From e80b3092a638562c4e5070891ff3ce881d418ff6 Mon Sep 17 00:00:00 2001 From: Misty Stanley-Jones Date: Wed, 14 Jan 2015 20:11:33 -0800 Subject: [PATCH] HBASE-12858 - remove extraneous Docbook files --- src/main/docbkx/appendix_acl_matrix.xml | 662 ---- ...appendix_contributing_to_documentation.xml | 426 -- src/main/docbkx/appendix_hfile_format.xml | 657 ---- src/main/docbkx/architecture.xml | 3489 ----------------- src/main/docbkx/asf.xml | 44 - src/main/docbkx/book.xml | 124 - src/main/docbkx/case_studies.xml | 239 -- src/main/docbkx/community.xml | 149 - src/main/docbkx/compression.xml | 535 --- src/main/docbkx/configuration.xml | 1653 -------- src/main/docbkx/cp.xml | 431 -- src/main/docbkx/customization-pdf.xsl | 129 - src/main/docbkx/customization.xsl | 49 - src/main/docbkx/datamodel.xml | 865 ---- src/main/docbkx/developer.xml | 2346 ----------- src/main/docbkx/external_apis.xml | 79 - src/main/docbkx/faq.xml | 270 -- src/main/docbkx/getting_started.xml | 728 ---- src/main/docbkx/hbase-default.xml | 561 --- src/main/docbkx/hbase_apis.xml | 133 - src/main/docbkx/hbase_history.xml | 41 - src/main/docbkx/hbck_in_depth.xml | 237 -- src/main/docbkx/mapreduce.xml | 630 --- src/main/docbkx/ops_mgt.xml | 2648 ------------- src/main/docbkx/orca.xml | 47 - src/main/docbkx/other_info.xml | 83 - src/main/docbkx/performance.xml | 1207 ------ src/main/docbkx/preface.xml | 83 - src/main/docbkx/rpc.xml | 301 -- src/main/docbkx/schema_design.xml | 1262 ------ src/main/docbkx/security.xml | 1925 --------- src/main/docbkx/shell.xml | 386 -- src/main/docbkx/sql.xml | 40 - src/main/docbkx/thrift_filter_language.xml | 757 ---- src/main/docbkx/tracing.xml | 187 - src/main/docbkx/troubleshooting.xml | 1700 -------- src/main/docbkx/unit_testing.xml | 330 -- src/main/docbkx/upgrading.xml | 833 ---- src/main/docbkx/ycsb.xml | 36 - src/main/docbkx/zookeeper.xml | 513 --- .../xslt/configuration_to_docbook_section.xsl | 66 - 41 files changed, 26881 deletions(-) delete mode 100644 src/main/docbkx/appendix_acl_matrix.xml delete mode 100644 src/main/docbkx/appendix_contributing_to_documentation.xml delete mode 100644 src/main/docbkx/appendix_hfile_format.xml delete mode 100644 src/main/docbkx/architecture.xml delete mode 100644 src/main/docbkx/asf.xml delete mode 100644 src/main/docbkx/book.xml delete mode 100644 src/main/docbkx/case_studies.xml delete mode 100644 src/main/docbkx/community.xml delete mode 100644 src/main/docbkx/compression.xml delete mode 100644 src/main/docbkx/configuration.xml delete mode 100644 src/main/docbkx/cp.xml delete mode 100644 src/main/docbkx/customization-pdf.xsl delete mode 100644 src/main/docbkx/customization.xsl delete mode 100644 src/main/docbkx/datamodel.xml delete mode 100644 src/main/docbkx/developer.xml delete mode 100644 src/main/docbkx/external_apis.xml delete mode 100644 src/main/docbkx/faq.xml delete mode 100644 src/main/docbkx/getting_started.xml delete mode 100644 src/main/docbkx/hbase-default.xml delete mode 100644 src/main/docbkx/hbase_apis.xml delete mode 100644 src/main/docbkx/hbase_history.xml delete mode 100644 src/main/docbkx/hbck_in_depth.xml delete mode 100644 src/main/docbkx/mapreduce.xml delete mode 100644 src/main/docbkx/ops_mgt.xml delete mode 100644 src/main/docbkx/orca.xml delete mode 100644 src/main/docbkx/other_info.xml delete mode 100644 src/main/docbkx/performance.xml delete mode 100644 src/main/docbkx/preface.xml delete mode 100644 src/main/docbkx/rpc.xml delete mode 100644 src/main/docbkx/schema_design.xml delete mode 100644 src/main/docbkx/security.xml delete mode 100644 src/main/docbkx/shell.xml delete mode 100644 src/main/docbkx/sql.xml delete mode 100644 src/main/docbkx/thrift_filter_language.xml delete mode 100644 src/main/docbkx/tracing.xml delete mode 100644 src/main/docbkx/troubleshooting.xml delete mode 100644 src/main/docbkx/unit_testing.xml delete mode 100644 src/main/docbkx/upgrading.xml delete mode 100644 src/main/docbkx/ycsb.xml delete mode 100644 src/main/docbkx/zookeeper.xml delete mode 100644 src/main/xslt/configuration_to_docbook_section.xsl diff --git a/src/main/docbkx/appendix_acl_matrix.xml b/src/main/docbkx/appendix_acl_matrix.xml deleted file mode 100644 index a0d46958c5b..00000000000 --- a/src/main/docbkx/appendix_acl_matrix.xml +++ /dev/null @@ -1,662 +0,0 @@ - - - - - Access Control Matrix - The following matrix shows the minimum permission set required to perform operations in - HBase. Before using the table, read through the information about how to interpret it. - - Interpreting the ACL Matrix Table - The following conventions are used in the ACL Matrix table: - - Scopes - - Permissions are evaluated starting at the widest scope and working to the - narrowest scope. A scope corresponds to a level of the data model. From broadest to - narrowest, the scopes are as follows:: - - Global - Namespace (NS) - Table - Column Family (CF) - Column Qualifier (CQ) - Cell - - For instance, a permission granted at table level dominates any grants done at - the Column Family, Column Qualifier, or cell level. The user can do what that - grant implies at any location in the table. A permission granted at global scope - dominates all: the user is always allowed to take that action everywhere. - - - - Permissions - - Possible permissions include the following: - - Superuser - a special user that belongs to group "supergroup" and has - unlimited access - Admin (A) - Create (C) - Write (W) - Read (R) - Execute (X) - - - - - - For the most part, permissions work in an expected way, with the following caveats: - - - Having Write permission does not imply Read permission. It is possible and sometimes - desirable for a user to be able to write data that same user cannot read. One such example - is a log-writing process. - - - The hbase:meta table is readable by every user, regardless - of the user's other grants or restrictions. This is a requirement for HBase to - function correctly. - - - CheckAndPut and CheckAndDelete operations will fail if - the user does not have both Write and Read permission. - - - Increment and Append operations do not require Read - access. - - - - The following table is sorted by the interface that provides each operation. In case the - table goes out of date, the unit tests which check for accuracy of permissions can be found - in - hbase-server/src/test/java/org/apache/hadoop/hbase/security/access/TestAccessController.java, - and the access controls themselves can be examined in - hbase-server/src/main/java/org/apache/hadoop/hbase/security/access/AccessController.java. - - - ACL Matrix - - - - Interface - Operation - Minimum Scope - Minimum Permission - - - - - - - Master - - - createTable - - - Global - - - C - - - - - modifyTable - - - Table - - - A|C - - - - - deleteTable - - - Table - - - A|C - - - - - truncateTable - - - Table - - - A|C - - - - - addColumn - - - Table - - - A|C - - - - - modifyColumn - - - Table - - - A|C - - - - - deleteColumn - - - Table - - - A|C - - - - - disableTable - - - Table - - - A|C - - - - - disableAclTable - - - None - - - Not allowed - - - - - enableTable - - - Table - - - A|C - - - - - move - - - Global - - - A - - - - - assign - - - Global - - - A - - - - - unassign - - - Global - - - A - - - - - regionOffline - - - Global - - - A - - - - - balance - - - Global - - - A - - - - - balanceSwitch - - - Global - - - A - - - - - shutdown - - - Global - - - A - - - - - stopMaster - - - Global - - - A - - - - - snapshot - - - Global - - - A - - - - - clone - - - Global - - - A - - - - - restore - - - Global - - - A - - - - - deleteSnapshot - - - Global - - - A - - - - - createNamespace - - - Global - - - A - - - - - deleteNamespace - - - Namespace - - - A - - - - - modifyNamespace - - - Namespace - - - A - - - - - flushTable - - - Table - - - A|C - - - - - getTableDescriptors - - - Global|Table - - - A - - - - - mergeRegions - - - Global - - - A - - - - Region - - open - Global - A - - - - openRegion - - - Global - - - A - - - - close - Global - A - - - - closeRegion - - - Global - - - A - - - - - stopRegionServer - - - Global - - - A - - - - - rollHLog - - - Global - - - A - - - - - mergeRegions - - - Global - - - A - - - - append - Table|CF|CQ - W - - - delete - Table|CF|CQ|Cell (if the user has write permission for all cells) - W - - - exists - Table|CF|CQ - R - - - get - Table|CF|CQ - R - - - getClosestRowBefore - Table|CF|CQ - R - - - increment - Table|CF|CQ - W - - - put - Table|CF|CQ - W - - - - flush - - - Global|Table - - - A|C - - - - - split - - - Global|Table - - - A - - - - - compact - - - Global|Table - - - A|C - - - - bulkLoadHFile - Table - W - - - prepareBulkLoad - Table - C - - - cleanupBulkLoad - Table - W - - - checkAndDelete - Table|CF|CQ - RW - - - checkAndPut - Table|CF|CQ - RW - - - incrementColumnValue - Table|CF|CQ - RW - - - scannerClose - Table - R - - - scannerNext - Table - R - - - scannerOpen - Table|CQ|CF - R - - - - Endpoint - - - invoke - - Endpoint - - X - - - - - AccessController - - - grant - - Global|Table|NS - - A - - - - - revoke - - Global|Table|NS - - A - - - - - getUserPermissions - - - Global|Table|NS - - - A - - - - - checkPermissions - - - Global|Table|NS - - - A - - - - -
- diff --git a/src/main/docbkx/appendix_contributing_to_documentation.xml b/src/main/docbkx/appendix_contributing_to_documentation.xml deleted file mode 100644 index 2f19c7b48e9..00000000000 --- a/src/main/docbkx/appendix_contributing_to_documentation.xml +++ /dev/null @@ -1,426 +0,0 @@ - - - - - Contributing to Documentation - The Apache HBase project welcomes contributions to all aspects of the project, including - the documentation. In HBase, documentation includes the following areas, and probably some - others: - - - The HBase Reference - Guide (this book) - - - The HBase websitee - - - The HBase - Wiki - - - API documentation - - - Command-line utility output and help text - - - Web UI strings, explicit help text, context-sensitive strings, and others - - - Log messages - - - Comments in source files, configuration files, and others - - - Localization of any of the above into target languages other than English - - - No matter which area you want to help out with, the first step is almost always to - download (typically by cloning the Git repository) and familiarize yourself with the HBase - source code. The only exception in the list above is the HBase Wiki, which is edited online. - For information on downloading and building the source, see . - -
- Getting Access to the Wiki - The HBase Wiki is not well-maintained and much of its content has been moved into the - HBase Reference Guide (this guide). However, some pages on the Wiki are well maintained, - and it would be great to have some volunteers willing to help out with the Wiki. To - request access to the Wiki, register a new account at https://wiki.apache.org/hadoop/Hbase?action=newaccount. Contact one of the - HBase committers, who can either give you access or refer you to someone who can. -
-
- Contributing to Documentation or Other Strings - If you spot an error in a string in a UI, utility, script, log message, or elsewhere, - or you think something could be made more clear, or you think text needs to be added - where it doesn't currently exist, the first step is to file a JIRA. Be sure to set the - component to Documentation in addition any other involved components. - Most components have one or more default owners, who monitor new issues which come into - those queues. Regardless of whether you feel able to fix the bug, you should still file - bugs where you see them. - If you want to try your hand at fixing your newly-filed bug, assign it to yourself. - You will need to clone the HBase Git repository to your local system and work on the - issue there. When you have developed a potential fix, submit it for review. If it - addresses the issue and is seen as an improvement, one of the HBase committers will - commit it to one or more branches, as appropriate. - - Suggested Work flow for Submitting Patches - This procedure goes into more detail than Git pros will need, but is included in - this appendix so that people unfamiliar with Git can feel confident contributing to - HBase while they learn. - - If you have not already done so, clone the Git repository locally. You only - need to do this once. - - - Fairly often, pull remote changes into your local repository by using the - git pull command, while your master branch is checked - out. - - - For each issue you work on, create a new branch. One convention that works - well for naming the branches is to name a given branch the same as the JIRA it - relates to: - $ git checkout -b HBASE-123456 - - - Make your suggested changes on your branch, committing your changes to your - local repository often. If you need to switch to working on a different issue, - remember to check out the appropriate branch. - - - When you are ready to submit your patch, first be sure that HBase builds - cleanly and behaves as expected in your modified branch. If you have made - documentation changes, be sure the documentation and website builds. - - Before you use the site target the very first time, be - sure you have built HBase at least once, in order to fetch all the Maven - dependencies you need. - - $ mvn clean install -DskipTests # Builds HBase - $ mvn clean site -DskipTests # Builds the website and documentation - If any errors occur, address them. - - - If it takes you several days or weeks to implement your fix, or you know that - the area of the code you are working in has had a lot of changes lately, make - sure you rebase your branch against the remote master and take care of any - conflicts before submitting your patch. - -$ git checkout HBASE-123456 -$ git rebase origin/master - - - - Generate your patch against the remote master. Run the following command from - the top level of your git repository (usually called - hbase): - $ git diff --no-prefix origin/master > HBASE-123456.patch - The name of the patch should contain the JIRA ID. Look over the patch file to - be sure that you did not change any additional files by accident and that there - are no other surprises. When you are satisfied, attach the patch to the JIRA and - click the Patch Available button. A reviewer - will review your patch. If you need to submit a new version of the patch, leave - the old one on the JIRA and add a version number to the name of the new - patch. - - - After a change has been committed, there is no need to keep your local branch - around. Instead you should run git pull to get the new change - into your master branch. - - -
- -
- Editing the HBase Website - The source for the HBase website is in the HBase source, in the - src/main/site/ directory. Within this directory, source for the - individual pages is in the xdocs/ directory, and images referenced - in those pages are in the images/ directory. This directory also - stores images used in the HBase Reference Guide. - The website's pages are written in an HTML-like XML dialect called xdoc, which has a - reference guide at http://maven.apache.org/archives/maven-1.x/plugins/xdoc/reference/xdocs.html. - You can edit these files in a plain-text editor, an IDE, or an XML editor such as - XML Mind XML Editor (XXE) or Oxygen XML Author. - To preview your changes, build the website using the mvn clean site - -DskipTests command. The HTML output resides in the - target/site/ directory. When you are satisfied with your - changes, follow the procedure in to submit - your patch. -
- -
- Editing the HBase Reference Guide - The source for the HBase Reference Guide is in the HBase source, in the - src/main/docbkx/ directory. It is written in Docbook XML. Docbook can be - intimidating, but you can typically follow the formatting of the surrounding file to get - an idea of the mark-up. You can edit Docbook XML files using a plain-text editor, an - XML-aware IDE, or a specialized XML editor. - Docbook's syntax can be picky. Before submitting a patch, be sure to build the output - locally using the mvn site command. If you do not get any build - errors, that means that the XML is well-formed, which means that each opening tag is - balanced by a closing tag. Well-formedness is not exactly the same as validity. Check - the output in target/docbkx/ for any surprises before submitting a - patch. -
- -
- Auto-Generated Content - Some parts of the HBase Reference Guide, most notably , - are generated automatically, so that this area of the documentation stays in sync with - the code. This is done by means of an XSLT transform, which you can examine in the - source at src/main/xslt/configuration_to_docbook_section.xsl. This - transforms the hbase-common/src/main/resources/hbase-default.xml - file into a Docbook output which can be included in the Reference Guide. Sometimes, it - is necessary to add configuration parameters or modify their descriptions. Make the - modifications to the source file, and they will be included in the Reference Guide when - it is rebuilt. - It is possible that other types of content can and will be automatically generated - from HBase source files in the future. -
- -
- Multi-Page and Single-Page Output - You can examine the site target in the Maven - pom.xml file included at the top level of the HBase source for - details on the process of building the website and documentation. The Reference Guide is - built twice, once as a single-page output and once with one HTML file per chapter. The - single-page output is located in target/docbkx/book.html, while the - multi-page output's index page is at target/docbkx/book/book.html. - Each of these outputs has its own images/ and - css/ directories, which are created at build time. -
- -
- Images in the HBase Reference Guide - You can include images in the HBase Reference Guide. For accessibility reasons, it is - recommended that you use a <figure> Docbook element for an image. This allows - screen readers to navigate to the image and also provides alternative text for the - image. The following is an example of a <figure> element. - - HFile Version 1 - - - - - - HFile Version 1 - - -]]> - - The <textobject> can contain a few sentences describing the image, rather than - simply reiterating the title. You can optionally specify alignment and size options in - the <imagedata> element. - When doing a local build, save the image to the - src/main/site/resources/images/ directory. In the - <imagedata> element, refer to the image as above, with no directory component. The - image will be copied to the appropriate target location during the build of the - output. - When you submit a patch which includes adding an image to the HBase Reference Guide, - attach the image to the JIRA. If the committer asks where the image should be committed, - it should go into the above directory. -
- -
- Adding a New Chapter to the HBase Reference Guide - If you want to add a new chapter to the HBase Reference Guide, the easiest way is to - copy an existing chapter file, rename it, and change the ID and title elements near the - top of the file. Delete the existing content and create the new content. Then open the - book.xml file, which is the main file for the HBase Reference - Guide, and use an <xi:include> element to include your new chapter in the - appropriate location. Be sure to add your new file to your Git repository before - creating your patch. Note that the book.xml file currently contains - many chapters. You can only include a chapter at the same nesting levels as the other - chapters in the file. When in doubt, check to see how other files have been - included. -
- -
- Docbook Common Issues - The following Docbook issues come up often. Some of these are preferences, but others - can create mysterious build errors or other problems. - - - - What can go where? - - - There is often confusion about which child elements are valid in a given - context. When in doubt, Docbook: The - Definitive Guide is the best resource. It has an appendix which - is indexed by element and contains all valid child and parent elements of - any given element. If you edit Docbook often, a schema-aware XML editor - makes things easier. - - - - - Paragraphs and Admonitions - - - It is a common pattern, and it is technically valid, to put an admonition - such as a <note> inside a <para> element. Because admonitions - render as block-level elements (they take the whole width of the page), it - is better to mark them up as siblings to the paragraphs around them, like - this: - This is the paragraph. - - This is an admonition which occurs after the paragraph. -]]> - - - - - Wrap textual <listitem> and <entry> contents in <para> - elements. - - - Because the contents of a <listitem> (an element in an itemized, - ordered, or variable list) or an <entry> (a cell in a table) can - consist of things other than plain text, they need to be wrapped in some - element. If they are plain text, they need to be inclosed in <para> - tags. This is tedious but necessary for validity. - - - This is a paragraph. - - - This is screen output. - -]]> - - - - - When to use <command>, <code>, <programlisting>, - <screen> - - - The first two are in-line tags, which can occur within the flow of - paragraphs or titles. The second two are block elements. - Use <command> to mention a command such as hbase - shell in the flow of a sentence. Use <code> for other - inline text referring to code. Incidentally, use <literal> to specify - literal strings that should be typed or entered exactly as shown. Within a - <screen> listing, it can be helpful to use the <userinput> and - <computeroutput> elements to mark up the text further. - Use <screen> to display input and output as the user would - see it on the screen, in a log file, etc. Use - <programlisting> only for blocks of code that occur within a file, - such as Java or XML code, or a Bash shell script. - - - - - How to escape XML elements so that they show up as XML - - - For one-off instances or short in-line mentions, use the &lt; and - &gt; encoded characters. For longer mentions, or blocks of code, enclose - it with , which is much easier to maintain and - parse in the source files.. - - - - - Tips and tricks for making screen output look good - - - Text within <screen> and <programlisting> elements is shown - exactly as it appears in the source, including indentation, tabs, and line - wrap. - - - Indent the starting and closing XML elements, but do not indent - the content. Also, to avoid having an extra blank line at the - beginning of the programlisting output, do not put the CDATA - element on its own line. For example: - -case $1 in - --cleanZk|--cleanHdfs|--cleanAll) - matches="yes" ;; - *) ;; -esac - ]]> - - - After pasting code into a programlisting, fix the indentation - manually, using two spaces per desired - indentation. For screen output, be sure to include line breaks so - that the text is no longer than 100 characters. - - - - - - - Isolate Changes for Easy Diff Review. - - - Be careful with pretty-printing or re-formatting an entire XML file, even - if the formatting has degraded over time. If you need to reformat a file, do - that in a separate JIRA where you do not change any content. Be careful - because some XML editors do a bulk-reformat when you open a new file, - especially if you use GUI mode in the editor. - - - - - Syntax Highlighting - - - The HBase Reference Guide uses the XSLT Syntax Highlighting Maven module for syntax highlighting. - To enable syntax highlighting for a given <programlisting> or - <screen> (or possibly other elements), add the attribute - language=LANGUAGE_OF_CHOICE - to the element, as in the following example: - - bar - foo -]]> - Several syntax types are supported. The most interesting ones for the - HBase Reference Guide are java, xml, - sql, and bourne (for BASH shell - output or Linux command-line examples). - - - -
- - - \ No newline at end of file diff --git a/src/main/docbkx/appendix_hfile_format.xml b/src/main/docbkx/appendix_hfile_format.xml deleted file mode 100644 index ee430314f44..00000000000 --- a/src/main/docbkx/appendix_hfile_format.xml +++ /dev/null @@ -1,657 +0,0 @@ - - - - HFile format - This appendix describes the evolution of the HFile format. - -
- HBase File Format (version 1) - As we will be discussing changes to the HFile format, it is useful to give a short overview of the original (HFile version 1) format. -
- Overview of Version 1 - An HFile in version 1 format is structured as follows: -
- HFile V1 Format - - - - - - HFile Version 1 - - Image courtesy of Lars George, hbase-architecture-101-storage.html. - -
- -
-
Block index format in version 1 - The block index in version 1 is very straightforward. For each entry, it contains: - - - Offset (long) - - - Uncompressed size (int) - - - Key (a serialized byte array written using Bytes.writeByteArray) - - - Key length as a variable-length integer (VInt) - - - - - Key bytes - - - - - - The number of entries in the block index is stored in the fixed file trailer, and has to be passed in to the method that reads the block index. One of the limitations of the block index in version 1 is that it does not provide the compressed size of a block, which turns out to be necessary for decompression. Therefore, the HFile reader has to infer this compressed size from the offset difference between blocks. We fix this limitation in version 2, where we store on-disk block size instead of uncompressed size, and get uncompressed size from the block header. -
-
-
- HBase file format with inline blocks (version 2) - - Note: this feature was introduced in HBase 0.92 -
Motivation - We found it necessary to revise the HFile format after encountering high memory usage and slow startup times caused by large Bloom filters and block indexes in the region server. Bloom filters can get as large as 100 MB per HFile, which adds up to 2 GB when aggregated over 20 regions. Block indexes can grow as large as 6 GB in aggregate size over the same set of regions. A region is not considered opened until all of its block index data is loaded. Large Bloom filters produce a different performance problem: the first get request that requires a Bloom filter lookup will incur the latency of loading the entire Bloom filter bit array. - To speed up region server startup we break Bloom filters and block indexes into multiple blocks and write those blocks out as they fill up, which also reduces the HFile writer’s memory footprint. In the Bloom filter case, “filling up a block” means accumulating enough keys to efficiently utilize a fixed-size bit array, and in the block index case we accumulate an “index block” of the desired size. Bloom filter blocks and index blocks (we call these “inline blocks”) become interspersed with data blocks, and as a side effect we can no longer rely on the difference between block offsets to determine data block length, as it was done in version 1. - HFile is a low-level file format by design, and it should not deal with application-specific details such as Bloom filters, which are handled at StoreFile level. Therefore, we call Bloom filter blocks in an HFile "inline" blocks. We also supply HFile with an interface to write those inline blocks. - Another format modification aimed at reducing the region server startup time is to use a contiguous “load-on-open” section that has to be loaded in memory at the time an HFile is being opened. Currently, as an HFile opens, there are separate seek operations to read the trailer, data/meta indexes, and file info. To read the Bloom filter, there are two more seek operations for its “data” and “meta” portions. In version 2, we seek once to read the trailer and seek again to read everything else we need to open the file from a contiguous block.
-
- Overview of Version 2 - The version of HBase introducing the above features reads both version 1 and 2 HFiles, but only writes version 2 HFiles. A version 2 HFile is structured as follows: - - - - - - HFile Version 2 - - - - -
-
Unified version 2 block format - In the version 2 every block in the data section contains the following fields: - - - 8 bytes: Block type, a sequence of bytes equivalent to version 1's "magic records". Supported block types are: - - - DATA – data blocks - - - - - LEAF_INDEX – leaf-level index blocks in a multi-level-block-index - - - - - BLOOM_CHUNK – Bloom filter chunks - - - - - META – meta blocks (not used for Bloom filters in version 2 anymore) - - - - - INTERMEDIATE_INDEX – intermediate-level index blocks in a multi-level blockindex - - - - - ROOT_INDEX – root>level index blocks in a multi>level block index - - - - - FILE_INFO – the “file info” block, a small key>value map of metadata - - - - - BLOOM_META – a Bloom filter metadata block in the load>on>open section - - - - - TRAILER – a fixed>size file trailer. As opposed to the above, this is not an - HFile v2 block but a fixed>size (for each HFile version) data structure - - - - - INDEX_V1 – this block type is only used for legacy HFile v1 block - - - - - - Compressed size of the block's data, not including the header (int). - - -Can be used for skipping the current data block when scanning HFile data. - - - - Uncompressed size of the block's data, not including the header (int) - - This is equal to the compressed size if the compression algorithm is NONE - - - - File offset of the previous block of the same type (long) - - Can be used for seeking to the previous data/index block - - - - Compressed data (or uncompressed data if the compression algorithm is NONE). - - - The above format of blocks is used in the following HFile sections: - - - Scanned block section. The section is named so because it contains all data blocks that need to be read when an HFile is scanned sequentially.  Also contains leaf block index and Bloom chunk blocks. - - - Non-scanned block section. This section still contains unified-format v2 blocks but it does not have to be read when doing a sequential scan. This section contains “meta” blocks and intermediate-level index blocks. - - - - We are supporting “meta” blocks in version 2 the same way they were supported in version 1, even though we do not store Bloom filter data in these blocks anymore.
- -
Block index in version 2 - There are three types of block indexes in HFile version 2, stored in two different formats (root and non-root): - - - Data index — version 2 multi-level block index, consisting of: - - - - Version 2 root index, stored in the data block index section of the file - - - - -Optionally, version 2 intermediate levels, stored in the non%root format in the data index section of the file. Intermediate levels can only be present if leaf level blocks are present - - - - -Optionally, version 2 leaf levels, stored in the non%root format inline with data blocks - - - - - - Meta index — version 2 root index format only, stored in the meta index section of the file - - - Bloom index — version 2 root index format only, stored in the “load-on-open” section as part of Bloom filter metadata. - -
-
- Root block index format in version 2 - This format applies to: - - - Root level of the version 2 data index - - - Entire meta and Bloom indexes in version 2, which are always single-level. - - - A version 2 root index block is a sequence of entries of the following format, similar to entries of a version 1 block index, but storing on-disk size instead of uncompressed size. - - - Offset (long) - -This offset may point to a data block or to a deeper>level index block. - - - - On-disk size (int) - - - Key (a serialized byte array stored using Bytes.writeByteArray) - - - Key (VInt) - - - - Key bytes - - - - - - A single-level version 2 block index consists of just a single root index block. To read a root index block of version 2, one needs to know the number of entries. For the data index and the meta index the number of entries is stored in the trailer, and for the Bloom index it is stored in the compound Bloom filter metadata. - - For a multi-level block index we also store the following fields in the root index block in the load-on-open section of the HFile, in addition to the data structure described above: - - - Middle leaf index block offset - - - Middle leaf block on-disk size (meaning the leaf index block containing the reference to the “middle” data block of the file) - - - The index of the mid-key (defined below) in the middle leaf-level block. - - - - These additional fields are used to efficiently retrieve the mid-key of the HFile used in HFile splits, which we define as the first key of the block with a zero-based index of (n – 1) / 2, if the total number of blocks in the HFile is n. This definition is consistent with how the mid-key was determined in HFile version 1, and is reasonable in general, because blocks are likely to be the same size on average, but we don’t have any estimates on individual key/value pair sizes. - - When writing a version 2 HFile, the total number of data blocks pointed to by every leaf-level index block is kept track of. When we finish writing and the total number of leaf-level blocks is determined, it is clear which leaf-level block contains the mid-key, and the fields listed above are computed.  When reading the HFile and the mid-key is requested, we retrieve the middle leaf index block (potentially from the block cache) and get the mid-key value from the appropriate position inside that leaf block.
-
- Non-root block index format in version 2 - This format applies to intermediate-level and leaf index blocks of a version 2 multi-level data block index. Every non-root index block is structured as follows. - - - numEntries: the number of entries (int). - - - entryOffsets: the “secondary index” of offsets of entries in the block, to facilitate a quick binary search on the key (numEntries + 1 int values). The last value is the total length of all entries in this index block. For example, in a non-root index block with entry sizes 60, 80, 50 the “secondary index” will contain the following int array: {0, 60, 140, 190}. - - - Entries. Each entry contains: - - - -Offset of the block referenced by this entry in the file (long) - - - - -On>disk size of the referenced block (int) - - - - -Key. The length can be calculated from entryOffsets. - - - - - -
- Bloom filters in version 2 - In contrast with version 1, in a version 2 HFile Bloom filter metadata is stored in the load-on-open section of the HFile for quick startup. - - - A compound Bloom filter. - - - - Bloom filter version = 3 (int). There used to be a DynamicByteBloomFilter class that had the Bloom filter version number 2 - - - - -The total byte size of all compound Bloom filter chunks (long) - - - - - Number of hash functions (int - - - - -Type of hash functions (int) - - - - -The total key count inserted into the Bloom filter (long) - - - - -The maximum total number of keys in the Bloom filter (long) - - - - -The number of chunks (int) - - - - -Comparator class used for Bloom filter keys, a UTF>8 encoded string stored using Bytes.writeByteArray - - - - - Bloom block index in the version 2 root block index format - - - - -
File Info format in versions 1 and 2 - The file info block is a serialized HbaseMapWritable (essentially a map from byte arrays to byte arrays) with the following keys, among others. StoreFile-level logic adds more keys to this. - - - - hfile.LASTKEY - - - The last key of the file (byte array) - - - - - hfile.AVG_KEY_LEN - - - The average key length in the file (int) - - - - - hfile.AVG_VALUE_LEN - - - The average value length in the file (int) - - - - File info format did not change in version 2. However, we moved the file info to the final section of the file, which can be loaded as one block at the time the HFile is being opened. Also, we do not store comparator in the version 2 file info anymore. Instead, we store it in the fixed file trailer. This is because we need to know the comparator at the time of parsing the load-on-open section of the HFile.
- Fixed file trailer format differences between versions 1 and 2 - The following table shows common and different fields between fixed file trailers in versions 1 and 2. Note that the size of the trailer is different depending on the version, so it is “fixed” only within one version. However, the version is always stored as the last four-byte integer in the file. - - - - - - - - - Version 1 - - - Version 2 - - - - - File info offset (long) - - - - - Data index offset (long) - - - loadOnOpenOffset (long) - The offset of the section that we need toload when opening the file. - - - - - Number of data index entries (int) - - - - - metaIndexOffset (long) - This field is not being used by the version 1 reader, so we removed it from version 2. - - - uncompressedDataIndexSize (long) - The total uncompressed size of the whole data block index, including root-level, intermediate-level, and leaf-level blocks. - - - - - Number of meta index entries (int) - - - - - Total uncompressed bytes (long) - - - - - numEntries (int) - - - numEntries (long) - - - - - Compression codec: 0 = LZO, 1 = GZ, 2 = NONE (int) - - - - - - - - The number of levels in the data block index (int) - - - - - - - - firstDataBlockOffset (long) - The offset of the first first data block. Used when scanning. - - - - - - - - lastDataBlockEnd (long) - The offset of the first byte after the last key/value data block. We don't need to go beyond this offset when scanning. - - - - - Version: 1 (int) - - - Version: 2 (int) - - - -
-
getShortMidpointKey(an optimization for data index block) - Note: this optimization was introduced in HBase 0.95+ - HFiles contain many blocks that contain a range of sorted Cells. Each cell has a key. To save IO when reading Cells, the HFile also has an index that maps a Cell's start key to the offset of the beginning of a particular block. Prior to this optimization, HBase would use the key of the first cell in each data block as the index key. - In HBASE-7845, we generate a new key that is lexicographically larger than the last key of the previous block and lexicographically equal or smaller than the start key of the current block. While actual keys can potentially be very long, this "fake key" or "virtual key" can be much shorter. For example, if the stop key of previous block is "the quick brown fox", the start key of current block is "the who", we could use "the r" as our virtual key in our hfile index. - There are two benefits to this: - - having shorter keys reduces the hfile index size, (allowing us to keep more indexes in memory), and - using something closer to the end key of the previous block allows us to avoid a potential extra IO when the target key lives in between the "virtual key" and the key of the first element in the target block. - - This optimization (implemented by the getShortMidpointKey method) is inspired by LevelDB's ByteWiseComparatorImpl::FindShortestSeparator() and FindShortSuccessor(). -
-
-
- HBase File Format with Security Enhancements (version 3) - Note: this feature was introduced in HBase 0.98 -
- Motivation - - Version 3 of HFile makes changes needed to ease management of encryption at rest and - cell-level metadata (which in turn is needed for cell-level ACLs and cell-level visibility - labels). For more information see , - , , and - . - -
-
- Overview - - The version of HBase introducing the above features reads HFiles in versions 1, 2, and 3 but - only writes version 3 HFiles. Version 3 HFiles are structured the same as version 2 HFiles. - For more information see . - -
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- File Info Block in Version 3 - - Version 3 added two additional pieces of information to the reserved keys in the file info - block. - - - - - - hfile.MAX_TAGS_LEN - - - - The maximum number of bytes needed to store the serialized tags for any single - cell in this hfile (int) - - - - - - hfile.TAGS_COMPRESSED - - - Does the block encoder for this hfile compress tags? (boolean) - - Should only be present if hfile.MAX_TAGS_LEN is also - present. - - - - - - - - - When reading a Version 3 HFile the presence of MAX_TAGS_LEN is used - to determine how to deserialize the cells within a data block. Therefore, consumers must - read the file's info block prior to reading any data blocks. - - - When writing a Version 3 HFile, HBase will always include MAX_TAGS_LEN - when flushing the memstore to underlying filesystem and when using prefix tree - encoding for data blocks, as described in . When compacting - extant files, the default writer will omit MAX_TAGS_LEN if all of the - files selected do not themselves contain any cells with tags. See - for details on the compaction file selection algorithm. - -
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- Data Blocks in Version 3 - - Within an HFile, HBase cells are stored in data blocks as a sequence of KeyValues (see , or Lars George's - excellent introduction to HBase Storage). In version 3, these KeyValue optionally - will include a set of 0 or more tags: - - - - - - - - Version 1 & 2 - Version 3 without MAX_TAGS_LEN - - Version 3 with MAX_TAGS_LEN - - - - Key Length (4 bytes) - - - - - Value Length (4 bytes) - - - - - Key bytes (variable) - - - - - Value bytes (variable) - - - - - Tags Length (2 bytes) - - - - - Tags bytes (variable) - - - - - - - - If the info block for a given HFile contains an entry for - MAX_TAGS_LEN each cell will have the length of that cell's tags - included, even if that length is zero. The actual tags are stored as a sequence of tag - length (2 bytes), tag type (1 byte), tag bytes (variable). The format an individual tag's - bytes depends on the tag type. - - - Note that the dependence on the contents of the info block implies that prior to reading - any data blocks you must first process a file's info block. It also implies that prior to - writing a data block you must know if the file's info block will include - MAX_TAGS_LEN. - -
-
- Fixed File Trailer in Version 3 - - The fixed file trailers written with HFile version 3 are always serialized with protocol - buffers. Additionally, it adds an optional field to the version 2 protocol buffer named - encryption_key. If HBase is configured to encrypt HFiles this field will store a data - encryption key for this particular HFile, encrypted with the current cluster master key - using AES. For more information see . - -
-
- diff --git a/src/main/docbkx/architecture.xml b/src/main/docbkx/architecture.xml deleted file mode 100644 index 16b298acae3..00000000000 --- a/src/main/docbkx/architecture.xml +++ /dev/null @@ -1,3489 +0,0 @@ - - - - - Architecture -
- Overview -
- NoSQL? - HBase is a type of "NoSQL" database. "NoSQL" is a general term meaning that the database isn't an RDBMS which - supports SQL as its primary access language, but there are many types of NoSQL databases: BerkeleyDB is an - example of a local NoSQL database, whereas HBase is very much a distributed database. Technically speaking, - HBase is really more a "Data Store" than "Data Base" because it lacks many of the features you find in an RDBMS, - such as typed columns, secondary indexes, triggers, and advanced query languages, etc. - - However, HBase has many features which supports both linear and modular scaling. HBase clusters expand - by adding RegionServers that are hosted on commodity class servers. If a cluster expands from 10 to 20 - RegionServers, for example, it doubles both in terms of storage and as well as processing capacity. - RDBMS can scale well, but only up to a point - specifically, the size of a single database server - and for the best - performance requires specialized hardware and storage devices. HBase features of note are: - - Strongly consistent reads/writes: HBase is not an "eventually consistent" DataStore. This - makes it very suitable for tasks such as high-speed counter aggregation. - Automatic sharding: HBase tables are distributed on the cluster via regions, and regions are - automatically split and re-distributed as your data grows. - Automatic RegionServer failover - Hadoop/HDFS Integration: HBase supports HDFS out of the box as its distributed file system. - MapReduce: HBase supports massively parallelized processing via MapReduce for using HBase as both - source and sink. - Java Client API: HBase supports an easy to use Java API for programmatic access. - Thrift/REST API: HBase also supports Thrift and REST for non-Java front-ends. - Block Cache and Bloom Filters: HBase supports a Block Cache and Bloom Filters for high volume query optimization. - Operational Management: HBase provides build-in web-pages for operational insight as well as JMX metrics. - - -
- -
- When Should I Use HBase? - HBase isn't suitable for every problem. - First, make sure you have enough data. If you have hundreds of millions or billions of rows, then - HBase is a good candidate. If you only have a few thousand/million rows, then using a traditional RDBMS - might be a better choice due to the fact that all of your data might wind up on a single node (or two) and - the rest of the cluster may be sitting idle. - - Second, make sure you can live without all the extra features that an RDBMS provides (e.g., typed columns, - secondary indexes, transactions, advanced query languages, etc.) An application built against an RDBMS cannot be - "ported" to HBase by simply changing a JDBC driver, for example. Consider moving from an RDBMS to HBase as a - complete redesign as opposed to a port. - - Third, make sure you have enough hardware. Even HDFS doesn't do well with anything less than - 5 DataNodes (due to things such as HDFS block replication which has a default of 3), plus a NameNode. - - HBase can run quite well stand-alone on a laptop - but this should be considered a development - configuration only. - -
-
- What Is The Difference Between HBase and Hadoop/HDFS? - HDFS is a distributed file system that is well suited for the storage of large files. - Its documentation states that it is not, however, a general purpose file system, and does not provide fast individual record lookups in files. - HBase, on the other hand, is built on top of HDFS and provides fast record lookups (and updates) for large tables. - This can sometimes be a point of conceptual confusion. HBase internally puts your data in indexed "StoreFiles" that exist - on HDFS for high-speed lookups. See the and the rest of this chapter for more information on how HBase achieves its goals. - -
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- -
- Catalog Tables - The catalog table hbase:meta exists as an HBase table and is filtered out of the HBase - shell's list command, but is in fact a table just like any other. -
- -ROOT- - - The -ROOT- table was removed in HBase 0.96.0. Information here should - be considered historical. - - The -ROOT- table kept track of the location of the - .META table (the previous name for the table now called hbase:meta) prior to HBase - 0.96. The -ROOT- table structure was as follows: - - Key - - .META. region key (.META.,,1) - - - - - Values - - info:regioninfo (serialized HRegionInfo - instance of hbase:meta) - - - info:server (server:port of the RegionServer holding - hbase:meta) - - - info:serverstartcode (start-time of the RegionServer process holding - hbase:meta) - - -
-
- hbase:meta - The hbase:meta table (previously called .META.) keeps a list - of all regions in the system. The location of hbase:meta was previously - tracked within the -ROOT- table, but is now stored in Zookeeper. - The hbase:meta table structure is as follows: - - Key - - Region key of the format ([table],[region start key],[region - id]) - - - - Values - - info:regioninfo (serialized - HRegionInfo instance for this region) - - - info:server (server:port of the RegionServer containing this - region) - - - info:serverstartcode (start-time of the RegionServer process - containing this region) - - - When a table is in the process of splitting, two other columns will be created, called - info:splitA and info:splitB. These columns represent the two - daughter regions. The values for these columns are also serialized HRegionInfo instances. - After the region has been split, eventually this row will be deleted. - - Note on HRegionInfo - The empty key is used to denote table start and table end. A region with an empty - start key is the first region in a table. If a region has both an empty start and an - empty end key, it is the only region in the table - - In the (hopefully unlikely) event that programmatic processing of catalog metadata is - required, see the Writables - utility. -
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- Startup Sequencing - First, the location of hbase:meta is looked up in Zookeeper. Next, - hbase:meta is updated with server and startcode values. - For information on region-RegionServer assignment, see . -
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- -
- Client - The HBase client finds the RegionServers that are serving the particular row range of - interest. It does this by querying the hbase:meta table. See for details. After locating the required region(s), the - client contacts the RegionServer serving that region, rather than going through the master, - and issues the read or write request. This information is cached in the client so that - subsequent requests need not go through the lookup process. Should a region be reassigned - either by the master load balancer or because a RegionServer has died, the client will - requery the catalog tables to determine the new location of the user region. - - See for more information about the impact of the Master on HBase - Client communication. - Administrative functions are done via an instance of Admin - - -
- Cluster Connections - The API changed in HBase 1.0. Its been cleaned up and users are returned - Interfaces to work against rather than particular types. In HBase 1.0, - obtain a cluster Connection from ConnectionFactory and thereafter, get from it - instances of Table, Admin, and RegionLocator on an as-need basis. When done, close - obtained instances. Finally, be sure to cleanup your Connection instance before - exiting. Connections are heavyweight objects. Create once and keep an instance around. - Table, Admin and RegionLocator instances are lightweight. Create as you go and then - let go as soon as you are done by closing them. See the - Client Package Javadoc Description for example usage of the new HBase 1.0 API. - - For connection configuration information, see . - - Table - instances are not thread-safe. Only one thread can use an instance of Table at - any given time. When creating Table instances, it is advisable to use the same HBaseConfiguration - instance. This will ensure sharing of ZooKeeper and socket instances to the RegionServers - which is usually what you want. For example, this is preferred: - HBaseConfiguration conf = HBaseConfiguration.create(); -HTable table1 = new HTable(conf, "myTable"); -HTable table2 = new HTable(conf, "myTable"); - as opposed to this: - HBaseConfiguration conf1 = HBaseConfiguration.create(); -HTable table1 = new HTable(conf1, "myTable"); -HBaseConfiguration conf2 = HBaseConfiguration.create(); -HTable table2 = new HTable(conf2, "myTable"); - - For more information about how connections are handled in the HBase client, - see HConnectionManager. - -
Connection Pooling - For applications which require high-end multithreaded access (e.g., web-servers or application servers that may serve many application threads - in a single JVM), you can pre-create an HConnection, as shown in - the following example: - - Pre-Creating a <code>HConnection</code> - // Create a connection to the cluster. -HConnection connection = HConnectionManager.createConnection(Configuration); -HTableInterface table = connection.getTable("myTable"); -// use table as needed, the table returned is lightweight -table.close(); -// use the connection for other access to the cluster -connection.close(); - - Constructing HTableInterface implementation is very lightweight and resources are - controlled. - - <code>HTablePool</code> is Deprecated - Previous versions of this guide discussed HTablePool, which was - deprecated in HBase 0.94, 0.95, and 0.96, and removed in 0.98.1, by HBASE-6500. - Please use HConnection instead. - -
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WriteBuffer and Batch Methods - If is turned off on - HTable, - Puts are sent to RegionServers when the writebuffer - is filled. The writebuffer is 2MB by default. Before an HTable instance is - discarded, either close() or - flushCommits() should be invoked so Puts - will not be lost. - - Note: htable.delete(Delete); does not go in the writebuffer! This only applies to Puts. - - For additional information on write durability, review the ACID semantics page. - - For fine-grained control of batching of - Puts or Deletes, - see the batch methods on HTable. - -
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External Clients - Information on non-Java clients and custom protocols is covered in - -
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- -
Client Request Filters - Get and Scan instances can be - optionally configured with filters which are applied on the RegionServer. - - Filters can be confusing because there are many different types, and it is best to approach them by understanding the groups - of Filter functionality. - -
Structural - Structural Filters contain other Filters. -
FilterList - FilterList - represents a list of Filters with a relationship of FilterList.Operator.MUST_PASS_ALL or - FilterList.Operator.MUST_PASS_ONE between the Filters. The following example shows an 'or' between two - Filters (checking for either 'my value' or 'my other value' on the same attribute). - -FilterList list = new FilterList(FilterList.Operator.MUST_PASS_ONE); -SingleColumnValueFilter filter1 = new SingleColumnValueFilter( - cf, - column, - CompareOp.EQUAL, - Bytes.toBytes("my value") - ); -list.add(filter1); -SingleColumnValueFilter filter2 = new SingleColumnValueFilter( - cf, - column, - CompareOp.EQUAL, - Bytes.toBytes("my other value") - ); -list.add(filter2); -scan.setFilter(list); - -
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- Column Value -
- SingleColumnValueFilter - SingleColumnValueFilter - can be used to test column values for equivalence (CompareOp.EQUAL - ), inequality (CompareOp.NOT_EQUAL), or ranges (e.g., - CompareOp.GREATER). The following is example of testing equivalence a - column to a String value "my value"... - -SingleColumnValueFilter filter = new SingleColumnValueFilter( - cf, - column, - CompareOp.EQUAL, - Bytes.toBytes("my value") - ); -scan.setFilter(filter); - -
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- Column Value Comparators - There are several Comparator classes in the Filter package that deserve special - mention. These Comparators are used in concert with other Filters, such as . -
- RegexStringComparator - RegexStringComparator - supports regular expressions for value comparisons. - -RegexStringComparator comp = new RegexStringComparator("my."); // any value that starts with 'my' -SingleColumnValueFilter filter = new SingleColumnValueFilter( - cf, - column, - CompareOp.EQUAL, - comp - ); -scan.setFilter(filter); - - See the Oracle JavaDoc for supported - RegEx patterns in Java. -
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- SubstringComparator - SubstringComparator - can be used to determine if a given substring exists in a value. The comparison is - case-insensitive. - -SubstringComparator comp = new SubstringComparator("y val"); // looking for 'my value' -SingleColumnValueFilter filter = new SingleColumnValueFilter( - cf, - column, - CompareOp.EQUAL, - comp - ); -scan.setFilter(filter); - -
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- BinaryPrefixComparator - See BinaryPrefixComparator. -
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- BinaryComparator - See BinaryComparator. -
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- KeyValue Metadata - As HBase stores data internally as KeyValue pairs, KeyValue Metadata Filters evaluate - the existence of keys (i.e., ColumnFamily:Column qualifiers) for a row, as opposed to - values the previous section. -
- FamilyFilter - FamilyFilter - can be used to filter on the ColumnFamily. It is generally a better idea to select - ColumnFamilies in the Scan than to do it with a Filter. -
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- QualifierFilter - QualifierFilter - can be used to filter based on Column (aka Qualifier) name. -
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- ColumnPrefixFilter - ColumnPrefixFilter - can be used to filter based on the lead portion of Column (aka Qualifier) names. - A ColumnPrefixFilter seeks ahead to the first column matching the prefix in each row - and for each involved column family. It can be used to efficiently get a subset of the - columns in very wide rows. - Note: The same column qualifier can be used in different column families. This - filter returns all matching columns. - Example: Find all columns in a row and family that start with "abc" - -HTableInterface t = ...; -byte[] row = ...; -byte[] family = ...; -byte[] prefix = Bytes.toBytes("abc"); -Scan scan = new Scan(row, row); // (optional) limit to one row -scan.addFamily(family); // (optional) limit to one family -Filter f = new ColumnPrefixFilter(prefix); -scan.setFilter(f); -scan.setBatch(10); // set this if there could be many columns returned -ResultScanner rs = t.getScanner(scan); -for (Result r = rs.next(); r != null; r = rs.next()) { - for (KeyValue kv : r.raw()) { - // each kv represents a column - } -} -rs.close(); - -
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- MultipleColumnPrefixFilter - MultipleColumnPrefixFilter - behaves like ColumnPrefixFilter but allows specifying multiple prefixes. - Like ColumnPrefixFilter, MultipleColumnPrefixFilter efficiently seeks ahead to the - first column matching the lowest prefix and also seeks past ranges of columns between - prefixes. It can be used to efficiently get discontinuous sets of columns from very wide - rows. - Example: Find all columns in a row and family that start with "abc" or "xyz" - -HTableInterface t = ...; -byte[] row = ...; -byte[] family = ...; -byte[][] prefixes = new byte[][] {Bytes.toBytes("abc"), Bytes.toBytes("xyz")}; -Scan scan = new Scan(row, row); // (optional) limit to one row -scan.addFamily(family); // (optional) limit to one family -Filter f = new MultipleColumnPrefixFilter(prefixes); -scan.setFilter(f); -scan.setBatch(10); // set this if there could be many columns returned -ResultScanner rs = t.getScanner(scan); -for (Result r = rs.next(); r != null; r = rs.next()) { - for (KeyValue kv : r.raw()) { - // each kv represents a column - } -} -rs.close(); - -
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- ColumnRangeFilter - A ColumnRangeFilter - allows efficient intra row scanning. - A ColumnRangeFilter can seek ahead to the first matching column for each involved - column family. It can be used to efficiently get a 'slice' of the columns of a very wide - row. i.e. you have a million columns in a row but you only want to look at columns - bbbb-bbdd. - Note: The same column qualifier can be used in different column families. This - filter returns all matching columns. - Example: Find all columns in a row and family between "bbbb" (inclusive) and "bbdd" - (inclusive) - -HTableInterface t = ...; -byte[] row = ...; -byte[] family = ...; -byte[] startColumn = Bytes.toBytes("bbbb"); -byte[] endColumn = Bytes.toBytes("bbdd"); -Scan scan = new Scan(row, row); // (optional) limit to one row -scan.addFamily(family); // (optional) limit to one family -Filter f = new ColumnRangeFilter(startColumn, true, endColumn, true); -scan.setFilter(f); -scan.setBatch(10); // set this if there could be many columns returned -ResultScanner rs = t.getScanner(scan); -for (Result r = rs.next(); r != null; r = rs.next()) { - for (KeyValue kv : r.raw()) { - // each kv represents a column - } -} -rs.close(); - - Note: Introduced in HBase 0.92 -
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RowKey -
RowFilter - It is generally a better idea to use the startRow/stopRow methods on Scan for row selection, however - RowFilter can also be used. -
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Utility -
FirstKeyOnlyFilter - This is primarily used for rowcount jobs. - See FirstKeyOnlyFilter. -
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- -
Master - HMaster is the implementation of the Master Server. The Master server is - responsible for monitoring all RegionServer instances in the cluster, and is the interface - for all metadata changes. In a distributed cluster, the Master typically runs on the . J Mohamed Zahoor goes into some more detail on the Master - Architecture in this blog posting, HBase HMaster - Architecture . -
Startup Behavior - If run in a multi-Master environment, all Masters compete to run the cluster. If the active - Master loses its lease in ZooKeeper (or the Master shuts down), then then the remaining Masters jostle to - take over the Master role. - -
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- Runtime Impact - A common dist-list question involves what happens to an HBase cluster when the Master - goes down. Because the HBase client talks directly to the RegionServers, the cluster can - still function in a "steady state." Additionally, per , hbase:meta exists as an HBase table and is not - resident in the Master. However, the Master controls critical functions such as - RegionServer failover and completing region splits. So while the cluster can still run for - a short time without the Master, the Master should be restarted as soon as possible. - -
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Interface - The methods exposed by HMasterInterface are primarily metadata-oriented methods: - - Table (createTable, modifyTable, removeTable, enable, disable) - - ColumnFamily (addColumn, modifyColumn, removeColumn) - - Region (move, assign, unassign) - - - For example, when the HBaseAdmin method disableTable is invoked, it is serviced by the Master server. - -
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Processes - The Master runs several background threads: - -
LoadBalancer - Periodically, and when there are no regions in transition, - a load balancer will run and move regions around to balance the cluster's load. - See for configuring this property. - See for more information on region assignment. - -
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CatalogJanitor - Periodically checks and cleans up the hbase:meta table. See for more information on META. -
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- RegionServer - HRegionServer is the RegionServer implementation. It is responsible for - serving and managing regions. In a distributed cluster, a RegionServer runs on a . -
- Interface - The methods exposed by HRegionRegionInterface contain both data-oriented - and region-maintenance methods: - - Data (get, put, delete, next, etc.) - - - Region (splitRegion, compactRegion, etc.) - - For example, when the HBaseAdmin method - majorCompact is invoked on a table, the client is actually iterating - through all regions for the specified table and requesting a major compaction directly to - each region. -
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- Processes - The RegionServer runs a variety of background threads: -
- CompactSplitThread - Checks for splits and handle minor compactions. -
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- MajorCompactionChecker - Checks for major compactions. -
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- MemStoreFlusher - Periodically flushes in-memory writes in the MemStore to StoreFiles. -
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- LogRoller - Periodically checks the RegionServer's WAL. -
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- Coprocessors - Coprocessors were added in 0.92. There is a thorough Blog Overview - of CoProcessors posted. Documentation will eventually move to this reference - guide, but the blog is the most current information available at this time. -
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- Block Cache - - HBase provides two different BlockCache implementations: the default onheap - LruBlockCache and BucketCache, which is (usually) offheap. This section - discusses benefits and drawbacks of each implementation, how to choose the appropriate - option, and configuration options for each. - - Block Cache Reporting: UI - See the RegionServer UI for detail on caching deploy. Since HBase-0.98.4, the - Block Cache detail has been significantly extended showing configurations, - sizings, current usage, time-in-the-cache, and even detail on block counts and types. - - -
- - Cache Choices - LruBlockCache is the original implementation, and is - entirely within the Java heap. BucketCache is mainly - intended for keeping blockcache data offheap, although BucketCache can also - keep data onheap and serve from a file-backed cache. - BucketCache is production ready as of hbase-0.98.6 - To run with BucketCache, you need HBASE-11678. This was included in - hbase-0.98.6. - - - - - Fetching will always be slower when fetching from BucketCache, - as compared to the native onheap LruBlockCache. However, latencies tend to be - less erratic across time, because there is less garbage collection when you use - BucketCache since it is managing BlockCache allocations, not the GC. If the - BucketCache is deployed in offheap mode, this memory is not managed by the - GC at all. This is why you'd use BucketCache, so your latencies are less erratic and to mitigate GCs - and heap fragmentation. See Nick Dimiduk's BlockCache 101 for - comparisons running onheap vs offheap tests. Also see - Comparing BlockCache Deploys - which finds that if your dataset fits inside your LruBlockCache deploy, use it otherwise - if you are experiencing cache churn (or you want your cache to exist beyond the - vagaries of java GC), use BucketCache. - - - When you enable BucketCache, you are enabling a two tier caching - system, an L1 cache which is implemented by an instance of LruBlockCache and - an offheap L2 cache which is implemented by BucketCache. Management of these - two tiers and the policy that dictates how blocks move between them is done by - CombinedBlockCache. It keeps all DATA blocks in the L2 - BucketCache and meta blocks -- INDEX and BLOOM blocks -- - onheap in the L1 LruBlockCache. - See for more detail on going offheap. -
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- General Cache Configurations - Apart from the cache implementation itself, you can set some general configuration - options to control how the cache performs. See . After setting any of these options, restart or rolling restart your cluster for the - configuration to take effect. Check logs for errors or unexpected behavior. - See also , which discusses a new option - introduced in HBASE-9857. -
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- LruBlockCache Design - The LruBlockCache is an LRU cache that contains three levels of block priority to - allow for scan-resistance and in-memory ColumnFamilies: - - - Single access priority: The first time a block is loaded from HDFS it normally - has this priority and it will be part of the first group to be considered during - evictions. The advantage is that scanned blocks are more likely to get evicted than - blocks that are getting more usage. - - - Mutli access priority: If a block in the previous priority group is accessed - again, it upgrades to this priority. It is thus part of the second group considered - during evictions. - - - In-memory access priority: If the block's family was configured to be - "in-memory", it will be part of this priority disregarding the number of times it - was accessed. Catalog tables are configured like this. This group is the last one - considered during evictions. - To mark a column family as in-memory, call - HColumnDescriptor.setInMemory(true); if creating a table from java, - or set IN_MEMORY => true when creating or altering a table in - the shell: e.g. hbase(main):003:0> create 't', {NAME => 'f', IN_MEMORY => 'true'} - - - For more information, see the LruBlockCache - source - -
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- LruBlockCache Usage - Block caching is enabled by default for all the user tables which means that any - read operation will load the LRU cache. This might be good for a large number of use - cases, but further tunings are usually required in order to achieve better performance. - An important concept is the working set size, or - WSS, which is: "the amount of memory needed to compute the answer to a problem". For a - website, this would be the data that's needed to answer the queries over a short amount - of time. - The way to calculate how much memory is available in HBase for caching is: - - number of region servers * heap size * hfile.block.cache.size * 0.99 - - The default value for the block cache is 0.25 which represents 25% of the available - heap. The last value (99%) is the default acceptable loading factor in the LRU cache - after which eviction is started. The reason it is included in this equation is that it - would be unrealistic to say that it is possible to use 100% of the available memory - since this would make the process blocking from the point where it loads new blocks. - Here are some examples: - - - One region server with the default heap size (1 GB) and the default block cache - size will have 253 MB of block cache available. - - - 20 region servers with the heap size set to 8 GB and a default block cache size - will have 39.6 of block cache. - - - 100 region servers with the heap size set to 24 GB and a block cache size of 0.5 - will have about 1.16 TB of block cache. - - - Your data is not the only resident of the block cache. Here are others that you may have to take into account: - - - - Catalog Tables - - The -ROOT- (prior to HBase 0.96. See ) and hbase:meta tables are forced - into the block cache and have the in-memory priority which means that they are - harder to evict. The former never uses more than a few hundreds of bytes while the - latter can occupy a few MBs (depending on the number of regions). - - - - HFiles Indexes - - An hfile is the file format that HBase uses to store - data in HDFS. It contains a multi-layered index which allows HBase to seek to the - data without having to read the whole file. The size of those indexes is a factor - of the block size (64KB by default), the size of your keys and the amount of data - you are storing. For big data sets it's not unusual to see numbers around 1GB per - region server, although not all of it will be in cache because the LRU will evict - indexes that aren't used. - - - - Keys - - The values that are stored are only half the picture, since each value is - stored along with its keys (row key, family qualifier, and timestamp). See . - - - - Bloom Filters - - Just like the HFile indexes, those data structures (when enabled) are stored - in the LRU. - - - - Currently the recommended way to measure HFile indexes and bloom filters sizes is to - look at the region server web UI and checkout the relevant metrics. For keys, sampling - can be done by using the HFile command line tool and look for the average key size - metric. Since HBase 0.98.3, you can view detail on BlockCache stats and metrics - in a special Block Cache section in the UI. - It's generally bad to use block caching when the WSS doesn't fit in memory. This is - the case when you have for example 40GB available across all your region servers' block - caches but you need to process 1TB of data. One of the reasons is that the churn - generated by the evictions will trigger more garbage collections unnecessarily. Here are - two use cases: - - - Fully random reading pattern: This is a case where you almost never access the - same row twice within a short amount of time such that the chance of hitting a - cached block is close to 0. Setting block caching on such a table is a waste of - memory and CPU cycles, more so that it will generate more garbage to pick up by the - JVM. For more information on monitoring GC, see . - - - Mapping a table: In a typical MapReduce job that takes a table in input, every - row will be read only once so there's no need to put them into the block cache. The - Scan object has the option of turning this off via the setCaching method (set it to - false). You can still keep block caching turned on on this table if you need fast - random read access. An example would be counting the number of rows in a table that - serves live traffic, caching every block of that table would create massive churn - and would surely evict data that's currently in use. - - -
- Caching META blocks only (DATA blocks in fscache) - An interesting setup is one where we cache META blocks only and we read DATA - blocks in on each access. If the DATA blocks fit inside fscache, this alternative - may make sense when access is completely random across a very large dataset. - To enable this setup, alter your table and for each column family - set BLOCKCACHE => 'false'. You are 'disabling' the - BlockCache for this column family only you can never disable the caching of - META blocks. Since - HBASE-4683 Always cache index and bloom blocks, - we will cache META blocks even if the BlockCache is disabled. - -
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- Offheap Block Cache -
- How to Enable BucketCache - The usual deploy of BucketCache is via a managing class that sets up two caching tiers: an L1 onheap cache - implemented by LruBlockCache and a second L2 cache implemented with BucketCache. The managing class is CombinedBlockCache by default. - The just-previous link describes the caching 'policy' implemented by CombinedBlockCache. In short, it works - by keeping meta blocks -- INDEX and BLOOM in the L1, onheap LruBlockCache tier -- and DATA - blocks are kept in the L2, BucketCache tier. It is possible to amend this behavior in - HBase since version 1.0 and ask that a column family have both its meta and DATA blocks hosted onheap in the L1 tier by - setting cacheDataInL1 via - (HColumnDescriptor.setCacheDataInL1(true) - or in the shell, creating or amending column families setting CACHE_DATA_IN_L1 - to true: e.g. hbase(main):003:0> create 't', {NAME => 't', CONFIGURATION => {CACHE_DATA_IN_L1 => 'true'}} - - The BucketCache Block Cache can be deployed onheap, offheap, or file based. - You set which via the - hbase.bucketcache.ioengine setting. Setting it to - heap will have BucketCache deployed inside the - allocated java heap. Setting it to offheap will have - BucketCache make its allocations offheap, - and an ioengine setting of file:PATH_TO_FILE will direct - BucketCache to use a file caching (Useful in particular if you have some fast i/o attached to the box such - as SSDs). - - It is possible to deploy an L1+L2 setup where we bypass the CombinedBlockCache - policy and have BucketCache working as a strict L2 cache to the L1 - LruBlockCache. For such a setup, set CacheConfig.BUCKET_CACHE_COMBINED_KEY to - false. In this mode, on eviction from L1, blocks go to L2. - When a block is cached, it is cached first in L1. When we go to look for a cached block, - we look first in L1 and if none found, then search L2. Let us call this deploy format, - Raw L1+L2. - Other BucketCache configs include: specifying a location to persist cache to across - restarts, how many threads to use writing the cache, etc. See the - CacheConfig.html - class for configuration options and descriptions. - - - BucketCache Example Configuration - This sample provides a configuration for a 4 GB offheap BucketCache with a 1 GB - onheap cache. Configuration is performed on the RegionServer. Setting - hbase.bucketcache.ioengine and - hbase.bucketcache.size > 0 enables CombinedBlockCache. - Let us presume that the RegionServer has been set to run with a 5G heap: - i.e. HBASE_HEAPSIZE=5g. - - - First, edit the RegionServer's hbase-env.sh and set - HBASE_OFFHEAPSIZE to a value greater than the offheap size wanted, in - this case, 4 GB (expressed as 4G). Lets set it to 5G. That'll be 4G - for our offheap cache and 1G for any other uses of offheap memory (there are - other users of offheap memory other than BlockCache; e.g. DFSClient - in RegionServer can make use of offheap memory). See . - HBASE_OFFHEAPSIZE=5G - - - Next, add the following configuration to the RegionServer's - hbase-site.xml. - - - hbase.bucketcache.ioengine - offheap - - - hfile.block.cache.size - 0.2 - - - hbase.bucketcache.size - 4196 -]]> - - - - Restart or rolling restart your cluster, and check the logs for any - issues. - - - In the above, we set bucketcache to be 4G. The onheap lrublockcache we - configured to have 0.2 of the RegionServer's heap size (0.2 * 5G = 1G). - In other words, you configure the L1 LruBlockCache as you would normally, - as you would when there is no L2 BucketCache present. - - HBASE-10641 introduced the ability to configure multiple sizes for the - buckets of the bucketcache, in HBase 0.98 and newer. To configurable multiple bucket - sizes, configure the new property (instead of - ) to a comma-separated list of block sizes, - ordered from smallest to largest, with no spaces. The goal is to optimize the bucket - sizes based on your data access patterns. The following example configures buckets of - size 4096 and 8192. - - hfile.block.cache.sizes - 4096,8192 - - ]]> - - Direct Memory Usage In HBase - The default maximum direct memory varies by JVM. Traditionally it is 64M - or some relation to allocated heap size (-Xmx) or no limit at all (JDK7 apparently). - HBase servers use direct memory, in particular short-circuit reading, the hosted DFSClient will - allocate direct memory buffers. If you do offheap block caching, you'll - be making use of direct memory. Starting your JVM, make sure - the -XX:MaxDirectMemorySize setting in - conf/hbase-env.sh is set to some value that is - higher than what you have allocated to your offheap blockcache - (hbase.bucketcache.size). It should be larger than your offheap block - cache and then some for DFSClient usage (How much the DFSClient uses is not - easy to quantify; it is the number of open hfiles * hbase.dfs.client.read.shortcircuit.buffer.size - where hbase.dfs.client.read.shortcircuit.buffer.size is set to 128k in HBase -- see hbase-default.xml - default configurations). - Direct memory, which is part of the Java process heap, is separate from the object - heap allocated by -Xmx. The value allocated by MaxDirectMemorySize must not exceed - physical RAM, and is likely to be less than the total available RAM due to other - memory requirements and system constraints. - - You can see how much memory -- onheap and offheap/direct -- a RegionServer is - configured to use and how much it is using at any one time by looking at the - Server Metrics: Memory tab in the UI. It can also be gotten - via JMX. In particular the direct memory currently used by the server can be found - on the java.nio.type=BufferPool,name=direct bean. Terracotta has - a good write up on using offheap memory in java. It is for their product - BigMemory but alot of the issues noted apply in general to any attempt at going - offheap. Check it out. - - hbase.bucketcache.percentage.in.combinedcache - This is a pre-HBase 1.0 configuration removed because it - was confusing. It was a float that you would set to some value - between 0.0 and 1.0. Its default was 0.9. If the deploy was using - CombinedBlockCache, then the LruBlockCache L1 size was calculated to - be (1 - hbase.bucketcache.percentage.in.combinedcache) * size-of-bucketcache - and the BucketCache size was hbase.bucketcache.percentage.in.combinedcache * size-of-bucket-cache. - where size-of-bucket-cache itself is EITHER the value of the configuration hbase.bucketcache.size - IF it was specified as megabytes OR hbase.bucketcache.size * -XX:MaxDirectMemorySize if - hbase.bucketcache.size between 0 and 1.0. - - In 1.0, it should be more straight-forward. L1 LruBlockCache size - is set as a fraction of java heap using hfile.block.cache.size setting - (not the best name) and L2 is set as above either in absolute - megabytes or as a fraction of allocated maximum direct memory. - - -
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- Comprewssed Blockcache - HBASE-11331 introduced lazy blockcache decompression, more simply referred to - as compressed blockcache. When compressed blockcache is enabled. data and encoded data - blocks are cached in the blockcache in their on-disk format, rather than being - decompressed and decrypted before caching. - For a RegionServer - hosting more data than can fit into cache, enabling this feature with SNAPPY compression - has been shown to result in 50% increase in throughput and 30% improvement in mean - latency while, increasing garbage collection by 80% and increasing overall CPU load by - 2%. See HBASE-11331 for more details about how performance was measured and achieved. - For a RegionServer hosting data that can comfortably fit into cache, or if your workload - is sensitive to extra CPU or garbage-collection load, you may receive less - benefit. - Compressed blockcache is disabled by default. To enable it, set - hbase.block.data.cachecompressed to true in - hbase-site.xml on all RegionServers. -
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- Write Ahead Log (WAL) - -
- Purpose - The Write Ahead Log (WAL) records all changes to data in - HBase, to file-based storage. Under normal operations, the WAL is not needed because - data changes move from the MemStore to StoreFiles. However, if a RegionServer crashes or - becomes unavailable before the MemStore is flushed, the WAL ensures that the changes to - the data can be replayed. If writing to the WAL fails, the entire operation to modify the - data fails. - - HBase uses an implementation of the WAL interface. Usually, there is only one instance of a WAL per RegionServer. - The RegionServer records Puts and Deletes to it, before recording them to the for the affected . - - - The HLog - - Prior to 2.0, the interface for WALs in HBase was named HLog. - In 0.94, HLog was the name of the implementation of the WAL. You will likely find - references to the HLog in documentation tailored to these older versions. - - - The WAL resides in HDFS in the /hbase/WALs/ directory (prior to - HBase 0.94, they were stored in /hbase/.logs/), with subdirectories per - region. - For more general information about the concept of write ahead logs, see the - Wikipedia Write-Ahead Log - article. -
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- WAL Flushing - TODO (describe). -
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- WAL Splitting - - A RegionServer serves many regions. All of the regions in a region server share the - same active WAL file. Each edit in the WAL file includes information about which region - it belongs to. When a region is opened, the edits in the WAL file which belong to that - region need to be replayed. Therefore, edits in the WAL file must be grouped by region - so that particular sets can be replayed to regenerate the data in a particular region. - The process of grouping the WAL edits by region is called log - splitting. It is a critical process for recovering data if a region server - fails. - Log splitting is done by the HMaster during cluster start-up or by the ServerShutdownHandler - as a region server shuts down. So that consistency is guaranteed, affected regions - are unavailable until data is restored. All WAL edits need to be recovered and replayed - before a given region can become available again. As a result, regions affected by - log splitting are unavailable until the process completes. - - Log Splitting, Step by Step - - The <filename>/hbase/WALs/<host>,<port>,<startcode></filename> directory is renamed. - Renaming the directory is important because a RegionServer may still be up and - accepting requests even if the HMaster thinks it is down. If the RegionServer does - not respond immediately and does not heartbeat its ZooKeeper session, the HMaster - may interpret this as a RegionServer failure. Renaming the logs directory ensures - that existing, valid WAL files which are still in use by an active but busy - RegionServer are not written to by accident. - The new directory is named according to the following pattern: - ,,-splitting]]> - An example of such a renamed directory might look like the following: - /hbase/WALs/srv.example.com,60020,1254173957298-splitting - - - Each log file is split, one at a time. - The log splitter reads the log file one edit entry at a time and puts each edit - entry into the buffer corresponding to the edit’s region. At the same time, the - splitter starts several writer threads. Writer threads pick up a corresponding - buffer and write the edit entries in the buffer to a temporary recovered edit - file. The temporary edit file is stored to disk with the following naming pattern: - //recovered.edits/.temp]]> - This file is used to store all the edits in the WAL log for this region. After - log splitting completes, the .temp file is renamed to the - sequence ID of the first log written to the file. - To determine whether all edits have been written, the sequence ID is compared to - the sequence of the last edit that was written to the HFile. If the sequence of the - last edit is greater than or equal to the sequence ID included in the file name, it - is clear that all writes from the edit file have been completed. - - - After log splitting is complete, each affected region is assigned to a - RegionServer. - When the region is opened, the recovered.edits folder is checked for recovered - edits files. If any such files are present, they are replayed by reading the edits - and saving them to the MemStore. After all edit files are replayed, the contents of - the MemStore are written to disk (HFile) and the edit files are deleted. - - - -
- Handling of Errors During Log Splitting - - If you set the hbase.hlog.split.skip.errors option to - true, errors are treated as follows: - - - Any error encountered during splitting will be logged. - - - The problematic WAL log will be moved into the .corrupt - directory under the hbase rootdir, - - - Processing of the WAL will continue - - - If the hbase.hlog.split.skip.errors optionset to - false, the default, the exception will be propagated and the - split will be logged as failed. See HBASE-2958 When - hbase.hlog.split.skip.errors is set to false, we fail the split but thats - it. We need to do more than just fail split if this flag is set. - -
- How EOFExceptions are treated when splitting a crashed RegionServers' - WALs - - If an EOFException occurs while splitting logs, the split proceeds even when - hbase.hlog.split.skip.errors is set to - false. An EOFException while reading the last log in the set of - files to split is likely, because the RegionServer is likely to be in the process of - writing a record at the time of a crash. For background, see HBASE-2643 - Figure how to deal with eof splitting logs -
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- Performance Improvements during Log Splitting - - WAL log splitting and recovery can be resource intensive and take a long time, - depending on the number of RegionServers involved in the crash and the size of the - regions. and were developed to improve - performance during log splitting. - -
- Distributed Log Splitting - Distributed Log Splitting was added in HBase version 0.92 - (HBASE-1364) - by Prakash Khemani from Facebook. It reduces the time to complete log splitting - dramatically, improving the availability of regions and tables. For - example, recovering a crashed cluster took around 9 hours with single-threaded log - splitting, but only about six minutes with distributed log splitting. - The information in this section is sourced from Jimmy Xiang's blog post at . - - - Enabling or Disabling Distributed Log Splitting - Distributed log processing is enabled by default since HBase 0.92. The setting - is controlled by the hbase.master.distributed.log.splitting - property, which can be set to true or false, - but defaults to true. - - - Distributed Log Splitting, Step by Step - After configuring distributed log splitting, the HMaster controls the process. - The HMaster enrolls each RegionServer in the log splitting process, and the actual - work of splitting the logs is done by the RegionServers. The general process for - log splitting, as described in still applies here. - - If distributed log processing is enabled, the HMaster creates a - split log manager instance when the cluster is started. - The split log manager manages all log files which need - to be scanned and split. The split log manager places all the logs into the - ZooKeeper splitlog node (/hbase/splitlog) as tasks. You can - view the contents of the splitlog by issuing the following - zkcli command. Example output is shown. - ls /hbase/splitlog -[hdfs%3A%2F%2Fhost2.sample.com%3A56020%2Fhbase%2F.logs%2Fhost8.sample.com%2C57020%2C1340474893275-splitting%2Fhost8.sample.com%253A57020.1340474893900, -hdfs%3A%2F%2Fhost2.sample.com%3A56020%2Fhbase%2F.logs%2Fhost3.sample.com%2C57020%2C1340474893299-splitting%2Fhost3.sample.com%253A57020.1340474893931, -hdfs%3A%2F%2Fhost2.sample.com%3A56020%2Fhbase%2F.logs%2Fhost4.sample.com%2C57020%2C1340474893287-splitting%2Fhost4.sample.com%253A57020.1340474893946] - - The output contains some non-ASCII characters. When decoded, it looks much - more simple: - -[hdfs://host2.sample.com:56020/hbase/.logs -/host8.sample.com,57020,1340474893275-splitting -/host8.sample.com%3A57020.1340474893900, -hdfs://host2.sample.com:56020/hbase/.logs -/host3.sample.com,57020,1340474893299-splitting -/host3.sample.com%3A57020.1340474893931, -hdfs://host2.sample.com:56020/hbase/.logs -/host4.sample.com,57020,1340474893287-splitting -/host4.sample.com%3A57020.1340474893946] - - The listing represents WAL file names to be scanned and split, which is a - list of log splitting tasks. - - - The split log manager monitors the log-splitting tasks and workers. - The split log manager is responsible for the following ongoing tasks: - - - Once the split log manager publishes all the tasks to the splitlog - znode, it monitors these task nodes and waits for them to be - processed. - - - Checks to see if there are any dead split log - workers queued up. If it finds tasks claimed by unresponsive workers, it - will resubmit those tasks. If the resubmit fails due to some ZooKeeper - exception, the dead worker is queued up again for retry. - - - Checks to see if there are any unassigned - tasks. If it finds any, it create an ephemeral rescan node so that each - split log worker is notified to re-scan unassigned tasks via the - nodeChildrenChanged ZooKeeper event. - - - Checks for tasks which are assigned but expired. If any are found, they - are moved back to TASK_UNASSIGNED state again so that they can - be retried. It is possible that these tasks are assigned to slow workers, or - they may already be finished. This is not a problem, because log splitting - tasks have the property of idempotence. In other words, the same log - splitting task can be processed many times without causing any - problem. - - - The split log manager watches the HBase split log znodes constantly. If - any split log task node data is changed, the split log manager retrieves the - node data. The - node data contains the current state of the task. You can use the - zkcli get command to retrieve the - current state of a task. In the example output below, the first line of the - output shows that the task is currently unassigned. - -get /hbase/splitlog/hdfs%3A%2F%2Fhost2.sample.com%3A56020%2Fhbase%2F.logs%2Fhost6.sample.com%2C57020%2C1340474893287-splitting%2Fhost6.sample.com%253A57020.1340474893945 - -unassigned host2.sample.com:57000 -cZxid = 0×7115 -ctime = Sat Jun 23 11:13:40 PDT 2012 -... - - Based on the state of the task whose data is changed, the split log - manager does one of the following: - - - - Resubmit the task if it is unassigned - - - Heartbeat the task if it is assigned - - - Resubmit or fail the task if it is resigned (see ) - - - Resubmit or fail the task if it is completed with errors (see ) - - - Resubmit or fail the task if it could not complete due to - errors (see ) - - - Delete the task if it is successfully completed or failed - - - - Reasons a Task Will Fail - The task has been deleted. - The node no longer exists. - The log status manager failed to move the state of the task - to TASK_UNASSIGNED. - The number of resubmits is over the resubmit - threshold. - - - - - - Each RegionServer's split log worker performs the log-splitting tasks. - Each RegionServer runs a daemon thread called the split log - worker, which does the work to split the logs. The daemon thread - starts when the RegionServer starts, and registers itself to watch HBase znodes. - If any splitlog znode children change, it notifies a sleeping worker thread to - wake up and grab more tasks. If if a worker's current task’s node data is - changed, the worker checks to see if the task has been taken by another worker. - If so, the worker thread stops work on the current task. - The worker monitors - the splitlog znode constantly. When a new task appears, the split log worker - retrieves the task paths and checks each one until it finds an unclaimed task, - which it attempts to claim. If the claim was successful, it attempts to perform - the task and updates the task's state property based on the - splitting outcome. At this point, the split log worker scans for another - unclaimed task. - - How the Split Log Worker Approaches a Task - - - It queries the task state and only takes action if the task is in - TASK_UNASSIGNED state. - - - If the task is is in TASK_UNASSIGNED state, the - worker attempts to set the state to TASK_OWNED by itself. - If it fails to set the state, another worker will try to grab it. The split - log manager will also ask all workers to rescan later if the task remains - unassigned. - - - If the worker succeeds in taking ownership of the task, it tries to get - the task state again to make sure it really gets it asynchronously. In the - meantime, it starts a split task executor to do the actual work: - - - Get the HBase root folder, create a temp folder under the root, and - split the log file to the temp folder. - - - If the split was successful, the task executor sets the task to - state TASK_DONE. - - - If the worker catches an unexpected IOException, the task is set to - state TASK_ERR. - - - If the worker is shutting down, set the the task to state - TASK_RESIGNED. - - - If the task is taken by another worker, just log it. - - - - - - - The split log manager monitors for uncompleted tasks. - The split log manager returns when all tasks are completed successfully. If - all tasks are completed with some failures, the split log manager throws an - exception so that the log splitting can be retried. Due to an asynchronous - implementation, in very rare cases, the split log manager loses track of some - completed tasks. For that reason, it periodically checks for remaining - uncompleted task in its task map or ZooKeeper. If none are found, it throws an - exception so that the log splitting can be retried right away instead of hanging - there waiting for something that won’t happen. - - -
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- Distributed Log Replay - After a RegionServer fails, its failed region is assigned to another - RegionServer, which is marked as "recovering" in ZooKeeper. A split log worker directly - replays edits from the WAL of the failed region server to the region at its new - location. When a region is in "recovering" state, it can accept writes but no reads - (including Append and Increment), region splits or merges. - Distributed Log Replay extends the framework. It works by - directly replaying WAL edits to another RegionServer instead of creating - recovered.edits files. It provides the following advantages - over distributed log splitting alone: - - It eliminates the overhead of writing and reading a large number of - recovered.edits files. It is not unusual for thousands of - recovered.edits files to be created and written concurrently - during a RegionServer recovery. Many small random writes can degrade overall - system performance. - It allows writes even when a region is in recovering state. It only takes seconds for a recovering region to accept writes again. - - - - Enabling Distributed Log Replay - To enable distributed log replay, set hbase.master.distributed.log.replay to - true. This will be the default for HBase 0.99 (HBASE-10888). - - You must also enable HFile version 3 (which is the default HFile format starting - in HBase 0.99. See HBASE-10855). - Distributed log replay is unsafe for rolling upgrades. -
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- Disabling the WAL - It is possible to disable the WAL, to improve performace in certain specific - situations. However, disabling the WAL puts your data at risk. The only situation where - this is recommended is during a bulk load. This is because, in the event of a problem, - the bulk load can be re-run with no risk of data loss. - The WAL is disabled by calling the HBase client field - Mutation.writeToWAL(false). Use the - Mutation.setDurability(Durability.SKIP_WAL) and Mutation.getDurability() - methods to set and get the field's value. There is no way to disable the WAL for only a - specific table. - - If you disable the WAL for anything other than bulk loads, your data is at - risk. -
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- -
- -
- Regions - Regions are the basic element of availability and - distribution for tables, and are comprised of a Store per Column Family. The heirarchy of objects - is as follows: - -Table (HBase table) - Region (Regions for the table) - Store (Store per ColumnFamily for each Region for the table) - MemStore (MemStore for each Store for each Region for the table) - StoreFile (StoreFiles for each Store for each Region for the table) - Block (Blocks within a StoreFile within a Store for each Region for the table) - - For a description of what HBase files look like when written to HDFS, see . - -
- Considerations for Number of Regions - In general, HBase is designed to run with a small (20-200) number of relatively large (5-20Gb) regions per server. The considerations for this are as follows: -
- Why cannot I have too many regions? - - Typically you want to keep your region count low on HBase for numerous reasons. - Usually right around 100 regions per RegionServer has yielded the best results. - Here are some of the reasons below for keeping region count low: - - - MSLAB requires 2mb per memstore (that's 2mb per family per region). - 1000 regions that have 2 families each is 3.9GB of heap used, and it's not even storing data yet. NB: the 2MB value is configurable. - - If you fill all the regions at somewhat the same rate, the global memory usage makes it that it forces tiny - flushes when you have too many regions which in turn generates compactions. - Rewriting the same data tens of times is the last thing you want. - An example is filling 1000 regions (with one family) equally and let's consider a lower bound for global memstore - usage of 5GB (the region server would have a big heap). - Once it reaches 5GB it will force flush the biggest region, - at that point they should almost all have about 5MB of data so - it would flush that amount. 5MB inserted later, it would flush another - region that will now have a bit over 5MB of data, and so on. - This is currently the main limiting factor for the number of regions; see - for detailed formula. - - The master as is is allergic to tons of regions, and will - take a lot of time assigning them and moving them around in batches. - The reason is that it's heavy on ZK usage, and it's not very async - at the moment (could really be improved -- and has been imporoved a bunch - in 0.96 hbase). - - - In older versions of HBase (pre-v2 hfile, 0.90 and previous), tons of regions - on a few RS can cause the store file index to rise, increasing heap usage and potentially - creating memory pressure or OOME on the RSs - - - Another issue is the effect of the number of regions on mapreduce jobs; it is typical to have one mapper per HBase region. - Thus, hosting only 5 regions per RS may not be enough to get sufficient number of tasks for a mapreduce job, while 1000 regions will generate far too many tasks. - - See for configuration guidelines. - -
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- -
- Region-RegionServer Assignment - This section describes how Regions are assigned to RegionServers. - - -
- Startup - When HBase starts regions are assigned as follows (short version): - - The Master invokes the AssignmentManager upon startup. - - The AssignmentManager looks at the existing region assignments in META. - - If the region assignment is still valid (i.e., if the RegionServer is still online) - then the assignment is kept. - - If the assignment is invalid, then the LoadBalancerFactory is invoked to assign the - region. The DefaultLoadBalancer will randomly assign the region to a RegionServer. - - META is updated with the RegionServer assignment (if needed) and the RegionServer start codes - (start time of the RegionServer process) upon region opening by the RegionServer. - - - -
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- Failover - When a RegionServer fails: - - The regions immediately become unavailable because the RegionServer is - down. - - - The Master will detect that the RegionServer has failed. - - - The region assignments will be considered invalid and will be re-assigned just - like the startup sequence. - - - In-flight queries are re-tried, and not lost. - - - Operations are switched to a new RegionServer within the following amount of - time: - ZooKeeper session timeout + split time + assignment/replay time - - - -
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- Region Load Balancing - - Regions can be periodically moved by the . - -
- -
- Region State Transition - HBase maintains a state for each region and persists the state in META. The state - of the META region itself is persisted in ZooKeeper. You can see the states of regions - in transition in the Master web UI. Following is the list of possible region - states. - - - Possible Region States - - OFFLINE: the region is offline and not opening - - - OPENING: the region is in the process of being opened - - - OPEN: the region is open and the region server has notified the master - - - FAILED_OPEN: the region server failed to open the region - - - CLOSING: the region is in the process of being closed - - - CLOSED: the region server has closed the region and notified the master - - - FAILED_CLOSE: the region server failed to close the region - - - SPLITTING: the region server notified the master that the region is - splitting - - - SPLIT: the region server notified the master that the region has finished - splitting - - - SPLITTING_NEW: this region is being created by a split which is in - progress - - - MERGING: the region server notified the master that this region is being merged - with another region - - - MERGED: the region server notified the master that this region has been - merged - - - MERGING_NEW: this region is being created by a merge of two regions - - - -
- Region State Transitions - - - - - - This graph shows all allowed transitions a region can undergo. In the graph, - each node is a state. A node has a color based on the state type, for readability. - A directed line in the graph is a possible state transition. - - -
- - - Graph Legend - - Brown: Offline state, a special state that can be transient (after closed before - opening), terminal (regions of disabled tables), or initial (regions of newly - created tables) - - Palegreen: Online state that regions can serve requests - - Lightblue: Transient states - - Red: Failure states that need OPS attention - - Gold: Terminal states of regions split/merged - - Grey: Initial states of regions created through split/merge - - - - Region State Transitions Explained - - The master moves a region from OFFLINE to - OPENING state and tries to assign the region to a region - server. The region server may or may not have received the open region request. The - master retries sending the open region request to the region server until the RPC - goes through or the master runs out of retries. After the region server receives the - open region request, the region server begins opening the region. - - - If the master is running out of retries, the master prevents the region server - from opening the region by moving the region to CLOSING state and - trying to close it, even if the region server is starting to open the region. - - - After the region server opens the region, it continues to try to notify the - master until the master moves the region to OPEN state and - notifies the region server. The region is now open. - - - If the region server cannot open the region, it notifies the master. The master - moves the region to CLOSED state and tries to open the region on - a different region server. - - - If the master cannot open the region on any of a certain number of regions, it - moves the region to FAILED_OPEN state, and takes no further - action until an operator intervenes from the HBase shell, or the server is - dead. - - - The master moves a region from OPEN to - CLOSING state. The region server holding the region may or may - not have received the close region request. The master retries sending the close - request to the server until the RPC goes through or the master runs out of - retries. - - - If the region server is not online, or throws - NotServingRegionException, the master moves the region to - OFFLINE state and re-assigns it to a different region - server. - - - If the region server is online, but not reachable after the master runs out of - retries, the master moves the region to FAILED_CLOSE state and - takes no further action until an operator intervenes from the HBase shell, or the - server is dead. - - - If the region server gets the close region request, it closes the region and - notifies the master. The master moves the region to CLOSED state - and re-assigns it to a different region server. - - - Before assigning a region, the master moves the region to - OFFLINE state automatically if it is in - CLOSED state. - - - When a region server is about to split a region, it notifies the master. The - master moves the region to be split from OPEN to - SPLITTING state and add the two new regions to be created to - the region server. These two regions are in SPLITING_NEW state - initially. - - - After notifying the master, the region server starts to split the region. Once - past the point of no return, the region server notifies the master again so the - master can update the META. However, the master does not update the region states - until it is notified by the server that the split is done. If the split is - successful, the splitting region is moved from SPLITTING to - SPLIT state and the two new regions are moved from - SPLITTING_NEW to OPEN state. - - - If the split fails, the splitting region is moved from - SPLITTING back to OPEN state, and the two - new regions which were created are moved from SPLITTING_NEW to - OFFLINE state. - - - When a region server is about to merge two regions, it notifies the master - first. The master moves the two regions to be merged from OPEN to - MERGINGstate, and adds the new region which will hold the - contents of the merged regions region to the region server. The new region is in - MERGING_NEW state initially. - - - After notifying the master, the region server starts to merge the two regions. - Once past the point of no return, the region server notifies the master again so the - master can update the META. However, the master does not update the region states - until it is notified by the region server that the merge has completed. If the merge - is successful, the two merging regions are moved from MERGING to - MERGED state and the new region is moved from - MERGING_NEW to OPEN state. - - - If the merge fails, the two merging regions are moved from - MERGING back to OPEN state, and the new - region which was created to hold the contents of the merged regions is moved from - MERGING_NEW to OFFLINE state. - - - For regions in FAILED_OPEN or FAILED_CLOSE - states , the master tries to close them again when they are reassigned by an - operator via HBase Shell. - - -
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- Region-RegionServer Locality - Over time, Region-RegionServer locality is achieved via HDFS block replication. - The HDFS client does the following by default when choosing locations to write replicas: - - First replica is written to local node - - Second replica is written to a random node on another rack - - Third replica is written on the same rack as the second, but on a different node chosen randomly - - Subsequent replicas are written on random nodes on the cluster. See Replica Placement: The First Baby Steps on this page: HDFS Architecture - - - Thus, HBase eventually achieves locality for a region after a flush or a compaction. - In a RegionServer failover situation a RegionServer may be assigned regions with non-local - StoreFiles (because none of the replicas are local), however as new data is written - in the region, or the table is compacted and StoreFiles are re-written, they will become "local" - to the RegionServer. - - For more information, see Replica Placement: The First Baby Steps on this page: HDFS Architecture - and also Lars George's blog on HBase and HDFS locality. - -
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- Region Splits - Regions split when they reach a configured threshold. - Below we treat the topic in short. For a longer exposition, - see Apache HBase Region Splitting and Merging - by our Enis Soztutar. - - - Splits run unaided on the RegionServer; i.e. the Master does not - participate. The RegionServer splits a region, offlines the split - region and then adds the daughter regions to META, opens daughters on - the parent's hosting RegionServer and then reports the split to the - Master. See for how to manually manage - splits (and for why you might do this) -
- Custom Split Policies - The default split policy can be overwritten using a custom RegionSplitPolicy (HBase 0.94+). - Typically a custom split policy should extend HBase's default split policy: ConstantSizeRegionSplitPolicy. - - The policy can set globally through the HBaseConfiguration used or on a per table basis: - -HTableDescriptor myHtd = ...; -myHtd.setValue(HTableDescriptor.SPLIT_POLICY, MyCustomSplitPolicy.class.getName()); - - -
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- Manual Region Splitting - It is possible to manually split your table, either at table creation (pre-splitting), - or at a later time as an administrative action. You might choose to split your region for - one or more of the following reasons. There may be other valid reasons, but the need to - manually split your table might also point to problems with your schema design. - - Reasons to Manually Split Your Table - - Your data is sorted by timeseries or another similar algorithm that sorts new data - at the end of the table. This means that the Region Server holding the last region is - always under load, and the other Region Servers are idle, or mostly idle. See also - . - - - You have developed an unexpected hotspot in one region of your table. For - instance, an application which tracks web searches might be inundated by a lot of - searches for a celebrity in the event of news about that celebrity. See for more discussion about this particular - scenario. - - - After a big increase to the number of Region Servers in your cluster, to get the - load spread out quickly. - - - Before a bulk-load which is likely to cause unusual and uneven load across - regions. - - - See for a discussion about the dangers and - possible benefits of managing splitting completely manually. -
- Determining Split Points - The goal of splitting your table manually is to improve the chances of balancing the - load across the cluster in situations where good rowkey design alone won't get you - there. Keeping that in mind, the way you split your regions is very dependent upon the - characteristics of your data. It may be that you already know the best way to split your - table. If not, the way you split your table depends on what your keys are like. - - - Alphanumeric Rowkeys - - If your rowkeys start with a letter or number, you can split your table at - letter or number boundaries. For instance, the following command creates a table - with regions that split at each vowel, so the first region has A-D, the second - region has E-H, the third region has I-N, the fourth region has O-V, and the fifth - region has U-Z. - hbase> create 'test_table', 'f1', SPLITS=> ['a', 'e', 'i', 'o', 'u'] - The following command splits an existing table at split point '2'. - hbase> split 'test_table', '2' - You can also split a specific region by referring to its ID. You can find the - region ID by looking at either the table or region in the Web UI. It will be a - long number such as - t2,1,1410227759524.829850c6eaba1acc689480acd8f081bd.. The - format is table_name,start_key,region_idTo split that - region into two, as close to equally as possible (at the nearest row boundary), - issue the following command. - hbase> split 't2,1,1410227759524.829850c6eaba1acc689480acd8f081bd.' - The split key is optional. If it is omitted, the table or region is split in - half. - The following example shows how to use the RegionSplitter to create 10 - regions, split at hexadecimal values. - hbase org.apache.hadoop.hbase.util.RegionSplitter test_table HexStringSplit -c 10 -f f1 - - - - Using a Custom Algorithm - - The RegionSplitter tool is provided with HBase, and uses a SplitAlgorithm to determine split points for you. As - parameters, you give it the algorithm, desired number of regions, and column - families. It includes two split algorithms. The first is the HexStringSplit algorithm, which assumes the row keys are - hexadecimal strings. The second, UniformSplit, assumes the row keys are random byte arrays. You will - probably need to develop your own SplitAlgorithm, using the provided ones as - models. - - - -
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- Online Region Merges - - Both Master and Regionserver participate in the event of online region merges. - Client sends merge RPC to master, then master moves the regions together to the - same regionserver where the more heavily loaded region resided, finally master - send merge request to this regionserver and regionserver run the region merges. - Similar with process of region splits, region merges run as a local transaction - on the regionserver, offlines the regions and then merges two regions on the file - system, atomically delete merging regions from META and add merged region to the META, - opens merged region on the regionserver and reports the merge to Master at last. - - An example of region merges in the hbase shell - $ hbase> merge_region 'ENCODED_REGIONNAME', 'ENCODED_REGIONNAME' - hbase> merge_region 'ENCODED_REGIONNAME', 'ENCODED_REGIONNAME', true - - It's an asynchronous operation and call returns immediately without waiting merge completed. - Passing 'true' as the optional third parameter will force a merge ('force' merges regardless - else merge will fail unless passed adjacent regions. 'force' is for expert use only) - -
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- Store - A Store hosts a MemStore and 0 or more StoreFiles (HFiles). A Store corresponds to a column family for a table for a given region. - -
- MemStore - The MemStore holds in-memory modifications to the Store. Modifications are - Cells/KeyValues. When a flush is requested, the current memstore is moved to a snapshot and is - cleared. HBase continues to serve edits from the new memstore and backing snapshot until - the flusher reports that the flush succeeded. At this point, the snapshot is discarded. - Note that when the flush happens, Memstores that belong to the same region will all be - flushed. -
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- MemStoreFlush - A MemStore flush can be triggered under any of the conditions listed below. The - minimum flush unit is per region, not at individual MemStore level. - - - When a MemStore reaches the value specified by - hbase.hregion.memstore.flush.size, all MemStores that belong to - its region will be flushed out to disk. - - - When overall memstore usage reaches the value specified by - hbase.regionserver.global.memstore.upperLimit, MemStores from - various regions will be flushed out to disk to reduce overall MemStore usage in a - Region Server. The flush order is based on the descending order of a region's - MemStore usage. Regions will have their MemStores flushed until the overall MemStore - usage drops to or slightly below - hbase.regionserver.global.memstore.lowerLimit. - - - When the number of WAL per region server reaches the value specified in - hbase.regionserver.max.logs, MemStores from various regions - will be flushed out to disk to reduce WAL count. The flush order is based on time. - Regions with the oldest MemStores are flushed first until WAL count drops below - hbase.regionserver.max.logs. - - -
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- Scans - - - When a client issues a scan against a table, HBase generates - RegionScanner objects, one per region, to serve the scan request. - - - - The RegionScanner object contains a list of - StoreScanner objects, one per column family. - - - Each StoreScanner object further contains a list of - StoreFileScanner objects, corresponding to each StoreFile and - HFile of the corresponding column family, and a list of - KeyValueScanner objects for the MemStore. - - - The two lists are merge into one, which is sorted in ascending order with the - scan object for the MemStore at the end of the list. - - - When a StoreFileScanner object is constructed, it is associated - with a MultiVersionConsistencyControl read point, which is the - current memstoreTS, filtering out any new updates beyond the read - point. - - -
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- StoreFile (HFile) - StoreFiles are where your data lives. - -
HFile Format - The hfile file format is based on - the SSTable file described in the BigTable [2006] paper and on - Hadoop's tfile - (The unit test suite and the compression harness were taken directly from tfile). - Schubert Zhang's blog post on HFile: A Block-Indexed File Format to Store Sorted Key-Value Pairs makes for a thorough introduction to HBase's hfile. Matteo Bertozzi has also put up a - helpful description, HBase I/O: HFile. - - For more information, see the HFile source code. - Also see for information about the HFile v2 format that was included in 0.92. - -
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- HFile Tool - - To view a textualized version of hfile content, you can do use - the org.apache.hadoop.hbase.io.hfile.HFile - tool. Type the following to see usage:$ ${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.io.hfile.HFile For - example, to view the content of the file - hdfs://10.81.47.41:8020/hbase/TEST/1418428042/DSMP/4759508618286845475, - type the following: $ ${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.io.hfile.HFile -v -f hdfs://10.81.47.41:8020/hbase/TEST/1418428042/DSMP/4759508618286845475 If - you leave off the option -v to see just a summary on the hfile. See - usage for other things to do with the HFile - tool. -
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- StoreFile Directory Structure on HDFS - For more information of what StoreFiles look like on HDFS with respect to the directory structure, see . - -
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- Blocks - StoreFiles are composed of blocks. The blocksize is configured on a per-ColumnFamily basis. - - Compression happens at the block level within StoreFiles. For more information on compression, see . - - For more information on blocks, see the HFileBlock source code. - -
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- KeyValue - The KeyValue class is the heart of data storage in HBase. KeyValue wraps a byte array and takes offsets and lengths into passed array - at where to start interpreting the content as KeyValue. - - The KeyValue format inside a byte array is: - - keylength - valuelength - key - value - - - The Key is further decomposed as: - - rowlength - row (i.e., the rowkey) - columnfamilylength - columnfamily - columnqualifier - timestamp - keytype (e.g., Put, Delete, DeleteColumn, DeleteFamily) - - - KeyValue instances are not split across blocks. - For example, if there is an 8 MB KeyValue, even if the block-size is 64kb this KeyValue will be read - in as a coherent block. For more information, see the KeyValue source code. - -
Example - To emphasize the points above, examine what happens with two Puts for two different columns for the same row: - - Put #1: rowkey=row1, cf:attr1=value1 - Put #2: rowkey=row1, cf:attr2=value2 - - Even though these are for the same row, a KeyValue is created for each column: - Key portion for Put #1: - - rowlength ------------> 4 - row -----------------> row1 - columnfamilylength ---> 2 - columnfamily --------> cf - columnqualifier ------> attr1 - timestamp -----------> server time of Put - keytype -------------> Put - - - Key portion for Put #2: - - rowlength ------------> 4 - row -----------------> row1 - columnfamilylength ---> 2 - columnfamily --------> cf - columnqualifier ------> attr2 - timestamp -----------> server time of Put - keytype -------------> Put - - - - It is critical to understand that the rowkey, ColumnFamily, and column (aka columnqualifier) are embedded within - the KeyValue instance. The longer these identifiers are, the bigger the KeyValue is. -
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- Compaction - - Ambiguous Terminology - A StoreFile is a facade of HFile. In terms of compaction, use of - StoreFile seems to have prevailed in the past. - A Store is the same thing as a ColumnFamily. - StoreFiles are related to a Store, or ColumnFamily. - - If you want to read more about StoreFiles versus HFiles and Stores versus - ColumnFamilies, see HBASE-11316. - - - When the MemStore reaches a given size - (hbase.hregion.memstore.flush.size), it flushes its contents to a - StoreFile. The number of StoreFiles in a Store increases over time. - Compaction is an operation which reduces the number of - StoreFiles in a Store, by merging them together, in order to increase performance on - read operations. Compactions can be resource-intensive to perform, and can either help - or hinder performance depending on many factors. - Compactions fall into two categories: minor and major. Minor and major compactions - differ in the following ways. - Minor compactions usually select a small number of small, - adjacent StoreFiles and rewrite them as a single StoreFile. Minor compactions do not - drop (filter out) deletes or expired versions, because of potential side effects. See and for information on how deletes and versions are - handled in relation to compactions. The end result of a minor compaction is fewer, - larger StoreFiles for a given Store. - The end result of a major compaction is a single StoreFile - per Store. Major compactions also process delete markers and max versions. See and for information on how deletes and versions are - handled in relation to compactions. - - - Compaction and Deletions - When an explicit deletion occurs in HBase, the data is not actually deleted. - Instead, a tombstone marker is written. The tombstone marker - prevents the data from being returned with queries. During a major compaction, the - data is actually deleted, and the tombstone marker is removed from the StoreFile. If - the deletion happens because of an expired TTL, no tombstone is created. Instead, the - expired data is filtered out and is not written back to the compacted - StoreFile. - - - - Compaction and Versions - When you create a Column Family, you can specify the maximum number of versions - to keep, by specifying HColumnDescriptor.setMaxVersions(int - versions). The default value is 3. If more versions - than the specified maximum exist, the excess versions are filtered out and not written - back to the compacted StoreFile. - - - - Major Compactions Can Impact Query Results - In some situations, older versions can be inadvertently resurrected if a newer - version is explicitly deleted. See for a more in-depth explanation. - This situation is only possible before the compaction finishes. - - - In theory, major compactions improve performance. However, on a highly loaded - system, major compactions can require an inappropriate number of resources and adversely - affect performance. In a default configuration, major compactions are scheduled - automatically to run once in a 7-day period. This is sometimes inappropriate for systems - in production. You can manage major compactions manually. See . - Compactions do not perform region merges. See for more information on region merging. -
- Compaction Policy - HBase 0.96.x and newer - Compacting large StoreFiles, or too many StoreFiles at once, can cause more IO - load than your cluster is able to handle without causing performance problems. The - method by which HBase selects which StoreFiles to include in a compaction (and whether - the compaction is a minor or major compaction) is called the compaction - policy. - Prior to HBase 0.96.x, there was only one compaction policy. That original - compaction policy is still available as - RatioBasedCompactionPolicy The new compaction default - policy, called ExploringCompactionPolicy, was subsequently - backported to HBase 0.94 and HBase 0.95, and is the default in HBase 0.96 and newer. - It was implemented in HBASE-7842. In - short, ExploringCompactionPolicy attempts to select the best - possible set of StoreFiles to compact with the least amount of work, while the - RatioBasedCompactionPolicy selects the first set that meets - the criteria. - Regardless of the compaction policy used, file selection is controlled by several - configurable parameters and happens in a multi-step approach. These parameters will be - explained in context, and then will be given in a table which shows their - descriptions, defaults, and implications of changing them. - -
- Being Stuck - When the MemStore gets too large, it needs to flush its contents to a StoreFile. - However, a Store can only have hbase.hstore.blockingStoreFiles - files, so the MemStore needs to wait for the number of StoreFiles to be reduced by - one or more compactions. However, if the MemStore grows larger than - hbase.hregion.memstore.flush.size, it is not able to flush its - contents to a StoreFile. If the MemStore is too large and the number of StpreFo;es - is also too high, the algorithm is said to be "stuck". The compaction algorithm - checks for this "stuck" situation and provides mechanisms to alleviate it. -
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- The ExploringCompactionPolicy Algorithm - The ExploringCompactionPolicy algorithm considers each possible set of - adjacent StoreFiles before choosing the set where compaction will have the most - benefit. - One situation where the ExploringCompactionPolicy works especially well is when - you are bulk-loading data and the bulk loads create larger StoreFiles than the - StoreFiles which are holding data older than the bulk-loaded data. This can "trick" - HBase into choosing to perform a major compaction each time a compaction is needed, - and cause a lot of extra overhead. With the ExploringCompactionPolicy, major - compactions happen much less frequently because minor compactions are more - efficient. - In general, ExploringCompactionPolicy is the right choice for most situations, - and thus is the default compaction policy. You can also use - ExploringCompactionPolicy along with . - The logic of this policy can be examined in - hbase-server/src/main/java/org/apache/hadoop/hbase/regionserver/compactions/ExploringCompactionPolicy.java. - The following is a walk-through of the logic of the - ExploringCompactionPolicy. - - - Make a list of all existing StoreFiles in the Store. The rest of the - algorithm filters this list to come up with the subset of HFiles which will be - chosen for compaction. - - - If this was a user-requested compaction, attempt to perform the requested - compaction type, regardless of what would normally be chosen. Note that even if - the user requests a major compaction, it may not be possible to perform a major - compaction. This may be because not all StoreFiles in the Column Family are - available to compact or because there are too many Stores in the Column - Family. - - - Some StoreFiles are automatically excluded from consideration. These - include: - - - StoreFiles that are larger than - hbase.hstore.compaction.max.size - - - StoreFiles that were created by a bulk-load operation which explicitly - excluded compaction. You may decide to exclude StoreFiles resulting from - bulk loads, from compaction. To do this, specify the - hbase.mapreduce.hfileoutputformat.compaction.exclude - parameter during the bulk load operation. - - - - - Iterate through the list from step 1, and make a list of all potential sets - of StoreFiles to compact together. A potential set is a grouping of - hbase.hstore.compaction.min contiguous StoreFiles in the - list. For each set, perform some sanity-checking and figure out whether this is - the best compaction that could be done: - - - If the number of StoreFiles in this set (not the size of the StoreFiles) - is fewer than hbase.hstore.compaction.min or more than - hbase.hstore.compaction.max, take it out of - consideration. - - - Compare the size of this set of StoreFiles with the size of the smallest - possible compaction that has been found in the list so far. If the size of - this set of StoreFiles represents the smallest compaction that could be - done, store it to be used as a fall-back if the algorithm is "stuck" and no - StoreFiles would otherwise be chosen. See . - - - Do size-based sanity checks against each StoreFile in this set of - StoreFiles. - - - If the size of this StoreFile is larger than - hbase.hstore.compaction.max.size, take it out of - consideration. - - - If the size is greater than or equal to - hbase.hstore.compaction.min.size, sanity-check it - against the file-based ratio to see whether it is too large to be - considered. The sanity-checking is successful if: - - - There is only one StoreFile in this set, or - - - For each StoreFile, its size multiplied by - hbase.hstore.compaction.ratio (or - hbase.hstore.compaction.ratio.offpeak if - off-peak hours are configured and it is during off-peak hours) is - less than the sum of the sizes of the other HFiles in the - set. - - - - - - - - - If this set of StoreFiles is still in consideration, compare it to the - previously-selected best compaction. If it is better, replace the - previously-selected best compaction with this one. - - - When the entire list of potential compactions has been processed, perform - the best compaction that was found. If no StoreFiles were selected for - compaction, but there are multiple StoreFiles, assume the algorithm is stuck - (see ) and if so, perform the smallest - compaction that was found in step 3. - - -
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- RatioBasedCompactionPolicy Algorithm - The RatioBasedCompactionPolicy was the only compaction policy prior to HBase - 0.96, though ExploringCompactionPolicy has now been backported to HBase 0.94 and - 0.95. To use the RatioBasedCompactionPolicy rather than the - ExploringCompactionPolicy, set - hbase.hstore.defaultengine.compactionpolicy.class to - RatioBasedCompactionPolicy in the - hbase-site.xml file. To switch back to the - ExploringCompactionPolicy, remove the setting from the - hbase-site.xml. - The following section walks you through the algorithm used to select StoreFiles - for compaction in the RatioBasedCompactionPolicy. - - - The first phase is to create a list of all candidates for compaction. A list - is created of all StoreFiles not already in the compaction queue, and all - StoreFiles newer than the newest file that is currently being compacted. This - list of StoreFiles is ordered by the sequence ID. The sequence ID is generated - when a Put is appended to the write-ahead log (WAL), and is stored in the - metadata of the HFile. - - - Check to see if the algorithm is stuck (see , and if so, a major compaction is forced. - This is a key area where is often a better choice than the - RatioBasedCompactionPolicy. - - - If the compaction was user-requested, try to perform the type of compaction - that was requested. Note that a major compaction may not be possible if all - HFiles are not available for compaction or if too may StoreFiles exist (more - than hbase.hstore.compaction.max). - - - Some StoreFiles are automatically excluded from consideration. These - include: - - - StoreFiles that are larger than - hbase.hstore.compaction.max.size - - - StoreFiles that were created by a bulk-load operation which explicitly - excluded compaction. You may decide to exclude StoreFiles resulting from - bulk loads, from compaction. To do this, specify the - hbase.mapreduce.hfileoutputformat.compaction.exclude - parameter during the bulk load operation. - - - - - The maximum number of StoreFiles allowed in a major compaction is controlled - by the hbase.hstore.compaction.max parameter. If the list - contains more than this number of StoreFiles, a minor compaction is performed - even if a major compaction would otherwise have been done. However, a - user-requested major compaction still occurs even if there are more than - hbase.hstore.compaction.max StoreFiles to compact. - - - If the list contains fewer than - hbase.hstore.compaction.min StoreFiles to compact, a minor - compaction is aborted. Note that a major compaction can be performed on a single - HFile. Its function is to remove deletes and expired versions, and reset - locality on the StoreFile. - - - The value of the hbase.hstore.compaction.ratio parameter - is multiplied by the sum of StoreFiles smaller than a given file, to determine - whether that StoreFile is selected for compaction during a minor compaction. For - instance, if hbase.hstore.compaction.ratio is 1.2, FileX is 5 mb, FileY is 2 mb, - and FileZ is 3 mb: - 5 <= 1.2 x (2 + 3) or 5 <= 6 - In this scenario, FileX is eligible for minor compaction. If FileX were 7 - mb, it would not be eligible for minor compaction. This ratio favors smaller - StoreFile. You can configure a different ratio for use in off-peak hours, using - the parameter hbase.hstore.compaction.ratio.offpeak, if you - also configure hbase.offpeak.start.hour and - hbase.offpeak.end.hour. - - - - If the last major compaction was too long ago and there is more than one - StoreFile to be compacted, a major compaction is run, even if it would otherwise - have been minor. By default, the maximum time between major compactions is 7 - days, plus or minus a 4.8 hour period, and determined randomly within those - parameters. Prior to HBase 0.96, the major compaction period was 24 hours. See - hbase.hregion.majorcompaction in the table below to tune or - disable time-based major compactions. - - -
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- - Parameters Used by Compaction Algorithm - This table contains the main configuration parameters for compaction. This list - is not exhaustive. To tune these parameters from the defaults, edit the - hbase-default.xml file. For a full list of all configuration - parameters available, see - - - - - Parameter - Description - Default - - - - - hbase.hstore.compaction.min - The minimum number of StoreFiles which must be eligible for - compaction before compaction can run. - The goal of tuning hbase.hstore.compaction.min - is to avoid ending up with too many tiny StoreFiles to compact. Setting - this value to 2 would cause a minor compaction each - time you have two StoreFiles in a Store, and this is probably not - appropriate. If you set this value too high, all the other values will - need to be adjusted accordingly. For most cases, the default value is - appropriate. - In previous versions of HBase, the parameter - hbase.hstore.compaction.min was called - hbase.hstore.compactionThreshold. - - 3 - - - hbase.hstore.compaction.max - The maximum number of StoreFiles which will be selected for a - single minor compaction, regardless of the number of eligible - StoreFiles. - Effectively, the value of - hbase.hstore.compaction.max controls the length of - time it takes a single compaction to complete. Setting it larger means - that more StoreFiles are included in a compaction. For most cases, the - default value is appropriate. - - 10 - - - hbase.hstore.compaction.min.size - A StoreFile smaller than this size will always be eligible for - minor compaction. StoreFiles this size or larger are evaluated by - hbase.hstore.compaction.ratio to determine if they are - eligible. - Because this limit represents the "automatic include" limit for - all StoreFiles smaller than this value, this value may need to be reduced - in write-heavy environments where many files in the 1-2 MB range are being - flushed, because every StoreFile will be targeted for compaction and the - resulting StoreFiles may still be under the minimum size and require - further compaction. - If this parameter is lowered, the ratio check is triggered more - quickly. This addressed some issues seen in earlier versions of HBase but - changing this parameter is no longer necessary in most situations. - - 128 MB - - - hbase.hstore.compaction.max.size - An StoreFile larger than this size will be excluded from - compaction. The effect of raising - hbase.hstore.compaction.max.size is fewer, larger - StoreFiles that do not get compacted often. If you feel that compaction is - happening too often without much benefit, you can try raising this - value. - Long.MAX_VALUE - - - hbase.hstore.compaction.ratio - For minor compaction, this ratio is used to determine whether a - given StoreFile which is larger than - hbase.hstore.compaction.min.size is eligible for - compaction. Its effect is to limit compaction of large StoreFile. The - value of hbase.hstore.compaction.ratio is expressed as - a floating-point decimal. - A large ratio, such as 10, will produce a - single giant StoreFile. Conversely, a value of .25, - will produce behavior similar to the BigTable compaction algorithm, - producing four StoreFiles. - A moderate value of between 1.0 and 1.4 is recommended. When - tuning this value, you are balancing write costs with read costs. Raising - the value (to something like 1.4) will have more write costs, because you - will compact larger StoreFiles. However, during reads, HBase will need to seek - through fewer StpreFo;es to accomplish the read. Consider this approach if you - cannot take advantage of . - Alternatively, you can lower this value to something like 1.0 to - reduce the background cost of writes, and use to limit the number of StoreFiles touched - during reads. - For most cases, the default value is appropriate. - - 1.2F - - - hbase.hstore.compaction.ratio.offpeak - The compaction ratio used during off-peak compactions, if off-peak - hours are also configured (see below). Expressed as a floating-point - decimal. This allows for more aggressive (or less aggressive, if you set it - lower than hbase.hstore.compaction.ratio) compaction - during a set time period. Ignored if off-peak is disabled (default). This - works the same as hbase.hstore.compaction.ratio. - 5.0F - - - hbase.offpeak.start.hour - The start of off-peak hours, expressed as an integer between 0 and 23, - inclusive. Set to -1 to disable off-peak. - -1 (disabled) - - - hbase.offpeak.end.hour - The end of off-peak hours, expressed as an integer between 0 and 23, - inclusive. Set to -1 to disable off-peak. - -1 (disabled) - - - hbase.regionserver.thread.compaction.throttle - There are two different thread pools for compactions, one for - large compactions and the other for small compactions. This helps to keep - compaction of lean tables (such as hbase:meta) - fast. If a compaction is larger than this threshold, it goes into the - large compaction pool. In most cases, the default value is - appropriate. - 2 x hbase.hstore.compaction.max x hbase.hregion.memstore.flush.size - (which defaults to 128) - - - hbase.hregion.majorcompaction - Time between major compactions, expressed in milliseconds. Set to - 0 to disable time-based automatic major compactions. User-requested and - size-based major compactions will still run. This value is multiplied by - hbase.hregion.majorcompaction.jitter to cause - compaction to start at a somewhat-random time during a given window of - time. - 7 days (604800000 milliseconds) - - - hbase.hregion.majorcompaction.jitter - A multiplier applied to - hbase.hregion.majorcompaction to cause compaction to - occur a given amount of time either side of - hbase.hregion.majorcompaction. The smaller the - number, the closer the compactions will happen to the - hbase.hregion.majorcompaction interval. Expressed as - a floating-point decimal. - .50F - - - - -
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- Compaction File Selection - - Legacy Information - This section has been preserved for historical reasons and refers to the way - compaction worked prior to HBase 0.96.x. You can still use this behavior if you - enable For information on - the way that compactions work in HBase 0.96.x and later, see . - - To understand the core algorithm for StoreFile selection, there is some ASCII-art - in the Store - source code that will serve as useful reference. It has been copied below: - -/* normal skew: - * - * older ----> newer - * _ - * | | _ - * | | | | _ - * --|-|- |-|- |-|---_-------_------- minCompactSize - * | | | | | | | | _ | | - * | | | | | | | | | | | | - * | | | | | | | | | | | | - */ - - Important knobs: - - hbase.hstore.compaction.ratio Ratio used in compaction file - selection algorithm (default 1.2f). - - - hbase.hstore.compaction.min (.90 - hbase.hstore.compactionThreshold) (files) Minimum number of StoreFiles per Store - to be selected for a compaction to occur (default 2). - - - hbase.hstore.compaction.max (files) Maximum number of - StoreFiles to compact per minor compaction (default 10). - - - hbase.hstore.compaction.min.size (bytes) Any StoreFile smaller - than this setting with automatically be a candidate for compaction. Defaults to - hbase.hregion.memstore.flush.size (128 mb). - - - hbase.hstore.compaction.max.size (.92) (bytes) Any StoreFile - larger than this setting with automatically be excluded from compaction (default - Long.MAX_VALUE). - - - - The minor compaction StoreFile selection logic is size based, and selects a file - for compaction when the file <= sum(smaller_files) * - hbase.hstore.compaction.ratio. - -
- Minor Compaction File Selection - Example #1 (Basic Example) - This example mirrors an example from the unit test - TestCompactSelection. - - - hbase.hstore.compaction.ratio = 1.0f - - - hbase.hstore.compaction.min = 3 (files) - - - hbase.hstore.compaction.max = 5 (files) - - - hbase.hstore.compaction.min.size = 10 (bytes) - - - hbase.hstore.compaction.max.size = 1000 (bytes) - - - The following StoreFiles exist: 100, 50, 23, 12, and 12 bytes apiece (oldest to - newest). With the above parameters, the files that would be selected for minor - compaction are 23, 12, and 12. - Why? - - 100 --> No, because sum(50, 23, 12, 12) * 1.0 = 97. - - - 50 --> No, because sum(23, 12, 12) * 1.0 = 47. - - - 23 --> Yes, because sum(12, 12) * 1.0 = 24. - - - 12 --> Yes, because the previous file has been included, and because this - does not exceed the the max-file limit of 5 - - - 12 --> Yes, because the previous file had been included, and because this - does not exceed the the max-file limit of 5. - - - -
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- Minor Compaction File Selection - Example #2 (Not Enough Files To - Compact) - This example mirrors an example from the unit test - TestCompactSelection. - - hbase.hstore.compaction.ratio = 1.0f - - - hbase.hstore.compaction.min = 3 (files) - - - hbase.hstore.compaction.max = 5 (files) - - - hbase.hstore.compaction.min.size = 10 (bytes) - - - hbase.hstore.compaction.max.size = 1000 (bytes) - - - - The following StoreFiles exist: 100, 25, 12, and 12 bytes apiece (oldest to - newest). With the above parameters, no compaction will be started. - Why? - - 100 --> No, because sum(25, 12, 12) * 1.0 = 47 - - - 25 --> No, because sum(12, 12) * 1.0 = 24 - - - 12 --> No. Candidate because sum(12) * 1.0 = 12, there are only 2 files - to compact and that is less than the threshold of 3 - - - 12 --> No. Candidate because the previous StoreFile was, but there are - not enough files to compact - - - -
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- Minor Compaction File Selection - Example #3 (Limiting Files To Compact) - This example mirrors an example from the unit test - TestCompactSelection. - - hbase.hstore.compaction.ratio = 1.0f - - - hbase.hstore.compaction.min = 3 (files) - - - hbase.hstore.compaction.max = 5 (files) - - - hbase.hstore.compaction.min.size = 10 (bytes) - - - hbase.hstore.compaction.max.size = 1000 (bytes) - - The following StoreFiles exist: 7, 6, 5, 4, 3, 2, and 1 bytes apiece - (oldest to newest). With the above parameters, the files that would be selected for - minor compaction are 7, 6, 5, 4, 3. - Why? - - 7 --> Yes, because sum(6, 5, 4, 3, 2, 1) * 1.0 = 21. Also, 7 is less than - the min-size - - - 6 --> Yes, because sum(5, 4, 3, 2, 1) * 1.0 = 15. Also, 6 is less than - the min-size. - - - 5 --> Yes, because sum(4, 3, 2, 1) * 1.0 = 10. Also, 5 is less than the - min-size. - - - 4 --> Yes, because sum(3, 2, 1) * 1.0 = 6. Also, 4 is less than the - min-size. - - - 3 --> Yes, because sum(2, 1) * 1.0 = 3. Also, 3 is less than the - min-size. - - - 2 --> No. Candidate because previous file was selected and 2 is less than - the min-size, but the max-number of files to compact has been reached. - - - 1 --> No. Candidate because previous file was selected and 1 is less than - the min-size, but max-number of files to compact has been reached. - - - -
- Impact of Key Configuration Options - - This information is now included in the configuration parameter table in . - -
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- Experimental: Stripe Compactions - Stripe compactions is an experimental feature added in HBase 0.98 which aims to - improve compactions for large regions or non-uniformly distributed row keys. In order - to achieve smaller and/or more granular compactions, the StoreFiles within a region - are maintained separately for several row-key sub-ranges, or "stripes", of the region. - The stripes are transparent to the rest of HBase, so other operations on the HFiles or - data work without modification. - Stripe compactions change the HFile layout, creating sub-regions within regions. - These sub-regions are easier to compact, and should result in fewer major compactions. - This approach alleviates some of the challenges of larger regions. - Stripe compaction is fully compatible with and works in conjunction with either the - ExploringCompactionPolicy or RatioBasedCompactionPolicy. It can be enabled for - existing tables, and the table will continue to operate normally if it is disabled - later. -
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- When To Use Stripe Compactions - Consider using stripe compaction if you have either of the following: - - - Large regions. You can get the positive effects of smaller regions without - additional overhead for MemStore and region management overhead. - - - Non-uniform keys, such as time dimension in a key. Only the stripes receiving - the new keys will need to compact. Old data will not compact as often, if at - all - - - - Performance Improvements - Performance testing has shown that the performance of reads improves somewhat, - and variability of performance of reads and writes is greatly reduced. An overall - long-term performance improvement is seen on large non-uniform-row key regions, such - as a hash-prefixed timestamp key. These performance gains are the most dramatic on a - table which is already large. It is possible that the performance improvement might - extend to region splits. - -
- Enabling Stripe Compaction - You can enable stripe compaction for a table or a column family, by setting its - hbase.hstore.engine.class to - org.apache.hadoop.hbase.regionserver.StripeStoreEngine. You - also need to set the hbase.hstore.blockingStoreFiles to a high - number, such as 100 (rather than the default value of 10). - - Enable Stripe Compaction - - If the table already exists, disable the table. - - - Run one of following commands in the HBase shell. Replace the table name - orders_table with the name of your table. - -alter 'orders_table', CONFIGURATION => {'hbase.hstore.engine.class' => 'org.apache.hadoop.hbase.regionserver.StripeStoreEngine', 'hbase.hstore.blockingStoreFiles' => '100'} -alter 'orders_table', {NAME => 'blobs_cf', CONFIGURATION => {'hbase.hstore.engine.class' => 'org.apache.hadoop.hbase.regionserver.StripeStoreEngine', 'hbase.hstore.blockingStoreFiles' => '100'}} -create 'orders_table', 'blobs_cf', CONFIGURATION => {'hbase.hstore.engine.class' => 'org.apache.hadoop.hbase.regionserver.StripeStoreEngine', 'hbase.hstore.blockingStoreFiles' => '100'} - - - - Configure other options if needed. See for more information. - - - Enable the table. - - - - - Disable Stripe Compaction - - Disable the table. - - - Set the hbase.hstore.engine.class option to either nil or - org.apache.hadoop.hbase.regionserver.DefaultStoreEngine. - Either option has the same effect. - -alter 'orders_table', CONFIGURATION => {'hbase.hstore.engine.class' => ''} - - - - Enable the table. - - - When you enable a large table after changing the store engine either way, a - major compaction will likely be performed on most regions. This is not necessary on - new tables. -
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- Configuring Stripe Compaction - Each of the settings for stripe compaction should be configured at the table or - column family, after disabling the table. If you use HBase shell, the general - command pattern is as follows: - - -alter 'orders_table', CONFIGURATION => {'key' => 'value', ..., 'key' => 'value'}} - -
- Region and stripe sizing - You can configure your stripe sizing bsaed upon your region sizing. By - default, your new regions will start with one stripe. On the next compaction after - the stripe has grown too large (16 x MemStore flushes size), it is split into two - stripes. Stripe splitting continues as the region grows, until the region is large - enough to split. - You can improve this pattern for your own data. A good rule is to aim for a - stripe size of at least 1 GB, and about 8-12 stripes for uniform row keys. For - example, if your regions are 30 GB, 12 x 2.5 GB stripes might be a good starting - point. - - - Stripe Sizing Settings - - - - - - Setting - Notes - - - - - - hbase.store.stripe.initialStripeCount - - - The number of stripes to create when stripe compaction is enabled. - You can use it as follows: - - For relatively uniform row keys, if you know the approximate - target number of stripes from the above, you can avoid some - splitting overhead by starting with several stripes (2, 5, 10...). - If the early data is not representative of overall row key - distribution, this will not be as efficient. - - - For existing tables with a large amount of data, this setting - will effectively pre-split your stripes. - - - For keys such as hash-prefixed sequential keys, with more than - one hash prefix per region, pre-splitting may make sense. - - - - - - - hbase.store.stripe.sizeToSplit - - The maximum size a stripe grows before splitting. Use this in - conjunction with hbase.store.stripe.splitPartCount to - control the target stripe size (sizeToSplit = splitPartsCount * target - stripe size), according to the above sizing considerations. - - - - hbase.store.stripe.splitPartCount - - The number of new stripes to create when splitting a stripe. The - default is 2, which is appropriate for most cases. For non-uniform row - keys, you can experiment with increasing the number to 3 or 4, to isolate - the arriving updates into narrower slice of the region without additional - splits being required. - - - -
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- MemStore Size Settings - By default, the flush creates several files from one MemStore, according to - existing stripe boundaries and row keys to flush. This approach minimizes write - amplification, but can be undesirable if the MemStore is small and there are many - stripes, because the files will be too small. - In this type of situation, you can set - hbase.store.stripe.compaction.flushToL0 to - true. This will cause a MemStore flush to create a single - file instead. When at least - hbase.store.stripe.compaction.minFilesL0 such files (by - default, 4) accumulate, they will be compacted into striped files. -
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- Normal Compaction Configuration and Stripe Compaction - All the settings that apply to normal compactions (see ) apply to stripe compactions. - The exceptions are the minimum and maximum number of files, which are set to - higher values by default because the files in stripes are smaller. To control - these for stripe compactions, use - hbase.store.stripe.compaction.minFiles and - hbase.store.stripe.compaction.maxFiles, rather than - hbase.hstore.compaction.min and - hbase.hstore.compaction.max. -
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Bulk Loading -
Overview - - HBase includes several methods of loading data into tables. - The most straightforward method is to either use the TableOutputFormat - class from a MapReduce job, or use the normal client APIs; however, - these are not always the most efficient methods. - - - The bulk load feature uses a MapReduce job to output table data in HBase's internal - data format, and then directly loads the generated StoreFiles into a running - cluster. Using bulk load will use less CPU and network resources than - simply using the HBase API. - -
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Bulk Load Limitations - As bulk loading bypasses the write path, the WAL doesn’t get written to as part of the process. - Replication works by reading the WAL files so it won’t see the bulk loaded data – and the same goes for the edits that use Put.setWriteToWAL(true). - One way to handle that is to ship the raw files or the HFiles to the other cluster and do the other processing there. -
-
Bulk Load Architecture - - The HBase bulk load process consists of two main steps. - -
Preparing data via a MapReduce job - - The first step of a bulk load is to generate HBase data files (StoreFiles) from - a MapReduce job using HFileOutputFormat. This output format writes - out data in HBase's internal storage format so that they can be - later loaded very efficiently into the cluster. - - - In order to function efficiently, HFileOutputFormat must be - configured such that each output HFile fits within a single region. - In order to do this, jobs whose output will be bulk loaded into HBase - use Hadoop's TotalOrderPartitioner class to partition the map output - into disjoint ranges of the key space, corresponding to the key - ranges of the regions in the table. - - - HFileOutputFormat includes a convenience function, - configureIncrementalLoad(), which automatically sets up - a TotalOrderPartitioner based on the current region boundaries of a - table. - -
-
Completing the data load - - After the data has been prepared using - HFileOutputFormat, it is loaded into the cluster using - completebulkload. This command line tool iterates - through the prepared data files, and for each one determines the - region the file belongs to. It then contacts the appropriate Region - Server which adopts the HFile, moving it into its storage directory - and making the data available to clients. - - - If the region boundaries have changed during the course of bulk load - preparation, or between the preparation and completion steps, the - completebulkloads utility will automatically split the - data files into pieces corresponding to the new boundaries. This - process is not optimally efficient, so users should take care to - minimize the delay between preparing a bulk load and importing it - into the cluster, especially if other clients are simultaneously - loading data through other means. - -
-
-
Importing the prepared data using the completebulkload tool - - After a data import has been prepared, either by using the - importtsv tool with the - "importtsv.bulk.output" option or by some other MapReduce - job using the HFileOutputFormat, the - completebulkload tool is used to import the data into the - running cluster. - - - The completebulkload tool simply takes the output path - where importtsv or your MapReduce job put its results, and - the table name to import into. For example: - - $ hadoop jar hbase-server-VERSION.jar completebulkload [-c /path/to/hbase/config/hbase-site.xml] /user/todd/myoutput mytable - - The -c config-file option can be used to specify a file - containing the appropriate hbase parameters (e.g., hbase-site.xml) if - not supplied already on the CLASSPATH (In addition, the CLASSPATH must - contain the directory that has the zookeeper configuration file if - zookeeper is NOT managed by HBase). - - - Note: If the target table does not already exist in HBase, this - tool will create the table automatically. - - This tool will run quickly, after which point the new data will be visible in - the cluster. - -
-
See Also - For more information about the referenced utilities, see and . - - - See How-to: Use HBase Bulk Loading, and Why - for a recent blog on current state of bulk loading. - -
-
Advanced Usage - - Although the importtsv tool is useful in many cases, advanced users may - want to generate data programatically, or import data from other formats. To get - started doing so, dig into ImportTsv.java and check the JavaDoc for - HFileOutputFormat. - - - The import step of the bulk load can also be done programatically. See the - LoadIncrementalHFiles class for more information. - -
-
- -
HDFS - As HBase runs on HDFS (and each StoreFile is written as a file on HDFS), - it is important to have an understanding of the HDFS Architecture - especially in terms of how it stores files, handles failovers, and replicates blocks. - - See the Hadoop documentation on HDFS Architecture - for more information. - -
NameNode - The NameNode is responsible for maintaining the filesystem metadata. See the above HDFS Architecture link - for more information. - -
-
DataNode - The DataNodes are responsible for storing HDFS blocks. See the above HDFS Architecture link - for more information. - -
-
- -
- Timeline-consistent High Available Reads -
- Introduction - - HBase, architecturally, always had the strong consistency guarantee from the start. All reads and writes are routed through a single region server, which guarantees that all writes happen in an order, and all reads are seeing the most recent committed data. - - However, because of this single homing of the reads to a single location, if the server becomes unavailable, the regions of the table that were hosted in the region server become unavailable for some time. There are three phases in the region recovery process - detection, assignment, and recovery. Of these, the detection is usually the longest and is presently in the order of 20-30 seconds depending on the zookeeper session timeout. During this time and before the recovery is complete, the clients will not be able to read the region data. - - However, for some use cases, either the data may be read-only, or doing reads againsts some stale data is acceptable. With timeline-consistent high available reads, HBase can be used for these kind of latency-sensitive use cases where the application can expect to have a time bound on the read completion. - - For achieving high availability for reads, HBase provides a feature called “region replication”. In this model, for each region of a table, there will be multiple replicas that are opened in different region servers. By default, the region replication is set to 1, so only a single region replica is deployed and there will not be any changes from the original model. If region replication is set to 2 or more, than the master will assign replicas of the regions of the table. The Load Balancer ensures that the region replicas are not co-hosted in the same region servers and also in the same rack (if possible). - - All of the replicas for a single region will have a unique replica_id, starting from 0. The region replica having replica_id==0 is called the primary region, and the others “secondary regions” or secondaries. Only the primary can accept writes from the client, and the primary will always contain the latest changes. Since all writes still have to go through the primary region, the writes are not highly-available (meaning they might block for some time if the region becomes unavailable). - - The writes are asynchronously sent to the secondary region replicas using an “Async WAL replication” feature. This works similarly to HBase’s multi-datacenter replication, but instead the data from a region is replicated to the secondary regions. Each secondary replica always receives and observes the writes in the same order that the primary region committed them. This ensures that the secondaries won’t diverge from the primary regions data, but since the log replication is asnyc, the data might be stale in secondary regions. In some sense, this design can be thought of as “in-cluster replication”, where instead of replicating to a different datacenter, the data goes to a secondary region to keep secondary region’s in-memory state up to date. The data files are shared between the primary region and the other replicas, so that there is no extra storage overhead. However, the secondary regions will have recent non-flushed data in their memstores, which increases the memory overhead. - - Async WAL replication feature is being implemented in Phase 2 of issue HBASE-10070. Before this, region replicas will only be updated with flushed data files from the primary (see hbase.regionserver.storefile.refresh.period below). It is also possible to use this without setting storefile.refresh.period for read only tables. - -
-
- Timeline Consistency - - With this feature, HBase introduces a Consistency definition, which can be provided per read operation (get or scan). - -public enum Consistency { - STRONG, - TIMELINE -} - - Consistency.STRONG is the default consistency model provided by HBase. In case the table has region replication = 1, or in a table with region replicas but the reads are done with this consistency, the read is always performed by the primary regions, so that there will not be any change from the previous behaviour, and the client always observes the latest data. - - In case a read is performed with Consistency.TIMELINE, then the read RPC will be sent to the primary region server first. After a short interval (hbase.client.primaryCallTimeout.get, 10ms by default), parallel RPC for secondary region replicas will also be sent if the primary does not respond back. After this, the result is returned from whichever RPC is finished first. If the response came back from the primary region replica, we can always know that the data is latest. For this Result.isStale() API has been added to inspect the staleness. If the result is from a secondary region, then Result.isStale() will be set to true. The user can then inspect this field to possibly reason about the data. - - In terms of semantics, TIMELINE consistency as implemented by HBase differs from pure eventual - consistency in these respects: - - - Single homed and ordered updates: Region replication or not, on the write side, - there is still only 1 defined replica (primary) which can accept writes. This - replica is responsible for ordering the edits and preventing conflicts. This - guarantees that two different writes are not committed at the same time by different - replicas and the data diverges. With this, there is no need to do read-repair or - last-timestamp-wins kind of conflict resolution. - - - The secondaries also apply the edits in the order that the primary committed - them. This way the secondaries will contain a snapshot of the primaries data at any - point in time. This is similar to RDBMS replications and even HBase’s own - multi-datacenter replication, however in a single cluster. - - - On the read side, the client can detect whether the read is coming from - up-to-date data or is stale data. Also, the client can issue reads with different - consistency requirements on a per-operation basis to ensure its own semantic - guarantees. - - - The client can still observe edits out-of-order, and can go back in time, if it - observes reads from one secondary replica first, then another secondary replica. - There is no stickiness to region replicas or a transaction-id based guarantee. If - required, this can be implemented later though. - - - -
- HFile Version 1 - - - - - - HFile Version 1 - - -
- - To better understand the TIMELINE semantics, lets look at the above diagram. Lets say that there are two clients, and the first one writes x=1 at first, then x=2 and x=3 later. As above, all writes are handled by the primary region replica. The writes are saved in the write ahead log (WAL), and replicated to the other replicas asynchronously. In the above diagram, notice that replica_id=1 received 2 updates, and it’s data shows that x=2, while the replica_id=2 only received a single update, and its data shows that x=1. - - If client1 reads with STRONG consistency, it will only talk with the replica_id=0, and thus is guaranteed to observe the latest value of x=3. In case of a client issuing TIMELINE consistency reads, the RPC will go to all replicas (after primary timeout) and the result from the first response will be returned back. Thus the client can see either 1, 2 or 3 as the value of x. Let’s say that the primary region has failed and log replication cannot continue for some time. If the client does multiple reads with TIMELINE consistency, she can observe x=2 first, then x=1, and so on. - - -
-
- Tradeoffs - Having secondary regions hosted for read availability comes with some tradeoffs which - should be carefully evaluated per use case. Following are advantages and - disadvantages. - - Advantages - - High availability for read-only tables. - - - High availability for stale reads - - - Ability to do very low latency reads with very high percentile (99.9%+) latencies - for stale reads - - - - - Disadvantages - - Double / Triple memstore usage (depending on region replication count) for tables - with region replication > 1 - - - Increased block cache usage - - - Extra network traffic for log replication - - - Extra backup RPCs for replicas - - - To serve the region data from multiple replicas, HBase opens the regions in secondary - mode in the region servers. The regions opened in secondary mode will share the same data - files with the primary region replica, however each secondary region replica will have its - own memstore to keep the unflushed data (only primary region can do flushes). Also to - serve reads from secondary regions, the blocks of data files may be also cached in the - block caches for the secondary regions. -
-
- Configuration properties - - To use highly available reads, you should set the following properties in hbase-site.xml file. There is no specific configuration to enable or disable region replicas. Instead you can change the number of region replicas per table to increase or decrease at the table creation or with alter table. - -
- Server side properties - - hbase.regionserver.storefile.refresh.period - 0 - - The period (in milliseconds) for refreshing the store files for the secondary regions. 0 means this feature is disabled. Secondary regions sees new files (from flushes and compactions) from primary once the secondary region refreshes the list of files in the region. But too frequent refreshes might cause extra Namenode pressure. If the files cannot be refreshed for longer than HFile TTL (hbase.master.hfilecleaner.ttl) the requests are rejected. Configuring HFile TTL to a larger value is also recommended with this setting. - - -]]> - - One thing to keep in mind also is that, region replica placement policy is only - enforced by the StochasticLoadBalancer which is the default balancer. If - you are using a custom load balancer property in hbase-site.xml - (hbase.master.loadbalancer.class) replicas of regions might end up being - hosted in the same server. -
-
- Client side properties - Ensure to set the following for all clients (and servers) that will use region - replicas. - - hbase.ipc.client.allowsInterrupt - true - - Whether to enable interruption of RPC threads at the client side. This is required for region replicas with fallback RPC’s to secondary regions. - - - - hbase.client.primaryCallTimeout.get - 10000 - - The timeout (in microseconds), before secondary fallback RPC’s are submitted for get requests with Consistency.TIMELINE to the secondary replicas of the regions. Defaults to 10ms. Setting this lower will increase the number of RPC’s, but will lower the p99 latencies. - - - - hbase.client.primaryCallTimeout.multiget - 10000 - - The timeout (in microseconds), before secondary fallback RPC’s are submitted for multi-get requests (HTable.get(List)) with Consistency.TIMELINE to the secondary replicas of the regions. Defaults to 10ms. Setting this lower will increase the number of RPC’s, but will lower the p99 latencies. - - - - hbase.client.replicaCallTimeout.scan - 1000000 - - The timeout (in microseconds), before secondary fallback RPC’s are submitted for scan requests with Consistency.TIMELINE to the secondary replicas of the regions. Defaults to 1 sec. Setting this lower will increase the number of RPC’s, but will lower the p99 latencies. - - -]]> - -
-
-
- Creating a table with region replication - - Region replication is a per-table property. All tables have REGION_REPLICATION = 1 by default, which means that there is only one replica per region. You can set and change the number of replicas per region of a table by supplying the REGION_REPLICATION property in the table descriptor. - -
Shell - 2} - -describe 't1' -for i in 1..100 -put 't1', "r#{i}", 'f1:c1', i -end -flush 't1' -]]> - -
-
Java - - - You can also use setRegionReplication() and alter table to increase, decrease the - region replication for a table. -
-
-
- Region splits and merges - Region splits and merges are not compatible with regions with replicas yet. So you - have to pre-split the table, and disable the region splits. Also you should not execute - region merges on tables with region replicas. To disable region splits you can use - DisabledRegionSplitPolicy as the split policy. -
-
- User Interface - In the masters user interface, the region replicas of a table are also shown together - with the primary regions. You can notice that the replicas of a region will share the same - start and end keys and the same region name prefix. The only difference would be the - appended replica_id (which is encoded as hex), and the region encoded name will be - different. You can also see the replica ids shown explicitly in the UI. -
-
- API and Usage -
- Shell - You can do reads in shell using a the Consistency.TIMELINE semantics as follows - - get 't1','r6', {CONSISTENCY => "TIMELINE"} -]]> - You can simulate a region server pausing or becoming unavailable and do a read from - the secondary replica: - - -hbase(main):001:0> get 't1','r6', {CONSISTENCY => "TIMELINE"} -]]> - Using scans is also similar - scan 't1', {CONSISTENCY => 'TIMELINE'} -]]> -
-
- Java - You can set set the consistency for Gets and Scans and do requests as - follows. - - You can also pass multiple gets: - gets = new ArrayList(); -gets.add(get1); -... -Result[] results = table.get(gets); -]]> - And Scans: - - You can inspect whether the results are coming from primary region or not by calling - the Result.isStale() method: - - -
-
- -
- Resources - - - More information about the design and implementation can be found at the jira - issue: HBASE-10070 - - - - HBaseCon 2014 talk also contains some - details and slides. - - -
-
- - - diff --git a/src/main/docbkx/asf.xml b/src/main/docbkx/asf.xml deleted file mode 100644 index 1455b4acb42..00000000000 --- a/src/main/docbkx/asf.xml +++ /dev/null @@ -1,44 +0,0 @@ - - - - HBase and the Apache Software Foundation - HBase is a project in the Apache Software Foundation and as such there are responsibilities to the ASF to ensure - a healthy project. -
ASF Development Process - See the Apache Development Process page - for all sorts of information on how the ASF is structured (e.g., PMC, committers, contributors), to tips on contributing - and getting involved, and how open-source works at ASF. - -
-
ASF Board Reporting - Once a quarter, each project in the ASF portfolio submits a report to the ASF board. This is done by the HBase project - lead and the committers. See ASF board reporting for more information. - -
- diff --git a/src/main/docbkx/book.xml b/src/main/docbkx/book.xml deleted file mode 100644 index 30100551b51..00000000000 --- a/src/main/docbkx/book.xml +++ /dev/null @@ -1,124 +0,0 @@ - - - - - - - <link - xlink:href="http://www.hbase.org"> The Apache HBase™ Reference Guide </link> - - - - - - - - - - - - - - - 2014 - Apache Software Foundation. All Rights Reserved. Apache Hadoop, Hadoop, MapReduce, - HDFS, Zookeeper, HBase, and the HBase project logo are trademarks of the Apache Software - Foundation. - - - This is the official reference guide of Apache HBase™, a distributed, versioned, big - data store built on top of Apache Hadoop™ and Apache ZooKeeper™. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Index - - diff --git a/src/main/docbkx/case_studies.xml b/src/main/docbkx/case_studies.xml deleted file mode 100644 index 332caf873a1..00000000000 --- a/src/main/docbkx/case_studies.xml +++ /dev/null @@ -1,239 +0,0 @@ - - - - Apache HBase Case Studies -
- Overview - This chapter will describe a variety of performance and troubleshooting case studies that - can provide a useful blueprint on diagnosing Apache HBase cluster issues. - For more information on Performance and Troubleshooting, see and . -
- -
- Schema Design - See the schema design case studies here: - - -
- - -
- Performance/Troubleshooting - -
- Case Study #1 (Performance Issue On A Single Node) -
- Scenario - Following a scheduled reboot, one data node began exhibiting unusual behavior. - Routine MapReduce jobs run against HBase tables which regularly completed in five or six - minutes began taking 30 or 40 minutes to finish. These jobs were consistently found to be - waiting on map and reduce tasks assigned to the troubled data node (e.g., the slow map - tasks all had the same Input Split). The situation came to a head during a distributed - copy, when the copy was severely prolonged by the lagging node. -
-
- Hardware - - Datanodes: - - Two 12-core processors - - - Six Enerprise SATA disks - - - 24GB of RAM - - - Two bonded gigabit NICs - - - - Network: - - 10 Gigabit top-of-rack switches - - - 20 Gigabit bonded interconnects between racks. - - -
-
- Hypotheses -
- HBase "Hot Spot" Region - We hypothesized that we were experiencing a familiar point of pain: a "hot spot" - region in an HBase table, where uneven key-space distribution can funnel a huge number - of requests to a single HBase region, bombarding the RegionServer process and cause slow - response time. Examination of the HBase Master status page showed that the number of - HBase requests to the troubled node was almost zero. Further, examination of the HBase - logs showed that there were no region splits, compactions, or other region transitions - in progress. This effectively ruled out a "hot spot" as the root cause of the observed - slowness. -
-
- HBase Region With Non-Local Data - Our next hypothesis was that one of the MapReduce tasks was requesting data from - HBase that was not local to the datanode, thus forcing HDFS to request data blocks from - other servers over the network. Examination of the datanode logs showed that there were - very few blocks being requested over the network, indicating that the HBase region was - correctly assigned, and that the majority of the necessary data was located on the node. - This ruled out the possibility of non-local data causing a slowdown. -
-
- Excessive I/O Wait Due To Swapping Or An Over-Worked Or Failing Hard Disk - After concluding that the Hadoop and HBase were not likely to be the culprits, we - moved on to troubleshooting the datanode's hardware. Java, by design, will periodically - scan its entire memory space to do garbage collection. If system memory is heavily - overcommitted, the Linux kernel may enter a vicious cycle, using up all of its resources - swapping Java heap back and forth from disk to RAM as Java tries to run garbage - collection. Further, a failing hard disk will often retry reads and/or writes many times - before giving up and returning an error. This can manifest as high iowait, as running - processes wait for reads and writes to complete. Finally, a disk nearing the upper edge - of its performance envelope will begin to cause iowait as it informs the kernel that it - cannot accept any more data, and the kernel queues incoming data into the dirty write - pool in memory. However, using vmstat(1) and free(1), we could - see that no swap was being used, and the amount of disk IO was only a few kilobytes per - second. -
-
- Slowness Due To High Processor Usage - Next, we checked to see whether the system was performing slowly simply due to very - high computational load. top(1) showed that the system load was higher than - normal, but vmstat(1) and mpstat(1) showed that the amount of - processor being used for actual computation was low. -
-
- Network Saturation (The Winner) - Since neither the disks nor the processors were being utilized heavily, we moved on - to the performance of the network interfaces. The datanode had two gigabit ethernet - adapters, bonded to form an active-standby interface. ifconfig(8) showed - some unusual anomalies, namely interface errors, overruns, framing errors. While not - unheard of, these kinds of errors are exceedingly rare on modern hardware which is - operating as it should: - -$ /sbin/ifconfig bond0 -bond0 Link encap:Ethernet HWaddr 00:00:00:00:00:00 -inet addr:10.x.x.x Bcast:10.x.x.255 Mask:255.255.255.0 -UP BROADCAST RUNNING MASTER MULTICAST MTU:1500 Metric:1 -RX packets:2990700159 errors:12 dropped:0 overruns:1 frame:6 <--- Look Here! Errors! -TX packets:3443518196 errors:0 dropped:0 overruns:0 carrier:0 -collisions:0 txqueuelen:0 -RX bytes:2416328868676 (2.4 TB) TX bytes:3464991094001 (3.4 TB) - - These errors immediately lead us to suspect that one or more of the ethernet - interfaces might have negotiated the wrong line speed. This was confirmed both by - running an ICMP ping from an external host and observing round-trip-time in excess of - 700ms, and by running ethtool(8) on the members of the bond interface and - discovering that the active interface was operating at 100Mbs/, full duplex. - -$ sudo ethtool eth0 -Settings for eth0: -Supported ports: [ TP ] -Supported link modes: 10baseT/Half 10baseT/Full - 100baseT/Half 100baseT/Full - 1000baseT/Full -Supports auto-negotiation: Yes -Advertised link modes: 10baseT/Half 10baseT/Full - 100baseT/Half 100baseT/Full - 1000baseT/Full -Advertised pause frame use: No -Advertised auto-negotiation: Yes -Link partner advertised link modes: Not reported -Link partner advertised pause frame use: No -Link partner advertised auto-negotiation: No -Speed: 100Mb/s <--- Look Here! Should say 1000Mb/s! -Duplex: Full -Port: Twisted Pair -PHYAD: 1 -Transceiver: internal -Auto-negotiation: on -MDI-X: Unknown -Supports Wake-on: umbg -Wake-on: g -Current message level: 0x00000003 (3) -Link detected: yes - - In normal operation, the ICMP ping round trip time should be around 20ms, and the - interface speed and duplex should read, "1000MB/s", and, "Full", respectively. -
-
-
- Resolution - After determining that the active ethernet adapter was at the incorrect speed, we - used the ifenslave(8) command to make the standby interface the active - interface, which yielded an immediate improvement in MapReduce performance, and a 10 times - improvement in network throughput: - On the next trip to the datacenter, we determined that the line speed issue was - ultimately caused by a bad network cable, which was replaced. -
-
- -
- Case Study #2 (Performance Research 2012) - Investigation results of a self-described "we're not sure what's wrong, but it seems - slow" problem. http://gbif.blogspot.com/2012/03/hbase-performance-evaluation-continued.html - -
- -
- Case Study #3 (Performance Research 2010)) - Investigation results of general cluster performance from 2010. Although this research - is on an older version of the codebase, this writeup is still very useful in terms of - approach. http://hstack.org/hbase-performance-testing/ - -
- -
- Case Study #4 (max.transfer.threads Config) - Case study of configuring max.transfer.threads (previously known as - xcievers) and diagnosing errors from misconfigurations. http://www.larsgeorge.com/2012/03/hadoop-hbase-and-xceivers.html - - See also . -
- -
- - - diff --git a/src/main/docbkx/community.xml b/src/main/docbkx/community.xml deleted file mode 100644 index 813f3569994..00000000000 --- a/src/main/docbkx/community.xml +++ /dev/null @@ -1,149 +0,0 @@ - - - - Community -
- Decisions -
- Feature Branches - Feature Branches are easy to make. You do not have to be a committer to make one. Just - request the name of your branch be added to JIRA up on the developer's mailing list and a - committer will add it for you. Thereafter you can file issues against your feature branch in - Apache HBase JIRA. Your code you keep elsewhere -- it should be public so it can be observed - -- and you can update dev mailing list on progress. When the feature is ready for commit, 3 - +1s from committers will get your feature merged. See HBase, mail # dev - Thoughts - about large feature dev branches -
-
- Patch +1 Policy - The below policy is something we put in place 09/2012. It is a suggested policy rather - than a hard requirement. We want to try it first to see if it works before we cast it in - stone. - Apache HBase is made of components. - Components have one or more s. See the 'Description' field on the components - JIRA page for who the current owners are by component. - Patches that fit within the scope of a single Apache HBase component require, at least, - a +1 by one of the component's owners before commit. If owners are absent -- busy or - otherwise -- two +1s by non-owners will suffice. - Patches that span components need at least two +1s before they can be committed, - preferably +1s by owners of components touched by the x-component patch (TODO: This needs - tightening up but I think fine for first pass). - Any -1 on a patch by anyone vetos a patch; it cannot be committed until the - justification for the -1 is addressed. -
-
- How to set fix version in JIRA on issue resolve - Here is how we agreed to set versions in - JIRA when we resolve an issue. If trunk is going to be 0.98.0 then: - - - Commit only to trunk: Mark with 0.98 - - - Commit to 0.95 and trunk : Mark with 0.98, and 0.95.x - - - Commit to 0.94.x and 0.95, and trunk: Mark with 0.98, 0.95.x, and 0.94.x - - - Commit to 89-fb: Mark with 89-fb. - - - Commit site fixes: no version - - -
-
- Policy on when to set a RESOLVED JIRA as CLOSED - We agreed that for issues that - list multiple releases in their Fix Version/s field, CLOSE the issue on - the release of any of the versions listed; subsequent change to the issue must happen in a - new JIRA. -
-
- Only transient state in ZooKeeper! - You should be able to kill the data in zookeeper and hbase should ride over it - recreating the zk content as it goes. This is an old adage around these parts. We just made - note of it now. We also are currently in violation of this basic tenet -- replication at - least keeps permanent state in zk -- but we are working to undo this breaking of a golden - rule. -
-
-
- Community Roles -
- Component Owner/Lieutenant - Component owners are listed in the description field on this Apache HBase JIRA components - page. The owners are listed in the 'Description' field rather than in the 'Component Lead' - field because the latter only allows us list one individual whereas it is encouraged that - components have multiple owners. - Owners or component lieutenants are volunteers who are (usually, but not necessarily) - expert in their component domain and may have an agenda on how they think their Apache HBase - component should evolve. - - Component Owner Duties - - Owners will try and review patches that land within their component's scope. - - - - If applicable, if an owner has an agenda, they will publish their goals or the - design toward which they are driving their component - - - If you would like to be volunteer as a component owner, just write the dev list and - we'll sign you up. Owners do not need to be committers. -
-
-
- Commit Message format - We agreed to the following SVN - commit message format: - HBASE-xxxxx <title>. (<contributor>) If the person - making the commit is the contributor, leave off the '(<contributor>)' element. -
- diff --git a/src/main/docbkx/compression.xml b/src/main/docbkx/compression.xml deleted file mode 100644 index d1971b1c34c..00000000000 --- a/src/main/docbkx/compression.xml +++ /dev/null @@ -1,535 +0,0 @@ - - - - - Compression and Data Block Encoding In - HBase<indexterm><primary>Compression</primary><secondary>Data Block - Encoding</secondary><seealso>codecs</seealso></indexterm> - - Codecs mentioned in this section are for encoding and decoding data blocks or row keys. - For information about replication codecs, see . - - Some of the information in this section is pulled from a discussion on the - HBase Development mailing list. - HBase supports several different compression algorithms which can be enabled on a - ColumnFamily. Data block encoding attempts to limit duplication of information in keys, taking - advantage of some of the fundamental designs and patterns of HBase, such as sorted row keys - and the schema of a given table. Compressors reduce the size of large, opaque byte arrays in - cells, and can significantly reduce the storage space needed to store uncompressed - data. - Compressors and data block encoding can be used together on the same ColumnFamily. - - - Changes Take Effect Upon Compaction - If you change compression or encoding for a ColumnFamily, the changes take effect during - compaction. - - - Some codecs take advantage of capabilities built into Java, such as GZip compression. - Others rely on native libraries. Native libraries may be available as part of Hadoop, such as - LZ4. In this case, HBase only needs access to the appropriate shared library. Other codecs, - such as Google Snappy, need to be installed first. Some codecs are licensed in ways that - conflict with HBase's license and cannot be shipped as part of HBase. - - This section discusses common codecs that are used and tested with HBase. No matter what - codec you use, be sure to test that it is installed correctly and is available on all nodes in - your cluster. Extra operational steps may be necessary to be sure that codecs are available on - newly-deployed nodes. You can use the utility to check that a given codec is correctly - installed. - - To configure HBase to use a compressor, see . To enable a compressor for a ColumnFamily, see . To enable data block encoding for a ColumnFamily, see - . - - Block Compressors - - none - - - Snappy - - - LZO - - - LZ4 - - - GZ - - - - - - Data Block Encoding Types - - Prefix - Often, keys are very similar. Specifically, keys often share a common prefix - and only differ near the end. For instance, one key might be - RowKey:Family:Qualifier0 and the next key might be - RowKey:Family:Qualifier1. In Prefix encoding, an extra column is - added which holds the length of the prefix shared between the current key and the previous - key. Assuming the first key here is totally different from the key before, its prefix - length is 0. The second key's prefix length is 23, since they have the - first 23 characters in common. - Obviously if the keys tend to have nothing in common, Prefix will not provide much - benefit. - The following image shows a hypothetical ColumnFamily with no data block encoding. -
- ColumnFamily with No Encoding - - - - - A ColumnFamily with no encoding> - -
- Here is the same data with prefix data encoding. -
- ColumnFamily with Prefix Encoding - - - - - A ColumnFamily with prefix encoding - -
- - - Diff - Diff encoding expands upon Prefix encoding. Instead of considering the key - sequentially as a monolithic series of bytes, each key field is split so that each part of - the key can be compressed more efficiently. Two new fields are added: timestamp and type. - If the ColumnFamily is the same as the previous row, it is omitted from the current row. - If the key length, value length or type are the same as the previous row, the field is - omitted. In addition, for increased compression, the timestamp is stored as a Diff from - the previous row's timestamp, rather than being stored in full. Given the two row keys in - the Prefix example, and given an exact match on timestamp and the same type, neither the - value length, or type needs to be stored for the second row, and the timestamp value for - the second row is just 0, rather than a full timestamp. - Diff encoding is disabled by default because writing and scanning are slower but more - data is cached. - This image shows the same ColumnFamily from the previous images, with Diff encoding. -
- ColumnFamily with Diff Encoding - - - - - A ColumnFamily with diff encoding - -
- - - Fast Diff - Fast Diff works similar to Diff, but uses a faster implementation. It also - adds another field which stores a single bit to track whether the data itself is the same - as the previous row. If it is, the data is not stored again. Fast Diff is the recommended - codec to use if you have long keys or many columns. The data format is nearly identical to - Diff encoding, so there is not an image to illustrate it. - - - Prefix Tree encoding was introduced as an experimental feature in HBase 0.96. It - provides similar memory savings to the Prefix, Diff, and Fast Diff encoder, but provides - faster random access at a cost of slower encoding speed. Prefix Tree may be appropriate - for applications that have high block cache hit ratios. It introduces new 'tree' fields - for the row and column. The row tree field contains a list of offsets/references - corresponding to the cells in that row. This allows for a good deal of compression. For - more details about Prefix Tree encoding, see HBASE-4676. It is - difficult to graphically illustrate a prefix tree, so no image is included. See the - Wikipedia article for Trie for more general information - about this data structure. - - - -
- Which Compressor or Data Block Encoder To Use - The compression or codec type to use depends on the characteristics of your data. - Choosing the wrong type could cause your data to take more space rather than less, and can - have performance implications. In general, you need to weigh your options between smaller - size and faster compression/decompression. Following are some general guidelines, expanded from a discussion at Documenting Guidance on compression and codecs. - - - If you have long keys (compared to the values) or many columns, use a prefix - encoder. FAST_DIFF is recommended, as more testing is needed for Prefix Tree - encoding. - - - If the values are large (and not precompressed, such as images), use a data block - compressor. - - - Use GZIP for cold data, which is accessed infrequently. GZIP - compression uses more CPU resources than Snappy or LZO, but provides a higher - compression ratio. - - - Use Snappy or LZO for hot data, which is accessed - frequently. Snappy and LZO use fewer CPU resources than GZIP, but do not provide as high - of a compression ratio. - - - In most cases, enabling Snappy or LZO by default is a good choice, because they have - a low performance overhead and provide space savings. - - - Before Snappy became available by Google in 2011, LZO was the default. Snappy has - similar qualities as LZO but has been shown to perform better. - - -
-
- Making use of Hadoop Native Libraries in HBase - The Hadoop shared library has a bunch of facility including - compression libraries and fast crc'ing. To make this facility available - to HBase, do the following. HBase/Hadoop will fall back to use - alternatives if it cannot find the native library versions -- or - fail outright if you asking for an explicit compressor and there is - no alternative available. - If you see the following in your HBase logs, you know that HBase was unable - to locate the Hadoop native libraries: - 2014-08-07 09:26:20,139 WARN [main] util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable - If the libraries loaded successfully, the WARN message does not show. - - Lets presume your Hadoop shipped with a native library that - suits the platform you are running HBase on. To check if the Hadoop - native library is available to HBase, run the following tool (available in - Hadoop 2.1 and greater): - $ ./bin/hbase --config ~/conf_hbase org.apache.hadoop.util.NativeLibraryChecker -2014-08-26 13:15:38,717 WARN [main] util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable -Native library checking: -hadoop: false -zlib: false -snappy: false -lz4: false -bzip2: false -2014-08-26 13:15:38,863 INFO [main] util.ExitUtil: Exiting with status 1 -Above shows that the native hadoop library is not available in HBase context. - - To fix the above, either copy the Hadoop native libraries local or symlink to - them if the Hadoop and HBase stalls are adjacent in the filesystem. - You could also point at their location by setting the LD_LIBRARY_PATH environment - variable. - Where the JVM looks to find native librarys is "system dependent" - (See java.lang.System#loadLibrary(name)). On linux, by default, - is going to look in lib/native/PLATFORM where PLATFORM - is the label for the platform your HBase is installed on. - On a local linux machine, it seems to be the concatenation of the java properties - os.name and os.arch followed by whether 32 or 64 bit. - HBase on startup prints out all of the java system properties so find the os.name and os.arch - in the log. For example: - .... - 2014-08-06 15:27:22,853 INFO [main] zookeeper.ZooKeeper: Client environment:os.name=Linux - 2014-08-06 15:27:22,853 INFO [main] zookeeper.ZooKeeper: Client environment:os.arch=amd64 - ... - - So in this case, the PLATFORM string is Linux-amd64-64. - Copying the Hadoop native libraries or symlinking at lib/native/Linux-amd64-64 - will ensure they are found. Check with the Hadoop NativeLibraryChecker. - - - Here is example of how to point at the Hadoop libs with LD_LIBRARY_PATH - environment variable: - $ LD_LIBRARY_PATH=~/hadoop-2.5.0-SNAPSHOT/lib/native ./bin/hbase --config ~/conf_hbase org.apache.hadoop.util.NativeLibraryChecker -2014-08-26 13:42:49,332 INFO [main] bzip2.Bzip2Factory: Successfully loaded & initialized native-bzip2 library system-native -2014-08-26 13:42:49,337 INFO [main] zlib.ZlibFactory: Successfully loaded & initialized native-zlib library -Native library checking: -hadoop: true /home/stack/hadoop-2.5.0-SNAPSHOT/lib/native/libhadoop.so.1.0.0 -zlib: true /lib64/libz.so.1 -snappy: true /usr/lib64/libsnappy.so.1 -lz4: true revision:99 -bzip2: true /lib64/libbz2.so.1 -Set in hbase-env.sh the LD_LIBRARY_PATH environment variable when starting your HBase. - -
- -
- Compressor Configuration, Installation, and Use -
- Configure HBase For Compressors - Before HBase can use a given compressor, its libraries need to be available. Due to - licensing issues, only GZ compression is available to HBase (via native Java libraries) in - a default installation. Other compression libraries are available via the shared library - bundled with your hadoop. The hadoop native library needs to be findable when HBase - starts. See -
- Compressor Support On the Master - A new configuration setting was introduced in HBase 0.95, to check the Master to - determine which data block encoders are installed and configured on it, and assume that - the entire cluster is configured the same. This option, - hbase.master.check.compression, defaults to true. This - prevents the situation described in HBASE-6370, where - a table is created or modified to support a codec that a region server does not support, - leading to failures that take a long time to occur and are difficult to debug. - If hbase.master.check.compression is enabled, libraries for all desired - compressors need to be installed and configured on the Master, even if the Master does - not run a region server. -
-
- Install GZ Support Via Native Libraries - HBase uses Java's built-in GZip support unless the native Hadoop libraries are - available on the CLASSPATH. The recommended way to add libraries to the CLASSPATH is to - set the environment variable HBASE_LIBRARY_PATH for the user running - HBase. If native libraries are not available and Java's GZIP is used, Got - brand-new compressor reports will be present in the logs. See ). -
-
- Install LZO Support - HBase cannot ship with LZO because of incompatibility between HBase, which uses an - Apache Software License (ASL) and LZO, which uses a GPL license. See the Using LZO - Compression wiki page for information on configuring LZO support for HBase. - If you depend upon LZO compression, consider configuring your RegionServers to fail - to start if LZO is not available. See . -
-
- Configure LZ4 Support - LZ4 support is bundled with Hadoop. Make sure the hadoop shared library - (libhadoop.so) is accessible when you start - HBase. After configuring your platform (see ), you can make a symbolic link from HBase to the native Hadoop - libraries. This assumes the two software installs are colocated. For example, if my - 'platform' is Linux-amd64-64: - $ cd $HBASE_HOME -$ mkdir lib/native -$ ln -s $HADOOP_HOME/lib/native lib/native/Linux-amd64-64 - Use the compression tool to check that LZ4 is installed on all nodes. Start up (or restart) - HBase. Afterward, you can create and alter tables to enable LZ4 as a - compression codec.: - -hbase(main):003:0> alter 'TestTable', {NAME => 'info', COMPRESSION => 'LZ4'} - - -
-
- Install Snappy Support - HBase does not ship with Snappy support because of licensing issues. You can install - Snappy binaries (for instance, by using yum install snappy on CentOS) - or build Snappy from source. After installing Snappy, search for the shared library, - which will be called libsnappy.so.X where X is a number. If you - built from source, copy the shared library to a known location on your system, such as - /opt/snappy/lib/. - In addition to the Snappy library, HBase also needs access to the Hadoop shared - library, which will be called something like libhadoop.so.X.Y, - where X and Y are both numbers. Make note of the location of the Hadoop library, or copy - it to the same location as the Snappy library. - - The Snappy and Hadoop libraries need to be available on each node of your cluster. - See to find out how to test that this is the case. - See to configure your RegionServers to fail to - start if a given compressor is not available. - - Each of these library locations need to be added to the environment variable - HBASE_LIBRARY_PATH for the operating system user that runs HBase. You - need to restart the RegionServer for the changes to take effect. -
- - -
- CompressionTest - You can use the CompressionTest tool to verify that your compressor is available to - HBase: - - $ hbase org.apache.hadoop.hbase.util.CompressionTest hdfs://host/path/to/hbase snappy - -
- - -
- Enforce Compression Settings On a RegionServer - You can configure a RegionServer so that it will fail to restart if compression is - configured incorrectly, by adding the option hbase.regionserver.codecs to the - hbase-site.xml, and setting its value to a comma-separated list - of codecs that need to be available. For example, if you set this property to - lzo,gz, the RegionServer would fail to start if both compressors - were not available. This would prevent a new server from being added to the cluster - without having codecs configured properly. -
-
- -
- Enable Compression On a ColumnFamily - To enable compression for a ColumnFamily, use an alter command. You do - not need to re-create the table or copy data. If you are changing codecs, be sure the old - codec is still available until all the old StoreFiles have been compacted. - - Enabling Compression on a ColumnFamily of an Existing Table using HBase - Shell - disable 'test' -hbase> alter 'test', {NAME => 'cf', COMPRESSION => 'GZ'} -hbase> enable 'test']]> - - - - Creating a New Table with Compression On a ColumnFamily - create 'test2', { NAME => 'cf2', COMPRESSION => 'SNAPPY' } - ]]> - - - Verifying a ColumnFamily's Compression Settings - describe 'test' -DESCRIPTION ENABLED - 'test', {NAME => 'cf', DATA_BLOCK_ENCODING => 'NONE false - ', BLOOMFILTER => 'ROW', REPLICATION_SCOPE => '0', - VERSIONS => '1', COMPRESSION => 'GZ', MIN_VERSIONS - => '0', TTL => 'FOREVER', KEEP_DELETED_CELLS => 'fa - lse', BLOCKSIZE => '65536', IN_MEMORY => 'false', B - LOCKCACHE => 'true'} -1 row(s) in 0.1070 seconds - ]]> - -
- -
- Testing Compression Performance - HBase includes a tool called LoadTestTool which provides mechanisms to test your - compression performance. You must specify either -write or - -update-read as your first parameter, and if you do not specify another - parameter, usage advice is printed for each option. - - <command>LoadTestTool</command> Usage - -Options: - -batchupdate Whether to use batch as opposed to separate - updates for every column in a row - -bloom Bloom filter type, one of [NONE, ROW, ROWCOL] - -compression Compression type, one of [LZO, GZ, NONE, SNAPPY, - LZ4] - -data_block_encoding Encoding algorithm (e.g. prefix compression) to - use for data blocks in the test column family, one - of [NONE, PREFIX, DIFF, FAST_DIFF, PREFIX_TREE]. - -encryption Enables transparent encryption on the test table, - one of [AES] - -generator The class which generates load for the tool. Any - args for this class can be passed as colon - separated after class name - -h,--help Show usage - -in_memory Tries to keep the HFiles of the CF inmemory as far - as possible. Not guaranteed that reads are always - served from inmemory - -init_only Initialize the test table only, don't do any - loading - -key_window The 'key window' to maintain between reads and - writes for concurrent write/read workload. The - default is 0. - -max_read_errors The maximum number of read errors to tolerate - before terminating all reader threads. The default - is 10. - -multiput Whether to use multi-puts as opposed to separate - puts for every column in a row - -num_keys The number of keys to read/write - -num_tables A positive integer number. When a number n is - speicfied, load test tool will load n table - parallely. -tn parameter value becomes table name - prefix. Each table name is in format - _1..._n - -read [:<#threads=20>] - -regions_per_server A positive integer number. When a number n is - specified, load test tool will create the test - table with n regions per server - -skip_init Skip the initialization; assume test table already - exists - -start_key The first key to read/write (a 0-based index). The - default value is 0. - -tn The name of the table to read or write - -update [:<#threads=20>][:<#whether to - ignore nonce collisions=0>] - -write :[:<#threads=20>] - -zk ZK quorum as comma-separated host names without - port numbers - -zk_root name of parent znode in zookeeper - ]]> - - - Example Usage of LoadTestTool - -$ hbase org.apache.hadoop.hbase.util.LoadTestTool -write 1:10:100 -num_keys 1000000 - -read 100:30 -num_tables 1 -data_block_encoding NONE -tn load_test_tool_NONE - - -
-
- -
- Enable Data Block Encoding - Codecs are built into HBase so no extra configuration is needed. Codecs are enabled on a - table by setting the DATA_BLOCK_ENCODING property. Disable the table before - altering its DATA_BLOCK_ENCODING setting. Following is an example using HBase Shell: - - Enable Data Block Encoding On a Table - disable 'test' -hbase> alter 'test', { NAME => 'cf', DATA_BLOCK_ENCODING => 'FAST_DIFF' } -Updating all regions with the new schema... -0/1 regions updated. -1/1 regions updated. -Done. -0 row(s) in 2.2820 seconds -hbase> enable 'test' -0 row(s) in 0.1580 seconds - ]]> - - - Verifying a ColumnFamily's Data Block Encoding - describe 'test' -DESCRIPTION ENABLED - 'test', {NAME => 'cf', DATA_BLOCK_ENCODING => 'FAST true - _DIFF', BLOOMFILTER => 'ROW', REPLICATION_SCOPE => - '0', VERSIONS => '1', COMPRESSION => 'GZ', MIN_VERS - IONS => '0', TTL => 'FOREVER', KEEP_DELETED_CELLS = - > 'false', BLOCKSIZE => '65536', IN_MEMORY => 'fals - e', BLOCKCACHE => 'true'} -1 row(s) in 0.0650 seconds - ]]> - -
- - - diff --git a/src/main/docbkx/configuration.xml b/src/main/docbkx/configuration.xml deleted file mode 100644 index cbf8d1f03ff..00000000000 --- a/src/main/docbkx/configuration.xml +++ /dev/null @@ -1,1653 +0,0 @@ - - - - Apache HBase Configuration - This chapter expands upon the chapter to further explain - configuration of Apache HBase. Please read this chapter carefully, especially to ensure that your HBase testing and deployment goes - smoothly, and prevent data loss. - - Apache HBase uses the same configuration system as Apache Hadoop. All configuration files - are located in the conf/ directory, which needs to be kept in sync for each - node on your cluster. - - - HBase Configuration Files - - backup-masters - - Not present by default. A plain-text file which lists hosts on which the Master should - start a backup Master process, one host per line. - - - - hadoop-metrics2-hbase.properties - - Used to connect HBase Hadoop's Metrics2 framework. See the Hadoop Wiki - entry for more information on Metrics2. Contains only commented-out examples by - default. - - - - hbase-env.cmd and hbase-env.sh - - Script for Windows and Linux / Unix environments to set up the working environment for - HBase, including the location of Java, Java options, and other environment variables. The - file contains many commented-out examples to provide guidance. - - In HBase 0.98.5 and newer, you must set JAVA_HOME on each node of - your cluster. hbase-env.sh provides a handy mechanism to do - this. - - - - - hbase-policy.xml - - The default policy configuration file used by RPC servers to make authorization - decisions on client requests. Only used if HBase security () is enabled. - - - - hbase-site.xml - - The main HBase configuration file. This file specifies configuration options which - override HBase's default configuration. You can view (but do not edit) the default - configuration file at docs/hbase-default.xml. You can also view the - entire effective configuration for your cluster (defaults and overrides) in the - HBase Configuration tab of the HBase Web UI. - - - - log4j.properties - - Configuration file for HBase logging via log4j. - - - - regionservers - - A plain-text file containing a list of hosts which should run a RegionServer in your - HBase cluster. By default this file contains the single entry - localhost. It should contain a list of hostnames or IP addresses, one - per line, and should only contain localhost if each node in your - cluster will run a RegionServer on its localhost interface. - - - - - - Checking XML Validity - When you edit XML, it is a good idea to use an XML-aware editor to be sure that your - syntax is correct and your XML is well-formed. You can also use the xmllint - utility to check that your XML is well-formed. By default, xmllint re-flows - and prints the XML to standard output. To check for well-formedness and only print output if - errors exist, use the command xmllint -noout - filename.xml. - - - - Keep Configuration In Sync Across the Cluster - When running in distributed mode, after you make an edit to an HBase configuration, make - sure you copy the content of the conf/ directory to all nodes of the - cluster. HBase will not do this for you. Use rsync, scp, - or another secure mechanism for copying the configuration files to your nodes. For most - configuration, a restart is needed for servers to pick up changes An exception is dynamic - configuration. to be described later below. - - -
- Basic Prerequisites - This section lists required services and some required system configuration. - - - Java - - HBase requires at least Java 6 from Oracle. The following table lists - which JDK version are compatible with each version of HBase. - - - - - HBase Version - JDK 6 - JDK 7 - JDK 8 - - - - - 1.0 - Not Supported - yes - Running with JDK 8 will work but is not well tested. - - - 0.98 - yes - yes - Running with JDK 8 works but is not well tested. Building with JDK 8 would - require removal of the deprecated remove() method of the PoolMap class and is under - consideration. See ee HBASE-7608 - for more information about JDK 8 support. - - - 0.96 - yes - yes - - - - 0.94 - yes - yes - - - - -
- - - In HBase 0.98.5 and newer, you must set JAVA_HOME on each node of - your cluster. hbase-env.sh provides a handy mechanism to do - this. - - - - Operating System Utilities - - ssh - - HBase uses the Secure Shell (ssh) command and utilities extensively to communicate - between cluster nodes. Each server in the cluster must be running ssh - so that the Hadoop and HBase daemons can be managed. You must be able to connect to all - nodes via SSH, including the local node, from the Master as well as any backup Master, - using a shared key rather than a password. You can see the basic methodology for such a - set-up in Linux or Unix systems at . If your cluster nodes use OS X, see the - section, SSH: - Setting up Remote Desktop and Enabling Self-Login on the Hadoop wiki. - - - - DNS - - HBase uses the local hostname to self-report its IP address. Both forward and - reverse DNS resolving must work in versions of HBase previous to 0.92.0. The hadoop-dns-checker - tool can be used to verify DNS is working correctly on the cluster. The project - README file provides detailed instructions on usage. - - If your server has multiple network interfaces, HBase defaults to using the - interface that the primary hostname resolves to. To override this behavior, set the - hbase.regionserver.dns.interface property to a different interface. This - will only work if each server in your cluster uses the same network interface - configuration. - - To choose a different DNS nameserver than the system default, set the - hbase.regionserver.dns.nameserver property to the IP address of - that nameserver. - - - - Loopback IP - - Prior to hbase-0.96.0, HBase only used the IP address - 127.0.0.1 to refer to localhost, and this could - not be configured. See . - - - - NTP - - The clocks on cluster nodes should be synchronized. A small amount of variation is - acceptable, but larger amounts of skew can cause erratic and unexpected behavior. Time - synchronization is one of the first things to check if you see unexplained problems in - your cluster. It is recommended that you run a Network Time Protocol (NTP) service, or - another time-synchronization mechanism, on your cluster, and that all nodes look to the - same service for time synchronization. See the Basic NTP - Configuration at The Linux Documentation Project (TLDP) - to set up NTP. - - - - - Limits on Number of Files and Processes (ulimit) - - ulimit - - nproc - - - - - Apache HBase is a database. It requires the ability to open a large number of files - at once. Many Linux distributions limit the number of files a single user is allowed to - open to 1024 (or 256 on older versions of OS X). - You can check this limit on your servers by running the command ulimit - -n when logged in as the user which runs HBase. See for some of the problems you may - experience if the limit is too low. You may also notice errors such as the - following: - -2010-04-06 03:04:37,542 INFO org.apache.hadoop.hdfs.DFSClient: Exception increateBlockOutputStream java.io.EOFException -2010-04-06 03:04:37,542 INFO org.apache.hadoop.hdfs.DFSClient: Abandoning block blk_-6935524980745310745_1391901 - - It is recommended to raise the ulimit to at least 10,000, but more likely 10,240, - because the value is usually expressed in multiples of 1024. Each ColumnFamily has at - least one StoreFile, and possibly more than 6 StoreFiles if the region is under load. - The number of open files required depends upon the number of ColumnFamilies and the - number of regions. The following is a rough formula for calculating the potential number - of open files on a RegionServer. - - Calculate the Potential Number of Open Files - (StoreFiles per ColumnFamily) x (regions per RegionServer) - - For example, assuming that a schema had 3 ColumnFamilies per region with an average - of 3 StoreFiles per ColumnFamily, and there are 100 regions per RegionServer, the JVM - will open 3 * 3 * 100 = 900 file descriptors, not counting open JAR files, configuration - files, and others. Opening a file does not take many resources, and the risk of allowing - a user to open too many files is minimal. - Another related setting is the number of processes a user is allowed to run at once. - In Linux and Unix, the number of processes is set using the ulimit -u - command. This should not be confused with the nproc command, which - controls the number of CPUs available to a given user. Under load, a - nproc that is too low can cause OutOfMemoryError exceptions. See - Jack Levin's major - hdfs issues thread on the hbase-users mailing list, from 2011. - Configuring the fmaximum number of ile descriptors and processes for the user who is - running the HBase process is an operating system configuration, rather than an HBase - configuration. It is also important to be sure that the settings are changed for the - user that actually runs HBase. To see which user started HBase, and that user's ulimit - configuration, look at the first line of the HBase log for that instance. A useful read - setting config on you hadoop cluster is Aaron Kimballs' Configuration Parameters: What can you just ignore? - - <command>ulimit</command> Settings on Ubuntu - To configure ulimit settings on Ubuntu, edit - /etc/security/limits.conf, which is a space-delimited file with - four columns. Refer to the man - page for limits.conf for details about the format of this file. In the - following example, the first line sets both soft and hard limits for the number of - open files (nofile) to 32768 for the operating - system user with the username hadoop. The second line sets the - number of processes to 32000 for the same user. - - -hadoop - nofile 32768 -hadoop - nproc 32000 - - The settings are only applied if the Pluggable Authentication Module (PAM) - environment is directed to use them. To configure PAM to use these limits, be sure that - the /etc/pam.d/common-session file contains the following line: - session required pam_limits.so - - - - - Windows - - - Prior to HBase 0.96, testing for running HBase on Microsoft Windows was limited. - Running a on Windows nodes is not recommended for production systems. - - To run versions of HBase prior to 0.96 on Microsoft Windows, you must install Cygwin and run HBase within the Cygwin - environment. This provides support for Linux/Unix commands and scripts. The full details are explained in the Windows Installation guide. Also search - our user mailing list to pick up latest fixes figured by Windows users. - Post-hbase-0.96.0, hbase runs natively on windows with supporting - *.cmd scripts bundled. - - - - - -
- <link - xlink:href="http://hadoop.apache.org">Hadoop</link><indexterm> - <primary>Hadoop</primary> - </indexterm> - The following table summarizes the versions of Hadoop supported with each version of - HBase. Based on the version of HBase, you should select the most - appropriate version of Hadoop. You can use Apache Hadoop, or a vendor's distribution of - Hadoop. No distinction is made here. See - for information about vendors of Hadoop. - - Hadoop 2.x is recommended. - Hadoop 2.x is faster and includes features, such as short-circuit reads, which will - help improve your HBase random read profile. Hadoop 2.x also includes important bug fixes - that will improve your overall HBase experience. HBase 0.98 drops support for Hadoop 1.0, deprecates use of Hadoop 1.1+, - and HBase 1.0 will not support Hadoop 1.x. - - Use the following legend to interpret this table: - - S = supported and tested, - X = not supported, - NT = it should run, but not tested enough. - - - - Hadoop version support matrix - - - - - - - - - - - HBase-0.92.x - HBase-0.94.x - HBase-0.96.x - HBase-0.98.x (Support for Hadoop 1.1+ is deprecated.) - HBase-1.0.x (Hadoop 1.x is NOT supported) - - - - - Hadoop-0.20.205 - S - X - X - X - X - - - Hadoop-0.22.x - S - X - X - X - X - - - Hadoop-1.0.x - X - X - X - X - X - - - Hadoop-1.1.x - NT - S - S - NT - X - - - Hadoop-0.23.x - X - S - NT - X - X - - - Hadoop-2.0.x-alpha - X - NT - X - X - X - - - Hadoop-2.1.0-beta - X - NT - S - X - X - - - Hadoop-2.2.0 - X - NT - S - S - NT - - - Hadoop-2.3.x - X - NT - S - S - NT - - - Hadoop-2.4.x - X - NT - S - S - S - - - Hadoop-2.5.x - X - NT - S - S - S - - - - -
- - - Replace the Hadoop Bundled With HBase! - Because HBase depends on Hadoop, it bundles an instance of the Hadoop jar under its - lib directory. The bundled jar is ONLY for use in standalone mode. - In distributed mode, it is critical that the version of Hadoop that - is out on your cluster match what is under HBase. Replace the hadoop jar found in the - HBase lib directory with the hadoop jar you are running on your cluster to avoid version - mismatch issues. Make sure you replace the jar in HBase everywhere on your cluster. Hadoop - version mismatch issues have various manifestations but often all looks like its hung up. - - -
- Apache HBase 0.94 with Hadoop 2 - To get 0.94.x to run on hadoop 2.2.0, you need to change the hadoop - 2 and protobuf versions in the pom.xml: Here is a diff with - pom.xml changes: - 1.4.3 - 1.2.16 - 1.8.5 -- 2.4.0a -+ 2.5.0 - 1.0.1 - 0.8.0 - 3.4.5 -@@ -2241,7 +2241,7 @@ - - - -- 2.0.0-alpha -+ 2.2.0 - 1.6.1 - - ]]> - - The next step is to regenerate Protobuf files and assuming that the Protobuf - has been installed: - - - Go to the hbase root folder, using the command line; - - - Type the following commands: - - - - - - - - - Building against the hadoop 2 profile by running something like the - following command: - $ mvn clean install assembly:single -Dhadoop.profile=2.0 -DskipTests -
-
- Apache HBase 0.92 and 0.94 - HBase 0.92 and 0.94 versions can work with Hadoop versions, 0.20.205, 0.22.x, 1.0.x, - and 1.1.x. HBase-0.94 can additionally work with Hadoop-0.23.x and 2.x, but you may have - to recompile the code using the specific maven profile (see top level pom.xml) -
- -
- Apache HBase 0.96 - As of Apache HBase 0.96.x, Apache Hadoop 1.0.x at least is required. Hadoop 2 is - strongly encouraged (faster but also has fixes that help MTTR). We will no longer run - properly on older Hadoops such as 0.20.205 or branch-0.20-append. Do not move to Apache - HBase 0.96.x if you cannot upgrade your Hadoop.. See HBase, mail # dev - DISCUSS: - Have hbase require at least hadoop 1.0.0 in hbase 0.96.0? -
- -
- Hadoop versions 0.20.x - 1.x - HBase will lose data unless it is running on an HDFS that has a durable - sync implementation. DO NOT use Hadoop 0.20.2, Hadoop 0.20.203.0, and - Hadoop 0.20.204.0 which DO NOT have this attribute. Currently only Hadoop versions - 0.20.205.x or any release in excess of this version -- this includes hadoop-1.0.0 -- have - a working, durable sync. The Cloudera blog post An - update on Apache Hadoop 1.0 by Charles Zedlweski has a nice exposition on how all - the Hadoop versions relate. Its worth checking out if you are having trouble making sense - of the Hadoop version morass. - Sync has to be explicitly enabled by setting - dfs.support.append equal to true on both the client side -- in - hbase-site.xml -- and on the serverside in - hdfs-site.xml (The sync facility HBase needs is a subset of the - append code path). - - dfs.support.append - true -]]> - You will have to restart your cluster after making this edit. Ignore the - chicken-little comment you'll find in the hdfs-default.xml in the - description for the dfs.support.append configuration. -
-
- Apache HBase on Secure Hadoop - Apache HBase will run on any Hadoop 0.20.x that incorporates Hadoop security features - as long as you do as suggested above and replace the Hadoop jar that ships with HBase with - the secure version. If you want to read more about how to setup Secure HBase, see . -
- -
- <varname>dfs.datanode.max.transfer.threads</varname><indexterm> - <primary>dfs.datanode.max.transfer.threads</primary> - </indexterm> - - An HDFS datanode has an upper bound on the number of files that it will serve - at any one time. Before doing any loading, make sure you have configured - Hadoop's conf/hdfs-site.xml, setting the - dfs.datanode.max.transfer.threads value to at least the following: - - - dfs.datanode.max.transfer.threads - 4096 - - ]]> - - Be sure to restart your HDFS after making the above configuration. - - Not having this configuration in place makes for strange-looking failures. One - manifestation is a complaint about missing blocks. For example: - 10/12/08 20:10:31 INFO hdfs.DFSClient: Could not obtain block - blk_XXXXXXXXXXXXXXXXXXXXXX_YYYYYYYY from any node: java.io.IOException: No live nodes - contain current block. Will get new block locations from namenode and retry... - See also and note that this - property was previously known as dfs.datanode.max.xcievers (e.g. - - Hadoop HDFS: Deceived by Xciever). - - - -
-
- -
- ZooKeeper Requirements - ZooKeeper 3.4.x is required as of HBase 1.0.0. HBase makes use of the - multi functionality that is only available since 3.4.0 - (The useMulti is defaulted true in HBase 1.0.0). - See HBASE-12241 The crash of regionServer when taking deadserver's replication queue breaks replication - and Use ZK.multi when available for HBASE-6710 0.92/0.94 compatibility fix for background. -
-
- -
- HBase run modes: Standalone and Distributed - - HBase has two run modes: and . Out of the box, HBase runs in standalone mode. Whatever your mode, - you will need to configure HBase by editing files in the HBase conf - directory. At a minimum, you must edit conf/hbase-env.sh to tell HBase which - java to use. In this file you set HBase environment variables such as the - heapsize and other options for the JVM, the preferred location for - log files, etc. Set JAVA_HOME to point at the root of your - java install. - -
- Standalone HBase - - This is the default mode. Standalone mode is what is described in the section. In standalone mode, HBase does not use HDFS -- it uses - the local filesystem instead -- and it runs all HBase daemons and a local ZooKeeper all up - in the same JVM. Zookeeper binds to a well known port so clients may talk to HBase. -
- -
- Distributed - - Distributed mode can be subdivided into distributed but all daemons run on a single node - -- a.k.a pseudo-distributed-- and - fully-distributed where the daemons are spread across all nodes in - the cluster. The pseudo-distributed vs fully-distributed nomenclature comes from Hadoop. - - Pseudo-distributed mode can run against the local filesystem or it can run against an - instance of the Hadoop Distributed File System (HDFS). - Fully-distributed mode can ONLY run on HDFS. See the Hadoop - requirements and instructions for how to set up HDFS for Hadoop 1.x. A good - walk-through for setting up HDFS on Hadoop 2 is at http://www.alexjf.net/blog/distributed-systems/hadoop-yarn-installation-definitive-guide. - - Below we describe the different distributed setups. Starting, verification and - exploration of your install, whether a pseudo-distributed or - fully-distributed configuration is described in a section that - follows, . The same verification script applies to both deploy types. -
- Pseudo-distributed - - Pseudo-Distributed Quickstart - A quickstart has been added to the chapter. See . Some of the information that was originally in this - section has been moved there. - - - A pseudo-distributed mode is simply a fully-distributed mode run on a single host. Use - this configuration testing and prototyping on HBase. Do not use this configuration for - production nor for evaluating HBase performance. - -
- -
- -
- Fully-distributed - By default, HBase runs in standalone mode. Both standalone mode and pseudo-distributed - mode are provided for the purposes of small-scale testing. For a production environment, - distributed mode is appropriate. In distributed mode, multiple instances of HBase daemons - run on multiple servers in the cluster. - Just as in pseudo-distributed mode, a fully distributed configuration requires that you - set the hbase-cluster.distributed property to true. - Typically, the hbase.rootdir is configured to point to a highly-available HDFS - filesystem. - In addition, the cluster is configured so that multiple cluster nodes enlist as - RegionServers, ZooKeeper QuorumPeers, and backup HMaster servers. These configuration basics - are all demonstrated in . - - - Distributed RegionServers - Typically, your cluster will contain multiple RegionServers all running on different - servers, as well as primary and backup Master and Zookeeper daemons. The - conf/regionservers file on the master server contains a list of - hosts whose RegionServers are associated with this cluster. Each host is on a separate - line. All hosts listed in this file will have their RegionServer processes started and - stopped when the master server starts or stops. - - - - ZooKeeper and HBase - See section for ZooKeeper setup for HBase. - - - - Example Distributed HBase Cluster - This is a bare-bones conf/hbase-site.xml for a distributed HBase - cluster. A cluster that is used for real-world work would contain more custom - configuration parameters. Most HBase configuration directives have default values, which - are used unless the value is overridden in the hbase-site.xml. See for more information. - - - hbase.rootdir - hdfs://namenode.example.org:8020/hbase - - - hbase.cluster.distributed - true - - - hbase.zookeeper.quorum - node-a.example.com,node-b.example.com,node-c.example.com - - -]]> - - This is an example conf/regionservers file, which contains a list - of each node that should run a RegionServer in the cluster. These nodes need HBase - installed and they need to use the same contents of the conf/ - directory as the Master server.. - -node-a.example.com -node-b.example.com -node-c.example.com - - This is an example conf/backup-masters file, which contains a - list of each node that should run a backup Master instance. The backup Master instances - will sit idle unless the main Master becomes unavailable. - -node-b.example.com -node-c.example.com - - - - Distributed HBase Quickstart - See for a walk-through of a simple three-node - cluster configuration with multiple ZooKeeper, backup HMaster, and RegionServer - instances. - - - - HDFS Client Configuration - - Of note, if you have made HDFS client configuration on your Hadoop cluster, such as - configuration directives for HDFS clients, as opposed to server-side configurations, you - must use one of the following methods to enable HBase to see and use these configuration - changes: - - - Add a pointer to your HADOOP_CONF_DIR to the - HBASE_CLASSPATH environment variable in - hbase-env.sh. - - - - Add a copy of hdfs-site.xml (or - hadoop-site.xml) or, better, symlinks, under - ${HBASE_HOME}/conf, or - - - - if only a small set of HDFS client configurations, add them to - hbase-site.xml. - - - - - An example of such an HDFS client configuration is dfs.replication. - If for example, you want to run with a replication factor of 5, hbase will create files with - the default of 3 unless you do the above to make the configuration available to - HBase. -
-
- -
- Running and Confirming Your Installation - - - - Make sure HDFS is running first. Start and stop the Hadoop HDFS daemons by running - bin/start-hdfs.sh over in the HADOOP_HOME - directory. You can ensure it started properly by testing the put and - get of files into the Hadoop filesystem. HBase does not normally use - the mapreduce daemons. These do not need to be started. - If you are managing your own ZooKeeper, start it and confirm its - running else, HBase will start up ZooKeeper for you as part of its start process. - Start HBase with the following command: - bin/start-hbase.sh - Run the above from the HBASE_HOME directory. - You should now have a running HBase instance. HBase logs can be found in the - logs subdirectory. Check them out especially if HBase had trouble - starting. - - HBase also puts up a UI listing vital attributes. By default its deployed on the Master - host at port 16010 (HBase RegionServers listen on port 16020 by default and put up an - informational http server at 16030). If the Master were running on a host named - master.example.org on the default port, to see the Master's homepage - you'd point your browser at http://master.example.org:16010. - - Prior to HBase 0.98, the default ports the master ui was deployed on port 16010, and the - HBase RegionServers would listen on port 16020 by default and put up an informational http - server at 16030. - - Once HBase has started, see the for how to create tables, add data, scan your insertions, and - finally disable and drop your tables. - - To stop HBase after exiting the HBase shell enter - $ ./bin/stop-hbase.sh -stopping hbase............... - Shutdown can take a moment to complete. It can take longer if your cluster is comprised - of many machines. If you are running a distributed operation, be sure to wait until HBase - has shut down completely before stopping the Hadoop daemons. -
- - - - - -
- Configuration Files - -
- <filename>hbase-site.xml</filename> and <filename>hbase-default.xml</filename> - Just as in Hadoop where you add site-specific HDFS configuration to the - hdfs-site.xml file, for HBase, site specific customizations go into - the file conf/hbase-site.xml. For the list of configurable properties, - see below or view the raw - hbase-default.xml source file in the HBase source code at - src/main/resources. - Not all configuration options make it out to hbase-default.xml. - Configuration that it is thought rare anyone would change can exist only in code; the only - way to turn up such configurations is via a reading of the source code itself. - Currently, changes here will require a cluster restart for HBase to notice the change. - - - - -
- - <para> - <emphasis>This file is fallback content</emphasis>. If you are seeing this, something - is wrong with the build of the HBase documentation or you are doing pre-build - verification. </para> - <para> The file hbase-default.xml is generated as part of the build of the hbase site. - See the hbase <filename>pom.xml</filename>. The generated file is a docbook glossary. </para> - <section> - <title>IDs that are auto-generated and cause validation errors if not present - Each of these is a reference to a configuration file parameter which will cause - an error if you are using the fallback content here. This is a dirty dirty hack. -
- fail.fast.expired.active.master - -
-
- "hbase.hregion.memstore.flush.size" - -
-
- hbase.hstore.bytes.per.checksum - -
-
- hbase.online.schema.update.enable - -
-
- hbase.regionserver.global.memstore.size - -
-
- hbase.hregion.max.filesize - -
-
- hbase.hstore.BlockingStoreFiles - -
-
- hfile.block.cache.size - -
-
- copy.table - -
-
- hbase.hstore.checksum.algorithm - -
-
- hbase.zookeeper.useMulti - -
-
- hbase.hregion.memstore.block.multiplier - -
-
- hbase.regionserver.global.memstore.size.lower.limit - -
-
-
- - -
- -
- <filename>hbase-env.sh</filename> - Set HBase environment variables in this file. Examples include options to pass the JVM - on start of an HBase daemon such as heap size and garbage collector configs. You can also - set configurations for HBase configuration, log directories, niceness, ssh options, where to - locate process pid files, etc. Open the file at conf/hbase-env.sh and - peruse its content. Each option is fairly well documented. Add your own environment - variables here if you want them read by HBase daemons on startup. - Changes here will require a cluster restart for HBase to notice the change. -
- -
- <filename>log4j.properties</filename> - Edit this file to change rate at which HBase files are rolled and to change the level at - which HBase logs messages. - Changes here will require a cluster restart for HBase to notice the change though log - levels can be changed for particular daemons via the HBase UI. -
- -
- Client configuration and dependencies connecting to an HBase cluster - If you are running HBase in standalone mode, you don't need to configure anything for - your client to work provided that they are all on the same machine. - Since the HBase Master may move around, clients bootstrap by looking to ZooKeeper for - current critical locations. ZooKeeper is where all these values are kept. Thus clients - require the location of the ZooKeeper ensemble information before they can do anything else. - Usually this the ensemble location is kept out in the hbase-site.xml - and is picked up by the client from the CLASSPATH. - - If you are configuring an IDE to run a HBase client, you should include the - conf/ directory on your classpath so - hbase-site.xml settings can be found (or add - src/test/resources to pick up the hbase-site.xml used by tests). - Minimally, a client of HBase needs several libraries in its - CLASSPATH when connecting to a cluster, including: - -commons-configuration (commons-configuration-1.6.jar) -commons-lang (commons-lang-2.5.jar) -commons-logging (commons-logging-1.1.1.jar) -hadoop-core (hadoop-core-1.0.0.jar) -hbase (hbase-0.92.0.jar) -log4j (log4j-1.2.16.jar) -slf4j-api (slf4j-api-1.5.8.jar) -slf4j-log4j (slf4j-log4j12-1.5.8.jar) -zookeeper (zookeeper-3.4.2.jar) - - An example basic hbase-site.xml for client only might look as - follows: - - - - hbase.zookeeper.quorum - example1,example2,example3 - The directory shared by region servers. - - - -]]> - - -
- Java client configuration - The configuration used by a Java client is kept in an HBaseConfiguration - instance. The factory method on HBaseConfiguration, - HBaseConfiguration.create();, on invocation, will read in the content of - the first hbase-site.xml found on the client's - CLASSPATH, if one is present (Invocation will also factor in any - hbase-default.xml found; an hbase-default.xml ships inside the - hbase.X.X.X.jar). It is also possible to specify configuration - directly without having to read from a hbase-site.xml. For example, - to set the ZooKeeper ensemble for the cluster programmatically do as follows: - Configuration config = HBaseConfiguration.create(); -config.set("hbase.zookeeper.quorum", "localhost"); // Here we are running zookeeper locally - If multiple ZooKeeper instances make up your ZooKeeper ensemble, they may be specified in - a comma-separated list (just as in the hbase-site.xml file). This - populated Configuration instance can then be passed to an HTable, - and so on. -
-
- - - - -
- Example Configurations - -
- Basic Distributed HBase Install - - Here is an example basic configuration for a distributed ten node cluster. The nodes are - named example0, example1, etc., through node - example9 in this example. The HBase Master and the HDFS namenode are - running on the node example0. RegionServers run on nodes - example1-example9. A 3-node ZooKeeper ensemble runs - on example1, example2, and example3 - on the default ports. ZooKeeper data is persisted to the directory - /export/zookeeper. Below we show what the main configuration files -- - hbase-site.xml, regionservers, and - hbase-env.sh -- found in the HBase conf - directory might look like. - -
- <filename>hbase-site.xml</filename> - - - - - - - hbase.zookeeper.quorum - example1,example2,example3 - The directory shared by RegionServers. - - - - hbase.zookeeper.property.dataDir - /export/zookeeper - Property from ZooKeeper config zoo.cfg. - The directory where the snapshot is stored. - - - - hbase.rootdir - hdfs://example0:8020/hbase - The directory shared by RegionServers. - - - - hbase.cluster.distributed - true - The mode the cluster will be in. Possible values are - false: standalone and pseudo-distributed setups with managed Zookeeper - true: fully-distributed with unmanaged Zookeeper Quorum (see hbase-env.sh) - - - -]]> - -
- -
- <filename>regionservers</filename> - - In this file you list the nodes that will run RegionServers. In our case, these nodes - are example1-example9. - - -example1 -example2 -example3 -example4 -example5 -example6 -example7 -example8 -example9 - -
- -
- <filename>hbase-env.sh</filename> - - The following lines in the hbase-env.sh file show how to set the - JAVA_HOME environment variable (required for HBase 0.98.5 and newer) and - set the heap to 4 GB (rather than the default value of 1 GB). If you copy and paste this - example, be sure to adjust the JAVA_HOME to suit your environment. - - -# The java implementation to use. -export JAVA_HOME=/usr/java/jdk1.7.0/ - -# The maximum amount of heap to use, in MB. Default is 1000. -export HBASE_HEAPSIZE=4096 - - - Use rsync to copy the content of the conf - directory to all nodes of the cluster. -
-
-
- - - -
- The Important Configurations - Below we list what the important Configurations. We've divided this - section into required configuration and worth-a-look recommended configs. - - -
- Required Configurations - Review the and sections. -
- Big Cluster Configurations - If a cluster with a lot of regions, it is possible if an eager beaver regionserver - checks in soon after master start while all the rest in the cluster are laggardly, this - first server to checkin will be assigned all regions. If lots of regions, this first - server could buckle under the load. To prevent the above scenario happening up the - hbase.master.wait.on.regionservers.mintostart from its default value - of 1. See HBASE-6389 Modify the - conditions to ensure that Master waits for sufficient number of Region Servers before - starting region assignments for more detail. -
-
- If a backup Master, making primary Master fail fast - If the primary Master loses its connection with ZooKeeper, it will fall into a loop - where it keeps trying to reconnect. Disable this functionality if you are running more - than one Master: i.e. a backup Master. Failing to do so, the dying Master may continue to - receive RPCs though another Master has assumed the role of primary. See the configuration . -
-
- -
- Recommended Configurations -
- ZooKeeper Configuration -
- <varname>zookeeper.session.timeout</varname> - The default timeout is three minutes (specified in milliseconds). This means that if - a server crashes, it will be three minutes before the Master notices the crash and - starts recovery. You might like to tune the timeout down to a minute or even less so the - Master notices failures the sooner. Before changing this value, be sure you have your - JVM garbage collection configuration under control otherwise, a long garbage collection - that lasts beyond the ZooKeeper session timeout will take out your RegionServer (You - might be fine with this -- you probably want recovery to start on the server if a - RegionServer has been in GC for a long period of time). - - To change this configuration, edit hbase-site.xml, copy the - changed file around the cluster and restart. - - We set this value high to save our having to field noob questions up on the mailing - lists asking why a RegionServer went down during a massive import. The usual cause is - that their JVM is untuned and they are running into long GC pauses. Our thinking is that - while users are getting familiar with HBase, we'd save them having to know all of its - intricacies. Later when they've built some confidence, then they can play with - configuration such as this. -
-
- Number of ZooKeeper Instances - See . -
-
-
- HDFS Configurations -
- dfs.datanode.failed.volumes.tolerated - This is the "...number of volumes that are allowed to fail before a datanode stops - offering service. By default any volume failure will cause a datanode to shutdown" from - the hdfs-default.xml description. If you have > three or four - disks, you might want to set this to 1 or if you have many disks, two or more. -
-
-
- <varname>hbase.regionserver.handler.count</varname> - This setting defines the number of threads that are kept open to answer incoming - requests to user tables. The rule of thumb is to keep this number low when the payload per - request approaches the MB (big puts, scans using a large cache) and high when the payload - is small (gets, small puts, ICVs, deletes). The total size of the queries in progress is - limited by the setting "hbase.ipc.server.max.callqueue.size". - It is safe to set that number to the maximum number of incoming clients if their - payload is small, the typical example being a cluster that serves a website since puts - aren't typically buffered and most of the operations are gets. - The reason why it is dangerous to keep this setting high is that the aggregate size - of all the puts that are currently happening in a region server may impose too much - pressure on its memory, or even trigger an OutOfMemoryError. A region server running on - low memory will trigger its JVM's garbage collector to run more frequently up to a point - where GC pauses become noticeable (the reason being that all the memory used to keep all - the requests' payloads cannot be trashed, no matter how hard the garbage collector tries). - After some time, the overall cluster throughput is affected since every request that hits - that region server will take longer, which exacerbates the problem even more. - You can get a sense of whether you have too little or too many handlers by on an individual RegionServer then tailing its logs (Queued - requests consume memory). -
-
- Configuration for large memory machines - HBase ships with a reasonable, conservative configuration that will work on nearly - all machine types that people might want to test with. If you have larger machines -- - HBase has 8G and larger heap -- you might the following configuration options helpful. - TODO. - -
- -
- Compression - You should consider enabling ColumnFamily compression. There are several options that - are near-frictionless and in most all cases boost performance by reducing the size of - StoreFiles and thus reducing I/O. - See for more information. -
-
- Configuring the size and number of WAL files - HBase uses to recover the memstore data that has not been flushed to disk in case - of an RS failure. These WAL files should be configured to be slightly smaller than HDFS - block (by default, HDFS block is 64Mb and WAL file is ~60Mb). - HBase also has a limit on number of WAL files, designed to ensure there's never too - much data that needs to be replayed during recovery. This limit needs to be set according - to memstore configuration, so that all the necessary data would fit. It is recommended to - allocated enough WAL files to store at least that much data (when all memstores are close - to full). For example, with 16Gb RS heap, default memstore settings (0.4), and default WAL - file size (~60Mb), 16Gb*0.4/60, the starting point for WAL file count is ~109. However, as - all memstores are not expected to be full all the time, less WAL files can be - allocated. -
-
- Managed Splitting - HBase generally handles splitting your regions, based upon the settings in your - hbase-default.xml and hbase-site.xml - configuration files. Important settings include - hbase.regionserver.region.split.policy, - hbase.hregion.max.filesize, - hbase.regionserver.regionSplitLimit. A simplistic view of splitting - is that when a region grows to hbase.hregion.max.filesize, it is split. - For most use patterns, most of the time, you should use automatic splitting. See for more information about manual region - splitting. - Instead of allowing HBase to split your regions automatically, you can choose to - manage the splitting yourself. This feature was added in HBase 0.90.0. Manually managing - splits works if you know your keyspace well, otherwise let HBase figure where to split for you. - Manual splitting can mitigate region creation and movement under load. It also makes it so - region boundaries are known and invariant (if you disable region splitting). If you use manual - splits, it is easier doing staggered, time-based major compactions spread out your network IO - load. - - - Disable Automatic Splitting - To disable automatic splitting, set hbase.hregion.max.filesize to - a very large value, such as 100 GB It is not recommended to set it to - its absolute maximum value of Long.MAX_VALUE. - - - Automatic Splitting Is Recommended - If you disable automatic splits to diagnose a problem or during a period of fast - data growth, it is recommended to re-enable them when your situation becomes more - stable. The potential benefits of managing region splits yourself are not - undisputed. - - - Determine the Optimal Number of Pre-Split Regions - The optimal number of pre-split regions depends on your application and environment. - A good rule of thumb is to start with 10 pre-split regions per server and watch as data - grows over time. It is better to err on the side of too few regions and perform rolling - splits later. The optimal number of regions depends upon the largest StoreFile in your - region. The size of the largest StoreFile will increase with time if the amount of data - grows. The goal is for the largest region to be just large enough that the compaction - selection algorithm only compacts it during a timed major compaction. Otherwise, the - cluster can be prone to compaction storms where a large number of regions under - compaction at the same time. It is important to understand that the data growth causes - compaction storms, and not the manual split decision. - - If the regions are split into too many large regions, you can increase the major - compaction interval by configuring HConstants.MAJOR_COMPACTION_PERIOD. - HBase 0.90 introduced org.apache.hadoop.hbase.util.RegionSplitter, - which provides a network-IO-safe rolling split of all regions. -
-
- Managed Compactions - By default, major compactions are scheduled to run once in a 7-day period. Prior to HBase 0.96.x, major - compactions were scheduled to happen once per day by default. - If you need to control exactly when and how often major compaction runs, you can - disable managed major compactions. See the entry for - hbase.hregion.majorcompaction in the table for details. - - Do Not Disable Major Compactions - Major compactions are absolutely necessary for StoreFile clean-up. Do not disable - them altogether. You can run major compactions manually via the HBase shell or via the HBaseAdmin - API. - - For more information about compactions and the compaction file selection process, see -
- -
- Speculative Execution - Speculative Execution of MapReduce tasks is on by default, and for HBase clusters it - is generally advised to turn off Speculative Execution at a system-level unless you need - it for a specific case, where it can be configured per-job. Set the properties - mapreduce.map.speculative and - mapreduce.reduce.speculative to false. -
-
-
Other Configurations -
Balancer - The balancer is a periodic operation which is run on the master to redistribute regions on the cluster. It is configured via - hbase.balancer.period and defaults to 300000 (5 minutes). - See for more information on the LoadBalancer. - -
-
Disabling Blockcache - Do not turn off block cache (You'd do it by setting hbase.block.cache.size to zero). - Currently we do not do well if you do this because the regionserver will spend all its time loading hfile - indices over and over again. If your working set it such that block cache does you no good, at least - size the block cache such that hfile indices will stay up in the cache (you can get a rough idea - on the size you need by surveying regionserver UIs; you'll see index block size accounted near the - top of the webpage). -
-
- <link xlink:href="http://en.wikipedia.org/wiki/Nagle's_algorithm">Nagle's</link> or the small package problem - If a big 40ms or so occasional delay is seen in operations against HBase, - try the Nagles' setting. For example, see the user mailing list thread, - Inconsistent scan performance with caching set to 1 - and the issue cited therein where setting notcpdelay improved scan speeds. You might also - see the graphs on the tail of HBASE-7008 Set scanner caching to a better default - where our Lars Hofhansl tries various data sizes w/ Nagle's on and off measuring the effect. -
-
- Better Mean Time to Recover (MTTR) - This section is about configurations that will make servers come back faster after a fail. - See the Deveraj Das an Nicolas Liochon blog post - Introduction to HBase Mean Time to Recover (MTTR) - for a brief introduction. - The issue HBASE-8354 forces Namenode into loop with lease recovery requests - is messy but has a bunch of good discussion toward the end on low timeouts and how to effect faster recovery including citation of fixes - added to HDFS. Read the Varun Sharma comments. The below suggested configurations are Varun's suggestions distilled and tested. Make sure you are - running on a late-version HDFS so you have the fixes he refers too and himself adds to HDFS that help HBase MTTR - (e.g. HDFS-3703, HDFS-3712, and HDFS-4791 -- hadoop 2 for sure has them and late hadoop 1 has some). - Set the following in the RegionServer. - - - - hbase.lease.recovery.dfs.timeout - 23000 - How much time we allow elapse between calls to recover lease. - Should be larger than the dfs timeout. - - - dfs.client.socket-timeout - 10000 - Down the DFS timeout from 60 to 10 seconds. - -]]> - - And on the namenode/datanode side, set the following to enable 'staleness' introduced - in HDFS-3703, HDFS-3912. - - dfs.client.socket-timeout - 10000 - Down the DFS timeout from 60 to 10 seconds. - - - dfs.datanode.socket.write.timeout - 10000 - Down the DFS timeout from 8 * 60 to 10 seconds. - - - ipc.client.connect.timeout - 3000 - Down from 60 seconds to 3. - - - ipc.client.connect.max.retries.on.timeouts - 2 - Down from 45 seconds to 3 (2 == 3 retries). - - - dfs.namenode.avoid.read.stale.datanode - true - Enable stale state in hdfs - - - dfs.namenode.stale.datanode.interval - 20000 - Down from default 30 seconds - - - dfs.namenode.avoid.write.stale.datanode - true - Enable stale state in hdfs - -]]> -
- -
- JMX - JMX(Java Management Extensions) provides built-in instrumentation that enables you - to monitor and manage the Java VM. To enable monitoring and management from remote - systems, you need to set system property com.sun.management.jmxremote.port(the port - number through which you want to enable JMX RMI connections) when you start the Java VM. - See - official document for more information. Historically, besides above port mentioned, - JMX opens 2 additional random TCP listening ports, which could lead to port conflict - problem.(See HBASE-10289 - for details) - - As an alternative, You can use the coprocessor-based JMX implementation provided - by HBase. To enable it in 0.99 or above, add below property in - hbase-site.xml: - - hbase.coprocessor.regionserver.classes - org.apache.hadoop.hbase.JMXListener - -]]> - NOTE: DO NOT set com.sun.management.jmxremote.port for Java VM at the same time. - - Currently it supports Master and RegionServer Java VM. The reason why you only - configure coprocessor for 'regionserver' is that, starting from HBase 0.99, - a Master IS also a RegionServer. (See HBASE-10569 - for more information.) - By default, the JMX listens on TCP port 10102, you can further configure the port - using below properties: - - - regionserver.rmi.registry.port - 61130 - - - regionserver.rmi.connector.port - 61140 - -]]> - The registry port can be shared with connector port in most cases, so you only - need to configure regionserver.rmi.registry.port. However if you want to use SSL - communication, the 2 ports must be configured to different values. - - - By default the password authentication and SSL communication is disabled. - To enable password authentication, you need to update hbase-env.sh - like below: - -export HBASE_JMX_BASE="-Dcom.sun.management.jmxremote.authenticate=true \ - -Dcom.sun.management.jmxremote.password.file=your_password_file \ - -Dcom.sun.management.jmxremote.access.file=your_access_file" - -export HBASE_MASTER_OPTS="$HBASE_MASTER_OPTS $HBASE_JMX_BASE " -export HBASE_REGIONSERVER_OPTS="$HBASE_REGIONSERVER_OPTS $HBASE_JMX_BASE " - - See example password/access file under $JRE_HOME/lib/management. - - - To enable SSL communication with password authentication, follow below steps: - -#1. generate a key pair, stored in myKeyStore -keytool -genkey -alias jconsole -keystore myKeyStore - -#2. export it to file jconsole.cert -keytool -export -alias jconsole -keystore myKeyStore -file jconsole.cert - -#3. copy jconsole.cert to jconsole client machine, import it to jconsoleKeyStore -keytool -import -alias jconsole -keystore jconsoleKeyStore -file jconsole.cert - - And then update hbase-env.sh like below: - -export HBASE_JMX_BASE="-Dcom.sun.management.jmxremote.ssl=true \ - -Djavax.net.ssl.keyStore=/home/tianq/myKeyStore \ - -Djavax.net.ssl.keyStorePassword=your_password_in_step_1 \ - -Dcom.sun.management.jmxremote.authenticate=true \ - -Dcom.sun.management.jmxremote.password.file=your_password file \ - -Dcom.sun.management.jmxremote.access.file=your_access_file" - -export HBASE_MASTER_OPTS="$HBASE_MASTER_OPTS $HBASE_JMX_BASE " -export HBASE_REGIONSERVER_OPTS="$HBASE_REGIONSERVER_OPTS $HBASE_JMX_BASE " - - - Finally start jconsole on client using the key store: - -jconsole -J-Djavax.net.ssl.trustStore=/home/tianq/jconsoleKeyStore - - - NOTE: for HBase 0.98, To enable the HBase JMX implementation on Master, you also - need to add below property in hbase-site.xml: - - hbase.coprocessor.master.classes - org.apache.hadoop.hbase.JMXListener - -]]> - The corresponding properties for port configuration are master.rmi.registry.port - (by default 10101) and master.rmi.connector.port(by default the same as registry.port) - -
- -
- -
- -
- Dynamic Configuration - Changing Configuration Without Restarting Servers - Since HBase 1.0.0, it is possible to change a subset of the configuration without - requiring a server restart. In the hbase shell, there are new operators, - update_config and update_all_config that - will prompt a server or all servers to reload configuration. - Only a subset of all configurations can currently be changed in the running server. - Here is an incomplete list: - hbase.regionserver.thread.compaction.large, - hbase.regionserver.thread.compaction.small, - hbase.regionserver.thread.split, - hbase.regionserver.thread.merge, as well as compaction - policy and configurations and adjustment to offpeak hours. - For the full list consult the patch attached to - HBASE-12147 Porting Online Config Change from 89-fb. - - -
- diff --git a/src/main/docbkx/cp.xml b/src/main/docbkx/cp.xml deleted file mode 100644 index 862430923d0..00000000000 --- a/src/main/docbkx/cp.xml +++ /dev/null @@ -1,431 +0,0 @@ - - - - Apache HBase Coprocessors - HBase coprocessors are modeled after the coprocessors which are part of Google's BigTable - (, pages - 66-67.). Coprocessors function in a similar way to Linux kernel modules. They provide a way to - run server-level code against locally-stored data. The functionality they provide is very - powerful, but also carries great risk and can have adverse effects on the system, at the level - of the operating system. The information in this chapter is primarily sourced and heavily reused - from Mingjie Lai's blog post at . - - Coprocessors are not designed to be used by end users of HBase, but by HBase developers who - need to add specialized functionality to HBase. One example of the use of coprocessors is - pluggable compaction and scan policies, which are provided as coprocessors in HBASE-6427. - -
- Coprocessor Framework - The implementation of HBase coprocessors diverges from the BigTable implementation. The - HBase framework provides a library and runtime environment for executing user code within the - HBase region server and master processes. - The framework API is provided in the coprocessor - package. - Two different types of coprocessors are provided by the framework, based on their - scope. - - Types of Coprocessors - - System Coprocessors - - System coprocessors are loaded globally on all tables and regions hosted by a region - server. - - - - Table Coprocessors - - You can specify which coprocessors should be loaded on all regions for a table on a - per-table basis. - - - - - The framework provides two different aspects of extensions as well: - observers and endpoints. - - - Observers - - Observers are analogous to triggers in conventional databases. They allow you to - insert user code by overriding upcall methods provided by the coprocessor framework. - Callback functions are executed from core HBase code when events occur. Callbacks are - handled by the framework, and the coprocessor itself only needs to insert the extended - or alternate functionality. - - Provided Observer Interfaces - - RegionObserver - - A RegionObserver provides hooks for data manipulation events, such as Get, - Put, Delete, Scan. An instance of a RegionObserver coprocessor exists for each - table region. The scope of the observations a RegionObserver can make is - constrained to that region. - - - - RegionServerObserver - - A RegionServerObserver provides for operations related to the RegionServer, - such as stopping the RegionServer and performing operations before or after - merges, commits, or rollbacks. - - - - WALObserver - - A WALObserver provides hooks for operations related to the write-ahead log - (WAL). You can observe or intercept WAL writing and reconstruction events. A - WALObserver runs in the context of WAL processing. A single WALObserver exists on - a single region server. - - - - MasterObserver - - A MasterObserver provides hooks for DDL-type operation, such as create, - delete, modify table. The MasterObserver runs within the context of the HBase - master. - - - - More than one observer of a given type can be loaded at once. Multiple observers are - chained to execute sequentially by order of assigned priority. Nothing prevents a - coprocessor implementor from communicating internally among its installed - observers. - An observer of a higher priority can preempt lower-priority observers by throwing an - IOException or a subclass of IOException. - - - - Endpoints (HBase 0.96.x and later) - - The implementation for endpoints changed significantly in HBase 0.96.x due to the - introduction of protocol buffers (protobufs) (HBASE-5488). If - you created endpoints before 0.96.x, you will need to rewrite them. Endpoints are now - defined and callable as protobuf services, rather than endpoint invocations passed - through as Writable blobs - Dynamic RPC endpoints resemble stored procedures. An endpoint can be invoked at any - time from the client. When it is invoked, it is executed remotely at the target region - or regions, and results of the executions are returned to the client. - The endpoint implementation is installed on the server and is invoked using HBase - RPC. The client library provides convenience methods for invoking these dynamic - interfaces. - An endpoint, like an observer, can communicate with any installed observers. This - allows you to plug new features into HBase without modifying or recompiling HBase - itself. - - Steps to Implement an Endpoint - Define the coprocessor service and related messages in a .proto file - Run the protoc command to generate the code. - Write code to implement the following: - - the generated protobuf Service interface - - the new org.apache.hadoop.hbase.coprocessor.CoprocessorService - interface (required for the RegionCoprocessorHost - to register the exposed service) - - - The client calls the new HTable.coprocessorService() methods to perform the endpoint RPCs. - - - For more information and examples, refer to the API documentation for the coprocessor - package, as well as the included RowCount example in the - /hbase-examples/src/test/java/org/apache/hadoop/hbase/coprocessor/example/ - of the HBase source. - - - - Endpoints (HBase 0.94.x and earlier) - - Dynamic RPC endpoints resemble stored procedures. An endpoint can be invoked at any - time from the client. When it is invoked, it is executed remotely at the target region - or regions, and results of the executions are returned to the client. - The endpoint implementation is installed on the server and is invoked using HBase - RPC. The client library provides convenience methods for invoking these dynamic - interfaces. - An endpoint, like an observer, can communicate with any installed observers. This - allows you to plug new features into HBase without modifying or recompiling HBase - itself. - - Steps to Implement an Endpoint - - Server-Side - - - Create new protocol interface which extends CoprocessorProtocol. - - - Implement the Endpoint interface. The implementation will be loaded into and - executed from the region context. - - - Extend the abstract class BaseEndpointCoprocessor. This convenience class - hides some internal details that the implementer does not need to be concerned - about, ˆ such as coprocessor framework class loading. - - - - - Client-Side - Endpoint can be invoked by two new HBase client APIs: - - - HTableInterface.coprocessorProxy(Class<T> protocol, byte[] - row) for executing against a single region - - - HTableInterface.coprocessorExec(Class<T> protocol, byte[] - startKey, byte[] endKey, Batch.Call<T,R> callable) for executing - over a range of regions - - - - - - - -
- -
- Examples - An example of an observer is included in - hbase-examples/src/test/java/org/apache/hadoop/hbase/coprocessor/example/TestZooKeeperScanPolicyObserver.java. - Several endpoint examples are included in the same directory. -
- - - -
- Building A Coprocessor - - Before you can build a processor, it must be developed, compiled, and packaged in a JAR - file. The next step is to configure the coprocessor framework to use your coprocessor. You can - load the coprocessor from your HBase configuration, so that the coprocessor starts with HBase, - or you can configure the coprocessor from the HBase shell, as a table attribute, so that it is - loaded dynamically when the table is opened or reopened. -
- Load from Configuration - To configure a coprocessor to be loaded when HBase starts, modify the RegionServer's - hbase-site.xml and configure one of the following properties, based - on the type of observer you are configuring: - - - hbase.coprocessor.region.classesfor RegionObservers and - Endpoints - - - hbase.coprocessor.wal.classesfor WALObservers - - - hbase.coprocessor.master.classesfor MasterObservers - - - - Example RegionObserver Configuration - In this example, one RegionObserver is configured for all the HBase tables. - - hbase.coprocessor.region.classes - org.apache.hadoop.hbase.coprocessor.AggregateImplementation - ]]> - - - - If multiple classes are specified for loading, the class names must be comma-separated. - The framework attempts to load all the configured classes using the default class loader. - Therefore, the jar file must reside on the server-side HBase classpath. - - Coprocessors which are loaded in this way will be active on all regions of - all tables. These are the system coprocessor introduced earlier. The first listed - coprocessors will be assigned the priority Coprocessor.Priority.SYSTEM. - Each subsequent coprocessor in the list will have its priority value incremented by one - (which reduces its priority, because priorities have the natural sort order of Integers). - When calling out to registered observers, the framework executes their callbacks methods - in the sorted order of their priority. Ties are broken arbitrarily. -
- -
- Load from the HBase Shell - You can load a coprocessor on a specific table via a table attribute. The following - example will load the FooRegionObserver observer when table - t1 is read or re-read. - - Load a Coprocessor On a Table Using HBase Shell - -hbase(main):005:0> alter 't1', METHOD => 'table_att', - 'coprocessor'=>'hdfs:///foo.jar|com.foo.FooRegionObserver|1001|arg1=1,arg2=2' -Updating all regions with the new schema... -1/1 regions updated. -Done. -0 row(s) in 1.0730 seconds - -hbase(main):006:0> describe 't1' -DESCRIPTION ENABLED - {NAME => 't1', coprocessor$1 => 'hdfs:///foo.jar|com.foo.FooRegio false - nObserver|1001|arg1=1,arg2=2', FAMILIES => [{NAME => 'c1', DATA_B - LOCK_ENCODING => 'NONE', BLOOMFILTER => 'NONE', REPLICATION_SCOPE - => '0', VERSIONS => '3', COMPRESSION => 'NONE', MIN_VERSIONS => - '0', TTL => '2147483647', KEEP_DELETED_CELLS => 'false', BLOCKSIZ - E => '65536', IN_MEMORY => 'false', ENCODE_ON_DISK => 'true', BLO - CKCACHE => 'true'}, {NAME => 'f1', DATA_BLOCK_ENCODING => 'NONE', - BLOOMFILTER => 'NONE', REPLICATION_SCOPE => '0', VERSIONS => '3' - , COMPRESSION => 'NONE', MIN_VERSIONS => '0', TTL => '2147483647' - , KEEP_DELETED_CELLS => 'false', BLOCKSIZE => '65536', IN_MEMORY - => 'false', ENCODE_ON_DISK => 'true', BLOCKCACHE => 'true'}]} -1 row(s) in 0.0190 seconds - - - - The coprocessor framework will try to read the class information from the coprocessor - table attribute value. The value contains four pieces of information which are separated by - the | character. - - - - File path: The jar file containing the coprocessor implementation must be in a - location where all region servers can read it. You could copy the file onto the local - disk on each region server, but it is recommended to store it in HDFS. - - - Class name: The full class name of the coprocessor. - - - Priority: An integer. The framework will determine the execution sequence of all - configured observers registered at the same hook using priorities. This field can be - left blank. In that case the framework will assign a default priority value. - - - Arguments: This field is passed to the coprocessor implementation. - - - - Unload a Coprocessor From a Table Using HBase Shell - -hbase(main):007:0> alter 't1', METHOD => 'table_att_unset', -hbase(main):008:0* NAME => 'coprocessor$1' -Updating all regions with the new schema... -1/1 regions updated. -Done. -0 row(s) in 1.1130 seconds - -hbase(main):009:0> describe 't1' -DESCRIPTION ENABLED - {NAME => 't1', FAMILIES => [{NAME => 'c1', DATA_BLOCK_ENCODING => false - 'NONE', BLOOMFILTER => 'NONE', REPLICATION_SCOPE => '0', VERSION - S => '3', COMPRESSION => 'NONE', MIN_VERSIONS => '0', TTL => '214 - 7483647', KEEP_DELETED_CELLS => 'false', BLOCKSIZE => '65536', IN - _MEMORY => 'false', ENCODE_ON_DISK => 'true', BLOCKCACHE => 'true - '}, {NAME => 'f1', DATA_BLOCK_ENCODING => 'NONE', BLOOMFILTER => - 'NONE', REPLICATION_SCOPE => '0', VERSIONS => '3', COMPRESSION => - 'NONE', MIN_VERSIONS => '0', TTL => '2147483647', KEEP_DELETED_C - ELLS => 'false', BLOCKSIZE => '65536', IN_MEMORY => 'false', ENCO - DE_ON_DISK => 'true', BLOCKCACHE => 'true'}]} -1 row(s) in 0.0180 seconds - - - - There is no guarantee that the framework will load a given coprocessor successfully. - For example, the shell command neither guarantees a jar file exists at a particular - location nor verifies whether the given class is actually contained in the jar file. - - -
-
-
- Check the Status of a Coprocessor - To check the status of a coprocessor after it has been configured, use the - status HBase Shell command. - -hbase(main):020:0> status 'detailed' -version 0.92-tm-6 -0 regionsInTransition -master coprocessors: [] -1 live servers - localhost:52761 1328082515520 - requestsPerSecond=3, numberOfOnlineRegions=3, usedHeapMB=32, maxHeapMB=995 - -ROOT-,,0 - numberOfStores=1, numberOfStorefiles=1, storefileUncompressedSizeMB=0, storefileSizeMB=0, memstoreSizeMB=0, -storefileIndexSizeMB=0, readRequestsCount=54, writeRequestsCount=1, rootIndexSizeKB=0, totalStaticIndexSizeKB=0, -totalStaticBloomSizeKB=0, totalCompactingKVs=0, currentCompactedKVs=0, compactionProgressPct=NaN, coprocessors=[] - .META.,,1 - numberOfStores=1, numberOfStorefiles=0, storefileUncompressedSizeMB=0, storefileSizeMB=0, memstoreSizeMB=0, -storefileIndexSizeMB=0, readRequestsCount=97, writeRequestsCount=4, rootIndexSizeKB=0, totalStaticIndexSizeKB=0, -totalStaticBloomSizeKB=0, totalCompactingKVs=0, currentCompactedKVs=0, compactionProgressPct=NaN, coprocessors=[] - t1,,1328082575190.c0491168a27620ffe653ec6c04c9b4d1. - numberOfStores=2, numberOfStorefiles=1, storefileUncompressedSizeMB=0, storefileSizeMB=0, memstoreSizeMB=0, -storefileIndexSizeMB=0, readRequestsCount=0, writeRequestsCount=0, rootIndexSizeKB=0, totalStaticIndexSizeKB=0, -totalStaticBloomSizeKB=0, totalCompactingKVs=0, currentCompactedKVs=0, compactionProgressPct=NaN, -coprocessors=[AggregateImplementation] -0 dead servers - -
-
- Monitor Time Spent in Coprocessors - HBase 0.98.5 introduced the ability to monitor some statistics relating to the amount of - time spent executing a given coprocessor. You can see these statistics via the HBase Metrics - framework (see or the Web UI for a given Region Server, via - the Coprocessor Metrics tab. These statistics are valuable for debugging - and benchmarking the performance impact of a given coprocessor on your cluster. Tracked - statistics include min, max, average, and 90th, 95th, and 99th percentile. All times are shown - in milliseconds. The statistics are calculated over coprocessor - execution samples recorded during the reporting interval, which is 10 seconds by default. The - metrics sampling rate as described in . -
- Coprocessor Metrics UI - - - - - - The Coprocessor Metrics UI shows statistics about time spent executing a given - coprocessor, including min, max, average, and 90th, 95th, and 99th percentile. - - -
-
-
- Status of Coprocessors in HBase - Coprocessors and the coprocessor framework are evolving rapidly and work is ongoing on - several different JIRAs. -
- diff --git a/src/main/docbkx/customization-pdf.xsl b/src/main/docbkx/customization-pdf.xsl deleted file mode 100644 index b21236fc3e3..00000000000 --- a/src/main/docbkx/customization-pdf.xsl +++ /dev/null @@ -1,129 +0,0 @@ - - - - - - - - - - - - 0 - 0 - 0 - - - 5mm - 10mm - 10mm - - 15mm - 10mm - 0mm - - 18mm - 18mm - - - 0pc - - - - - justify - true - - - 11 - 8 - - - 1.4 - - - /&? - ​ - - - - - - - 0.8em - wrap - true - - - - - - page - - - - - page - - - - - page - - - - 2 - - - - - - - Courier - 8pt - always - - - - - - #E8E8E8 - 0.5pt - solid - #575757 - 3pt - - - - - - 90 - - - - - diff --git a/src/main/docbkx/customization.xsl b/src/main/docbkx/customization.xsl deleted file mode 100644 index 5d0ec2ca471..00000000000 --- a/src/main/docbkx/customization.xsl +++ /dev/null @@ -1,49 +0,0 @@ - - - - - - - - - - - - -
- - -comments powered by Disqus - - - diff --git a/src/main/docbkx/datamodel.xml b/src/main/docbkx/datamodel.xml deleted file mode 100644 index bdf697d6fde..00000000000 --- a/src/main/docbkx/datamodel.xml +++ /dev/null @@ -1,865 +0,0 @@ - - - - - Data Model - In HBase, data is stored in tables, which have rows and columns. This is a terminology - overlap with relational databases (RDBMSs), but this is not a helpful analogy. Instead, it can - be helpful to think of an HBase table as a multi-dimensional map. - - HBase Data Model Terminology - - Table - - An HBase table consists of multiple rows. - - - - Row - - A row in HBase consists of a row key and one or more columns with values associated - with them. Rows are sorted alphabetically by the row key as they are stored. For this - reason, the design of the row key is very important. The goal is to store data in such a - way that related rows are near each other. A common row key pattern is a website domain. - If your row keys are domains, you should probably store them in reverse (org.apache.www, - org.apache.mail, org.apache.jira). This way, all of the Apache domains are near each - other in the table, rather than being spread out based on the first letter of the - subdomain. - - - - Column - - A column in HBase consists of a column family and a column qualifier, which are - delimited by a : (colon) character. - - - - Column Family - - Column families physically colocate a set of columns and their values, often for - performance reasons. Each column family has a set of storage properties, such as whether - its values should be cached in memory, how its data is compressed or its row keys are - encoded, and others. Each row in a table has the same column - families, though a given row might not store anything in a given column family. - Column families are specified when you create your table, and influence the way your - data is stored in the underlying filesystem. Therefore, the column families should be - considered carefully during schema design. - - - - Column Qualifier - - A column qualifier is added to a column family to provide the index for a given - piece of data. Given a column family content, a column qualifier - might be content:html, and another might be - content:pdf. Though column families are fixed at table creation, - column qualifiers are mutable and may differ greatly between rows. - - - - Cell - - A cell is a combination of row, column family, and column qualifier, and contains a - value and a timestamp, which represents the value's version. - A cell's value is an uninterpreted array of bytes. - - - - Timestamp - - A timestamp is written alongside each value, and is the identifier for a given - version of a value. By default, the timestamp represents the time on the RegionServer - when the data was written, but you can specify a different timestamp value when you put - data into the cell. - - Direct manipulation of timestamps is an advanced feature which is only exposed for - special cases that are deeply integrated with HBase, and is discouraged in general. - Encoding a timestamp at the application level is the preferred pattern. - - You can specify the maximum number of versions of a value that HBase retains, per column - family. When the maximum number of versions is reached, the oldest versions are - eventually deleted. By default, only the newest version is kept. - - - - -
- Conceptual View - You can read a very understandable explanation of the HBase data model in the blog post Understanding - HBase and BigTable by Jim R. Wilson. Another good explanation is available in the - PDF Introduction - to Basic Schema Design by Amandeep Khurana. It may help to read different - perspectives to get a solid understanding of HBase schema design. The linked articles cover - the same ground as the information in this section. - The following example is a slightly modified form of the one on page 2 of the BigTable paper. There - is a table called webtable that contains two rows - (com.cnn.www - and com.example.www), three column families named - contents, anchor, and people. In - this example, for the first row (com.cnn.www), - anchor contains two columns (anchor:cssnsi.com, - anchor:my.look.ca) and contents contains one column - (contents:html). This example contains 5 versions of the row with the - row key com.cnn.www, and one version of the row with the row key - com.example.www. The contents:html column qualifier contains the entire - HTML of a given website. Qualifiers of the anchor column family each - contain the external site which links to the site represented by the row, along with the - text it used in the anchor of its link. The people column family represents - people associated with the site. - - - Column Names - By convention, a column name is made of its column family prefix and a - qualifier. For example, the column - contents:html is made up of the column family - contents and the html qualifier. The colon - character (:) delimits the column family from the column family - qualifier. - - - Table <varname>webtable</varname> - - - - - - - - - Row Key - Time Stamp - ColumnFamily contents - ColumnFamily anchor - ColumnFamily people - - - - - "com.cnn.www" - t9 - - anchor:cnnsi.com = "CNN" - - - - "com.cnn.www" - t8 - - anchor:my.look.ca = "CNN.com" - - - - "com.cnn.www" - t6 - contents:html = "<html>..." - - - - - "com.cnn.www" - t5 - contents:html = "<html>..." - - - - - "com.cnn.www" - t3 - contents:html = "<html>..." - - - - - "com.example.www" - t5 - contents:html = "<html>..." - - people:author = "John Doe" - - - -
- Cells in this table that appear to be empty do not take space, or in fact exist, in - HBase. This is what makes HBase "sparse." A tabular view is not the only possible way to - look at data in HBase, or even the most accurate. The following represents the same - information as a multi-dimensional map. This is only a mock-up for illustrative - purposes and may not be strictly accurate. - ..." - t5: contents:html: "..." - t3: contents:html: "..." - } - anchor: { - t9: anchor:cnnsi.com = "CNN" - t8: anchor:my.look.ca = "CNN.com" - } - people: {} - } - "com.example.www": { - contents: { - t5: contents:html: "..." - } - anchor: {} - people: { - t5: people:author: "John Doe" - } - } -} - ]]> - -
-
- Physical View - Although at a conceptual level tables may be viewed as a sparse set of rows, they are - physically stored by column family. A new column qualifier (column_family:column_qualifier) - can be added to an existing column family at any time. - - ColumnFamily <varname>anchor</varname> - - - - - - - Row Key - Time Stamp - Column Family anchor - - - - - "com.cnn.www" - t9 - anchor:cnnsi.com = "CNN" - - - "com.cnn.www" - t8 - anchor:my.look.ca = "CNN.com" - - - -
- - ColumnFamily <varname>contents</varname> - - - - - - - Row Key - Time Stamp - ColumnFamily "contents:" - - - - - "com.cnn.www" - t6 - contents:html = "<html>..." - - - "com.cnn.www" - t5 - contents:html = "<html>..." - - - "com.cnn.www" - t3 - contents:html = "<html>..." - - - -
- The empty cells shown in the - conceptual view are not stored at all. - Thus a request for the value of the contents:html column at time stamp - t8 would return no value. Similarly, a request for an - anchor:my.look.ca value at time stamp t9 would - return no value. However, if no timestamp is supplied, the most recent value for a - particular column would be returned. Given multiple versions, the most recent is also the - first one found, since timestamps - are stored in descending order. Thus a request for the values of all columns in the row - com.cnn.www if no timestamp is specified would be: the value of - contents:html from timestamp t6, the value of - anchor:cnnsi.com from timestamp t9, the value of - anchor:my.look.ca from timestamp t8. - For more information about the internals of how Apache HBase stores data, see . -
- -
- Namespace - A namespace is a logical grouping of tables analogous to a database in relation - database systems. This abstraction lays the groundwork for upcoming multi-tenancy related - features: - - Quota Management (HBASE-8410) - Restrict the amount of resources (ie regions, - tables) a namespace can consume. - - - Namespace Security Administration (HBASE-9206) - provide another level of security - administration for tenants. - - - Region server groups (HBASE-6721) - A namespace/table can be pinned onto a subset - of regionservers thus guaranteeing a course level of isolation. - - - -
- Namespace management - A namespace can be created, removed or altered. Namespace membership is determined - during table creation by specifying a fully-qualified table name of the form: - - :]]> - - - - Examples - - -#Create a namespace -create_namespace 'my_ns' - - -#create my_table in my_ns namespace -create 'my_ns:my_table', 'fam' - - -#drop namespace -drop_namespace 'my_ns' - - -#alter namespace -alter_namespace 'my_ns', {METHOD => 'set', 'PROPERTY_NAME' => 'PROPERTY_VALUE'} - - - -
- Predefined namespaces - There are two predefined special namespaces: - - - hbase - system namespace, used to contain hbase internal tables - - - default - tables with no explicit specified namespace will automatically fall into - this namespace. - - - - Examples - - -#namespace=foo and table qualifier=bar -create 'foo:bar', 'fam' - -#namespace=default and table qualifier=bar -create 'bar', 'fam' - - -
- - -
- Table - Tables are declared up front at schema definition time. -
- -
- Row - Row keys are uninterrpreted bytes. Rows are lexicographically sorted with the lowest - order appearing first in a table. The empty byte array is used to denote both the start and - end of a tables' namespace. -
- -
- Column Family<indexterm><primary>Column Family</primary></indexterm> - Columns in Apache HBase are grouped into column families. All - column members of a column family have the same prefix. For example, the columns - courses:history and courses:math are both - members of the courses column family. The colon character - (:) delimits the column family from the column - family qualifierColumn Family Qualifier. - The column family prefix must be composed of printable characters. The - qualifying tail, the column family qualifier, can be made of any - arbitrary bytes. Column families must be declared up front at schema definition time whereas - columns do not need to be defined at schema time but can be conjured on the fly while the - table is up an running. - Physically, all column family members are stored together on the filesystem. Because - tunings and storage specifications are done at the column family level, it is advised that - all column family members have the same general access pattern and size - characteristics. - -
-
- Cells<indexterm><primary>Cells</primary></indexterm> - A {row, column, version} tuple exactly specifies a - cell in HBase. Cell content is uninterrpreted bytes -
-
- Data Model Operations - The four primary data model operations are Get, Put, Scan, and Delete. Operations are - applied via Table - instances. - -
- Get - Get - returns attributes for a specified row. Gets are executed via - Table.get. -
-
- Put - Put - either adds new rows to a table (if the key is new) or can update existing rows (if the - key already exists). Puts are executed via - Table.put (writeBuffer) or - Table.batch (non-writeBuffer). -
-
- Scans - Scan - allow iteration over multiple rows for specified attributes. - The following is an example of a Scan on a Table instance. Assume that a table is - populated with rows with keys "row1", "row2", "row3", and then another set of rows with - the keys "abc1", "abc2", and "abc3". The following example shows how to set a Scan - instance to return the rows beginning with "row". - -public static final byte[] CF = "cf".getBytes(); -public static final byte[] ATTR = "attr".getBytes(); -... - -Table table = ... // instantiate a Table instance - -Scan scan = new Scan(); -scan.addColumn(CF, ATTR); -scan.setRowPrefixFilter(Bytes.toBytes("row")); -ResultScanner rs = table.getScanner(scan); -try { - for (Result r = rs.next(); r != null; r = rs.next()) { - // process result... -} finally { - rs.close(); // always close the ResultScanner! - - Note that generally the easiest way to specify a specific stop point for a scan is by - using the InclusiveStopFilter - class. -
-
- Delete - Delete - removes a row from a table. Deletes are executed via - HTable.delete. - HBase does not modify data in place, and so deletes are handled by creating new - markers called tombstones. These tombstones, along with the dead - values, are cleaned up on major compactions. - See for more information on deleting versions of columns, and - see for more information on compactions. - -
- -
- - -
- Versions<indexterm><primary>Versions</primary></indexterm> - - A {row, column, version} tuple exactly specifies a - cell in HBase. It's possible to have an unbounded number of cells where - the row and column are the same but the cell address differs only in its version - dimension. - - While rows and column keys are expressed as bytes, the version is specified using a long - integer. Typically this long contains time instances such as those returned by - java.util.Date.getTime() or System.currentTimeMillis(), that is: - the difference, measured in milliseconds, between the current time and midnight, - January 1, 1970 UTC. - - The HBase version dimension is stored in decreasing order, so that when reading from a - store file, the most recent values are found first. - - There is a lot of confusion over the semantics of cell versions, in - HBase. In particular: - - - If multiple writes to a cell have the same version, only the last written is - fetchable. - - - - It is OK to write cells in a non-increasing version order. - - - - Below we describe how the version dimension in HBase currently works. See HBASE-2406 for - discussion of HBase versions. Bending time in HBase - makes for a good read on the version, or time, dimension in HBase. It has more detail on - versioning than is provided here. As of this writing, the limiitation - Overwriting values at existing timestamps mentioned in the - article no longer holds in HBase. This section is basically a synopsis of this article - by Bruno Dumon. - -
- Specifying the Number of Versions to Store - The maximum number of versions to store for a given column is part of the column - schema and is specified at table creation, or via an alter command, via - HColumnDescriptor.DEFAULT_VERSIONS. Prior to HBase 0.96, the default number - of versions kept was 3, but in 0.96 and newer has been changed to - 1. - - Modify the Maximum Number of Versions for a Column - This example uses HBase Shell to keep a maximum of 5 versions of column - f1. You could also use HColumnDescriptor. - alter ‘t1′, NAME => ‘f1′, VERSIONS => 5]]> - - - Modify the Minimum Number of Versions for a Column - You can also specify the minimum number of versions to store. By default, this is - set to 0, which means the feature is disabled. The following example sets the minimum - number of versions on field f1 to 2, via HBase Shell. - You could also use HColumnDescriptor. - alter ‘t1′, NAME => ‘f1′, MIN_VERSIONS => 2]]> - - Starting with HBase 0.98.2, you can specify a global default for the maximum number of - versions kept for all newly-created columns, by setting - in hbase-site.xml. See - . -
- -
- Versions and HBase Operations - - In this section we look at the behavior of the version dimension for each of the core - HBase operations. - -
- Get/Scan - - Gets are implemented on top of Scans. The below discussion of Get - applies equally to Scans. - - By default, i.e. if you specify no explicit version, when doing a - get, the cell whose version has the largest value is returned - (which may or may not be the latest one written, see later). The default behavior can be - modified in the following ways: - - - - to return more than one version, see Get.setMaxVersions() - - - - to return versions other than the latest, see Get.setTimeRange() - - To retrieve the latest version that is less than or equal to a given value, thus - giving the 'latest' state of the record at a certain point in time, just use a range - from 0 to the desired version and set the max versions to 1. - - - -
-
- Default Get Example - The following Get will only retrieve the current version of the row - -public static final byte[] CF = "cf".getBytes(); -public static final byte[] ATTR = "attr".getBytes(); -... -Get get = new Get(Bytes.toBytes("row1")); -Result r = table.get(get); -byte[] b = r.getValue(CF, ATTR); // returns current version of value - -
-
- Versioned Get Example - The following Get will return the last 3 versions of the row. - -public static final byte[] CF = "cf".getBytes(); -public static final byte[] ATTR = "attr".getBytes(); -... -Get get = new Get(Bytes.toBytes("row1")); -get.setMaxVersions(3); // will return last 3 versions of row -Result r = table.get(get); -byte[] b = r.getValue(CF, ATTR); // returns current version of value -List<KeyValue> kv = r.getColumn(CF, ATTR); // returns all versions of this column - -
- -
- Put - - Doing a put always creates a new version of a cell, at a certain - timestamp. By default the system uses the server's currentTimeMillis, - but you can specify the version (= the long integer) yourself, on a per-column level. - This means you could assign a time in the past or the future, or use the long value for - non-time purposes. - - To overwrite an existing value, do a put at exactly the same row, column, and - version as that of the cell you would overshadow. -
- Implicit Version Example - The following Put will be implicitly versioned by HBase with the current - time. - -public static final byte[] CF = "cf".getBytes(); -public static final byte[] ATTR = "attr".getBytes(); -... -Put put = new Put(Bytes.toBytes(row)); -put.add(CF, ATTR, Bytes.toBytes( data)); -table.put(put); - -
-
- Explicit Version Example - The following Put has the version timestamp explicitly set. - -public static final byte[] CF = "cf".getBytes(); -public static final byte[] ATTR = "attr".getBytes(); -... -Put put = new Put( Bytes.toBytes(row)); -long explicitTimeInMs = 555; // just an example -put.add(CF, ATTR, explicitTimeInMs, Bytes.toBytes(data)); -table.put(put); - - Caution: the version timestamp is internally by HBase for things like time-to-live - calculations. It's usually best to avoid setting this timestamp yourself. Prefer using - a separate timestamp attribute of the row, or have the timestamp a part of the rowkey, - or both. -
- -
- -
- Delete - - There are three different types of internal delete markers. See Lars Hofhansl's blog - for discussion of his attempt adding another, Scanning - in HBase: Prefix Delete Marker. - - - Delete: for a specific version of a column. - - - Delete column: for all versions of a column. - - - Delete family: for all columns of a particular ColumnFamily - - - When deleting an entire row, HBase will internally create a tombstone for each - ColumnFamily (i.e., not each individual column). - Deletes work by creating tombstone markers. For example, let's - suppose we want to delete a row. For this you can specify a version, or else by default - the currentTimeMillis is used. What this means is delete all - cells where the version is less than or equal to this version. HBase never - modifies data in place, so for example a delete will not immediately delete (or mark as - deleted) the entries in the storage file that correspond to the delete condition. - Rather, a so-called tombstone is written, which will mask the - deleted values. When HBase does a major compaction, the tombstones are processed to - actually remove the dead values, together with the tombstones themselves. If the version - you specified when deleting a row is larger than the version of any value in the row, - then you can consider the complete row to be deleted. - For an informative discussion on how deletes and versioning interact, see the thread Put w/ - timestamp -> Deleteall -> Put w/ timestamp fails up on the user mailing - list. - Also see for more information on the internal KeyValue format. - Delete markers are purged during the next major compaction of the store, unless the - option is set in the column family. To keep the - deletes for a configurable amount of time, you can set the delete TTL via the - property in - hbase-site.xml. If - is not set, or set to 0, all - delete markers, including those with timestamps in the future, are purged during the - next major compaction. Otherwise, a delete marker with a timestamp in the future is kept - until the major compaction which occurs after the time represented by the marker's - timestamp plus the value of , in - milliseconds. - - This behavior represents a fix for an unexpected change that was introduced in - HBase 0.94, and was fixed in HBASE-10118. - The change has been backported to HBase 0.94 and newer branches. - -
-
- -
- Current Limitations - -
- Deletes mask Puts - - Deletes mask puts, even puts that happened after the delete - was entered. See HBASE-2256. Remember that a delete writes a tombstone, which only - disappears after then next major compaction has run. Suppose you do - a delete of everything <= T. After this you do a new put with a - timestamp <= T. This put, even if it happened after the delete, - will be masked by the delete tombstone. Performing the put will not - fail, but when you do a get you will notice the put did have no - effect. It will start working again after the major compaction has - run. These issues should not be a problem if you use - always-increasing versions for new puts to a row. But they can occur - even if you do not care about time: just do delete and put - immediately after each other, and there is some chance they happen - within the same millisecond. -
- -
- Major compactions change query results - - ...create three cell versions at t1, t2 and t3, with a maximum-versions - setting of 2. So when getting all versions, only the values at t2 and t3 will be - returned. But if you delete the version at t2 or t3, the one at t1 will appear again. - Obviously, once a major compaction has run, such behavior will not be the case - anymore... (See Garbage Collection in Bending time in - HBase.) -
-
-
-
- Sort Order - All data model operations HBase return data in sorted order. First by row, - then by ColumnFamily, followed by column qualifier, and finally timestamp (sorted - in reverse, so newest records are returned first). - -
-
- Column Metadata - There is no store of column metadata outside of the internal KeyValue instances for a ColumnFamily. - Thus, while HBase can support not only a wide number of columns per row, but a heterogenous set of columns - between rows as well, it is your responsibility to keep track of the column names. - - The only way to get a complete set of columns that exist for a ColumnFamily is to process all the rows. - For more information about how HBase stores data internally, see . - -
-
Joins - Whether HBase supports joins is a common question on the dist-list, and there is a simple answer: it doesn't, - at not least in the way that RDBMS' support them (e.g., with equi-joins or outer-joins in SQL). As has been illustrated - in this chapter, the read data model operations in HBase are Get and Scan. - - However, that doesn't mean that equivalent join functionality can't be supported in your application, but - you have to do it yourself. The two primary strategies are either denormalizing the data upon writing to HBase, - or to have lookup tables and do the join between HBase tables in your application or MapReduce code (and as RDBMS' - demonstrate, there are several strategies for this depending on the size of the tables, e.g., nested loops vs. - hash-joins). So which is the best approach? It depends on what you are trying to do, and as such there isn't a single - answer that works for every use case. - -
-
ACID - See ACID Semantics. - Lars Hofhansl has also written a note on - ACID in HBase. -
- diff --git a/src/main/docbkx/developer.xml b/src/main/docbkx/developer.xml deleted file mode 100644 index 47c78b42624..00000000000 --- a/src/main/docbkx/developer.xml +++ /dev/null @@ -1,2346 +0,0 @@ - - - - Building and Developing Apache HBase - This chapter contains information and guidelines for building and releasing HBase code and - documentation. Being familiar with these guidelines will help the HBase committers to use - your contributions more easily. -
- Getting Involved - Apache HBase gets better only when people contribute! If you are looking to contribute - to Apache HBase, look for issues in JIRA tagged with the label 'beginner'. These are issues HBase - contributors have deemed worthy but not of immediate priority and a good way to ramp on - HBase internals. See What label - is used for issues that are good on ramps for new contributors? from the dev - mailing list for background. - Before you get started submitting code to HBase, please refer to . - As Apache HBase is an Apache Software Foundation project, see - for more information about how the ASF functions. -
- Mailing Lists - Sign up for the dev-list and the user-list. See the - mailing lists page. - Posing questions - and helping to answer other people's questions - is encouraged! - There are varying levels of experience on both lists so patience and politeness are encouraged (and please - stay on topic.) - -
-
- Internet Relay Chat (IRC) - For real-time questions and discussions, use the #hbase IRC - channel on the FreeNode IRC network. - FreeNode offers a web-based client, but most people prefer a native client, and - several clients are available for each operating system. -
-
- Jira - Check for existing issues in Jira. If it's - either a new feature request, enhancement, or a bug, file a ticket. - To check for existing issues which you can tackle as a beginner, search for issues in JIRA tagged with the label 'beginner'. - - JIRA Priorities - - Blocker: Should only be used if the issue WILL cause data loss or cluster - instability reliably. - - - Critical: The issue described can cause data loss or cluster instability - in some cases. - - - Major: Important but not tragic issues, like updates to the client API - that will add a lot of much-needed functionality or significant bugs that - need to be fixed but that don't cause data loss. - - - Minor: Useful enhancements and annoying but not damaging bugs. - - - Trivial: Useful enhancements but generally cosmetic. - - - - Code Blocks in Jira Comments - A commonly used macro in Jira is {code}. Everything inside the tags is - preformatted, as in this example. - -{code} -code snippet -{code} - - -
- -
- -
- Apache HBase Repositories - There are two different repositories for Apache HBase: Subversion (SVN) and Git. GIT - is our repository of record for all but the Apache HBase website. We used to be on SVN. - We migrated. See Migrate Apache HBase SVN Repos to Git. Updating hbase.apache.org still - requires use of SVN (See ). See Source Code - Management page for contributor and committer links or seach for HBase on the - Apache Git page. -
- -
- IDEs -
- Eclipse -
- Code Formatting - Under the dev-support/ folder, you will find - hbase_eclipse_formatter.xml. We encourage you to have - this formatter in place in eclipse when editing HBase code. - - Load the HBase Formatter Into Eclipse - - Open the - Eclipse - Preferences - menu item. - - - In Preferences, click the - Java - Code Style - Formatter - menu item. - - - Click Import and browse to the location of the - hbase_eclipse_formatter.xml file, which is in - the dev-support/ directory. Click - Apply. - - - Still in Preferences, click - Java Editor - Save Actions - . Be sure the following options are selected: - - Perform the selected actions on save - Format source code - Format edited lines - - Click Apply. Close all dialog boxes and return - to the main window. - - - - In addition to the automatic formatting, make sure you follow the style - guidelines explained in - Also, no @author tags - that's a rule. Quality Javadoc comments - are appreciated. And include the Apache license. -
-
- Eclipse Git Plugin - If you cloned the project via git, download and install the Git plugin (EGit). - Attach to your local git repo (via the Git Repositories - window) and you'll be able to see file revision history, generate patches, - etc. -
-
- HBase Project Setup in Eclipse using <code>m2eclipse</code> - The easiest way is to use the m2eclipse plugin for Eclipse. - Eclipse Indigo or newer includes m2eclipse, or you can - download it from http://www.eclipse.org/m2e//. It provides Maven integration for - Eclipse, and even lets you use the direct Maven commands from within Eclipse to - compile and test your project. - To import the project, click - File - Import - Maven - Existing Maven Projects - and select the HBase root directory. m2eclipse - locates all the hbase modules for you. - If you install m2eclipse and import HBase in your - workspace, do the following to fix your eclipse Build Path. - - - Remove target folder - - - Add target/generated-jamon and - target/generated-sources/java folders. - - - Remove from your Build Path the exclusions on the - src/main/resources and - src/test/resources to avoid error message in - the console, such as the following: - Failed to execute goal -org.apache.maven.plugins:maven-antrun-plugin:1.6:run (default) on project hbase: -'An Ant BuildException has occured: Replace: source file .../target/classes/hbase-default.xml -doesn't exist - This will also reduce the eclipse build cycles and make your life - easier when developing. - - -
-
- HBase Project Setup in Eclipse Using the Command Line - Instead of using m2eclipse, you can generate the Eclipse files - from the command line. - - - First, run the following command, which builds HBase. You only need to - do this once. - mvn clean install -DskipTests - - - Close Eclipse, and execute the following command from the terminal, in - your local HBase project directory, to generate new - .project and .classpath - files. - mvn eclipse:eclipse - - - Reopen Eclipse and import the .project file in - the HBase directory to a workspace. - - -
-
- Maven Classpath Variable - The $M2_REPO classpath variable needs to be set up for the - project. This needs to be set to your local Maven repository, which is usually - ~/.m2/repository - If this classpath variable is not configured, you will see compile errors in - Eclipse like this: - -Description Resource Path Location Type -The project cannot be built until build path errors are resolved hbase Unknown Java Problem -Unbound classpath variable: 'M2_REPO/asm/asm/3.1/asm-3.1.jar' in project 'hbase' hbase Build path Build Path Problem -Unbound classpath variable: 'M2_REPO/com/google/guava/guava/r09/guava-r09.jar' in project 'hbase' hbase Build path Build Path Problem -Unbound classpath variable: 'M2_REPO/com/google/protobuf/protobuf-java/2.3.0/protobuf-java-2.3.0.jar' in project 'hbase' hbase Build path Build Path Problem Unbound classpath variable: - -
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- Eclipse Known Issues - Eclipse will currently complain about Bytes.java. It is - not possible to turn these errors off. - -Description Resource Path Location Type -Access restriction: The method arrayBaseOffset(Class) from the type Unsafe is not accessible due to restriction on required library /System/Library/Java/JavaVirtualMachines/1.6.0.jdk/Contents/Classes/classes.jar Bytes.java /hbase/src/main/java/org/apache/hadoop/hbase/util line 1061 Java Problem -Access restriction: The method arrayIndexScale(Class) from the type Unsafe is not accessible due to restriction on required library /System/Library/Java/JavaVirtualMachines/1.6.0.jdk/Contents/Classes/classes.jar Bytes.java /hbase/src/main/java/org/apache/hadoop/hbase/util line 1064 Java Problem -Access restriction: The method getLong(Object, long) from the type Unsafe is not accessible due to restriction on required library /System/Library/Java/JavaVirtualMachines/1.6.0.jdk/Contents/Classes/classes.jar Bytes.java /hbase/src/main/java/org/apache/hadoop/hbase/util line 1111 Java Problem - -
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- Eclipse - More Information - For additional information on setting up Eclipse for HBase development on - Windows, see Michael Morello's blog on the topic. -
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- IntelliJ IDEA - You can set up IntelliJ IDEA for similar functinoality as Eclipse. Follow these steps. - - Project Setup in IntelliJ IDEA - - Select - Import Project - Import Project From External Model - Maven - - - - You do not need to select a profile. Be sure Maven project - required is selected, and click - Next. - - - Select the location for the JDK. - - - - Using the HBase Formatter in IntelliJ IDEA - Using the Eclipse Code Formatter plugin for IntelliJ IDEA, you can import the - HBase code formatter described in . - -
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- Other IDEs - It would be userful to mirror the set-up instructions - for other IDEs. If you would like to assist, please have a look at HBASE-11704. -
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- -
- Building Apache HBase -
- Basic Compile - HBase is compiled using Maven. You must use Maven 3.x. To check your Maven - version, run the command mvn -version. - - JDK Version Requirements - Starting with HBase 1.0 you must use Java 7 or later to build from source - code. See for more complete information about supported - JDK versions. - -
- Maven Build Commands - All commands are executed from the local HBase project directory. -
- Package - The simplest command to compile HBase from its java source code is to use - the package target, which builds JARs with the compiled - files. - mvn package -DskipTests - Or, to clean up before compiling: - mvn clean package -DskipTests - With Eclipse set up as explained above in , you - can also use the Build command in Eclipse. To create the - full installable HBase package takes a little bit more work, so read on. - -
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- Compile - The compile target does not create the JARs with the compiled - files. - mvn compile - mvn clean compile -
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- Install - To install the JARs in your ~/.m2/ directory, use the - install target. - mvn install - mvn clean install - mvn clean install -DskipTests -
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- Running all or individual Unit Tests - See the section in -
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- Building against various hadoop versions. - As of 0.96, Apache HBase supports building against Apache Hadoop versions: - 1.0.3, 2.0.0-alpha and 3.0.0-SNAPSHOT. By default, in 0.96 and earlier, we will - build with Hadoop-1.0.x. As of 0.98, Hadoop 1.x is deprecated and Hadoop 2.x is - the default. To change the version to build against, add a hadoop.profile - property when you invoke mvn: - mvn -Dhadoop.profile=1.0 ... - The above will build against whatever explicit hadoop 1.x version we have in - our pom.xml as our '1.0' version. Tests may not all pass so - you may need to pass -DskipTests unless you are inclined to fix the - failing tests. - - 'dependencyManagement.dependencies.dependency.artifactId' for - org.apache.hbase:${compat.module}:test-jar with value '${compat.module}' - does not match a valid id pattern - You will see ERRORs like the above title if you pass the - default profile; e.g. if you pass - hadoop.profile=1.1 when building 0.96 or - hadoop.profile=2.0 when building hadoop 0.98; just - drop the hadoop.profile stipulation in this case to get your build to run - again. This seems to be a maven pecularity that is probably fixable but - we've not spent the time trying to figure it. - - - Similarly, for 3.0, you would just replace the profile value. Note that - Hadoop-3.0.0-SNAPSHOT does not currently have a deployed maven artificat - you - will need to build and install your own in your local maven repository if you - want to run against this profile. - In earilier versions of Apache HBase, you can build against older versions of - Apache Hadoop, notably, Hadoop 0.22.x and 0.23.x. If you are running, for - example HBase-0.94 and wanted to build against Hadoop 0.23.x, you would run - with: - mvn -Dhadoop.profile=22 ... -
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- Build Protobuf - You may need to change the protobuf definitions that reside in the - hbase-protocol module or other modules. - The protobuf files are located in - hbase-protocol/src/main/protobuf. For the change to be - effective, you will need to regenerate the classes. You can use maven profile - compile-protobuf to do this. - mvn compile -Pcompile-protobuf - You may also want to define protoc.path for the protoc - binary, using the following command: - -mvn compile -Pcompile-protobuf -Dprotoc.path=/opt/local/bin/protoc - - Read the hbase-protocol/README.txt for more details. - -
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- Build Thrift - You may need to change the thrift definitions that reside in the - hbase-thrift module or other modules. - The thrift files are located in - hbase-thrift/src/main/resources. - For the change to be effective, you will need to regenerate the classes. - You can use maven profile compile-thrift to do this. - mvn compile -Pcompile-thrift - You may also want to define thrift.path for the thrift - binary, using the following command: - - mvn compile -Pcompile-thrift -Dthrift.path=/opt/local/bin/thrift - -
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- Build a Tarball - You can build a tarball without going through the release process described in - , by running the following command: - mvn -DskipTests clean install && mvn -DskipTests package assembly:single - The distribution tarball is built in - hbase-assembly/target/hbase-<version>-bin.tar.gz. -
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- Build Gotchas - If you see Unable to find resource 'VM_global_library.vm', ignore - it. Its not an error. It is officially - ugly though. -
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- Building in snappy compression support - Pass -Psnappy to trigger the hadoop-snappy maven profile - for building Google Snappy native libraries into HBase. See also -
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- Releasing Apache HBase - - Building against HBase 1.x - HBase 1.x requires Java 7 to build. See for Java - requirements per HBase release. - -
- Building against HBase 0.96-0.98 - HBase 0.96.x will run on Hadoop 1.x or Hadoop 2.x. HBase 0.98 still runs on both, - but HBase 0.98 deprecates use of Hadoop 1. HBase 1.x will not - run on Hadoop 1. In the following procedures, we make a distinction between HBase - 1.x builds and the awkward process involved building HBase 0.96/0.98 for either - Hadoop 1 or Hadoop 2 targets. - You must choose which Hadoop to build against. It is not possible to build a - single HBase binary that runs against both Hadoop 1 and Hadoop 2. Hadoop is included - in the build, because it is needed to run HBase in standalone mode. Therefore, the - set of modules included in the tarball changes, depending on the build target. To - determine which HBase you have, look at the HBase version. The Hadoop version is - embedded within it. - Maven, our build system, natively does not allow a single product to be built - against different dependencies. Also, Maven cannot change the set of included - modules and write out the correct pom.xml files with - appropriate dependencies, even using two build targets, one for Hadoop 1 and another - for Hadoop 2. A prerequisite step is required, which takes as input the current - pom.xmls and generates Hadoop 1 or Hadoop 2 versions using - a script in the dev-tools/ directory, called - generate-hadoopX-poms.sh - where X is either 1 or - 2. You then reference these generated poms when you build. - For now, just be aware of the difference between HBase 1.x builds and those of HBase - 0.96-0.98. This difference is important to the build instructions. - - - - Example <filename>~/.m2/settings.xml</filename> File - Publishing to maven requires you sign the artifacts you want to upload. For - the build to sign them for you, you a properly configured - settings.xml in your local repository under - .m2, such as the following. - - - - - apache.snapshots.https - YOUR_APACHE_ID - - YOUR_APACHE_PASSWORD - - - - - - apache.releases.https - YOUR_APACHE_ID - - YOUR_APACHE_PASSWORD - - - - - - apache-release - - YOUR_KEYNAME - - YOUR_KEY_PASSWORD - - - - -]]> - - - -
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- Making a Release Candidate - - These instructions are for building HBase 1.0.x. For building earlier - versions, the process is different. See this section under the respective - release documentation folders. - - Point Releases - If you are making a point release (for example to quickly address a critical - incompatability or security problem) off of a release branch instead of a - development branch, the tagging instructions are slightly different. I'll prefix - those special steps with Point Release Only. - - - - Before You Begin - Before you make a release candidate, do a practice run by deploying a - snapshot. Before you start, check to be sure recent builds have been passing for - the branch from where you are going to take your release. You should also have - tried recent branch tips out on a cluster under load, perhaps by running the - hbase-it integration test suite for a few hours to 'burn in' - the near-candidate bits. - - - Point Release Only - At this point you should tag the previous release branch (ex: 0.96.1) with the - new point release tag (e.g. 0.96.1.1 tag). Any commits with changes for the - point release should be appled to the new tag. - - - - The Hadoop How To - Release wiki page is used as a model for most of the instructions below, - and may have more detail on particular sections, so it is worth review. - - - Specifying the Heap Space for Maven on OSX - On OSX, you may need to specify the heap space for Maven commands, by setting - the MAVEN_OPTS variable to -Xmx3g. You can - prefix the variable to the Maven command, as in the following example: - MAVEN_OPTS="-Xmx2g" mvn package - You could also set this in an environment variable or alias in your - shell. - - - Release Procedure - The script dev-support/make_rc.sh automates many of these - steps. It does not do the modification of the CHANGES.txt - for the release, the close of the staging repository in Apache Maven (human - intervention is needed here), the checking of the produced artifacts to ensure - they are 'good' -- e.g. extracting the produced tarballs, verifying that they - look right, then starting HBase and checking that everything is running - correctly, then the signing and pushing of the tarballs to people.apache.org. The script - handles everything else, and comes in handy. - - Update the <filename>CHANGES.txt</filename> file and the POM files. - Update CHANGES.txt with the changes since the last - release. Make sure the URL to the JIRA points to the proper location which - lists fixes for this release. Adjust the version in all the POM files - appropriately. If you are making a release candidate, you must remove the - -SNAPSHOT label from all versions. If you are running - this receipe to publish a snapshot, you must keep the - -SNAPSHOT suffix on the hbase version. The Versions - Maven Plugin can be of use here. To set a version in all the many - poms of the hbase multi-module project, use a command like the - following: - -$ mvn clean org.codehaus.mojo:versions-maven-plugin:1.3.1:set -DnewVersion=0.96.0 - - Checkin the CHANGES.txt and any version - changes. - - - Update the documentation. - Update the documentation under src/main/docbkx. This - usually involves copying the latest from trunk and making version-particular - adjustments to suit this release candidate version. - - - Build the source tarball. - Now, build the source tarball. This tarball is Hadoop-version-independent. - It is just the pure source code and documentation without a particular - hadoop taint, etc. Add the -Prelease profile when - building. It checks files for licenses and will fail the build if unlicensed - files are present. - -$ mvn clean install -DskipTests assembly:single -Dassembly.file=hbase-assembly/src/main/assembly/src.xml -Prelease - - Extract the tarball and make sure it looks good. A good test for the src - tarball being 'complete' is to see if you can build new tarballs from this - source bundle. If the source tarball is good, save it off to a - version directory, a directory somewhere where you - are collecting all of the tarballs you will publish as part of the release - candidate. For example if you were building a hbase-0.96.0 release - candidate, you might call the directory - hbase-0.96.0RC0. Later you will publish this directory - as our release candidate up on people.apache.org/~YOU/. - - - Build the binary tarball. - Next, build the binary tarball. Add the -Prelease - profile when building. It checks files for licenses and will fail the build - if unlicensed files are present. Do it in two steps. - - - First install into the local repository - -$ mvn clean install -DskipTests -Prelease - - - Next, generate documentation and assemble the tarball. - -$ mvn install -DskipTests site assembly:single -Prelease - - - Otherwise, the build complains that hbase modules are not in the maven - repository when you try to do it at once, especially on fresh repository. It - seems that you need the install goal in both steps. - Extract the generated tarball and check it out. Look at the documentation, - see if it runs, etc. If good, copy the tarball to the above mentioned - version directory. - - - Create a new tag. - - Point Release Only - The following step that creates a new tag can be skipped since you've - already created the point release tag - - Tag the release at this point since it looks good. If you find an issue - later, you can delete the tag and start over. Release needs to be tagged for - the next step. - - - Deploy to the Maven Repository. - Next, deploy HBase to the Apache Maven repository, using the - apache-release profile instead of the - release profile when running the mvn - deploy command. This profile invokes the Apache pom referenced - by our pom files, and also signs your artifacts published to Maven, as long - as the settings.xml is configured correctly, as - described in . - -$ mvn deploy -DskipTests -Papache-release - This command copies all artifacts up to a temporary staging Apache mvn - repository in an 'open' state. More work needs to be done on these maven - artifacts to make them generally available. - - - Make the Release Candidate available. - The artifacts are in the maven repository in the staging area in the - 'open' state. While in this 'open' state you can check out what you've - published to make sure all is good. To do this, login at repository.apache.org - using your Apache ID. Find your artifacts in the staging repository. Browse - the content. Make sure all artifacts made it up and that the poms look - generally good. If it checks out, 'close' the repo. This will make the - artifacts publically available. You will receive an email with the URL to - give out for the temporary staging repository for others to use trying out - this new release candidate. Include it in the email that announces the - release candidate. Folks will need to add this repo URL to their local poms - or to their local settings.xml file to pull the - published release candidate artifacts. If the published artifacts are - incomplete or have problems, just delete the 'open' staged artifacts. - - hbase-downstreamer - See the hbase-downstreamer test for a simple example of a project - that is downstream of HBase an depends on it. Check it out and run its - simple test to make sure maven artifacts are properly deployed to the - maven repository. Be sure to edit the pom to point to the proper staging - repository. Make sure you are pulling from the repository when tests run - and that you are not getting from your local repository, by either - passing the -U flag or deleting your local repo content and - check maven is pulling from remote out of the staging repository. - - - See Publishing Maven Artifacts for some pointers on this maven - staging process. - - We no longer publish using the maven release plugin. Instead we do - mvn deploy. It seems to give us a backdoor to - maven release publishing. If there is no -SNAPSHOT - on the version string, then we are 'deployed' to the apache maven - repository staging directory from which we can publish URLs for - candidates and later, if they pass, publish as release (if a - -SNAPSHOT on the version string, deploy will - put the artifacts up into apache snapshot repos). - - If the HBase version ends in -SNAPSHOT, the artifacts - go elsewhere. They are put into the Apache snapshots repository directly and - are immediately available. Making a SNAPSHOT release, this is what you want - to happen. - - - If you used the <filename>make_rc.sh</filename> script instead of doing - the above manually, do your sanity checks now. - At this stage, you have two tarballs in your 'version directory' and a - set of artifacts in a staging area of the maven repository, in the 'closed' - state. These are publicly accessible in a temporary staging repository whose - URL you should have gotten in an email. The above mentioned script, - make_rc.sh does all of the above for you minus the - check of the artifacts built, the closing of the staging repository up in - maven, and the tagging of the release. If you run the script, do your checks - at this stage verifying the src and bin tarballs and checking what is up in - staging using hbase-downstreamer project. Tag before you start the build. - You can always delete it if the build goes haywire. - - - Sign, upload, and 'stage' your version directory to <link - xlink:href="http://people.apache.org">people.apache.org</link> (TODO: - There is a new location to stage releases using svnpubsub. See - (<link xlink:href="https://issues.apache.org/jira/browse/HBASE-10554">HBASE-10554 Please delete old releases from mirroring system</link>). - If all checks out, next put the version directory up - on people.apache.org. You - will need to sign and fingerprint them before you push them up. In the - version directory run the following commands: - - -$ for i in *.tar.gz; do echo $i; gpg --print-mds $i > $i.mds ; done -$ for i in *.tar.gz; do echo $i; gpg --armor --output $i.asc --detach-sig $i ; done -$ cd .. -# Presuming our 'version directory' is named 0.96.0RC0, now copy it up to people.apache.org. -$ rsync -av 0.96.0RC0 people.apache.org:public_html - - Make sure the people.apache.org directory is showing and that the mvn repo - URLs are good. Announce the release candidate on the mailing list and call a - vote. - - -
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- Publishing a SNAPSHOT to maven - Make sure your settings.xml is set up properly, as in . Make sure the hbase version includes - -SNAPSHOT as a suffix. Following is an example of publishing - SNAPSHOTS of a release that had an hbase version of 0.96.0 in its poms. - - $ mvn clean install -DskipTests javadoc:aggregate site assembly:single -Prelease - $ mvn -DskipTests deploy -Papache-release - The make_rc.sh script mentioned above (see ) can help you publish SNAPSHOTS. - Make sure your hbase.version has a -SNAPSHOT - suffix before running the script. It will put a snapshot up into the apache snapshot - repository for you. -
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- Voting on Release Candidates - Everyone is encouraged to try and vote on HBase release candidates. Only the votes of - PMC members are binding. PMC members, please read this WIP doc on policy voting for a - release candidate, Release - Policy. Before casting +1 binding votes, individuals are required to - download the signed source code package onto their own hardware, compile it as - provided, and test the resulting executable on their own platform, along with also - validating cryptographic signatures and verifying that the package meets the - requirements of the ASF policy on releases. Regards the latter, run - mvn apache-rat:check to verify all files are suitably licensed. - See HBase, mail # dev - On - recent discussion clarifying ASF release policy. for how we arrived at this - process. -
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- Generating the HBase Reference Guide - The manual is marked up using docbook. We then use the docbkx maven plugin to - transform the markup to html. This plugin is run when you specify the - site goal as in when you run mvn site or you - can call the plugin explicitly to just generate the manual by doing mvn - docbkx:generate-html. When you run mvn site, the - documentation is generated twice, once to generate the multipage manual and then again - for the single page manual, which is easier to search. See for more information on building - the documentation. -
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- Updating <link xlink:href="http://hbase.apache.org">hbase.apache.org</link> -
- Contributing to hbase.apache.org - See for more information - on contributing to the documentation or website. -
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- Publishing <link xlink:href="http://hbase.apache.org" - >hbase.apache.org</link> - As of INFRA-5680 Migrate apache hbase website, to publish the website, build - it using Maven, and then deploy it over a checkout of - https://svn.apache.org/repos/asf/hbase/hbase.apache.org/trunk - and check in your changes. The script - dev-scripts/publish_hbase_website.sh is provided to - automate this process and to be sure that stale files are removed from SVN. Review - the script even if you decide to publish the website manually. Use the script as - follows: - $ publish_hbase_website.sh -h -] [-s ]]]> - -h Show this message - -i Prompts the user for input - -a Does not prompt the user. Potentially dangerous. - -g The local location of the HBase git repository - -s The local location of the HBase svn checkout - Either --interactive or --silent is required. - Edit the script to set default Git and SVN directories. - - The SVN commit takes a long time. -
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- Tests - - Developers, at a minimum, should familiarize themselves with the unit test detail; - unit tests in HBase have a character not usually seen in other projects. - This information is about unit tests for HBase itself. For developing unit tests for - your HBase applications, see . -
- Apache HBase Modules - As of 0.96, Apache HBase is split into multiple modules. This creates - "interesting" rules for how and where tests are written. If you are writing code for - hbase-server, see for - how to write your tests. These tests can spin up a minicluster and will need to be - categorized. For any other module, for example hbase-common, - the tests must be strict unit tests and just test the class under test - no use of - the HBaseTestingUtility or minicluster is allowed (or even possible given the - dependency tree). -
- Testing the HBase Shell - - The HBase shell and its tests are predominantly written in jruby. In order to make these - tests run as a part of the standard build, there is a single JUnit test, - TestShell, that takes care of loading the jruby implemented tests and - running them. You can run all of these tests from the top level with: - - - mvn clean test -Dtest=TestShell - - - Alternatively, you may limit the shell tests that run using the system variable - shell.test. This value should specify the ruby literal equivalent of a - particular test case by name. For example, the tests that cover the shell commands for - altering tables are contained in the test case AdminAlterTableTest - and you can run them with: - - - mvn clean test -Dtest=TestShell -Dshell.test=/AdminAlterTableTest/ - - - You may also use a Ruby Regular Expression - literal (in the /pattern/ style) to select a set of test cases. - You can run all of the HBase admin related tests, including both the normal administration and - the security administration, with the command: - - - mvn clean test -Dtest=TestShell -Dshell.test=/.*Admin.*Test/ - - - In the event of a test failure, you can see details by examining the XML version of the - surefire report results - - - vim hbase-shell/target/surefire-reports/TEST-org.apache.hadoop.hbase.client.TestShell.xml - -
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- Running Tests in other Modules - If the module you are developing in has no other dependencies on other HBase modules, then - you can cd into that module and just run: - mvn test - which will just run the tests IN THAT MODULE. If there are other dependencies on other modules, - then you will have run the command from the ROOT HBASE DIRECTORY. This will run the tests in the other - modules, unless you specify to skip the tests in that module. For instance, to skip the tests in the hbase-server module, - you would run: - mvn clean test -PskipServerTests - from the top level directory to run all the tests in modules other than hbase-server. Note that you - can specify to skip tests in multiple modules as well as just for a single module. For example, to skip - the tests in hbase-server and hbase-common, you would run: - mvn clean test -PskipServerTests -PskipCommonTests - Also, keep in mind that if you are running tests in the hbase-server module you will need to - apply the maven profiles discussed in to get the tests to run properly. -
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-Unit Tests -Apache HBase unit tests are subdivided into four categories: small, medium, large, and -integration with corresponding JUnit categories: -SmallTests, MediumTests, -LargeTests, IntegrationTests. -JUnit categories are denoted using java annotations and look like this in your unit test code. -... -@Category(SmallTests.class) -public class TestHRegionInfo { - @Test - public void testCreateHRegionInfoName() throws Exception { - // ... - } -} - The above example shows how to mark a unit test as belonging to the - small category. All unit tests in HBase have a - categorization. - The first three categories, small, medium, - and large, are for tests run when you type $ mvn - test. In other words, these three categorizations are for HBase unit - tests. The integration category is not for unit tests, but for - integration tests. These are run when you invoke $ mvn verify. - Integration tests are described in . - HBase uses a patched maven surefire plugin and maven profiles to implement - its unit test characterizations. - Keep reading to figure which annotation of the set small, medium, and large to - put on your new HBase unit test. - - - Categorizing Tests - - Small TestsSmallTests - - - Small tests are executed in a shared JVM. We put in - this category all the tests that can be executed quickly in a shared - JVM. The maximum execution time for a small test is 15 seconds, and - small tests should not use a (mini)cluster. - - - - - Medium TestsMediumTests - - Medium tests represent tests that must be - executed before proposing a patch. They are designed to run in less than - 30 minutes altogether, and are quite stable in their results. They are - designed to last less than 50 seconds individually. They can use a - cluster, and each of them is executed in a separate JVM. - - - - - Large TestsLargeTests - - Large tests are everything else. They are - typically large-scale tests, regression tests for specific bugs, timeout - tests, performance tests. They are executed before a commit on the - pre-integration machines. They can be run on the developer machine as - well. - - - - Integration - TestsIntegrationTests - - Integration tests are system level tests. See - for more info. - - - -
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- Running tests - -
- Default: small and medium category tests - Running mvn test will - execute all small tests in a single JVM (no fork) and then medium tests in a - separate JVM for each test instance. Medium tests are NOT executed if there is - an error in a small test. Large tests are NOT executed. There is one report for - small tests, and one report for medium tests if they are executed. -
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- Running all tests - Running - mvn test -P runAllTests will - execute small tests in a single JVM then medium and large tests in a separate - JVM for each test. Medium and large tests are NOT executed if there is an error - in a small test. Large tests are NOT executed if there is an error in a small or - medium test. There is one report for small tests, and one report for medium and - large tests if they are executed. -
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- Running a single test or all tests in a package - To run an individual test, e.g. MyTest, rum mvn test -Dtest=MyTest You can also pass multiple, - individual tests as a comma-delimited list: mvn test - -Dtest=MyTest1,MyTest2,MyTest3 You can also pass a package, which - will run all tests under the package: mvn test - '-Dtest=org.apache.hadoop.hbase.client.*' - - - When -Dtest is specified, the localTests profile - will be used. It will use the official release of maven surefire, rather than - our custom surefire plugin, and the old connector (The HBase build uses a - patched version of the maven surefire plugin). Each junit test is executed in a - separate JVM (A fork per test class). There is no parallelization when tests are - running in this mode. You will see a new message at the end of the -report: - "[INFO] Tests are skipped". It's harmless. However, you - need to make sure the sum of Tests run: in the Results - : section of test reports matching the number of tests you specified - because no error will be reported when a non-existent test case is specified. - -
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- Other test invocation permutations - Running mvn test -P runSmallTests will execute "small" - tests only, using a single JVM. - Running mvn test -P runMediumTests will execute "medium" - tests only, launching a new JVM for each test-class. - Running mvn test -P runLargeTests will execute "large" - tests only, launching a new JVM for each test-class. - For convenience, you can run mvn test -P runDevTests to - execute both small and medium tests, using a single JVM. -
- -
- Running tests faster - By default, $ mvn test -P runAllTests runs 5 tests in parallel. - It can be increased on a developer's machine. Allowing that you can have 2 tests - in parallel per core, and you need about 2GB of memory per test (at the - extreme), if you have an 8 core, 24GB box, you can have 16 tests in parallel. - but the memory available limits it to 12 (24/2), To run all tests with 12 tests - in parallel, do this: mvn test -P runAllTests - -Dsurefire.secondPartForkCount=12. If using a version earlier than - 2.0, do: mvn test -P runAllTests -Dsurefire.secondPartThreadCount=12 - . To increase the speed, you can as well use a ramdisk. You will need 2GB - of memory to run all tests. You will also need to delete the files between two - test run. The typical way to configure a ramdisk on Linux is: - $ sudo mkdir /ram2G -sudo mount -t tmpfs -o size=2048M tmpfs /ram2G - You can then use it to run all HBase tests on 2.0 with the command: - mvn test - -P runAllTests -Dsurefire.secondPartForkCount=12 - -Dtest.build.data.basedirectory=/ram2G - On earlier versions, use: - mvn test - -P runAllTests -Dsurefire.secondPartThreadCount=12 - -Dtest.build.data.basedirectory=/ram2G -
- -
- <command>hbasetests.sh</command> - It's also possible to use the script hbasetests.sh. This - script runs the medium and large tests in parallel with two maven instances, and - provides a single report. This script does not use the hbase version of surefire - so no parallelization is being done other than the two maven instances the - script sets up. It must be executed from the directory which contains the - pom.xml. - For example running ./dev-support/hbasetests.sh will - execute small and medium tests. Running ./dev-support/hbasetests.sh - runAllTests will execute all tests. Running - ./dev-support/hbasetests.sh replayFailed will rerun the - failed tests a second time, in a separate jvm and without parallelisation. - -
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- Test Resource Checker<indexterm><primary>Test Resource - Checker</primary></indexterm> - A custom Maven SureFire plugin listener checks a number of resources before - and after each HBase unit test runs and logs its findings at the end of the test - output files which can be found in target/surefire-reports - per Maven module (Tests write test reports named for the test class into this - directory. Check the *-out.txt files). The resources - counted are the number of threads, the number of file descriptors, etc. If the - number has increased, it adds a LEAK? comment in the logs. - As you can have an HBase instance running in the background, some threads can be - deleted/created without any specific action in the test. However, if the test - does not work as expected, or if the test should not impact these resources, - it's worth checking these log lines - ...hbase.ResourceChecker(157): before... - and ...hbase.ResourceChecker(157): after.... - For example: - 2012-09-26 09:22:15,315 INFO [pool-1-thread-1] -hbase.ResourceChecker(157): after: -regionserver.TestColumnSeeking#testReseeking Thread=65 (was 65), -OpenFileDescriptor=107 (was 107), MaxFileDescriptor=10240 (was 10240), -ConnectionCount=1 (was 1) -
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- -
- Writing Tests -
- General rules - - - As much as possible, tests should be written as category small - tests. - - - All tests must be written to support parallel execution on the same - machine, hence they should not use shared resources as fixed ports or - fixed file names. - - - Tests should not overlog. More than 100 lines/second makes the logs - complex to read and use i/o that are hence not available for the other - tests. - - - Tests can be written with HBaseTestingUtility. - This class offers helper functions to create a temp directory and do the - cleanup, or to start a cluster. - - -
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- Categories and execution time - - - All tests must be categorized, if not they could be skipped. - - - All tests should be written to be as fast as possible. - - - Small category tests should last less than 15 seconds, and must not - have any side effect. - - - Medium category tests should last less than 50 seconds. - - - Large category tests should last less than 3 minutes. This should - ensure a good parallelization for people using it, and ease the analysis - when the test fails. - - -
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- Sleeps in tests - Whenever possible, tests should not use Thread.sleep, - but rather waiting for the real event they need. This is faster and clearer for - the reader. Tests should not do a Thread.sleep without - testing an ending condition. This allows understanding what the test is waiting - for. Moreover, the test will work whatever the machine performance is. Sleep - should be minimal to be as fast as possible. Waiting for a variable should be - done in a 40ms sleep loop. Waiting for a socket operation should be done in a - 200 ms sleep loop. -
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- Tests using a cluster - - Tests using a HRegion do not have to start a cluster: A region can use the - local file system. Start/stopping a cluster cost around 10 seconds. They should - not be started per test method but per test class. Started cluster must be - shutdown using HBaseTestingUtility#shutdownMiniCluster, - which cleans the directories. As most as possible, tests should use the default - settings for the cluster. When they don't, they should document it. This will - allow to share the cluster later. -
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- -
- Integration Tests - HBase integration/system tests are tests that are beyond HBase unit tests. They - are generally long-lasting, sizeable (the test can be asked to 1M rows or 1B rows), - targetable (they can take configuration that will point them at the ready-made - cluster they are to run against; integration tests do not include cluster start/stop - code), and verifying success, integration tests rely on public APIs only; they do - not attempt to examine server internals asserting success/fail. Integration tests - are what you would run when you need to more elaborate proofing of a release - candidate beyond what unit tests can do. They are not generally run on the Apache - Continuous Integration build server, however, some sites opt to run integration - tests as a part of their continuous testing on an actual cluster. - Integration tests currently live under the src/test - directory in the hbase-it submodule and will match the regex: - **/IntegrationTest*.java. All integration tests are also - annotated with @Category(IntegrationTests.class). - - Integration tests can be run in two modes: using a mini cluster, or against an - actual distributed cluster. Maven failsafe is used to run the tests using the mini - cluster. IntegrationTestsDriver class is used for executing the tests against a - distributed cluster. Integration tests SHOULD NOT assume that they are running - against a mini cluster, and SHOULD NOT use private API's to access cluster state. To - interact with the distributed or mini cluster uniformly, - IntegrationTestingUtility, and HBaseCluster classes, - and public client API's can be used. - - On a distributed cluster, integration tests that use ChaosMonkey or otherwise - manipulate services thru cluster manager (e.g. restart regionservers) use SSH to do - it. To run these, test process should be able to run commands on remote end, so ssh - should be configured accordingly (for example, if HBase runs under hbase user in - your cluster, you can set up passwordless ssh for that user and run the test also - under it). To facilitate that, hbase.it.clustermanager.ssh.user, - hbase.it.clustermanager.ssh.opts and - hbase.it.clustermanager.ssh.cmd configuration settings can be used. - "User" is the remote user that cluster manager should use to perform ssh commands. - "Opts" contains additional options that are passed to SSH (for example, "-i - /tmp/my-key"). Finally, if you have some custom environment setup, "cmd" is the - override format for the entire tunnel (ssh) command. The default string is - {/usr/bin/ssh %1$s %2$s%3$s%4$s "%5$s"} and is a good starting - point. This is a standard Java format string with 5 arguments that is used to - execute the remote command. The argument 1 (%1$s) is SSH options set the via opts - setting or via environment variable, 2 is SSH user name, 3 is "@" if username is set - or "" otherwise, 4 is the target host name, and 5 is the logical command to execute - (that may include single quotes, so don't use them). For example, if you run the - tests under non-hbase user and want to ssh as that user and change to hbase on - remote machine, you can use {/usr/bin/ssh %1$s %2$s%3$s%4$s "su hbase - -c - \"%5$s\""}. That way, to kill RS (for example) integration tests may run - {/usr/bin/ssh some-hostname "su hbase - -c \"ps aux | ... | kill - ...\""}. The command is logged in the test logs, so you can verify it is - correct for your environment. - To disable the running of Integration Tests, pass the following profile on the - command line -PskipIntegrationTests. For example, - $ mvn clean install test -Dtest=TestZooKeeper -PskipIntegrationTests - -
- Running integration tests against mini cluster - HBase 0.92 added a verify maven target. Invoking it, for - example by doing mvn verify, will run all the phases up to and - including the verify phase via the maven failsafe - plugin, running all the above mentioned HBase unit tests as well as - tests that are in the HBase integration test group. After you have completed - mvn install -DskipTests You can run just the integration - tests by invoking: - -cd hbase-it -mvn verify - If you just want to run the integration tests in top-level, you need to run - two commands. First: mvn failsafe:integration-test This - actually runs ALL the integration tests. - - This command will always output BUILD SUCCESS even if there - are test failures. - - At this point, you could grep the output by hand looking for failed tests. - However, maven will do this for us; just use: mvn - failsafe:verify The above command basically looks at all the test - results (so don't remove the 'target' directory) for test failures and reports - the results. - -
- Running a subset of Integration tests - This is very similar to how you specify running a subset of unit tests - (see above), but use the property it.test instead of - test. To just run - IntegrationTestClassXYZ.java, use: mvn - failsafe:integration-test -Dit.test=IntegrationTestClassXYZ - The next thing you might want to do is run groups of integration tests, say - all integration tests that are named IntegrationTestClassX*.java: - mvn failsafe:integration-test -Dit.test=*ClassX* This - runs everything that is an integration test that matches *ClassX*. This - means anything matching: "**/IntegrationTest*ClassX*". You can also run - multiple groups of integration tests using comma-delimited lists (similar to - unit tests). Using a list of matches still supports full regex matching for - each of the groups.This would look something like: mvn - failsafe:integration-test -Dit.test=*ClassX*, *ClassY - -
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-
- Running integration tests against distributed cluster - If you have an already-setup HBase cluster, you can launch the integration - tests by invoking the class IntegrationTestsDriver. You may have to - run test-compile first. The configuration will be picked by the bin/hbase - script. mvn test-compile Then - launch the tests with: - bin/hbase [--config config_dir] org.apache.hadoop.hbase.IntegrationTestsDriver - Pass -h to get usage on this sweet tool. Running the - IntegrationTestsDriver without any argument will launch tests found under - hbase-it/src/test, having - @Category(IntegrationTests.class) annotation, and a name - starting with IntegrationTests. See the usage, by passing -h, to - see how to filter test classes. You can pass a regex which is checked against - the full class name; so, part of class name can be used. IntegrationTestsDriver - uses Junit to run the tests. Currently there is no support for running - integration tests against a distributed cluster using maven (see HBASE-6201). - - The tests interact with the distributed cluster by using the methods in the - DistributedHBaseCluster (implementing - HBaseCluster) class, which in turn uses a pluggable - ClusterManager. Concrete implementations provide actual - functionality for carrying out deployment-specific and environment-dependent - tasks (SSH, etc). The default ClusterManager is - HBaseClusterManager, which uses SSH to remotely execute - start/stop/kill/signal commands, and assumes some posix commands (ps, etc). Also - assumes the user running the test has enough "power" to start/stop servers on - the remote machines. By default, it picks up HBASE_SSH_OPTS, HBASE_HOME, - HBASE_CONF_DIR from the env, and uses - bin/hbase-daemon.sh to carry out the actions. Currently tarball - deployments, deployments which uses hbase-daemons.sh, and Apache Ambari - deployments are supported. /etc/init.d/ scripts are not supported for now, but - it can be easily added. For other deployment options, a ClusterManager can be - implemented and plugged in. -
- -
- Destructive integration / system tests - In 0.96, a tool named ChaosMonkey has been introduced. It is - modeled after the same-named tool by Netflix. Some of the tests use ChaosMonkey to - simulate faults in the running cluster in the way of killing random servers, - disconnecting servers, etc. ChaosMonkey can also be used as a stand-alone tool - to run a (misbehaving) policy while you are running other tests. - - ChaosMonkey defines Action's and Policy's. Actions are sequences of events. - We have at least the following actions: - - - Restart active master (sleep 5 sec) - - - Restart random regionserver (sleep 5 sec) - - - Restart random regionserver (sleep 60 sec) - - - Restart META regionserver (sleep 5 sec) - - - Restart ROOT regionserver (sleep 5 sec) - - - Batch restart of 50% of regionservers (sleep 5 sec) - - - Rolling restart of 100% of regionservers (sleep 5 sec) - - - Policies on the other hand are responsible for executing the actions based on - a strategy. The default policy is to execute a random action every minute based - on predefined action weights. ChaosMonkey executes predefined named policies - until it is stopped. More than one policy can be active at any time. - - To run ChaosMonkey as a standalone tool deploy your HBase cluster as usual. - ChaosMonkey uses the configuration from the bin/hbase script, thus no extra - configuration needs to be done. You can invoke the ChaosMonkey by - running: - bin/hbase org.apache.hadoop.hbase.util.ChaosMonkey - This will output smt like: - -12/11/19 23:21:57 INFO util.ChaosMonkey: Using ChaosMonkey Policy: class org.apache.hadoop.hbase.util.ChaosMonkey$PeriodicRandomActionPolicy, period:60000 -12/11/19 23:21:57 INFO util.ChaosMonkey: Sleeping for 26953 to add jitter -12/11/19 23:22:24 INFO util.ChaosMonkey: Performing action: Restart active master -12/11/19 23:22:24 INFO util.ChaosMonkey: Killing master:master.example.com,60000,1353367210440 -12/11/19 23:22:24 INFO hbase.HBaseCluster: Aborting Master: master.example.com,60000,1353367210440 -12/11/19 23:22:24 INFO hbase.ClusterManager: Executing remote command: ps aux | grep master | grep -v grep | tr -s ' ' | cut -d ' ' -f2 | xargs kill -s SIGKILL , hostname:master.example.com -12/11/19 23:22:25 INFO hbase.ClusterManager: Executed remote command, exit code:0 , output: -12/11/19 23:22:25 INFO hbase.HBaseCluster: Waiting service:master to stop: master.example.com,60000,1353367210440 -12/11/19 23:22:25 INFO hbase.ClusterManager: Executing remote command: ps aux | grep master | grep -v grep | tr -s ' ' | cut -d ' ' -f2 , hostname:master.example.com -12/11/19 23:22:25 INFO hbase.ClusterManager: Executed remote command, exit code:0 , output: -12/11/19 23:22:25 INFO util.ChaosMonkey: Killed master server:master.example.com,60000,1353367210440 -12/11/19 23:22:25 INFO util.ChaosMonkey: Sleeping for:5000 -12/11/19 23:22:30 INFO util.ChaosMonkey: Starting master:master.example.com -12/11/19 23:22:30 INFO hbase.HBaseCluster: Starting Master on: master.example.com -12/11/19 23:22:30 INFO hbase.ClusterManager: Executing remote command: /homes/enis/code/hbase-0.94/bin/../bin/hbase-daemon.sh --config /homes/enis/code/hbase-0.94/bin/../conf start master , hostname:master.example.com -12/11/19 23:22:31 INFO hbase.ClusterManager: Executed remote command, exit code:0 , output:starting master, logging to /homes/enis/code/hbase-0.94/bin/../logs/hbase-enis-master-master.example.com.out -.... -12/11/19 23:22:33 INFO util.ChaosMonkey: Started master: master.example.com,60000,1353367210440 -12/11/19 23:22:33 INFO util.ChaosMonkey: Sleeping for:51321 -12/11/19 23:23:24 INFO util.ChaosMonkey: Performing action: Restart random region server -12/11/19 23:23:24 INFO util.ChaosMonkey: Killing region server:rs3.example.com,60020,1353367027826 -12/11/19 23:23:24 INFO hbase.HBaseCluster: Aborting RS: rs3.example.com,60020,1353367027826 -12/11/19 23:23:24 INFO hbase.ClusterManager: Executing remote command: ps aux | grep regionserver | grep -v grep | tr -s ' ' | cut -d ' ' -f2 | xargs kill -s SIGKILL , hostname:rs3.example.com -12/11/19 23:23:25 INFO hbase.ClusterManager: Executed remote command, exit code:0 , output: -12/11/19 23:23:25 INFO hbase.HBaseCluster: Waiting service:regionserver to stop: rs3.example.com,60020,1353367027826 -12/11/19 23:23:25 INFO hbase.ClusterManager: Executing remote command: ps aux | grep regionserver | grep -v grep | tr -s ' ' | cut -d ' ' -f2 , hostname:rs3.example.com -12/11/19 23:23:25 INFO hbase.ClusterManager: Executed remote command, exit code:0 , output: -12/11/19 23:23:25 INFO util.ChaosMonkey: Killed region server:rs3.example.com,60020,1353367027826. Reported num of rs:6 -12/11/19 23:23:25 INFO util.ChaosMonkey: Sleeping for:60000 -12/11/19 23:24:25 INFO util.ChaosMonkey: Starting region server:rs3.example.com -12/11/19 23:24:25 INFO hbase.HBaseCluster: Starting RS on: rs3.example.com -12/11/19 23:24:25 INFO hbase.ClusterManager: Executing remote command: /homes/enis/code/hbase-0.94/bin/../bin/hbase-daemon.sh --config /homes/enis/code/hbase-0.94/bin/../conf start regionserver , hostname:rs3.example.com -12/11/19 23:24:26 INFO hbase.ClusterManager: Executed remote command, exit code:0 , output:starting regionserver, logging to /homes/enis/code/hbase-0.94/bin/../logs/hbase-enis-regionserver-rs3.example.com.out - -12/11/19 23:24:27 INFO util.ChaosMonkey: Started region server:rs3.example.com,60020,1353367027826. Reported num of rs:6 - - As you can see from the log, ChaosMonkey started the default - PeriodicRandomActionPolicy, which is configured with all the available actions, - and ran RestartActiveMaster and RestartRandomRs actions. ChaosMonkey tool, if - run from command line, will keep on running until the process is killed. -
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- Passing individual Chaos Monkey per-test Settings/Properties - Since HBase version 1.0.0 (HBASE-11348), the chaos monkeys is used to run integration tests can - be configured per test run. Users can create a java properties file and and pass - this to the chaos monkey with timing configurations. The properties file needs - to be in the HBase classpath. The various properties that can be configured and - their default values can be found listed in the - org.apache.hadoop.hbase.chaos.factories.MonkeyConstants - class. If any chaos monkey configuration is missing from the property file, then - the default values are assumed. For example: - -$bin/hbase org.apache.hadoop.hbase.IntegrationTestIngest -m slowDeterministic -monkeyProps monkey.properties - - The above command will start the integration tests and chaos monkey passing - the properties file monkey.properties. Here is an example - chaos monkey file: - -sdm.action1.period=120000 -sdm.action2.period=40000 -move.regions.sleep.time=80000 -move.regions.max.time=1000000 -move.regions.sleep.time=80000 -batch.restart.rs.ratio=0.4f - -
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- -
- Developer Guidelines -
Codelines - Most development is done on the master branch, which is named - master in the Git repository. Previously, HBase used Subversion, in - which the master branch was called TRUNK. Branches exist for minor - releases, and important features and bug fixes are often back-ported. -
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- Release Managers - Each maintained release branch has a release manager, who volunteers to coordinate - new features and bug fixes are backported to that release. The release managers are - committers. If - you would like your feature or bug fix to be included in a given release, - communicate with that release manager. If this list goes out of date or you can't - reach the listed person, reach out to someone else on the list. - - End-of-life releases are not included in this list. - -
- Release Managers - - - - Release - Release Manager - - - - - 0.98 - Andrew Purtell - - - 1.0 - Enis Soztutar - - - -
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- -
- Code Standards - See and . -
- Interface Classifications - Interfaces are classified both by audience and by stability level. These - labels appear at the head of a class. The conventions followed by HBase are - inherited by its parent project, Hadoop. - The following interface classifications are commonly used: - - @InterfaceAudience - - @InterfaceAudience.Public - - APIs for users and HBase applications. These APIs will be - deprecated through major versions of HBase. - - - - @InterfaceAudience.Private - - APIs for HBase internals developers. No guarantees on - compatibility or availability in future versions. Private interfaces - do not need an @InterfaceStability - classification. - - - - @InterfaceAudience.LimitedPrivate(HBaseInterfaceAudience.COPROC) - - APIs for HBase coprocessor writers. As of HBase - 0.92/0.94/0.96/0.98 this api is still unstable. No guarantees on - compatibility with future versions. - - - - No @InterfaceAudience Classification - - Packages without an @InterfaceAudience label are - considered private. Mark your new packages if publicly - accessible. - - - - - Excluding Non-Public Interfaces from API Documentation - Only interfaces classified @InterfaceAudience.Public should - be included in API documentation (Javadoc). Committers must add new package - excludes ExcludePackageNames section of the - pom.xml for new packages which do not contain - public classes. - - - @InterfaceStability - @InterfaceStability is important for packages marked - @InterfaceAudience.Public. - - @InterfaceStability.Stable - - Public packages marked as stable cannot be changed without a - deprecation path or a very good reason. - - - - @InterfaceStability.Unstable - - Public packages marked as unstable can be changed without a - deprecation path. - - - - @InterfaceStability.Evolving - - Public packages marked as evolving may be changed, but it is - discouraged. - - - - No @InterfaceStability Label - - Public classes with no @InterfaceStability label are - discouraged, and should be considered implicitly unstable. - - - - If you are unclear about how to mark packages, ask on the development list. - -
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- Code Formatting Conventions - Please adhere to the following guidelines so that your patches can be reviewed - more quickly. These guidelines have been developed based upon common feedback on - patches from new contributors. - See the Code - Conventions for the Java Programming Language for more information on - coding conventions in Java. -
- Space Invaders - Do not use extra spaces around brackets. Use the second style, rather than - the first. - -if ( foo.equals( bar ) ) { // don't do this - - -if (foo.equals(bar)) { - - -foo = barArray[ i ]; // don't do this - - -foo = barArray[i]; - -
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- Auto Generated Code - Auto-generated code in Eclipse often uses bad variable names such as - arg0. Use more informative variable names. Use code - like the second example here. - - public void readFields(DataInput arg0) throws IOException { // don't do this - foo = arg0.readUTF(); // don't do this - - - public void readFields(DataInput di) throws IOException { - foo = di.readUTF(); - -
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- Long Lines - Keep lines less than 100 characters. You can configure your IDE to do - this automatically. - -Bar bar = foo.veryLongMethodWithManyArguments(argument1, argument2, argument3, argument4, argument5, argument6, argument7, argument8, argument9); // don't do this - - -Bar bar = foo.veryLongMethodWithManyArguments( - argument1, argument2, argument3,argument4, argument5, argument6, argument7, argument8, argument9); - -
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- Trailing Spaces - Trailing spaces are a common problem. Be sure there is a line break after - the end of your code, and avoid lines with nothing but whitespace. This - makes diffs more meaningful. You can configure your IDE to help with - this. - -Bar bar = foo.getBar(); <--- imagine there is an extra space(s) after the semicolon. - -
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- API Documentation (Javadoc) - This is also a very common feedback item. Don't forget Javadoc! - Javadoc warnings are checked during precommit. If the precommit tool gives - you a '-1', please fix the javadoc issue. Your patch won't be committed if - it adds such warnings. -
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- Findbugs - - Findbugs is used to detect common bugs pattern. It is checked - during the precommit build by Apache's Jenkins. If errors are found, please - fix them. You can run findbugs locally with mvn - findbugs:findbugs, which will generate the - findbugs files locally. Sometimes, you may have to write - code smarter than findbugs. You can annotate your code to tell - findbugs you know what you're doing, by annotating your - class with the following annotation: - @edu.umd.cs.findbugs.annotations.SuppressWarnings( -value="HE_EQUALS_USE_HASHCODE", -justification="I know what I'm doing") - It is important to use the Apache-licensed version of the annotations. - -
- -
- Javadoc - Useless Defaults - Don't just leave the @param arguments the way your IDE generated - them.: - - /** - * - * @param bar <---- don't do this!!!! - * @return <---- or this!!!! - */ - public Foo getFoo(Bar bar); - - Either add something descriptive to the @param and - @return lines, or just remove them. The preference is to - add something descriptive and useful. -
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- One Thing At A Time, Folks - If you submit a patch for one thing, don't do auto-reformatting or - unrelated reformatting of code on a completely different area of code. - Likewise, don't add unrelated cleanup or refactorings outside the scope of - your Jira. -
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- Ambigious Unit Tests - Make sure that you're clear about what you are testing in your unit tests - and why. -
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- Implementing Writable - - Applies pre-0.96 only - In 0.96, HBase moved to protocol buffers (protobufs). The below - section on Writables applies to 0.94.x and previous, not to 0.96 and - beyond. - - Every class returned by RegionServers must implement the - Writable interface. If you are creating a new class that - needs to implement this interface, do not forget the default constructor. - -
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- -
- Invariants - We don't have many but what we have we list below. All are subject to challenge of - course but until then, please hold to the rules of the road. - -
- No permanent state in ZooKeeper - ZooKeeper state should transient (treat it like memory). If ZooKeeper state is - deleted, hbase should be able to recover and essentially be in the same - state. - - Exceptions - - There are currently a few exceptions that we need to fix around - whether a table is enabled or disabled. - - - Replication data is currently stored only in ZooKeeper. Deleting - ZooKeeper data related to replication may cause replication to be - disabled. Do not delete the replication tree, - /hbase/replication/. - - Replication may be disrupted and data loss may occur if you delete - the replication tree (/hbase/replication/) from - ZooKeeper. Follow progress on this issue at HBASE-10295. - - - -
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- -
- Running In-Situ - If you are developing Apache HBase, frequently it is useful to test your changes against a more-real cluster than what you find in unit tests. In this case, HBase can be run directly from the source in local-mode. - All you need to do is run: - - ${HBASE_HOME}/bin/start-hbase.sh - - This will spin up a full local-cluster, just as if you had packaged up HBase and installed it on your machine. - - Keep in mind that you will need to have installed HBase into your local maven repository for the in-situ cluster to work properly. That is, you will need to run: - mvn clean install -DskipTests - to ensure that maven can find the correct classpath and dependencies. Generally, the above command - is just a good thing to try running first, if maven is acting oddly. -
- -
- Adding Metrics - - After adding a new feature a developer might want to add metrics. HBase exposes - metrics using the Hadoop Metrics 2 system, so adding a new metric involves - exposing that metric to the hadoop system. Unfortunately the API of metrics2 - changed from hadoop 1 to hadoop 2. In order to get around this a set of - interfaces and implementations have to be loaded at runtime. To get an in-depth - look at the reasoning and structure of these classes you can read the blog post - located here. - To add a metric to an existing MBean follow the short guide below: - -
- Add Metric name and Function to Hadoop Compat Interface. - - Inside of the source interface the corresponds to where the metrics are - generated (eg MetricsMasterSource for things coming from HMaster) create new - static strings for metric name and description. Then add a new method that - will be called to add new reading. - -
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- Add the Implementation to Both Hadoop 1 and Hadoop 2 Compat modules. - - Inside of the implementation of the source (eg. MetricsMasterSourceImpl - in the above example) create a new histogram, counter, gauge, or stat in the - init method. Then in the method that was added to the interface wire up the - parameter passed in to the histogram. - - - Now add tests that make sure the data is correctly exported to the metrics 2 - system. For this the MetricsAssertHelper is provided. - -
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- -
- Git Best Practices - - - Use the correct method to create patches. See . - - - Avoid git merges. Use git pull --rebase or git - fetch followed by git rebase. - - - Do not use git push --force. If the push does not work, fix - the problem or ask for help. - - - Please contribute to this document if you think of other Git best - practices. -
- <code>rebase_all_git_branches.sh</code> - The dev-support/rebase_all_git_branches.sh script is - provided to help keep your Git repository clean. Use the -h - parameter to get usage instructions. The script automatically refreshes your - tracking branches, attempts an automatic rebase of each local branch against its - remote branch, and gives you the option to delete any branch which represents a - closed HBASE- JIRA. The script has one optional configuration - option, the location of your Git directory. You can set a default by editing the - script. Otherwise, you can pass the git directory manually by using the - -d parameter, followed by an absolute or relative directory - name, or even '.' for the current working directory. The script checks the - directory for sub-directory called .git/, before - proceeding. -
-
- -
- Submitting Patches - HBase moved to GIT from SVN. Until we develop our own documentation for how to - contribute patches in our new GIT context, caveat the fact that we have a different - branching model and that we don't currently do the merge practice described in the - following, the accumulo doc - on how to contribute and develop after our move to GIT is worth a read. - See also . - - If you are new to submitting patches to open source or new to submitting patches - to Apache, start by reading the On Contributing - Patches page from Apache - Commons Project. It provides a nice overview that applies equally to the - Apache HBase Project. -
- Create Patch - The script dev-support/make_patch.sh has been provided to - help you adhere to patch-creation guidelines. The script has the following syntax: - - $ make_patch.sh [-a] [-p <patch_dir>] - - - If you do not pass a patch_dir, the script defaults to - ~/patches/. If the patch_dir does - not exist, it is created. - - - By default, if an existing patch exists with the JIRA ID, the version - of the new patch is incremented - (HBASE-XXXX-v3.patch). If the -a - option is passed, the version is not incremented, but the suffix - -addendum is added - (HBASE-XXXX-v2-addendum.patch). A second - addendum to a given version is not supported. - - - Detects whether you have more than one local commit on your branch. If - you do, the script offers you the chance to run git rebase - -i to squash the changes into a single commit so that it - can use git format-patch. If you decline, the script - uses git diff instead. The patch is saved in a - configurable directory and is ready to be attached to your JIRA. - - - - Patching Workflow - - Always patch against the master branch - first, even if you want to patch in another branch. HBase committers - always apply patches first to the master branch, and backport if - necessary. - - - Submit one single patch for a fix. If necessary, squash local commits - to merge local commits into a single one first. See this Stack Overflow question for more information about squashing - commits. - - - The patch should have the JIRA ID in the name. If you are generating - from a branch, include the target branch in the filename. A common - naming scheme for patches is: - HBASE-XXXX.patch - HBASE-XXXX-0.90.patch # to denote that the patch is against branch 0.90 - HBASE-XXXX-v3.patch # to denote that this is the third version of the patch - - - To submit a patch, first create it using one of the methods in . Next, attach the patch to the JIRA - (one patch for the whole fix), using the - More - Attach Files - dialog. Next, click the Patch - Available button, which triggers the Hudson job which - checks the patch for validity. - Please understand that not every patch may get committed, and that - feedback will likely be provided on the patch. - - - If your patch is longer than a single screen, also attach a Review - Board to the case. See . - - - If you need to revise your patch, leave the previous patch file(s) - attached to the JIRA, and upload the new one, following the naming - conventions in . Cancel the - Patch Available flag and then re-trigger it, by toggling the - Patch Available button in JIRA. JIRA sorts - attached files by the time they were attached, and has no problem with - multiple attachments with the same name. However, at times it is easier - to refer to different version of a patch if you add - -vX, where the X is - the version (starting with 2). - - - If you need to submit your patch against multiple branches, rather - than just master, name each version of the patch with the branch it is - for, following the naming conventions in . - - - - Methods to Create Patches - - Eclipse - - Select the - Team - Create Patch - menu item. - - - - Git - - git format-patch is preferred because it preserves - commit messages. Use git rebase -i first, to combine - (squash) smaller commits into a single larger one. - - Do not use --no-prefix, even if you were in the - habit of doing it previously. - - - - git format-patch origin/master --stdout > HBASE-XXXX.patch - - - git diff origin/master > HBASE-XXXX.patch - - - If your branch is based upon a different remote branch, replace - origin/master with the remote to - compare against. - If you are new to creating patches, it's a good idea to check out - a fresh branch and try to apply your patch to it. If you used - git format-patch, apply the patch using - git am. Otherwise, use git - apply. If the patch does not apply correctly, fix the - problem and try again. - - - - Subversion - - svn diff > HBASE-XXXX.patch - - - - Make sure you review and for code style. If your patch was - generated incorrectly or your code does not adhere to the code formatting - guidelines, you may be asked to redo some work. -
- -
- Unit Tests - Yes, please. Please try to include unit tests with every code patch (and - especially new classes and large changes). Make sure unit tests pass locally - before submitting the patch. - Also, see . - If you are creating a new unit test class, notice how other unit test classes - have classification/sizing annotations at the top and a static method on the - end. Be sure to include these in any new unit test files you generate. See for more on how the annotations work. -
- -
- Integration Tests - Significant new features should provide an integration test in addition to - unit tests, suitable for exercising the new feature at different points in its - configuration space. -
- - - - -
- ReviewBoard - Patches larger than one screen, or patches that will be tricky to review, - should go through ReviewBoard. - - Use ReviewBoard - - Register for an account if you don't already have one. It does not use - the credentials from issues.apache.org. Log in. - - - Click New Review Request. - - - Choose the hbase-git repository. Click Choose File - to select the diff and optionally a parent diff. Click Create - Review Request. - - - Fill in the fields as required. At the minimum, fill in the - Summary and choose hbase as - the Review Group. If you fill in the - Bugs field, the review board links back to the - relevant JIRA. The more fields you fill in, the better. Click - Publish to make your review request public. - An email will be sent to everyone in the hbase group, - to review the patch. - - - Back in your JIRA, click - More - Link - Web Link - , and paste in the URL of your ReviewBoard request. This - attaches the ReviewBoard to the JIRA, for easy access. - - - To cancel the request, click - Close - Discarded - . - - - For more information on how to use ReviewBoard, see the ReviewBoard - documentation. -
- - -
- Guide for HBase Committers - -
- New committers - New committers are encouraged to first read Apache's generic committer - documentation: - - - Apache New Committer Guide - - - - Apache Committer FAQ - - - -
- -
- Review - HBase committers should, as often as possible, attempt to review patches - submitted by others. Ideally every submitted patch will get reviewed by a - committer within a few days. If a committer reviews a - patch they have not authored, and believe it to be of sufficient quality, - then they can commit the patch, otherwise the patch should be cancelled with - a clear explanation for why it was rejected. - The list of submitted patches is in the HBase Review Queue, which is ordered by time of last - modification. Committers should scan the list from top to bottom, looking - for patches that they feel qualified to review and possibly commit. - For non-trivial changes, it is required to get another committer to review - your own patches before commit. Use the Submit Patch - button in JIRA, just like other contributors, and then wait for a - +1 response from another committer before committing. - -
- -
- Reject - Patches which do not adhere to the guidelines in HowToContribute and to the code review checklist should be rejected. Committers should - always be polite to contributors and try to instruct and encourage them to - contribute better patches. If a committer wishes to improve an unacceptable - patch, then it should first be rejected, and a new patch should be attached - by the committer for review. -
- -
- Commit - Committers commit patches to the Apache HBase GIT repository. - - Before you commit!!!! - Make sure your local configuration is correct, especially your - identity and email. Examine the output of the $ git config - --list command and be sure it is correct. See this GitHub - article, Set Up Git if you need pointers. - - When you commit a patch, please: - - - Include the Jira issue id in the commit message, along with a - short description of the change and the name of the contributor if - it is not you. Be sure to get the issue ID right, as this causes - Jira to link to the change in Git (use the issue's - "All" tab to see these). - - - Commit the patch to a new branch based off master or other - intended branch. It's a good idea to call this branch by the JIRA - ID. Then check out the relevant target branch where you want to - commit, make sure your local branch has all remote changes, by doing - a git pull --rebase or another similar command, - cherry-pick the change into each relevant branch (such as master), - and do git push <remote-server> - <remote-branch>. - - If you do not have all remote changes, the push will fail. If - the push fails for any reason, fix the problem or ask for help. - Do not do a git push --force. - - Before you can commit a patch, you need to determine how the patch - was created. The instructions and preferences around the way to - create patches have changed, and there will be a transition - periond. - - Determine How a Patch Was Created - - If the first few lines of the patch look like the headers - of an email, with a From, Date, and Subject, it was created - using git format-patch. This is the - preference, because you can reuse the submitter's commit - message. If the commit message is not appropriate, you can - still use the commit, then run the command git - rebase -i origin/master, and squash and reword - as appropriate. - - - If the first line of the patch looks similar to the - following, it was created using git diff - without --no-prefix. This is acceptable too. - Notice the a and b in - front of the file names. This is the indication that the - patch was not created with --no-prefix. - diff --git a/src/main/docbkx/developer.xml b/src/main/docbkx/developer.xml - - - If the first line of the patch looks similar to the - following (without the a and - b), the patch was created with - git diff --no-prefix and you need to - add -p0 to the git apply - command below. - diff --git src/main/docbkx/developer.xml src/main/docbkx/developer.xml - - - - Example of Committing a Patch - One thing you will notice with these examples is that there - are a lot of git pull commands. The only - command that actually writes anything to the remote repository - is git push, and you need to make absolutely - sure you have the correct versions of everything and don't have - any conflicts before pushing. The extra git - pull commands are usually redundant, but better - safe than sorry. - The first example shows how to apply a patch that was - generated with git format-patch and apply it - to the master and branch-1 branches. - - The directive to use git format-patch - rather than git diff, and not to use - --no-prefix, is a new one. See the second - example for how to apply a patch created with git - diff, and educate the person who created the - patch. - $ git checkout -b HBASE-XXXX -$ git am ~/Downloads/HBASE-XXXX-v2.patch -$ git checkout master -$ git pull --rebase -$ git cherry-pick <sha-from-commit> -# Resolve conflicts if necessary or ask the submitter to do it -$ git pull --rebase # Better safe than sorry -$ git push origin master -$ git checkout branch-1 -$ git pull --rebase -$ git cherry-pick <sha-from-commit> -# Resolve conflicts if necessary -$ git pull --rebase # Better safe than sorry -$ git push origin branch-1 -$ git branch -D HBASE-XXXX - - This example shows how to commit a patch that was created - using git diff without - --no-prefix. If the patch was created with - --no-prefix, add -p0 to the - git apply command. - $ git apply ~/Downloads/HBASE-XXXX-v2.patch -$ git commit -m "HBASE-XXXX Really Good Code Fix (Joe Schmo)" -a # This extra step is needed for patches created with 'git diff' -$ git checkout master -$ git pull --rebase -$ git cherry-pick <sha-from-commit> -# Resolve conflicts if necessary or ask the submitter to do it -$ git pull --rebase # Better safe than sorry -$ git push origin master -$ git checkout branch-1 -$ git pull --rebase -$ git cherry-pick <sha-from-commit> -# Resolve conflicts if necessary or ask the submitter to do it -$ git pull --rebase # Better safe than sorry -$ git push origin branch-1 -$ git branch -D HBASE-XXXX - - - - Resolve the issue as fixed, thanking the contributor. Always set - the "Fix Version" at this point, but please only set a - single fix version for each branch where the change was committed, - the earliest release in that branch in which the change will appear. - - - - -
- Commit Message Format - The commit message should contain the JIRA ID and a description of - what the patch does. The preferred commit message format is: - <jira-id> <jira-title> (<contributor-name-if-not-commit-author>) - HBASE-12345 Fix All The Things (jane@example.com) - If the contributor used git format-patch to - generate the patch, their commit message is in their patch and you can - use that, but be sure the JIRA ID is at the front of the commit message, - even if the contributor left it out. -
-
- Add Amending-Author when a conflict cherrypick backporting - We've established the practice of committing to trunk and then cherry - picking back to branches whenever possible. When there is a minor - conflict we can fix it up and just proceed with the commit. The - resulting commit retains the original author. When the amending author - is different from the original committer, add notice of this at the end - of the commit message as: Amending-Author: Author - <committer&apache> See discussion at HBase, mail # dev - - [DISCUSSION] Best practice when amending commits cherry picked - from master to branch. -
- -
- Committers are responsible for making sure commits do not break the - build or tests - If a committer commits a patch, it is their responsibility to make - sure it passes the test suite. It is helpful if contributors keep an eye - out that their patch does not break the hbase build and/or tests, but - ultimately, a contributor cannot be expected to be aware of all the - particular vagaries and interconnections that occur in a project like - HBase. A committer should. -
-
- Patching Etiquette - In the thread HBase, mail # dev - ANNOUNCEMENT: Git Migration In Progress (WAS => - Re: Git Migration), it was agreed on the following patch flow - - Develop and commit the patch against trunk/master - first. - - - Try to cherry-pick the patch when backporting if - possible. - - - If this does not work, manually commit the patch to the - branch. - - - -
- -
- Merge Commits - Avoid merge commits, as they create problems in the git - history. -
-
- Committing Documentation - See . -
-
- -
- -
- Dialog - Committers should hang out in the #hbase room on irc.freenode.net for - real-time discussions. However any substantive discussion (as with any off-list - project-related discussion) should be re-iterated in Jira or on the developer - list. -
-
- Do not edit JIRA comments - Misspellings and/or bad grammar is preferable to the disruption a JIRA comment - edit causes: See the discussion at Re:(HBASE-451) Remove HTableDescriptor from HRegionInfo - -
-
-
- diff --git a/src/main/docbkx/external_apis.xml b/src/main/docbkx/external_apis.xml deleted file mode 100644 index fa1abdc95d0..00000000000 --- a/src/main/docbkx/external_apis.xml +++ /dev/null @@ -1,79 +0,0 @@ - - - - Apache HBase External APIs - This chapter will cover access to Apache HBase either through non-Java languages, or - through custom protocols. For information on using the native HBase APIs, refer to User API Reference and the new chapter. -
- Non-Java Languages Talking to the JVM - Currently the documentation on this topic in the - Apache HBase Wiki. - See also the Thrift API Javadoc. - -
- -
- REST - Currently most of the documentation on REST exists in the - Apache HBase Wiki on REST (The REST gateway used to be - called 'Stargate'). There are also a nice set of blogs on How-to: Use the Apache HBase REST Interface - by Jesse Anderson. - - - To run your REST server under SSL, set hbase.rest.ssl.enabled to true and also set the - following configs when you launch the REST server:(See example commands in - ) - -hbase.rest.ssl.keystore.store -hbase.rest.ssl.keystore.password -hbase.rest.ssl.keystore.keypassword - - - HBase ships a simple REST client, see REST client package for details. - To enable SSL support for it, please also import your certificate into local java - cacerts keystore: - keytool -import -trustcacerts -file /home/user/restserver.cert -keystore $JAVA_HOME/jre/lib/security/cacerts - -
- -
- Thrift - Documentation about Thrift has moved to . -
- -
- C/C++ Apache HBase Client - FB's Chip Turner wrote a pure C/C++ client. Check it out. - -
- - diff --git a/src/main/docbkx/faq.xml b/src/main/docbkx/faq.xml deleted file mode 100644 index d7bcb0cfc4e..00000000000 --- a/src/main/docbkx/faq.xml +++ /dev/null @@ -1,270 +0,0 @@ - - - - FAQ - - General - - When should I use HBase? - - See the in the Architecture chapter. - - - - - Are there other HBase FAQs? - - - See the FAQ that is up on the wiki, HBase Wiki FAQ. - - - - - Does HBase support SQL? - - - Not really. SQL-ish support for HBase via Hive is in development, however Hive is based on MapReduce which is not generally suitable for low-latency requests. - See the section for examples on the HBase client. - - - - - How can I find examples of NoSQL/HBase? - - See the link to the BigTable paper in in the appendix, as - well as the other papers. - - - - - What is the history of HBase? - - See . - - - - - - Upgrading - - - How do I upgrade Maven-managed projects from HBase 0.94 to HBase 0.96+? - - - In HBase 0.96, the project moved to a modular structure. Adjust your project's - dependencies to rely upon the hbase-client module or another - module as appropriate, rather than a single JAR. You can model your Maven depency - after one of the following, depending on your targeted version of HBase. See or for more - information. - - Maven Dependency for HBase 0.98 - - org.apache.hbase - hbase-client - 0.98.5-hadoop2 - - ]]> - - - Maven Dependency for HBase 0.96 - - org.apache.hbase - hbase-client - 0.96.2-hadoop2 - - ]]> - - - Maven Dependency for HBase 0.94 - - org.apache.hbase - hbase - 0.94.3 - - ]]> - - - - - Architecture - - How does HBase handle Region-RegionServer assignment and locality? - - - See . - - - - - Configuration - - How can I get started with my first cluster? - - - See . - - - - - Where can I learn about the rest of the configuration options? - - - See . - - - - - Schema Design / Data Access - - How should I design my schema in HBase? - - - See and - - - - - - How can I store (fill in the blank) in HBase? - - - - See . - - - - - - How can I handle secondary indexes in HBase? - - - - See - - - - - Can I change a table's rowkeys? - - This is a very common question. You can't. See . - - - - What APIs does HBase support? - - - See , and . - - - - - MapReduce - - How can I use MapReduce with HBase? - - - See - - - - - Performance and Troubleshooting - - - How can I improve HBase cluster performance? - - - - See . - - - - - - How can I troubleshoot my HBase cluster? - - - - See . - - - - - Amazon EC2 - - - I am running HBase on Amazon EC2 and... - - - - EC2 issues are a special case. See Troubleshooting and Performance sections. - - - - - Operations - - - How do I manage my HBase cluster? - - - - See - - - - - - How do I back up my HBase cluster? - - - - See - - - - - HBase in Action - - Where can I find interesting videos and presentations on HBase? - - - See - - - - - - - diff --git a/src/main/docbkx/getting_started.xml b/src/main/docbkx/getting_started.xml deleted file mode 100644 index 79478bab838..00000000000 --- a/src/main/docbkx/getting_started.xml +++ /dev/null @@ -1,728 +0,0 @@ - - - - Getting Started - -
- Introduction - - will get you up and running on a single-node, standalone - instance of HBase, followed by a pseudo-distributed single-machine instance, and finally a - fully-distributed cluster. -
- -
- Quick Start - Standalone HBase - - This guide describes setup of a standalone HBase instance running against the local - filesystem. This is not an appropriate configuration for a production instance of HBase, but - will allow you to experiment with HBase. This section shows you how to create a table in - HBase using the hbase shell CLI, insert rows into the table, perform put - and scan operations against the table, enable or disable the table, and start and stop HBase. - Apart from downloading HBase, this procedure should take less than 10 minutes. - - Local Filesystem and Durability - The below advice is for HBase 0.98.2 and earlier releases only. This is fixed - in HBase 0.98.3 and beyond. See HBASE-11272 and - HBASE-11218. - Using HBase with a local filesystem does not guarantee durability. The HDFS - local filesystem implementation will lose edits if files are not properly closed. This is - very likely to happen when you are experimenting with new software, starting and stopping - the daemons often and not always cleanly. You need to run HBase on HDFS - to ensure all writes are preserved. Running against the local filesystem is intended as a - shortcut to get you familiar with how the general system works, as the very first phase of - evaluation. See and its associated issues - for more details about the issues of running on the local filesystem. - - - Loopback IP - HBase 0.94.x and earlier - The below advice is for hbase-0.94.x and older versions only. This is fixed in - hbase-0.96.0 and beyond. - - Prior to HBase 0.94.x, HBase expected the loopback IP address to be 127.0.0.1. Ubuntu - and some other distributions default to 127.0.1.1 and this will cause problems for you . See Why does HBase - care about /etc/hosts? for detail. - - Example /etc/hosts File for Ubuntu - The following /etc/hosts file works correctly for HBase 0.94.x - and earlier, on Ubuntu. Use this as a template if you run into trouble. - -127.0.0.1 localhost -127.0.0.1 ubuntu.ubuntu-domain ubuntu - - - - -
- JDK Version Requirements - HBase requires that a JDK be installed. See for information - about supported JDK versions. -
- -
- Get Started with HBase - - - Download, Configure, and Start HBase - - Choose a download site from this list of Apache Download Mirrors. - Click on the suggested top link. This will take you to a mirror of HBase - Releases. Click on the folder named stable and then - download the binary file that ends in .tar.gz to your local filesystem. Be - sure to choose the version that corresponds with the version of Hadoop you are likely to use - later. In most cases, you should choose the file for Hadoop 2, which will be called something - like hbase-0.98.3-hadoop2-bin.tar.gz. Do not download the file ending in - src.tar.gz for now. - - - Extract the downloaded file, and change to the newly-created directory. - -$ tar xzvf hbase-]]>-hadoop2-bin.tar.gz -$ cd hbase-]]>-hadoop2/ - - - - For HBase 0.98.5 and later, you are required to set the JAVA_HOME - environment variable before starting HBase. Prior to 0.98.5, HBase attempted to detect - the location of Java if the variables was not set. You can set the variable via your - operating system's usual mechanism, but HBase provides a central mechanism, - conf/hbase-env.sh. Edit this file, uncomment the line starting - with JAVA_HOME, and set it to the appropriate location for your - operating system. The JAVA_HOME variable should be set to a directory - which contains the executable file bin/java. Most modern Linux - operating systems provide a mechanism, such as /usr/bin/alternatives on RHEL or CentOS, - for transparently switching between versions of executables such as Java. In this case, - you can set JAVA_HOME to the directory containing the symbolic link to - bin/java, which is usually /usr. - JAVA_HOME=/usr - - These instructions assume that each node of your cluster uses the same - configuration. If this is not the case, you may need to set JAVA_HOME - separately for each node. - - - - Edit conf/hbase-site.xml, which is the main HBase configuration - file. At this time, you only need to specify the directory on the local filesystem where - HBase and Zookeeper write data. By default, a new directory is created under /tmp. Many - servers are configured to delete the contents of /tmp upon reboot, so you should store - the data elsewhere. The following configuration will store HBase's data in the - hbase directory, in the home directory of the user called - testuser. Paste the <property> tags beneath the - <configuration> tags, which should be empty in a new HBase install. - - Example <filename>hbase-site.xml</filename> for Standalone HBase - - - hbase.rootdir - file:///home/testuser/hbase - - - hbase.zookeeper.property.dataDir - /home/testuser/zookeeper - - - ]]> - - - You do not need to create the HBase data directory. HBase will do this for you. If - you create the directory, HBase will attempt to do a migration, which is not what you - want. - - - The bin/start-hbase.sh script is provided as a convenient way - to start HBase. Issue the command, and if all goes well, a message is logged to standard - output showing that HBase started successfully. You can use the jps - command to verify that you have one running process called HMaster. - In standalone mode HBase runs all daemons within this single JVM, i.e. the HMaster, a - single HRegionServer, and the ZooKeeper daemon. - Java needs to be installed and available. If you get an error indicating that - Java is not installed, but it is on your system, perhaps in a non-standard location, - edit the conf/hbase-env.sh file and modify the - JAVA_HOME setting to point to the directory that contains - bin/java your system. - - - - - Use HBase For the First Time - - Connect to HBase. - Connect to your running instance of HBase using the hbase shell - command, located in the bin/ directory of your HBase - install. In this example, some usage and version information that is printed when you - start HBase Shell has been omitted. The HBase Shell prompt ends with a - > character. - -$ ./bin/hbase shell -hbase(main):001:0> - - - - Display HBase Shell Help Text. - Type help and press Enter, to display some basic usage - information for HBase Shell, as well as several example commands. Notice that table - names, rows, columns all must be enclosed in quote characters. - - - Create a table. - Use the create command to create a new table. You must specify the - table name and the ColumnFamily name. - -hbase> create 'test', 'cf' -0 row(s) in 1.2200 seconds - - - - List Information About your Table - Use the list command to - -hbase> list 'test' -TABLE -test -1 row(s) in 0.0350 seconds - -=> ["test"] - - - - Put data into your table. - To put data into your table, use the put command. - -hbase> put 'test', 'row1', 'cf:a', 'value1' -0 row(s) in 0.1770 seconds - -hbase> put 'test', 'row2', 'cf:b', 'value2' -0 row(s) in 0.0160 seconds - -hbase> put 'test', 'row3', 'cf:c', 'value3' -0 row(s) in 0.0260 seconds - - Here, we insert three values, one at a time. The first insert is at - row1, column cf:a, with a value of - value1. Columns in HBase are comprised of a column family prefix, - cf in this example, followed by a colon and then a column qualifier - suffix, a in this case. - - - Scan the table for all data at once. - One of the ways to get data from HBase is to scan. Use the scan - command to scan the table for data. You can limit your scan, but for now, all data is - fetched. - -hbase> scan 'test' -ROW COLUMN+CELL - row1 column=cf:a, timestamp=1403759475114, value=value1 - row2 column=cf:b, timestamp=1403759492807, value=value2 - row3 column=cf:c, timestamp=1403759503155, value=value3 -3 row(s) in 0.0440 seconds - - - - Get a single row of data. - To get a single row of data at a time, use the get command. - -hbase> get 'test', 'row1' -COLUMN CELL - cf:a timestamp=1403759475114, value=value1 -1 row(s) in 0.0230 seconds - - - - Disable a table. - If you want to delete a table or change its settings, as well as in some other - situations, you need to disable the table first, using the disable - command. You can re-enable it using the enable command. - -hbase> disable 'test' -0 row(s) in 1.6270 seconds - -hbase> enable 'test' -0 row(s) in 0.4500 seconds - - Disable the table again if you tested the enable command above: - -hbase> disable 'test' -0 row(s) in 1.6270 seconds - - - - Drop the table. - To drop (delete) a table, use the drop command. - -hbase> drop 'test' -0 row(s) in 0.2900 seconds - - - - Exit the HBase Shell. - To exit the HBase Shell and disconnect from your cluster, use the - quit command. HBase is still running in the background. - - - - - Stop HBase - - In the same way that the bin/start-hbase.sh script is provided - to conveniently start all HBase daemons, the bin/stop-hbase.sh - script stops them. - -$ ./bin/stop-hbase.sh -stopping hbase.................... -$ - - - - After issuing the command, it can take several minutes for the processes to shut - down. Use the jps to be sure that the HMaster and HRegionServer - processes are shut down. - - -
- -
- Intermediate - Pseudo-Distributed Local Install - After working your way through , you can re-configure HBase - to run in pseudo-distributed mode. Pseudo-distributed mode means - that HBase still runs completely on a single host, but each HBase daemon (HMaster, - HRegionServer, and Zookeeper) runs as a separate process. By default, unless you configure the - hbase.rootdir property as described in , your data - is still stored in /tmp/. In this walk-through, we store your data in - HDFS instead, assuming you have HDFS available. You can skip the HDFS configuration to - continue storing your data in the local filesystem. - - Hadoop Configuration - This procedure assumes that you have configured Hadoop and HDFS on your local system - and or a remote system, and that they are running and available. It also assumes you are - using Hadoop 2. Currently, the documentation on the Hadoop website does not include a - quick start for Hadoop 2, but the guide at http://www.alexjf.net/blog/distributed-systems/hadoop-yarn-installation-definitive-guide - is a good starting point. - - - - Stop HBase if it is running. - If you have just finished and HBase is still running, - stop it. This procedure will create a totally new directory where HBase will store its - data, so any databases you created before will be lost. - - - Configure HBase. - - Edit the hbase-site.xml configuration. First, add the following - property. which directs HBase to run in distributed mode, with one JVM instance per - daemon. - - - hbase.cluster.distributed - true - - ]]> - Next, change the hbase.rootdir from the local filesystem to the address - of your HDFS instance, using the hdfs://// URI syntax. In this example, - HDFS is running on the localhost at port 8020. - - hbase.rootdir - hdfs://localhost:8020/hbase - - ]]> - - You do not need to create the directory in HDFS. HBase will do this for you. If you - create the directory, HBase will attempt to do a migration, which is not what you - want. - - - Start HBase. - Use the bin/start-hbase.sh command to start HBase. If your - system is configured correctly, the jps command should show the - HMaster and HRegionServer processes running. - - - Check the HBase directory in HDFS. - If everything worked correctly, HBase created its directory in HDFS. In the - configuration above, it is stored in /hbase/ on HDFS. You can use - the hadoop fs command in Hadoop's bin/ directory - to list this directory. - -$ ./bin/hadoop fs -ls /hbase -Found 7 items -drwxr-xr-x - hbase users 0 2014-06-25 18:58 /hbase/.tmp -drwxr-xr-x - hbase users 0 2014-06-25 21:49 /hbase/WALs -drwxr-xr-x - hbase users 0 2014-06-25 18:48 /hbase/corrupt -drwxr-xr-x - hbase users 0 2014-06-25 18:58 /hbase/data --rw-r--r-- 3 hbase users 42 2014-06-25 18:41 /hbase/hbase.id --rw-r--r-- 3 hbase users 7 2014-06-25 18:41 /hbase/hbase.version -drwxr-xr-x - hbase users 0 2014-06-25 21:49 /hbase/oldWALs - - - - Create a table and populate it with data. - You can use the HBase Shell to create a table, populate it with data, scan and get - values from it, using the same procedure as in . - - - Start and stop a backup HBase Master (HMaster) server. - - Running multiple HMaster instances on the same hardware does not make sense in a - production environment, in the same way that running a pseudo-distributed cluster does - not make sense for production. This step is offered for testing and learning purposes - only. - - The HMaster server controls the HBase cluster. You can start up to 9 backup HMaster - servers, which makes 10 total HMasters, counting the primary. To start a backup HMaster, - use the local-master-backup.sh. For each backup master you want to - start, add a parameter representing the port offset for that master. Each HMaster uses - three ports (16010, 16020, and 16030 by default). The port offset is added to these ports, so - using an offset of 2, the backup HMaster would use ports 16012, 16022, and 16032. The - following command starts 3 backup servers using ports 16012/16022/16032, 16013/16023/16033, - and 16015/16025/16035. - -$ ./bin/local-master-backup.sh 2 3 5 - - To kill a backup master without killing the entire cluster, you need to find its - process ID (PID). The PID is stored in a file with a name like - /tmp/hbase-USER-X-master.pid. - The only contents of the file are the PID. You can use the kill -9 - command to kill that PID. The following command will kill the master with port offset 1, - but leave the cluster running: - -$ cat /tmp/hbase-testuser-1-master.pid |xargs kill -9 - - - - Start and stop additional RegionServers - The HRegionServer manages the data in its StoreFiles as directed by the HMaster. - Generally, one HRegionServer runs per node in the cluster. Running multiple - HRegionServers on the same system can be useful for testing in pseudo-distributed mode. - The local-regionservers.sh command allows you to run multiple - RegionServers. It works in a similar way to the - local-master-backup.sh command, in that each parameter you provide - represents the port offset for an instance. Each RegionServer requires two ports, and - the default ports are 16020 and 16030. However, the base ports for additional RegionServers - are not the default ports since the default ports are used by the HMaster, which is also - a RegionServer since HBase version 1.0.0. The base ports are 16200 and 16300 instead. - You can run 99 additional RegionServers that are not a HMaster or backup HMaster, - on a server. The following command starts four additional RegionServers, running on - sequential ports starting at 16202/16302 (base ports 16200/16300 plus 2). - -$ .bin/local-regionservers.sh start 2 3 4 5 - - To stop a RegionServer manually, use the local-regionservers.sh - command with the stop parameter and the offset of the server to - stop. - $ .bin/local-regionservers.sh stop 3 - - - Stop HBase. - You can stop HBase the same way as in the procedure, using the - bin/stop-hbase.sh command. - - -
- -
- Advanced - Fully Distributed - In reality, you need a fully-distributed configuration to fully test HBase and to use it - in real-world scenarios. In a distributed configuration, the cluster contains multiple - nodes, each of which runs one or more HBase daemon. These include primary and backup Master - instances, multiple Zookeeper nodes, and multiple RegionServer nodes. - This advanced quickstart adds two more nodes to your cluster. The architecture will be - as follows: - - Distributed Cluster Demo Architecture - - - - Node Name - Master - ZooKeeper - RegionServer - - - - - node-a.example.com - yes - yes - no - - - node-b.example.com - backup - yes - yes - - - node-c.example.com - no - yes - yes - - - -
- This quickstart assumes that each node is a virtual machine and that they are all on the - same network. It builds upon the previous quickstart, , - assuming that the system you configured in that procedure is now node-a. Stop HBase on node-a - before continuing. - - Be sure that all the nodes have full access to communicate, and that no firewall rules - are in place which could prevent them from talking to each other. If you see any errors like - no route to host, check your firewall. - - - Configure Password-Less SSH Access - node-a needs to be able to log into node-b and - node-c (and to itself) in order to start the daemons. The easiest way to accomplish this is - to use the same username on all hosts, and configure password-less SSH login from - node-a to each of the others. - - On <code>node-a</code>, generate a key pair. - While logged in as the user who will run HBase, generate a SSH key pair, using the - following command: - - $ ssh-keygen -t rsa - If the command succeeds, the location of the key pair is printed to standard output. - The default name of the public key is id_rsa.pub. - - - Create the directory that will hold the shared keys on the other nodes. - On node-b and node-c, log in as the HBase user and create - a .ssh/ directory in the user's home directory, if it does not - already exist. If it already exists, be aware that it may already contain other keys. - - - Copy the public key to the other nodes. - Securely copy the public key from node-a to each of the nodes, by - using the scp or some other secure means. On each of the other nodes, - create a new file called .ssh/authorized_keys if it does - not already exist, and append the contents of the - id_rsa.pub file to the end of it. Note that you also need to do - this for node-a itself. - $ cat id_rsa.pub >> ~/.ssh/authorized_keys - - - Test password-less login. - If you performed the procedure correctly, if you SSH from node-a to - either of the other nodes, using the same username, you should not be prompted for a password. - - - - Since node-b will run a backup Master, repeat the procedure above, - substituting node-b everywhere you see node-a. Be sure not to - overwrite your existing .ssh/authorized_keys files, but concatenate - the new key onto the existing file using the >> operator rather than - the > operator. - - - - - Prepare <code>node-a</code> - node-a will run your primary master and ZooKeeper processes, but no - RegionServers. - - Stop the RegionServer from starting on <code>node-a</code>. - Edit conf/regionservers and remove the line which contains - localhost. Add lines with the hostnames or IP addresses for - node-b and node-c. Even if you did want to run a - RegionServer on node-a, you should refer to it by the hostname the other - servers would use to communicate with it. In this case, that would be - node-a.example.com. This enables you to distribute the - configuration to each node of your cluster any hostname conflicts. Save the file. - - - Configure HBase to use <code>node-b</code> as a backup master. - Create a new file in conf/ called - backup-masters, and add a new line to it with the hostname for - node-b. In this demonstration, the hostname is - node-b.example.com. - - - Configure ZooKeeper - In reality, you should carefully consider your ZooKeeper configuration. You can find - out more about configuring ZooKeeper in . This configuration will direct HBase to start and manage a - ZooKeeper instance on each node of the cluster. - On node-a, edit conf/hbase-site.xml and add the - following properties. - - hbase.zookeeper.quorum - node-a.example.com,node-b.example.com,node-c.example.com - - - hbase.zookeeper.property.dataDir - /usr/local/zookeeper - - ]]> - - - Everywhere in your configuration that you have referred to node-a as - localhost, change the reference to point to the hostname that - the other nodes will use to refer to node-a. In these examples, the - hostname is node-a.example.com. - - - - Prepare <code>node-b</code> and <code>node-c</code> - node-b will run a backup master server and a ZooKeeper instance. - - Download and unpack HBase. - Download and unpack HBase to node-b, just as you did for the standalone - and pseudo-distributed quickstarts. - - - Copy the configuration files from <code>node-a</code> to <code>node-b</code>.and - <code>node-c</code>. - Each node of your cluster needs to have the same configuration information. Copy the - contents of the conf/ directory to the conf/ - directory on node-b and node-c. - - - - - Start and Test Your Cluster - - Be sure HBase is not running on any node. - If you forgot to stop HBase from previous testing, you will have errors. Check to - see whether HBase is running on any of your nodes by using the jps - command. Look for the processes HMaster, - HRegionServer, and HQuorumPeer. If they exist, - kill them. - - - Start the cluster. - On node-a, issue the start-hbase.sh command. Your - output will be similar to that below. - -$ bin/start-hbase.sh -node-c.example.com: starting zookeeper, logging to /home/hbuser/hbase-0.98.3-hadoop2/bin/../logs/hbase-hbuser-zookeeper-node-c.example.com.out -node-a.example.com: starting zookeeper, logging to /home/hbuser/hbase-0.98.3-hadoop2/bin/../logs/hbase-hbuser-zookeeper-node-a.example.com.out -node-b.example.com: starting zookeeper, logging to /home/hbuser/hbase-0.98.3-hadoop2/bin/../logs/hbase-hbuser-zookeeper-node-b.example.com.out -starting master, logging to /home/hbuser/hbase-0.98.3-hadoop2/bin/../logs/hbase-hbuser-master-node-a.example.com.out -node-c.example.com: starting regionserver, logging to /home/hbuser/hbase-0.98.3-hadoop2/bin/../logs/hbase-hbuser-regionserver-node-c.example.com.out -node-b.example.com: starting regionserver, logging to /home/hbuser/hbase-0.98.3-hadoop2/bin/../logs/hbase-hbuser-regionserver-node-b.example.com.out -node-b.example.com: starting master, logging to /home/hbuser/hbase-0.98.3-hadoop2/bin/../logs/hbase-hbuser-master-nodeb.example.com.out - - ZooKeeper starts first, followed by the master, then the RegionServers, and finally - the backup masters. - - - Verify that the processes are running. - On each node of the cluster, run the jps command and verify that - the correct processes are running on each server. You may see additional Java processes - running on your servers as well, if they are used for other purposes. - - <code>node-a</code> <command>jps</command> Output - -$ jps -20355 Jps -20071 HQuorumPeer -20137 HMaster - - - - <code>node-b</code> <command>jps</command> Output - -$ jps -15930 HRegionServer -16194 Jps -15838 HQuorumPeer -16010 HMaster - - - - <code>node-c</code> <command>jps</command> Output - -$ jps -13901 Jps -13639 HQuorumPeer -13737 HRegionServer - - - - ZooKeeper Process Name - The HQuorumPeer process is a ZooKeeper instance which is controlled - and started by HBase. If you use ZooKeeper this way, it is limited to one instance per - cluster node, , and is appropriate for testing only. If ZooKeeper is run outside of - HBase, the process is called QuorumPeer. For more about ZooKeeper - configuration, including using an external ZooKeeper instance with HBase, see . - - - - Browse to the Web UI. - - Web UI Port Changes - In HBase newer than 0.98.x, the HTTP ports used by the HBase Web UI changed from - 60010 for the Master and 60030 for each RegionServer to 16610 for the Master and 16030 - for the RegionServer. - - If everything is set up correctly, you should be able to connect to the UI for the - Master http://node-a.example.com:60110/ or the secondary master at - http://node-b.example.com:60110/ for the secondary master, using a - web browser. If you can connect via localhost but not from another host, - check your firewall rules. You can see the web UI for each of the RegionServers at port - 60130 of their IP addresses, or by clicking their links in the web UI for the - Master. - - - Test what happens when nodes or services disappear. - With a three-node cluster like you have configured, things will not be very - resilient. Still, you can test what happens when the primary Master or a RegionServer - disappears, by killing the processes and watching the logs. - - -
- -
- Where to go next - - The next chapter, , gives more information about the different HBase run modes, - system requirements for running HBase, and critical configuration areas for setting up a - distributed HBase cluster. -
-
- diff --git a/src/main/docbkx/hbase-default.xml b/src/main/docbkx/hbase-default.xml deleted file mode 100644 index f221950336b..00000000000 --- a/src/main/docbkx/hbase-default.xml +++ /dev/null @@ -1,561 +0,0 @@ - - -HBase Default Configuration -The documentation below is generated using the default hbase configuration file, -hbase-default.xml, as source. -hbase.tmp.dirTemporary directory on the local filesystem. - Change this setting to point to a location more permanent - than '/tmp', the usual resolve for java.io.tmpdir, as the - '/tmp' directory is cleared on machine restart.Default${java.io.tmpdir}/hbase-${user.name}hbase.rootdirThe directory shared by region servers and into - which HBase persists. The URL should be 'fully-qualified' - to include the filesystem scheme. For example, to specify the - HDFS directory '/hbase' where the HDFS instance's namenode is - running at namenode.example.org on port 9000, set this value to: - hdfs://namenode.example.org:9000/hbase. By default, we write - to whatever ${hbase.tmp.dir} is set too -- usually /tmp -- - so change this configuration or else all data will be lost on - machine restart.Default${hbase.tmp.dir}/hbasehbase.cluster.distributedThe mode the cluster will be in. Possible values are - false for standalone mode and true for distributed mode. If - false, startup will run all HBase and ZooKeeper daemons together - in the one JVM.Defaultfalsehbase.zookeeper.quorumComma separated list of servers in the ZooKeeper ensemble - (This config. should have been named hbase.zookeeper.ensemble). - For example, "host1.mydomain.com,host2.mydomain.com,host3.mydomain.com". - By default this is set to localhost for local and pseudo-distributed modes - of operation. For a fully-distributed setup, this should be set to a full - list of ZooKeeper ensemble servers. If HBASE_MANAGES_ZK is set in hbase-env.sh - this is the list of servers which hbase will start/stop ZooKeeper on as - part of cluster start/stop. Client-side, we will take this list of - ensemble members and put it together with the hbase.zookeeper.clientPort - config. and pass it into zookeeper constructor as the connectString - parameter.Defaultlocalhosthbase.local.dirDirectory on the local filesystem to be used - as a local storage.Default${hbase.tmp.dir}/local/hbase.master.info.portThe port for the HBase Master web UI. - Set to -1 if you do not want a UI instance run.Default16010hbase.master.info.bindAddressThe bind address for the HBase Master web UI - Default0.0.0.0hbase.master.logcleaner.pluginsA comma-separated list of BaseLogCleanerDelegate invoked by - the LogsCleaner service. These WAL cleaners are called in order, - so put the cleaner that prunes the most files in front. To - implement your own BaseLogCleanerDelegate, just put it in HBase's classpath - and add the fully qualified class name here. Always add the above - default log cleaners in the list.Defaultorg.apache.hadoop.hbase.master.cleaner.TimeToLiveLogCleanerhbase.master.logcleaner.ttlMaximum time a WAL can stay in the .oldlogdir directory, - after which it will be cleaned by a Master thread.Default600000hbase.master.hfilecleaner.pluginsA comma-separated list of BaseHFileCleanerDelegate invoked by - the HFileCleaner service. These HFiles cleaners are called in order, - so put the cleaner that prunes the most files in front. To - implement your own BaseHFileCleanerDelegate, just put it in HBase's classpath - and add the fully qualified class name here. Always add the above - default log cleaners in the list as they will be overwritten in - hbase-site.xml.Defaultorg.apache.hadoop.hbase.master.cleaner.TimeToLiveHFileCleanerhbase.master.catalog.timeoutTimeout value for the Catalog Janitor from the master to - META.Default600000hbase.master.infoserver.redirectWhether or not the Master listens to the Master web - UI port (hbase.master.info.port) and redirects requests to the web - UI server shared by the Master and RegionServer.Defaulttruehbase.regionserver.portThe port the HBase RegionServer binds to.Default16020hbase.regionserver.info.portThe port for the HBase RegionServer web UI - Set to -1 if you do not want the RegionServer UI to run.Default16030hbase.regionserver.info.bindAddressThe address for the HBase RegionServer web UIDefault0.0.0.0hbase.regionserver.info.port.autoWhether or not the Master or RegionServer - UI should search for a port to bind to. Enables automatic port - search if hbase.regionserver.info.port is already in use. - Useful for testing, turned off by default.Defaultfalsehbase.regionserver.handler.countCount of RPC Listener instances spun up on RegionServers. - Same property is used by the Master for count of master handlers.Default30hbase.ipc.server.callqueue.handler.factorFactor to determine the number of call queues. - A value of 0 means a single queue shared between all the handlers. - A value of 1 means that each handler has its own queue.Default0.1hbase.ipc.server.callqueue.read.ratioSplit the call queues into read and write queues. - The specified interval (which should be between 0.0 and 1.0) - will be multiplied by the number of call queues. - A value of 0 indicate to not split the call queues, meaning that both read and write - requests will be pushed to the same set of queues. - A value lower than 0.5 means that there will be less read queues than write queues. - A value of 0.5 means there will be the same number of read and write queues. - A value greater than 0.5 means that there will be more read queues than write queues. - A value of 1.0 means that all the queues except one are used to dispatch read requests. - - Example: Given the total number of call queues being 10 - a read.ratio of 0 means that: the 10 queues will contain both read/write requests. - a read.ratio of 0.3 means that: 3 queues will contain only read requests - and 7 queues will contain only write requests. - a read.ratio of 0.5 means that: 5 queues will contain only read requests - and 5 queues will contain only write requests. - a read.ratio of 0.8 means that: 8 queues will contain only read requests - and 2 queues will contain only write requests. - a read.ratio of 1 means that: 9 queues will contain only read requests - and 1 queues will contain only write requests. - Default0hbase.ipc.server.callqueue.scan.ratioGiven the number of read call queues, calculated from the total number - of call queues multiplied by the callqueue.read.ratio, the scan.ratio property - will split the read call queues into small-read and long-read queues. - A value lower than 0.5 means that there will be less long-read queues than short-read queues. - A value of 0.5 means that there will be the same number of short-read and long-read queues. - A value greater than 0.5 means that there will be more long-read queues than short-read queues - A value of 0 or 1 indicate to use the same set of queues for gets and scans. - - Example: Given the total number of read call queues being 8 - a scan.ratio of 0 or 1 means that: 8 queues will contain both long and short read requests. - a scan.ratio of 0.3 means that: 2 queues will contain only long-read requests - and 6 queues will contain only short-read requests. - a scan.ratio of 0.5 means that: 4 queues will contain only long-read requests - and 4 queues will contain only short-read requests. - a scan.ratio of 0.8 means that: 6 queues will contain only long-read requests - and 2 queues will contain only short-read requests. - Default0hbase.regionserver.msgintervalInterval between messages from the RegionServer to Master - in milliseconds.Default3000hbase.regionserver.regionSplitLimitLimit for the number of regions after which no more region - splitting should take place. This is not a hard limit for the number of - regions but acts as a guideline for the regionserver to stop splitting after - a certain limit. Default is MAX_INT; i.e. do not block splitting.Default2147483647hbase.regionserver.logroll.periodPeriod at which we will roll the commit log regardless - of how many edits it has.Default3600000hbase.regionserver.logroll.errors.toleratedThe number of consecutive WAL close errors we will allow - before triggering a server abort. A setting of 0 will cause the - region server to abort if closing the current WAL writer fails during - log rolling. Even a small value (2 or 3) will allow a region server - to ride over transient HDFS errors.Default2hbase.regionserver.hlog.reader.implThe WAL file reader implementation.Defaultorg.apache.hadoop.hbase.regionserver.wal.ProtobufLogReaderhbase.regionserver.hlog.writer.implThe WAL file writer implementation.Defaultorg.apache.hadoop.hbase.regionserver.wal.ProtobufLogWriterhbase.master.distributed.log.replayEnable 'distributed log replay' as default engine splitting - WAL files on server crash. This default is new in hbase 1.0. To fall - back to the old mode 'distributed log splitter', set the value to - 'false'. 'Disributed log replay' improves MTTR because it does not - write intermediate files. 'DLR' required that 'hfile.format.version' - be set to version 3 or higher. - Defaulttruehbase.regionserver.global.memstore.sizeMaximum size of all memstores in a region server before new - updates are blocked and flushes are forced. Defaults to 40% of heap. - Updates are blocked and flushes are forced until size of all memstores - in a region server hits hbase.regionserver.global.memstore.size.lower.limit.Default0.4hbase.regionserver.global.memstore.size.lower.limitMaximum size of all memstores in a region server before flushes are forced. - Defaults to 95% of hbase.regionserver.global.memstore.size. - A 100% value for this value causes the minimum possible flushing to occur when updates are - blocked due to memstore limiting.Default0.95hbase.regionserver.optionalcacheflushinterval - Maximum amount of time an edit lives in memory before being automatically flushed. - Default 1 hour. Set it to 0 to disable automatic flushing.Default3600000hbase.regionserver.catalog.timeoutTimeout value for the Catalog Janitor from the regionserver to META.Default600000hbase.regionserver.dns.interfaceThe name of the Network Interface from which a region server - should report its IP address.Defaultdefaulthbase.regionserver.dns.nameserverThe host name or IP address of the name server (DNS) - which a region server should use to determine the host name used by the - master for communication and display purposes.Defaultdefaulthbase.regionserver.region.split.policy - A split policy determines when a region should be split. The various other split policies that - are available currently are ConstantSizeRegionSplitPolicy, DisabledRegionSplitPolicy, - DelimitedKeyPrefixRegionSplitPolicy, KeyPrefixRegionSplitPolicy etc. - Defaultorg.apache.hadoop.hbase.regionserver.IncreasingToUpperBoundRegionSplitPolicyzookeeper.session.timeoutZooKeeper session timeout in milliseconds. It is used in two different ways. - First, this value is used in the ZK client that HBase uses to connect to the ensemble. - It is also used by HBase when it starts a ZK server and it is passed as the 'maxSessionTimeout'. See - http://hadoop.apache.org/zookeeper/docs/current/zookeeperProgrammers.html#ch_zkSessions. - For example, if a HBase region server connects to a ZK ensemble that's also managed by HBase, then the - session timeout will be the one specified by this configuration. But, a region server that connects - to an ensemble managed with a different configuration will be subjected that ensemble's maxSessionTimeout. So, - even though HBase might propose using 90 seconds, the ensemble can have a max timeout lower than this and - it will take precedence. The current default that ZK ships with is 40 seconds, which is lower than HBase's. - Default90000zookeeper.znode.parentRoot ZNode for HBase in ZooKeeper. All of HBase's ZooKeeper - files that are configured with a relative path will go under this node. - By default, all of HBase's ZooKeeper file path are configured with a - relative path, so they will all go under this directory unless changed.Default/hbasezookeeper.znode.rootserverPath to ZNode holding root region location. This is written by - the master and read by clients and region servers. If a relative path is - given, the parent folder will be ${zookeeper.znode.parent}. By default, - this means the root location is stored at /hbase/root-region-server.Defaultroot-region-serverzookeeper.znode.acl.parentRoot ZNode for access control lists.Defaultaclhbase.zookeeper.dns.interfaceThe name of the Network Interface from which a ZooKeeper server - should report its IP address.Defaultdefaulthbase.zookeeper.dns.nameserverThe host name or IP address of the name server (DNS) - which a ZooKeeper server should use to determine the host name used by the - master for communication and display purposes.Defaultdefaulthbase.zookeeper.peerportPort used by ZooKeeper peers to talk to each other. - See http://hadoop.apache.org/zookeeper/docs/r3.1.1/zookeeperStarted.html#sc_RunningReplicatedZooKeeper - for more information.Default2888hbase.zookeeper.leaderportPort used by ZooKeeper for leader election. - See http://hadoop.apache.org/zookeeper/docs/r3.1.1/zookeeperStarted.html#sc_RunningReplicatedZooKeeper - for more information.Default3888hbase.zookeeper.useMultiInstructs HBase to make use of ZooKeeper's multi-update functionality. - This allows certain ZooKeeper operations to complete more quickly and prevents some issues - with rare Replication failure scenarios (see the release note of HBASE-2611 for an example). - IMPORTANT: only set this to true if all ZooKeeper servers in the cluster are on version 3.4+ - and will not be downgraded. ZooKeeper versions before 3.4 do not support multi-update and - will not fail gracefully if multi-update is invoked (see ZOOKEEPER-1495).Defaulttruehbase.config.read.zookeeper.config - Set to true to allow HBaseConfiguration to read the - zoo.cfg file for ZooKeeper properties. Switching this to true - is not recommended, since the functionality of reading ZK - properties from a zoo.cfg file has been deprecated.Defaultfalsehbase.zookeeper.property.initLimitProperty from ZooKeeper's config zoo.cfg. - The number of ticks that the initial synchronization phase can take.Default10hbase.zookeeper.property.syncLimitProperty from ZooKeeper's config zoo.cfg. - The number of ticks that can pass between sending a request and getting an - acknowledgment.Default5hbase.zookeeper.property.dataDirProperty from ZooKeeper's config zoo.cfg. - The directory where the snapshot is stored.Default${hbase.tmp.dir}/zookeeperhbase.zookeeper.property.clientPortProperty from ZooKeeper's config zoo.cfg. - The port at which the clients will connect.Default2181hbase.zookeeper.property.maxClientCnxnsProperty from ZooKeeper's config zoo.cfg. - Limit on number of concurrent connections (at the socket level) that a - single client, identified by IP address, may make to a single member of - the ZooKeeper ensemble. Set high to avoid zk connection issues running - standalone and pseudo-distributed.Default300hbase.client.write.bufferDefault size of the HTable client write buffer in bytes. - A bigger buffer takes more memory -- on both the client and server - side since server instantiates the passed write buffer to process - it -- but a larger buffer size reduces the number of RPCs made. - For an estimate of server-side memory-used, evaluate - hbase.client.write.buffer * hbase.regionserver.handler.countDefault2097152hbase.client.pauseGeneral client pause value. Used mostly as value to wait - before running a retry of a failed get, region lookup, etc. - See hbase.client.retries.number for description of how we backoff from - this initial pause amount and how this pause works w/ retries.Default100hbase.client.retries.numberMaximum retries. Used as maximum for all retryable - operations such as the getting of a cell's value, starting a row update, - etc. Retry interval is a rough function based on hbase.client.pause. At - first we retry at this interval but then with backoff, we pretty quickly reach - retrying every ten seconds. See HConstants#RETRY_BACKOFF for how the backup - ramps up. Change this setting and hbase.client.pause to suit your workload.Default35hbase.client.max.total.tasksThe maximum number of concurrent tasks a single HTable instance will - send to the cluster.Default100hbase.client.max.perserver.tasksThe maximum number of concurrent tasks a single HTable instance will - send to a single region server.Default5hbase.client.max.perregion.tasksThe maximum number of concurrent connections the client will - maintain to a single Region. That is, if there is already - hbase.client.max.perregion.tasks writes in progress for this region, new puts - won't be sent to this region until some writes finishes.Default1hbase.client.scanner.cachingNumber of rows that will be fetched when calling next - on a scanner if it is not served from (local, client) memory. Higher - caching values will enable faster scanners but will eat up more memory - and some calls of next may take longer and longer times when the cache is empty. - Do not set this value such that the time between invocations is greater - than the scanner timeout; i.e. hbase.client.scanner.timeout.periodDefault100hbase.client.keyvalue.maxsizeSpecifies the combined maximum allowed size of a KeyValue - instance. This is to set an upper boundary for a single entry saved in a - storage file. Since they cannot be split it helps avoiding that a region - cannot be split any further because the data is too large. It seems wise - to set this to a fraction of the maximum region size. Setting it to zero - or less disables the check.Default10485760hbase.client.scanner.timeout.periodClient scanner lease period in milliseconds.Default60000hbase.client.localityCheck.threadPoolSizeDefault2hbase.bulkload.retries.numberMaximum retries. This is maximum number of iterations - to atomic bulk loads are attempted in the face of splitting operations - 0 means never give up.Default10hbase.balancer.period - Period at which the region balancer runs in the Master.Default300000hbase.regions.slopRebalance if any regionserver has average + (average * slop) regions.Default0.2hbase.server.thread.wakefrequencyTime to sleep in between searches for work (in milliseconds). - Used as sleep interval by service threads such as log roller.Default10000hbase.server.versionfile.writeattempts - How many time to retry attempting to write a version file - before just aborting. Each attempt is seperated by the - hbase.server.thread.wakefrequency milliseconds.Default3hbase.hregion.memstore.flush.size - Memstore will be flushed to disk if size of the memstore - exceeds this number of bytes. Value is checked by a thread that runs - every hbase.server.thread.wakefrequency.Default134217728hbase.hregion.percolumnfamilyflush.size.lower.bound - If FlushLargeStoresPolicy is used, then every time that we hit the - total memstore limit, we find out all the column families whose memstores - exceed this value, and only flush them, while retaining the others whose - memstores are lower than this limit. If none of the families have their - memstore size more than this, all the memstores will be flushed - (just as usual). This value should be less than half of the total memstore - threshold (hbase.hregion.memstore.flush.size). - Default16777216hbase.hregion.preclose.flush.size - If the memstores in a region are this size or larger when we go - to close, run a "pre-flush" to clear out memstores before we put up - the region closed flag and take the region offline. On close, - a flush is run under the close flag to empty memory. During - this time the region is offline and we are not taking on any writes. - If the memstore content is large, this flush could take a long time to - complete. The preflush is meant to clean out the bulk of the memstore - before putting up the close flag and taking the region offline so the - flush that runs under the close flag has little to do.Default5242880hbase.hregion.memstore.block.multiplier - Block updates if memstore has hbase.hregion.memstore.block.multiplier - times hbase.hregion.memstore.flush.size bytes. Useful preventing - runaway memstore during spikes in update traffic. Without an - upper-bound, memstore fills such that when it flushes the - resultant flush files take a long time to compact or split, or - worse, we OOME.Default4hbase.hregion.memstore.mslab.enabled - Enables the MemStore-Local Allocation Buffer, - a feature which works to prevent heap fragmentation under - heavy write loads. This can reduce the frequency of stop-the-world - GC pauses on large heaps.Defaulttruehbase.hregion.max.filesize - Maximum HFile size. If the sum of the sizes of a region's HFiles has grown to exceed this - value, the region is split in two.Default10737418240hbase.hregion.majorcompactionTime between major compactions, expressed in milliseconds. Set to 0 to disable - time-based automatic major compactions. User-requested and size-based major compactions will - still run. This value is multiplied by hbase.hregion.majorcompaction.jitter to cause - compaction to start at a somewhat-random time during a given window of time. The default value - is 7 days, expressed in milliseconds. If major compactions are causing disruption in your - environment, you can configure them to run at off-peak times for your deployment, or disable - time-based major compactions by setting this parameter to 0, and run major compactions in a - cron job or by another external mechanism.Default604800000hbase.hregion.majorcompaction.jitterA multiplier applied to hbase.hregion.majorcompaction to cause compaction to occur - a given amount of time either side of hbase.hregion.majorcompaction. The smaller the number, - the closer the compactions will happen to the hbase.hregion.majorcompaction - interval.Default0.50hbase.hstore.compactionThreshold If more than this number of StoreFiles exist in any one Store - (one StoreFile is written per flush of MemStore), a compaction is run to rewrite all - StoreFiles into a single StoreFile. Larger values delay compaction, but when compaction does - occur, it takes longer to complete.Default3hbase.hstore.flusher.count The number of flush threads. With fewer threads, the MemStore flushes will be - queued. With more threads, the flushes will be executed in parallel, increasing the load on - HDFS, and potentially causing more compactions. Default2hbase.hstore.blockingStoreFiles If more than this number of StoreFiles exist in any one Store (one StoreFile - is written per flush of MemStore), updates are blocked for this region until a compaction is - completed, or until hbase.hstore.blockingWaitTime has been exceeded.Default10hbase.hstore.blockingWaitTime The time for which a region will block updates after reaching the StoreFile limit - defined by hbase.hstore.blockingStoreFiles. After this time has elapsed, the region will stop - blocking updates even if a compaction has not been completed.Default90000hbase.hstore.compaction.minThe minimum number of StoreFiles which must be eligible for compaction before - compaction can run. The goal of tuning hbase.hstore.compaction.min is to avoid ending up with - too many tiny StoreFiles to compact. Setting this value to 2 would cause a minor compaction - each time you have two StoreFiles in a Store, and this is probably not appropriate. If you - set this value too high, all the other values will need to be adjusted accordingly. For most - cases, the default value is appropriate. In previous versions of HBase, the parameter - hbase.hstore.compaction.min was named hbase.hstore.compactionThreshold.Default3hbase.hstore.compaction.maxThe maximum number of StoreFiles which will be selected for a single minor - compaction, regardless of the number of eligible StoreFiles. Effectively, the value of - hbase.hstore.compaction.max controls the length of time it takes a single compaction to - complete. Setting it larger means that more StoreFiles are included in a compaction. For most - cases, the default value is appropriate.Default10hbase.hstore.compaction.min.sizeA StoreFile smaller than this size will always be eligible for minor compaction. - HFiles this size or larger are evaluated by hbase.hstore.compaction.ratio to determine if - they are eligible. Because this limit represents the "automatic include"limit for all - StoreFiles smaller than this value, this value may need to be reduced in write-heavy - environments where many StoreFiles in the 1-2 MB range are being flushed, because every - StoreFile will be targeted for compaction and the resulting StoreFiles may still be under the - minimum size and require further compaction. If this parameter is lowered, the ratio check is - triggered more quickly. This addressed some issues seen in earlier versions of HBase but - changing this parameter is no longer necessary in most situations. Default: 128 MB expressed - in bytes.Default134217728hbase.hstore.compaction.max.sizeA StoreFile larger than this size will be excluded from compaction. The effect of - raising hbase.hstore.compaction.max.size is fewer, larger StoreFiles that do not get - compacted often. If you feel that compaction is happening too often without much benefit, you - can try raising this value. Default: the value of LONG.MAX_VALUE, expressed in bytes.Default9223372036854775807hbase.hstore.compaction.ratioFor minor compaction, this ratio is used to determine whether a given StoreFile - which is larger than hbase.hstore.compaction.min.size is eligible for compaction. Its - effect is to limit compaction of large StoreFiles. The value of hbase.hstore.compaction.ratio - is expressed as a floating-point decimal. A large ratio, such as 10, will produce a single - giant StoreFile. Conversely, a low value, such as .25, will produce behavior similar to the - BigTable compaction algorithm, producing four StoreFiles. A moderate value of between 1.0 and - 1.4 is recommended. When tuning this value, you are balancing write costs with read costs. - Raising the value (to something like 1.4) will have more write costs, because you will - compact larger StoreFiles. However, during reads, HBase will need to seek through fewer - StoreFiles to accomplish the read. Consider this approach if you cannot take advantage of - Bloom filters. Otherwise, you can lower this value to something like 1.0 to reduce the - background cost of writes, and use Bloom filters to control the number of StoreFiles touched - during reads. For most cases, the default value is appropriate.Default1.2Fhbase.hstore.compaction.ratio.offpeakAllows you to set a different (by default, more aggressive) ratio for determining - whether larger StoreFiles are included in compactions during off-peak hours. Works in the - same way as hbase.hstore.compaction.ratio. Only applies if hbase.offpeak.start.hour and - hbase.offpeak.end.hour are also enabled.Default5.0Fhbase.hstore.time.to.purge.deletesThe amount of time to delay purging of delete markers with future timestamps. If - unset, or set to 0, all delete markers, including those with future timestamps, are purged - during the next major compaction. Otherwise, a delete marker is kept until the major compaction - which occurs after the marker's timestamp plus the value of this setting, in milliseconds. - Default0hbase.offpeak.start.hourThe start of off-peak hours, expressed as an integer between 0 and 23, inclusive. - Set to -1 to disable off-peak.Default-1hbase.offpeak.end.hourThe end of off-peak hours, expressed as an integer between 0 and 23, inclusive. Set - to -1 to disable off-peak.Default-1hbase.regionserver.thread.compaction.throttleThere are two different thread pools for compactions, one for large compactions and - the other for small compactions. This helps to keep compaction of lean tables (such as - hbase:meta) fast. If a compaction is larger than this threshold, it - goes into the large compaction pool. In most cases, the default value is appropriate. Default: - 2 x hbase.hstore.compaction.max x hbase.hregion.memstore.flush.size (which defaults to 128MB). - The value field assumes that the value of hbase.hregion.memstore.flush.size is unchanged from - the default.Default2684354560hbase.hstore.compaction.kv.maxThe maximum number of KeyValues to read and then write in a batch when flushing or - compacting. Set this lower if you have big KeyValues and problems with Out Of Memory - Exceptions Set this higher if you have wide, small rows. Default10hbase.storescanner.parallel.seek.enable - Enables StoreFileScanner parallel-seeking in StoreScanner, - a feature which can reduce response latency under special conditions.Defaultfalsehbase.storescanner.parallel.seek.threads - The default thread pool size if parallel-seeking feature enabled.Default10hfile.block.cache.sizePercentage of maximum heap (-Xmx setting) to allocate to block cache - used by a StoreFile. Default of 0.4 means allocate 40%. - Set to 0 to disable but it's not recommended; you need at least - enough cache to hold the storefile indices.Default0.4hfile.block.index.cacheonwriteThis allows to put non-root multi-level index blocks into the block - cache at the time the index is being written.Defaultfalsehfile.index.block.max.sizeWhen the size of a leaf-level, intermediate-level, or root-level - index block in a multi-level block index grows to this size, the - block is written out and a new block is started.Default131072hbase.bucketcache.ioengineWhere to store the contents of the bucketcache. One of: onheap, - offheap, or file. If a file, set it to file:PATH_TO_FILE. See https://hbase.apache.org/apidocs/org/apache/hadoop/hbase/io/hfile/CacheConfig.html for more information. - Defaulthbase.bucketcache.combinedcache.enabledWhether or not the bucketcache is used in league with the LRU - on-heap block cache. In this mode, indices and blooms are kept in the LRU - blockcache and the data blocks are kept in the bucketcache.Defaulttruehbase.bucketcache.sizeThe size of the buckets for the bucketcache if you only use a single size. - Defaults to the default blocksize, which is 64 * 1024.Default65536hbase.bucketcache.sizesA comma-separated list of sizes for buckets for the bucketcache - if you use multiple sizes. Should be a list of block sizes in order from smallest - to largest. The sizes you use will depend on your data access patterns.Defaulthfile.format.versionThe HFile format version to use for new files. - Version 3 adds support for tags in hfiles (See http://hbase.apache.org/book.html#hbase.tags). - Distributed Log Replay requires that tags are enabled. Also see the configuration - 'hbase.replication.rpc.codec'. - Default3hfile.block.bloom.cacheonwriteEnables cache-on-write for inline blocks of a compound Bloom filter.Defaultfalseio.storefile.bloom.block.sizeThe size in bytes of a single block ("chunk") of a compound Bloom - filter. This size is approximate, because Bloom blocks can only be - inserted at data block boundaries, and the number of keys per data - block varies.Default131072hbase.rs.cacheblocksonwriteWhether an HFile block should be added to the block cache when the - block is finished.Defaultfalsehbase.rpc.timeoutThis is for the RPC layer to define how long HBase client applications - take for a remote call to time out. It uses pings to check connections - but will eventually throw a TimeoutException.Default60000hbase.rpc.shortoperation.timeoutThis is another version of "hbase.rpc.timeout". For those RPC operation - within cluster, we rely on this configuration to set a short timeout limitation - for short operation. For example, short rpc timeout for region server's trying - to report to active master can benefit quicker master failover process.Default10000hbase.ipc.client.tcpnodelaySet no delay on rpc socket connections. See - http://docs.oracle.com/javase/1.5.0/docs/api/java/net/Socket.html#getTcpNoDelay()Defaulttruehbase.master.keytab.fileFull path to the kerberos keytab file to use for logging in - the configured HMaster server principal.Defaulthbase.master.kerberos.principalEx. "hbase/_HOST@EXAMPLE.COM". The kerberos principal name - that should be used to run the HMaster process. The principal name should - be in the form: user/hostname@DOMAIN. If "_HOST" is used as the hostname - portion, it will be replaced with the actual hostname of the running - instance.Defaulthbase.regionserver.keytab.fileFull path to the kerberos keytab file to use for logging in - the configured HRegionServer server principal.Defaulthbase.regionserver.kerberos.principalEx. "hbase/_HOST@EXAMPLE.COM". The kerberos principal name - that should be used to run the HRegionServer process. The principal name - should be in the form: user/hostname@DOMAIN. If "_HOST" is used as the - hostname portion, it will be replaced with the actual hostname of the - running instance. An entry for this principal must exist in the file - specified in hbase.regionserver.keytab.fileDefaulthadoop.policy.fileThe policy configuration file used by RPC servers to make - authorization decisions on client requests. Only used when HBase - security is enabled.Defaulthbase-policy.xmlhbase.superuserList of users or groups (comma-separated), who are allowed - full privileges, regardless of stored ACLs, across the cluster. - Only used when HBase security is enabled.Defaulthbase.auth.key.update.intervalThe update interval for master key for authentication tokens - in servers in milliseconds. Only used when HBase security is enabled.Default86400000hbase.auth.token.max.lifetimeThe maximum lifetime in milliseconds after which an - authentication token expires. Only used when HBase security is enabled.Default604800000hbase.ipc.client.fallback-to-simple-auth-allowedWhen a client is configured to attempt a secure connection, but attempts to - connect to an insecure server, that server may instruct the client to - switch to SASL SIMPLE (unsecure) authentication. This setting controls - whether or not the client will accept this instruction from the server. - When false (the default), the client will not allow the fallback to SIMPLE - authentication, and will abort the connection.Defaultfalsehbase.display.keysWhen this is set to true the webUI and such will display all start/end keys - as part of the table details, region names, etc. When this is set to false, - the keys are hidden.Defaulttruehbase.coprocessor.region.classesA comma-separated list of Coprocessors that are loaded by - default on all tables. For any override coprocessor method, these classes - will be called in order. After implementing your own Coprocessor, just put - it in HBase's classpath and add the fully qualified class name here. - A coprocessor can also be loaded on demand by setting HTableDescriptor.Defaulthbase.rest.portThe port for the HBase REST server.Default8080hbase.rest.readonlyDefines the mode the REST server will be started in. Possible values are: - false: All HTTP methods are permitted - GET/PUT/POST/DELETE. - true: Only the GET method is permitted.Defaultfalsehbase.rest.threads.maxThe maximum number of threads of the REST server thread pool. - Threads in the pool are reused to process REST requests. This - controls the maximum number of requests processed concurrently. - It may help to control the memory used by the REST server to - avoid OOM issues. If the thread pool is full, incoming requests - will be queued up and wait for some free threads.Default100hbase.rest.threads.minThe minimum number of threads of the REST server thread pool. - The thread pool always has at least these number of threads so - the REST server is ready to serve incoming requests.Default2hbase.rest.support.proxyuserEnables running the REST server to support proxy-user mode.Defaultfalsehbase.defaults.for.version.skipSet to true to skip the 'hbase.defaults.for.version' check. - Setting this to true can be useful in contexts other than - the other side of a maven generation; i.e. running in an - ide. You'll want to set this boolean to true to avoid - seeing the RuntimException complaint: "hbase-default.xml file - seems to be for and old version of HBase (\${hbase.version}), this - version is X.X.X-SNAPSHOT"Defaultfalsehbase.coprocessor.master.classesA comma-separated list of - org.apache.hadoop.hbase.coprocessor.MasterObserver coprocessors that are - loaded by default on the active HMaster process. For any implemented - coprocessor methods, the listed classes will be called in order. After - implementing your own MasterObserver, just put it in HBase's classpath - and add the fully qualified class name here.Defaulthbase.coprocessor.abortonerrorSet to true to cause the hosting server (master or regionserver) - to abort if a coprocessor fails to load, fails to initialize, or throws an - unexpected Throwable object. Setting this to false will allow the server to - continue execution but the system wide state of the coprocessor in question - will become inconsistent as it will be properly executing in only a subset - of servers, so this is most useful for debugging only.Defaulttruehbase.online.schema.update.enableSet true to enable online schema changes.Defaulttruehbase.table.lock.enableSet to true to enable locking the table in zookeeper for schema change operations. - Table locking from master prevents concurrent schema modifications to corrupt table - state.Defaulttruehbase.table.max.rowsize - Maximum size of single row in bytes (default is 1 Gb) for Get'ting - or Scan'ning without in-row scan flag set. If row size exceeds this limit - RowTooBigException is thrown to client. - Default1073741824hbase.thrift.minWorkerThreadsThe "core size" of the thread pool. New threads are created on every - connection until this many threads are created.Default16hbase.thrift.maxWorkerThreadsThe maximum size of the thread pool. When the pending request queue - overflows, new threads are created until their number reaches this number. - After that, the server starts dropping connections.Default1000hbase.thrift.maxQueuedRequestsThe maximum number of pending Thrift connections waiting in the queue. If - there are no idle threads in the pool, the server queues requests. Only - when the queue overflows, new threads are added, up to - hbase.thrift.maxQueuedRequests threads.Default1000hbase.thrift.htablepool.size.maxThe upper bound for the table pool used in the Thrift gateways server. - Since this is per table name, we assume a single table and so with 1000 default - worker threads max this is set to a matching number. For other workloads this number - can be adjusted as needed. - Default1000hbase.regionserver.thrift.framedUse Thrift TFramedTransport on the server side. - This is the recommended transport for thrift servers and requires a similar setting - on the client side. Changing this to false will select the default transport, - vulnerable to DoS when malformed requests are issued due to THRIFT-601. - Defaultfalsehbase.regionserver.thrift.framed.max_frame_size_in_mbDefault frame size when using framed transportDefault2hbase.regionserver.thrift.compactUse Thrift TCompactProtocol binary serialization protocol.Defaultfalsehbase.data.umask.enableEnable, if true, that file permissions should be assigned - to the files written by the regionserverDefaultfalsehbase.data.umaskFile permissions that should be used to write data - files when hbase.data.umask.enable is trueDefault000hbase.metrics.showTableNameWhether to include the prefix "tbl.tablename" in per-column family metrics. - If true, for each metric M, per-cf metrics will be reported for tbl.T.cf.CF.M, if false, - per-cf metrics will be aggregated by column-family across tables, and reported for cf.CF.M. - In both cases, the aggregated metric M across tables and cfs will be reported.Defaulttruehbase.metrics.exposeOperationTimesWhether to report metrics about time taken performing an - operation on the region server. Get, Put, Delete, Increment, and Append can all - have their times exposed through Hadoop metrics per CF and per region.Defaulttruehbase.snapshot.enabledSet to true to allow snapshots to be taken / restored / cloned.Defaulttruehbase.snapshot.restore.take.failsafe.snapshotSet to true to take a snapshot before the restore operation. - The snapshot taken will be used in case of failure, to restore the previous state. - At the end of the restore operation this snapshot will be deletedDefaulttruehbase.snapshot.restore.failsafe.nameName of the failsafe snapshot taken by the restore operation. - You can use the {snapshot.name}, {table.name} and {restore.timestamp} variables - to create a name based on what you are restoring.Defaulthbase-failsafe-{snapshot.name}-{restore.timestamp}hbase.server.compactchecker.interval.multiplierThe number that determines how often we scan to see if compaction is necessary. - Normally, compactions are done after some events (such as memstore flush), but if - region didn't receive a lot of writes for some time, or due to different compaction - policies, it may be necessary to check it periodically. The interval between checks is - hbase.server.compactchecker.interval.multiplier multiplied by - hbase.server.thread.wakefrequency.Default1000hbase.lease.recovery.timeoutHow long we wait on dfs lease recovery in total before giving up.Default900000hbase.lease.recovery.dfs.timeoutHow long between dfs recover lease invocations. Should be larger than the sum of - the time it takes for the namenode to issue a block recovery command as part of - datanode; dfs.heartbeat.interval and the time it takes for the primary - datanode, performing block recovery to timeout on a dead datanode; usually - dfs.client.socket-timeout. See the end of HBASE-8389 for more.Default64000hbase.column.max.versionNew column family descriptors will use this value as the default number of versions - to keep.Default1hbase.dfs.client.read.shortcircuit.buffer.sizeIf the DFSClient configuration - dfs.client.read.shortcircuit.buffer.size is unset, we will - use what is configured here as the short circuit read default - direct byte buffer size. DFSClient native default is 1MB; HBase - keeps its HDFS files open so number of file blocks * 1MB soon - starts to add up and threaten OOME because of a shortage of - direct memory. So, we set it down from the default. Make - it > the default hbase block size set in the HColumnDescriptor - which is usually 64k. - Default131072hbase.regionserver.checksum.verify - If set to true (the default), HBase verifies the checksums for hfile - blocks. HBase writes checksums inline with the data when it writes out - hfiles. HDFS (as of this writing) writes checksums to a separate file - than the data file necessitating extra seeks. Setting this flag saves - some on i/o. Checksum verification by HDFS will be internally disabled - on hfile streams when this flag is set. If the hbase-checksum verification - fails, we will switch back to using HDFS checksums (so do not disable HDFS - checksums! And besides this feature applies to hfiles only, not to WALs). - If this parameter is set to false, then hbase will not verify any checksums, - instead it will depend on checksum verification being done in the HDFS client. - Defaulttruehbase.hstore.bytes.per.checksum - Number of bytes in a newly created checksum chunk for HBase-level - checksums in hfile blocks. - Default16384hbase.hstore.checksum.algorithm - Name of an algorithm that is used to compute checksums. Possible values - are NULL, CRC32, CRC32C. - DefaultCRC32hbase.status.published - This setting activates the publication by the master of the status of the region server. - When a region server dies and its recovery starts, the master will push this information - to the client application, to let them cut the connection immediately instead of waiting - for a timeout. - Defaultfalsehbase.status.publisher.class - Implementation of the status publication with a multicast message. - Defaultorg.apache.hadoop.hbase.master.ClusterStatusPublisher$MulticastPublisherhbase.status.listener.class - Implementation of the status listener with a multicast message. - Defaultorg.apache.hadoop.hbase.client.ClusterStatusListener$MulticastListenerhbase.status.multicast.address.ip - Multicast address to use for the status publication by multicast. - Default226.1.1.3hbase.status.multicast.address.port - Multicast port to use for the status publication by multicast. - Default16100hbase.dynamic.jars.dir - The directory from which the custom filter/co-processor jars can be loaded - dynamically by the region server without the need to restart. However, - an already loaded filter/co-processor class would not be un-loaded. See - HBASE-1936 for more details. - Default${hbase.rootdir}/libhbase.security.authentication - Controls whether or not secure authentication is enabled for HBase. - Possible values are 'simple' (no authentication), and 'kerberos'. - Defaultsimplehbase.rest.filter.classes - Servlet filters for REST service. - Defaultorg.apache.hadoop.hbase.rest.filter.GzipFilterhbase.master.loadbalancer.class - Class used to execute the regions balancing when the period occurs. - See the class comment for more on how it works - http://hbase.apache.org/devapidocs/org/apache/hadoop/hbase/master/balancer/StochasticLoadBalancer.html - It replaces the DefaultLoadBalancer as the default (since renamed - as the SimpleLoadBalancer). - Defaultorg.apache.hadoop.hbase.master.balancer.StochasticLoadBalancerhbase.security.exec.permission.checks - If this setting is enabled and ACL based access control is active (the - AccessController coprocessor is installed either as a system coprocessor - or on a table as a table coprocessor) then you must grant all relevant - users EXEC privilege if they require the ability to execute coprocessor - endpoint calls. EXEC privilege, like any other permission, can be - granted globally to a user, or to a user on a per table or per namespace - basis. For more information on coprocessor endpoints, see the coprocessor - section of the HBase online manual. For more information on granting or - revoking permissions using the AccessController, see the security - section of the HBase online manual. - Defaultfalsehbase.procedure.regionserver.classesA comma-separated list of - org.apache.hadoop.hbase.procedure.RegionServerProcedureManager procedure managers that are - loaded by default on the active HRegionServer process. The lifecycle methods (init/start/stop) - will be called by the active HRegionServer process to perform the specific globally barriered - procedure. After implementing your own RegionServerProcedureManager, just put it in - HBase's classpath and add the fully qualified class name here. - Defaulthbase.procedure.master.classesA comma-separated list of - org.apache.hadoop.hbase.procedure.MasterProcedureManager procedure managers that are - loaded by default on the active HMaster process. A procedure is identified by its signature and - users can use the signature and an instant name to trigger an execution of a globally barriered - procedure. After implementing your own MasterProcedureManager, just put it in HBase's classpath - and add the fully qualified class name here.Defaulthbase.coordinated.state.manager.classFully qualified name of class implementing coordinated state manager.Defaultorg.apache.hadoop.hbase.coordination.ZkCoordinatedStateManagerhbase.regionserver.storefile.refresh.period - The period (in milliseconds) for refreshing the store files for the secondary regions. 0 - means this feature is disabled. Secondary regions sees new files (from flushes and - compactions) from primary once the secondary region refreshes the list of files in the - region (there is no notification mechanism). But too frequent refreshes might cause - extra Namenode pressure. If the files cannot be refreshed for longer than HFile TTL - (hbase.master.hfilecleaner.ttl) the requests are rejected. Configuring HFile TTL to a larger - value is also recommended with this setting. - Default0hbase.region.replica.replication.enabled - Whether asynchronous WAL replication to the secondary region replicas is enabled or not. - If this is enabled, a replication peer named "region_replica_replication" will be created - which will tail the logs and replicate the mutatations to region replicas for tables that - have region replication > 1. If this is enabled once, disabling this replication also - requires disabling the replication peer using shell or ReplicationAdmin java class. - Replication to secondary region replicas works over standard inter-cluster replication. - So replication, if disabled explicitly, also has to be enabled by setting "hbase.replication" - to true for this feature to work. - Defaultfalsehbase.http.filter.initializers - A comma separated list of class names. Each class in the list must extend - org.apache.hadoop.hbase.http.FilterInitializer. The corresponding Filter will - be initialized. Then, the Filter will be applied to all user facing jsp - and servlet web pages. - The ordering of the list defines the ordering of the filters. - The default StaticUserWebFilter add a user principal as defined by the - hbase.http.staticuser.user property. - Defaultorg.apache.hadoop.hbase.http.lib.StaticUserWebFilterhbase.security.visibility.mutations.checkauths - This property if enabled, will check whether the labels in the visibility expression are associated - with the user issuing the mutation - Defaultfalsehbase.http.max.threads - The maximum number of threads that the HTTP Server will create in its - ThreadPool. - Default10hbase.replication.rpc.codec - The codec that is to be used when replication is enabled so that - the tags are also replicated. This is used along with HFileV3 which - supports tags in them. If tags are not used or if the hfile version used - is HFileV2 then KeyValueCodec can be used as the replication codec. Note that - using KeyValueCodecWithTags for replication when there are no tags causes no harm. - Defaultorg.apache.hadoop.hbase.codec.KeyValueCodecWithTagshbase.http.staticuser.user - The user name to filter as, on static web filters - while rendering content. An example use is the HDFS - web UI (user to be used for browsing files). - Defaultdr.stackhbase.regionserver.handler.abort.on.error.percentThe percent of region server RPC threads failed to abort RS. - -1 Disable aborting; 0 Abort if even a single handler has died; - 0.x Abort only when this percent of handlers have died; - 1 Abort only all of the handers have died.Default0.5 diff --git a/src/main/docbkx/hbase_apis.xml b/src/main/docbkx/hbase_apis.xml deleted file mode 100644 index bc35abac41b..00000000000 --- a/src/main/docbkx/hbase_apis.xml +++ /dev/null @@ -1,133 +0,0 @@ - - - - Apache HBase APIs - This chapter provides information about performing operations using HBase native APIs. This - information is not exhaustive, and provides a quick reference in addition to the User API - Reference. The examples here are not comprehensive or complete, and should be used for - purposes of illustration only. - Apache HBase also works with multiple external APIs. See - for more information. - - - Create a Table Using Java - This example has been tested on HBase 0.96.1.1. - -package com.example.hbase.admin; - -import java.io.IOException; - -import org.apache.hadoop.hbase.HBaseConfiguration; -import org.apache.hadoop.hbase.HColumnDescriptor; -import org.apache.hadoop.hbase.HTableDescriptor; -import org.apache.hadoop.hbase.TableName; -import org.apache.hadoop.hbase.client.HBaseAdmin; -import org.apache.hadoop.hbase.io.compress.Compression.Algorithm; -import org.apache.hadoop.conf.Configuration; - -import static com.example.hbase.Constants.*; - -public class CreateSchema { - - public static void createOrOverwrite(HBaseAdmin admin, HTableDescriptor table) throws IOException { - if (admin.tableExists(table.getName())) { - admin.disableTable(table.getName()); - admin.deleteTable(table.getName()); - } - admin.createTable(table); - } - - public static void createSchemaTables (Configuration config) { - try { - final HBaseAdmin admin = new HBaseAdmin(config); - HTableDescriptor table = new HTableDescriptor(TableName.valueOf(TABLE_NAME)); - table.addFamily(new HColumnDescriptor(CF_DEFAULT).setCompressionType(Algorithm.SNAPPY)); - - System.out.print("Creating table. "); - createOrOverwrite(admin, table); - System.out.println(" Done."); - - admin.close(); - } catch (Exception e) { - e.printStackTrace(); - System.exit(-1); - } - } - - -} - - - - - Add, Modify, and Delete a Table - This example has been tested on HBase 0.96.1.1. - -public static void upgradeFrom0 (Configuration config) { - - try { - final HBaseAdmin admin = new HBaseAdmin(config); - TableName tableName = TableName.valueOf(TABLE_ASSETMETA); - HTableDescriptor table_assetmeta = new HTableDescriptor(tableName); - table_assetmeta.addFamily(new HColumnDescriptor(CF_DEFAULT).setCompressionType(Algorithm.SNAPPY)); - - // Create a new table. - - System.out.print("Creating table_assetmeta. "); - admin.createTable(table_assetmeta); - System.out.println(" Done."); - - // Update existing table - HColumnDescriptor newColumn = new HColumnDescriptor("NEWCF"); - newColumn.setCompactionCompressionType(Algorithm.GZ); - newColumn.setMaxVersions(HConstants.ALL_VERSIONS); - admin.addColumn(tableName, newColumn); - - // Disable an existing table - admin.disableTable(tableName); - - // Delete an existing column family - admin.deleteColumn(tableName, CF_DEFAULT); - - // Delete a table (Need to be disabled first) - admin.deleteTable(tableName); - - - admin.close(); - } catch (Exception e) { - e.printStackTrace(); - System.exit(-1); - } - } - - - - diff --git a/src/main/docbkx/hbase_history.xml b/src/main/docbkx/hbase_history.xml deleted file mode 100644 index f7b90645af9..00000000000 --- a/src/main/docbkx/hbase_history.xml +++ /dev/null @@ -1,41 +0,0 @@ - - - - HBase History - - 2006: BigTable paper published by Google. - - 2006 (end of year): HBase development starts. - - 2008: HBase becomes Hadoop sub-project. - - 2010: HBase becomes Apache top-level project. - - - diff --git a/src/main/docbkx/hbck_in_depth.xml b/src/main/docbkx/hbck_in_depth.xml deleted file mode 100644 index e2ee34f3a46..00000000000 --- a/src/main/docbkx/hbck_in_depth.xml +++ /dev/null @@ -1,237 +0,0 @@ - - - - - hbck In Depth - HBaseFsck (hbck) is a tool for checking for region consistency and table integrity problems - and repairing a corrupted HBase. It works in two basic modes -- a read-only inconsistency - identifying mode and a multi-phase read-write repair mode. - -
- Running hbck to identify inconsistencies - To check to see if your HBase cluster has corruptions, run hbck against your HBase cluster: - -$ ./bin/hbase hbck - - - At the end of the commands output it prints OK or tells you the number of INCONSISTENCIES - present. You may also want to run run hbck a few times because some inconsistencies can be - transient (e.g. cluster is starting up or a region is splitting). Operationally you may want to run - hbck regularly and setup alert (e.g. via nagios) if it repeatedly reports inconsistencies . - A run of hbck will report a list of inconsistencies along with a brief description of the regions and - tables affected. The using the -details option will report more details including a representative - listing of all the splits present in all the tables. - - -$ ./bin/hbase hbck -details - - If you just want to know if some tables are corrupted, you can limit hbck to identify inconsistencies - in only specific tables. For example the following command would only attempt to check table - TableFoo and TableBar. The benefit is that hbck will run in less time. - -$ ./bin/hbase hbck TableFoo TableBar - -
-
Inconsistencies - - If after several runs, inconsistencies continue to be reported, you may have encountered a - corruption. These should be rare, but in the event they occur newer versions of HBase include - the hbck tool enabled with automatic repair options. - - - There are two invariants that when violated create inconsistencies in HBase: - - - HBase’s region consistency invariant is satisfied if every region is assigned and - deployed on exactly one region server, and all places where this state kept is in - accordance. - - HBase’s table integrity invariant is satisfied if for each table, every possible row key - resolves to exactly one region. - - - - Repairs generally work in three phases -- a read-only information gathering phase that identifies - inconsistencies, a table integrity repair phase that restores the table integrity invariant, and then - finally a region consistency repair phase that restores the region consistency invariant. - Starting from version 0.90.0, hbck could detect region consistency problems report on a subset - of possible table integrity problems. It also included the ability to automatically fix the most - common inconsistency, region assignment and deployment consistency problems. This repair - could be done by using the -fix command line option. These problems close regions if they are - open on the wrong server or on multiple region servers and also assigns regions to region - servers if they are not open. - - - Starting from HBase versions 0.90.7, 0.92.2 and 0.94.0, several new command line options are - introduced to aid repairing a corrupted HBase. This hbck sometimes goes by the nickname - “uberhbck”. Each particular version of uber hbck is compatible with the HBase’s of the same - major version (0.90.7 uberhbck can repair a 0.90.4). However, versions <=0.90.6 and versions - <=0.92.1 may require restarting the master or failing over to a backup master. - -
-
Localized repairs - - When repairing a corrupted HBase, it is best to repair the lowest risk inconsistencies first. - These are generally region consistency repairs -- localized single region repairs, that only modify - in-memory data, ephemeral zookeeper data, or patch holes in the META table. - Region consistency requires that the HBase instance has the state of the region’s data in HDFS - (.regioninfo files), the region’s row in the hbase:meta table., and region’s deployment/assignments on - region servers and the master in accordance. Options for repairing region consistency include: - - -fixAssignments (equivalent to the 0.90 -fix option) repairs unassigned, incorrectly - assigned or multiply assigned regions. - - -fixMeta which removes meta rows when corresponding regions are not present in - HDFS and adds new meta rows if they regions are present in HDFS while not in META. - - - To fix deployment and assignment problems you can run this command: - - -$ ./bin/hbase hbck -fixAssignments - - To fix deployment and assignment problems as well as repairing incorrect meta rows you can - run this command: - -$ ./bin/hbase hbck -fixAssignments -fixMeta - - There are a few classes of table integrity problems that are low risk repairs. The first two are - degenerate (startkey == endkey) regions and backwards regions (startkey > endkey). These are - automatically handled by sidelining the data to a temporary directory (/hbck/xxxx). - The third low-risk class is hdfs region holes. This can be repaired by using the: - - -fixHdfsHoles option for fabricating new empty regions on the file system. - If holes are detected you can use -fixHdfsHoles and should include -fixMeta and -fixAssignments to make the new region consistent. - - - -$ ./bin/hbase hbck -fixAssignments -fixMeta -fixHdfsHoles - - Since this is a common operation, we’ve added a the -repairHoles flag that is equivalent to the - previous command: - -$ ./bin/hbase hbck -repairHoles - - If inconsistencies still remain after these steps, you most likely have table integrity problems - related to orphaned or overlapping regions. -
-
Region Overlap Repairs - Table integrity problems can require repairs that deal with overlaps. This is a riskier operation - because it requires modifications to the file system, requires some decision making, and may - require some manual steps. For these repairs it is best to analyze the output of a hbck -details - run so that you isolate repairs attempts only upon problems the checks identify. Because this is - riskier, there are safeguard that should be used to limit the scope of the repairs. - WARNING: This is a relatively new and have only been tested on online but idle HBase instances - (no reads/writes). Use at your own risk in an active production environment! - The options for repairing table integrity violations include: - - -fixHdfsOrphans option for “adopting” a region directory that is missing a region - metadata file (the .regioninfo file). - - -fixHdfsOverlaps ability for fixing overlapping regions - - - When repairing overlapping regions, a region’s data can be modified on the file system in two - ways: 1) by merging regions into a larger region or 2) by sidelining regions by moving data to - “sideline” directory where data could be restored later. Merging a large number of regions is - technically correct but could result in an extremely large region that requires series of costly - compactions and splitting operations. In these cases, it is probably better to sideline the regions - that overlap with the most other regions (likely the largest ranges) so that merges can happen on - a more reasonable scale. Since these sidelined regions are already laid out in HBase’s native - directory and HFile format, they can be restored by using HBase’s bulk load mechanism. - The default safeguard thresholds are conservative. These options let you override the default - thresholds and to enable the large region sidelining feature. - - -maxMerge <n> maximum number of overlapping regions to merge - - -sidelineBigOverlaps if more than maxMerge regions are overlapping, sideline attempt - to sideline the regions overlapping with the most other regions. - - -maxOverlapsToSideline <n> if sidelining large overlapping regions, sideline at most n - regions. - - - - Since often times you would just want to get the tables repaired, you can use this option to turn - on all repair options: - - -repair includes all the region consistency options and only the hole repairing table - integrity options. - - - Finally, there are safeguards to limit repairs to only specific tables. For example the following - command would only attempt to check and repair table TableFoo and TableBar. - -$ ./bin/hbase hbck -repair TableFoo TableBar - -
Special cases: Meta is not properly assigned - There are a few special cases that hbck can handle as well. - Sometimes the meta table’s only region is inconsistently assigned or deployed. In this case - there is a special -fixMetaOnly option that can try to fix meta assignments. - -$ ./bin/hbase hbck -fixMetaOnly -fixAssignments - -
-
Special cases: HBase version file is missing - HBase’s data on the file system requires a version file in order to start. If this flie is missing, you - can use the -fixVersionFile option to fabricating a new HBase version file. This assumes that - the version of hbck you are running is the appropriate version for the HBase cluster. -
-
Special case: Root and META are corrupt. - The most drastic corruption scenario is the case where the ROOT or META is corrupted and - HBase will not start. In this case you can use the OfflineMetaRepair tool create new ROOT - and META regions and tables. - This tool assumes that HBase is offline. It then marches through the existing HBase home - directory, loads as much information from region metadata files (.regioninfo files) as possible - from the file system. If the region metadata has proper table integrity, it sidelines the original root - and meta table directories, and builds new ones with pointers to the region directories and their - data. - -$ ./bin/hbase org.apache.hadoop.hbase.util.hbck.OfflineMetaRepair - - NOTE: This tool is not as clever as uberhbck but can be used to bootstrap repairs that uberhbck - can complete. - If the tool succeeds you should be able to start hbase and run online repairs if necessary. -
-
Special cases: Offline split parent - - Once a region is split, the offline parent will be cleaned up automatically. Sometimes, daughter regions - are split again before their parents are cleaned up. HBase can clean up parents in the right order. However, - there could be some lingering offline split parents sometimes. They are in META, in HDFS, and not deployed. - But HBase can't clean them up. In this case, you can use the -fixSplitParents option to reset - them in META to be online and not split. Therefore, hbck can merge them with other regions if fixing - overlapping regions option is used. - - - This option should not normally be used, and it is not in -fixAll. - -
-
- - diff --git a/src/main/docbkx/mapreduce.xml b/src/main/docbkx/mapreduce.xml deleted file mode 100644 index 9e9e47427e1..00000000000 --- a/src/main/docbkx/mapreduce.xml +++ /dev/null @@ -1,630 +0,0 @@ - - - - - HBase and MapReduce - Apache MapReduce is a software framework used to analyze large amounts of data, and is - the framework used most often with Apache Hadoop. MapReduce itself is out of the - scope of this document. A good place to get started with MapReduce is . MapReduce version - 2 (MR2)is now part of YARN. - - This chapter discusses specific configuration steps you need to take to use MapReduce on - data within HBase. In addition, it discusses other interactions and issues between HBase and - MapReduce jobs. - - mapred and mapreduce - There are two mapreduce packages in HBase as in MapReduce itself: org.apache.hadoop.hbase.mapred - and org.apache.hadoop.hbase.mapreduce. The former does old-style API and the latter - the new style. The latter has more facility though you can usually find an equivalent in the older - package. Pick the package that goes with your mapreduce deploy. When in doubt or starting over, pick the - org.apache.hadoop.hbase.mapreduce. In the notes below, we refer to - o.a.h.h.mapreduce but replace with the o.a.h.h.mapred if that is what you are using. - - - - -
- HBase, MapReduce, and the CLASSPATH - By default, MapReduce jobs deployed to a MapReduce cluster do not have access to either - the HBase configuration under $HBASE_CONF_DIR or the HBase classes. - To give the MapReduce jobs the access they need, you could add - hbase-site.xml to the - $HADOOP_HOME/conf/ directory and add the - HBase JARs to the HADOOP_HOME/conf/ - directory, then copy these changes across your cluster. You could add hbase-site.xml to - $HADOOP_HOME/conf and add HBase jars to the $HADOOP_HOME/lib. You would then need to copy - these changes across your cluster or edit - $HADOOP_HOMEconf/hadoop-env.sh and add - them to the HADOOP_CLASSPATH variable. However, this approach is not - recommended because it will pollute your Hadoop install with HBase references. It also - requires you to restart the Hadoop cluster before Hadoop can use the HBase data. - Since HBase 0.90.x, HBase adds its dependency JARs to the job configuration itself. The - dependencies only need to be available on the local CLASSPATH. The following example runs - the bundled HBase RowCounter - MapReduce job against a table named usertable If you have not set - the environment variables expected in the command (the parts prefixed by a - $ sign and curly braces), you can use the actual system paths instead. - Be sure to use the correct version of the HBase JAR for your system. The backticks - (` symbols) cause ths shell to execute the sub-commands, setting the - CLASSPATH as part of the command. This example assumes you use a BASH-compatible shell. - $ HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-server-VERSION.jar rowcounter usertable - When the command runs, internally, the HBase JAR finds the dependencies it needs for - zookeeper, guava, and its other dependencies on the passed HADOOP_CLASSPATH - and adds the JARs to the MapReduce job configuration. See the source at - TableMapReduceUtil#addDependencyJars(org.apache.hadoop.mapreduce.Job) for how this is done. - - The example may not work if you are running HBase from its build directory rather - than an installed location. You may see an error like the following: - java.lang.RuntimeException: java.lang.ClassNotFoundException: org.apache.hadoop.hbase.mapreduce.RowCounter$RowCounterMapper - If this occurs, try modifying the command as follows, so that it uses the HBase JARs - from the target/ directory within the build environment. - $ HADOOP_CLASSPATH=${HBASE_HOME}/hbase-server/target/hbase-server-VERSION-SNAPSHOT.jar:`${HBASE_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-server/target/hbase-server-VERSION-SNAPSHOT.jar rowcounter usertable - - - Notice to Mapreduce users of HBase 0.96.1 and above - Some mapreduce jobs that use HBase fail to launch. The symptom is an exception similar - to the following: - -Exception in thread "main" java.lang.IllegalAccessError: class - com.google.protobuf.ZeroCopyLiteralByteString cannot access its superclass - com.google.protobuf.LiteralByteString - at java.lang.ClassLoader.defineClass1(Native Method) - at java.lang.ClassLoader.defineClass(ClassLoader.java:792) - at java.security.SecureClassLoader.defineClass(SecureClassLoader.java:142) - at java.net.URLClassLoader.defineClass(URLClassLoader.java:449) - at java.net.URLClassLoader.access$100(URLClassLoader.java:71) - at java.net.URLClassLoader$1.run(URLClassLoader.java:361) - at java.net.URLClassLoader$1.run(URLClassLoader.java:355) - at java.security.AccessController.doPrivileged(Native Method) - at java.net.URLClassLoader.findClass(URLClassLoader.java:354) - at java.lang.ClassLoader.loadClass(ClassLoader.java:424) - at java.lang.ClassLoader.loadClass(ClassLoader.java:357) - at - org.apache.hadoop.hbase.protobuf.ProtobufUtil.toScan(ProtobufUtil.java:818) - at - org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.convertScanToString(TableMapReduceUtil.java:433) - at - org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.initTableMapperJob(TableMapReduceUtil.java:186) - at - org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.initTableMapperJob(TableMapReduceUtil.java:147) - at - org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.initTableMapperJob(TableMapReduceUtil.java:270) - at - org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.initTableMapperJob(TableMapReduceUtil.java:100) -... - - This is caused by an optimization introduced in HBASE-9867 that - inadvertently introduced a classloader dependency. - This affects both jobs using the -libjars option and "fat jar," those - which package their runtime dependencies in a nested lib folder. - In order to satisfy the new classloader requirements, hbase-protocol.jar must be - included in Hadoop's classpath. See for current recommendations for resolving - classpath errors. The following is included for historical purposes. - This can be resolved system-wide by including a reference to the hbase-protocol.jar in - hadoop's lib directory, via a symlink or by copying the jar into the new location. - This can also be achieved on a per-job launch basis by including it in the - HADOOP_CLASSPATH environment variable at job submission time. When - launching jobs that package their dependencies, all three of the following job launching - commands satisfy this requirement: - -$ HADOOP_CLASSPATH=/path/to/hbase-protocol.jar:/path/to/hbase/conf hadoop jar MyJob.jar MyJobMainClass -$ HADOOP_CLASSPATH=$(hbase mapredcp):/path/to/hbase/conf hadoop jar MyJob.jar MyJobMainClass -$ HADOOP_CLASSPATH=$(hbase classpath) hadoop jar MyJob.jar MyJobMainClass - - For jars that do not package their dependencies, the following command structure is - necessary: - -$ HADOOP_CLASSPATH=$(hbase mapredcp):/etc/hbase/conf hadoop jar MyApp.jar MyJobMainClass -libjars $(hbase mapredcp | tr ':' ',') ... - - See also HBASE-10304 for - further discussion of this issue. - -
- -
- MapReduce Scan Caching - TableMapReduceUtil now restores the option to set scanner caching (the number of rows - which are cached before returning the result to the client) on the Scan object that is - passed in. This functionality was lost due to a bug in HBase 0.95 (HBASE-11558), which - is fixed for HBase 0.98.5 and 0.96.3. The priority order for choosing the scanner caching is - as follows: - - - Caching settings which are set on the scan object. - - - Caching settings which are specified via the configuration option - , which can either be set manually in - hbase-site.xml or via the helper method - TableMapReduceUtil.setScannerCaching(). - - - The default value HConstants.DEFAULT_HBASE_CLIENT_SCANNER_CACHING, which is set to - 100. - - - Optimizing the caching settings is a balance between the time the client waits for a - result and the number of sets of results the client needs to receive. If the caching setting - is too large, the client could end up waiting for a long time or the request could even time - out. If the setting is too small, the scan needs to return results in several pieces. - If you think of the scan as a shovel, a bigger cache setting is analogous to a bigger - shovel, and a smaller cache setting is equivalent to more shoveling in order to fill the - bucket. - The list of priorities mentioned above allows you to set a reasonable default, and - override it for specific operations. - See the API documentation for Scan for more details. -
- -
- Bundled HBase MapReduce Jobs - The HBase JAR also serves as a Driver for some bundled mapreduce jobs. To learn about - the bundled MapReduce jobs, run the following command. - - $ ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-server-VERSION.jar -An example program must be given as the first argument. -Valid program names are: - copytable: Export a table from local cluster to peer cluster - completebulkload: Complete a bulk data load. - export: Write table data to HDFS. - import: Import data written by Export. - importtsv: Import data in TSV format. - rowcounter: Count rows in HBase table - - Each of the valid program names are bundled MapReduce jobs. To run one of the jobs, - model your command after the following example. - $ ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-server-VERSION.jar rowcounter myTable -
- -
- HBase as a MapReduce Job Data Source and Data Sink - HBase can be used as a data source, TableInputFormat, - and data sink, TableOutputFormat - or MultiTableOutputFormat, - for MapReduce jobs. Writing MapReduce jobs that read or write HBase, it is advisable to - subclass TableMapper - and/or TableReducer. - See the do-nothing pass-through classes IdentityTableMapper - and IdentityTableReducer - for basic usage. For a more involved example, see RowCounter - or review the org.apache.hadoop.hbase.mapreduce.TestTableMapReduce unit test. - If you run MapReduce jobs that use HBase as source or sink, need to specify source and - sink table and column names in your configuration. - - When you read from HBase, the TableInputFormat requests the list of regions - from HBase and makes a map, which is either a map-per-region or - mapreduce.job.maps map, whichever is smaller. If your job only has two maps, - raise mapreduce.job.maps to a number greater than the number of regions. Maps - will run on the adjacent TaskTracker if you are running a TaskTracer and RegionServer per - node. When writing to HBase, it may make sense to avoid the Reduce step and write back into - HBase from within your map. This approach works when your job does not need the sort and - collation that MapReduce does on the map-emitted data. On insert, HBase 'sorts' so there is - no point double-sorting (and shuffling data around your MapReduce cluster) unless you need - to. If you do not need the Reduce, you myour map might emit counts of records processed for - reporting at the end of the jobj, or set the number of Reduces to zero and use - TableOutputFormat. If running the Reduce step makes sense in your case, you should typically - use multiple reducers so that load is spread across the HBase cluster. - - A new HBase partitioner, the HRegionPartitioner, - can run as many reducers the number of existing regions. The HRegionPartitioner is suitable - when your table is large and your upload will not greatly alter the number of existing - regions upon completion. Otherwise use the default partitioner. -
- -
- Writing HFiles Directly During Bulk Import - If you are importing into a new table, you can bypass the HBase API and write your - content directly to the filesystem, formatted into HBase data files (HFiles). Your import - will run faster, perhaps an order of magnitude faster. For more on how this mechanism works, - see . -
- -
- RowCounter Example - The included RowCounter - MapReduce job uses TableInputFormat and does a count of all rows in the specified - table. To run it, use the following command: - $ ./bin/hadoop jar hbase-X.X.X.jar - This will - invoke the HBase MapReduce Driver class. Select rowcounter from the choice of jobs - offered. This will print rowcouner usage advice to standard output. Specify the tablename, - column to count, and output - directory. If you have classpath errors, see . -
- -
- Map-Task Splitting -
- The Default HBase MapReduce Splitter - When TableInputFormat - is used to source an HBase table in a MapReduce job, its splitter will make a map task for - each region of the table. Thus, if there are 100 regions in the table, there will be 100 - map-tasks for the job - regardless of how many column families are selected in the - Scan. -
-
- Custom Splitters - For those interested in implementing custom splitters, see the method - getSplits in TableInputFormatBase. - That is where the logic for map-task assignment resides. -
-
-
- HBase MapReduce Examples -
- HBase MapReduce Read Example - The following is an example of using HBase as a MapReduce source in read-only manner. - Specifically, there is a Mapper instance but no Reducer, and nothing is being emitted from - the Mapper. There job would be defined as follows... - -Configuration config = HBaseConfiguration.create(); -Job job = new Job(config, "ExampleRead"); -job.setJarByClass(MyReadJob.class); // class that contains mapper - -Scan scan = new Scan(); -scan.setCaching(500); // 1 is the default in Scan, which will be bad for MapReduce jobs -scan.setCacheBlocks(false); // don't set to true for MR jobs -// set other scan attrs -... - -TableMapReduceUtil.initTableMapperJob( - tableName, // input HBase table name - scan, // Scan instance to control CF and attribute selection - MyMapper.class, // mapper - null, // mapper output key - null, // mapper output value - job); -job.setOutputFormatClass(NullOutputFormat.class); // because we aren't emitting anything from mapper - -boolean b = job.waitForCompletion(true); -if (!b) { - throw new IOException("error with job!"); -} - - ...and the mapper instance would extend TableMapper... - -public static class MyMapper extends TableMapper<Text, Text> { - - public void map(ImmutableBytesWritable row, Result value, Context context) throws InterruptedException, IOException { - // process data for the row from the Result instance. - } -} - -
-
- HBase MapReduce Read/Write Example - The following is an example of using HBase both as a source and as a sink with - MapReduce. This example will simply copy data from one table to another. - -Configuration config = HBaseConfiguration.create(); -Job job = new Job(config,"ExampleReadWrite"); -job.setJarByClass(MyReadWriteJob.class); // class that contains mapper - -Scan scan = new Scan(); -scan.setCaching(500); // 1 is the default in Scan, which will be bad for MapReduce jobs -scan.setCacheBlocks(false); // don't set to true for MR jobs -// set other scan attrs - -TableMapReduceUtil.initTableMapperJob( - sourceTable, // input table - scan, // Scan instance to control CF and attribute selection - MyMapper.class, // mapper class - null, // mapper output key - null, // mapper output value - job); -TableMapReduceUtil.initTableReducerJob( - targetTable, // output table - null, // reducer class - job); -job.setNumReduceTasks(0); - -boolean b = job.waitForCompletion(true); -if (!b) { - throw new IOException("error with job!"); -} - - An explanation is required of what TableMapReduceUtil is doing, - especially with the reducer. TableOutputFormat - is being used as the outputFormat class, and several parameters are being set on the - config (e.g., TableOutputFormat.OUTPUT_TABLE), as well as setting the reducer output key - to ImmutableBytesWritable and reducer value to - Writable. These could be set by the programmer on the job and - conf, but TableMapReduceUtil tries to make things easier. - The following is the example mapper, which will create a Put - and matching the input Result and emit it. Note: this is what the - CopyTable utility does. - -public static class MyMapper extends TableMapper<ImmutableBytesWritable, Put> { - - public void map(ImmutableBytesWritable row, Result value, Context context) throws IOException, InterruptedException { - // this example is just copying the data from the source table... - context.write(row, resultToPut(row,value)); - } - - private static Put resultToPut(ImmutableBytesWritable key, Result result) throws IOException { - Put put = new Put(key.get()); - for (KeyValue kv : result.raw()) { - put.add(kv); - } - return put; - } -} - - There isn't actually a reducer step, so TableOutputFormat takes - care of sending the Put to the target table. - This is just an example, developers could choose not to use - TableOutputFormat and connect to the target table themselves. - -
-
- HBase MapReduce Read/Write Example With Multi-Table Output - TODO: example for MultiTableOutputFormat. -
-
- HBase MapReduce Summary to HBase Example - The following example uses HBase as a MapReduce source and sink with a summarization - step. This example will count the number of distinct instances of a value in a table and - write those summarized counts in another table. - -Configuration config = HBaseConfiguration.create(); -Job job = new Job(config,"ExampleSummary"); -job.setJarByClass(MySummaryJob.class); // class that contains mapper and reducer - -Scan scan = new Scan(); -scan.setCaching(500); // 1 is the default in Scan, which will be bad for MapReduce jobs -scan.setCacheBlocks(false); // don't set to true for MR jobs -// set other scan attrs - -TableMapReduceUtil.initTableMapperJob( - sourceTable, // input table - scan, // Scan instance to control CF and attribute selection - MyMapper.class, // mapper class - Text.class, // mapper output key - IntWritable.class, // mapper output value - job); -TableMapReduceUtil.initTableReducerJob( - targetTable, // output table - MyTableReducer.class, // reducer class - job); -job.setNumReduceTasks(1); // at least one, adjust as required - -boolean b = job.waitForCompletion(true); -if (!b) { - throw new IOException("error with job!"); -} - - In this example mapper a column with a String-value is chosen as the value to summarize - upon. This value is used as the key to emit from the mapper, and an - IntWritable represents an instance counter. - -public static class MyMapper extends TableMapper<Text, IntWritable> { - public static final byte[] CF = "cf".getBytes(); - public static final byte[] ATTR1 = "attr1".getBytes(); - - private final IntWritable ONE = new IntWritable(1); - private Text text = new Text(); - - public void map(ImmutableBytesWritable row, Result value, Context context) throws IOException, InterruptedException { - String val = new String(value.getValue(CF, ATTR1)); - text.set(val); // we can only emit Writables... - - context.write(text, ONE); - } -} - - In the reducer, the "ones" are counted (just like any other MR example that does this), - and then emits a Put. - -public static class MyTableReducer extends TableReducer<Text, IntWritable, ImmutableBytesWritable> { - public static final byte[] CF = "cf".getBytes(); - public static final byte[] COUNT = "count".getBytes(); - - public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { - int i = 0; - for (IntWritable val : values) { - i += val.get(); - } - Put put = new Put(Bytes.toBytes(key.toString())); - put.add(CF, COUNT, Bytes.toBytes(i)); - - context.write(null, put); - } -} - - -
-
- HBase MapReduce Summary to File Example - This very similar to the summary example above, with exception that this is using - HBase as a MapReduce source but HDFS as the sink. The differences are in the job setup and - in the reducer. The mapper remains the same. - -Configuration config = HBaseConfiguration.create(); -Job job = new Job(config,"ExampleSummaryToFile"); -job.setJarByClass(MySummaryFileJob.class); // class that contains mapper and reducer - -Scan scan = new Scan(); -scan.setCaching(500); // 1 is the default in Scan, which will be bad for MapReduce jobs -scan.setCacheBlocks(false); // don't set to true for MR jobs -// set other scan attrs - -TableMapReduceUtil.initTableMapperJob( - sourceTable, // input table - scan, // Scan instance to control CF and attribute selection - MyMapper.class, // mapper class - Text.class, // mapper output key - IntWritable.class, // mapper output value - job); -job.setReducerClass(MyReducer.class); // reducer class -job.setNumReduceTasks(1); // at least one, adjust as required -FileOutputFormat.setOutputPath(job, new Path("/tmp/mr/mySummaryFile")); // adjust directories as required - -boolean b = job.waitForCompletion(true); -if (!b) { - throw new IOException("error with job!"); -} - - As stated above, the previous Mapper can run unchanged with this example. As for the - Reducer, it is a "generic" Reducer instead of extending TableMapper and emitting - Puts. - - public static class MyReducer extends Reducer<Text, IntWritable, Text, IntWritable> { - - public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { - int i = 0; - for (IntWritable val : values) { - i += val.get(); - } - context.write(key, new IntWritable(i)); - } -} - -
-
- HBase MapReduce Summary to HBase Without Reducer - It is also possible to perform summaries without a reducer - if you use HBase as the - reducer. - An HBase target table would need to exist for the job summary. The Table method - incrementColumnValue would be used to atomically increment values. From a - performance perspective, it might make sense to keep a Map of values with their values to - be incremeneted for each map-task, and make one update per key at during the - cleanup method of the mapper. However, your milage may vary depending on the - number of rows to be processed and unique keys. - In the end, the summary results are in HBase. -
-
- HBase MapReduce Summary to RDBMS - Sometimes it is more appropriate to generate summaries to an RDBMS. For these cases, - it is possible to generate summaries directly to an RDBMS via a custom reducer. The - setup method can connect to an RDBMS (the connection information can be - passed via custom parameters in the context) and the cleanup method can close the - connection. - It is critical to understand that number of reducers for the job affects the - summarization implementation, and you'll have to design this into your reducer. - Specifically, whether it is designed to run as a singleton (one reducer) or multiple - reducers. Neither is right or wrong, it depends on your use-case. Recognize that the more - reducers that are assigned to the job, the more simultaneous connections to the RDBMS will - be created - this will scale, but only to a point. - - public static class MyRdbmsReducer extends Reducer<Text, IntWritable, Text, IntWritable> { - - private Connection c = null; - - public void setup(Context context) { - // create DB connection... - } - - public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { - // do summarization - // in this example the keys are Text, but this is just an example - } - - public void cleanup(Context context) { - // close db connection - } - -} - - In the end, the summary results are written to your RDBMS table/s. -
- -
- -
- Accessing Other HBase Tables in a MapReduce Job - Although the framework currently allows one HBase table as input to a MapReduce job, - other HBase tables can be accessed as lookup tables, etc., in a MapReduce job via creating - an Table instance in the setup method of the Mapper. - public class MyMapper extends TableMapper<Text, LongWritable> { - private Table myOtherTable; - - public void setup(Context context) { - // In here create a Connection to the cluster and save it or use the Connection - // from the existing table - myOtherTable = connection.getTable("myOtherTable"); - } - - public void map(ImmutableBytesWritable row, Result value, Context context) throws IOException, InterruptedException { - // process Result... - // use 'myOtherTable' for lookups - } - - - -
-
- Speculative Execution - It is generally advisable to turn off speculative execution for MapReduce jobs that use - HBase as a source. This can either be done on a per-Job basis through properties, on on the - entire cluster. Especially for longer running jobs, speculative execution will create - duplicate map-tasks which will double-write your data to HBase; this is probably not what - you want. - See for more information. -
- - diff --git a/src/main/docbkx/ops_mgt.xml b/src/main/docbkx/ops_mgt.xml deleted file mode 100644 index 20788bf8bab..00000000000 --- a/src/main/docbkx/ops_mgt.xml +++ /dev/null @@ -1,2648 +0,0 @@ - - - - Apache HBase Operational Management - This chapter will cover operational tools and practices required of a running Apache HBase - cluster. The subject of operations is related to the topics of , , and but is a distinct topic in itself. - -
- HBase Tools and Utilities - - HBase provides several tools for administration, analysis, and debugging of your cluster. - The entry-point to most of these tools is the bin/hbase command, though - some tools are available in the dev-support/ directory. - To see usage instructions for bin/hbase command, run it with no - arguments, or with the argument. These are the usage instructions for - HBase 0.98.x. Some commands, such as version, pe, - ltt, clean, are not available in previous - versions. - -$ bin/hbase -] []]]> -Options: - --config DIR Configuration direction to use. Default: ./conf - --hosts HOSTS Override the list in 'regionservers' file - -Commands: -Some commands take arguments. Pass no args or -h for usage. - shell Run the HBase shell - hbck Run the hbase 'fsck' tool - hlog Write-ahead-log analyzer - hfile Store file analyzer - zkcli Run the ZooKeeper shell - upgrade Upgrade hbase - master Run an HBase HMaster node - regionserver Run an HBase HRegionServer node - zookeeper Run a Zookeeper server - rest Run an HBase REST server - thrift Run the HBase Thrift server - thrift2 Run the HBase Thrift2 server - clean Run the HBase clean up script - classpath Dump hbase CLASSPATH - mapredcp Dump CLASSPATH entries required by mapreduce - pe Run PerformanceEvaluation - ltt Run LoadTestTool - version Print the version - CLASSNAME Run the class named CLASSNAME - - Some of the tools and utilities below are Java classes which are passed directly to the - bin/hbase command, as referred to in the last line of the usage - instructions. Others, such as hbase shell (), - hbase upgrade (), and hbase - thrift (), are documented elsewhere in this guide. -
- Canary - There is a Canary class can help users to canary-test the HBase cluster status, with - every column-family for every regions or regionservers granularity. To see the usage, use - the --help parameter. - $ ${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.tool.Canary -help - -Usage: bin/hbase org.apache.hadoop.hbase.tool.Canary [opts] [table1 [table2]...] | [regionserver1 [regionserver2]..] - where [opts] are: - -help Show this help and exit. - -regionserver replace the table argument to regionserver, - which means to enable regionserver mode - -daemon Continuous check at defined intervals. - -interval <N> Interval between checks (sec) - -e Use region/regionserver as regular expression - which means the region/regionserver is regular expression pattern - -f <B> stop whole program if first error occurs, default is true - -t <N> timeout for a check, default is 600000 (milliseconds) - This tool will return non zero error codes to user for collaborating with other - monitoring tools, such as Nagios. The error code definitions are: - private static final int USAGE_EXIT_CODE = 1; -private static final int INIT_ERROR_EXIT_CODE = 2; -private static final int TIMEOUT_ERROR_EXIT_CODE = 3; -private static final int ERROR_EXIT_CODE = 4; - Here are some examples based on the following given case. There are two HTable called - test-01 and test-02, they have two column family cf1 and cf2 respectively, and deployed on - the 3 regionservers. see following table. - - - - - - - - - RegionServer - test-01 - test-02 - - - - - rs1 - r1 - r2 - - - rs2 - r2 - - - - rs3 - r2 - r1 - - - - - Following are some examples based on the previous given case. -
- Canary test for every column family (store) of every region of every table - $ ${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.tool.Canary - -3/12/09 03:26:32 INFO tool.Canary: read from region test-01,,1386230156732.0e3c7d77ffb6361ea1b996ac1042ca9a. column family cf1 in 2ms -13/12/09 03:26:32 INFO tool.Canary: read from region test-01,,1386230156732.0e3c7d77ffb6361ea1b996ac1042ca9a. column family cf2 in 2ms -13/12/09 03:26:32 INFO tool.Canary: read from region test-01,0004883,1386230156732.87b55e03dfeade00f441125159f8ca87. column family cf1 in 4ms -13/12/09 03:26:32 INFO tool.Canary: read from region test-01,0004883,1386230156732.87b55e03dfeade00f441125159f8ca87. column family cf2 in 1ms -... -13/12/09 03:26:32 INFO tool.Canary: read from region test-02,,1386559511167.aa2951a86289281beee480f107bb36ee. column family cf1 in 5ms -13/12/09 03:26:32 INFO tool.Canary: read from region test-02,,1386559511167.aa2951a86289281beee480f107bb36ee. column family cf2 in 3ms -13/12/09 03:26:32 INFO tool.Canary: read from region test-02,0004883,1386559511167.cbda32d5e2e276520712d84eaaa29d84. column family cf1 in 31ms -13/12/09 03:26:32 INFO tool.Canary: read from region test-02,0004883,1386559511167.cbda32d5e2e276520712d84eaaa29d84. column family cf2 in 8ms - - So you can see, table test-01 has two regions and two column families, so the Canary - tool will pick 4 small piece of data from 4 (2 region * 2 store) different stores. This is - a default behavior of the this tool does. -
- -
- Canary test for every column family (store) of every region of specific - table(s) - You can also test one or more specific tables. - $ ${HBASE_HOME}/bin/hbase orghapache.hadoop.hbase.tool.Canary test-01 test-02 -
- -
- Canary test with regionserver granularity - This will pick one small piece of data from each regionserver, and can also put your - resionserver name as input options for canary-test specific regionservers. - $ ${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.tool.Canary -regionserver - -13/12/09 06:05:17 INFO tool.Canary: Read from table:test-01 on region server:rs2 in 72ms -13/12/09 06:05:17 INFO tool.Canary: Read from table:test-02 on region server:rs3 in 34ms -13/12/09 06:05:17 INFO tool.Canary: Read from table:test-01 on region server:rs1 in 56ms -
-
- Canary test with regular expression pattern - This will test both table test-01 and test-02. - $ ${HBASE_HOME}/bin/hbase orghapache.hadoop.hbase.tool.Canary -e test-0[1-2] -
- -
- Run canary test as daemon mode - Run repeatedly with interval defined in option -interval whose default value is 6 - seconds. This daemon will stop itself and return non-zero error code if any error occurs, - due to the default value of option -f is true. - $ ${HBASE_HOME}/bin/hbase orghapache.hadoop.hbase.tool.Canary -daemon - Run repeatedly with internal 5 seconds and will not stop itself even error occurs in - the test. - $ ${HBASE_HOME}/bin/hbase orghapache.hadoop.hbase.tool.Canary -daemon -interval 50000 -f false -
- -
- Force timeout if canary test stuck - In some cases, we suffered the request stucked on the regionserver and not response - back to the client. The regionserver in problem, would also not indicated to be dead by - Master, which would bring the clients hung. So we provide the timeout option to kill the - canary test forcefully and return non-zero error code as well. This run sets the timeout - value to 60 seconds, the default value is 600 seconds. - $ ${HBASE_HOME}/bin/hbase orghapache.hadoop.hbase.tool.Canary -t 600000 -
- -
- Running Canary in a Kerberos-enabled Cluster - To run Canary in a Kerberos-enabled cluster, configure the following two properties in - hbase-site.xml: - - - hbase.client.keytab.file - - - hbase.client.kerberos.principal - - - Kerberos credentials are refreshed every 30 seconds when Canary runs in daemon - mode. - To configure the DNS interface for the client, configure the following optional - properties in hbase-site.xml. - - - hbase.client.dns.interface - - - hbase.client.dns.nameserver - - - - Canary in a Kerberos-Enabled Cluster - This example shows each of the properties with valid values. - - hbase.client.kerberos.principal - hbase/_HOST@YOUR-REALM.COM - - - hbase.client.keytab.file - /etc/hbase/conf/keytab.krb5 - - -property> - hbase.client.dns.interface - default - - - hbase.client.dns.nameserver - default - - ]]> - -
-
- -
- Health Checker - You can configure HBase to run a script on a period and if it fails N times - (configurable), have the server exit. See HBASE-7351 Periodic health check script for configurations and - detail. -
- -
- Driver - Several frequently-accessed utilities are provided as Driver classes, and executed by - the bin/hbase command. These utilities represent MapReduce jobs which - run on your cluster. They are run in the following way, replacing - UtilityName with the utility you want to run. This command - assumes you have set the environment variable HBASE_HOME to the directory - where HBase is unpacked on your server. - -${HBASE_HOME}/bin/hbase org.apache.hadoop.hbase.mapreduce.UtilityName - - The following utilities are available: - - - LoadIncrementalHFiles - Complete a bulk data load. - - - CopyTable - Export a table from the local cluster to a peer cluster. - - - Export - Write table data to HDFS. - - - Import - Import data written by a previous Export operation. - - - ImportTsv - Import data in TSV format. - - - RowCounter - Count rows in an HBase table. - - - replication.VerifyReplication - Compare the data from tables in two different clusters. WARNING: It - doesn't work for incrementColumnValues'd cells since the timestamp is changed. Note that - this command is in a different package than the others. - - - Each command except RowCounter accepts a single - --help argument to print usage instructions. -
-
- HBase <application>hbck</application> - An fsck for your HBase install - To run hbck against your HBase cluster run $ - ./bin/hbase hbck At the end of the command's output it prints - OK or INCONSISTENCY. If your cluster reports - inconsistencies, pass -details to see more detail emitted. If - inconsistencies, run hbck a few times because the inconsistency may be - transient (e.g. cluster is starting up or a region is splitting). Passing - -fix may correct the inconsistency (This latter is an experimental - feature). - For more information, see . -
-
- HFile Tool - See . -
-
- WAL Tools - -
- <classname>FSHLog</classname> tool - - The main method on FSHLog offers manual split and dump - facilities. Pass it WALs or the product of a split, the content of the - recovered.edits. directory. - - You can get a textual dump of a WAL file content by doing the following: - $ ./bin/hbase org.apache.hadoop.hbase.regionserver.wal.FSHLog --dump hdfs://example.org:8020/hbase/.logs/example.org,60020,1283516293161/10.10.21.10%3A60020.1283973724012 - The return code will be non-zero if issues with the file so you can test wholesomeness - of file by redirecting STDOUT to /dev/null and testing the - program return. - - Similarly you can force a split of a log file directory by doing: - $ ./bin/hbase org.apache.hadoop.hbase.regionserver.wal.FSHLog --split hdfs://example.org:8020/hbase/.logs/example.org,60020,1283516293161/ - -
- WAL Pretty Printer - The WAL Pretty Printer is a tool with configurable options to - print the contents of a WAL. You can invoke it via the hbase cli with the 'wal' command. - - $ ./bin/hbase wal hdfs://example.org:8020/hbase/.logs/example.org,60020,1283516293161/10.10.21.10%3A60020.1283973724012 - - WAL Printing in older versions of HBase - Prior to version 2.0, the WAL Pretty Printer was called the - HLogPrettyPrinter, after an internal name for HBase's write - ahead log. In those versions, you can pring the contents of a WAL using the same - configuration as above, but with the 'hlog' command. - - $ ./bin/hbase hlog hdfs://example.org:8020/hbase/.logs/example.org,60020,1283516293161/10.10.21.10%3A60020.1283973724012 - -
- -
-
-
- Compression Tool - See . -
-
- CopyTable - CopyTable is a utility that can copy part or of all of a table, either to the same - cluster or another cluster. The target table must first exist. The usage is as - follows: - - -$ ./bin/hbase org.apache.hadoop.hbase.mapreduce.CopyTable --help -/bin/hbase org.apache.hadoop.hbase.mapreduce.CopyTable --help -Usage: CopyTable [general options] [--starttime=X] [--endtime=Y] [--new.name=NEW] [--peer.adr=ADR] <tablename> - -Options: - rs.class hbase.regionserver.class of the peer cluster, - specify if different from current cluster - rs.impl hbase.regionserver.impl of the peer cluster, - startrow the start row - stoprow the stop row - starttime beginning of the time range (unixtime in millis) - without endtime means from starttime to forever - endtime end of the time range. Ignored if no starttime specified. - versions number of cell versions to copy - new.name new table's name - peer.adr Address of the peer cluster given in the format - hbase.zookeeer.quorum:hbase.zookeeper.client.port:zookeeper.znode.parent - families comma-separated list of families to copy - To copy from cf1 to cf2, give sourceCfName:destCfName. - To keep the same name, just give "cfName" - all.cells also copy delete markers and deleted cells - -Args: - tablename Name of the table to copy - -Examples: - To copy 'TestTable' to a cluster that uses replication for a 1 hour window: - $ bin/hbase org.apache.hadoop.hbase.mapreduce.CopyTable --starttime=1265875194289 --endtime=1265878794289 --peer.adr=server1,server2,server3:2181:/hbase --families=myOldCf:myNewCf,cf2,cf3 TestTable - -For performance consider the following general options: - It is recommended that you set the following to >=100. A higher value uses more memory but - decreases the round trip time to the server and may increase performance. - -Dhbase.client.scanner.caching=100 - The following should always be set to false, to prevent writing data twice, which may produce - inaccurate results. - -Dmapred.map.tasks.speculative.execution=false - - - Scanner Caching - Caching for the input Scan is configured via hbase.client.scanner.caching - in the job configuration. - - - Versions - By default, CopyTable utility only copies the latest version of row cells unless - --versions=n is explicitly specified in the command. - - See Jonathan Hsieh's Online - HBase Backups with CopyTable blog post for more on CopyTable. - -
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- Export - Export is a utility that will dump the contents of table to HDFS in a sequence file. - Invoke via: - $ bin/hbase org.apache.hadoop.hbase.mapreduce.Export <tablename> <outputdir> [<versions> [<starttime> [<endtime>]]] - - - Note: caching for the input Scan is configured via - hbase.client.scanner.caching in the job configuration. -
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- Import - Import is a utility that will load data that has been exported back into HBase. Invoke - via: - $ bin/hbase org.apache.hadoop.hbase.mapreduce.Import <tablename> <inputdir> - - To import 0.94 exported files in a 0.96 cluster or onwards, you need to set system - property "hbase.import.version" when running the import command as below: - $ bin/hbase -Dhbase.import.version=0.94 org.apache.hadoop.hbase.mapreduce.Import <tablename> <inputdir> - -
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- ImportTsv - ImportTsv is a utility that will load data in TSV format into HBase. It has two distinct - usages: loading data from TSV format in HDFS into HBase via Puts, and preparing StoreFiles - to be loaded via the completebulkload. - To load data via Puts (i.e., non-bulk loading): - $ bin/hbase org.apache.hadoop.hbase.mapreduce.ImportTsv -Dimporttsv.columns=a,b,c <tablename> <hdfs-inputdir> - - - To generate StoreFiles for bulk-loading: - $ bin/hbase org.apache.hadoop.hbase.mapreduce.ImportTsv -Dimporttsv.columns=a,b,c -Dimporttsv.bulk.output=hdfs://storefile-outputdir <tablename> <hdfs-data-inputdir> - - These generated StoreFiles can be loaded into HBase via . -
- ImportTsv Options - Running ImportTsv with no arguments prints brief usage - information: - -Usage: importtsv -Dimporttsv.columns=a,b,c <tablename> <inputdir> - -Imports the given input directory of TSV data into the specified table. - -The column names of the TSV data must be specified using the -Dimporttsv.columns -option. This option takes the form of comma-separated column names, where each -column name is either a simple column family, or a columnfamily:qualifier. The special -column name HBASE_ROW_KEY is used to designate that this column should be used -as the row key for each imported record. You must specify exactly one column -to be the row key, and you must specify a column name for every column that exists in the -input data. - -By default importtsv will load data directly into HBase. To instead generate -HFiles of data to prepare for a bulk data load, pass the option: - -Dimporttsv.bulk.output=/path/for/output - Note: the target table will be created with default column family descriptors if it does not already exist. - -Other options that may be specified with -D include: - -Dimporttsv.skip.bad.lines=false - fail if encountering an invalid line - '-Dimporttsv.separator=|' - eg separate on pipes instead of tabs - -Dimporttsv.timestamp=currentTimeAsLong - use the specified timestamp for the import - -Dimporttsv.mapper.class=my.Mapper - A user-defined Mapper to use instead of org.apache.hadoop.hbase.mapreduce.TsvImporterMapper - -
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- ImportTsv Example - For example, assume that we are loading data into a table called 'datatsv' with a - ColumnFamily called 'd' with two columns "c1" and "c2". - Assume that an input file exists as follows: - -row1 c1 c2 -row2 c1 c2 -row3 c1 c2 -row4 c1 c2 -row5 c1 c2 -row6 c1 c2 -row7 c1 c2 -row8 c1 c2 -row9 c1 c2 -row10 c1 c2 - - - For ImportTsv to use this imput file, the command line needs to look like this: - - HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-server-VERSION.jar importtsv -Dimporttsv.columns=HBASE_ROW_KEY,d:c1,d:c2 -Dimporttsv.bulk.output=hdfs://storefileoutput datatsv hdfs://inputfile - - ... and in this example the first column is the rowkey, which is why the - HBASE_ROW_KEY is used. The second and third columns in the file will be imported as "d:c1" - and "d:c2", respectively. -
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- ImportTsv Warning - If you have preparing a lot of data for bulk loading, make sure the target HBase table - is pre-split appropriately. -
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- See Also - For more information about bulk-loading HFiles into HBase, see -
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- -
- CompleteBulkLoad - The completebulkload utility will move generated StoreFiles into an HBase - table. This utility is often used in conjunction with output from . - There are two ways to invoke this utility, with explicit classname and via the - driver: - $ bin/hbase org.apache.hadoop.hbase.mapreduce.LoadIncrementalHFiles <hdfs://storefileoutput> <tablename> - - .. and via the Driver.. - HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-server-VERSION.jar completebulkload <hdfs://storefileoutput> <tablename> - -
- CompleteBulkLoad Warning - Data generated via MapReduce is often created with file permissions that are not - compatible with the running HBase process. Assuming you're running HDFS with permissions - enabled, those permissions will need to be updated before you run CompleteBulkLoad. - For more information about bulk-loading HFiles into HBase, see . -
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- WALPlayer - WALPlayer is a utility to replay WAL files into HBase. - The WAL can be replayed for a set of tables or all tables, and a timerange can be - provided (in milliseconds). The WAL is filtered to this set of tables. The output can - optionally be mapped to another set of tables. - WALPlayer can also generate HFiles for later bulk importing, in that case only a single - table and no mapping can be specified. - Invoke via: - $ bin/hbase org.apache.hadoop.hbase.mapreduce.WALPlayer [options] <wal inputdir> <tables> [<tableMappings>]> - - For example: - $ bin/hbase org.apache.hadoop.hbase.mapreduce.WALPlayer /backuplogdir oldTable1,oldTable2 newTable1,newTable2 - - WALPlayer, by default, runs as a mapreduce job. To NOT run WALPlayer as a mapreduce job - on your cluster, force it to run all in the local process by adding the flags - -Dmapreduce.jobtracker.address=local on the command line. -
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- RowCounter and CellCounter - RowCounter - is a mapreduce job to count all the rows of a table. This is a good utility to use as a - sanity check to ensure that HBase can read all the blocks of a table if there are any - concerns of metadata inconsistency. It will run the mapreduce all in a single process but it - will run faster if you have a MapReduce cluster in place for it to exploit. - $ bin/hbase org.apache.hadoop.hbase.mapreduce.RowCounter <tablename> [<column1> <column2>...] - - Note: caching for the input Scan is configured via - hbase.client.scanner.caching in the job configuration. - HBase ships another diagnostic mapreduce job called CellCounter. - Like RowCounter, it gathers more fine-grained statistics about your table. The statistics - gathered by RowCounter are more fine-grained and include: - - - Total number of rows in the table. - - - Total number of CFs across all rows. - - - Total qualifiers across all rows. - - - Total occurrence of each CF. - - - Total occurrence of each qualifier. - - - Total number of versions of each qualifier. - - - The program allows you to limit the scope of the run. Provide a row regex or prefix to - limit the rows to analyze. Use hbase.mapreduce.scan.column.family to specify - scanning a single column family. - $ bin/hbase org.apache.hadoop.hbase.mapreduce.CellCounter <tablename> <outputDir> [regex or prefix] - Note: just like RowCounter, caching for the input Scan is configured via - hbase.client.scanner.caching in the job configuration. -
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- mlockall - It is possible to optionally pin your servers in physical memory making them less likely - to be swapped out in oversubscribed environments by having the servers call mlockall on startup. See HBASE-4391 Add ability to - start RS as root and call mlockall for how to build the optional library and have - it run on startup. -
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- Offline Compaction Tool - See the usage for the Compaction - Tool. Run it like this ./bin/hbase - org.apache.hadoop.hbase.regionserver.CompactionTool - -
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- <command>hbase clean</command> - The hbase clean command cleans HBase data from ZooKeeper, HDFS, or - both. It is appropriate to use for testing. Run it with no options for usage instructions. - The hbase clean command was introduced in HBase 0.98. - -$ bin/hbase clean -Usage: hbase clean (--cleanZk|--cleanHdfs|--cleanAll) -Options: - --cleanZk cleans hbase related data from zookeeper. - --cleanHdfs cleans hbase related data from hdfs. - --cleanAll cleans hbase related data from both zookeeper and hdfs. - -
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- <command>hbase pe</command> - The hbase pe command is a shortcut provided to run the - org.apache.hadoop.hbase.PerformanceEvaluation tool, which is used for - testing. The hbase pe command was introduced in HBase 0.98.4. - The PerformanceEvaluation tool accepts many different options and commands. For usage - instructions, run the command with no options. - To run PerformanceEvaluation prior to HBase 0.98.4, issue the command - hbase org.apache.hadoop.hbase.PerformanceEvaluation. - The PerformanceEvaluation tool has received many updates in recent HBase releases, - including support for namespaces, support for tags, cell-level ACLs and visibility labels, - multiget support for RPC calls, increased sampling sizes, an option to randomly sleep during - testing, and ability to "warm up" the cluster before testing starts. -
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- <command>hbase ltt</command> - The hbase ltt command is a shortcut provided to run the - org.apache.hadoop.hbase.util.LoadTestTool utility, which is used for - testing. The hbase ltt command was introduced in HBase 0.98.4. - You must specify either or as the - first option. For general usage instructions, pass the option. - To run LoadTestTool prior to HBase 0.98.4, issue the command hbase - org.apache.hadoop.hbase.util.LoadTestTool. - The LoadTestTool has received many updates in recent HBase releases, including support - for namespaces, support for tags, cell-level ACLS and visibility labels, testing - security-related features, ability to specify the number of regions per server, tests for - multi-get RPC calls, and tests relating to replication. -
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- - -
- Region Management -
- Major Compaction - Major compactions can be requested via the HBase shell or HBaseAdmin.majorCompact. - Note: major compactions do NOT do region merges. See for more information about compactions. -
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- Merge - Merge is a utility that can merge adjoining regions in the same table (see - org.apache.hadoop.hbase.util.Merge). - $ bin/hbase org.apache.hadoop.hbase.util.Merge <tablename> <region1> <region2> - - If you feel you have too many regions and want to consolidate them, Merge is the utility - you need. Merge must run be done when the cluster is down. See the O'Reilly HBase - Book for an example of usage. - You will need to pass 3 parameters to this application. The first one is the table name. - The second one is the fully qualified name of the first region to merge, like - "table_name,\x0A,1342956111995.7cef47f192318ba7ccc75b1bbf27a82b.". The third one is the - fully qualified name for the second region to merge. - Additionally, there is a Ruby script attached to HBASE-1621 for region - merging. -
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- Node Management -
- Node Decommission - You can stop an individual RegionServer by running the following script in the HBase - directory on the particular node: - $ ./bin/hbase-daemon.sh stop regionserver - The RegionServer will first close all regions and then shut itself down. On shutdown, - the RegionServer's ephemeral node in ZooKeeper will expire. The master will notice the - RegionServer gone and will treat it as a 'crashed' server; it will reassign the nodes the - RegionServer was carrying. - - Disable the Load Balancer before Decommissioning a node - If the load balancer runs while a node is shutting down, then there could be - contention between the Load Balancer and the Master's recovery of the just decommissioned - RegionServer. Avoid any problems by disabling the balancer first. See below. - - - Kill Node Tool - In hbase-2.0, in the bin directory, we added a script named - considerAsDead.sh that can be used to kill a regionserver. - Hardware issues could be detected by specialized monitoring tools before the - zookeeper timeout has expired. considerAsDead.sh is a - simple function to mark a RegionServer as dead. It deletes all the znodes - of the server, starting the recovery process. Plug in the script into - your monitoring/fault detection tools to initiate faster failover. Be - careful how you use this disruptive tool. Copy the script if you need to - make use of it in a version of hbase previous to hbase-2.0. - - - A downside to the above stop of a RegionServer is that regions could be offline for a - good period of time. Regions are closed in order. If many regions on the server, the first - region to close may not be back online until all regions close and after the master notices - the RegionServer's znode gone. In Apache HBase 0.90.2, we added facility for having a node - gradually shed its load and then shutdown itself down. Apache HBase 0.90.2 added the - graceful_stop.sh script. Here is its usage: - $ ./bin/graceful_stop.sh -Usage: graceful_stop.sh [--config &conf-dir>] [--restart] [--reload] [--thrift] [--rest] &hostname> - thrift If we should stop/start thrift before/after the hbase stop/start - rest If we should stop/start rest before/after the hbase stop/start - restart If we should restart after graceful stop - reload Move offloaded regions back on to the stopped server - debug Move offloaded regions back on to the stopped server - hostname Hostname of server we are to stop - To decommission a loaded RegionServer, run the following: $ - ./bin/graceful_stop.sh HOSTNAME where HOSTNAME is the host - carrying the RegionServer you would decommission. - - On <varname>HOSTNAME</varname> - The HOSTNAME passed to graceful_stop.sh must - match the hostname that hbase is using to identify RegionServers. Check the list of - RegionServers in the master UI for how HBase is referring to servers. Its usually hostname - but can also be FQDN. Whatever HBase is using, this is what you should pass the - graceful_stop.sh decommission script. If you pass IPs, the script - is not yet smart enough to make a hostname (or FQDN) of it and so it will fail when it - checks if server is currently running; the graceful unloading of regions will not run. - - - The graceful_stop.sh script will move the regions off the - decommissioned RegionServer one at a time to minimize region churn. It will verify the - region deployed in the new location before it will moves the next region and so on until the - decommissioned server is carrying zero regions. At this point, the - graceful_stop.sh tells the RegionServer stop. The - master will at this point notice the RegionServer gone but all regions will have already - been redeployed and because the RegionServer went down cleanly, there will be no WAL logs to - split. - - Load Balancer - It is assumed that the Region Load Balancer is disabled while the - graceful_stop script runs (otherwise the balancer and the - decommission script will end up fighting over region deployments). Use the shell to - disable the balancer: - hbase(main):001:0> balance_switch false -true -0 row(s) in 0.3590 seconds - This turns the balancer OFF. To reenable, do: - hbase(main):001:0> balance_switch true -false -0 row(s) in 0.3590 seconds - The graceful_stop will check the balancer and if enabled, will turn - it off before it goes to work. If it exits prematurely because of error, it will not have - reset the balancer. Hence, it is better to manage the balancer apart from - graceful_stop reenabling it after you are done w/ graceful_stop. - - -
- Decommissioning several Regions Servers concurrently - If you have a large cluster, you may want to decommission more than one machine at a - time by gracefully stopping mutiple RegionServers concurrently. To gracefully drain - multiple regionservers at the same time, RegionServers can be put into a "draining" state. - This is done by marking a RegionServer as a draining node by creating an entry in - ZooKeeper under the hbase_root/draining znode. This znode has format - name,port,startcode just like the regionserver entries under - hbase_root/rs znode. - Without this facility, decommissioning mulitple nodes may be non-optimal because - regions that are being drained from one region server may be moved to other regionservers - that are also draining. Marking RegionServers to be in the draining state prevents this - from happening. See this blog - post for more details. -
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- Bad or Failing Disk - It is good having set if you have a decent number of - disks per machine for the case where a disk plain dies. But usually disks do the "John - Wayne" -- i.e. take a while to go down spewing errors in dmesg -- or - for some reason, run much slower than their companions. In this case you want to - decommission the disk. You have two options. You can decommission - the datanode or, less disruptive in that only the bad disks data will be - rereplicated, can stop the datanode, unmount the bad volume (You can't umount a volume - while the datanode is using it), and then restart the datanode (presuming you have set - dfs.datanode.failed.volumes.tolerated > 0). The regionserver will throw some errors in its - logs as it recalibrates where to get its data from -- it will likely roll its WAL log too - -- but in general but for some latency spikes, it should keep on chugging. - - Short Circuit Reads - If you are doing short-circuit reads, you will have to move the regions off the - regionserver before you stop the datanode; when short-circuiting reading, though chmod'd - so regionserver cannot have access, because it already has the files open, it will be - able to keep reading the file blocks from the bad disk even though the datanode is down. - Move the regions back after you restart the datanode. - -
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- Rolling Restart - - Some cluster configuration changes require either the entire cluster, or the - RegionServers, to be restarted in order to pick up the changes. In addition, rolling - restarts are supported for upgrading to a minor or maintenance release, and to a major - release if at all possible. See the release notes for release you want to upgrade to, to - find out about limitations to the ability to perform a rolling upgrade. - There are multiple ways to restart your cluster nodes, depending on your situation. - These methods are detailed below. -
- Using the <command>rolling-restart.sh</command> Script - - HBase ships with a script, bin/rolling-restart.sh, that allows - you to perform rolling restarts on the entire cluster, the master only, or the - RegionServers only. The script is provided as a template for your own script, and is not - explicitly tested. It requires password-less SSH login to be configured and assumes that - you have deployed using a tarball. The script requires you to set some environment - variables before running it. Examine the script and modify it to suit your needs. - - <filename>rolling-restart.sh</filename> General Usage - -$ ./bin/rolling-restart.sh --help] [--rs-only] [--master-only] [--graceful] [--maxthreads xx] - ]]> - - - - Rolling Restart on RegionServers Only - - To perform a rolling restart on the RegionServers only, use the - --rs-only option. This might be necessary if you need to reboot the - individual RegionServer or if you make a configuration change that only affects - RegionServers and not the other HBase processes. - If you need to restart only a single RegionServer, or if you need to do extra - actions during the restart, use the bin/graceful_stop.sh - command instead. See . - - - - Rolling Restart on Masters Only - - To perform a rolling restart on the active and backup Masters, use the - --master-only option. You might use this if you know that your - configuration change only affects the Master and not the RegionServers, or if you - need to restart the server where the active Master is running. - If you are not running backup Masters, the Master is simply restarted. If you - are running backup Masters, they are all stopped before any are restarted, to avoid - a race condition in ZooKeeper to determine which is the new Master. First the main - Master is restarted, then the backup Masters are restarted. Directly after restart, - it checks for and cleans out any regions in transition before taking on its normal - workload. - - - - Graceful Restart - - If you specify the --graceful option, RegionServers are restarted - using the bin/graceful_stop.sh script, which moves regions off - a RegionServer before restarting it. This is safer, but can delay the - restart. - - - - Limiting the Number of Threads - - To limit the rolling restart to using only a specific number of threads, use the - --maxthreads option. - - - -
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- Manual Rolling Restart - To retain more control over the process, you may wish to manually do a rolling restart - across your cluster. This uses the graceful-stop.sh command . In this method, you can restart each RegionServer - individually and then move its old regions back into place, retaining locality. If you - also need to restart the Master, you need to do it separately, and restart the Master - before restarting the RegionServers using this method. The following is an example of such - a command. You may need to tailor it to your environment. This script does a rolling - restart of RegionServers only. It disables the load balancer before moving the - regions. - /tmp/log.txt &; - ]]> - Monitor the output of the /tmp/log.txt file to follow the - progress of the script. -
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- Logic for Crafting Your Own Rolling Restart Script - Use the following guidelines if you want to create your own rolling restart script. - - - Extract the new release, verify its configuration, and synchronize it to all nodes - of your cluster using rsync, scp, or another - secure synchronization mechanism. - Use the hbck utility to ensure that the cluster is consistent. - -$ ./bin/hbck - - Perform repairs if required. See for details. - - Restart the master first. You may need to modify these commands if your - new HBase directory is different from the old one, such as for an upgrade. - -$ ./bin/hbase-daemon.sh stop master; ./bin/hbase-daemon.sh start master - - - Gracefully restart each RegionServer, using a script such as the - following, from the Master. - /tmp/log.txt & - ]]> - If you are running Thrift or REST servers, pass the --thrift or --rest options. - For other available options, run the bin/graceful-stop.sh --help - command. - It is important to drain HBase regions slowly when restarting multiple - RegionServers. Otherwise, multiple regions go offline simultaneously and must be - reassigned to other nodes, which may also go offline soon. This can negatively affect - performance. You can inject delays into the script above, for instance, by adding a - Shell command such as sleep. To wait for 5 minutes between each - RegionServer restart, modify the above script to the following: - /tmp/log.txt & - ]]> - - Restart the Master again, to clear out the dead servers list and re-enable - the load balancer. - Run the hbck utility again, to be sure the cluster is - consistent. - -
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- Adding a New Node - Adding a new regionserver in HBase is essentially free, you simply start it like this: - $ ./bin/hbase-daemon.sh start regionserver and it will register itself - with the master. Ideally you also started a DataNode on the same machine so that the RS can - eventually start to have local files. If you rely on ssh to start your daemons, don't forget - to add the new hostname in conf/regionservers on the master. - At this point the region server isn't serving data because no regions have moved to it - yet. If the balancer is enabled, it will start moving regions to the new RS. On a - small/medium cluster this can have a very adverse effect on latency as a lot of regions will - be offline at the same time. It is thus recommended to disable the balancer the same way - it's done when decommissioning a node and move the regions manually (or even better, using a - script that moves them one by one). - The moved regions will all have 0% locality and won't have any blocks in cache so the - region server will have to use the network to serve requests. Apart from resulting in higher - latency, it may also be able to use all of your network card's capacity. For practical - purposes, consider that a standard 1GigE NIC won't be able to read much more than - 100MB/s. In this case, or if you are in a OLAP environment and - require having locality, then it is recommended to major compact the moved regions. - -
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- HBase Metrics - HBase emits metrics which adhere to the Hadoop metrics API. Starting with HBase 0.95The Metrics system was redone in - HBase 0.96. See Migration - to the New Metrics Hotness – Metrics2 by Elliot Clark for detail, - HBase is configured to emit a default - set of metrics with a default sampling period of every 10 seconds. You can use HBase - metrics in conjunction with Ganglia. You can also filter which metrics are emitted and extend - the metrics framework to capture custom metrics appropriate for your environment. -
- Metric Setup - For HBase 0.95 and newer, HBase ships with a default metrics configuration, or - sink. This includes a wide variety of individual metrics, and emits - them every 10 seconds by default. To configure metrics for a given region server, edit the - conf/hadoop-metrics2-hbase.properties file. Restart the region server - for the changes to take effect. - To change the sampling rate for the default sink, edit the line beginning with - *.period. To filter which metrics are emitted or to extend the metrics - framework, see - - - HBase Metrics and Ganglia - By default, HBase emits a large number of metrics per region server. Ganglia may have - difficulty processing all these metrics. Consider increasing the capacity of the Ganglia - server or reducing the number of metrics emitted by HBase. See Metrics Filtering. - -
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- Disabling Metrics - To disable metrics for a region server, edit the - conf/hadoop-metrics2-hbase.properties file and comment out any - uncommented lines. Restart the region server for the changes to take effect. -
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- Discovering Available Metrics - Rather than listing each metric which HBase emits by default, you can browse through the - available metrics, either as a JSON output or via JMX. Different metrics are - exposed for the Master process and each region server process. - - Access a JSON Output of Available Metrics - - After starting HBase, access the region server's web UI, at - http://REGIONSERVER_HOSTNAME:60030 by default (or port 16030 in HBase 1.0+). - - - Click the Metrics Dump link near the top. The metrics for the region server are - presented as a dump of the JMX bean in JSON format. This will dump out all metrics names and their - values. - To include metrics descriptions in the listing — this can be useful when you are exploring - what is available — add a query string of - ?description=true so your URL becomes - http://REGIONSERVER_HOSTNAME:60030/jmx?description=true. - Not all beans and attributes have descriptions. - - - - To view metrics for the Master, connect to the Master's web UI instead (defaults to - http://localhost:60010 or port 16010 in HBase 1.0+) and click its Metrics - Dump link. - To include metrics descriptions in the listing — this can be useful when you are exploring - what is available — add a query string of - ?description=true so your URL becomes - http://REGIONSERVER_HOSTNAME:60010/jmx?description=true. - Not all beans and attributes have descriptions. - - - - - - Browse the JMX Output of Available Metrics - You can use many different tools to view JMX content by browsing MBeans. This - procedure uses jvisualvm, which is an application usually available in the JDK. - - - Start HBase, if it is not already running. - - - Run the command jvisualvm command on a host with a GUI display. - You can launch it from the command line or another method appropriate for your operating - system. - - - Be sure the VisualVM-MBeans plugin is installed. Browse to - Tools - Plugins - . Click Installed and check whether the plugin is - listed. If not, click Available Plugins, select it, and click - Install. When finished, click - Close. - - - To view details for a given HBase process, double-click the process in the - Local sub-tree in the left-hand panel. A detailed view opens in - the right-hand panel. Click the MBeans tab which appears as a tab - in the top of the right-hand panel. - - - To access the HBase metrics, navigate to the appropriate sub-bean: - - - Master: - Hadoop - HBase - Master - Server - - - - RegionServer: - Hadoop - HBase - RegionServer - Server - - - - - - The name of each metric and its current value is displayed in the - Attributes tab. For a view which includes more details, including - the description of each attribute, click the Metadata tab. - - -
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- Units of Measure for Metrics - Different metrics are expressed in different units, as appropriate. Often, the unit of - measure is in the name (as in the metric shippedKBs). Otherwise, use the - following guidelines. When in doubt, you may need to examine the source for a given - metric. - - - Metrics that refer to a point in time are usually expressed as a timestamp. - - - Metrics that refer to an age (such as ageOfLastShippedOp) are usually - expressed in milliseconds. - - - Metrics that refer to memory sizes are in bytes. - - - Sizes of queues (such as sizeOfLogQueue) are expressed as the number of - items in the queue. Determine the size by multiplying by the block size (default is 64 - MB in HDFS). - - - Metrics that refer to things like the number of a given type of operations (such as - logEditsRead) are expressed as an integer. - - -
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- Most Important Master Metrics - Note: Counts are usually over the last metrics reporting interval. - - - hbase.master.numRegionServers - Number of live regionservers - - - hbase.master.numDeadRegionServers - Number of dead regionservers - - - hbase.master.ritCount - The number of regions in transition - - - hbase.master.ritCountOverThreshold - The number of regions that have been in transition longer than - a threshold time (default: 60 seconds) - - - hbase.master.ritOldestAge - The age of the longest region in transition, in milliseconds - - - -
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- Most Important RegionServer Metrics - Note: Counts are usually over the last metrics reporting interval. - - - hbase.regionserver.regionCount - The number of regions hosted by the regionserver - - - hbase.regionserver.storeFileCount - The number of store files on disk currently managed by the - regionserver - - - hbase.regionserver.storeFileSize - Aggregate size of the store files on disk - - - hbase.regionserver.hlogFileCount - The number of write ahead logs not yet archived - - - hbase.regionserver.totalRequestCount - The total number of requests received - - - hbase.regionserver.readRequestCount - The number of read requests received - - - hbase.regionserver.writeRequestCount - The number of write requests received - - - hbase.regionserver.numOpenConnections - The number of open connections at the RPC layer - - - hbase.regionserver.numActiveHandler - The number of RPC handlers actively servicing requests - - - hbase.regionserver.numCallsInGeneralQueue - The number of currently enqueued user requests - - - hbase.regionserver.numCallsInReplicationQueue - The number of currently enqueued operations received from - replication - - - hbase.regionserver.numCallsInPriorityQueue - The number of currently enqueued priority (internal housekeeping) - requests - - - hbase.regionserver.flushQueueLength - Current depth of the memstore flush queue. If increasing, we are falling - behind with clearing memstores out to HDFS. - - - hbase.regionserver.updatesBlockedTime - Number of milliseconds updates have been blocked so the memstore can be - flushed - - - hbase.regionserver.compactionQueueLength - Current depth of the compaction request queue. If increasing, we are - falling behind with storefile compaction. - - - hbase.regionserver.blockCacheHitCount - The number of block cache hits - - - hbase.regionserver.blockCacheMissCount - The number of block cache misses - - - hbase.regionserver.blockCacheExpressHitPercent - The percent of the time that requests with the cache turned on hit the - cache - - - hbase.regionserver.percentFilesLocal - Percent of store file data that can be read from the local DataNode, - 0-100 - - - hbase.regionserver.<op>_<measure> - Operation latencies, where <op> is one of Append, Delete, Mutate, - Get, Replay, Increment; and where <measure> is one of min, max, mean, median, - 75th_percentile, 95th_percentile, 99th_percentile - - - hbase.regionserver.slow<op>Count - The number of operations we thought were slow, where <op> is one - of the list above - - - hbase.regionserver.GcTimeMillis - Time spent in garbage collection, in milliseconds - - - hbase.regionserver.GcTimeMillisParNew - Time spent in garbage collection of the young generation, in - milliseconds - - - hbase.regionserver.GcTimeMillisConcurrentMarkSweep - Time spent in garbage collection of the old generation, in - milliseconds - - - hbase.regionserver.authenticationSuccesses - Number of client connections where authentication succeeded - - - hbase.regionserver.authenticationFailures - Number of client connection authentication failures - - - hbase.regionserver.mutationsWithoutWALCount - Count of writes submitted with a flag indicating they should bypass the - write ahead log - - -
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- -
- HBase Monitoring -
- Overview - The following metrics are arguably the most important to monitor for each RegionServer - for "macro monitoring", preferably with a system like OpenTSDB. If your cluster is having performance - issues it's likely that you'll see something unusual with this group. - - HBase: - - See - - - - - OS: - - IO Wait - - - User CPU - - - - Java: - - GC - - - For more information on HBase metrics, see . -
- -
- Slow Query Log - The HBase slow query log consists of parseable JSON structures describing the properties - of those client operations (Gets, Puts, Deletes, etc.) that either took too long to run, or - produced too much output. The thresholds for "too long to run" and "too much output" are - configurable, as described below. The output is produced inline in the main region server - logs so that it is easy to discover further details from context with other logged events. - It is also prepended with identifying tags (responseTooSlow), - (responseTooLarge), (operationTooSlow), and - (operationTooLarge) in order to enable easy filtering with grep, in - case the user desires to see only slow queries. - -
- Configuration - There are two configuration knobs that can be used to adjust the thresholds for when - queries are logged. - - - - hbase.ipc.warn.response.time Maximum number of milliseconds - that a query can be run without being logged. Defaults to 10000, or 10 seconds. Can be - set to -1 to disable logging by time. - - - hbase.ipc.warn.response.size Maximum byte size of response that - a query can return without being logged. Defaults to 100 megabytes. Can be set to -1 - to disable logging by size. - - -
- -
- Metrics - The slow query log exposes to metrics to JMX. - - - hadoop.regionserver_rpc_slowResponse a global metric reflecting - the durations of all responses that triggered logging. - - - hadoop.regionserver_rpc_methodName.aboveOneSec A metric - reflecting the durations of all responses that lasted for more than one second. - - - -
- -
- Output - The output is tagged with operation e.g. (operationTooSlow) if - the call was a client operation, such as a Put, Get, or Delete, which we expose detailed - fingerprint information for. If not, it is tagged (responseTooSlow) - and still produces parseable JSON output, but with less verbose information solely - regarding its duration and size in the RPC itself. TooLarge is - substituted for TooSlow if the response size triggered the logging, - with TooLarge appearing even in the case that both size and duration - triggered logging. -
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- Example - - 2011-09-08 10:01:25,824 WARN org.apache.hadoop.ipc.HBaseServer: (operationTooSlow): {"tables":{"riley2":{"puts":[{"totalColumns":11,"families":{"actions":[{"timestamp":1315501284459,"qualifier":"0","vlen":9667580},{"timestamp":1315501284459,"qualifier":"1","vlen":10122412},{"timestamp":1315501284459,"qualifier":"2","vlen":11104617},{"timestamp":1315501284459,"qualifier":"3","vlen":13430635}]},"row":"cfcd208495d565ef66e7dff9f98764da:0"}],"families":["actions"]}},"processingtimems":956,"client":"10.47.34.63:33623","starttimems":1315501284456,"queuetimems":0,"totalPuts":1,"class":"HRegionServer","responsesize":0,"method":"multiPut"} - - - Note that everything inside the "tables" structure is output produced by MultiPut's - fingerprint, while the rest of the information is RPC-specific, such as processing time - and client IP/port. Other client operations follow the same pattern and the same general - structure, with necessary differences due to the nature of the individual operations. In - the case that the call is not a client operation, that detailed fingerprint information - will be completely absent. - - This particular example, for example, would indicate that the likely cause of slowness - is simply a very large (on the order of 100MB) multiput, as we can tell by the "vlen," or - value length, fields of each put in the multiPut. -
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- Block Cache Monitoring - Starting with HBase 0.98, the HBase Web UI includes the ability to monitor and report on - the performance of the block cache. To view the block cache reports, click - Tasks - Show Non-RPC Tasks - Block Cache - . Following are a few examples of the reporting capabilities. -
- Basic Info - - - - - - Shows the cache implementation - - -
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- Config - - - - - - Shows all cache configuration options. - - -
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- Stats - - - - - - Shows statistics about the performance of the cache. - - -
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- L1 and L2 - - - - - - - - - Shows information about the L1 and L2 caches. - - -
- This is not an exhaustive list of all the screens and reports available. Have a look in - the Web UI. -
- - - -
- -
- Cluster Replication - - This information was previously available at Cluster Replication. - - HBase provides a cluster replication mechanism which allows you to keep one cluster's - state synchronized with that of another cluster, using the write-ahead log (WAL) of the source - cluster to propagate the changes. Some use cases for cluster replication include: - - Backup and disaster recovery - Data aggregation - Geographic data distribution - Online data ingestion combined with offline data analytics - - Replication is enabled at the granularity of the column family. Before enabling - replication for a column family, create the table and all column families to be replicated, on - the destination cluster. - Cluster replication uses a source-push methodology. An HBase cluster can be a source (also - called master or active, meaning that it is the originator of new data), a destination (also - called slave or passive, meaning that it receives data via replication), or can fulfill both - roles at once. Replication is asynchronous, and the goal of replication is eventual - consistency. When the source receives an edit to a column family with replication enabled, - that edit is propagated to all destination clusters using the WAL for that for that column - family on the RegionServer managing the relevant region. - When data is replicated from one cluster to another, the original source of the data is - tracked via a cluster ID which is part of the metadata. In HBase 0.96 and newer (HBASE-7709), all - clusters which have already consumed the data are also tracked. This prevents replication - loops. - The WALs for each region server must be kept in HDFS as long as they are needed to - replicate data to any slave cluster. Each region server reads from the oldest log it needs to - replicate and keeps track of its progress processing WALs inside ZooKeeper to simplify failure - recovery. The position marker which indicates a slave cluster's progress, as well as the queue - of WALs to process, may be different for every slave cluster. - The clusters participating in replication can be of different sizes. The master cluster - relies on randomization to attempt to balance the stream of replication on the slave clusters. - It is expected that the slave cluster has storage capacity to hold the replicated data, as - well as any data it is responsible for ingesting. If a slave cluster does run out of room, or - is inaccessible for other reasons, it throws an error and the master retains the WAL and - retries the replication at intervals. - - Terminology Changes - Previously, terms such as master-master, - master-slave, and cyclical were used to - describe replication relationships in HBase. These terms added confusion, and have been - abandoned in favor of discussions about cluster topologies appropriate for different - scenarios. - - - Cluster Topologies - - A central source cluster might propagate changes out to multiple destination clusters, - for failover or due to geographic distribution. - - - A source cluster might push changes to a destination cluster, which might also push - its own changes back to the original cluster. - - Many different low-latency clusters might push changes to one centralized cluster for - backup or resource-intensive data analytics jobs. The processed data might then be - replicated back to the low-latency clusters. - - - Multiple levels of replication may be chained together to suit your organization's needs. - The following diagram shows a hypothetical scenario. Use the arrows to follow the data - paths. -
- Example of a Complex Cluster Replication Configuration - - - - - At the top of the diagram, the San Jose and Tokyo clusters, shown in red, - replicate changes to each other, and each also replicates changes to a User Data and a - Payment Data cluster. - Each cluster in the second row, shown in blue, replicates its changes to the All Data - Backup 1 cluster, shown in grey. The All Data Backup 1 cluster replicates changes to the - All Data Backup 2 cluster (also shown in grey), as well as the Data Analysis cluster - (shown in green). All Data Backup 2 also propagates any of its own changes back to All - Data Backup 1. - The Data Analysis cluster runs MapReduce jobs on its data, and then pushes the - processed data back to the San Jose and Tokyo clusters. - -
- - HBase replication borrows many concepts from the statement-based replication design used by MySQL. Instead of SQL - statements, entire WALEdits (consisting of multiple cell inserts coming from Put and Delete - operations on the clients) are replicated in order to maintain atomicity. - -
- Configuring Cluster Replication - The following is a simplified procedure for configuring cluster replication. It may not - cover every edge case. For more information, see the API documentation for replication. - - - Configure and start the source and destination clusters. Create tables with the same - names and column families on both the source and destination clusters, so that the - destination cluster knows where to store data it will receive. All hosts in the source - and destination clusters should be reachable to each other. - - - On the source cluster, enable replication by setting hbase.replication - to true in hbase-site.xml. - - - On the source cluster, in HBase Shell, add the destination cluster as a peer, using - the add_peer command. The syntax is as follows: - hbase> add_peer 'ID' 'CLUSTER_KEY' - The ID is a string (prior to HBASE-11367, it - was a short integer), which must not contain a hyphen (see HBASE-11394). To - compose the CLUSTER_KEY, use the following template: - hbase.zookeeper.quorum:hbase.zookeeper.property.clientPort:zookeeper.znode.parent - If both clusters use the same ZooKeeper cluster, you must use a different - zookeeper.znode.parent, because they cannot write in the same folder. - - - On the source cluster, configure each column family to be replicated by setting its - REPLICATION_SCOPE to 1, using commands such as the following in HBase Shell. - hbase> disable 'example_table' -hbase> alter 'example_table', {NAME => 'example_family', REPLICATION_SCOPE => '1'} -hbase> enable 'example_table' - - You can verify that replication is taking place by examining the logs on the - source cluster for messages such as the following. - Considering 1 rs, with ratio 0.1 -Getting 1 rs from peer cluster # 0 -Choosing peer 10.10.1.49:62020 - - - To verify the validity of replicated data, you can use the included - VerifyReplication MapReduce job on the source cluster, providing it with - the ID of the replication peer and table name to verify. Other options are possible, - such as a time range or specific families to verify. - The command has the following form: - hbase org.apache.hadoop.hbase.mapreduce.replication.VerifyReplication [--starttime=timestamp1] [--stoptime=timestamp [--families=comma separated list of families] <peerId><tablename> - The VerifyReplication command prints out GOODROWS - and BADROWS counters to indicate rows that did and did not replicate - correctly. - - -
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- Detailed Information About Cluster Replication - -
- Replication Architecture Overview - - - - - - Illustration of the replication architecture in HBase, as described in the prior - text. - - -
- - -
- Life of a WAL Edit - A single WAL edit goes through several steps in order to be replicated to a slave - cluster. - - - When the slave responds correctly: - - An HBase client uses a Put or Delete operation to manipulate data in HBase. - - - The region server writes the request to the WAL in a way allows it to be replayed - if it is not written successfully. - - - If the changed cell corresponds to a column family that is scoped for replication, - the edit is added to the queue for replication. - - - In a separate thread, the edit is read from the log, as part of a batch process. - Only the KeyValues that are eligible for replication are kept. Replicable KeyValues - are part of a column family whose schema is scoped GLOBAL, are not part of a catalog - such as hbase:meta, did not originate from the target slave cluster, and - have not already been consumed by the target slave cluster. - - - The edit is tagged with the master's UUID and added to a buffer. When the buffer - is filled, or the reader reaches the end of the file, the buffer is sent to a random - region server on the slave cluster. - - - The region server reads the edits sequentially and separates them into buffers, - one buffer per table. After all edits are read, each buffer is flushed using HTable, HBase's normal client. The master's UUID and the UUIDs of slaves - which have already consumed the data are preserved in the edits they are applied, in - order to prevent replication loops. - - - In the master, the offset for the WAL that is currently being replicated is - registered in ZooKeeper. - - - - When the slave does not respond: - - The first three steps, where the edit is inserted, are identical. - - - Again in a separate thread, the region server reads, filters, and edits the log - edits in the same way as above. The slave region server does not answer the RPC - call. - - - The master sleeps and tries again a configurable number of times. - - - If the slave region server is still not available, the master selects a new subset - of region server to replicate to, and tries again to send the buffer of edits. - - - Meanwhile, the WALs are rolled and stored in a queue in ZooKeeper. Logs that are - archived by their region server, by moving them from the - region server's log directory to a central log directory, will update their paths in - the in-memory queue of the replicating thread. - - - When the slave cluster is finally available, the buffer is applied in the same way - as during normal processing. The master region server will then replicate the backlog - of logs that accumulated during the outage. - - - - - Spreading Queue Failover Load - When replication is active, a subset of region servers in the source cluster is - responsible for shipping edits to the sink. This responsibility must be failed over like - all other region server functions should a process or node crash. The following - configuration settings are recommended for maintaining an even distribution of - replication activity over the remaining live servers in the source cluster: - - - - Set replication.source.maxretriesmultiplier to - 300. - - - Set replication.source.sleepforretries to 1 (1 - second). This value, combined with the value of - replication.source.maxretriesmultiplier, causes the retry cycle to last - about 5 minutes. - - - Set replication.sleep.before.failover to 30000 (30 - seconds) in the source cluster site configuration. - - - - - Preserving Tags During Replication - By default, the codec used for replication between clusters strips tags, such as - cell-level ACLs, from cells. To prevent the tags from being stripped, you can use a - different codec which does not strip them. Configure - hbase.replication.rpc.codec to use - org.apache.hadoop.hbase.codec.KeyValueCodecWithTags, on both the - source and sink RegionServers involved in the replication. This option was introduced in - HBASE-10322. - -
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- Replication Internals - - - Replication State in ZooKeeper - - HBase replication maintains its state in ZooKeeper. By default, the state is - contained in the base node /hbase/replication. This node - contains two child nodes, the Peers znode and the RS - znode. - - Replication may be disrupted and data loss may occur if you delete the - replication tree (/hbase/replication/) from ZooKeeper. This - is despite the information about invariants at . Follow progress on this issue at HBASE-10295. - - - - - The Peers Znode - - The peers znode is stored in - /hbase/replication/peers by default. It consists of a list of - all peer replication clusters, along with the status of each of them. The value of - each peer is its cluster key, which is provided in the HBase Shell. The cluster key - contains a list of ZooKeeper nodes in the cluster's quorum, the client port for the - ZooKeeper quorum, and the base znode for HBase in HDFS on that cluster. - -/hbase/replication/peers - /1 [Value: zk1.host.com,zk2.host.com,zk3.host.com:2181:/hbase] - /2 [Value: zk5.host.com,zk6.host.com,zk7.host.com:2181:/hbase] - - Each peer has a child znode which indicates whether or not replication is - enabled on that cluster. These peer-state znodes do not contain any child znodes, - but only contain a Boolean value. This value is read and maintained by the - ReplicationPeer.PeerStateTracker class. - -/hbase/replication/peers - /1/peer-state [Value: ENABLED] - /2/peer-state [Value: DISABLED] - - - - - The RS Znode - - The rs znode contains a list of WAL logs which need to be - replicated. This list is divided into a set of queues organized by region server and - the peer cluster the region server is shipping the logs to. The rs znode has one - child znode for each region server in the cluster. The child znode name is the - region server's hostname, client port, and start code. This list includes both live - and dead region servers. - -/hbase/replication/rs - /hostname.example.org,6020,1234 - /hostname2.example.org,6020,2856 - - Each rs znode contains a list of WAL replication queues, one queue - for each peer cluster it replicates to. These queues are represented by child znodes - named by the cluster ID of the peer cluster they represent. - -/hbase/replication/rs - /hostname.example.org,6020,1234 - /1 - /2 - - Each queue has one child znode for each WAL log that still needs to be - replicated. the value of these child znodes is the last position that was - replicated. This position is updated each time a WAL log is replicated. - -/hbase/replication/rs - /hostname.example.org,6020,1234 - /1 - 23522342.23422 [VALUE: 254] - 12340993.22342 [VALUE: 0] - - - - -
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- Choosing Region Servers to Replicate To - When a master cluster region server initiates a replication source to a slave cluster, - it first connects to the slave's ZooKeeper ensemble using the provided cluster key . It - then scans the rs/ directory to discover all the available sinks - (region servers that are accepting incoming streams of edits to replicate) and randomly - chooses a subset of them using a configured ratio which has a default value of 10%. For - example, if a slave cluster has 150 machines, 15 will be chosen as potential recipient for - edits that this master cluster region server sends. Because this selection is performed by - each master region server, the probability that all slave region servers are used is very - high, and this method works for clusters of any size. For example, a master cluster of 10 - machines replicating to a slave cluster of 5 machines with a ratio of 10% causes the - master cluster region servers to choose one machine each at random. - A ZooKeeper watcher is placed on the - ${zookeeper.znode.parent}/rs node of - the slave cluster by each of the master cluster's region servers. This watch is used to - monitor changes in the composition of the slave cluster. When nodes are removed from the - slave cluster, or if nodes go down or come back up, the master cluster's region servers - will respond by selecting a new pool of slave region servers to replicate to. -
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- Keeping Track of Logs - Each master cluster region server has its own znode in the replication znodes - hierarchy. It contains one znode per peer cluster (if 5 slave clusters, 5 znodes are - created), and each of these contain a queue of WALs to process. Each of these queues will - track the WALs created by that region server, but they can differ in size. For example, if - one slave cluster becomes unavailable for some time, the WALs should not be deleted, so - they need to stay in the queue while the others are processed. See for an example. - When a source is instantiated, it contains the current WAL that the region server is - writing to. During log rolling, the new file is added to the queue of each slave cluster's - znode just before it is made available. This ensures that all the sources are aware that a - new log exists before the region server is able to append edits into it, but this - operations is now more expensive. The queue items are discarded when the replication - thread cannot read more entries from a file (because it reached the end of the last block) - and there are other files in the queue. This means that if a source is up to date and - replicates from the log that the region server writes to, reading up to the "end" of the - current file will not delete the item in the queue. - A log can be archived if it is no longer used or if the number of logs exceeds - hbase.regionserver.maxlogs because the insertion rate is faster than - regions are flushed. When a log is archived, the source threads are notified that the path - for that log changed. If a particular source has already finished with an archived log, it - will just ignore the message. If the log is in the queue, the path will be updated in - memory. If the log is currently being replicated, the change will be done atomically so - that the reader doesn't attempt to open the file when has already been moved. Because - moving a file is a NameNode operation , if the reader is currently reading the log, it - won't generate any exception. -
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- Reading, Filtering and Sending Edits - By default, a source attempts to read from a WAL and ship log entries to a sink as - quickly as possible. Speed is limited by the filtering of log entries Only KeyValues that - are scoped GLOBAL and that do not belong to catalog tables will be retained. Speed is also - limited by total size of the list of edits to replicate per slave, which is limited to 64 - MB by default. With this configuration, a master cluster region server with three slaves - would use at most 192 MB to store data to replicate. This does not account for the data - which was filtered but not garbage collected. - Once the maximum size of edits has been buffered or the reader reaces the end of the - WAL, the source thread stops reading and chooses at random a sink to replicate to (from - the list that was generated by keeping only a subset of slave region servers). It directly - issues a RPC to the chosen region server and waits for the method to return. If the RPC - was successful, the source determines whether the current file has been emptied or it - contains more data which needs to be read. If the file has been emptied, the source - deletes the znode in the queue. Otherwise, it registers the new offset in the log's znode. - If the RPC threw an exception, the source will retry 10 times before trying to find a - different sink. -
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- Cleaning Logs - If replication is not enabled, the master's log-cleaning thread deletes old logs using - a configured TTL. This TTL-based method does not work well with replication, because - archived logs which have exceeded their TTL may still be in a queue. The default behavior - is augmented so that if a log is past its TTL, the cleaning thread looks up every queue - until it finds the log, while caching queues it has found. If the log is not found in any - queues, the log will be deleted. The next time the cleaning process needs to look for a - log, it starts by using its cached list. -
-
- Region Server Failover - When no region servers are failing, keeping track of the logs in ZooKeeper adds no - value. Unfortunately, region servers do fail, and since ZooKeeper is highly available, it - is useful for managing the transfer of the queues in the event of a failure. - Each of the master cluster region servers keeps a watcher on every other region - server, in order to be notified when one dies (just as the master does). When a failure - happens, they all race to create a znode called lock inside the dead - region server's znode that contains its queues. The region server that creates it - successfully then transfers all the queues to its own znode, one at a time since ZooKeeper - does not support renaming queues. After queues are all transferred, they are deleted from - the old location. The znodes that were recovered are renamed with the ID of the slave - cluster appended with the name of the dead server. - Next, the master cluster region server creates one new source thread per copied queue, - and each of the source threads follows the read/filter/ship pattern. The main difference - is that those queues will never receive new data, since they do not belong to their new - region server. When the reader hits the end of the last log, the queue's znode is deleted - and the master cluster region server closes that replication source. - Given a master cluster with 3 region servers replicating to a single slave with id - 2, the following hierarchy represents what the znodes layout could be - at some point in time. The region servers' znodes all contain a peers - znode which contains a single queue. The znode names in the queues represent the actual - file names on HDFS in the form - address,port.timestamp. - -/hbase/replication/rs/ - 1.1.1.1,60020,123456780/ - 2/ - 1.1.1.1,60020.1234 (Contains a position) - 1.1.1.1,60020.1265 - 1.1.1.2,60020,123456790/ - 2/ - 1.1.1.2,60020.1214 (Contains a position) - 1.1.1.2,60020.1248 - 1.1.1.2,60020.1312 - 1.1.1.3,60020, 123456630/ - 2/ - 1.1.1.3,60020.1280 (Contains a position) - - Assume that 1.1.1.2 loses its ZooKeeper session. The survivors will race to create a - lock, and, arbitrarily, 1.1.1.3 wins. It will then start transferring all the queues to - its local peers znode by appending the name of the dead server. Right before 1.1.1.3 is - able to clean up the old znodes, the layout will look like the following: - -/hbase/replication/rs/ - 1.1.1.1,60020,123456780/ - 2/ - 1.1.1.1,60020.1234 (Contains a position) - 1.1.1.1,60020.1265 - 1.1.1.2,60020,123456790/ - lock - 2/ - 1.1.1.2,60020.1214 (Contains a position) - 1.1.1.2,60020.1248 - 1.1.1.2,60020.1312 - 1.1.1.3,60020,123456630/ - 2/ - 1.1.1.3,60020.1280 (Contains a position) - - 2-1.1.1.2,60020,123456790/ - 1.1.1.2,60020.1214 (Contains a position) - 1.1.1.2,60020.1248 - 1.1.1.2,60020.1312 - - Some time later, but before 1.1.1.3 is able to finish replicating the last WAL from - 1.1.1.2, it dies too. Some new logs were also created in the normal queues. The last - region server will then try to lock 1.1.1.3's znode and will begin transferring all the - queues. The new layout will be: - -/hbase/replication/rs/ - 1.1.1.1,60020,123456780/ - 2/ - 1.1.1.1,60020.1378 (Contains a position) - - 2-1.1.1.3,60020,123456630/ - 1.1.1.3,60020.1325 (Contains a position) - 1.1.1.3,60020.1401 - - 2-1.1.1.2,60020,123456790-1.1.1.3,60020,123456630/ - 1.1.1.2,60020.1312 (Contains a position) - 1.1.1.3,60020,123456630/ - lock - 2/ - 1.1.1.3,60020.1325 (Contains a position) - 1.1.1.3,60020.1401 - - 2-1.1.1.2,60020,123456790/ - 1.1.1.2,60020.1312 (Contains a position) - -
-
-
- Replication Metrics - The following metrics are exposed at the global region server level and (since HBase - 0.95) at the peer level: - - - source.sizeOfLogQueue - - number of WALs to process (excludes the one which is being processed) at the - Replication source - - - - source.shippedOps - - number of mutations shipped - - - - source.logEditsRead - - number of mutations read from WALs at the replication source - - - - source.ageOfLastShippedOp - - age of last batch that was shipped by the replication source - - - -
-
- Replication Configuration Options - - - - - Option - Description - Default - - - - - zookeeper.znode.parent - The name of the base ZooKeeper znode used for HBase - /hbase - - - zookeeper.znode.replication - The name of the base znode used for replication - replication - - - zookeeper.znode.replication.peers - The name of the peer znode - peers - - - zookeeper.znode.replication.peers.state - The name of peer-state znode - peer-state - - - zookeeper.znode.replication.rs - The name of the rs znode - rs - - - hbase.replication - Whether replication is enabled or disabled on a given - cluster - false - - - eplication.sleep.before.failover - How many milliseconds a worker should sleep before attempting to replicate - a dead region server's WAL queues. - - - - replication.executor.workers - The number of region servers a given region server should attempt to - failover simultaneously. - 1 - - - - -
-
-
- HBase Backup - There are two broad strategies for performing HBase backups: backing up with a full - cluster shutdown, and backing up on a live cluster. Each approach has pros and cons. - For additional information, see HBase Backup - Options over on the Sematext Blog. -
- Full Shutdown Backup - Some environments can tolerate a periodic full shutdown of their HBase cluster, for - example if it is being used a back-end analytic capacity and not serving front-end - web-pages. The benefits are that the NameNode/Master are RegionServers are down, so there is - no chance of missing any in-flight changes to either StoreFiles or metadata. The obvious con - is that the cluster is down. The steps include: -
- Stop HBase - -
-
- Distcp - Distcp could be used to either copy the contents of the HBase directory in HDFS to - either the same cluster in another directory, or to a different cluster. - Note: Distcp works in this situation because the cluster is down and there are no - in-flight edits to files. Distcp-ing of files in the HBase directory is not generally - recommended on a live cluster. -
-
- Restore (if needed) - The backup of the hbase directory from HDFS is copied onto the 'real' hbase directory - via distcp. The act of copying these files creates new HDFS metadata, which is why a - restore of the NameNode edits from the time of the HBase backup isn't required for this - kind of restore, because it's a restore (via distcp) of a specific HDFS directory (i.e., - the HBase part) not the entire HDFS file-system. -
-
-
- Live Cluster Backup - Replication - This approach assumes that there is a second cluster. See the HBase page on replication for more - information. -
-
- Live Cluster Backup - CopyTable - The utility could either be used to copy data from one table to another - on the same cluster, or to copy data to another table on another cluster. - Since the cluster is up, there is a risk that edits could be missed in the copy process. - -
-
- Live Cluster Backup - Export - The approach dumps the content of a table to HDFS on the same cluster. To - restore the data, the utility would be used. - Since the cluster is up, there is a risk that edits could be missed in the export - process. -
-
- - -
- HBase Snapshots - HBase Snapshots allow you to take a snapshot of a table without too much impact on Region - Servers. Snapshot, Clone and restore operations don't involve data copying. Also, Exporting - the snapshot to another cluster doesn't have impact on the Region Servers. - Prior to version 0.94.6, the only way to backup or to clone a table is to use - CopyTable/ExportTable, or to copy all the hfiles in HDFS after disabling the table. The - disadvantages of these methods are that you can degrade region server performance (Copy/Export - Table) or you need to disable the table, that means no reads or writes; and this is usually - unacceptable. -
- Configuration - To turn on the snapshot support just set the hbase.snapshot.enabled - property to true. (Snapshots are enabled by default in 0.95+ and off by default in - 0.94.6+) - - <property> - <name>hbase.snapshot.enabled</name> - <value>true</value> - </property> - -
-
- Take a Snapshot - You can take a snapshot of a table regardless of whether it is enabled or disabled. The - snapshot operation doesn't involve any data copying. - -$ ./bin/hbase shell -hbase> snapshot 'myTable', 'myTableSnapshot-122112' - - - Take a Snapshot Without Flushing - The default behavior is to perform a flush of data in memory before the snapshot is - taken. This means that data in memory is included in the snapshot. In most cases, this is - the desired behavior. However, if your set-up can tolerate data in memory being excluded - from the snapshot, you can use the option of the - snapshot command to disable and flushing while taking the - snapshot. - - hbase> snapshot 'mytable', 'snapshot123', {SKIP_FLUSH => true} - - There is no way to determine or predict whether a very concurrent insert or update - will be included in a given snapshot, whether flushing is enabled or disabled. A snapshot - is only a representation of a table during a window of time. The amount of time the - snapshot operation will take to reach each Region Server may vary from a few seconds to a - minute, depending on the resource load and speed of the hardware or network, among other - factors. There is also no way to know whether a given insert or update is in memory or has - been flushed. - -
-
- Listing Snapshots - List all snapshots taken (by printing the names and relative information). - -$ ./bin/hbase shell -hbase> list_snapshots - -
-
- Deleting Snapshots - You can remove a snapshot, and the files retained for that snapshot will be removed if - no longer needed. - -$ ./bin/hbase shell -hbase> delete_snapshot 'myTableSnapshot-122112' - -
-
- Clone a table from snapshot - From a snapshot you can create a new table (clone operation) with the same data that you - had when the snapshot was taken. The clone operation, doesn't involve data copies, and a - change to the cloned table doesn't impact the snapshot or the original table. - -$ ./bin/hbase shell -hbase> clone_snapshot 'myTableSnapshot-122112', 'myNewTestTable' - -
-
- Restore a snapshot - The restore operation requires the table to be disabled, and the table will be restored - to the state at the time when the snapshot was taken, changing both data and schema if - required. - -$ ./bin/hbase shell -hbase> disable 'myTable' -hbase> restore_snapshot 'myTableSnapshot-122112' - - - Since Replication works at log level and snapshots at file-system level, after a - restore, the replicas will be in a different state from the master. If you want to use - restore, you need to stop replication and redo the bootstrap. - - In case of partial data-loss due to misbehaving client, instead of a full restore that - requires the table to be disabled, you can clone the table from the snapshot and use a - Map-Reduce job to copy the data that you need, from the clone to the main one. -
-
- Snapshots operations and ACLs - If you are using security with the AccessController Coprocessor (See ), only a global administrator can take, - clone, or restore a snapshot, and these actions do not capture the ACL rights. This means - that restoring a table preserves the ACL rights of the existing table, while cloning a table - creates a new table that has no ACL rights until the administrator adds them. -
-
- Export to another cluster - The ExportSnapshot tool copies all the data related to a snapshot (hfiles, logs, - snapshot metadata) to another cluster. The tool executes a Map-Reduce job, similar to - distcp, to copy files between the two clusters, and since it works at file-system level the - hbase cluster does not have to be online. - To copy a snapshot called MySnapshot to an HBase cluster srv2 (hdfs:///srv2:8082/hbase) - using 16 mappers: - $ bin/hbase class org.apache.hadoop.hbase.snapshot.ExportSnapshot -snapshot MySnapshot -copy-to hdfs://srv2:8082/hbase -mappers 16 - - Limiting Bandwidth Consumption - You can limit the bandwidth consumption when exporting a snapshot, by specifying the - -bandwidth parameter, which expects an integer representing megabytes per - second. The following example limits the above example to 200 MB/sec. - - $ bin/hbase class org.apache.hadoop.hbase.snapshot.ExportSnapshot -snapshot MySnapshot -copy-to hdfs://srv2:8082/hbase -mappers 16 -bandwidth 200 -
-
- - -
- Capacity Planning and Region Sizing - There are several considerations when planning the capacity for an HBase cluster and - performing the initial configuration. Start with a solid understanding of how HBase handles - data internally. -
- Node count and hardware/VM configuration -
- Physical data size - Physical data size on disk is distinct from logical size of your data and is affected - by the following: - - - Increased by HBase overhead - - - See and . At least 24 bytes per key-value (cell), can be more. Small - keys/values means more relative overhead. - - - KeyValue instances are aggregated into blocks, which are indexed. Indexes also - have to be stored. Blocksize is configurable on a per-ColumnFamily basis. See . - - - - - Decreased by and data block encoding, depending on data. See - also this thread. You might - want to test what compression and encoding (if any) make sense for your data. - - - Increased by size of region server (usually fixed and negligible - less than half of RS - memory size, per RS). - - - Increased by HDFS replication - usually x3. - - - Aside from the disk space necessary to store the data, one RS may not be able to serve - arbitrarily large amounts of data due to some practical limits on region count and size - (see ). -
- -
- Read/Write throughput - Number of nodes can also be driven by required thoughput for reads and/or writes. The - throughput one can get per node depends a lot on data (esp. key/value sizes) and request - patterns, as well as node and system configuration. Planning should be done for peak load - if it is likely that the load would be the main driver of the increase of the node count. - PerformanceEvaluation and tools can be used to test single node or a test - cluster. - For write, usually 5-15Mb/s per RS can be expected, since every region server has only - one active WAL. There's no good estimate for reads, as it depends vastly on data, - requests, and cache hit rate. might be helpful. -
- -
- JVM GC limitations - RS cannot currently utilize very large heap due to cost of GC. There's also no good - way of running multiple RS-es per server (other than running several VMs per machine). - Thus, ~20-24Gb or less memory dedicated to one RS is recommended. GC tuning is required - for large heap sizes. See , and elsewhere (TODO: where?) -
- -
- -
- Determining region count and size - Generally less regions makes for a smoother running cluster (you can always manually - split the big regions later (if necessary) to spread the data, or request load, over the - cluster); 20-200 regions per RS is a reasonable range. The number of regions cannot be - configured directly (unless you go for fully ); adjust the region size to achieve the target - region size given table size. - When configuring regions for multiple tables, note that most region settings can be set - on a per-table basis via HTableDescriptor, - as well as shell commands. These settings will override the ones in - hbase-site.xml. That is useful if your tables have different - workloads/use cases. - Also note that in the discussion of region sizes here, HDFS replication factor is not (and should not be) taken into account, whereas - other factors should be. So, if your data is compressed and - replicated 3 ways by HDFS, "9 Gb region" means 9 Gb of compressed data. HDFS replication - factor only affects your disk usage and is invisible to most HBase code. -
- Viewing the Current Number of Regions - You can view the current number of regions for a given table using the HMaster UI. In - the Tables section, the number of online regions for each table is - listed in the Online Regions column. This total only includes the - in-memory state and does not include disabled or offline regions. If you do not want to - use the HMaster UI, you can determine the number of regions by counting the number of - subdirectories of the /hbase/<table>/ subdirectories in HDFS, or by running the - bin/hbase hbck command. Each of these methods may return a slightly - different number, depending on the status of each region. -
-
- Number of regions per RS - upper bound - In production scenarios, where you have a lot of data, you are normally concerned with - the maximum number of regions you can have per server. - has technical discussion on the subject. Basically, the maximum number of regions is - mostly determined by memstore memory usage. Each region has its own memstores; these grow - up to a configurable size; usually in 128-256 MB range, see . One memstore exists per column family (so - there's only one per region if there's one CF in the table). The RS dedicates some - fraction of total memory to its memstores (see ). If this memory is exceeded (too - much memstore usage), it can cause undesirable consequences such as unresponsive server or - compaction storms. A good starting point for the number of regions per RS (assuming one - table) is: - - ((RS memory) * (total memstore fraction)) / ((memstore size)*(# column families)) - This formula is pseudo-code. Here are two formulas using the actual tunable - parameters, first for HBase 0.98+ and second for HBase 0.94.x. - - HBase 0.98.x:((RS Xmx) * hbase.regionserver.global.memstore.size) / - (hbase.hregion.memstore.flush.size * (# column families)) - HBase 0.94.x:((RS Xmx) * hbase.regionserver.global.memstore.upperLimit) / - (hbase.hregion.memstore.flush.size * (# column families)) - - If a given RegionServer has 16 GB of RAM, with default settings, the formula works out - to 16384*0.4/128 ~ 51 regions per RS is a starting point. The formula can be extended to - multiple tables; if they all have the same configuration, just use the total number of - families. - This number can be adjusted; the formula above assumes all your regions are filled at - approximately the same rate. If only a fraction of your regions are going to be actively - written to, you can divide the result by that fraction to get a larger region count. Then, - even if all regions are written to, all region memstores are not filled evenly, and - eventually jitter appears even if they are (due to limited number of concurrent flushes). - Thus, one can have as many as 2-3 times more regions than the starting point; however, - increased numbers carry increased risk. - For write-heavy workload, memstore fraction can be increased in configuration at the - expense of block cache; this will also allow one to have more regions. -
- -
- Number of regions per RS - lower bound - HBase scales by having regions across many servers. Thus if you have 2 regions for - 16GB data, on a 20 node machine your data will be concentrated on just a few machines - - nearly the entire cluster will be idle. This really can't be stressed enough, since a - common problem is loading 200MB data into HBase and then wondering why your awesome 10 - node cluster isn't doing anything. - On the other hand, if you have a very large amount of data, you may also want to go - for a larger number of regions to avoid having regions that are too large. -
- -
- Maximum region size - For large tables in production scenarios, maximum region size is mostly limited by - compactions - very large compactions, esp. major, can degrade cluster performance. - Currently, the recommended maximum region size is 10-20Gb, and 5-10Gb is optimal. For - older 0.90.x codebase, the upper-bound of regionsize is about 4Gb, with a default of - 256Mb. - The size at which the region is split into two is generally configured via ; for details, see . - If you cannot estimate the size of your tables well, when starting off, it's probably - best to stick to the default region size, perhaps going smaller for hot tables (or - manually split hot regions to spread the load over the cluster), or go with larger region - sizes if your cell sizes tend to be largish (100k and up). - In HBase 0.98, experimental stripe compactions feature was added that would allow for - larger regions, especially for log data. See . -
- -
- Total data size per region server - According to above numbers for region size and number of regions per region server, in - an optimistic estimate 10 GB x 100 regions per RS will give up to 1TB served per region - server, which is in line with some of the reported multi-PB use cases. However, it is - important to think about the data vs cache size ratio at the RS level. With 1TB of data - per server and 10 GB block cache, only 1% of the data will be cached, which may barely - cover all block indices. -
- -
- -
- Initial configuration and tuning - First, see . Note that some configurations, more than others, - depend on specific scenarios. Pay special attention to: - - - - request handler thread count, vital - for high-throughput workloads. - - - - the blocking number of WAL files depends on your memstore - configuration and should be set accordingly to prevent potential blocking when doing - high volume of writes. - - - Then, there are some considerations when setting up your cluster and tables. -
- Compactions - Depending on read/write volume and latency requirements, optimal compaction settings - may be different. See for some details. - When provisioning for large data sizes, however, it's good to keep in mind that - compactions can affect write throughput. Thus, for write-intensive workloads, you may opt - for less frequent compactions and more store files per regions. Minimum number of files - for compactions (hbase.hstore.compaction.min) can be set to higher - value; should also be increased, as more files - might accumulate in such case. You may also consider manually managing compactions: -
- -
- Pre-splitting the table - Based on the target number of the regions per RS (see ) and number of RSes, one can pre-split the table at - creation time. This would both avoid some costly splitting as the table starts to fill up, - and ensure that the table starts out already distributed across many servers. - If the table is expected to grow large enough to justify that, at least one region per - RS should be created. It is not recommended to split immediately into the full target - number of regions (e.g. 50 * number of RSes), but a low intermediate value can be chosen. - For multiple tables, it is recommended to be conservative with presplitting (e.g. - pre-split 1 region per RS at most), especially if you don't know how much each table will - grow. If you split too much, you may end up with too many regions, with some tables having - too many small regions. - For pre-splitting howto, see and - . -
- -
- -
- -
- Table Rename - In versions 0.90.x of hbase and earlier, we had a simple script that would rename the hdfs - table directory and then do an edit of the hbase:meta table replacing all mentions of the old - table name with the new. The script was called ./bin/rename_table.rb. The - script was deprecated and removed mostly because it was unmaintained and the operation - performed by the script was brutal. - As of hbase 0.94.x, you can use the snapshot facility renaming a table. Here is how you - would do it using the hbase shell: - disable 'tableName' -hbase shell> snapshot 'tableName', 'tableSnapshot' -hbase shell> clone_snapshot 'tableSnapshot', 'newTableName' -hbase shell> delete_snapshot 'tableSnapshot' -hbase shell> drop 'tableName']]> - or in code it would be as follows: - void rename(HBaseAdmin admin, String oldTableName, String newTableName) { - String snapshotName = randomName(); - admin.disableTable(oldTableName); - admin.snapshot(snapshotName, oldTableName); - admin.cloneSnapshot(snapshotName, newTableName); - admin.deleteSnapshot(snapshotName); - admin.deleteTable(oldTableName); -} - -
- - diff --git a/src/main/docbkx/orca.xml b/src/main/docbkx/orca.xml deleted file mode 100644 index 29d8727f0f1..00000000000 --- a/src/main/docbkx/orca.xml +++ /dev/null @@ -1,47 +0,0 @@ - - - - Apache HBase Orca -
- Apache HBase Orca - - - - - -
- An Orca is the Apache - HBase mascot. - See NOTICES.txt. Our Orca logo we got here: http://www.vectorfree.com/jumping-orca - It is licensed Creative Commons Attribution 3.0. See https://creativecommons.org/licenses/by/3.0/us/ - We changed the logo by stripping the colored background, inverting - it and then rotating it some. - - diff --git a/src/main/docbkx/other_info.xml b/src/main/docbkx/other_info.xml deleted file mode 100644 index 72ff2745865..00000000000 --- a/src/main/docbkx/other_info.xml +++ /dev/null @@ -1,83 +0,0 @@ - - - - Other Information About HBase -
HBase Videos - Introduction to HBase - - Introduction to HBase by Todd Lipcon (Chicago Data Summit 2011). - - Introduction to HBase by Todd Lipcon (2010). - - - - Building Real Time Services at Facebook with HBase by Jonathan Gray (Hadoop World 2011). - - HBase and Hadoop, Mixing Real-Time and Batch Processing at StumbleUpon by JD Cryans (Hadoop World 2010). - -
-
HBase Presentations (Slides) - Advanced HBase Schema Design by Lars George (Hadoop World 2011). - - Introduction to HBase by Todd Lipcon (Chicago Data Summit 2011). - - Getting The Most From Your HBase Install by Ryan Rawson, Jonathan Gray (Hadoop World 2009). - -
-
HBase Papers - BigTable by Google (2006). - - HBase and HDFS Locality by Lars George (2010). - - No Relation: The Mixed Blessings of Non-Relational Databases by Ian Varley (2009). - -
-
HBase Sites - Cloudera's HBase Blog has a lot of links to useful HBase information. - - CAP Confusion is a relevant entry for background information on - distributed storage systems. - - - - HBase Wiki has a page with a number of presentations. - - HBase RefCard from DZone. - -
-
HBase Books - HBase: The Definitive Guide by Lars George. - -
-
Hadoop Books - Hadoop: The Definitive Guide by Tom White. - -
- - diff --git a/src/main/docbkx/performance.xml b/src/main/docbkx/performance.xml deleted file mode 100644 index 42ed79bc935..00000000000 --- a/src/main/docbkx/performance.xml +++ /dev/null @@ -1,1207 +0,0 @@ - - - - Apache HBase Performance Tuning - -
- Operating System -
- Memory - RAM, RAM, RAM. Don't starve HBase. -
-
- 64-bit - Use a 64-bit platform (and 64-bit JVM). -
-
- Swapping - Watch out for swapping. Set swappiness to 0. -
-
-
- Network - Perhaps the most important factor in avoiding network issues degrading Hadoop and HBase - performance is the switching hardware that is used, decisions made early in the scope of the - project can cause major problems when you double or triple the size of your cluster (or more). - Important items to consider: - - Switching capacity of the device - - - Number of systems connected - - - Uplink capacity - - - -
- Single Switch - The single most important factor in this configuration is that the switching capacity of - the hardware is capable of handling the traffic which can be generated by all systems - connected to the switch. Some lower priced commodity hardware can have a slower switching - capacity than could be utilized by a full switch. -
-
- Multiple Switches - Multiple switches are a potential pitfall in the architecture. The most common - configuration of lower priced hardware is a simple 1Gbps uplink from one switch to another. - This often overlooked pinch point can easily become a bottleneck for cluster communication. - Especially with MapReduce jobs that are both reading and writing a lot of data the - communication across this uplink could be saturated. - Mitigation of this issue is fairly simple and can be accomplished in multiple ways: - - - Use appropriate hardware for the scale of the cluster which you're attempting to - build. - - - Use larger single switch configurations i.e. single 48 port as opposed to 2x 24 - port - - - Configure port trunking for uplinks to utilize multiple interfaces to increase cross - switch bandwidth. - - -
-
- Multiple Racks - Multiple rack configurations carry the same potential issues as multiple switches, and - can suffer performance degradation from two main areas: - - - Poor switch capacity performance - - - Insufficient uplink to another rack - - - If the the switches in your rack have appropriate switching capacity to handle all the - hosts at full speed, the next most likely issue will be caused by homing more of your - cluster across racks. The easiest way to avoid issues when spanning multiple racks is to use - port trunking to create a bonded uplink to other racks. The downside of this method however, - is in the overhead of ports that could potentially be used. An example of this is, creating - an 8Gbps port channel from rack A to rack B, using 8 of your 24 ports to communicate between - racks gives you a poor ROI, using too few however can mean you're not getting the most out - of your cluster. - Using 10Gbe links between racks will greatly increase performance, and assuming your - switches support a 10Gbe uplink or allow for an expansion card will allow you to save your - ports for machines as opposed to uplinks. -
-
- Network Interfaces - Are all the network interfaces functioning correctly? Are you sure? See the - Troubleshooting Case Study in . -
-
- - -
- Java - -
- The Garbage Collector and Apache HBase - -
- Long GC pauses - - In his presentation, Avoiding Full GCs - with MemStore-Local Allocation Buffers, Todd Lipcon describes two cases of - stop-the-world garbage collections common in HBase, especially during loading; CMS failure - modes and old generation heap fragmentation brought. To address the first, start the CMS - earlier than default by adding -XX:CMSInitiatingOccupancyFraction and setting - it down from defaults. Start at 60 or 70 percent (The lower you bring down the threshold, - the more GCing is done, the more CPU used). To address the second fragmentation issue, - Todd added an experimental facility, MSLAB, that - must be explicitly enabled in Apache HBase 0.90.x (Its defaulted to be on in Apache 0.92.x - HBase). See hbase.hregion.memstore.mslab.enabled to true in your - Configuration. See the cited slides for background and detail. - The latest jvms do better regards fragmentation so make sure you are running a recent - release. Read down in the message, Identifying - concurrent mode failures caused by fragmentation. Be aware that when enabled, - each MemStore instance will occupy at least an MSLAB instance of memory. If you have - thousands of regions or lots of regions each with many column families, this allocation of - MSLAB may be responsible for a good portion of your heap allocation and in an extreme case - cause you to OOME. Disable MSLAB in this case, or lower the amount of memory it uses or - float less regions per server. - If you have a write-heavy workload, check out HBASE-8163 - MemStoreChunkPool: An improvement for JAVA GC when using MSLAB. It describes - configurations to lower the amount of young GC during write-heavy loadings. If you do not - have HBASE-8163 installed, and you are trying to improve your young GC times, one trick to - consider -- courtesy of our Liang Xie -- is to set the GC config - -XX:PretenureSizeThreshold in hbase-env.sh to be - just smaller than the size of hbase.hregion.memstore.mslab.chunksize so - MSLAB allocations happen in the tenured space directly rather than first in the young gen. - You'd do this because these MSLAB allocations are going to likely make it to the old gen - anyways and rather than pay the price of a copies between s0 and s1 in eden space followed - by the copy up from young to old gen after the MSLABs have achieved sufficient tenure, - save a bit of YGC churn and allocate in the old gen directly. - For more information about GC logs, see . - Consider also enabling the offheap Block Cache. This has been shown to mitigate - GC pause times. See -
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- HBase Configurations - - See . - -
- Managing Compactions - - For larger systems, managing compactions and splits may be - something you want to consider. -
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- <varname>hbase.regionserver.handler.count</varname> - See . - -
- - - -
- <varname>hfile.block.cache.size</varname> - See . - A memory setting for the RegionServer process. - -
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- Prefetch Option for Blockcache - HBASE-9857 - adds a new option to prefetch HFile contents when opening the blockcache, if a columnfamily - or regionserver property is set. This option is available for HBase 0.98.3 and later. The - purpose is to warm the blockcache as rapidly as possible after the cache is opened, using - in-memory table data, and not counting the prefetching as cache misses. This is great for - fast reads, but is not a good idea if the data to be preloaded will not fit into the - blockcache. It is useful for tuning the IO impact of prefetching versus the time before all - data blocks are in cache. - To enable prefetching on a given column family, you can use HBase Shell or use the - API. - - Enable Prefetch Using HBase Shell - hbase> create 'MyTable', { NAME => 'myCF', PREFETCH_BLOCKS_ON_OPEN => 'true' } - - - Enable Prefetch Using the API - -// ... -HTableDescriptor tableDesc = new HTableDescriptor("myTable"); -HColumnDescriptor cfDesc = new HColumnDescriptor("myCF"); -cfDesc.setPrefetchBlocksOnOpen(true); -tableDesc.addFamily(cfDesc); -// ... - - - See the API documentation for CacheConfig. -
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- <varname>hbase.regionserver.global.memstore.size</varname> - See . - This memory setting is often adjusted for the RegionServer process depending on needs. - -
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- <varname>hbase.regionserver.global.memstore.size.lower.limit</varname> - See . - This memory setting is often adjusted for the RegionServer process depending on needs. - -
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- <varname>hbase.hstore.blockingStoreFiles</varname> - See . - If there is blocking in the RegionServer logs, increasing this can help. - -
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- <varname>hbase.hregion.memstore.block.multiplier</varname> - See . - If there is enough RAM, increasing this can help. - -
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- <varname>hbase.regionserver.checksum.verify</varname> - Have HBase write the checksum into the datablock and save - having to do the checksum seek whenever you read. - - See , - and - For more information see the - release note on HBASE-5074 support checksums in HBase block cache. - -
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- Tuning <code>callQueue</code> Options - HBASE-11355 - introduces several callQueue tuning mechanisms which can increase performance. See the JIRA - for some benchmarking information. - - - To increase the number of callqueues, set - to a value greater than - 1. - - - To split the callqueue into separate read and write queues, set - hbase.ipc.server.callqueue.read.ratio to a value between - 0 and 1. This factor weights the queues toward - writes (if below .5) or reads (if above .5). Another way to say this is that the factor - determines what percentage of the split queues are used for reads. The following - examples illustrate some of the possibilities. Note that you always have at least one - write queue, no matter what setting you use. - - - The default value of 0 does not split the queue. - - - A value of .3 uses 30% of the queues for reading and 60% for - writing. Given a value of 10 for - , 3 queues would be used for reads - and 7 for writes. - - - A value of .5 uses the same number of read queues and write - queues. Given a value of 10 for - , 5 queues would be used for reads - and 5 for writes. - - - A value of .6 uses 60% of the queues for reading and 30% for - reading. Given a value of 10 for - , 7 queues would be used for reads - and 3 for writes. - - - A value of 1.0 uses one queue to process write requests, and - all other queues process read requests. A value higher than 1.0 - has the same effect as a value of 1.0. Given a value of - 10 for , 9 - queues would be used for reads and 1 for writes. - - - - - You can also split the read queues so that separate queues are used for short reads - (from Get operations) and long reads (from Scan operations), by setting the - option. This option is a factor - between 0 and 1, which determine the ratio of read queues used for Gets and Scans. More - queues are used for Gets if the value is below .5 and more are used - for scans if the value is above .5. No matter what setting you use, - at least one read queue is used for Get operations. - - - A value of 0 does not split the read queue. - - - A value of .3 uses 60% of the read queues for Gets and 30% - for Scans. Given a value of 20 for - and a value of .5 - for , 10 queues - would be used for reads, out of those 10, 7 would be used for Gets and 3 for - Scans. - - - A value of .5 uses half the read queues for Gets and half for - Scans. Given a value of 20 for - and a value of .5 - for , 10 queues - would be used for reads, out of those 10, 5 would be used for Gets and 5 for - Scans. - - - A value of .6 uses 30% of the read queues for Gets and 60% - for Scans. Given a value of 20 for - and a value of .5 - for , 10 queues - would be used for reads, out of those 10, 3 would be used for Gets and 7 for - Scans. - - - A value of 1.0 uses all but one of the read queues for Scans. - Given a value of 20 for - and a value of .5 - for , 10 queues - would be used for reads, out of those 10, 1 would be used for Gets and 9 for - Scans. - - - - - You can use the new option - to programmatically tune - the number of queues: - - - A value of 0 uses a single shared queue between all the - handlers. - - - A value of 1 uses a separate queue for each handler. - - - A value between 0 and 1 tunes the number - of queues against the number of handlers. For instance, a value of - .5 shares one queue between each two handlers. - - - Having more queues, such as in a situation where you have one queue per handler, - reduces contention when adding a task to a queue or selecting it from a queue. The - trade-off is that if you have some queues with long-running tasks, a handler may end up - waiting to execute from that queue rather than processing another queue which has - waiting tasks. - - - For these values to take effect on a given Region Server, the Region Server must be - restarted. These parameters are intended for testing purposes and should be used - carefully. -
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- - - - -
- ZooKeeper - See for information on configuring ZooKeeper, and see the part about - having a dedicated disk. -
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- Schema Design - -
- Number of Column Families - See . -
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- Key and Attribute Lengths - See . See also for compression caveats. -
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- Table RegionSize - The regionsize can be set on a per-table basis via setFileSize on HTableDescriptor - in the event where certain tables require different regionsizes than the configured default - regionsize. - See for more information. -
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- Bloom Filters - A Bloom filter, named for its creator, Burton Howard Bloom, is a data structure which is - designed to predict whether a given element is a member of a set of data. A positive result - from a Bloom filter is not always accurate, but a negative result is guaranteed to be - accurate. Bloom filters are designed to be "accurate enough" for sets of data which are so - large that conventional hashing mechanisms would be impractical. For more information about - Bloom filters in general, refer to . - In terms of HBase, Bloom filters provide a lightweight in-memory structure to reduce the - number of disk reads for a given Get operation (Bloom filters do not work with Scans) to only the StoreFiles likely to - contain the desired Row. The potential performance gain increases with the number of - parallel reads. - The Bloom filters themselves are stored in the metadata of each HFile and never need to - be updated. When an HFile is opened because a region is deployed to a RegionServer, the - Bloom filter is loaded into memory. - HBase includes some tuning mechanisms for folding the Bloom filter to reduce the size - and keep the false positive rate within a desired range. - Bloom filters were introduced in HBASE-1200. Since - HBase 0.96, row-based Bloom filters are enabled by default. (HBASE-) - For more information on Bloom filters in relation to HBase, see for more information, or the following Quora discussion: How are bloom - filters used in HBase?. - -
- When To Use Bloom Filters - Since HBase 0.96, row-based Bloom filters are enabled by default. You may choose to - disable them or to change some tables to use row+column Bloom filters, depending on the - characteristics of your data and how it is loaded into HBase. - - To determine whether Bloom filters could have a positive impact, check the value of - blockCacheHitRatio in the RegionServer metrics. If Bloom filters are enabled, the value of - blockCacheHitRatio should increase, because the Bloom filter is filtering out blocks that - are definitely not needed. - You can choose to enable Bloom filters for a row or for a row+column combination. If - you generally scan entire rows, the row+column combination will not provide any benefit. A - row-based Bloom filter can operate on a row+column Get, but not the other way around. - However, if you have a large number of column-level Puts, such that a row may be present - in every StoreFile, a row-based filter will always return a positive result and provide no - benefit. Unless you have one column per row, row+column Bloom filters require more space, - in order to store more keys. Bloom filters work best when the size of each data entry is - at least a few kilobytes in size. - Overhead will be reduced when your data is stored in a few larger StoreFiles, to avoid - extra disk IO during low-level scans to find a specific row. - Bloom filters need to be rebuilt upon deletion, so may not be appropriate in - environments with a large number of deletions. -
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- Enabling Bloom Filters - Bloom filters are enabled on a Column Family. You can do this by using the - setBloomFilterType method of HColumnDescriptor or using the HBase API. Valid values are - NONE (the default), ROW, or - ROWCOL. See for more information on ROW versus - ROWCOL. See also the API documentation for HColumnDescriptor. - The following example creates a table and enables a ROWCOL Bloom filter on the - colfam1 column family. - -hbase> create 'mytable',{NAME => 'colfam1', BLOOMFILTER => 'ROWCOL'} - -
- -
- Configuring Server-Wide Behavior of Bloom Filters - You can configure the following settings in the hbase-site.xml. - - - - - - Parameter - Default - Description - - - - - io.hfile.bloom.enabled - yes - Set to no to kill bloom filters server-wide if - something goes wrong - - - io.hfile.bloom.error.rate - .01 - The average false positive rate for bloom filters. Folding is used to - maintain the false positive rate. Expressed as a decimal representation of a - percentage. - - - io.hfile.bloom.max.fold - 7 - The guaranteed maximum fold rate. Changing this setting should not be - necessary and is not recommended. - - - io.storefile.bloom.max.keys - 128000000 - For default (single-block) Bloom filters, this specifies the maximum - number of keys. - - - io.storefile.delete.family.bloom.enabled - true - Master switch to enable Delete Family Bloom filters and store them in - the StoreFile. - - - io.storefile.bloom.block.size - 65536 - Target Bloom block size. Bloom filter blocks of approximately this size - are interleaved with data blocks. - - - hfile.block.bloom.cacheonwrite - false - Enables cache-on-write for inline blocks of a compound Bloom filter. - - - - -
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- ColumnFamily BlockSize - The blocksize can be configured for each ColumnFamily in a table, and this defaults to - 64k. Larger cell values require larger blocksizes. There is an inverse relationship between - blocksize and the resulting StoreFile indexes (i.e., if the blocksize is doubled then the - resulting indexes should be roughly halved). - See HColumnDescriptor - and for more information. -
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- In-Memory ColumnFamilies - ColumnFamilies can optionally be defined as in-memory. Data is still persisted to disk, - just like any other ColumnFamily. In-memory blocks have the highest priority in the , but it is not a guarantee that the entire table will be in - memory. - See HColumnDescriptor - for more information. -
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- Compression - Production systems should use compression with their ColumnFamily definitions. See for more information. -
- However... - Compression deflates data on disk. When it's in-memory (e.g., in - the MemStore) or on the wire (e.g., transferring between RegionServer and Client) it's - inflated. So while using ColumnFamily compression is a best practice, but it's not going - to completely eliminate the impact of over-sized Keys, over-sized ColumnFamily names, or - over-sized Column names. - See on for schema design tips, and for more information on HBase stores data internally. -
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- - -
- HBase General Patterns -
- Constants - When people get started with HBase they have a tendency to write code that looks like - this: - -Get get = new Get(rowkey); -Result r = htable.get(get); -byte[] b = r.getValue(Bytes.toBytes("cf"), Bytes.toBytes("attr")); // returns current version of value - - But especially when inside loops (and MapReduce jobs), converting the columnFamily and - column-names to byte-arrays repeatedly is surprisingly expensive. It's better to use - constants for the byte-arrays, like this: - -public static final byte[] CF = "cf".getBytes(); -public static final byte[] ATTR = "attr".getBytes(); -... -Get get = new Get(rowkey); -Result r = htable.get(get); -byte[] b = r.getValue(CF, ATTR); // returns current version of value - -
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- Writing to HBase - -
- Batch Loading - Use the bulk load tool if you can. See . Otherwise, pay attention to the below. -
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- Table Creation: Pre-Creating Regions - Tables in HBase are initially created with one region by default. For bulk imports, - this means that all clients will write to the same region until it is large enough to split - and become distributed across the cluster. A useful pattern to speed up the bulk import - process is to pre-create empty regions. Be somewhat conservative in this, because too-many - regions can actually degrade performance. - There are two different approaches to pre-creating splits. The first approach is to rely - on the default HBaseAdmin strategy (which is implemented in - Bytes.split)... - -byte[] startKey = ...; // your lowest key -byte[] endKey = ...; // your highest key -int numberOfRegions = ...; // # of regions to create -admin.createTable(table, startKey, endKey, numberOfRegions); - - And the other approach is to define the splits yourself... - -byte[][] splits = ...; // create your own splits -admin.createTable(table, splits); - - See for issues related to understanding your - keyspace and pre-creating regions. See - for discussion on manually pre-splitting regions. -
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- Table Creation: Deferred Log Flush - The default behavior for Puts using the Write Ahead Log (WAL) is that - WAL edits will be written immediately. If deferred log flush is - used, WAL edits are kept in memory until the flush period. The benefit is aggregated and - asynchronous WAL- writes, but the potential downside is that if the - RegionServer goes down the yet-to-be-flushed edits are lost. This is safer, however, than - not using WAL at all with Puts. - Deferred log flush can be configured on tables via HTableDescriptor. - The default value of hbase.regionserver.optionallogflushinterval is - 1000ms. -
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- HBase Client: AutoFlush - - When performing a lot of Puts, make sure that setAutoFlush is set to false on your HTable - instance. Otherwise, the Puts will be sent one at a time to the RegionServer. Puts added via - htable.add(Put) and htable.add( <List> Put) wind up in - the same write buffer. If autoFlush = false, these messages are not sent until - the write-buffer is filled. To explicitly flush the messages, call - flushCommits. Calling close on the - HTable instance will invoke - flushCommits. -
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- HBase Client: Turn off WAL on Puts - A frequent request is to disable the WAL to increase performance of Puts. This is only - appropriate for bulk loads, as it puts your data at risk by removing the protection of the - WAL in the event of a region server crash. Bulk loads can be re-run in the event of a crash, - with little risk of data loss. - - If you disable the WAL for anything other than bulk loads, your data is at - risk. - In general, it is best to use WAL for Puts, and where loading throughput is a concern to - use bulk loading techniques instead. For normal - Puts, you are not likely to see a performance improvement which would outweigh the risk. To - disable the WAL, see . -
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- HBase Client: Group Puts by RegionServer - In addition to using the writeBuffer, grouping Puts by - RegionServer can reduce the number of client RPC calls per writeBuffer flush. There is a - utility HTableUtil currently on TRUNK that does this, but you can - either copy that or implement your own version for those still on 0.90.x or earlier. -
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- MapReduce: Skip The Reducer - When writing a lot of data to an HBase table from a MR job (e.g., with TableOutputFormat), - and specifically where Puts are being emitted from the Mapper, skip the Reducer step. When a - Reducer step is used, all of the output (Puts) from the Mapper will get spooled to disk, - then sorted/shuffled to other Reducers that will most likely be off-node. It's far more - efficient to just write directly to HBase. - For summary jobs where HBase is used as a source and a sink, then writes will be coming - from the Reducer step (e.g., summarize values then write out result). This is a different - processing problem than from the the above case. -
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- Anti-Pattern: One Hot Region - If all your data is being written to one region at a time, then re-read the section on - processing timeseries data. - Also, if you are pre-splitting regions and all your data is still - winding up in a single region even though your keys aren't monotonically increasing, confirm - that your keyspace actually works with the split strategy. There are a variety of reasons - that regions may appear "well split" but won't work with your data. As the HBase client - communicates directly with the RegionServers, this can be obtained via HTable.getRegionLocation. - See , as well as - -
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- Reading from HBase - The mailing list can help if you are having performance issues. For example, here is a - good general thread on what to look at addressing read-time issues: HBase Random Read latency > - 100ms -
- Scan Caching - - If HBase is used as an input source for a MapReduce job, for example, make sure that the - input Scan - instance to the MapReduce job has setCaching set to something - greater than the default (which is 1). Using the default value means that the map-task will - make call back to the region-server for every record processed. Setting this value to 500, - for example, will transfer 500 rows at a time to the client to be processed. There is a - cost/benefit to have the cache value be large because it costs more in memory for both - client and RegionServer, so bigger isn't always better. -
- Scan Caching in MapReduce Jobs - Scan settings in MapReduce jobs deserve special attention. Timeouts can result (e.g., - UnknownScannerException) in Map tasks if it takes longer to process a batch of records - before the client goes back to the RegionServer for the next set of data. This problem can - occur because there is non-trivial processing occuring per row. If you process rows - quickly, set caching higher. If you process rows more slowly (e.g., lots of - transformations per row, writes), then set caching lower. - Timeouts can also happen in a non-MapReduce use case (i.e., single threaded HBase - client doing a Scan), but the processing that is often performed in MapReduce jobs tends - to exacerbate this issue. -
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- Scan Attribute Selection - - Whenever a Scan is used to process large numbers of rows (and especially when used as a - MapReduce source), be aware of which attributes are selected. If scan.addFamily - is called then all of the attributes in the specified ColumnFamily will - be returned to the client. If only a small number of the available attributes are to be - processed, then only those attributes should be specified in the input scan because - attribute over-selection is a non-trivial performance penalty over large datasets. -
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- Avoid scan seeks - When columns are selected explicitly with scan.addColumn, HBase will - schedule seek operations to seek between the selected columns. When rows have few columns - and each column has only a few versions this can be inefficient. A seek operation is - generally slower if does not seek at least past 5-10 columns/versions or 512-1024 - bytes. - In order to opportunistically look ahead a few columns/versions to see if the next - column/version can be found that way before a seek operation is scheduled, a new attribute - Scan.HINT_LOOKAHEAD can be set the on Scan object. The following code - instructs the RegionServer to attempt two iterations of next before a seek is - scheduled: - -Scan scan = new Scan(); -scan.addColumn(...); -scan.setAttribute(Scan.HINT_LOOKAHEAD, Bytes.toBytes(2)); -table.getScanner(scan); - -
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- MapReduce - Input Splits - For MapReduce jobs that use HBase tables as a source, if there a pattern where the - "slow" map tasks seem to have the same Input Split (i.e., the RegionServer serving the - data), see the Troubleshooting Case Study in . -
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- Close ResultScanners - - This isn't so much about improving performance but rather avoiding - performance problems. If you forget to close ResultScanners - you can cause problems on the RegionServers. Always have ResultScanner processing enclosed - in try/catch blocks... - -Scan scan = new Scan(); -// set attrs... -ResultScanner rs = htable.getScanner(scan); -try { - for (Result r = rs.next(); r != null; r = rs.next()) { - // process result... -} finally { - rs.close(); // always close the ResultScanner! -} -htable.close(); - -
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- Block Cache - - Scan - instances can be set to use the block cache in the RegionServer via the - setCacheBlocks method. For input Scans to MapReduce jobs, this - should be false. For frequently accessed rows, it is advisable to use the - block cache. - - Cache more data by moving your Block Cache offheap. See -
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- Optimal Loading of Row Keys - When performing a table scan - where only the row keys are needed (no families, qualifiers, values or timestamps), add a - FilterList with a MUST_PASS_ALL operator to the scanner using - setFilter. The filter list should include both a FirstKeyOnlyFilter - and a KeyOnlyFilter. - Using this filter combination will result in a worst case scenario of a RegionServer reading - a single value from disk and minimal network traffic to the client for a single row. -
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- Concurrency: Monitor Data Spread - When performing a high number of concurrent reads, monitor the data spread of the target - tables. If the target table(s) have too few regions then the reads could likely be served - from too few nodes. - See , as well as - -
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- Bloom Filters - Enabling Bloom Filters can save your having to go to disk and can help improve read - latencies. - Bloom filters were developed - over in HBase-1200 Add - bloomfilters. For description of the development process -- why static blooms rather than dynamic - -- and for an overview of the unique properties that pertain to blooms in HBase, as well - as possible future directions, see the Development Process section - of the document BloomFilters - in HBase attached to HBase-1200. The bloom filters described here are actually version two of blooms in HBase. In - versions up to 0.19.x, HBase had a dynamic bloom option based on work done by the European Commission One-Lab Project 034819. - The core of the HBase bloom work was later pulled up into Hadoop to implement - org.apache.hadoop.io.BloomMapFile. Version 1 of HBase blooms never worked that well. - Version 2 is a rewrite from scratch though again it starts with the one-lab work. - - See also . - -
- Bloom StoreFile footprint - - Bloom filters add an entry to the StoreFile general - FileInfo data structure and then two extra entries to the - StoreFile metadata section. - -
- BloomFilter in the <classname>StoreFile</classname> - <classname>FileInfo</classname> data structure - - FileInfo has a BLOOM_FILTER_TYPE entry - which is set to NONE, ROW or - ROWCOL. -
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- BloomFilter entries in <classname>StoreFile</classname> metadata - - BLOOM_FILTER_META holds Bloom Size, Hash Function used, etc. Its - small in size and is cached on StoreFile.Reader load - BLOOM_FILTER_DATA is the actual bloomfilter data. Obtained - on-demand. Stored in the LRU cache, if it is enabled (Its enabled by default). -
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- Bloom Filter Configuration -
- <varname>io.hfile.bloom.enabled</varname> global kill switch - - io.hfile.bloom.enabled in Configuration serves - as the kill switch in case something goes wrong. Default = - true. -
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- <varname>io.hfile.bloom.error.rate</varname> - - io.hfile.bloom.error.rate = average false positive rate. Default - = 1%. Decrease rate by ½ (e.g. to .5%) == +1 bit per bloom entry. -
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- <varname>io.hfile.bloom.max.fold</varname> - - io.hfile.bloom.max.fold = guaranteed minimum fold rate. Most - people should leave this alone. Default = 7, or can collapse to at least 1/128th of - original size. See the Development Process section of the document BloomFilters - in HBase for more on what this option means. -
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- Hedged Reads - Hedged reads are a feature of HDFS, introduced in HDFS-5776. Normally, a - single thread is spawned for each read request. However, if hedged reads are enabled, the - client waits some configurable amount of time, and if the read does not return, the client - spawns a second read request, against a different block replica of the same data. Whichever - read returns first is used, and the other read request is discarded. Hedged reads can be - helpful for times where a rare slow read is caused by a transient error such as a failing - disk or flaky network connection. - Because a HBase RegionServer is a HDFS client, you can enable hedged reads in HBase, by - adding the following properties to the RegionServer's hbase-site.xml and tuning the values - to suit your environment. - - Configuration for Hedged Reads - - dfs.client.hedged.read.threadpool.size - the number of threads - dedicated to servicing hedged reads. If this is set to 0 (the default), hedged reads are - disabled. - - - dfs.client.hedged.read.threshold.millis - the number of milliseconds to - wait before spawning a second read thread. - - - - Hedged Reads Configuration Example - - dfs.client.hedged.read.threadpool.size - 20 - - - dfs.client.hedged.read.threshold.millis - 10 -]]> - - Use the following metrics to tune the settings for hedged reads on - your cluster. See for more information. - - Metrics for Hedged Reads - - hedgedReadOps - the number of times hedged read threads have been triggered. This - could indicate that read requests are often slow, or that hedged reads are triggered too - quickly. - - - hedgeReadOpsWin - the number of times the hedged read thread was faster than the - original thread. This could indicate that a given RegionServer is having trouble - servicing requests. - - -
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- Deleting from HBase -
- Using HBase Tables as Queues - HBase tables are sometimes used as queues. In this case, special care must be taken to - regularly perform major compactions on tables used in this manner. As is documented in , marking rows as deleted creates additional StoreFiles which then - need to be processed on reads. Tombstones only get cleaned up with major compactions. - See also and HBaseAdmin.majorCompact. - -
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- Delete RPC Behavior - Be aware that htable.delete(Delete) doesn't use the writeBuffer. It will - execute an RegionServer RPC with each invocation. For a large number of deletes, consider - htable.delete(List). - See - -
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- - -
- HDFS - Because HBase runs on it is important to understand how it works and how it affects HBase. -
- Current Issues With Low-Latency Reads - The original use-case for HDFS was batch processing. As such, there low-latency reads - were historically not a priority. With the increased adoption of Apache HBase this is - changing, and several improvements are already in development. See the Umbrella Jira Ticket for HDFS - Improvements for HBase. -
-
- Leveraging local data - Since Hadoop 1.0.0 (also 0.22.1, 0.23.1, CDH3u3 and HDP 1.0) via HDFS-2246, it is - possible for the DFSClient to take a "short circuit" and read directly from the disk instead - of going through the DataNode when the data is local. What this means for HBase is that the - RegionServers can read directly off their machine's disks instead of having to open a socket - to talk to the DataNode, the former being generally much faster. See JD's Performance - Talk. Also see HBase, mail # dev - read short - circuit thread for more discussion around short circuit reads. - To enable "short circuit" reads, it will depend on your version of Hadoop. The original - shortcircuit read patch was much improved upon in Hadoop 2 in HDFS-347. See - for details on the difference between the old and new implementations. See Hadoop - shortcircuit reads configuration page for how to enable the latter, better version - of shortcircuit. For example, here is a minimal config. enabling short-circuit reads added - to hbase-site.xml: - - dfs.client.read.shortcircuit - true - - This configuration parameter turns on short-circuit local reads. - - - - dfs.domain.socket.path - /home/stack/sockets/short_circuit_read_socket_PORT - - Optional. This is a path to a UNIX domain socket that will be used for - communication between the DataNode and local HDFS clients. - If the string "_PORT" is present in this path, it will be replaced by the - TCP port of the DataNode. - -]]> - Be careful about permissions for the directory that hosts the shared domain socket; - dfsclient will complain if open to other than the hbase user. - If you are running on an old Hadoop, one that is without HDFS-347 but that has - HDFS-2246, you - must set two configurations. First, the hdfs-site.xml needs to be amended. Set the property - dfs.block.local-path-access.user to be the only - user that can use the shortcut. This has to be the user that started HBase. Then in - hbase-site.xml, set dfs.client.read.shortcircuit to be - true - - - Services -- at least the HBase RegionServers -- will need to be restarted in order to - pick up the new configurations. - - dfs.client.read.shortcircuit.buffer.size - The default for this value is too high when running on a highly trafficed HBase. In - HBase, if this value has not been set, we set it down from the default of 1M to 128k - (Since HBase 0.98.0 and 0.96.1). See HBASE-8143 HBase on Hadoop - 2 with local short circuit reads (ssr) causes OOM). The Hadoop DFSClient in HBase - will allocate a direct byte buffer of this size for each block it has - open; given HBase keeps its HDFS files open all the time, this can add up quickly. - -
- -
- Performance Comparisons of HBase vs. HDFS - A fairly common question on the dist-list is why HBase isn't as performant as HDFS files - in a batch context (e.g., as a MapReduce source or sink). The short answer is that HBase is - doing a lot more than HDFS (e.g., reading the KeyValues, returning the most current row or - specified timestamps, etc.), and as such HBase is 4-5 times slower than HDFS in this - processing context. There is room for improvement and this gap will, over time, be reduced, - but HDFS will always be faster in this use-case. -
-
- -
- Amazon EC2 - Performance questions are common on Amazon EC2 environments because it is a shared - environment. You will not see the same throughput as a dedicated server. In terms of running - tests on EC2, run them several times for the same reason (i.e., it's a shared environment and - you don't know what else is happening on the server). - If you are running on EC2 and post performance questions on the dist-list, please state - this fact up-front that because EC2 issues are practically a separate class of performance - issues. -
- -
- Collocating HBase and MapReduce - It is often recommended to have different clusters for HBase and MapReduce. A better - qualification of this is: don't collocate a HBase that serves live requests with a heavy MR - workload. OLTP and OLAP-optimized systems have conflicting requirements and one will lose to - the other, usually the former. For example, short latency-sensitive disk reads will have to - wait in line behind longer reads that are trying to squeeze out as much throughput as - possible. MR jobs that write to HBase will also generate flushes and compactions, which will - in turn invalidate blocks in the . - If you need to process the data from your live HBase cluster in MR, you can ship the - deltas with or use replication to get the new data in real time on the OLAP - cluster. In the worst case, if you really need to collocate both, set MR to use less Map and - Reduce slots than you'd normally configure, possibly just one. - When HBase is used for OLAP operations, it's preferable to set it up in a hardened way - like configuring the ZooKeeper session timeout higher and giving more memory to the MemStores - (the argument being that the Block Cache won't be used much since the workloads are usually - long scans). -
- -
- Case Studies - For Performance and Troubleshooting Case Studies, see . -
- diff --git a/src/main/docbkx/preface.xml b/src/main/docbkx/preface.xml deleted file mode 100644 index a8f68959ebc..00000000000 --- a/src/main/docbkx/preface.xml +++ /dev/null @@ -1,83 +0,0 @@ - - - - Preface - - This is the official reference guide for the HBase version it ships with. Herein you - will find either the definitive documentation on an HBase topic as of its standing when the - referenced HBase version shipped, or it will point to the location in javadoc, JIRA or wiki where the pertinent - information can be found. - - - About This Guide - This reference guide is a work in progress. The source for this guide can be found in - the src/main/docbkx directory of the HBase source. This reference - guide is marked up using DocBook from - which the the finished guide is generated as part of the 'site' build target. Run - mvn site to generate this documentation. Amendments and - improvements to the documentation are welcomed. Click this link to file a new documentation bug against Apache HBase with some - values pre-selected. - - - Contributing to the Documentation - For an overview of Docbook and suggestions to get started contributing to the documentation, see . - - - Providing Feedback - This guide allows you to leave comments or questions on any page, using Disqus. Look - for the Comments area at the bottom of the page. Answering these questions is a - volunteer effort, and may be delayed. - - - - Heads-up if this is your first foray into the world of distributed - computing... - If this is your first foray into the wonderful world of Distributed Computing, then - you are in for some interesting times. First off, distributed systems are hard; making a - distributed system hum requires a disparate skillset that spans systems (hardware and - software) and networking. Your cluster' operation can hiccup because of any of a myriad - set of reasons from bugs in HBase itself through misconfigurations -- misconfiguration - of HBase but also operating system misconfigurations -- through to hardware problems - whether it be a bug in your network card drivers or an underprovisioned RAM bus (to - mention two recent examples of hardware issues that manifested as "HBase is slow"). You - will also need to do a recalibration if up to this your computing has been bound to a - single box. Here is one good starting point: Fallacies - of Distributed Computing. That said, you are welcome. Its a fun place to be. - Yours, the HBase Community. - - diff --git a/src/main/docbkx/rpc.xml b/src/main/docbkx/rpc.xml deleted file mode 100644 index 2e5dd5f71f9..00000000000 --- a/src/main/docbkx/rpc.xml +++ /dev/null @@ -1,301 +0,0 @@ - - - - - 0.95 RPC Specification - In 0.95, all client/server communication is done with protobuf’ed Messages rather than - with Hadoop - Writables. Our RPC wire format therefore changes. This document describes the - client/server request/response protocol and our new RPC wire-format. - - For what RPC is like in 0.94 and previous, see Benoît/Tsuna’s Unofficial - Hadoop / HBase RPC protocol documentation. For more background on how we arrived - at this spec., see HBase - RPC: WIP - -
- Goals - - - - A wire-format we can evolve - - - A format that does not require our rewriting server core or radically - changing its current architecture (for later). - - - -
-
- TODO - - - - List of problems with currently specified format and where we would like - to go in a version2, etc. For example, what would we have to change if - anything to move server async or to support streaming/chunking? - - - Diagram on how it works - - - A grammar that succinctly describes the wire-format. Currently we have - these words and the content of the rpc protobuf idl but a grammar for the - back and forth would help with groking rpc. Also, a little state machine on - client/server interactions would help with understanding (and ensuring - correct implementation). - - - -
-
- RPC - The client will send setup information on connection establish. Thereafter, the client - invokes methods against the remote server sending a protobuf Message and receiving a - protobuf Message in response. Communication is synchronous. All back and forth is - preceded by an int that has the total length of the request/response. Optionally, - Cells(KeyValues) can be passed outside of protobufs in follow-behind Cell blocks - (because we - can’t protobuf megabytes of KeyValues or Cells). These CellBlocks are encoded - and optionally compressed. - - For more detail on the protobufs involved, see the RPC.proto - file in trunk. - -
- Connection Setup - Client initiates connection. -
- Client - On connection setup, client sends a preamble followed by a connection header. - -
- <preamble> - <MAGIC 4 byte integer> <1 byte RPC Format Version> <1 byte auth type> - We need the auth method spec. here so the connection header is encoded if auth enabled. - E.g.: HBas0x000x50 -- 4 bytes of MAGIC -- ‘HBas’ -- plus one-byte of - version, 0 in this case, and one byte, 0x50 (SIMPLE). of an auth - type. -
- -
- <Protobuf ConnectionHeader Message> - Has user info, and “protocol”, as well as the encoders and compression the - client will use sending CellBlocks. CellBlock encoders and compressors are - for the life of the connection. CellBlock encoders implement - org.apache.hadoop.hbase.codec.Codec. CellBlocks may then also be compressed. - Compressors implement org.apache.hadoop.io.compress.CompressionCodec. This - protobuf is written using writeDelimited so is prefaced by a pb varint with - its serialized length -
-
- - -
- Server - After client sends preamble and connection header, server does NOT respond if - successful connection setup. No response means server is READY to accept - requests and to give out response. If the version or authentication in the - preamble is not agreeable or the server has trouble parsing the preamble, it - will throw a org.apache.hadoop.hbase.ipc.FatalConnectionException explaining the - error and will then disconnect. If the client in the connection header -- i.e. - the protobuf’d Message that comes after the connection preamble -- asks for for - a Service the server does not support or a codec the server does not have, again - we throw a FatalConnectionException with explanation. -
-
- -
- Request - After a Connection has been set up, client makes requests. Server responds. - A request is made up of a protobuf RequestHeader followed by a protobuf Message - parameter. The header includes the method name and optionally, metadata on the - optional CellBlock that may be following. The parameter type suits the method being - invoked: i.e. if we are doing a getRegionInfo request, the protobuf Message param - will be an instance of GetRegionInfoRequest. The response will be a - GetRegionInfoResponse. The CellBlock is optionally used ferrying the bulk of the RPC - data: i.e Cells/KeyValues. -
- Request Parts -
- <Total Length> - The request is prefaced by an int that holds the total length of what - follows. -
-
- <Protobuf RequestHeader Message> - Will have call.id, trace.id, and method name, etc. including optional - Metadata on the Cell block IFF one is following. Data is protobuf’d inline - in this pb Message or optionally comes in the following CellBlock -
-
- <Protobuf Param Message> - If the method being invoked is getRegionInfo, if you study the Service - descriptor for the client to regionserver protocol, you will find that the - request sends a GetRegionInfoRequest protobuf Message param in this - position. -
-
- <CellBlock> - An encoded and optionally compressed Cell block. -
-
- -
- - -
- Response - Same as Request, it is a protobuf ResponseHeader followed by a protobuf Message - response where the Message response type suits the method invoked. Bulk of the data - may come in a following CellBlock. -
- Response Parts -
- <Total Length> - The response is prefaced by an int that holds the total length of what - follows. -
-
- <Protobuf ResponseHeader Message> - Will have call.id, etc. Will include exception if failed processing. -  Optionally includes metadata on optional, IFF there is a CellBlock - following. -
- -
- <Protobuf Response Message> - Return or may be nothing if exception. If the method being invoked is - getRegionInfo, if you study the Service descriptor for the client to - regionserver protocol, you will find that the response sends a - GetRegionInfoResponse protobuf Message param in this position. -
-
- <CellBlock> - An encoded and optionally compressed Cell block. -
-
- -
- - -
- Exceptions - There are two distinct types. There is the request failed which is encapsulated - inside the response header for the response. The connection stays open to receive - new requests. The second type, the FatalConnectionException, kills the - connection. - Exceptions can carry extra information. See the ExceptionResponse protobuf type. - It has a flag to indicate do-no-retry as well as other miscellaneous payload to help - improve client responsiveness. -
-
- CellBlocks - These are not versioned. Server can do the codec or it cannot. If new version of a - codec with say, tighter encoding, then give it a new class name. Codecs will live on - the server for all time so old clients can connect. -
-
- - -
- Notes -
- Constraints - In some part, current wire-format -- i.e. all requests and responses preceeded by - a length -- has been dictated by current server non-async architecture. -
-
- One fat pb request or header+param - We went with pb header followed by pb param making a request and a pb header - followed by pb response for now. Doing header+param rather than a single protobuf - Message with both header and param content: - - - - Is closer to what we currently have - - - Having a single fat pb requires extra copying putting the already pb’d - param into the body of the fat request pb (and same making - result) - - - We can decide whether to accept the request or not before we read the - param; for example, the request might be low priority.  As is, we read - header+param in one go as server is currently implemented so this is a - TODO. - - - - The advantages are minor.  If later, fat request has clear advantage, can roll out - a v2 later. -
-
- RPC Configurations -
- CellBlock Codecs - To enable a codec other than the default KeyValueCodec, - set hbase.client.rpc.codec to the name of the Codec class to - use. Codec must implement hbase's Codec Interface. After - connection setup, all passed cellblocks will be sent with this codec. The server - will return cellblocks using this same codec as long as the codec is on the - servers' CLASSPATH (else you will get - UnsupportedCellCodecException). - To change the default codec, set - hbase.client.default.rpc.codec. - To disable cellblocks completely and to go pure protobuf, set the default to - the empty String and do not specify a codec in your Configuration. So, set - hbase.client.default.rpc.codec to the empty string and do - not set hbase.client.rpc.codec. This will cause the client to - connect to the server with no codec specified. If a server sees no codec, it - will return all responses in pure protobuf. Running pure protobuf all the time - will be slower than running with cellblocks. -
-
- Compression - Uses hadoops compression codecs. To enable compressing of passed CellBlocks, - set hbase.client.rpc.compressor to the name of the Compressor - to use. Compressor must implement Hadoops' CompressionCodec Interface. After - connection setup, all passed cellblocks will be sent compressed. The server will - return cellblocks compressed using this same compressor as long as the - compressor is on its CLASSPATH (else you will get - UnsupportedCompressionCodecException). -
-
-
- diff --git a/src/main/docbkx/schema_design.xml b/src/main/docbkx/schema_design.xml deleted file mode 100644 index e4632eca29f..00000000000 --- a/src/main/docbkx/schema_design.xml +++ /dev/null @@ -1,1262 +0,0 @@ - - - - HBase and Schema Design - A good general introduction on the strength and weaknesses modelling on the various - non-rdbms datastores is Ian Varley's Master thesis, No Relation: - The Mixed Blessings of Non-Relational Databases. Recommended. Also, read for how HBase stores data internally, and the section on . -
- Schema Creation - HBase schemas can be created or updated with or by using HBaseAdmin - in the Java API. - Tables must be disabled when making ColumnFamily modifications, for example: - -Configuration config = HBaseConfiguration.create(); -HBaseAdmin admin = new HBaseAdmin(conf); -String table = "myTable"; - -admin.disableTable(table); - -HColumnDescriptor cf1 = ...; -admin.addColumn(table, cf1); // adding new ColumnFamily -HColumnDescriptor cf2 = ...; -admin.modifyColumn(table, cf2); // modifying existing ColumnFamily - -admin.enableTable(table); - - See for more information about configuring client - connections. - Note: online schema changes are supported in the 0.92.x codebase, but the 0.90.x codebase - requires the table to be disabled. -
- Schema Updates - When changes are made to either Tables or ColumnFamilies (e.g., region size, block - size), these changes take effect the next time there is a major compaction and the - StoreFiles get re-written. - See for more information on StoreFiles. -
-
-
- On the number of column families - HBase currently does not do well with anything above two or three column families so keep - the number of column families in your schema low. Currently, flushing and compactions are done - on a per Region basis so if one column family is carrying the bulk of the data bringing on - flushes, the adjacent families will also be flushed though the amount of data they carry is - small. When many column families the flushing and compaction interaction can make for a bunch - of needless i/o loading (To be addressed by changing flushing and compaction to work on a per - column family basis). For more information on compactions, see . - Try to make do with one column family if you can in your schemas. Only introduce a second - and third column family in the case where data access is usually column scoped; i.e. you query - one column family or the other but usually not both at the one time. -
- Cardinality of ColumnFamilies - Where multiple ColumnFamilies exist in a single table, be aware of the cardinality - (i.e., number of rows). If ColumnFamilyA has 1 million rows and ColumnFamilyB has 1 billion - rows, ColumnFamilyA's data will likely be spread across many, many regions (and - RegionServers). This makes mass scans for ColumnFamilyA less efficient. -
-
-
- Rowkey Design -
- Hotspotting - Rows in HBase are sorted lexicographically by row key. This design optimizes for scans, - allowing you to store related rows, or rows that will be read together, near each other. - However, poorly designed row keys are a common source of hotspotting. - Hotspotting occurs when a large amount of client traffic is directed at one node, or only a - few nodes, of a cluster. This traffic may represent reads, writes, or other operations. The - traffic overwhelms the single machine responsible for hosting that region, causing - performance degradation and potentially leading to region unavailability. This can also have - adverse effects on other regions hosted by the same region server as that host is unable to - service the requested load. It is important to design data access patterns such that the - cluster is fully and evenly utilized. - To prevent hotspotting on writes, design your row keys such that rows that truly do need - to be in the same region are, but in the bigger picture, data is being written to multiple - regions across the cluster, rather than one at a time. Some common techniques for avoiding - hotspotting are described below, along with some of their advantages and drawbacks. - - Salting - Salting in this sense has nothing to do with cryptography, but refers to adding random - data to the start of a row key. In this case, salting refers to adding a randomly-assigned - prefix to the row key to cause it to sort differently than it otherwise would. The number - of possible prefixes correspond to the number of regions you want to spread the data - across. Salting can be helpful if you have a few "hot" row key patterns which come up over - and over amongst other more evenly-distributed rows. Consider the following example, which - shows that salting can spread write load across multiple regionservers, and illustrates - some of the negative implications for reads. - - - Salting Example - Suppose you have the following list of row keys, and your table is split such that - there is one region for each letter of the alphabet. Prefix 'a' is one region, prefix 'b' - is another. In this table, all rows starting with 'f' are in the same region. This example - focuses on rows with keys like the following: - -foo0001 -foo0002 -foo0003 -foo0004 - - Now, imagine that you would like to spread these across four different regions. You - decide to use four different salts: a, b, - c, and d. In this scenario, each of these letter - prefixes will be on a different region. After applying the salts, you have the following - rowkeys instead. Since you can now write to four separate regions, you theoretically have - four times the throughput when writing that you would have if all the writes were going to - the same region. - -a-foo0003 -b-foo0001 -c-foo0004 -d-foo0002 - - Then, if you add another row, it will randomly be assigned one of the four possible - salt values and end up near one of the existing rows. - -a-foo0003 -b-foo0001 -c-foo0003 -c-foo0004 -d-foo0002 - - Since this assignment will be random, you will need to do more work if you want to - retrieve the rows in lexicographic order. In this way, salting attempts to increase - throughput on writes, but has a cost during reads. - - - - Hashing - Instead of a random assignment, you could use a one-way hash - that would cause a given row to always be "salted" with the same prefix, in a way that - would spread the load across the regionservers, but allow for predictability during reads. - Using a deterministic hash allows the client to reconstruct the complete rowkey and use a - Get operation to retrieve that row as normal. - - - Hashing Example - Given the same situation in the salting example above, you could instead apply a - one-way hash that would cause the row with key foo0003 to always, and - predictably, receive the a prefix. Then, to retrieve that row, you - would already know the key. You could also optimize things so that certain pairs of keys - were always in the same region, for instance. - - - Reversing the Key - A third common trick for preventing hotspotting is to reverse a fixed-width or numeric - row key so that the part that changes the most often (the least significant digit) is first. - This effectively randomizes row keys, but sacrifices row ordering properties. - - See https://communities.intel.com/community/itpeernetwork/datastack/blog/2013/11/10/discussion-on-designing-hbase-tables, - and article on Salted Tables - from the Phoenix project, and the discussion in the comments of HBASE-11682 for more - information about avoiding hotspotting. -
-
- Monotonically Increasing Row Keys/Timeseries Data - In the HBase chapter of Tom White's book Hadoop: The Definitive Guide - (O'Reilly) there is a an optimization note on watching out for a phenomenon where an import - process walks in lock-step with all clients in concert pounding one of the table's regions - (and thus, a single node), then moving onto the next region, etc. With monotonically - increasing row-keys (i.e., using a timestamp), this will happen. See this comic by IKai Lan - on why monotonically increasing row keys are problematic in BigTable-like datastores: monotonically - increasing values are bad. The pile-up on a single region brought on by - monotonically increasing keys can be mitigated by randomizing the input records to not be in - sorted order, but in general it's best to avoid using a timestamp or a sequence (e.g. 1, 2, - 3) as the row-key. - If you do need to upload time series data into HBase, you should study OpenTSDB as a successful example. It has a page - describing the schema it uses in HBase. The key - format in OpenTSDB is effectively [metric_type][event_timestamp], which would appear at - first glance to contradict the previous advice about not using a timestamp as the key. - However, the difference is that the timestamp is not in the lead - position of the key, and the design assumption is that there are dozens or hundreds (or - more) of different metric types. Thus, even with a continual stream of input data with a mix - of metric types, the Puts are distributed across various points of regions in the table. - See for some rowkey design examples. -
-
- Try to minimize row and column sizes - Or why are my StoreFile indices large? - In HBase, values are always freighted with their coordinates; as a cell value passes - through the system, it'll be accompanied by its row, column name, and timestamp - always. If - your rows and column names are large, especially compared to the size of the cell value, - then you may run up against some interesting scenarios. One such is the case described by - Marc Limotte at the tail of HBASE-3551 - (recommended!). Therein, the indices that are kept on HBase storefiles () to facilitate random access may end up occupyng large chunks of the - HBase allotted RAM because the cell value coordinates are large. Mark in the above cited - comment suggests upping the block size so entries in the store file index happen at a larger - interval or modify the table schema so it makes for smaller rows and column names. - Compression will also make for larger indices. See the thread a - question storefileIndexSize up on the user mailing list. - Most of the time small inefficiencies don't matter all that much. Unfortunately, this is - a case where they do. Whatever patterns are selected for ColumnFamilies, attributes, and - rowkeys they could be repeated several billion times in your data. - See for more information on HBase stores data internally to see why this - is important. -
- Column Families - Try to keep the ColumnFamily names as small as possible, preferably one character - (e.g. "d" for data/default). - See for more information on HBase stores data internally to see why - this is important. -
-
- Attributes - Although verbose attribute names (e.g., "myVeryImportantAttribute") are easier to - read, prefer shorter attribute names (e.g., "via") to store in HBase. - See for more information on HBase stores data internally to see why - this is important. -
-
- Rowkey Length - Keep them as short as is reasonable such that they can still be useful for required - data access (e.g., Get vs. Scan). A short key that is useless for data access is not - better than a longer key with better get/scan properties. Expect tradeoffs when designing - rowkeys. -
-
- Byte Patterns - A long is 8 bytes. You can store an unsigned number up to 18,446,744,073,709,551,615 - in those eight bytes. If you stored this number as a String -- presuming a byte per - character -- you need nearly 3x the bytes. - Not convinced? Below is some sample code that you can run on your own. - -// long -// -long l = 1234567890L; -byte[] lb = Bytes.toBytes(l); -System.out.println("long bytes length: " + lb.length); // returns 8 - -String s = "" + l; -byte[] sb = Bytes.toBytes(s); -System.out.println("long as string length: " + sb.length); // returns 10 - -// hash -// -MessageDigest md = MessageDigest.getInstance("MD5"); -byte[] digest = md.digest(Bytes.toBytes(s)); -System.out.println("md5 digest bytes length: " + digest.length); // returns 16 - -String sDigest = new String(digest); -byte[] sbDigest = Bytes.toBytes(sDigest); -System.out.println("md5 digest as string length: " + sbDigest.length); // returns 26 - - Unfortunately, using a binary representation of a type will make your data harder to - read outside of your code. For example, this is what you will see in the shell when you - increment a value: - -hbase(main):001:0> incr 't', 'r', 'f:q', 1 -COUNTER VALUE = 1 - -hbase(main):002:0> get 't', 'r' -COLUMN CELL - f:q timestamp=1369163040570, value=\x00\x00\x00\x00\x00\x00\x00\x01 -1 row(s) in 0.0310 seconds - - The shell makes a best effort to print a string, and it this case it decided to just - print the hex. The same will happen to your row keys inside the region names. It can be - okay if you know what's being stored, but it might also be unreadable if arbitrary data - can be put in the same cells. This is the main trade-off. -
- -
-
- Reverse Timestamps - - Reverse Scan API - - HBASE-4811 - implements an API to scan a table or a range within a table in reverse, reducing the need - to optimize your schema for forward or reverse scanning. This feature is available in - HBase 0.98 and later. See - for more information. - - - A common problem in database processing is quickly finding the most recent version of a - value. A technique using reverse timestamps as a part of the key can help greatly with a - special case of this problem. Also found in the HBase chapter of Tom White's book Hadoop: - The Definitive Guide (O'Reilly), the technique involves appending (Long.MAX_VALUE - - timestamp) to the end of any key, e.g., [key][reverse_timestamp]. - The most recent value for [key] in a table can be found by performing a Scan for [key] - and obtaining the first record. Since HBase keys are in sorted order, this key sorts before - any older row-keys for [key] and thus is first. - This technique would be used instead of using where the intent is to hold onto all versions "forever" (or a - very long time) and at the same time quickly obtain access to any other version by using the - same Scan technique. -
-
- Rowkeys and ColumnFamilies - Rowkeys are scoped to ColumnFamilies. Thus, the same rowkey could exist in each - ColumnFamily that exists in a table without collision. -
-
- Immutability of Rowkeys - Rowkeys cannot be changed. The only way they can be "changed" in a table is if the row - is deleted and then re-inserted. This is a fairly common question on the HBase dist-list so - it pays to get the rowkeys right the first time (and/or before you've inserted a lot of - data). -
-
- Relationship Between RowKeys and Region Splits - If you pre-split your table, it is critical to understand how your - rowkey will be distributed across the region boundaries. As an example of why this is - important, consider the example of using displayable hex characters as the lead position of - the key (e.g., "0000000000000000" to "ffffffffffffffff"). Running those key ranges through - Bytes.split (which is the split strategy used when creating regions in - HBaseAdmin.createTable(byte[] startKey, byte[] endKey, numRegions) for 10 - regions will generate the following splits... - -48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 // 0 -54 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 -10 // 6 -61 -67 -67 -67 -67 -67 -67 -67 -67 -67 -67 -67 -67 -67 -67 -68 // = -68 -124 -124 -124 -124 -124 -124 -124 -124 -124 -124 -124 -124 -124 -124 -126 // D -75 75 75 75 75 75 75 75 75 75 75 75 75 75 75 72 // K -82 18 18 18 18 18 18 18 18 18 18 18 18 18 18 14 // R -88 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -40 -44 // X -95 -97 -97 -97 -97 -97 -97 -97 -97 -97 -97 -97 -97 -97 -97 -102 // _ -102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 // f - - ... (note: the lead byte is listed to the right as a comment.) Given that the first - split is a '0' and the last split is an 'f', everything is great, right? Not so fast. - The problem is that all the data is going to pile up in the first 2 regions and the last - region thus creating a "lumpy" (and possibly "hot") region problem. To understand why, refer - to an ASCII Table. '0' is byte 48, and 'f' is byte - 102, but there is a huge gap in byte values (bytes 58 to 96) that will never - appear in this keyspace because the only values are [0-9] and [a-f]. Thus, the - middle regions regions will never be used. To make pre-spliting work with this example - keyspace, a custom definition of splits (i.e., and not relying on the built-in split method) - is required. - Lesson #1: Pre-splitting tables is generally a best practice, but you need to pre-split - them in such a way that all the regions are accessible in the keyspace. While this example - demonstrated the problem with a hex-key keyspace, the same problem can happen with - any keyspace. Know your data. - Lesson #2: While generally not advisable, using hex-keys (and more generally, - displayable data) can still work with pre-split tables as long as all the created regions - are accessible in the keyspace. - To conclude this example, the following is an example of how appropriate splits can be - pre-created for hex-keys:. - -
-
- -
- Number of Versions -
- Maximum Number of Versions - The maximum number of row versions to store is configured per column family via HColumnDescriptor. - The default for max versions is 1. This is an important parameter because as described in section HBase does not overwrite row values, - but rather stores different values per row by time (and qualifier). Excess versions are - removed during major compactions. The number of max versions may need to be increased or - decreased depending on application needs. - It is not recommended setting the number of max versions to an exceedingly high level - (e.g., hundreds or more) unless those old values are very dear to you because this will - greatly increase StoreFile size. -
-
- Minimum Number of Versions - Like maximum number of row versions, the minimum number of row versions to keep is - configured per column family via HColumnDescriptor. - The default for min versions is 0, which means the feature is disabled. The minimum number - of row versions parameter is used together with the time-to-live parameter and can be - combined with the number of row versions parameter to allow configurations such as "keep the - last T minutes worth of data, at most N versions, but keep at least M versions - around" (where M is the value for minimum number of row versions, M<N). This - parameter should only be set when time-to-live is enabled for a column family and must be - less than the number of row versions. -
-
-
- Supported Datatypes - HBase supports a "bytes-in/bytes-out" interface via Put - and Result, - so anything that can be converted to an array of bytes can be stored as a value. Input could - be strings, numbers, complex objects, or even images as long as they can rendered as bytes. - There are practical limits to the size of values (e.g., storing 10-50MB objects in HBase - would probably be too much to ask); search the mailling list for conversations on this topic. - All rows in HBase conform to the , and that includes versioning. Take that into consideration when - making your design, as well as block size for the ColumnFamily. - -
- Counters - One supported datatype that deserves special mention are "counters" (i.e., the ability - to do atomic increments of numbers). See Increment - in HTable. - Synchronization on counters are done on the RegionServer, not in the client. -
-
-
- Joins - If you have multiple tables, don't forget to factor in the potential for into the schema design. -
-
- Time To Live (TTL) - ColumnFamilies can set a TTL length in seconds, and HBase will automatically delete rows - once the expiration time is reached. This applies to all versions of a - row - even the current one. The TTL time encoded in the HBase for the row is specified in UTC. - - Store files which contains only expired rows are deleted on minor compaction. Setting - hbase.store.delete.expired.storefile to false disables this - feature. Setting minimum number of versions to other - than 0 also disables this. - See HColumnDescriptor for more information. - Recent versions of HBase also support setting time to live on a per cell basis. See HBASE-10560 for more - information. Cell TTLs are submitted as an attribute on mutation requests (Appends, - Increments, Puts, etc.) using Mutation#setTTL. If the TTL attribute is set, it will be applied - to all cells updated on the server by the operation. There are two notable differences - between cell TTL handling and ColumnFamily TTLs: - - - Cell TTLs are expressed in units of milliseconds instead of seconds. - - - A cell TTLs cannot extend the effective lifetime of a cell beyond a ColumnFamily level - TTL setting. - - -
-
- Keeping Deleted Cells - By default, delete markers extend back to the beginning of time. Therefore, Get - or Scan - operations will not see a deleted cell (row or column), even when the Get or Scan operation - indicates a time range - before the delete marker was placed. - ColumnFamilies can optionally keep deleted cells. In this case, deleted cells can still be - retrieved, as long as these operations specify a time range that ends before the timestamp of - any delete that would affect the cells. This allows for point-in-time queries even in the - presence of deletes. - Deleted cells are still subject to TTL and there will never be more than "maximum number - of versions" deleted cells. A new "raw" scan options returns all deleted rows and the delete - markers. - - Change the Value of <code>KEEP_DELETED_CELLS</code> Using HBase Shell - hbase> hbase> alter ‘t1′, NAME => ‘f1′, KEEP_DELETED_CELLS => true - - - Change the Value of <code>KEEP_DELETED_CELLS</code> Using the API - ... -HColumnDescriptor.setKeepDeletedCells(true); -... - - - See the API documentation for KEEP_DELETED_CELLS for more information. -
-
- Secondary Indexes and Alternate Query Paths - This section could also be titled "what if my table rowkey looks like - this but I also want to query my table like that." - A common example on the dist-list is where a row-key is of the format "user-timestamp" but - there are reporting requirements on activity across users for certain time ranges. Thus, - selecting by user is easy because it is in the lead position of the key, but time is not. - There is no single answer on the best way to handle this because it depends on... - - - Number of users - - - Data size and data arrival rate - - - Flexibility of reporting requirements (e.g., completely ad-hoc date selection vs. - pre-configured ranges) - - - Desired execution speed of query (e.g., 90 seconds may be reasonable to some for an - ad-hoc report, whereas it may be too long for others) - - - ... and solutions are also influenced by the size of the cluster and how much processing - power you have to throw at the solution. Common techniques are in sub-sections below. This is - a comprehensive, but not exhaustive, list of approaches. - It should not be a surprise that secondary indexes require additional cluster space and - processing. This is precisely what happens in an RDBMS because the act of creating an - alternate index requires both space and processing cycles to update. RDBMS products are more - advanced in this regard to handle alternative index management out of the box. However, HBase - scales better at larger data volumes, so this is a feature trade-off. - Pay attention to when implementing any of these approaches. - Additionally, see the David Butler response in this dist-list thread HBase, - mail # user - Stargate+hbase - -
- Filter Query - Depending on the case, it may be appropriate to use . In this case, no secondary index is created. However, don't - try a full-scan on a large table like this from an application (i.e., single-threaded - client). -
-
- Periodic-Update Secondary Index - A secondary index could be created in an other table which is periodically updated via a - MapReduce job. The job could be executed intra-day, but depending on load-strategy it could - still potentially be out of sync with the main data table. - See for more information. -
-
- Dual-Write Secondary Index - Another strategy is to build the secondary index while publishing data to the cluster - (e.g., write to data table, write to index table). If this is approach is taken after a data - table already exists, then bootstrapping will be needed for the secondary index with a - MapReduce job (see ). -
-
- Summary Tables - Where time-ranges are very wide (e.g., year-long report) and where the data is - voluminous, summary tables are a common approach. These would be generated with MapReduce - jobs into another table. - See for more information. -
-
- Coprocessor Secondary Index - Coprocessors act like RDBMS triggers. These were added in 0.92. For more information, - see - -
-
-
- Constraints - HBase currently supports 'constraints' in traditional (SQL) database parlance. The advised - usage for Constraints is in enforcing business rules for attributes in the table (eg. make - sure values are in the range 1-10). Constraints could also be used to enforce referential - integrity, but this is strongly discouraged as it will dramatically decrease the write - throughput of the tables where integrity checking is enabled. Extensive documentation on using - Constraints can be found at: Constraint - since version 0.94. -
-
- Schema Design Case Studies - The following will describe some typical data ingestion use-cases with HBase, and how the - rowkey design and construction can be approached. Note: this is just an illustration of - potential approaches, not an exhaustive list. Know your data, and know your processing - requirements. - It is highly recommended that you read the rest of the first, before reading these case studies. - The following case studies are described: - - - Log Data / Timeseries Data - - - Log Data / Timeseries on Steroids - - - Customer/Order - - - Tall/Wide/Middle Schema Design - - - List Data - - -
- Case Study - Log Data and Timeseries Data - Assume that the following data elements are being collected. - - - Hostname - - - Timestamp - - - Log event - - - Value/message - - - We can store them in an HBase table called LOG_DATA, but what will the rowkey be? From - these attributes the rowkey will be some combination of hostname, timestamp, and log-event - - but what specifically? -
- Timestamp In The Rowkey Lead Position - The rowkey [timestamp][hostname][log-event] suffers from the - monotonically increasing rowkey problem described in . - There is another pattern frequently mentioned in the dist-lists about “bucketing” - timestamps, by performing a mod operation on the timestamp. If time-oriented scans are - important, this could be a useful approach. Attention must be paid to the number of - buckets, because this will require the same number of scans to return results. - -long bucket = timestamp % numBuckets; - - … to construct: - -[bucket][timestamp][hostname][log-event] - - As stated above, to select data for a particular timerange, a Scan will need to be - performed for each bucket. 100 buckets, for example, will provide a wide distribution in - the keyspace but it will require 100 Scans to obtain data for a single timestamp, so there - are trade-offs. -
- -
- Host In The Rowkey Lead Position - The rowkey [hostname][log-event][timestamp] is a candidate if there is a - large-ish number of hosts to spread the writes and reads across the keyspace. This - approach would be useful if scanning by hostname was a priority. -
- -
- Timestamp, or Reverse Timestamp? - If the most important access path is to pull most recent events, then storing the - timestamps as reverse-timestamps (e.g., timestamp = Long.MAX_VALUE – - timestamp) will create the property of being able to do a Scan on - [hostname][log-event] to obtain the quickly obtain the most recently - captured events. - Neither approach is wrong, it just depends on what is most appropriate for the - situation. - - Reverse Scan API - - HBASE-4811 - implements an API to scan a table or a range within a table in reverse, reducing the - need to optimize your schema for forward or reverse scanning. This feature is available - in HBase 0.98 and later. See - for more information. - -
- -
- Variangle Length or Fixed Length Rowkeys? - It is critical to remember that rowkeys are stamped on every column in HBase. If the - hostname is “a” and the event type is “e1” then the resulting rowkey would be quite small. - However, what if the ingested hostname is “myserver1.mycompany.com” and the event type is - “com.package1.subpackage2.subsubpackage3.ImportantService”? - It might make sense to use some substitution in the rowkey. There are at least two - approaches: hashed and numeric. In the Hostname In The Rowkey Lead Position example, it - might look like this: - Composite Rowkey With Hashes: - - - [MD5 hash of hostname] = 16 bytes - - - [MD5 hash of event-type] = 16 bytes - - - [timestamp] = 8 bytes - - - Composite Rowkey With Numeric Substitution: - For this approach another lookup table would be needed in addition to LOG_DATA, called - LOG_TYPES. The rowkey of LOG_TYPES would be: - - - [type] (e.g., byte indicating hostname vs. event-type) - - - [bytes] variable length bytes for raw hostname or event-type. - - - A column for this rowkey could be a long with an assigned number, which could be - obtained by using an HBase - counter. - So the resulting composite rowkey would be: - - - [substituted long for hostname] = 8 bytes - - - [substituted long for event type] = 8 bytes - - - [timestamp] = 8 bytes - - - In either the Hash or Numeric substitution approach, the raw values for hostname and - event-type can be stored as columns. -
- -
- -
- Case Study - Log Data and Timeseries Data on Steroids - This effectively is the OpenTSDB approach. What OpenTSDB does is re-write data and pack - rows into columns for certain time-periods. For a detailed explanation, see: http://opentsdb.net/schema.html, and Lessons - Learned from OpenTSDB from HBaseCon2012. - But this is how the general concept works: data is ingested, for example, in this - manner… - -[hostname][log-event][timestamp1] -[hostname][log-event][timestamp2] -[hostname][log-event][timestamp3] - - … with separate rowkeys for each detailed event, but is re-written like this… - [hostname][log-event][timerange] - … and each of the above events are converted into columns stored with a time-offset - relative to the beginning timerange (e.g., every 5 minutes). This is obviously a very - advanced processing technique, but HBase makes this possible. -
- - -
- Case Study - Customer/Order - Assume that HBase is used to store customer and order information. There are two core - record-types being ingested: a Customer record type, and Order record type. - The Customer record type would include all the things that you’d typically expect: - - - Customer number - - - Customer name - - - Address (e.g., city, state, zip) - - - Phone numbers, etc. - - - The Order record type would include things like: - - - Customer number - - - Order number - - - Sales date - - - A series of nested objects for shipping locations and line-items (see for details) - - - Assuming that the combination of customer number and sales order uniquely identify an - order, these two attributes will compose the rowkey, and specifically a composite key such - as: - [customer number][order number] - … for a ORDER table. However, there are more design decisions to make: are the - raw values the best choices for rowkeys? - The same design questions in the Log Data use-case confront us here. What is the - keyspace of the customer number, and what is the format (e.g., numeric? alphanumeric?) As it - is advantageous to use fixed-length keys in HBase, as well as keys that can support a - reasonable spread in the keyspace, similar options appear: - Composite Rowkey With Hashes: - - - [MD5 of customer number] = 16 bytes - - - [MD5 of order number] = 16 bytes - - - Composite Numeric/Hash Combo Rowkey: - - - [substituted long for customer number] = 8 bytes - - - [MD5 of order number] = 16 bytes - - -
- Single Table? Multiple Tables? - A traditional design approach would have separate tables for CUSTOMER and SALES. - Another option is to pack multiple record types into a single table (e.g., CUSTOMER++). - Customer Record Type Rowkey: - - - [customer-id] - - - [type] = type indicating ‘1’ for customer record type - - - Order Record Type Rowkey: - - - [customer-id] - - - [type] = type indicating ‘2’ for order record type - - - [order] - - - The advantage of this particular CUSTOMER++ approach is that organizes many different - record-types by customer-id (e.g., a single scan could get you everything about that - customer). The disadvantage is that it’s not as easy to scan for a particular record-type. - -
-
- Order Object Design - Now we need to address how to model the Order object. Assume that the class structure - is as follows: - - - Order - - (an Order can have multiple ShippingLocations - - - - LineItem - - (a ShippingLocation can have multiple LineItems - - - - ... there are multiple options on storing this data. -
- Completely Normalized - With this approach, there would be separate tables for ORDER, SHIPPING_LOCATION, and - LINE_ITEM. - The ORDER table's rowkey was described above: - - The SHIPPING_LOCATION's composite rowkey would be something like this: - - - [order-rowkey] - - - [shipping location number] (e.g., 1st location, 2nd, etc.) - - - The LINE_ITEM table's composite rowkey would be something like this: - - - [order-rowkey] - - - [shipping location number] (e.g., 1st location, 2nd, etc.) - - - [line item number] (e.g., 1st lineitem, 2nd, etc.) - - - Such a normalized model is likely to be the approach with an RDBMS, but that's not - your only option with HBase. The cons of such an approach is that to retrieve - information about any Order, you will need: - - - Get on the ORDER table for the Order - - - Scan on the SHIPPING_LOCATION table for that order to get the ShippingLocation - instances - - - Scan on the LINE_ITEM for each ShippingLocation - - - ... granted, this is what an RDBMS would do under the covers anyway, but since there - are no joins in HBase you're just more aware of this fact. -
-
- Single Table With Record Types - With this approach, there would exist a single table ORDER that would contain - The Order rowkey was described above: - - - [order-rowkey] - - - [ORDER record type] - - - The ShippingLocation composite rowkey would be something like this: - - - [order-rowkey] - - - [SHIPPING record type] - - - [shipping location number] (e.g., 1st location, 2nd, etc.) - - - The LineItem composite rowkey would be something like this: - - - [order-rowkey] - - - [LINE record type] - - - [shipping location number] (e.g., 1st location, 2nd, etc.) - - - [line item number] (e.g., 1st lineitem, 2nd, etc.) - - -
-
- Denormalized - A variant of the Single Table With Record Types approach is to denormalize and - flatten some of the object hierarchy, such as collapsing the ShippingLocation attributes - onto each LineItem instance. - The LineItem composite rowkey would be something like this: - - - [order-rowkey] - - - [LINE record type] - - - [line item number] (e.g., 1st lineitem, 2nd, etc. - care must be taken that - there are unique across the entire order) - - - ... and the LineItem columns would be something like this: - - - itemNumber - - - quantity - - - price - - - shipToLine1 (denormalized from ShippingLocation) - - - shipToLine2 (denormalized from ShippingLocation) - - - shipToCity (denormalized from ShippingLocation) - - - shipToState (denormalized from ShippingLocation) - - - shipToZip (denormalized from ShippingLocation) - - - The pros of this approach include a less complex object heirarchy, but one of the - cons is that updating gets more complicated in case any of this information changes. - -
-
- Object BLOB - With this approach, the entire Order object graph is treated, in one way or another, - as a BLOB. For example, the ORDER table's rowkey was described above: , and a single column called "order" would - contain an object that could be deserialized that contained a container Order, - ShippingLocations, and LineItems. - There are many options here: JSON, XML, Java Serialization, Avro, Hadoop Writables, - etc. All of them are variants of the same approach: encode the object graph to a - byte-array. Care should be taken with this approach to ensure backward compatibilty in - case the object model changes such that older persisted structures can still be read - back out of HBase. - Pros are being able to manage complex object graphs with minimal I/O (e.g., a single - HBase Get per Order in this example), but the cons include the aforementioned warning - about backward compatiblity of serialization, language dependencies of serialization - (e.g., Java Serialization only works with Java clients), the fact that you have to - deserialize the entire object to get any piece of information inside the BLOB, and the - difficulty in getting frameworks like Hive to work with custom objects like this. - -
-
- -
- - -
- Case Study - "Tall/Wide/Middle" Schema Design Smackdown - This section will describe additional schema design questions that appear on the - dist-list, specifically about tall and wide tables. These are general guidelines and not - laws - each application must consider its own needs. -
- Rows vs. Versions - A common question is whether one should prefer rows or HBase's built-in-versioning. - The context is typically where there are "a lot" of versions of a row to be retained - (e.g., where it is significantly above the HBase default of 1 max versions). The - rows-approach would require storing a timestamp in some portion of the rowkey so that they - would not overwite with each successive update. - Preference: Rows (generally speaking). -
-
- Rows vs. Columns - Another common question is whether one should prefer rows or columns. The context is - typically in extreme cases of wide tables, such as having 1 row with 1 million attributes, - or 1 million rows with 1 columns apiece. - Preference: Rows (generally speaking). To be clear, this guideline is in the context - is in extremely wide cases, not in the standard use-case where one needs to store a few - dozen or hundred columns. But there is also a middle path between these two options, and - that is "Rows as Columns." -
-
- Rows as Columns - The middle path between Rows vs. Columns is packing data that would be a separate row - into columns, for certain rows. OpenTSDB is the best example of this case where a single - row represents a defined time-range, and then discrete events are treated as columns. This - approach is often more complex, and may require the additional complexity of re-writing - your data, but has the advantage of being I/O efficient. For an overview of this approach, - see . -
-
- -
- Case Study - List Data - The following is an exchange from the user dist-list regarding a fairly common question: - how to handle per-user list data in Apache HBase. - *** QUESTION *** - We're looking at how to store a large amount of (per-user) list data in HBase, and we - were trying to figure out what kind of access pattern made the most sense. One option is - store the majority of the data in a key, so we could have something like: - - :"" (no value) -:"" (no value) -:"" (no value) -]]> - - The other option we had was to do this entirely using: - :... -:... - ]]> - where each row would contain multiple values. So in one case reading the first thirty - values would be: - 'FixedWidthUsername' LIMIT => 30} - ]]> - And in the second case it would be - -get 'FixedWidthUserName\x00\x00\x00\x00' - - The general usage pattern would be to read only the first 30 values of these lists, - with infrequent access reading deeper into the lists. Some users would have <= 30 total - values in these lists, and some users would have millions (i.e. power-law distribution) - The single-value format seems like it would take up more space on HBase, but would - offer some improved retrieval / pagination flexibility. Would there be any significant - performance advantages to be able to paginate via gets vs paginating with scans? - My initial understanding was that doing a scan should be faster if our paging size is - unknown (and caching is set appropriately), but that gets should be faster if we'll always - need the same page size. I've ended up hearing different people tell me opposite things - about performance. I assume the page sizes would be relatively consistent, so for most use - cases we could guarantee that we only wanted one page of data in the fixed-page-length case. - I would also assume that we would have infrequent updates, but may have inserts into the - middle of these lists (meaning we'd need to update all subsequent rows). - Thanks for help / suggestions / follow-up questions. - *** ANSWER *** - If I understand you correctly, you're ultimately trying to store triples in the form - "user, valueid, value", right? E.g., something like: - -"user123, firstname, Paul", -"user234, lastname, Smith" - - (But the usernames are fixed width, and the valueids are fixed width). - And, your access pattern is along the lines of: "for user X, list the next 30 values, - starting with valueid Y". Is that right? And these values should be returned sorted by - valueid? - The tl;dr version is that you should probably go with one row per user+value, and not - build a complicated intra-row pagination scheme on your own unless you're really sure it is - needed. - Your two options mirror a common question people have when designing HBase schemas: - should I go "tall" or "wide"? Your first schema is "tall": each row represents one value for - one user, and so there are many rows in the table for each user; the row key is user + - valueid, and there would be (presumably) a single column qualifier that means "the value". - This is great if you want to scan over rows in sorted order by row key (thus my question - above, about whether these ids are sorted correctly). You can start a scan at any - user+valueid, read the next 30, and be done. What you're giving up is the ability to have - transactional guarantees around all the rows for one user, but it doesn't sound like you - need that. Doing it this way is generally recommended (see here http://hbase.apache.org/book.html#schema.smackdown). - Your second option is "wide": you store a bunch of values in one row, using different - qualifiers (where the qualifier is the valueid). The simple way to do that would be to just - store ALL values for one user in a single row. I'm guessing you jumped to the "paginated" - version because you're assuming that storing millions of columns in a single row would be - bad for performance, which may or may not be true; as long as you're not trying to do too - much in a single request, or do things like scanning over and returning all of the cells in - the row, it shouldn't be fundamentally worse. The client has methods that allow you to get - specific slices of columns. - Note that neither case fundamentally uses more disk space than the other; you're just - "shifting" part of the identifying information for a value either to the left (into the row - key, in option one) or to the right (into the column qualifiers in option 2). Under the - covers, every key/value still stores the whole row key, and column family name. (If this is - a bit confusing, take an hour and watch Lars George's excellent video about understanding - HBase schema design: http://www.youtube.com/watch?v=_HLoH_PgrLk). - A manually paginated version has lots more complexities, as you note, like having to - keep track of how many things are in each page, re-shuffling if new values are inserted, - etc. That seems significantly more complex. It might have some slight speed advantages (or - disadvantages!) at extremely high throughput, and the only way to really know that would be - to try it out. If you don't have time to build it both ways and compare, my advice would be - to start with the simplest option (one row per user+value). Start simple and iterate! :) - -
- - -
- -
- Operational and Performance Configuration Options - See the Performance section for more information operational and performance schema design - options, such as Bloom Filters, Table-configured regionsizes, compression, and blocksizes. - -
- - - diff --git a/src/main/docbkx/security.xml b/src/main/docbkx/security.xml deleted file mode 100644 index c9db37a65d0..00000000000 --- a/src/main/docbkx/security.xml +++ /dev/null @@ -1,1925 +0,0 @@ - - - - Securing Apache HBase - HBase provides mechanisms to secure various components and aspects of HBase and how it - relates to the rest of the Hadoop infrastructure, as well as clients and resources outside - Hadoop. -
- Using Secure HTTP (HTTPS) for the Web UI - A default HBase install uses insecure HTTP connections for web UIs for the master and - region servers. To enable secure HTTP (HTTPS) connections instead, set - hadoop.ssl.enabled to true in - hbase-site.xml. This does not change the port used by the Web UI. To - change the port for the web UI for a given HBase component, configure that port's setting in - hbase-site.xml. These settings are: - - hbase.master.info.port - hbase.regionserver.info.port - - - If you enable HTTPS, clients should avoid using the non-secure HTTP connection. - If you enable secure HTTP, clients should connect to HBase using the - https:// URL. Clients using the http:// URL will receive an HTTP - response of 200, but will not receive any data. The following exception is logged: - javax.net.ssl.SSLException: Unrecognized SSL message, plaintext connection? - This is because the same port is used for HTTP and HTTPS. - HBase uses Jetty for the Web UI. Without modifying Jetty itself, it does not seem - possible to configure Jetty to redirect one port to another on the same host. See Nick - Dimiduk's contribution on this Stack Overflow thread for more information. If you know how to fix this without - opening a second port for HTTPS, patches are appreciated. - -
-
- Secure Client Access to Apache HBase - Newer releases of Apache HBase (>= 0.92) support optional SASL authentication of clients. See also Matteo Bertozzi's article on Understanding - User Authentication and Authorization in Apache HBase. - This describes how to set up Apache HBase and clients for connection to secure HBase - resources. - -
- Prerequisites - - - Hadoop Authentication Configuration - - To run HBase RPC with strong authentication, you must set - hbase.security.authentication to true. In this case, - you must also set hadoop.security.authentication to - true. Otherwise, you would be using strong authentication for - HBase but not for the underlying HDFS, which would cancel out any benefit. - - - - - Kerberos KDC - - You need to have a working Kerberos KDC. - A HBase configured for secure client access is expected to be running on top of a - secured HDFS cluster. HBase must be able to authenticate to HDFS services. HBase needs - Kerberos credentials to interact with the Kerberos-enabled HDFS daemons. - Authenticating a service should be done using a keytab file. The procedure for - creating keytabs for HBase service is the same as for creating keytabs for Hadoop. - Those steps are omitted here. Copy the resulting keytab files to wherever HBase Master - and RegionServer processes are deployed and make them readable only to the user - account under which the HBase daemons will run. - A Kerberos principal has three parts, with the form - username/fully.qualified.domain.name@YOUR-REALM.COM. We recommend using - hbase as the username portion. - The following is an example of the configuration properties for Kerberos - operation that must be added to the hbase-site.xml file on every server - machine in the cluster. Required for even the most basic interactions with a secure - Hadoop configuration, independent of HBase security. - - hbase.regionserver.kerberos.principal - hbase/_HOST@YOUR-REALM.COM - - - hbase.regionserver.keytab.file - /etc/hbase/conf/keytab.krb5 - - - hbase.master.kerberos.principal - hbase/_HOST@YOUR-REALM.COM - - - hbase.master.keytab.file - /etc/hbase/conf/keytab.krb5 - - ]]> - Each HBase client user should also be given a Kerberos principal. This principal - should have a password assigned to it (as opposed to a keytab file). The client - principal's maxrenewlife should be set so that it can be renewed enough - times for the HBase client process to complete. For example, if a user runs a - long-running HBase client process that takes at most 3 days, we might create this - user's principal within kadmin with: addprinc -maxrenewlife - 3days - - Long running daemons with indefinite lifetimes that require client access to - HBase can instead be configured to log in from a keytab. For each host running such - daemons, create a keytab with kadmin or kadmin.local. The - procedure for creating keytabs for HBase service is the same as for creating keytabs - for Hadoop. Those steps are omitted here. Copy the resulting keytab files to where the - client daemon will execute and make them readable only to the user account under which - the daemon will run. - - - -
- -
- Server-side Configuration for Secure Operation - First, refer to and ensure that your - underlying HDFS configuration is secure. - Add the following to the hbase-site.xml file on every server machine in - the cluster: - - hbase.security.authentication - kerberos - - - hbase.security.authorization - true - - -hbase.coprocessor.region.classes - org.apache.hadoop.hbase.security.token.TokenProvider - - ]]> - A full shutdown and restart of HBase service is required when deploying these - configuration changes. -
- -
- Client-side Configuration for Secure Operation - First, refer to and ensure that your - underlying HDFS configuration is secure. - Add the following to the hbase-site.xml file on every client: - - hbase.security.authentication - kerberos - - ]]> - The client environment must be logged in to Kerberos from KDC or keytab via the - kinit command before communication with the HBase cluster will be possible. - Be advised that if the hbase.security.authentication in the client- and - server-side site files do not match, the client will not be able to communicate with the - cluster. - Once HBase is configured for secure RPC it is possible to optionally configure - encrypted communication. To do so, add the following to the hbase-site.xml file - on every client: - - hbase.rpc.protection - privacy - - ]]> - This configuration property can also be set on a per connection basis. Set it in the - Configuration supplied to HTable: - -Configuration conf = HBaseConfiguration.create(); -conf.set("hbase.rpc.protection", "privacy"); -HTable table = new HTable(conf, tablename); - - Expect a ~10% performance penalty for encrypted communication. -
- - -
- Client-side Configuration for Secure Operation - Thrift Gateway - Add the following to the hbase-site.xml file for every Thrift gateway: - hbase.thrift.keytab.file - /etc/hbase/conf/hbase.keytab - - - hbase.thrift.kerberos.principal - $USER/_HOST@HADOOP.LOCALDOMAIN - - - ]]> - - Substitute the appropriate credential and keytab for $USER - and $KEYTAB respectively. - In order to use the Thrift API principal to interact with HBase, it is also necessary to - add the hbase.thrift.kerberos.principal to the _acl_ table. For - example, to give the Thrift API principal, thrift_server, administrative - access, a command such as this one will suffice: - - For more information about ACLs, please see the Access Control section - - The Thrift gateway will authenticate with HBase using the supplied credential. No - authentication will be performed by the Thrift gateway itself. All client access via the - Thrift gateway will use the Thrift gateway's credential and have its privilege. -
-
- Configure the Thrift Gateway to Authenticate on Behalf of the Client - describes how to authenticate a Thrift client - to HBase using a fixed user. As an alternative, you can configure the Thrift gateway to - authenticate to HBase on the client's behalf, and to access HBase using a proxy user. This - was implemented in HBASE-11349 for Thrift 1, and HBASE-11474 for - Thrift 2. - - Limitations with Thrift Framed Transport - If you use framed transport, you cannot yet take advantage of this feature, because - SASL does not work with Thrift framed transport at this time. - - To enable it, do the following. - - - Be sure Thrift is running in secure mode, by following the procedure described in - . - - - Be sure that HBase is configured to allow proxy users, as described in . - - - In hbase-site.xml for each cluster node running a Thrift - gateway, set the property hbase.thrift.security.qop to one of the following - three values: - - - auth-conf - authentication, integrity, and confidentiality - checking - - - auth-int - authentication and integrity checking - - - auth - authentication checking only - - - - - Restart the Thrift gateway processes for the changes to take effect. If a node is - running Thrift, the output of the jps command will list a - ThriftServer process. To stop Thrift on a node, run the command - bin/hbase-daemon.sh stop thrift. To start Thrift on a node, run the - command bin/hbase-daemon.sh start thrift. - - -
- -
- Client-side Configuration for Secure Operation - REST Gateway - Add the following to the hbase-site.xml file for every REST gateway: - - hbase.rest.keytab.file - $KEYTAB - - - hbase.rest.kerberos.principal - $USER/_HOST@HADOOP.LOCALDOMAIN - - ]]> - Substitute the appropriate credential and keytab for $USER - and $KEYTAB respectively. - The REST gateway will authenticate with HBase using the supplied credential. No - authentication will be performed by the REST gateway itself. All client access via the REST - gateway will use the REST gateway's credential and have its privilege. - In order to use the REST API principal to interact with HBase, it is also necessary to - add the hbase.rest.kerberos.principal to the _acl_ table. For - example, to give the REST API principal, rest_server, administrative access, a - command such as this one will suffice: - - For more information about ACLs, please see the Access Control section - It should be possible for clients to authenticate with the HBase cluster through the - REST gateway in a pass-through manner via SPEGNO HTTP authentication. This is future work. - -
- -
- REST Gateway Impersonation Configuration - By default, the REST gateway doesn't support impersonation. It accesses the HBase on - behalf of clients as the user configured as in the previous section. To the HBase server, - all requests are from the REST gateway user. The actual users are unknown. You can turn on - the impersonation support. With impersonation, the REST gateway user is a proxy user. The - HBase server knows the acutal/real user of each request. So it can apply proper - authorizations. - To turn on REST gateway impersonation, we need to configure HBase servers (masters and - region servers) to allow proxy users; configure REST gateway to enable impersonation. - To allow proxy users, add the following to the hbase-site.xml file for - every HBase server: - - hadoop.security.authorization - true - - - hadoop.proxyuser.$USER.groups - $GROUPS - - - hadoop.proxyuser.$USER.hosts - $GROUPS - - ]]> - Substitute the REST gateway proxy user for $USER, and the allowed group list for - $GROUPS. - To enable REST gateway impersonation, add the following to the - hbase-site.xml file for every REST gateway. - - hbase.rest.authentication.type - kerberos - - - hbase.rest.authentication.kerberos.principal - HTTP/_HOST@HADOOP.LOCALDOMAIN - - - hbase.rest.authentication.kerberos.keytab - $KEYTAB - - ]]> - Substitute the keytab for HTTP for $KEYTAB. -
- -
- - -
- Simple User Access to Apache HBase - Newer releases of Apache HBase (>= 0.92) support optional SASL authentication of clients. See also Matteo Bertozzi's article on Understanding - User Authentication and Authorization in Apache HBase. - This describes how to set up Apache HBase and clients for simple user access to HBase - resources. - -
- Simple Versus Secure Access - The following section shows how to set up simple user access. Simple user access is not - a secure method of operating HBase. This method is used to prevent users from making - mistakes. It can be used to mimic the Access Control using on a development system without - having to set up Kerberos. - This method is not used to prevent malicious or hacking attempts. To make HBase secure - against these types of attacks, you must configure HBase for secure operation. Refer to the - section Secure Client Access to HBase and - complete all of the steps described there. - -
- Prerequisites - None - -
- Server-side Configuration for Simple User Access Operation - Add the following to the hbase-site.xml file on every server machine - in the cluster: - - hbase.security.authentication - simple - - - hbase.security.authorization - true - - - hbase.coprocessor.master.classes - org.apache.hadoop.hbase.security.access.AccessController - - - hbase.coprocessor.region.classes - org.apache.hadoop.hbase.security.access.AccessController - - - hbase.coprocessor.regionserver.classes - org.apache.hadoop.hbase.security.access.AccessController - - ]]> - For 0.94, add the following to the hbase-site.xml file on every server - machine in the cluster: - - hbase.rpc.engine - org.apache.hadoop.hbase.ipc.SecureRpcEngine - - - hbase.coprocessor.master.classes - org.apache.hadoop.hbase.security.access.AccessController - - - hbase.coprocessor.region.classes - org.apache.hadoop.hbase.security.access.AccessController - - ]]> - A full shutdown and restart of HBase service is required when deploying these - configuration changes. -
- -
- Client-side Configuration for Simple User Access Operation - Add the following to the hbase-site.xml file on every client: - - hbase.security.authentication - simple - - ]]> - For 0.94, add the following to the hbase-site.xml file on every server - machine in the cluster: - - hbase.rpc.engine - org.apache.hadoop.hbase.ipc.SecureRpcEngine - - ]]> - Be advised that if the hbase.security.authentication in the client- - and server-side site files do not match, the client will not be able to communicate with - the cluster. -
- -
- Client-side Configuration for Simple User Access Operation - Thrift Gateway - The Thrift gateway user will need access. For example, to give the Thrift API user, - thrift_server, administrative access, a command such as this one will - suffice: - - For more information about ACLs, please see the Access Control section - - The Thrift gateway will authenticate with HBase using the supplied credential. No - authentication will be performed by the Thrift gateway itself. All client access via the - Thrift gateway will use the Thrift gateway's credential and have its privilege. -
- -
- Client-side Configuration for Simple User Access Operation - REST Gateway - - The REST gateway will authenticate with HBase using the supplied credential. No - authentication will be performed by the REST gateway itself. All client access via the - REST gateway will use the REST gateway's credential and have its privilege. - The REST gateway user will need access. For example, to give the REST API user, - rest_server, administrative access, a command such as this one will - suffice: - - For more information about ACLs, please see the Access Control section - It should be possible for clients to authenticate with the HBase cluster through - the REST gateway in a pass-through manner via SPEGNO HTTP authentication. This is future - work. -
-
-
- -
- - -
- Securing Access To Your Data - After you have configured secure authentication between HBase client and server processes - and gateways, you need to consider the security of your data itself. HBase provides several - strategies for securing your data: - - - Role-based Access Control (RBAC) controls which users or groups can read and write to - a given HBase resource or execute a coprocessor endpoint, using the familiar paradigm of - roles. - - - Visibility Labels which allow you to label cells and control access to labelled cells, - to further restrict who can read or write to certain subsets of your data. Visibility - labels are stored as tags. See for more information. - - - Transparent encryption of data at rest on the underlying filesystem, both in HFiles - and in the WAL. This protects your data at rest from an attacker who has access to the - underlying filesystem, without the need to change the implementation of the client. It can - also protect against data leakage from improperly disposed disks, which can be important - for legal and regulatory compliance. - - - Server-side configuration, administration, and implementation details of each of these - features are discussed below, along with any performance trade-offs. An example security - configuration is given at the end, to show these features all used together, as they might be - in a real-world scenario. - - All aspects of security in HBase are in active development and evolving rapidly. Any - strategy you employ for security of your data should be thoroughly tested. In addition, some - of these features are still in the experimental stage of development. To take advantage of - many of these features, you must be running HBase 0.98+ and using the HFile v3 file - format. - - - - Protecting Sensitive Files - Several procedures in this section require you to copy files between cluster nodes. When - copying keys, configuration files, or other files containing sensitive strings, use a secure - method, such as ssh, to avoid leaking sensitive data. - - - - Basic Server-Side Configuration - - Enable HFile v3, by setting to 3 in - hbase-site.xml. This is the default for HBase 1.0 and - newer. - - hfile.format.version - 3 - - ]]> - - - Enable SASL and Kerberos authentication for RPC and ZooKeeper, as described in and . - - - -
- Tags - Tags are a feature of HFile v3. A tag is a piece of metadata - which is part of a cell, separate from the key, value, and version. Tags are an - implementation detail which provides a foundation for other security-related features such - as cell-level ACLs and visibility labels. Tags are stored in the HFiles themselves. It is - possible that in the future, tags will be used to implement other HBase features. You don't - need to know a lot about tags in order to use the security features they enable. -
- Implementation Details - Every cell can have zero or more tags. Every tag has a type and the actual tag byte - array. - Just as row keys, column families, qualifiers and values can be encoded (see ), tags can also be encoded as well. You can enable - or disable tag encoding at the level of the column family, and it is enabled by default. - Use the HColumnDescriptor#setCompressionTags(boolean compressTags) method to - manage encoding settings on a column family. You also need to enable the DataBlockEncoder - for the column family, for encoding of tags to take effect. - You can enable compression of each tag in the WAL, if WAL compression is also enabled, - by setting the value of to - true in hbase-site.xml. Tag compression uses - dictionary encoding. - Tag compression is not supported when using WAL encryption. -
-
- -
- Access Control Labels (ACLs) -
- How It Works - ACLs in HBase are based upon a user's membership in or exclusion from groups, and a - given group's permissions to access a given resource. ACLs are implemented as a - coprocessor called AccessController. - HBase does not maintain a private group mapping, but relies on a Hadoop - group mapper, which maps between entities in a directory such as LDAP or - Active Directory, and HBase users. Any supported Hadoop group mapper will work. Users are - then granted specific permissions (Read, Write, Execute, Create, Admin) against resources - (global, namespaces, tables, cells, or endpoints). - - With Kerberos and Access Control enabled, client access to HBase is authenticated - and user data is private unless access has been explicitly granted. - - HBase has a simpler security model than relational databases, especially in terms of - client operations. No distinction is made between an insert (new record) and update (of - existing record), for example, as both collapse down into a Put. -
- Understanding Access Levels - HBase access levels are granted independently of each other and allow for different - types of operations at a given scope. - - - Read (R) - can read data at the given scope - - - Write (W) - can write data at the given scope - - - Execute (X) - can execute coprocessor endpoints at the given - scope - - - Create (C) - can create tables or drop tables (even those - they did not create) at the given scope - - - Admin (A) - can perform cluster operations such as balancing - the cluster or assigning regions at the given scope - - - The possible scopes are: - - - Superuser - superusers can perform any operation available in - HBase, to any resource. The user who runs HBase on your cluster is a superuser, as - are any principals assigned to the configuration property - hbase.superuser in hbase-site.xml on the - HMaster. - - - Global - permissions granted at global - scope allow the admin to operate on all tables of the cluster. - - - Namespace - permissions granted at - namespace scope apply to all tables within a given - namespace. - - - Table - permissions granted at table - scope apply to data or metadata within a given table. - - - ColumnFamily - permissions granted at - ColumnFamily scope apply to cells within that - ColumnFamily. - - - Cell - permissions granted at cell scope - apply to that exact cell coordinate (key, value, timestamp). This allows for policy - evolution along with data. - To change an ACL on a specific cell, write an updated cell with new ACL to the - precise coordinates of the original. - If you have a multi-versioned schema and want to update ACLs on all visible - versions, you need to write new cells for all visible versions. The application - has complete control over policy evolution. - The exception to the above rule is append and - increment processing. Appends and increments can carry an ACL in the - operation. If one is included in the operation, then it will be applied to the - result of the append or increment. Otherwise, the ACL of - the existing cell you are appending to or incrementing is preserved. - - - The combination of access levels and scopes creates a matrix of possible access - levels that can be granted to a user. In a production environment, it is useful to think - of access levels in terms of what is needed to do a specific job. The following list - describes appropriate access levels for some common types of HBase users. It is - important not to grant more access than is required for a given user to perform their - required tasks. - - - Superusers - In a production system, only the HBase user should have superuser - access. In a development environment, an administrator may need superuser access in - order to quickly control and manage the cluster. However, this type of administrator - should usually be a Global Admin rather than a superuser. - - - Global Admins - A global admin can perform tasks and access every table in - HBase. In a typical production environment, an admin should not have Read or Write - permissions to data within tables. - - - A global admin with Admin permissions can perform cluster-wide operations on - the cluster, such as balancing, assigning or unassigning regions, or calling an - explicit major compaction. This is an operations role. - - - A global admin with Create permissions can create or drop any table within - HBase. This is more of a DBA-type role. - - - In a production environment, it is likely that different users will have only - one of Admin and Create permissions. - - In the current implementation, a Global Admin with Admin - permission can grant himself Read and Write permissions - on a table and gain access to that table's data. For this reason, only grant - Global Admin permissions to trusted user who actually need - them. - Also be aware that a Global Admin with Create - permission can perform a Put operation on the ACL table, simulating a - grant or revoke and circumventing the authorization - check for Global Admin permissions. - Due to these issues, be cautious with granting Global Admin - privileges. - - - - Namespace Admins - a namespace admin with Create - permissions can create or drop tables within that namespace, and take and restore - snapshots. A namespace admin with Admin permissions can perform - operations such as splits or major compactions on tables within that - namespace. - - - Table Admins - A table admin can perform administrative - operations only on that table. A table admin with Create permissions - can create snapshots from that table or restore that table from a snapshot. A table - admin with Admin permissions can perform operations such as splits or - major compactions on that table. - - - Users - Users can read or write data, or both. Users can also - execute coprocessor endpoints, if given Executable permissions. - - - - Real-World Example of Access Levels - - - - Job Title - Scope - Permissions - Description - - - - - Senior Administrator - Global - Access, Create - Manages the cluster and gives access to Junior - Administrators. - - - Junior Administrator - Global - Create - Creates tables and gives access to Table - Administrators. - - - Table Administrator - Table - Access - Maintains a table from an operations point of view. - - - Data Analyst - Table - Read - Creates reports from HBase data. - - - Web Application - Table - Read, Write - Puts data into HBase and uses HBase data to perform - operations. - - - - - -
This table shows how real-world titles might map to HBase permissions in - a hypothetical company.
- - ACL Matrix - For more details on how ACLs map to specific HBase operations and tasks, see . - -
-
- Implementation Details - Cell-level ACLs are implemented using tags (see ). In - order to use cell-level ACLs, you must be using HFile v3 and HBase 0.98 or newer. - - ACL Implementation Caveats - - Files created by HBase are owned by the operating system user running the HBase - process. To interact with HBase files, you should use the API or bulk load - facility. - - - HBase does not model "roles" internally in HBase. Instead, group names can be - granted permissions. This allows external modeling of roles via group membership. - Groups are created and manipulated externally to HBase, via the Hadoop group mapping - service. - - -
-
- Server-Side Configuration - - - As a prerequisite, perform the steps in . - - - Install and configure the AccessController coprocessor, by setting the following - properties in hbase-site.xml. These properties take a list of - classes. - - If you use the AccessController along with the VisibilityController, the - AccessController must come first in the list, because with both components active, - the VisibilityController will delegate access control on its system tables to the - AccessController. For an example of using both together, see . - - - hbase.coprocessor.region.classes - org.apache.hadoop.hbase.security.access.AccessController, org.apache.hadoop.hbase.security.token.TokenProvider - - - hbase.coprocessor.master.classes - org.apache.hadoop.hbase.security.access.AccessController - - - hbase.coprocessor.regionserver.classes - org.apache.hadoop.hbase.security.access.AccessController - - - hbase.security.exec.permission.checks - true - - ]]> - Optionally, you can enable transport security, by setting - to auth-conf. This - requires HBase 0.98.4 or newer. - - - Set up the Hadoop group mapper in the Hadoop namenode's - core-site.xml. This is a Hadoop file, not an HBase file. - Customize it to your site's needs. Following is an example. - - hadoop.security.group.mapping - org.apache.hadoop.security.LdapGroupsMapping - - - - hadoop.security.group.mapping.ldap.url - ldap://server - - - - hadoop.security.group.mapping.ldap.bind.user - Administrator@example-ad.local - - - - hadoop.security.group.mapping.ldap.bind.password - **** - - - - hadoop.security.group.mapping.ldap.base - dc=example-ad,dc=local - - - - hadoop.security.group.mapping.ldap.search.filter.user - (&(objectClass=user)(sAMAccountName={0})) - - - - hadoop.security.group.mapping.ldap.search.filter.group - (objectClass=group) - - - - hadoop.security.group.mapping.ldap.search.attr.member - member - - - - hadoop.security.group.mapping.ldap.search.attr.group.name - cn -]]> - - - - Optionally, enable the early-out evaluation strategy. Prior to HBase 0.98.0, if - a user was not granted access to a column family, or at least a column qualifier, an - AccessDeniedException would be thrown. HBase 0.98.0 removed this exception in order - to allow cell-level exceptional grants. To restore the old behavior in HBase - 0.98.0-0.98.6, set to - true in hbase-site.xml. In HBase 0.98.6, - the default has been returned to true. - - - Distribute your configuration and restart your cluster for changes to take - effect. - - - To test your configuration, log into HBase Shell as a given user and use the - whoami command to report the groups your user is part of. In - this example, the user is reported as being a member of the services - group. - -hbase> whoami -service (auth:KERBEROS) - groups: services - - - -
-
- Administration - Administration tasks can be performed from HBase Shell or via an API. - - API Examples - Many of the API examples below are taken from source files - hbase-server/src/test/java/org/apache/hadoop/hbase/security/access/TestAccessController.java - and - hbase-server/src/test/java/org/apache/hadoop/hbase/security/access/SecureTestUtil.java. - Neither the examples, nor the source files they are taken from, are part of the - public HBase API, and are provided for illustration only. Refer to the official API - for usage instructions. - - - - User and Group Administration - Users and groups are maintained external to HBase, in your directory. - - - Granting Access To A Namespace, Table, Column Family, or Cell - There are a few different types of syntax for grant statements. The first, and - most familiar, is as follows, with the table and column family being - optional: - grant 'user', 'RWXCA', 'TABLE', 'CF', 'CQ' - Groups and users are granted access in the same way, but groups are prefixed - with an @ symbol. In the same way, tables and namespaces are - specified in the same way, but namespaces are prefixed with an @ - symbol. - It is also possible to grant multiple permissions against the same resource in a - single statement, as in this example. The first sub-clause maps users to ACLs and - the second sub-clause specifies the resource. - - HBase Shell support for granting and revoking access at the cell level is for - testing and verification support, and should not be employed for production use - because it won't apply the permissions to cells that don't exist yet. The correct - way to apply cell level permissions is to do so in the application code when - storing the values. - - - ACL Granularity and Evaluation Order - ACLs are evaluated from least granular to most granular, and when an ACL is - reached that grants permission, evaluation stops. This means that cell ACLs do not - override ACLs at less granularity. - - - HBase Shell - - - Global: - hbase> grant '@admins', 'RWXCA' - - - Namespace: - hbase> grant 'service', 'RWXCA', '@test-NS' - - - Table: - hbase> grant 'service', 'RWXCA', 'user' - - - Column Family: - hbase> grant '@developers', 'RW', 'user', 'i' - - - Column Qualifier: - hbase> grant 'service, 'RW', 'user', 'i', 'foo' - - - Cell: - The syntax for granting cell ACLs uses the following syntax: - grant <table>, \ - { '<user-or-group>' => \ - '<permissions>', ... }, \ - { <scanner-specification> } - - - <user-or-group> is the user or group - name, prefixed with @ in the case of a group. - - - <permissions> is a string containing - any or all of "RWXCA", though only R and W are meaningful at cell - scope. - - - <scanner-specification> is the - scanner specification syntax and conventions used by the 'scan' shell - command. For some examples of scanner specifications, issue the following - HBase Shell command. - hbase> help "scan" - - - This example grants read access to the 'testuser' user and read/write - access to the 'developers' group, on cells in the 'pii' column which match the - filter. - hbase> grant 'user', \ - { '@developers' => 'RW', 'testuser' => 'R' }, \ - { COLUMNS => 'pii', FILTER => "(PrefixFilter ('test'))" } - The shell will run a scanner with the given criteria, rewrite the found - cells with new ACLs, and store them back to their exact coordinates. - - - - - API - The following example shows how to grant access at the table level. - () { - @Override - public Void call() throws Exception { - HTable acl = new HTable(util.getConfiguration(), AccessControlLists.ACL_TABLE_NAME); - try { - BlockingRpcChannel service = acl.coprocessorService(HConstants.EMPTY_START_ROW); - AccessControlService.BlockingInterface protocol = - AccessControlService.newBlockingStub(service); - ProtobufUtil.grant(protocol, user, table, family, qualifier, actions); - } finally { - acl.close(); - } - return null; - } - }); -} ]]> - - To grant permissions at the cell level, you can use the - Mutation.setACL method: - perms) - ]]> - - Specifically, this example provides read permission to a user called - user1 on any cells contained in a particular Put - operation: - - - - - Revoking Access Control From a Namespace, Table, Column Family, or Cell - The revoke command and API are twins of the grant command and - API, and the syntax is exactly the same. The only exception is that you cannot - revoke permissions at the cell level. You can only revoke access that has previously - been granted, and a revoke statement is not the same thing as - explicit denial to a resource. - - HBase Shell support for granting and revoking access is for testing and - verification support, and should not be employed for production use because it - won't apply the permissions to cells that don't exist yet. The correct way to - apply cell-level permissions is to do so in the application code when storing the - values. - - - Revoking Access To a Table - -() { - @Override - public Void call() throws Exception { - HTable acl = new HTable(util.getConfiguration(), AccessControlLists.ACL_TABLE_NAME); - try { - BlockingRpcChannel service = acl.coprocessorService(HConstants.EMPTY_START_ROW); - AccessControlService.BlockingInterface protocol = - AccessControlService.newBlockingStub(service); - ProtobufUtil.revoke(protocol, user, table, family, qualifier, actions); - } finally { - acl.close(); - } - return null; - } - }); -} ]]> - - - - - Showing a User's Effective Permissions - - HBase Shell - hbase> user_permission 'user' - hbase> user_permission '.*' - hbase> user_permission JAVA_REGEX - - - API - ) { - List results = (List) obj; - if (results != null && results.isEmpty()) { - fail("Empty non null results from action for user '" + user.getShortName() + "'"); - } - assertEquals(count, results.size()); - } - } catch (AccessDeniedException ade) { - fail("Expected action to pass for user '" + user.getShortName() + "' but was denied"); - } -}]]> - - - - -
-
-
- -
- Visibility Labels - Visibility labels control can be used to only permit users or principals associated with - a given label to read or access cells with that label. For instance, you might label a cell - top-secret, and only grant access to that label to the - managers group. Visibility labels are implemented using Tags, which are - a feature of HFile v3, and allow you to store metadata on a per-cell basis. A label is a - string, and labels can be combined into expressions by using logical operators (&, |, or - !), and using parentheses for grouping. HBase does not do any kind of validation of - expressions beyond basic well-formedness. Visibility labels have no meaning on their own, - and may be used to denote sensitivity level, privilege level, or any other arbitrary - semantic meaning. - If a user's labels do not match a cell's label or expression, the user is - denied access to the cell. - In HBase 0.98.6 and newer, UTF-8 encoding is supported for visibility labels and - expressions. When creating labels using the addLabels(conf, labels) method - provided by the org.apache.hadoop.hbase.security.visibility.VisibilityClient - class and passing labels in Authorizations via Scan or Get, labels can contain UTF-8 - characters, as well as the logical operators normally used in visibility labels, with normal - Java notations, without needing any escaping method. However, when you pass a CellVisibility - expression via a Mutation, you must enclose the expression with the - CellVisibility.quote() method if you use UTF-8 characters or logical - operators. See TestExpressionParser and the source file - hbase-client/src/test/java/org/apache/hadoop/hbase/client/TestScan.java. - - A user adds visibility expressions to a cell during a Put operation. In the default - configuration, the user does not need to access to a label in order to label cells with it. - This behavior is controlled by the configuration option - . If you set this option to - true, the labels the user is modifying as part of the mutation must be - associated with the user, or the mutation will fail. Whether a user is authorized to read a - labelled cell is determined during a Get or Scan, and results which the user is not allowed - to read are filtered out. This incurs the same I/O penalty as if the results were returned, - but reduces load on the network. - Visibility labels can also be specified during Delete operations. For details about - visibility labels and Deletes, see HBASE-10885. - The user's effective label set is built in the RPC context when a request is first - received by the RegionServer. The way that users are associated with labels is pluggable. - The default plugin passes through labels specified in Authorizations added to the Get or - Scan and checks those against the calling user's authenticated labels list. When the client - passes labels for which the user is not authenticated, the default plugin drops them. You - can pass a subset of user authenticated labels via the - Get#setAuthorizations(Authorizations(String,...)) and - Scan#setAuthorizations(Authorizations(String,...)); methods. - Visibility label access checking is performed by the VisibilityController coprocessor. - You can use interface VisibilityLabelService to provide a custom implementation - and/or control the way that visibility labels are stored with cells. See the source file - hbase-server/src/test/java/org/apache/hadoop/hbase/security/visibility/TestVisibilityLabelsWithCustomVisLabService.java - for one example. - - Visibility labels can be used in conjunction with ACLs. - - Examples of Visibility Expressions - - - - Expression - Interpretation - - - - - fulltime - Allow accesss to users associated with the - fulltime label. - - - !public - Allow access to users not associated with the - public label. - - - ( secret | topsecret ) & !probationary - Allow access to users associated with either the - secret or topsecret label and not - associated with the probationary label. - - - -
-
- Server-Side Configuration - - - As a prerequisite, perform the steps in . - - Install and configure the VisibilityController coprocessor by setting the - following properties in hbase-site.xml. These properties take a - list of class names. - - hbase.coprocessor.region.classes - org.apache.hadoop.hbase.security.visibility.VisibilityController - - - hbase.coprocessor.master.classes - org.apache.hadoop.hbase.security.visibility.VisibilityController - - ]]> - - If you use the AccessController and VisibilityController coprocessors together, - the AccessController must come first in the list, because with both components - active, the VisibilityController will delegate access control on its system tables - to the AccessController. - - - - Adjust Configuration - By default, users can label cells with any label, including labels they are not - associated with, which means that a user can Put data that he cannot read. For - example, a user could label a cell with the (hypothetical) 'topsecret' label even if - the user is not associated with that label. If you only want users to be able to label - cells with labels they are associated with, set - hbase.security.visibility.mutations.checkauths to - true. In that case, the mutation will fail if it makes use of - labels the user is not associated with. - - - Distribute your configuration and restart your cluster for changes to take - effect. - - -
-
- Administration - Administration tasks can be performed using the HBase Shell or the Java API. For - defining the list of visibility labels and associating labels with users, the - HBase Shell is probably simpler. - - API Examples - Many of the Java API examples in this section are taken from the source file - hbase-server/src/test/java/org/apache/hadoop/hbase/security/visibility/TestVisibilityLabels.java. - Refer to that file or the API documentation for more context. - Neither these examples, nor the source file they were taken from, are part of the - public HBase API, and are provided for illustration only. Refer to the official API - for usage instructions. - - - - Define the List of Visibility Labels - - HBase Shell - hbase> add_labels [ 'admin', 'service', 'developer', 'test' ] - - - Java API - action = - new PrivilegedExceptionAction() { - public VisibilityLabelsResponse run() throws Exception { - String[] labels = { SECRET, TOPSECRET, CONFIDENTIAL, PUBLIC, PRIVATE, COPYRIGHT, ACCENT, - UNICODE_VIS_TAG, UC1, UC2 }; - try { - VisibilityClient.addLabels(conf, labels); - } catch (Throwable t) { - throw new IOException(t); - } - return null; - } - }; - SUPERUSER.runAs(action); -} - ]]> - - - - Associate Labels with Users - - HBase Shell - hbase> set_auths 'service', [ 'service' ] - gbase> set_auths 'testuser', [ 'test' ] - hbase> set_auths 'qa', [ 'test', 'developer' ] - - - Java API - action = new PrivilegedExceptionAction() { - public Void run() throws Exception { - String[] auths = { SECRET, CONFIDENTIAL }; - try { - VisibilityClient.setAuths(conf, auths, user); - } catch (Throwable e) { - } - return null; - } - ... - ]]> - - - - Clear Labels From Users - - HBase Shell - hbase> clear_auths 'service', [ 'service' ] - hbase> clear_auths 'testuser', [ 'test' ] - hbase> clear_auths 'qa', [ 'test', 'developer' ] - - - Java API - - - - - Apply a Label or Expression to a Cell - The label is only applied when data is written. The label is associated with a - given version of the cell. - - HBase Shell - hbase> set_visibility 'user', 'admin|service|developer', \ - { COLUMNS => 'i' } - hbase> set_visibility 'user', 'admin|service', \ - { COLUMNS => ' pii' } - hbase> COLUMNS => [ 'i', 'pii' ], \ - FILTER => "(PrefixFilter ('test'))" } - - - HBase Shell support for applying labels or permissions to cells is for testing - and verification support, and should not be employed for production use because it - won't apply the labels to cells that don't exist yet. The correct way to apply cell - level labels is to do so in the application code when storing the values. - - - Java API - puts = new ArrayList(); - for (String labelExp : labelExps) { - Put put = new Put(Bytes.toBytes("row" + i)); - put.add(fam, qual, HConstants.LATEST_TIMESTAMP, value); - put.setCellVisibility(new CellVisibility(labelExp)); - puts.add(put); - i++; - } - table.put(puts); - } finally { - if (table != null) { - table.flushCommits(); - } - } - ]]> - - - -
-
- Implementing Your Own Visibility Label Algorithm - Interpreting the labels authenticated for a given get/scan request is a pluggable - algorithm. You can specify a custom plugin by using the property - hbase.regionserver.scan.visibility.label.generator.class. The default - implementation class is - org.apache.hadoop.hbase.security.visibility.DefaultScanLabelGenerator. You - can also configure a set of ScanLabelGenerators to be used by the system, as - a comma-separated list. -
-
- -
- Transparent Encryption of Data At Rest - HBase provides a mechanism for protecting your data at rest, in HFiles and the WAL, which - reside within HDFS or another distributed filesystem. A two-tier architecture is used for - flexible and non-intrusive key rotation. "Transparent" means that no implementation changes - are needed on the client side. When data is written, it is encrypted. When it is read, it is - decrypted on demand. -
- How It Works - The administrator provisions a master key for the cluster, which is stored in a key - provider accessible to every trusted HBase process, including the HMaster, RegionServers, - and clients (such as HBase Shell) on administrative workstations. The default key provider - is integrated with the Java KeyStore API and any key management systems with support for - it. Other custom key provider implementations are possible. The key retrieval mechanism is - configured in the hbase-site.xml configuration file. The master key - may be stored on the cluster servers, protected by a secure KeyStore file, or on an - external keyserver, or in a hardware security module. This master key is resolved as - needed by HBase processes through the configured key provider. - Next, encryption use can be specified in the schema, per column family, by creating - or modifying a column descriptor to include two additional attributes: the name of the - encryption algorithm to use (currently only "AES" is supported), and optionally, a data - key wrapped (encrypted) with the cluster master key. If a data key is not explictly - configured for a ColumnFamily, HBase will create a random data key per HFile. This - provides an incremental improvement in security over the alternative. Unless you need to - supply an explicit data key, such as in a case where you are generating encrypted HFiles - for bulk import with a given data key, only specify the encryption algorithm in the - ColumnFamily schema metadata and let HBase create data keys on demand. Per Column Family - keys facilitate low impact incremental key rotation and reduce the scope of any external - leak of key material. The wrapped data key is stored in the ColumnFamily schema metadata, - and in each HFile for the Column Family, encrypted with the cluster master key. After the - Column Family is configured for encryption, any new HFiles will be written encrypted. To - ensure encryption of all HFiles, trigger a major compaction after enabling this - feature. - When the HFile is opened, the data key is extracted from the HFile, decrypted with the - cluster master key, and used for decryption of the remainder of the HFile. The HFile will - be unreadable if the master key is not available. If a remote user somehow acquires access - to the HFile data because of some lapse in HDFS permissions, or from inappropriately - discarded media, it will not be possible to decrypt either the data key or the file - data. - It is also possible to encrypt the WAL. Even though WALs are transient, it is - necessary to encrypt the WALEdits to avoid circumventing HFile protections for encrypted - column families, in the event that the underlying filesystem is compromised. When WAL - encryption is enabled, all WALs are encrypted, regardless of whether the relevant HFiles - are encrypted. -
-
- Server-Side Configuration - This procedure assumes you are using the default Java keystore implementation. If you - are using a custom implementation, check its documentation and adjust accordingly. - - - Create a secret key of appropriate length for AES encryption, using the - <code>keytool</code> utility. - $ keytool -keystore /path/to/hbase/conf/hbase.jks \ - -storetype jceks -storepass **** \ - -genseckey -keyalg AES -keysize 128 \ - -alias <alias> - Replace **** with the password for the keystore file - and <alias> with the username of the HBase service account, or an arbitrary - string. If you use an arbitrary string, you will need to configure HBase to use it, - and that is covered below. Specify a keysize that is appropriate. Do not specify a - separate password for the key, but press Return when prompted. - - - Set appropriate permissions on the keyfile and distribute it to all the HBase - servers. - The previous command created a file called hbase.jks in the - HBase conf/ directory. Set the permissions and ownership on this - file such that only the HBase service account user can read the file, and securely - distribute the key to all HBase servers. - - - Configure the HBase daemons. - Set the following properties in hbase-site.xml on the region - servers, to configure HBase daemons to use a key provider backed by the KeyStore file - or retrieving the cluster master key. In the example below, replace - **** with the password. - - hbase.crypto.keyprovider - org.apache.hadoop.hbase.io.crypto.KeyStoreKeyProvider - - - hbase.crypto.keyprovider.parameters - jceks:///path/to/hbase/conf/hbase.jks?password=**** - - ]]> - By default, the HBase service account name will be used to resolve the cluster - master key. However, you can store it with an arbitrary alias (in the - keytool command). In that case, set the following property to the - alias you used. - - hbase.crypto.master.key.name - my-alias -]]> - - You also need to be sure your HFiles use HFile v3, in order to use transparent - encryption. This is the default configuration for HBase 1.0 onward. For previous - versions, set the following property in your hbase-site.xml - file. - - hfile.format.version - 3 -]]> - - Optionally, you can use a different cipher provider, either a Java Cryptography - Encryption (JCE) algorithm provider or a custom HBase cipher implementation. - - - JCE: - - - Install a signed JCE provider (supporting “AES/CTR/NoPadding” mode with - 128 bit keys) - - - Add it with highest preference to the JCE site configuration file - $JAVA_HOME/lib/security/java.security. - - - Update and - options in - hbase-site.xml. - - - - - Custom HBase Cipher: - - - Implement - org.apache.hadoop.hbase.io.crypto.CipherProvider. - - - Add the implementation to the server classpath. - - - Update in - hbase-site.xml. - - - - - - - Configure WAL encryption. - Configure WAL encryption in every RegionServer's - hbase-site.xml, by setting the following properties. You can - include these in the HMaster's hbase-site.xml as well, but the - HMaster does not have a WAL and will not use them. - - hbase.regionserver.hlog.reader.impl - org.apache.hadoop.hbase.regionserver.wal.SecureProtobufLogReader - - - hbase.regionserver.hlog.writer.impl - org.apache.hadoop.hbase.regionserver.wal.SecureProtobufLogWriter - - - hbase.regionserver.wal.encryption - true - - ]]> - - - Configure permissions on the <filename>hbase-site.xml</filename> file. - Because the keystore password is stored in the hbase-site.xml, you need to ensure - that only the HBase user can read the hbase-site.xml file, using - file ownership and permissions. - - - Restart your cluster. - Distribute the new configuration file to all nodes and restart your - cluster. - - -
-
- Administration - Administrative tasks can be performed in HBase Shell or the Java API. - - Java API - Java API examples in this section are taken from the source file - hbase-server/src/test/java/org/apache/hadoop/hbase/util/TestHBaseFsckEncryption.java. - . - Neither these examples, nor the source files they are taken from, are part of the - public HBase API, and are provided for illustration only. Refer to the official API - for usage instructions. - - - - Enable Encryption on a Column Family - - To enable encryption on a column family, you can either use HBase Shell or the - Java API. After enabling encryption, trigger a major compaction. When the major - compaction completes, the HFiles will be encrypted. - - HBase Shell - -hbase> disable 'mytable' -hbase> alter 'mytable', 'mycf', {ENCRYPTION => AES} -hbase> enable 'mytable' - - - - Java API - You can use the HBaseAdmin#modifyColumn API to modify the - ENCRYPTION attribute on a Column Family. Additionally, you - can specify the specific key to use as the wrapper, by setting the - ENCRYPTION_KEY attribute. This is only possible via the - Java API, and not the HBase Shell. The default behavior if you do not specify an - ENCRYPTION_KEY for a column family is for a random key to - be generated for each encrypted column family (per HFile). This provides - additional defense in the (unlikely, but theoretically possible) occurrence of - storing the same data in multiple HFiles with exactly the same block layout, the - same data key, and the same randomly-generated initialization vector. - This example shows how to programmatically set the transparent encryption both - in the server configuration and at the column family, as part of a test which uses - the Minicluster configuration. - -@Before -public void setUp() throws Exception { - conf = TEST_UTIL.getConfiguration(); - conf.setInt("hfile.format.version", 3); - conf.set(HConstants.CRYPTO_KEYPROVIDER_CONF_KEY, KeyProviderForTesting.class.getName()); - conf.set(HConstants.CRYPTO_MASTERKEY_NAME_CONF_KEY, "hbase"); - - // Create the test encryption key - SecureRandom rng = new SecureRandom(); - byte[] keyBytes = new byte[AES.KEY_LENGTH]; - rng.nextBytes(keyBytes); - cfKey = new SecretKeySpec(keyBytes, "AES"); - - // Start the minicluster - TEST_UTIL.startMiniCluster(3); - - // Create the table - htd = new HTableDescriptor(TableName.valueOf("default", "TestHBaseFsckEncryption")); - HColumnDescriptor hcd = new HColumnDescriptor("cf"); - hcd.setEncryptionType("AES"); - hcd.setEncryptionKey(EncryptionUtil.wrapKey(conf, - conf.get(HConstants.CRYPTO_MASTERKEY_NAME_CONF_KEY, User.getCurrent().getShortName()), - cfKey)); - htd.addFamily(hcd); - TEST_UTIL.getHBaseAdmin().createTable(htd); - TEST_UTIL.waitTableAvailable(htd.getName(), 5000); -} - - - - - - Rotate the Data Key - - To rotate the data key, first change the ColumnFamily key in the column - descriptor, then trigger a major compaction. When compaction is complete, all HFiles - will be re-encrypted using the new data key. Until the compaction completes, the - old HFiles will still be readable using the old key. - If you rely on HBase's default behavior of generating a random key for each - HFile, there is no need to rotate data keys. A major compaction will re-encrypt the - HFile with a new key. - - - - Switching Between Using a Random Data Key and Specifying A Key - - If you configured a column family to use a specific key and you want to return - to the default behavior of using a randomly-generated key for that column family, - use the Java API to alter the HColumnDescriptor so that no value is - sent with the key ENCRYPTION_KEY. - - - - Rotate the Master Key - - To rotate the master key, first generate and distribute the new key. Then update - the KeyStore to contain a new master key, and keep the old master key in the - KeyStore using a different alias. Next, configure fallback to the old master key in - the hbase-site.xml file. - - hbase.crypto.master.alternate.key.name - hbase.old - - ]]> - Rolling restart your cluster for this change to take effect. Trigger a major - compaction on each table. At the end of the major compaction, all HFiles will be - re-encrypted with data keys wrapped by the new cluster key. At this point, you can - remove the old master key from the KeyStore, remove the configuration for the - fallback master key from the hbase-site.xml, and perform a - second rolling restart at some point. This second rolling restart is not - time-sensitive. - - - - - - - - - -
-
- -
- Secure Bulk Load - Bulk loading in secure mode is a bit more involved than normal setup, since the client - has to transfer the ownership of the files generated from the mapreduce job to HBase. Secure - bulk loading is implemented by a coprocessor, named SecureBulkLoadEndpoint, which uses a staging directory configured by the - configuration property , which defaults to - /tmp/hbase-staging/. - - Secure Bulk Load Algorithm - - One time only, create a staging directory which is world-traversable and owned by - the user which runs HBase (mode 711, or rwx--x--x). A listing of this - directory will look similar to the following: - $ ls -ld /tmp/hbase-staging -drwx--x--x 2 hbase hbase 68 3 Sep 14:54 /tmp/hbase-staging - - - - A user writes out data to a secure output directory owned by that user. For example, - /user/foo/data. - - - Internally, HBase creates a secret staging directory which is globally - readable/writable (-rwxrwxrwx, 777). For example, - /tmp/hbase-staging/averylongandrandomdirectoryname. The name and - location of this directory is not exposed to the user. HBase manages creation and - deletion of this directory. - - - The user makes the data world-readable and world-writable, moves it into the random - staging directory, then calls the SecureBulkLoadClient#bulkLoadHFiles - method. - - - - The strength of the security lies in the length and randomness of the secret - directory. - To enable secure bulk load, add the following properties to - hbase-site.xml. - - - hbase.bulkload.staging.dir - /tmp/hbase-staging - - - hbase.coprocessor.region.classes - org.apache.hadoop.hbase.security.token.TokenProvider, - org.apache.hadoop.hbase.security.access.AccessController - - - hbase.coprocessor.regionserver.classes - org.apache.hadoop.hbase.security.token.TokenProvider, - org.apache.hadoop.hbase.security.access.AccessController,org.apache.hadoop.hbase.security.access.SecureBulkLoadEndpoint - - ]]> -
- -
- -
- Security Configuration Example - This configuration example includes support for HFile v3, ACLs, Visibility Labels, and - transparent encryption of data at rest and the WAL. All options have been discussed separately - in the sections above. - - Example Security Settings in <filename>hbase-site.xml</filename> - - - hfile.format.version - 3 - - - - hbase.superuser - hbase, admin - - - - hbase.coprocessor.region.classes - org.apache.hadoop.hbase.security.access.AccessController, - org.apache.hadoop.hbase.security.visibility.VisibilityController, - org.apache.hadoop.hbase.security.token.TokenProvider - - - hbase.coprocessor.master.classes - org.apache.hadoop.hbase.security.access.AccessController, - org.apache.hadoop.hbase.security.visibility.VisibilityController - - - hbase.coprocessor.regionserver.classes - org.apache.hadoop/hbase.security.access.AccessController, - org.apache.hadoop.hbase.security.access.VisibilityController - - - - hbase.security.exec.permission.checks - true - - - - hbase.security.visibility.mutations.checkauth - false - - - - hbase.rpc.protection - auth-conf - - - - hbase.crypto.keyprovider - org.apache.hadoop.hbase.io.crypto.KeyStoreKeyProvider - - - hbase.crypto.keyprovider.parameters - jceks:///path/to/hbase/conf/hbase.jks?password=*** - - - hbase.crypto.master.key.name - hbase - - - - hbase.regionserver.hlog.reader.impl - org.apache.hadoop.hbase.regionserver.wal.SecureProtobufLogReader - - - hbase.regionserver.hlog.writer.impl - org.apache.hadoop.hbase.regionserver.wal.SecureProtobufLogWriter - - - hbase.regionserver.wal.encryption - true - - - - hbase.crypto.master.alternate.key.name - hbase.old - - - - hbase.bulkload.staging.dir - /tmp/hbase-staging - - - hbase.coprocessor.region.classes - org.apache.hadoop.hbase.security.token.TokenProvider, - org.apache.hadoop.hbase.security.access.AccessController,org.apache.hadoop.hbase.security.access.SecureBulkLoadEndpoint - - ]]> - - - Example Group Mapper in Hadoop <filename>core-site.xml</filename> - Adjust these settings to suit your environment. - - hadoop.security.group.mapping - org.apache.hadoop.security.LdapGroupsMapping - - - hadoop.security.group.mapping.ldap.url - ldap://server - - - hadoop.security.group.mapping.ldap.bind.user - Administrator@example-ad.local - - - hadoop.security.group.mapping.ldap.bind.password - **** - - - hadoop.security.group.mapping.ldap.base - dc=example-ad,dc=local - - - hadoop.security.group.mapping.ldap.search.filter.user - (&(objectClass=user)(sAMAccountName={0})) - - - hadoop.security.group.mapping.ldap.search.filter.group - (objectClass=group) - - - hadoop.security.group.mapping.ldap.search.attr.member - member - - - hadoop.security.group.mapping.ldap.search.attr.group.name - cn - - ]]> - -
- diff --git a/src/main/docbkx/shell.xml b/src/main/docbkx/shell.xml deleted file mode 100644 index a400d8de05c..00000000000 --- a/src/main/docbkx/shell.xml +++ /dev/null @@ -1,386 +0,0 @@ - - - - The Apache HBase Shell - - The Apache HBase Shell is (J)Ruby's IRB with some HBase particular commands - added. Anything you can do in IRB, you should be able to do in the HBase Shell. - To run the HBase shell, do as follows: - $ ./bin/hbase shell - Type help and then <RETURN> to see a listing - of shell commands and options. Browse at least the paragraphs at the end of the help - emission for the gist of how variables and command arguments are entered into the HBase - shell; in particular note how table names, rows, and columns, etc., must be quoted. - See for example basic shell operation. - Here is a nicely formatted listing of all shell - commands by Rajeshbabu Chintaguntla. - -
- Scripting with Ruby - For examples scripting Apache HBase, look in the HBase bin - directory. Look at the files that end in *.rb. To run one of these - files, do as follows: - $ ./bin/hbase org.jruby.Main PATH_TO_SCRIPT -
- -
- Running the Shell in Non-Interactive Mode - A new non-interactive mode has been added to the HBase Shell (HBASE-11658). - Non-interactive mode captures the exit status (success or failure) of HBase Shell - commands and passes that status back to the command interpreter. If you use the normal - interactive mode, the HBase Shell will only ever return its own exit status, which will - nearly always be 0 for success. - To invoke non-interactive mode, pass the or - option to HBase Shell. -
- -
- HBase Shell in OS Scripts - You can use the HBase shell from within operating system script interpreters like the - Bash shell which is the default command interpreter for most Linux and UNIX - distributions. The following guidelines use Bash syntax, but could be adjusted to work - with C-style shells such as csh or tcsh, and could probably be modified to work with the - Microsoft Windows script interpreter as well. Submissions are welcome. - - Spawning HBase Shell commands in this way is slow, so keep that in mind when you - are deciding when combining HBase operations with the operating system command line - is appropriate. - - - Passing Commands to the HBase Shell - You can pass commands to the HBase Shell in non-interactive mode (see ) using the echo - command and the | (pipe) operator. Be sure to escape characters - in the HBase commands which would otherwise be interpreted by the shell. Some - debug-level output has been truncated from the example below. - $ echo "describe 'test1'" | ./hbase shell -n - -Version 0.98.3-hadoop2, rd5e65a9144e315bb0a964e7730871af32f5018d5, Sat May 31 19:56:09 PDT 2014 - -describe 'test1' - -DESCRIPTION ENABLED - 'test1', {NAME => 'cf', DATA_BLOCK_ENCODING => 'NON true - E', BLOOMFILTER => 'ROW', REPLICATION_SCOPE => '0', - VERSIONS => '1', COMPRESSION => 'NONE', MIN_VERSIO - NS => '0', TTL => 'FOREVER', KEEP_DELETED_CELLS => - 'false', BLOCKSIZE => '65536', IN_MEMORY => 'false' - , BLOCKCACHE => 'true'} -1 row(s) in 3.2410 seconds - - - To suppress all output, echo it to /dev/null: - $ echo "describe 'test'" | ./hbase shell -n > /dev/null 2>&1 - - - Checking the Result of a Scripted Command - Since scripts are not designed to be run interactively, you need a way to check - whether your command failed or succeeded. The HBase shell uses the standard - convention of returning a value of 0 for successful commands, and - some non-zero value for failed commands. Bash stores a command's return value in a - special environment variable called $?. Because that variable is - overwritten each time the shell runs any command, you should store the result in a - different, script-defined variable. - This is a naive script that shows one way to store the return value and make a - decision based upon it. - -#!/bin/bash - -echo "describe 'test'" | ./hbase shell -n > /dev/null 2>&1 -status=$? -echo "The status was " $status -if ($status == 0); then - echo "The command succeeded" -else - echo "The command may have failed." -fi -return $status - - -
- Checking for Success or Failure In Scripts - Getting an exit code of 0 means that the command you scripted definitely - succeeded. However, getting a non-zero exit code does not necessarily mean the - command failed. The command could have succeeded, but the client lost connectivity, - or some other event obscured its success. This is because RPC commands are - stateless. The only way to be sure of the status of an operation is to check. For - instance, if your script creates a table, but returns a non-zero exit value, you - should check whether the table was actually created before trying again to create - it. -
-
- -
- Read HBase Shell Commands from a Command File - You can enter HBase Shell commands into a text file, one command per line, and pass - that file to the HBase Shell. - - Example Command File - -create 'test', 'cf' -list 'test' -put 'test', 'row1', 'cf:a', 'value1' -put 'test', 'row2', 'cf:b', 'value2' -put 'test', 'row3', 'cf:c', 'value3' -put 'test', 'row4', 'cf:d', 'value4' -scan 'test' -get 'test', 'row1' -disable 'test' -enable 'test' - - - - Directing HBase Shell to Execute the Commands - Pass the path to the command file as the only argument to the hbase - shell command. Each command is executed and its output is shown. If - you do not include the exit command in your script, you are - returned to the HBase shell prompt. There is no way to programmatically check each - individual command for success or failure. Also, though you see the output for each - command, the commands themselves are not echoed to the screen so it can be difficult - to line up the command with its output. - -$ ./hbase shell ./sample_commands.txt -0 row(s) in 3.4170 seconds - -TABLE -test -1 row(s) in 0.0590 seconds - -0 row(s) in 0.1540 seconds - -0 row(s) in 0.0080 seconds - -0 row(s) in 0.0060 seconds - -0 row(s) in 0.0060 seconds - -ROW COLUMN+CELL - row1 column=cf:a, timestamp=1407130286968, value=value1 - row2 column=cf:b, timestamp=1407130286997, value=value2 - row3 column=cf:c, timestamp=1407130287007, value=value3 - row4 column=cf:d, timestamp=1407130287015, value=value4 -4 row(s) in 0.0420 seconds - -COLUMN CELL - cf:a timestamp=1407130286968, value=value1 -1 row(s) in 0.0110 seconds - -0 row(s) in 1.5630 seconds - -0 row(s) in 0.4360 seconds - - -
-
- Passing VM Options to the Shell - You can pass VM options to the HBase Shell using the HBASE_SHELL_OPTS - environment variable. You can set this in your environment, for instance by editing - ~/.bashrc, or set it as part of the command to launch HBase - Shell. The following example sets several garbage-collection-related variables, just for - the lifetime of the VM running the HBase Shell. The command should be run all on a - single line, but is broken by the \ character, for - readability. - -$ HBASE_SHELL_OPTS="-verbose:gc -XX:+PrintGCApplicationStoppedTime -XX:+PrintGCDateStamps \ - -XX:+PrintGCDetails -Xloggc:$HBASE_HOME/logs/gc-hbase.log" ./bin/hbase shell - -
-
- Shell Tricks -
- Table variables - - HBase 0.95 adds shell commands that provide a jruby-style object-oriented - references for tables. Previously all of the shell commands that act upon a table - have a procedural style that always took the name of the table as an argument. HBase - 0.95 introduces the ability to assign a table to a jruby variable. The table - reference can be used to perform data read write operations such as puts, scans, and - gets well as admin functionality such as disabling, dropping, describing tables. - - For example, previously you would always specify a table name: - -hbase(main):000:0> create ‘t’, ‘f’ -0 row(s) in 1.0970 seconds -hbase(main):001:0> put 't', 'rold', 'f', 'v' -0 row(s) in 0.0080 seconds - -hbase(main):002:0> scan 't' -ROW COLUMN+CELL - rold column=f:, timestamp=1378473207660, value=v -1 row(s) in 0.0130 seconds - -hbase(main):003:0> describe 't' -DESCRIPTION ENABLED - 't', {NAME => 'f', DATA_BLOCK_ENCODING => 'NONE', BLOOMFILTER => 'ROW', REPLICATION_ true - SCOPE => '0', VERSIONS => '1', COMPRESSION => 'NONE', MIN_VERSIONS => '0', TTL => '2 - 147483647', KEEP_DELETED_CELLS => 'false', BLOCKSIZE => '65536', IN_MEMORY => 'false - ', BLOCKCACHE => 'true'} -1 row(s) in 1.4430 seconds - -hbase(main):004:0> disable 't' -0 row(s) in 14.8700 seconds - -hbase(main):005:0> drop 't' -0 row(s) in 23.1670 seconds - -hbase(main):006:0> - - - Now you can assign the table to a variable and use the results in jruby shell - code. - -hbase(main):007 > t = create 't', 'f' -0 row(s) in 1.0970 seconds - -=> Hbase::Table - t -hbase(main):008 > t.put 'r', 'f', 'v' -0 row(s) in 0.0640 seconds -hbase(main):009 > t.scan -ROW COLUMN+CELL - r column=f:, timestamp=1331865816290, value=v -1 row(s) in 0.0110 seconds -hbase(main):010:0> t.describe -DESCRIPTION ENABLED - 't', {NAME => 'f', DATA_BLOCK_ENCODING => 'NONE', BLOOMFILTER => 'ROW', REPLICATION_ true - SCOPE => '0', VERSIONS => '1', COMPRESSION => 'NONE', MIN_VERSIONS => '0', TTL => '2 - 147483647', KEEP_DELETED_CELLS => 'false', BLOCKSIZE => '65536', IN_MEMORY => 'false - ', BLOCKCACHE => 'true'} -1 row(s) in 0.0210 seconds -hbase(main):038:0> t.disable -0 row(s) in 6.2350 seconds -hbase(main):039:0> t.drop -0 row(s) in 0.2340 seconds - - - If the table has already been created, you can assign a Table to a variable by - using the get_table method: - -hbase(main):011 > create 't','f' -0 row(s) in 1.2500 seconds - -=> Hbase::Table - t -hbase(main):012:0> tab = get_table 't' -0 row(s) in 0.0010 seconds - -=> Hbase::Table - t -hbase(main):013:0> tab.put ‘r1’ ,’f’, ‘v’ -0 row(s) in 0.0100 seconds -hbase(main):014:0> tab.scan -ROW COLUMN+CELL - r1 column=f:, timestamp=1378473876949, value=v -1 row(s) in 0.0240 seconds -hbase(main):015:0> - - - The list functionality has also been extended so that it returns a list of table - names as strings. You can then use jruby to script table operations based on these - names. The list_snapshots command also acts similarly. - -hbase(main):016 > tables = list(‘t.*’) -TABLE -t -1 row(s) in 0.1040 seconds - -=> #<#<Class:0x7677ce29>:0x21d377a4> -hbase(main):017:0> tables.map { |t| disable t ; drop t} -0 row(s) in 2.2510 seconds - -=> [nil] -hbase(main):018:0> - -
- -
- <filename>irbrc</filename> - Create an .irbrc file for yourself in your home directory. - Add customizations. A useful one is command history so commands are save across - Shell invocations: - -$ more .irbrc -require 'irb/ext/save-history' -IRB.conf[:SAVE_HISTORY] = 100 -IRB.conf[:HISTORY_FILE] = "#{ENV['HOME']}/.irb-save-history" - See the ruby documentation of - .irbrc to learn about other possible configurations. - -
-
- LOG data to timestamp - To convert the date '08/08/16 20:56:29' from an hbase log into a timestamp, - do: - -hbase(main):021:0> import java.text.SimpleDateFormat -hbase(main):022:0> import java.text.ParsePosition -hbase(main):023:0> SimpleDateFormat.new("yy/MM/dd HH:mm:ss").parse("08/08/16 20:56:29", ParsePosition.new(0)).getTime() => 1218920189000 - To go the other direction: - -hbase(main):021:0> import java.util.Date -hbase(main):022:0> Date.new(1218920189000).toString() => "Sat Aug 16 20:56:29 UTC 2008" - To output in a format that is exactly like that of the HBase log format will take - a little messing with SimpleDateFormat. - -
-
- Debug -
- Shell debug switch - You can set a debug switch in the shell to see more output -- e.g. more of the - stack trace on exception -- when you run a command: - hbase> debug <RETURN> -
-
- DEBUG log level - To enable DEBUG level logging in the shell, launch it with the - -d option. - $ ./bin/hbase shell -d -
-
-
- Commands -
- count - Count command returns the number of rows in a table. It's quite fast when - configured with the right CACHE - hbase> count '<tablename>', CACHE => 1000 - The above count fetches 1000 rows at a time. Set CACHE lower if your rows are - big. Default is to fetch one row at a time. -
-
- -
- diff --git a/src/main/docbkx/sql.xml b/src/main/docbkx/sql.xml deleted file mode 100644 index 40f43d60f8c..00000000000 --- a/src/main/docbkx/sql.xml +++ /dev/null @@ -1,40 +0,0 @@ - - - - SQL over HBase -
- Apache Phoenix - Apache Phoenix -
-
- Trafodion - Trafodion: Transactional SQL-on-HBase -
- - diff --git a/src/main/docbkx/thrift_filter_language.xml b/src/main/docbkx/thrift_filter_language.xml deleted file mode 100644 index 74da600fe8d..00000000000 --- a/src/main/docbkx/thrift_filter_language.xml +++ /dev/null @@ -1,757 +0,0 @@ - - - - - Thrift API and Filter Language - Apache Thrift is a cross-platform, - cross-language development framework. HBase includes a Thrift API and filter language. The - Thrift API relies on client and server processes. Documentation about the HBase Thrift API - is located at . - You can configure Thrift for secure authentication at the server and client side, by - following the procedures in and . - The rest of this chapter discusses the filter language provided by the Thrift API. -
- Filter Language - Thrift Filter Language was introduced in APache HBase 0.92. It allows - you to perform server-side filtering when accessing HBase over Thrift or in the HBase - shell. You can find out more about shell integration by using the scan help - command in the shell. - You specify a filter as a string, which is parsed on the server to construct the - filter. - -
- General Filter String Syntax - A simple filter expression is expressed as a string: - “FilterName (argument, argument,... , argument)” - Keep the following syntax guidelines in mind. - - - Specify the name of the filter followed by the comma-separated argument - list in parentheses. - - - If the argument represents a string, it should be enclosed in single - quotes ('). - - - Arguments which represent a boolean, an integer, or a comparison operator - (such as <, >, or !=), should not be enclosed in quotes - - - The filter name must be a single word. All ASCII characters are allowed - except for whitespace, single quotes and parentheses. - - - The filter’s arguments can contain any ASCII character. If single quotes - are present in the argument, they must be escaped by an additional preceding - single quote. - - -
- -
- Compound Filters and Operators - - Binary Operators - - AND - If the AND operator is used, the key-vallue - must satisfy both the filters. - - - OR - If the OR operator is used, the key-value - must satisfy at least one of the filters. - - - - - Unary Operators - - SKIP - For a particular row, if any of the key-values fail the - filter condition, the entire row is skipped. - - - WHILE - For a particular row, key-values will be emitted until a - key-value is reached t hat fails the filter condition. - - - - - Compound Operators - You can combine multiple operators to create a hierarchy of filters, such as - the following example: - (Filter1 AND Filter2) OR (Filter3 AND Filter4) - -
- -
- Order of Evaluation - - Parentheses have the highest precedence. - The unary operators SKIP and WHILE are next, and have the same - precedence. - The binary operators follow. AND has highest - precedence, followed by OR. - - - Precedence Example - Filter1 AND Filter2 OR Filter -is evaluated as -(Filter1 AND Filter2) OR Filter3 - Filter1 AND SKIP Filter2 OR Filter3 -is evaluated as -(Filter1 AND (SKIP Filter2)) OR Filter3 - - - You can use parentheses to explicitly control the order of evaluation. -
- -
- Compare Operator - The following compare operators are provided: - - - LESS (<) - - - LESS_OR_EQUAL (<=) - - - EQUAL (=) - - - NOT_EQUAL (!=) - - - GREATER_OR_EQUAL (>=) - - - GREATER (>) - - - NO_OP (no operation) - - - The client should use the symbols (<, <=, =, !=, >, >=) to express compare - operators. -
- -
- Comparator - A comparator can be any of the following: - - - BinaryComparator - This lexicographically - compares against the specified byte array using Bytes.compareTo(byte[], - byte[]) - - - BinaryPrefixComparator - This lexicographically - compares against a specified byte array. It only compares up to the length - of this byte array. - - - RegexStringComparator - This compares against the - specified byte array using the given regular expression. Only EQUAL and - NOT_EQUAL comparisons are valid with this comparator - - - SubStringComparator - This tests if the given - substring appears in a specified byte array. The comparison is case - insensitive. Only EQUAL and NOT_EQUAL comparisons are valid with this - comparator - - - The general syntax of a comparator is: - ComparatorType:ComparatorValue - The ComparatorType for the various comparators is as follows: - - - BinaryComparator - binary - - - BinaryPrefixComparator - binaryprefix - - - RegexStringComparator - regexstring - - - SubStringComparator - substring - - - The ComparatorValue can be any value. - - Example 1 - >, 'binary:abc' will match everything that is - lexicographically greater than "abc" - - - Example 2 - =, 'binaryprefix:abc' will match everything whose first 3 - characters are lexicographically equal to "abc" - - - Example 3 - !=, 'regexstring:ab*yz' will match everything that doesn't - begin with "ab" and ends with "yz" - - - Example 4 - =, 'substring:abc123' will match everything that begins - with the substring "abc123" - -
- -
- Example PHP Client Program that uses the Filter Language - ', ); - $hbase->open(); - $client = $hbase->getClient(); - $result = $client->scannerOpenWithFilterString('table_name', "(PrefixFilter ('row2') AND (QualifierFilter (>=, 'binary:xyz'))) AND (TimestampsFilter ( 123, 456))"); - $to_print = $client->scannerGetList($result,1); - while ($to_print) { - print_r($to_print); - $to_print = $client->scannerGetList($result,1); - } - $client->scannerClose($result); -?> - ]]> -
- -
- Example Filter Strings - - - - “PrefixFilter (‘Row’) AND PageFilter (1) AND FirstKeyOnlyFilter - ()” will return all key-value pairs that match the following - conditions: - - - The row containing the key-value should have prefix “Row” - - - - The key-value must be located in the first row of the table - - - - The key-value pair must be the first key-value in the row - - - - - - - - - - - - “(RowFilter (=, ‘binary:Row 1’) AND TimeStampsFilter (74689, - 89734)) OR ColumnRangeFilter (‘abc’, true, ‘xyz’, - false))” will return all key-value pairs that match both - the following conditions: - - - The key-value is in a row having row key “Row 1” - - - The key-value must have a timestamp of either 74689 or - 89734. - - - Or it must match the following condition: - - - The key-value pair must be in a column that is - lexicographically >= abc and < xyz  - - - - - - - - - - - - - “SKIP ValueFilter (0)” will skip the entire row if any of - the values in the row is not 0 - - - -
- -
- Individual Filter Syntax - - - KeyOnlyFilter - - This filter doesn’t take any arguments. It returns only the key - component of each key-value. - - Syntax - - KeyOnlyFilter () - - - - Example - - KeyOnlyFilter ()" - - - - - - FirstKeyOnlyFilter - - This filter doesn’t take any arguments. It returns only the first - key-value from each row. - - - Syntax - - FirstKeyOnlyFilter () - - - - Example - - FirstKeyOnlyFilter () - - - - - - PrefixFilter - - This filter takes one argument – a prefix of a row key. It returns - only those key-values present in a row that starts with the specified - row prefix - - Syntax - - PrefixFilter (‘<row_prefix>’) - - - - Example - - PrefixFilter (‘Row’) - - - - - - ColumnPrefixFilter - - This filter takes one argument – a column prefix. It returns only - those key-values present in a column that starts with the specified - column prefix. The column prefix must be of the form: - “qualifier”. - - Syntax - - ColumnPrefixFilter(‘<column_prefix>’) - - - - Example - - ColumnPrefixFilter(‘Col’) - - - - - - MultipleColumnPrefixFilter - - This filter takes a list of column prefixes. It returns key-values - that are present in a column that starts with any of the specified - column prefixes. Each of the column prefixes must be of the form: - “qualifier”. - - Syntax - - MultipleColumnPrefixFilter(‘<column_prefix>’, ‘<column_prefix>’, …, ‘<column_prefix>’) - - - - Example - - MultipleColumnPrefixFilter(‘Col1’, ‘Col2’) - - - - - - ColumnCountGetFilter - - This filter takes one argument – a limit. It returns the first limit - number of columns in the table. - - Syntax - - ColumnCountGetFilter - (‘<limit>’) - - - - Example - - ColumnCountGetFilter (4) - - - - - - PageFilter - - This filter takes one argument – a page size. It returns page size - number of rows from the table. - - Syntax - - PageFilter (‘<page_size>’) - - - - Example - - PageFilter (2) - - - - - - ColumnPaginationFilter - - This filter takes two arguments – a limit and offset. It returns limit - number of columns after offset number of columns. It does this for all - the rows. - - Syntax - - ColumnPaginationFilter(‘<limit>’, ‘<offset>’) - - - - Example - - ColumnPaginationFilter (3, 5) - - - - - - InclusiveStopFilter - - This filter takes one argument – a row key on which to stop scanning. - It returns all key-values present in rows up to and including the - specified row. - - Syntax - - InclusiveStopFilter(‘<stop_row_key>’) - - - - Example - - InclusiveStopFilter ('Row2') - - - - - - TimeStampsFilter - - This filter takes a list of timestamps. It returns those key-values - whose timestamps matches any of the specified timestamps. - - Syntax - - TimeStampsFilter (<timestamp>, <timestamp>, ... ,<timestamp>) - - - - Example - - TimeStampsFilter (5985489, 48895495, 58489845945) - - - - - - RowFilter - - This filter takes a compare operator and a comparator. It compares - each row key with the comparator using the compare operator and if the - comparison returns true, it returns all the key-values in that - row. - - Syntax - - RowFilter (<compareOp>, ‘<row_comparator>’) - - - - Example - - RowFilter (<=, ‘xyz) - - - - - - Family Filter - - This filter takes a compare operator and a comparator. It compares - each qualifier name with the comparator using the compare operator and - if the comparison returns true, it returns all the key-values in that - column. - - Syntax - - QualifierFilter (<compareOp>, ‘<qualifier_comparator>’) - - - - Example - - QualifierFilter (=, ‘Column1’) - - - - - - QualifierFilter - - This filter takes a compare operator and a comparator. It compares - each qualifier name with the comparator using the compare operator and - if the comparison returns true, it returns all the key-values in that - column. - - Syntax - - QualifierFilter (<compareOp>,‘<qualifier_comparator>’) - - - - Example - - QualifierFilter (=,‘Column1’) - - - - - - ValueFilter - - This filter takes a compare operator and a comparator. It compares - each value with the comparator using the compare operator and if the - comparison returns true, it returns that key-value. - - Syntax - - ValueFilter (<compareOp>,‘<value_comparator>’) - - - - Example - - ValueFilter (!=, ‘Value’) - - - - - - DependentColumnFilter - - This filter takes two arguments – a family and a qualifier. It tries - to locate this column in each row and returns all key-values in that row - that have the same timestamp. If the row doesn’t contain the specified - column – none of the key-values in that row will be returned. - The filter can also take an optional boolean argument – - dropDependentColumn. If set to true, the column we were depending on - doesn’t get returned. - The filter can also take two more additional optional arguments – a - compare operator and a value comparator, which are further checks in - addition to the family and qualifier. If the dependent column is found, - its value should also pass the value check and then only is its - timestamp taken into consideration. - - Syntax - - ’,‘’, , , ‘ - - - ’,‘’, )]]> - - - DependentColumnFilter (‘<family>’,‘<qualifier>’) - - - - Example - - DependentColumnFilter (‘conf’, ‘blacklist’, false, >=, ‘zebra’) - - - DependentColumnFilter (‘conf’, 'blacklist', true) - - - DependentColumnFilter (‘conf’, 'blacklist') - - - - - - SingleColumnValueFilter - - This filter takes a column family, a qualifier, a compare operator and - a comparator. If the specified column is not found – all the columns of - that row will be emitted. If the column is found and the comparison with - the comparator returns true, all the columns of the row will be emitted. - If the condition fails, the row will not be emitted. - This filter also takes two additional optional boolean arguments – - filterIfColumnMissing and setLatestVersionOnly - If the filterIfColumnMissing flag is set to true the columns of the - row will not be emitted if the specified column to check is not found in - the row. The default value is false. - If the setLatestVersionOnly flag is set to false, it will test - previous versions (timestamps) too. The default value is true. - These flags are optional and if you must set neither or both. - - Syntax - - SingleColumnValueFilter(‘<family>’,‘<qualifier>’, <compare operator>, ‘<comparator>’, <filterIfColumnMissing_boolean>, <latest_version_boolean>) - - - SingleColumnValueFilter(‘<family>’, ‘<qualifier>, <compare operator>, ‘<comparator>’) - - - - Example - - SingleColumnValueFilter (‘FamilyA’, ‘Column1’, <=, ‘abc’, true, false) - - - SingleColumnValueFilter (‘FamilyA’, ‘Column1’, <=, ‘abc’) - - - - - - SingleColumnValueExcludeFilter - - This filter takes the same arguments and behaves same as - SingleColumnValueFilter – however, if the column is found and the - condition passes, all the columns of the row will be emitted except for - the tested column value. - - Syntax - - SingleColumnValueExcludeFilter('<family>', '<qualifier>', <compare operator>, '<comparator>', <latest_version_boolean>, <filterIfColumnMissing_boolean>) - - - SingleColumnValueExcludeFilter('<family>', '<qualifier>', <compare operator>, '<comparator>') - - - - Example - - SingleColumnValueExcludeFilter (‘FamilyA’, ‘Column1’, ‘<=’, ‘abc’, ‘false’, ‘true’) - - - SingleColumnValueExcludeFilter (‘FamilyA’, ‘Column1’, ‘<=’, ‘abc’) - - - - - - ColumnRangeFilter - - This filter is used for selecting only those keys with columns that - are between minColumn and maxColumn. It also takes two boolean variables - to indicate whether to include the minColumn and maxColumn or - not. - If you don’t want to set the minColumn or the maxColumn – you can pass - in an empty argument. - - Syntax - - ColumnRangeFilter (‘<minColumn>’, <minColumnInclusive_bool>, ‘<maxColumn>’, <maxColumnInclusive_bool>) - - - - Example - - ColumnRangeFilter (‘abc’, true, ‘xyz’, false) - - - - - -
-
- - diff --git a/src/main/docbkx/tracing.xml b/src/main/docbkx/tracing.xml deleted file mode 100644 index b5dfd35968b..00000000000 --- a/src/main/docbkx/tracing.xml +++ /dev/null @@ -1,187 +0,0 @@ - - - - Enabling Dapper-like Tracing in HBase - - - HBASE-6449 added support - for tracing requests through HBase, using the open source tracing library, HTrace. Setting up tracing is quite - simple, however it currently requires some very minor changes to your client code (it would not - be very difficult to remove this requirement). - -
- SpanReceivers - The tracing system works by collecting information in structs called 'Spans'. It is up to - you to choose how you want to receive this information by implementing the - SpanReceiver interface, which defines one method: - - This method serves as a callback whenever a span is completed. HTrace allows you to use as - many SpanReceivers as you want so you can easily send trace information to multiple - destinations. - - Configure what SpanReceivers you'd like to us by putting a comma separated list of the - fully-qualified class name of classes implementing SpanReceiver in - hbase-site.xml property: - hbase.trace.spanreceiver.classes. - - HTrace includes a LocalFileSpanReceiver that writes all span - information to local files in a JSON-based format. The - LocalFileSpanReceiver looks in hbase-site.xml - for a hbase.local-file-span-receiver.path property with a value describing - the name of the file to which nodes should write their span information. - - hbase.trace.spanreceiver.classes - org.htrace.impl.LocalFileSpanReceiver - - - hbase.local-file-span-receiver.path - /var/log/hbase/htrace.out - -]]> - - HTrace also provides ZipkinSpanReceiver which converts spans to Zipkin span format and send them to - Zipkin server. In order to use this span receiver, you need to install the jar of - htrace-zipkin to your HBase's classpath on all of the nodes in your cluster. - - htrace-zipkin is published to the maven central repository. You could get - the latest version from there or just build it locally and then copy it out to all nodes, - change your config to use zipkin receiver, distribute the new configuration and then (rolling) - restart. - Here is the example of manual setup procedure. - - The ZipkinSpanReceiver looks in hbase-site.xml - for a hbase.zipkin.collector-hostname and - hbase.zipkin.collector-port property with a value describing the Zipkin - collector server to which span information are sent. - - hbase.trace.spanreceiver.classes - org.htrace.impl.ZipkinSpanReceiver - - - hbase.zipkin.collector-hostname - localhost - - - hbase.zipkin.collector-port - 9410 - -]]> - - If you do not want to use the included span receivers, you are encouraged to write your - own receiver (take a look at LocalFileSpanReceiver for an example). If - you think others would benefit from your receiver, file a JIRA or send a pull request to HTrace. -
- -
- Client Modifications - In order to turn on tracing in your client code, you must initialize the module sending - spans to receiver once per client process. - - Then you simply start tracing span before requests you think are interesting, and close it - when the request is done. For example, if you wanted to trace all of your get operations, you - change this: - - into: - - If you wanted to trace half of your 'get' operations, you would pass in: - - in lieu of Sampler.ALWAYS to Trace.startSpan(). - See the HTrace README for more information on Samplers. -
- -
- Tracing from HBase Shell - You can use trace command for tracing requests from HBase Shell. - trace 'start' command turns on tracing and trace - 'stop' command turns off tracing. - trace 'start' -hbase(main):002:0> put 'test', 'row1', 'f:', 'val1' # traced commands -hbase(main):003:0> trace 'stop' -]]> - - trace 'start' and trace 'stop' always returns boolean - value representing if or not there is ongoing tracing. As a result, trace - 'stop' returns false on suceess. trace 'status' just returns if - or not tracing is turned on. - trace 'start' -=> true - -hbase(main):002:0> trace 'status' -=> true - -hbase(main):003:0> trace 'stop' -=> false - -hbase(main):004:0> trace 'status' -=> false -]]> -
- - diff --git a/src/main/docbkx/troubleshooting.xml b/src/main/docbkx/troubleshooting.xml deleted file mode 100644 index d57bb08cf74..00000000000 --- a/src/main/docbkx/troubleshooting.xml +++ /dev/null @@ -1,1700 +0,0 @@ - - - - Troubleshooting and Debugging Apache HBase -
- General Guidelines - Always start with the master log (TODO: Which lines?). Normally it’s just printing the - same lines over and over again. If not, then there’s an issue. Google or search-hadoop.com should return some hits for - those exceptions you’re seeing. - An error rarely comes alone in Apache HBase, usually when something gets screwed up what - will follow may be hundreds of exceptions and stack traces coming from all over the place. The - best way to approach this type of problem is to walk the log up to where it all began, for - example one trick with RegionServers is that they will print some metrics when aborting so - grepping for Dump should get you around the start of the problem. - RegionServer suicides are “normal”, as this is what they do when something goes wrong. - For example, if ulimit and max transfer threads (the two most important initial settings, see - and ) aren’t - changed, it will make it impossible at some point for DataNodes - to create new threads that from the HBase point of view is seen as if HDFS was gone. Think - about what would happen if your MySQL database was suddenly unable to access files on your - local file system, well it’s the same with HBase and HDFS. Another very common reason to see - RegionServers committing seppuku is when they enter prolonged garbage collection pauses that - last longer than the default ZooKeeper session timeout. For more information on GC pauses, see - the 3 - part blog post by Todd Lipcon and above. -
-
- Logs - The key process logs are as follows... (replace <user> with the user that started - the service, and <hostname> for the machine name) - NameNode: - $HADOOP_HOME/logs/hadoop-<user>-namenode-<hostname>.log - - DataNode: - $HADOOP_HOME/logs/hadoop-<user>-datanode-<hostname>.log - - JobTracker: - $HADOOP_HOME/logs/hadoop-<user>-jobtracker-<hostname>.log - - TaskTracker: - $HADOOP_HOME/logs/hadoop-<user>-tasktracker-<hostname>.log - - HMaster: - $HBASE_HOME/logs/hbase-<user>-master-<hostname>.log - - RegionServer: - $HBASE_HOME/logs/hbase-<user>-regionserver-<hostname>.log - - ZooKeeper: TODO - -
- Log Locations - For stand-alone deployments the logs are obviously going to be on a single machine, - however this is a development configuration only. Production deployments need to run on a - cluster. -
- NameNode - The NameNode log is on the NameNode server. The HBase Master is typically run on the - NameNode server, and well as ZooKeeper. - For smaller clusters the JobTracker is typically run on the NameNode server as - well. -
-
- DataNode - Each DataNode server will have a DataNode log for HDFS, as well as a RegionServer log - for HBase. - Additionally, each DataNode server will also have a TaskTracker log for MapReduce task - execution. -
-
-
- Log Levels -
- Enabling RPC-level logging - Enabling the RPC-level logging on a RegionServer can often given insight on timings at - the server. Once enabled, the amount of log spewed is voluminous. It is not recommended - that you leave this logging on for more than short bursts of time. To enable RPC-level - logging, browse to the RegionServer UI and click on Log Level. Set - the log level to DEBUG for the package - org.apache.hadoop.ipc (Thats right, for - hadoop.ipc, NOT, hbase.ipc). Then tail the - RegionServers log. Analyze. - To disable, set the logging level back to INFO level. -
-
-
- JVM Garbage Collection Logs - HBase is memory intensive, and using the default GC you can see long pauses in all - threads including the Juliet Pause aka "GC of Death". To help debug - this or confirm this is happening GC logging can be turned on in the Java virtual machine. - To enable, in hbase-env.sh, uncomment one of the below lines - : - -# This enables basic gc logging to the .out file. -# export SERVER_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps" - -# This enables basic gc logging to its own file. -# export SERVER_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps -Xloggc:<FILE-PATH>" - -# This enables basic GC logging to its own file with automatic log rolling. Only applies to jdk 1.6.0_34+ and 1.7.0_2+. -# export SERVER_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps -Xloggc:<FILE-PATH> -XX:+UseGCLogFileRotation -XX:NumberOfGCLogFiles=1 -XX:GCLogFileSize=512M" - -# If <FILE-PATH> is not replaced, the log file(.gc) would be generated in the HBASE_LOG_DIR. - - At this point you should see logs like so: - -64898.952: [GC [1 CMS-initial-mark: 2811538K(3055704K)] 2812179K(3061272K), 0.0007360 secs] [Times: user=0.00 sys=0.00, real=0.00 secs] -64898.953: [CMS-concurrent-mark-start] -64898.971: [GC 64898.971: [ParNew: 5567K->576K(5568K), 0.0101110 secs] 2817105K->2812715K(3061272K), 0.0102200 secs] [Times: user=0.07 sys=0.00, real=0.01 secs] - - In this section, the first line indicates a 0.0007360 second pause for the CMS to - initially mark. This pauses the entire VM, all threads for that period of time. - The third line indicates a "minor GC", which pauses the VM for 0.0101110 seconds - aka - 10 milliseconds. It has reduced the "ParNew" from about 5.5m to 576k. Later on in this cycle - we see: - -64901.445: [CMS-concurrent-mark: 1.542/2.492 secs] [Times: user=10.49 sys=0.33, real=2.49 secs] -64901.445: [CMS-concurrent-preclean-start] -64901.453: [GC 64901.453: [ParNew: 5505K->573K(5568K), 0.0062440 secs] 2868746K->2864292K(3061272K), 0.0063360 secs] [Times: user=0.05 sys=0.00, real=0.01 secs] -64901.476: [GC 64901.476: [ParNew: 5563K->575K(5568K), 0.0072510 secs] 2869283K->2864837K(3061272K), 0.0073320 secs] [Times: user=0.05 sys=0.01, real=0.01 secs] -64901.500: [GC 64901.500: [ParNew: 5517K->573K(5568K), 0.0120390 secs] 2869780K->2865267K(3061272K), 0.0121150 secs] [Times: user=0.09 sys=0.00, real=0.01 secs] -64901.529: [GC 64901.529: [ParNew: 5507K->569K(5568K), 0.0086240 secs] 2870200K->2865742K(3061272K), 0.0087180 secs] [Times: user=0.05 sys=0.00, real=0.01 secs] -64901.554: [GC 64901.555: [ParNew: 5516K->575K(5568K), 0.0107130 secs] 2870689K->2866291K(3061272K), 0.0107820 secs] [Times: user=0.06 sys=0.00, real=0.01 secs] -64901.578: [CMS-concurrent-preclean: 0.070/0.133 secs] [Times: user=0.48 sys=0.01, real=0.14 secs] -64901.578: [CMS-concurrent-abortable-preclean-start] -64901.584: [GC 64901.584: [ParNew: 5504K->571K(5568K), 0.0087270 secs] 2871220K->2866830K(3061272K), 0.0088220 secs] [Times: user=0.05 sys=0.00, real=0.01 secs] -64901.609: [GC 64901.609: [ParNew: 5512K->569K(5568K), 0.0063370 secs] 2871771K->2867322K(3061272K), 0.0064230 secs] [Times: user=0.06 sys=0.00, real=0.01 secs] -64901.615: [CMS-concurrent-abortable-preclean: 0.007/0.037 secs] [Times: user=0.13 sys=0.00, real=0.03 secs] -64901.616: [GC[YG occupancy: 645 K (5568 K)]64901.616: [Rescan (parallel) , 0.0020210 secs]64901.618: [weak refs processing, 0.0027950 secs] [1 CMS-remark: 2866753K(3055704K)] 2867399K(3061272K), 0.0049380 secs] [Times: user=0.00 sys=0.01, real=0.01 secs] -64901.621: [CMS-concurrent-sweep-start] - - The first line indicates that the CMS concurrent mark (finding garbage) has taken 2.4 - seconds. But this is a _concurrent_ 2.4 seconds, Java has not been paused at any point in - time. - There are a few more minor GCs, then there is a pause at the 2nd last line: - -64901.616: [GC[YG occupancy: 645 K (5568 K)]64901.616: [Rescan (parallel) , 0.0020210 secs]64901.618: [weak refs processing, 0.0027950 secs] [1 CMS-remark: 2866753K(3055704K)] 2867399K(3061272K), 0.0049380 secs] [Times: user=0.00 sys=0.01, real=0.01 secs] - - - The pause here is 0.0049380 seconds (aka 4.9 milliseconds) to 'remark' the heap. - At this point the sweep starts, and you can watch the heap size go down: - -64901.637: [GC 64901.637: [ParNew: 5501K->569K(5568K), 0.0097350 secs] 2871958K->2867441K(3061272K), 0.0098370 secs] [Times: user=0.05 sys=0.00, real=0.01 secs] -... lines removed ... -64904.936: [GC 64904.936: [ParNew: 5532K->568K(5568K), 0.0070720 secs] 1365024K->1360689K(3061272K), 0.0071930 secs] [Times: user=0.05 sys=0.00, real=0.01 secs] -64904.953: [CMS-concurrent-sweep: 2.030/3.332 secs] [Times: user=9.57 sys=0.26, real=3.33 secs] - - At this point, the CMS sweep took 3.332 seconds, and heap went from about ~ 2.8 GB to - 1.3 GB (approximate). - The key points here is to keep all these pauses low. CMS pauses are always low, but if - your ParNew starts growing, you can see minor GC pauses approach 100ms, exceed 100ms and hit - as high at 400ms. - This can be due to the size of the ParNew, which should be relatively small. If your - ParNew is very large after running HBase for a while, in one example a ParNew was about - 150MB, then you might have to constrain the size of ParNew (The larger it is, the longer the - collections take but if its too small, objects are promoted to old gen too quickly). In the - below we constrain new gen size to 64m. - Add the below line in hbase-env.sh: - -export SERVER_GC_OPTS="$SERVER_GC_OPTS -XX:NewSize=64m -XX:MaxNewSize=64m" - - - Similarly, to enable GC logging for client processes, uncomment one of the below lines - in hbase-env.sh: - -# This enables basic gc logging to the .out file. -# export CLIENT_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps" - -# This enables basic gc logging to its own file. -# export CLIENT_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps -Xloggc:<FILE-PATH>" - -# This enables basic GC logging to its own file with automatic log rolling. Only applies to jdk 1.6.0_34+ and 1.7.0_2+. -# export CLIENT_GC_OPTS="-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps -Xloggc:<FILE-PATH> -XX:+UseGCLogFileRotation -XX:NumberOfGCLogFiles=1 -XX:GCLogFileSize=512M" - -# If <FILE-PATH> is not replaced, the log file(.gc) would be generated in the HBASE_LOG_DIR . - - For more information on GC pauses, see the 3 - part blog post by Todd Lipcon and above. -
-
-
- Resources -
- search-hadoop.com - - search-hadoop.com indexes all the mailing - lists and is great for historical searches. Search here first when you have an issue as its - more than likely someone has already had your problem. -
-
- Mailing Lists - Ask a question on the Apache - HBase mailing lists. The 'dev' mailing list is aimed at the community of developers - actually building Apache HBase and for features currently under development, and 'user' is - generally used for questions on released versions of Apache HBase. Before going to the - mailing list, make sure your question has not already been answered by searching the mailing - list archives first. Use . Take some time - crafting your question. See Getting Answers for ideas on crafting good questions. A quality question that - includes all context and exhibits evidence the author has tried to find answers in the - manual and out on lists is more likely to get a prompt response. -
-
- IRC - #hbase on irc.freenode.net -
-
- JIRA - - JIRA is also really - helpful when looking for Hadoop/HBase-specific issues. -
-
-
- Tools -
- Builtin Tools -
- Master Web Interface - The Master starts a web-interface on port 16010 by default. (Up to and including 0.98 - this was port 60010) - The Master web UI lists created tables and their definition (e.g., ColumnFamilies, - blocksize, etc.). Additionally, the available RegionServers in the cluster are listed - along with selected high-level metrics (requests, number of regions, usedHeap, maxHeap). - The Master web UI allows navigation to each RegionServer's web UI. -
-
- RegionServer Web Interface - RegionServers starts a web-interface on port 16030 by default. (Up to an including - 0.98 this was port 60030) - The RegionServer web UI lists online regions and their start/end keys, as well as - point-in-time RegionServer metrics (requests, regions, storeFileIndexSize, - compactionQueueSize, etc.). - See for more information in metric definitions. -
-
- zkcli - zkcli is a very useful tool for investigating ZooKeeper-related issues. - To invoke: - -./hbase zkcli -server host:port <cmd> <args> - - The commands (and arguments) are: - - connect host:port - get path [watch] - ls path [watch] - set path data [version] - delquota [-n|-b] path - quit - printwatches on|off - create [-s] [-e] path data acl - stat path [watch] - close - ls2 path [watch] - history - listquota path - setAcl path acl - getAcl path - sync path - redo cmdno - addauth scheme auth - delete path [version] - setquota -n|-b val path - -
-
-
- External Tools -
- tail - - tail is the command line tool that lets you look at the end of a file. Add - the “-f” option and it will refresh when new data is available. It’s useful when you are - wondering what’s happening, for example, when a cluster is taking a long time to shutdown - or startup as you can just fire a new terminal and tail the master log (and maybe a few - RegionServers). -
-
- top - - top is probably one of the most important tool when first trying to see - what’s running on a machine and how the resources are consumed. Here’s an example from - production system: - -top - 14:46:59 up 39 days, 11:55, 1 user, load average: 3.75, 3.57, 3.84 -Tasks: 309 total, 1 running, 308 sleeping, 0 stopped, 0 zombie -Cpu(s): 4.5%us, 1.6%sy, 0.0%ni, 91.7%id, 1.4%wa, 0.1%hi, 0.6%si, 0.0%st -Mem: 24414432k total, 24296956k used, 117476k free, 7196k buffers -Swap: 16008732k total, 14348k used, 15994384k free, 11106908k cached - - PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND -15558 hadoop 18 -2 3292m 2.4g 3556 S 79 10.4 6523:52 java -13268 hadoop 18 -2 8967m 8.2g 4104 S 21 35.1 5170:30 java - 8895 hadoop 18 -2 1581m 497m 3420 S 11 2.1 4002:32 java -… - - Here we can see that the system load average during the last five minutes is 3.75, - which very roughly means that on average 3.75 threads were waiting for CPU time during - these 5 minutes. In general, the “perfect” utilization equals to the number of cores, - under that number the machine is under utilized and over that the machine is over - utilized. This is an important concept, see this article to understand it more: http://www.linuxjournal.com/article/9001. - Apart from load, we can see that the system is using almost all its available RAM but - most of it is used for the OS cache (which is good). The swap only has a few KBs in it and - this is wanted, high numbers would indicate swapping activity which is the nemesis of - performance of Java systems. Another way to detect swapping is when the load average goes - through the roof (although this could also be caused by things like a dying disk, among - others). - The list of processes isn’t super useful by default, all we know is that 3 java - processes are using about 111% of the CPUs. To know which is which, simply type “c” and - each line will be expanded. Typing “1” will give you the detail of how each CPU is used - instead of the average for all of them like shown here. -
-
- jps - - jps is shipped with every JDK and gives the java process ids for the current - user (if root, then it gives the ids for all users). Example: - -hadoop@sv4borg12:~$ jps -1322 TaskTracker -17789 HRegionServer -27862 Child -1158 DataNode -25115 HQuorumPeer -2950 Jps -19750 ThriftServer -18776 jmx - - In order, we see a: - - - Hadoop TaskTracker, manages the local Childs - - - HBase RegionServer, serves regions - - - Child, its MapReduce task, cannot tell which type exactly - - - Hadoop TaskTracker, manages the local Childs - - - Hadoop DataNode, serves blocks - - - HQuorumPeer, a ZooKeeper ensemble member - - - Jps, well… it’s the current process - - - ThriftServer, it’s a special one will be running only if thrift was started - - - jmx, this is a local process that’s part of our monitoring platform ( poorly named - maybe). You probably don’t have that. - - - You can then do stuff like checking out the full command line that started the - process: - -hadoop@sv4borg12:~$ ps aux | grep HRegionServer -hadoop 17789 155 35.2 9067824 8604364 ? S<l Mar04 9855:48 /usr/java/jdk1.6.0_14/bin/java -Xmx8000m -XX:+DoEscapeAnalysis -XX:+AggressiveOpts -XX:+UseConcMarkSweepGC -XX:NewSize=64m -XX:MaxNewSize=64m -XX:CMSInitiatingOccupancyFraction=88 -verbose:gc -XX:+PrintGCDetails -XX:+PrintGCTimeStamps -Xloggc:/export1/hadoop/logs/gc-hbase.log -Dcom.sun.management.jmxremote.port=10102 -Dcom.sun.management.jmxremote.authenticate=true -Dcom.sun.management.jmxremote.ssl=false -Dcom.sun.management.jmxremote.password.file=/home/hadoop/hbase/conf/jmxremote.password -Dcom.sun.management.jmxremote -Dhbase.log.dir=/export1/hadoop/logs -Dhbase.log.file=hbase-hadoop-regionserver-sv4borg12.log -Dhbase.home.dir=/home/hadoop/hbase -Dhbase.id.str=hadoop -Dhbase.root.logger=INFO,DRFA -Djava.library.path=/home/hadoop/hbase/lib/native/Linux-amd64-64 -classpath /home/hadoop/hbase/bin/../conf:[many jars]:/home/hadoop/hadoop/conf org.apache.hadoop.hbase.regionserver.HRegionServer start - -
-
- jstack - - jstack is one of the most important tools when trying to figure out what a - java process is doing apart from looking at the logs. It has to be used in conjunction - with jps in order to give it a process id. It shows a list of threads, each one has a - name, and they appear in the order that they were created (so the top ones are the most - recent threads). Here’s a few example: - The main thread of a RegionServer that’s waiting for something to do from the - master: - -"regionserver60020" prio=10 tid=0x0000000040ab4000 nid=0x45cf waiting on condition [0x00007f16b6a96000..0x00007f16b6a96a70] -java.lang.Thread.State: TIMED_WAITING (parking) - at sun.misc.Unsafe.park(Native Method) - - parking to wait for <0x00007f16cd5c2f30> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject) - at java.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:198) - at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.awaitNanos(AbstractQueuedSynchronizer.java:1963) - at java.util.concurrent.LinkedBlockingQueue.poll(LinkedBlockingQueue.java:395) - at org.apache.hadoop.hbase.regionserver.HRegionServer.run(HRegionServer.java:647) - at java.lang.Thread.run(Thread.java:619) - - The MemStore flusher thread that is currently flushing to a file: -"regionserver60020.cacheFlusher" daemon prio=10 tid=0x0000000040f4e000 nid=0x45eb in Object.wait() [0x00007f16b5b86000..0x00007f16b5b87af0] -java.lang.Thread.State: WAITING (on object monitor) - at java.lang.Object.wait(Native Method) - at java.lang.Object.wait(Object.java:485) - at org.apache.hadoop.ipc.Client.call(Client.java:803) - - locked <0x00007f16cb14b3a8> (a org.apache.hadoop.ipc.Client$Call) - at org.apache.hadoop.ipc.RPC$Invoker.invoke(RPC.java:221) - at $Proxy1.complete(Unknown Source) - at sun.reflect.GeneratedMethodAccessor38.invoke(Unknown Source) - at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25) - at java.lang.reflect.Method.invoke(Method.java:597) - at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:82) - at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:59) - at $Proxy1.complete(Unknown Source) - at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.closeInternal(DFSClient.java:3390) - - locked <0x00007f16cb14b470> (a org.apache.hadoop.hdfs.DFSClient$DFSOutputStream) - at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.close(DFSClient.java:3304) - at org.apache.hadoop.fs.FSDataOutputStream$PositionCache.close(FSDataOutputStream.java:61) - at org.apache.hadoop.fs.FSDataOutputStream.close(FSDataOutputStream.java:86) - at org.apache.hadoop.hbase.io.hfile.HFile$Writer.close(HFile.java:650) - at org.apache.hadoop.hbase.regionserver.StoreFile$Writer.close(StoreFile.java:853) - at org.apache.hadoop.hbase.regionserver.Store.internalFlushCache(Store.java:467) - - locked <0x00007f16d00e6f08> (a java.lang.Object) - at org.apache.hadoop.hbase.regionserver.Store.flushCache(Store.java:427) - at org.apache.hadoop.hbase.regionserver.Store.access$100(Store.java:80) - at org.apache.hadoop.hbase.regionserver.Store$StoreFlusherImpl.flushCache(Store.java:1359) - at org.apache.hadoop.hbase.regionserver.HRegion.internalFlushcache(HRegion.java:907) - at org.apache.hadoop.hbase.regionserver.HRegion.internalFlushcache(HRegion.java:834) - at org.apache.hadoop.hbase.regionserver.HRegion.flushcache(HRegion.java:786) - at org.apache.hadoop.hbase.regionserver.MemStoreFlusher.flushRegion(MemStoreFlusher.java:250) - at org.apache.hadoop.hbase.regionserver.MemStoreFlusher.flushRegion(MemStoreFlusher.java:224) - at org.apache.hadoop.hbase.regionserver.MemStoreFlusher.run(MemStoreFlusher.java:146) - - A handler thread that’s waiting for stuff to do (like put, delete, scan, etc): - -"IPC Server handler 16 on 60020" daemon prio=10 tid=0x00007f16b011d800 nid=0x4a5e waiting on condition [0x00007f16afefd000..0x00007f16afefd9f0] - java.lang.Thread.State: WAITING (parking) - at sun.misc.Unsafe.park(Native Method) - - parking to wait for <0x00007f16cd3f8dd8> (a java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject) - at java.util.concurrent.locks.LockSupport.park(LockSupport.java:158) - at java.util.concurrent.locks.AbstractQueuedSynchronizer$ConditionObject.await(AbstractQueuedSynchronizer.java:1925) - at java.util.concurrent.LinkedBlockingQueue.take(LinkedBlockingQueue.java:358) - at org.apache.hadoop.hbase.ipc.HBaseServer$Handler.run(HBaseServer.java:1013) - - And one that’s busy doing an increment of a counter (it’s in the phase where it’s - trying to create a scanner in order to read the last value): - -"IPC Server handler 66 on 60020" daemon prio=10 tid=0x00007f16b006e800 nid=0x4a90 runnable [0x00007f16acb77000..0x00007f16acb77cf0] - java.lang.Thread.State: RUNNABLE - at org.apache.hadoop.hbase.regionserver.KeyValueHeap.<init>(KeyValueHeap.java:56) - at org.apache.hadoop.hbase.regionserver.StoreScanner.<init>(StoreScanner.java:79) - at org.apache.hadoop.hbase.regionserver.Store.getScanner(Store.java:1202) - at org.apache.hadoop.hbase.regionserver.HRegion$RegionScanner.<init>(HRegion.java:2209) - at org.apache.hadoop.hbase.regionserver.HRegion.instantiateInternalScanner(HRegion.java:1063) - at org.apache.hadoop.hbase.regionserver.HRegion.getScanner(HRegion.java:1055) - at org.apache.hadoop.hbase.regionserver.HRegion.getScanner(HRegion.java:1039) - at org.apache.hadoop.hbase.regionserver.HRegion.getLastIncrement(HRegion.java:2875) - at org.apache.hadoop.hbase.regionserver.HRegion.incrementColumnValue(HRegion.java:2978) - at org.apache.hadoop.hbase.regionserver.HRegionServer.incrementColumnValue(HRegionServer.java:2433) - at sun.reflect.GeneratedMethodAccessor20.invoke(Unknown Source) - at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25) - at java.lang.reflect.Method.invoke(Method.java:597) - at org.apache.hadoop.hbase.ipc.HBaseRPC$Server.call(HBaseRPC.java:560) - at org.apache.hadoop.hbase.ipc.HBaseServer$Handler.run(HBaseServer.java:1027) - - A thread that receives data from HDFS: - -"IPC Client (47) connection to sv4borg9/10.4.24.40:9000 from hadoop" daemon prio=10 tid=0x00007f16a02d0000 nid=0x4fa3 runnable [0x00007f16b517d000..0x00007f16b517dbf0] - java.lang.Thread.State: RUNNABLE - at sun.nio.ch.EPollArrayWrapper.epollWait(Native Method) - at sun.nio.ch.EPollArrayWrapper.poll(EPollArrayWrapper.java:215) - at sun.nio.ch.EPollSelectorImpl.doSelect(EPollSelectorImpl.java:65) - at sun.nio.ch.SelectorImpl.lockAndDoSelect(SelectorImpl.java:69) - - locked <0x00007f17d5b68c00> (a sun.nio.ch.Util$1) - - locked <0x00007f17d5b68be8> (a java.util.Collections$UnmodifiableSet) - - locked <0x00007f1877959b50> (a sun.nio.ch.EPollSelectorImpl) - at sun.nio.ch.SelectorImpl.select(SelectorImpl.java:80) - at org.apache.hadoop.net.SocketIOWithTimeout$SelectorPool.select(SocketIOWithTimeout.java:332) - at org.apache.hadoop.net.SocketIOWithTimeout.doIO(SocketIOWithTimeout.java:157) - at org.apache.hadoop.net.SocketInputStream.read(SocketInputStream.java:155) - at org.apache.hadoop.net.SocketInputStream.read(SocketInputStream.java:128) - at java.io.FilterInputStream.read(FilterInputStream.java:116) - at org.apache.hadoop.ipc.Client$Connection$PingInputStream.read(Client.java:304) - at java.io.BufferedInputStream.fill(BufferedInputStream.java:218) - at java.io.BufferedInputStream.read(BufferedInputStream.java:237) - - locked <0x00007f1808539178> (a java.io.BufferedInputStream) - at java.io.DataInputStream.readInt(DataInputStream.java:370) - at org.apache.hadoop.ipc.Client$Connection.receiveResponse(Client.java:569) - at org.apache.hadoop.ipc.Client$Connection.run(Client.java:477) - - And here is a master trying to recover a lease after a RegionServer died: - -"LeaseChecker" daemon prio=10 tid=0x00000000407ef800 nid=0x76cd waiting on condition [0x00007f6d0eae2000..0x00007f6d0eae2a70] --- - java.lang.Thread.State: WAITING (on object monitor) - at java.lang.Object.wait(Native Method) - at java.lang.Object.wait(Object.java:485) - at org.apache.hadoop.ipc.Client.call(Client.java:726) - - locked <0x00007f6d1cd28f80> (a org.apache.hadoop.ipc.Client$Call) - at org.apache.hadoop.ipc.RPC$Invoker.invoke(RPC.java:220) - at $Proxy1.recoverBlock(Unknown Source) - at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.processDatanodeError(DFSClient.java:2636) - at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.<init>(DFSClient.java:2832) - at org.apache.hadoop.hdfs.DFSClient.append(DFSClient.java:529) - at org.apache.hadoop.hdfs.DistributedFileSystem.append(DistributedFileSystem.java:186) - at org.apache.hadoop.fs.FileSystem.append(FileSystem.java:530) - at org.apache.hadoop.hbase.util.FSUtils.recoverFileLease(FSUtils.java:619) - at org.apache.hadoop.hbase.regionserver.wal.HLog.splitLog(HLog.java:1322) - at org.apache.hadoop.hbase.regionserver.wal.HLog.splitLog(HLog.java:1210) - at org.apache.hadoop.hbase.master.HMaster.splitLogAfterStartup(HMaster.java:648) - at org.apache.hadoop.hbase.master.HMaster.joinCluster(HMaster.java:572) - at org.apache.hadoop.hbase.master.HMaster.run(HMaster.java:503) - -
-
- OpenTSDB - - OpenTSDB is an excellent alternative to Ganglia - as it uses Apache HBase to store all the time series and doesn’t have to downsample. - Monitoring your own HBase cluster that hosts OpenTSDB is a good exercise. - Here’s an example of a cluster that’s suffering from hundreds of compactions launched - almost all around the same time, which severely affects the IO performance: (TODO: insert - graph plotting compactionQueueSize) - It’s a good practice to build dashboards with all the important graphs per machine - and per cluster so that debugging issues can be done with a single quick look. For - example, at StumbleUpon there’s one dashboard per cluster with the most important metrics - from both the OS and Apache HBase. You can then go down at the machine level and get even - more detailed metrics. -
-
- clusterssh+top - clusterssh+top, it’s like a poor man’s monitoring system and it can be quite useful - when you have only a few machines as it’s very easy to setup. Starting clusterssh will - give you one terminal per machine and another terminal in which whatever you type will be - retyped in every window. This means that you can type “top” once and it will start it for - all of your machines at the same time giving you full view of the current state of your - cluster. You can also tail all the logs at the same time, edit files, etc. -
-
-
- -
- Client - For more information on the HBase client, see . -
- ScannerTimeoutException or UnknownScannerException - This is thrown if the time between RPC calls from the client to RegionServer exceeds the - scan timeout. For example, if Scan.setCaching is set to 500, then there will be - an RPC call to fetch the next batch of rows every 500 .next() calls on the - ResultScanner because data is being transferred in blocks of 500 rows to the client. - Reducing the setCaching value may be an option, but setting this value too low makes for - inefficient processing on numbers of rows. - See . -
-
- Performance Differences in Thrift and Java APIs - Poor performance, or even ScannerTimeoutExceptions, can occur if - Scan.setCaching is too high, as discussed in . If the Thrift client uses the wrong caching - settings for a given workload, performance can suffer compared to the Java API. To set - caching for a given scan in the Thrift client, use the scannerGetList(scannerId, - numRows) method, where numRows is an integer representing the number - of rows to cache. In one case, it was found that reducing the cache for Thrift scans from - 1000 to 100 increased performance to near parity with the Java API given the same - queries. - See also Jesse Andersen's blog post - about using Scans with Thrift. -
-
- <classname>LeaseException</classname> when calling - <classname>Scanner.next</classname> - In some situations clients that fetch data from a RegionServer get a LeaseException - instead of the usual . Usually the source of the exception is - org.apache.hadoop.hbase.regionserver.Leases.removeLease(Leases.java:230) - (line number may vary). It tends to happen in the context of a slow/freezing - RegionServer#next call. It can be prevented by having hbase.rpc.timeout > - hbase.regionserver.lease.period. Harsh J investigated the issue as part - of the mailing list thread HBase, - mail # user - Lease does not exist exceptions - -
-
- Shell or client application throws lots of scary exceptions during normal - operation - Since 0.20.0 the default log level for org.apache.hadoop.hbase.*is DEBUG. - On your clients, edit $HBASE_HOME/conf/log4j.properties and change - this: log4j.logger.org.apache.hadoop.hbase=DEBUG to this: - log4j.logger.org.apache.hadoop.hbase=INFO, or even - log4j.logger.org.apache.hadoop.hbase=WARN. -
-
- Long Client Pauses With Compression - This is a fairly frequent question on the Apache HBase dist-list. The scenario is that a - client is typically inserting a lot of data into a relatively un-optimized HBase cluster. - Compression can exacerbate the pauses, although it is not the source of the problem. - See on the pattern for pre-creating regions and confirm that - the table isn't starting with a single region. - See for cluster configuration, particularly - hbase.hstore.blockingStoreFiles, - hbase.hregion.memstore.block.multiplier, MAX_FILESIZE (region - size), and MEMSTORE_FLUSHSIZE. - - A slightly longer explanation of why pauses can happen is as follows: Puts are sometimes - blocked on the MemStores which are blocked by the flusher thread which is blocked because - there are too many files to compact because the compactor is given too many small files to - compact and has to compact the same data repeatedly. This situation can occur even with - minor compactions. Compounding this situation, Apache HBase doesn't compress data in memory. - Thus, the 64MB that lives in the MemStore could become a 6MB file after compression - which - results in a smaller StoreFile. The upside is that more data is packed into the same region, - but performance is achieved by being able to write larger files - which is why HBase waits - until the flushize before writing a new StoreFile. And smaller StoreFiles become targets for - compaction. Without compression the files are much bigger and don't need as much compaction, - however this is at the expense of I/O. - For additional information, see this thread on Long - client pauses with compression. -
-
- Secure Client Connect ([Caused by GSSException: No valid credentials provided...]) - You may encounter the following error: - Secure Client Connect ([Caused by GSSException: No valid credentials provided - (Mechanism level: Request is a replay (34) V PROCESS_TGS)]) - This issue is caused by bugs in the MIT Kerberos replay_cache component, #1201 and #5924. These bugs - caused the old version of krb5-server to erroneously block subsequent requests sent from a - Principal. This caused krb5-server to block the connections sent from one Client (one HTable - instance with multi-threading connection instances for each regionserver); Messages, such as - Request is a replay (34), are logged in the client log You can ignore - the messages, because HTable will retry 5 * 10 (50) times for each failed connection by - default. HTable will throw IOException if any connection to the regionserver fails after the - retries, so that the user client code for HTable instance can handle it further. - Alternatively, update krb5-server to a version which solves these issues, such as - krb5-server-1.10.3. See JIRA HBASE-10379 for more - details. -
-
- ZooKeeper Client Connection Errors - Errors like this... - -11/07/05 11:26:41 WARN zookeeper.ClientCnxn: Session 0x0 for server null, - unexpected error, closing socket connection and attempting reconnect - java.net.ConnectException: Connection refused: no further information - at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method) - at sun.nio.ch.SocketChannelImpl.finishConnect(Unknown Source) - at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:1078) - 11/07/05 11:26:43 INFO zookeeper.ClientCnxn: Opening socket connection to - server localhost/127.0.0.1:2181 - 11/07/05 11:26:44 WARN zookeeper.ClientCnxn: Session 0x0 for server null, - unexpected error, closing socket connection and attempting reconnect - java.net.ConnectException: Connection refused: no further information - at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method) - at sun.nio.ch.SocketChannelImpl.finishConnect(Unknown Source) - at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:1078) - 11/07/05 11:26:45 INFO zookeeper.ClientCnxn: Opening socket connection to - server localhost/127.0.0.1:2181 - - ... are either due to ZooKeeper being down, or unreachable due to network issues. - The utility may help investigate ZooKeeper issues. -
-
- Client running out of memory though heap size seems to be stable (but the - off-heap/direct heap keeps growing) - You are likely running into the issue that is described and worked through in the mail - thread HBase, - mail # user - Suspected memory leak and continued over in HBase, - mail # dev - FeedbackRe: Suspected memory leak. A workaround is passing your - client-side JVM a reasonable value for -XX:MaxDirectMemorySize. By default, the - MaxDirectMemorySize is equal to your -Xmx max heapsize - setting (if -Xmx is set). Try seting it to something smaller (for example, one - user had success setting it to 1g when they had a client-side heap of - 12g). If you set it too small, it will bring on FullGCs so keep - it a bit hefty. You want to make this setting client-side only especially if you are running - the new experiemental server-side off-heap cache since this feature depends on being able to - use big direct buffers (You may have to keep separate client-side and server-side config - dirs). - -
-
- Client Slowdown When Calling Admin Methods (flush, compact, etc.) - This is a client issue fixed by HBASE-5073 in 0.90.6. - There was a ZooKeeper leak in the client and the client was getting pummeled by ZooKeeper - events with each additional invocation of the admin API. -
- -
- Secure Client Cannot Connect ([Caused by GSSException: No valid credentials provided - (Mechanism level: Failed to find any Kerberos tgt)]) - There can be several causes that produce this symptom. - First, check that you have a valid Kerberos ticket. One is required in order to set up - communication with a secure Apache HBase cluster. Examine the ticket currently in the - credential cache, if any, by running the klist command line utility. If no ticket is listed, - you must obtain a ticket by running the kinit command with either a keytab specified, or by - interactively entering a password for the desired principal. - Then, consult the Java - Security Guide troubleshooting section. The most common problem addressed there is - resolved by setting javax.security.auth.useSubjectCredsOnly system property value to false. - Because of a change in the format in which MIT Kerberos writes its credentials cache, - there is a bug in the Oracle JDK 6 Update 26 and earlier that causes Java to be unable to - read the Kerberos credentials cache created by versions of MIT Kerberos 1.8.1 or higher. If - you have this problematic combination of components in your environment, to work around this - problem, first log in with kinit and then immediately refresh the credential cache with - kinit -R. The refresh will rewrite the credential cache without the problematic formatting. - Finally, depending on your Kerberos configuration, you may need to install the Java - Cryptography Extension, or JCE. Insure the JCE jars are on the classpath on both - server and client systems. - You may also need to download the unlimited - strength JCE policy files. Uncompress and extract the downloaded file, and install - the policy jars into <java-home>/lib/security. -
- -
- -
- MapReduce -
- You Think You're On The Cluster, But You're Actually Local - This following stacktrace happened using ImportTsv, but things like this - can happen on any job with a mis-configuration. - - WARN mapred.LocalJobRunner: job_local_0001 -java.lang.IllegalArgumentException: Can't read partitions file - at org.apache.hadoop.hbase.mapreduce.hadoopbackport.TotalOrderPartitioner.setConf(TotalOrderPartitioner.java:111) - at org.apache.hadoop.util.ReflectionUtils.setConf(ReflectionUtils.java:62) - at org.apache.hadoop.util.ReflectionUtils.newInstance(ReflectionUtils.java:117) - at org.apache.hadoop.mapred.MapTask$NewOutputCollector.<init>(MapTask.java:560) - at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:639) - at org.apache.hadoop.mapred.MapTask.run(MapTask.java:323) - at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:210) -Caused by: java.io.FileNotFoundException: File _partition.lst does not exist. - at org.apache.hadoop.fs.RawLocalFileSystem.getFileStatus(RawLocalFileSystem.java:383) - at org.apache.hadoop.fs.FilterFileSystem.getFileStatus(FilterFileSystem.java:251) - at org.apache.hadoop.fs.FileSystem.getLength(FileSystem.java:776) - at org.apache.hadoop.io.SequenceFile$Reader.<init>(SequenceFile.java:1424) - at org.apache.hadoop.io.SequenceFile$Reader.<init>(SequenceFile.java:1419) - at org.apache.hadoop.hbase.mapreduce.hadoopbackport.TotalOrderPartitioner.readPartitions(TotalOrderPartitioner.java:296) - - .. see the critical portion of the stack? It's... - -at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:210) - - LocalJobRunner means the job is running locally, not on the cluster. - - To solve this problem, you should run your MR job with your - HADOOP_CLASSPATH set to include the HBase dependencies. The "hbase classpath" - utility can be used to do this easily. For example (substitute VERSION with your HBase - version): - - HADOOP_CLASSPATH=`hbase classpath` hadoop jar $HBASE_HOME/hbase-server-VERSION.jar rowcounter usertable - - See - http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/package-summary.html#classpath - for more information on HBase MapReduce jobs and classpaths. -
-
- Launching a job, you get java.lang.IllegalAccessError: com/google/protobuf/HBaseZeroCopyByteString or class com.google.protobuf.ZeroCopyLiteralByteString cannot access its superclass com.google.protobuf.LiteralByteString - See HBASE-10304 Running an hbase job jar: IllegalAccessError: class com.google.protobuf.ZeroCopyLiteralByteString cannot access its superclass com.google.protobuf.LiteralByteString and HBASE-11118 non environment variable solution for "IllegalAccessError: class com.google.protobuf.ZeroCopyLiteralByteString cannot access its superclass com.google.protobuf.LiteralByteString". The issue can also show up - when trying to run spark jobs. See HBASE-10877 HBase non-retriable exception list should be expanded. - -
-
- -
- NameNode - For more information on the NameNode, see . -
- HDFS Utilization of Tables and Regions - To determine how much space HBase is using on HDFS use the hadoop shell - commands from the NameNode. For example... - hadoop fs -dus /hbase/ ...returns the summarized disk - utilization for all HBase objects. - hadoop fs -dus /hbase/myTable ...returns the summarized - disk utilization for the HBase table 'myTable'. - hadoop fs -du /hbase/myTable ...returns a list of the - regions under the HBase table 'myTable' and their disk utilization. - For more information on HDFS shell commands, see the HDFS - FileSystem Shell documentation. -
-
- Browsing HDFS for HBase Objects - Sometimes it will be necessary to explore the HBase objects that exist on HDFS. These - objects could include the WALs (Write Ahead Logs), tables, regions, StoreFiles, etc. The - easiest way to do this is with the NameNode web application that runs on port 50070. The - NameNode web application will provide links to the all the DataNodes in the cluster so that - they can be browsed seamlessly. - The HDFS directory structure of HBase tables in the cluster is... - -/hbase - /<Table> (Tables in the cluster) - /<Region> (Regions for the table) - /<ColumnFamily> (ColumnFamilies for the Region for the table) - /<StoreFile> (StoreFiles for the ColumnFamily for the Regions for the table) - - - The HDFS directory structure of HBase WAL is.. - -/hbase - /.logs - /<RegionServer> (RegionServers) - /<WAL> (WAL files for the RegionServer) - - - See the HDFS User - Guide for other non-shell diagnostic utilities like fsck. -
- Zero size WALs with data in them - Problem: when getting a listing of all the files in a region server's .logs directory, - one file has a size of 0 but it contains data. - Answer: It's an HDFS quirk. A file that's currently being written to will appear to - have a size of 0 but once it's closed it will show its true size -
-
- Use Cases - Two common use-cases for querying HDFS for HBase objects is research the degree of - uncompaction of a table. If there are a large number of StoreFiles for each ColumnFamily - it could indicate the need for a major compaction. Additionally, after a major compaction - if the resulting StoreFile is "small" it could indicate the need for a reduction of - ColumnFamilies for the table. -
- -
-
- -
- Network -
- Network Spikes - If you are seeing periodic network spikes you might want to check the - compactionQueues to see if major compactions are happening. - See for more information on managing compactions. -
-
- Loopback IP - HBase expects the loopback IP Address to be 127.0.0.1. See the Getting Started section - on . -
-
- Network Interfaces - Are all the network interfaces functioning correctly? Are you sure? See the - Troubleshooting Case Study in . -
- -
- -
- RegionServer - For more information on the RegionServers, see . -
- Startup Errors -
- Master Starts, But RegionServers Do Not - The Master believes the RegionServers have the IP of 127.0.0.1 - which is localhost - and resolves to the master's own localhost. - The RegionServers are erroneously informing the Master that their IP addresses are - 127.0.0.1. - Modify /etc/hosts on the region servers, from... - -# Do not remove the following line, or various programs -# that require network functionality will fail. -127.0.0.1 fully.qualified.regionservername regionservername localhost.localdomain localhost -::1 localhost6.localdomain6 localhost6 - - ... to (removing the master node's name from localhost)... - -# Do not remove the following line, or various programs -# that require network functionality will fail. -127.0.0.1 localhost.localdomain localhost -::1 localhost6.localdomain6 localhost6 - -
- -
- Compression Link Errors - Since compression algorithms such as LZO need to be installed and configured on each - cluster this is a frequent source of startup error. If you see messages like - this... - -11/02/20 01:32:15 ERROR lzo.GPLNativeCodeLoader: Could not load native gpl library -java.lang.UnsatisfiedLinkError: no gplcompression in java.library.path - at java.lang.ClassLoader.loadLibrary(ClassLoader.java:1734) - at java.lang.Runtime.loadLibrary0(Runtime.java:823) - at java.lang.System.loadLibrary(System.java:1028) - - .. then there is a path issue with the compression libraries. See the Configuration - section on LZO compression configuration. -
-
-
- Runtime Errors - -
- RegionServer Hanging - Are you running an old JVM (< 1.6.0_u21?)? When you look at a thread dump, does it - look like threads are BLOCKED but no one holds the lock all are blocked on? See HBASE 3622 Deadlock in - HBaseServer (JVM bug?). Adding -XX:+UseMembar to the HBase - HBASE_OPTS in conf/hbase-env.sh may fix it. - -
-
- java.io.IOException...(Too many open files) - If you see log messages like this... - -2010-09-13 01:24:17,336 WARN org.apache.hadoop.hdfs.server.datanode.DataNode: -Disk-related IOException in BlockReceiver constructor. Cause is java.io.IOException: Too many open files - at java.io.UnixFileSystem.createFileExclusively(Native Method) - at java.io.File.createNewFile(File.java:883) - - ... see the Getting Started section on ulimit and nproc configuration. -
-
- xceiverCount 258 exceeds the limit of concurrent xcievers 256 - This typically shows up in the DataNode logs. - See the Getting Started section on xceivers configuration. -
-
- System instability, and the presence of "java.lang.OutOfMemoryError: unable to create - new native thread in exceptions" HDFS DataNode logs or that of any system daemon - See the Getting Started section on ulimit and nproc configuration. The default on recent Linux - distributions is 1024 - which is far too low for HBase. -
-
- DFS instability and/or RegionServer lease timeouts - If you see warning messages like this... - -2009-02-24 10:01:33,516 WARN org.apache.hadoop.hbase.util.Sleeper: We slept xxx ms, ten times longer than scheduled: 10000 -2009-02-24 10:01:33,516 WARN org.apache.hadoop.hbase.util.Sleeper: We slept xxx ms, ten times longer than scheduled: 15000 -2009-02-24 10:01:36,472 WARN org.apache.hadoop.hbase.regionserver.HRegionServer: unable to report to master for xxx milliseconds - retrying - - ... or see full GC compactions then you may be experiencing full GC's. -
-
- "No live nodes contain current block" and/or YouAreDeadException - These errors can happen either when running out of OS file handles or in periods of - severe network problems where the nodes are unreachable. - See the Getting Started section on ulimit and nproc configuration and check your network. -
-
- ZooKeeper SessionExpired events - Master or RegionServers shutting down with messages like those in the logs: - -WARN org.apache.zookeeper.ClientCnxn: Exception -closing session 0x278bd16a96000f to sun.nio.ch.SelectionKeyImpl@355811ec -java.io.IOException: TIMED OUT - at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:906) -WARN org.apache.hadoop.hbase.util.Sleeper: We slept 79410ms, ten times longer than scheduled: 5000 -INFO org.apache.zookeeper.ClientCnxn: Attempting connection to server hostname/IP:PORT -INFO org.apache.zookeeper.ClientCnxn: Priming connection to java.nio.channels.SocketChannel[connected local=/IP:PORT remote=hostname/IP:PORT] -INFO org.apache.zookeeper.ClientCnxn: Server connection successful -WARN org.apache.zookeeper.ClientCnxn: Exception closing session 0x278bd16a96000d to sun.nio.ch.SelectionKeyImpl@3544d65e -java.io.IOException: Session Expired - at org.apache.zookeeper.ClientCnxn$SendThread.readConnectResult(ClientCnxn.java:589) - at org.apache.zookeeper.ClientCnxn$SendThread.doIO(ClientCnxn.java:709) - at org.apache.zookeeper.ClientCnxn$SendThread.run(ClientCnxn.java:945) -ERROR org.apache.hadoop.hbase.regionserver.HRegionServer: ZooKeeper session expired - - The JVM is doing a long running garbage collecting which is pausing every threads - (aka "stop the world"). Since the RegionServer's local ZooKeeper client cannot send - heartbeats, the session times out. By design, we shut down any node that isn't able to - contact the ZooKeeper ensemble after getting a timeout so that it stops serving data that - may already be assigned elsewhere. - - - - Make sure you give plenty of RAM (in hbase-env.sh), the - default of 1GB won't be able to sustain long running imports. - - - Make sure you don't swap, the JVM never behaves well under swapping. - - - Make sure you are not CPU starving the RegionServer thread. For example, if you - are running a MapReduce job using 6 CPU-intensive tasks on a machine with 4 cores, you - are probably starving the RegionServer enough to create longer garbage collection - pauses. - - - Increase the ZooKeeper session timeout - - - If you wish to increase the session timeout, add the following to your - hbase-site.xml to increase the timeout from the default of 60 - seconds to 120 seconds. - - - zookeeper.session.timeout - 1200000 - - - hbase.zookeeper.property.tickTime - 6000 -]]> - - - - Be aware that setting a higher timeout means that the regions served by a failed RegionServer will take at least - that amount of time to be transfered to another RegionServer. For a production system serving live requests, we would instead - recommend setting it lower than 1 minute and over-provision your cluster in order the lower the memory load on each machines (hence having - less garbage to collect per machine). - - - If this is happening during an upload which only happens once (like initially loading all your data into HBase), consider bulk loading. - -See for other general information about ZooKeeper troubleshooting. -
-
- NotServingRegionException - This exception is "normal" when found in the RegionServer logs at DEBUG level. This exception is returned back to the client - and then the client goes back to hbase:meta to find the new location of the moved region. - However, if the NotServingRegionException is logged ERROR, then the client ran out of retries and something probably wrong. -
-
- Regions listed by domain name, then IP - - Fix your DNS. In versions of Apache HBase before 0.92.x, reverse DNS needs to give same answer - as forward lookup. See HBASE 3431 - RegionServer is not using the name given it by the master; double entry in master listing of servers for gorey details. - -
-
- Logs flooded with '2011-01-10 12:40:48,407 INFO org.apache.hadoop.io.compress.CodecPool: Got - brand-new compressor' messages - We are not using the native versions of compression - libraries. See HBASE-1900 Put back native support when hadoop 0.21 is released. - Copy the native libs from hadoop under hbase lib dir or - symlink them into place and the message should go away. - -
-
- Server handler X on 60020 caught: java.nio.channels.ClosedChannelException - - If you see this type of message it means that the region server was trying to read/send data from/to a client but - it already went away. Typical causes for this are if the client was killed (you see a storm of messages like this when a MapReduce - job is killed or fails) or if the client receives a SocketTimeoutException. It's harmless, but you should consider digging in - a bit more if you aren't doing something to trigger them. - -
- -
-
- Snapshot Errors Due to Reverse DNS - Several operations within HBase, including snapshots, rely on properly configured - reverse DNS. Some environments, such as Amazon EC2, have trouble with reverse DNS. If you - see errors like the following on your RegionServers, check your reverse DNS configuration: - -2013-05-01 00:04:56,356 DEBUG org.apache.hadoop.hbase.procedure.Subprocedure: Subprocedure 'backup1' -coordinator notified of 'acquire', waiting on 'reached' or 'abort' from coordinator. - - In general, the hostname reported by the RegionServer needs to be the same as the - hostname the Master is trying to reach. You can see a hostname mismatch by looking for the - following type of message in the RegionServer's logs at start-up. - -2013-05-01 00:03:00,614 INFO org.apache.hadoop.hbase.regionserver.HRegionServer: Master passed us hostname -to use. Was=myhost-1234, Now=ip-10-55-88-99.ec2.internal - -
-
- Shutdown Errors - -
- -
- -
- Master - For more information on the Master, see . - -
- Startup Errors -
- Master says that you need to run the hbase migrations script - Upon running that, the hbase migrations script says no files in root directory. - HBase expects the root directory to either not exist, or to have already been initialized by hbase running a previous time. If you create a new directory for HBase using Hadoop DFS, this error will occur. - Make sure the HBase root directory does not currently exist or has been initialized by a previous run of HBase. Sure fire solution is to just use Hadoop dfs to delete the HBase root and let HBase create and initialize the directory itself. - -
-
- Packet len6080218 is out of range! - If you have many regions on your cluster and you see an error - like that reported above in this sections title in your logs, see - HBASE-4246 Cluster with too many regions cannot withstand some master failover scenarios. -
- -
-
- Shutdown Errors - -
- -
- -
- ZooKeeper -
- Startup Errors -
- Could not find my address: xyz in list of ZooKeeper quorum servers - A ZooKeeper server wasn't able to start, throws that error. xyz is the name of your server. - This is a name lookup problem. HBase tries to start a ZooKeeper server on some machine but that machine isn't able to find itself in the hbase.zookeeper.quorum configuration. - - Use the hostname presented in the error message instead of the value you used. If you have a DNS server, you can set hbase.zookeeper.dns.interface and hbase.zookeeper.dns.nameserver in hbase-site.xml to make sure it resolves to the correct FQDN. - -
- -
-
- ZooKeeper, The Cluster Canary - ZooKeeper is the cluster's "canary in the mineshaft". It'll be the first to notice issues if any so making sure its happy is the short-cut to a humming cluster. - - - See the ZooKeeper Operating Environment Troubleshooting page. It has suggestions and tools for checking disk and networking performance; i.e. the operating environment your ZooKeeper and HBase are running in. - - Additionally, the utility may help investigate ZooKeeper issues. - -
- -
- -
- Amazon EC2 -
- ZooKeeper does not seem to work on Amazon EC2 - HBase does not start when deployed as Amazon EC2 instances. Exceptions like the below appear in the Master and/or RegionServer logs: - - 2009-10-19 11:52:27,030 INFO org.apache.zookeeper.ClientCnxn: Attempting - connection to server ec2-174-129-15-236.compute-1.amazonaws.com/10.244.9.171:2181 - 2009-10-19 11:52:27,032 WARN org.apache.zookeeper.ClientCnxn: Exception - closing session 0x0 to sun.nio.ch.SelectionKeyImpl@656dc861 - java.net.ConnectException: Connection refused - - - Security group policy is blocking the ZooKeeper port on a public address. - Use the internal EC2 host names when configuring the ZooKeeper quorum peer list. - -
-
- Instability on Amazon EC2 - Questions on HBase and Amazon EC2 come up frequently on the HBase dist-list. Search for old threads using Search Hadoop - -
-
- Remote Java Connection into EC2 Cluster Not Working - - See Andrew's answer here, up on the user list: Remote Java client connection into EC2 instance. - -
- -
- -
- HBase and Hadoop version issues -
- <code>NoClassDefFoundError</code> when trying to run 0.90.x on hadoop-0.20.205.x (or hadoop-1.0.x) - Apache HBase 0.90.x does not ship with hadoop-0.20.205.x, etc. To make it run, you need to replace the hadoop - jars that Apache HBase shipped with in its lib directory with those of the Hadoop you want to - run HBase on. If even after replacing Hadoop jars you get the below exception: - -sv4r6s38: Exception in thread "main" java.lang.NoClassDefFoundError: org/apache/commons/configuration/Configuration -sv4r6s38: at org.apache.hadoop.metrics2.lib.DefaultMetricsSystem.<init>(DefaultMetricsSystem.java:37) -sv4r6s38: at org.apache.hadoop.metrics2.lib.DefaultMetricsSystem.<clinit>(DefaultMetricsSystem.java:34) -sv4r6s38: at org.apache.hadoop.security.UgiInstrumentation.create(UgiInstrumentation.java:51) -sv4r6s38: at org.apache.hadoop.security.UserGroupInformation.initialize(UserGroupInformation.java:209) -sv4r6s38: at org.apache.hadoop.security.UserGroupInformation.ensureInitialized(UserGroupInformation.java:177) -sv4r6s38: at org.apache.hadoop.security.UserGroupInformation.isSecurityEnabled(UserGroupInformation.java:229) -sv4r6s38: at org.apache.hadoop.security.KerberosName.<clinit>(KerberosName.java:83) -sv4r6s38: at org.apache.hadoop.security.UserGroupInformation.initialize(UserGroupInformation.java:202) -sv4r6s38: at org.apache.hadoop.security.UserGroupInformation.ensureInitialized(UserGroupInformation.java:177) - - you need to copy under hbase/lib, the - commons-configuration-X.jar you find in your Hadoop's - lib directory. That should fix the above complaint. -
- -
- ...cannot communicate with client version... - If you see something like the following in your logs ... 2012-09-24 - 10:20:52,168 FATAL org.apache.hadoop.hbase.master.HMaster: Unhandled exception. Starting - shutdown. org.apache.hadoop.ipc.RemoteException: Server IPC version 7 cannot communicate - with client version 4 ... ...are you trying to talk to an Hadoop 2.0.x - from an HBase that has an Hadoop 1.0.x client? Use the HBase built against Hadoop 2.0 or - rebuild your HBase passing the -Dhadoop.profile=2.0 attribute to Maven - (See for more). - -
-
-
- IPC Configuration Conflicts with Hadoop - If the Hadoop configuration is loaded after the HBase configuration, and you have - configured custom IPC settings in both HBase and Hadoop, the Hadoop values may overwrite the - HBase values. There is normally no need to change these settings for HBase, so this problem is - an edge case. However, HBASE-11492 renames - these settings for HBase to remove the chance of a conflict. Each of the setting names have - been prefixed with hbase., as shown in the following table. No action is - required related to these changes unless you are already experiencing a conflict. - These changes were backported to HBase 0.98.x and apply to all newer versions. - - - - - Pre-0.98.x - 0.98-x And Newer - - - - - ipc.server.listen.queue.size - hbase.ipc.server.listen.queue.size - - - ipc.server.max.callqueue.size - hbase.ipc.server.max.callqueue.size - - - ipc.server.callqueue.handler.factor - hbase.ipc.server.callqueue.handler.factor - - - ipc.server.callqueue.read.share - hbase.ipc.server.callqueue.read.share - - - ipc.server.callqueue.type - hbase.ipc.server.callqueue.type - - - ipc.server.queue.max.call.delay - hbase.ipc.server.queue.max.call.delay - - - ipc.server.max.callqueue.length - hbase.ipc.server.max.callqueue.length - - - ipc.server.read.threadpool.size - hbase.ipc.server.read.threadpool.size - - - ipc.server.tcpkeepalive - hbase.ipc.server.tcpkeepalive - - - ipc.server.tcpnodelay - hbase.ipc.server.tcpnodelay - - - ipc.client.call.purge.timeout - hbase.ipc.client.call.purge.timeout - - - ipc.client.connection.maxidletime - hbase.ipc.client.connection.maxidletime - - - ipc.client.idlethreshold - hbase.ipc.client.idlethreshold - - - ipc.client.kill.max - hbase.ipc.client.kill.max - - - ipc.server.scan.vtime.weight - hbase.ipc.server.scan.vtime.weight - - - - -
- -
- HBase and HDFS - General configuration guidance for Apache HDFS is out of the scope of this guide. Refer to - the documentation available at http://hadoop.apache.org/ for extensive - information about configuring HDFS. This section deals with HDFS in terms of HBase. - - In most cases, HBase stores its data in Apache HDFS. This includes the HFiles containing - the data, as well as the write-ahead logs (WALs) which store data before it is written to the - HFiles and protect against RegionServer crashes. HDFS provides reliability and protection to - data in HBase because it is distributed. To operate with the most efficiency, HBase needs data - to be available locally. Therefore, it is a good practice to run an HDFS datanode on each - RegionServer. - - Important Information and Guidelines for HBase and HDFS - - HBase is a client of HDFS. - - HBase is an HDFS client, using the HDFS DFSClient class, and references - to this class appear in HBase logs with other HDFS client log messages. - - - - Configuration is necessary in multiple places. - - Some HDFS configurations relating to HBase need to be done at the HDFS (server) side. - Others must be done within HBase (at the client side). Other settings need - to be set at both the server and client side. - - - - - Write errors which affect HBase may be logged in the HDFS logs rather than HBase logs. - - When writing, HDFS pipelines communications from one datanode to another. HBase - communicates to both the HDFS namenode and datanode, using the HDFS client classes. - Communication problems between datanodes are logged in the HDFS logs, not the HBase - logs. - HDFS writes are always local when possible. HBase RegionServers should not - experience many write errors, because they write the local datanode. If the datanode - cannot replicate the blocks, the errors are logged in HDFS, not in the HBase - RegionServer logs. - - - - HBase communicates with HDFS using two different ports. - - HBase communicates with datanodes using the ipc.Client interface and - the DataNode class. References to these will appear in HBase logs. Each of - these communication channels use a different port (50010 and 50020 by default). The - ports are configured in the HDFS configuration, via the - dfs.datanode.address and dfs.datanode.ipc.address - parameters. - - - - Errors may be logged in HBase, HDFS, or both. - - When troubleshooting HDFS issues in HBase, check logs in both places for errors. - - - - HDFS takes a while to mark a node as dead. You can configure HDFS to avoid using stale - datanodes. - - By default, HDFS does not mark a node as dead until it is unreachable for 630 - seconds. In Hadoop 1.1 and Hadoop 2.x, this can be alleviated by enabling checks for - stale datanodes, though this check is disabled by default. You can enable the check for - reads and writes separately, via dfs.namenode.avoid.read.stale.datanode and - dfs.namenode.avoid.write.stale.datanode settings. A stale datanode is one - that has not been reachable for dfs.namenode.stale.datanode.interval - (default is 30 seconds). Stale datanodes are avoided, and marked as the last possible - target for a read or write operation. For configuration details, see the HDFS - documentation. - - - - Settings for HDFS retries and timeouts are important to HBase. - - You can configure settings for various retries and timeouts. Always refer to the - HDFS documentation for current recommendations and defaults. Some of the settings - important to HBase are listed here. Defaults are current as of Hadoop 2.3. Check the - Hadoop documentation for the most current values and recommendations. - - Retries - - ipc.client.connect.max.retries (default: 10) - - The number of times a client will attempt to establish a connection with the - server. This value sometimes needs to be increased. You can specify different - setting for the maximum number of retries if a timeout occurs. For SASL - connections, the number of retries is hard-coded at 15 and cannot be - configured. - - - ipc.client.connect.max.retries.on.timeouts (default: 45) - The number of times a client will attempt to establish a connection - with the server in the event of a timeout. If some retries are due to timeouts and - some are due to other reasons, this counter is added to - ipc.client.connect.max.retries, so the maximum number of retries for - all reasons could be the combined value. - - - dfs.client.block.write.retries (default: 3) - How many times the client attempts to write to the datanode. After the - number of retries is reached, the client reconnects to the namenode to get a new - location of a datanode. You can try increasing this value. - - - - HDFS Heartbeats - HDFS heartbeats are entirely on the HDFS side, between the namenode and datanodes. - - dfs.heartbeat.interval (default: 3) - The interval at which a node heartbeats. - - - dfs.namenode.heartbeat.recheck-interval (default: 300000) - - The interval of time between heartbeat checks. The total time before a node is - marked as stale is determined by the following formula, which works out to 10 - minutes and 30 seconds: - 2 * (dfs.namenode.heartbeat.recheck-interval) + 10 * 1000 * (dfs.heartbeat.interval) - - - - dfs.namenode.stale.datanode.interval (default: 3000) - - How long (in milliseconds) a node can go without a heartbeat before it is - determined to be stale, if the other options to do with stale datanodes are - configured (off by default). - - - - - - - Connection Timeouts - Connection timeouts occur between the client (HBASE) and the HDFS datanode. They may - occur when establishing a connection, attempting to read, or attempting to write. The two - settings below are used in combination, and affect connections between the DFSClient and the - datanode, the ipc.cClient and the datanode, and communication between two datanodes. - - dfs.client.socket-timeout (default: 60000) - - The amount of time before a client connection times out when establishing a - connection or reading. The value is expressed in milliseconds, so the default is 60 - seconds. - - - - dfs.datanode.socket.write.timeout (default: 480000) - - The amount of time before a write operation times out. The default is 8 - minutes, expressed as milliseconds. - - - - - Typical Error Logs - The following types of errors are often seen in the logs. - - INFO HDFS.DFSClient: Failed to connect to /xxx50010, add to deadNodes and - continue java.net.SocketTimeoutException: 60000 millis timeout while waiting for channel - to be ready for connect. ch : java.nio.channels.SocketChannel[connection-pending - remote=/region-server-1:50010] - - All datanodes for a block are dead, and recovery is not possible. Here is the - sequence of events that leads to this error: - - - The client attempts to connect to a dead datanode. - - - The connection fails, so the client moves down the list of datanodes and tries - the next one. It also fails. - - - When the client exhausts its entire list, it sleeps for 3 seconds and requests a - new list. It is very likely to receive the exact same list as before, in which case - the error occurs again. - - - - - - INFO org.apache.hadoop.HDFS.DFSClient: Exception in createBlockOutputStream - java.net.SocketTimeoutException: 69000 millis timeout while waiting for channel to be - ready for connect. ch : java.nio.channels.SocketChannel[connection-pending remote=/ - xxx:50010] - - This type of error indicates a write issue. In this case, the master wants to split - the log. It does not have a local datanode so it tries to connect to a remote datanode, - but the datanode is dead. - In this situation, there will be three retries (by default). If all retries fail, a - message like the following is logged: - -WARN HDFS.DFSClient: DataStreamer Exception: java.io.IOException: Unable to create new block - - If the operation was an attempt to split the log, the following type of message may - also appear: - -FATAL wal.WALSplitter: WriterThread-xxx Got while writing log entry to log - - - - -
- -
- Running unit or integration tests -
- Runtime exceptions from MiniDFSCluster when running tests - If you see something like the following - ... -java.lang.NullPointerException: null -at org.apache.hadoop.hdfs.MiniDFSCluster.startDataNodes -at org.apache.hadoop.hdfs.MiniDFSCluster.<init> -at org.apache.hadoop.hbase.MiniHBaseCluster.<init> -at org.apache.hadoop.hbase.HBaseTestingUtility.startMiniDFSCluster -at org.apache.hadoop.hbase.HBaseTestingUtility.startMiniCluster -... - or - ... -java.io.IOException: Shutting down -at org.apache.hadoop.hbase.MiniHBaseCluster.init -at org.apache.hadoop.hbase.MiniHBaseCluster.<init> -at org.apache.hadoop.hbase.MiniHBaseCluster.<init> -at org.apache.hadoop.hbase.HBaseTestingUtility.startMiniHBaseCluster -at org.apache.hadoop.hbase.HBaseTestingUtility.startMiniCluster -... - ... then try issuing the command umask 022 before launching tests. - This is a workaround for HDFS-2556 - -
- -
- -
- Case Studies - For Performance and Troubleshooting Case Studies, see . -
- -
- Cryptographic Features -
- sun.security.pkcs11.wrapper.PKCS11Exception: CKR_ARGUMENTS_BAD - This problem manifests as exceptions ultimately caused by: - -Caused by: sun.security.pkcs11.wrapper.PKCS11Exception: CKR_ARGUMENTS_BAD - at sun.security.pkcs11.wrapper.PKCS11.C_DecryptUpdate(Native Method) - at sun.security.pkcs11.P11Cipher.implDoFinal(P11Cipher.java:795) - - This problem appears to affect some versions of OpenJDK 7 shipped by some Linux - vendors. NSS is configured as the default provider. If the host has an x86_64 architecture, - depending on if the vendor packages contain the defect, the NSS provider will not function - correctly. - To work around this problem, find the JRE home directory and edit the file - lib/security/java.security. Edit the file to comment out the line: - -security.provider.1=sun.security.pkcs11.SunPKCS11 ${java.home}/lib/security/nss.cfg - - Then renumber the remaining providers accordingly. -
-
- -
- Operating System Specific Issues -
- Page Allocation Failure - This issue is known to affect CentOS 6.2 and possibly CentOS 6.5. It may also affect - some versions of Red Hat Enterprise Linux, according to . - Some users have reported seeing the following error: - kernel: java: page allocation failure. order:4, mode:0x20 - Raising the value of min_free_kbytes was reported to fix this problem. This - parameter is set to a percentage of the amount of RAM on your system, and is described in more - detail at . - To find the current value on your system, run the following command: - [user@host]# cat /proc/sys/vm/min_free_kbytes - Next, raise the value. Try doubling, then quadrupling the value. Note that setting the - value too low or too high could have detrimental effects on your system. Consult your - operating system vendor for specific recommendations. - Use the following command to modify the value of min_free_kbytes, - substituting <value> with your intended value: - [user@host]# echo <value> > /proc/sys/vm/min_free_kbytes -
-
-
- JDK Issues -
- NoSuchMethodError: java.util.concurrent.ConcurrentHashMap.keySet - -If you see this in your logs: - Caused by: java.lang.NoSuchMethodError: java.util.concurrent.ConcurrentHashMap.keySet()Ljava/util/concurrent/ConcurrentHashMap$KeySetView; - at org.apache.hadoop.hbase.master.ServerManager.findServerWithSameHostnamePortWithLock(ServerManager.java:393) - at org.apache.hadoop.hbase.master.ServerManager.checkAndRecordNewServer(ServerManager.java:307) - at org.apache.hadoop.hbase.master.ServerManager.regionServerStartup(ServerManager.java:244) - at org.apache.hadoop.hbase.master.MasterRpcServices.regionServerStartup(MasterRpcServices.java:304) - at org.apache.hadoop.hbase.protobuf.generated.RegionServerStatusProtos$RegionServerStatusService$2.callBlockingMethod(RegionServerStatusProtos.java:7910) - at org.apache.hadoop.hbase.ipc.RpcServer.call(RpcServer.java:2020) - ... 4 more -then check if you compiled with jdk8 and tried to run it on jdk7. If so, this won't work. -Run on jdk8 or recompile with jdk7. See HBASE-10607 [JDK8] NoSuchMethodError involving ConcurrentHashMap.keySet if running on JRE 7. - -
-
- - diff --git a/src/main/docbkx/unit_testing.xml b/src/main/docbkx/unit_testing.xml deleted file mode 100644 index 8d8c756ad5b..00000000000 --- a/src/main/docbkx/unit_testing.xml +++ /dev/null @@ -1,330 +0,0 @@ - - - - - Unit Testing HBase Applications - This chapter discusses unit testing your HBase application using JUnit, Mockito, MRUnit, - and HBaseTestingUtility. Much of the information comes from a community blog post about testing HBase applications. For information on unit - tests for HBase itself, see . - -
- JUnit - HBase uses JUnit 4 for unit tests - This example will add unit tests to the following example class: - -public class MyHBaseDAO { - - public static void insertRecord(HTableInterface table, HBaseTestObj obj) - throws Exception { - Put put = createPut(obj); - table.put(put); - } - - private static Put createPut(HBaseTestObj obj) { - Put put = new Put(Bytes.toBytes(obj.getRowKey())); - put.add(Bytes.toBytes("CF"), Bytes.toBytes("CQ-1"), - Bytes.toBytes(obj.getData1())); - put.add(Bytes.toBytes("CF"), Bytes.toBytes("CQ-2"), - Bytes.toBytes(obj.getData2())); - return put; - } -} - - The first step is to add JUnit dependencies to your Maven POM file: - - junit - junit - 4.11 - test - - ]]> - Next, add some unit tests to your code. Tests are annotated with - @Test. Here, the unit tests are in bold. - -public class TestMyHbaseDAOData { - @Test - public void testCreatePut() throws Exception { - HBaseTestObj obj = new HBaseTestObj(); - obj.setRowKey("ROWKEY-1"); - obj.setData1("DATA-1"); - obj.setData2("DATA-2"); - Put put = MyHBaseDAO.createPut(obj); - assertEquals(obj.getRowKey(), Bytes.toString(put.getRow())); - assertEquals(obj.getData1(), Bytes.toString(put.get(Bytes.toBytes("CF"), Bytes.toBytes("CQ-1")).get(0).getValue())); - assertEquals(obj.getData2(), Bytes.toString(put.get(Bytes.toBytes("CF"), Bytes.toBytes("CQ-2")).get(0).getValue())); - } -} - - These tests ensure that your createPut method creates, populates, and - returns a Put object with expected values. Of course, JUnit can do much - more than this. For an introduction to JUnit, see https://github.com/junit-team/junit/wiki/Getting-started. -
- -
- Mockito - Mockito is a mocking framework. It goes further than JUnit by allowing you to test the - interactions between objects without having to replicate the entire environment. You can - read more about Mockito at its project site, https://code.google.com/p/mockito/. - You can use Mockito to do unit testing on smaller units. For instance, you can mock a - org.apache.hadoop.hbase.Server instance or a - org.apache.hadoop.hbase.master.MasterServices interface - reference rather than a full-blown - org.apache.hadoop.hbase.master.HMaster. - This example builds upon the example code in , to test the - insertRecord method. - First, add a dependency for Mockito to your Maven POM file. - - org.mockito - mockito-all - 1.9.5 - test - - ]]> - Next, add a @RunWith annotation to your test class, to direct it to use - Mockito. - -@RunWith(MockitoJUnitRunner.class) -public class TestMyHBaseDAO{ - @Mock - private HTableInterface table; - @Mock - private HTablePool hTablePool; - @Captor - private ArgumentCaptor putCaptor; - - @Test - public void testInsertRecord() throws Exception { - //return mock table when getTable is called - when(hTablePool.getTable("tablename")).thenReturn(table); - //create test object and make a call to the DAO that needs testing - HBaseTestObj obj = new HBaseTestObj(); - obj.setRowKey("ROWKEY-1"); - obj.setData1("DATA-1"); - obj.setData2("DATA-2"); - MyHBaseDAO.insertRecord(table, obj); - verify(table).put(putCaptor.capture()); - Put put = putCaptor.getValue(); - - assertEquals(Bytes.toString(put.getRow()), obj.getRowKey()); - assert(put.has(Bytes.toBytes("CF"), Bytes.toBytes("CQ-1"))); - assert(put.has(Bytes.toBytes("CF"), Bytes.toBytes("CQ-2"))); - assertEquals(Bytes.toString(put.get(Bytes.toBytes("CF"),Bytes.toBytes("CQ-1")).get(0).getValue()), "DATA-1"); - assertEquals(Bytes.toString(put.get(Bytes.toBytes("CF"),Bytes.toBytes("CQ-2")).get(0).getValue()), "DATA-2"); - } -} - - This code populates HBaseTestObj with “ROWKEY-1”, “DATA-1”, “DATA-2” as - values. It then inserts the record into the mocked table. The Put that the DAO would - have inserted is captured, and values are tested to verify that they are what you - expected them to be. - The key here is to manage htable pool and htable instance creation outside the DAO. - This allows you to mock them cleanly and test Puts as shown above. Similarly, you can - now expand into other operations such as Get, Scan, or Delete. - -
-
- MRUnit - Apache MRUnit is a library that - allows you to unit-test MapReduce jobs. You can use it to test HBase jobs in the same - way as other MapReduce jobs. - Given a MapReduce job that writes to an HBase table called MyTest, - which has one column family called CF, the reducer of such a job - could look like the following: - { - public static final byte[] CF = "CF".getBytes(); - public static final byte[] QUALIFIER = "CQ-1".getBytes(); - public void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException { - //bunch of processing to extract data to be inserted, in our case, lets say we are simply - //appending all the records we receive from the mapper for this particular - //key and insert one record into HBase - StringBuffer data = new StringBuffer(); - Put put = new Put(Bytes.toBytes(key.toString())); - for (Text val : values) { - data = data.append(val); - } - put.add(CF, QUALIFIER, Bytes.toBytes(data.toString())); - //write to HBase - context.write(new ImmutableBytesWritable(Bytes.toBytes(key.toString())), put); - } - } ]]> - - To test this code, the first step is to add a dependency to MRUnit to your Maven POM - file. - - org.apache.mrunit - mrunit - 1.0.0 - test - - ]]> - Next, use the ReducerDriver provided by MRUnit, in your Reducer job. - reduceDriver; - byte[] CF = "CF".getBytes(); - byte[] QUALIFIER = "CQ-1".getBytes(); - - @Before - public void setUp() { - MyReducer reducer = new MyReducer(); - reduceDriver = ReduceDriver.newReduceDriver(reducer); - } - - @Test - public void testHBaseInsert() throws IOException { - String strKey = "RowKey-1", strValue = "DATA", strValue1 = "DATA1", -strValue2 = "DATA2"; - List list = new ArrayList(); - list.add(new Text(strValue)); - list.add(new Text(strValue1)); - list.add(new Text(strValue2)); - //since in our case all that the reducer is doing is appending the records that the mapper - //sends it, we should get the following back - String expectedOutput = strValue + strValue1 + strValue2; - //Setup Input, mimic what mapper would have passed - //to the reducer and run test - reduceDriver.withInput(new Text(strKey), list); - //run the reducer and get its output - List> result = reduceDriver.run(); - - //extract key from result and verify - assertEquals(Bytes.toString(result.get(0).getFirst().get()), strKey); - - //extract value for CF/QUALIFIER and verify - Put a = (Put)result.get(0).getSecond(); - String c = Bytes.toString(a.get(CF, QUALIFIER).get(0).getValue()); - assertEquals(expectedOutput,c ); - } - -} - ]]> - Your MRUnit test verifies that the output is as expected, the Put that is inserted - into HBase has the correct value, and the ColumnFamily and ColumnQualifier have the - correct values. - MRUnit includes a MapperDriver to test mapping jobs, and you can use MRUnit to test - other operations, including reading from HBase, processing data, or writing to - HDFS, -
- -
- Integration Testing with a HBase Mini-Cluster - HBase ships with HBaseTestingUtility, which makes it easy to write integration tests - using a mini-cluster. The first step is to add some dependencies - to your Maven POM file. Check the versions to be sure they are appropriate. - - org.apache.hadoop - hadoop-common - 2.0.0 - test-jar - test - - - - org.apache.hbase - hbase - 0.98.3 - test-jar - test - - - - org.apache.hadoop - hadoop-hdfs - 2.0.0 - test-jar - test - - - - org.apache.hadoop - hadoop-hdfs - 2.0.0 - test - - ]]> - This code represents an integration test for the MyDAO insert shown in . - -public class MyHBaseIntegrationTest { - private static HBaseTestingUtility utility; - byte[] CF = "CF".getBytes(); - byte[] QUALIFIER = "CQ-1".getBytes(); - - @Before - public void setup() throws Exception { - utility = new HBaseTestingUtility(); - utility.startMiniCluster(); - } - - @Test - public void testInsert() throws Exception { - HTableInterface table = utility.createTable(Bytes.toBytes("MyTest"), - Bytes.toBytes("CF")); - HBaseTestObj obj = new HBaseTestObj(); - obj.setRowKey("ROWKEY-1"); - obj.setData1("DATA-1"); - obj.setData2("DATA-2"); - MyHBaseDAO.insertRecord(table, obj); - Get get1 = new Get(Bytes.toBytes(obj.getRowKey())); - get1.addColumn(CF, CQ1); - Result result1 = table.get(get1); - assertEquals(Bytes.toString(result1.getRow()), obj.getRowKey()); - assertEquals(Bytes.toString(result1.value()), obj.getData1()); - Get get2 = new Get(Bytes.toBytes(obj.getRowKey())); - get2.addColumn(CF, CQ2); - Result result2 = table.get(get2); - assertEquals(Bytes.toString(result2.getRow()), obj.getRowKey()); - assertEquals(Bytes.toString(result2.value()), obj.getData2()); - } -} - - This code creates an HBase mini-cluster and starts it. Next, it creates a table called - MyTest with one column family, CF. A record is - inserted, a Get is performed from the same table, and the insertion is verified. - - Starting the mini-cluster takes about 20-30 seconds, but that should be - appropriate for integration testing. - - To use an HBase mini-cluster on Microsoft Windows, you need to use a Cygwin - environment. - See the paper at HBase Case-Study: Using HBaseTestingUtility for Local Testing and - Development (2010) for more information about HBaseTestingUtility. -
- - - - \ No newline at end of file diff --git a/src/main/docbkx/upgrading.xml b/src/main/docbkx/upgrading.xml deleted file mode 100644 index 5d71e0f2f1b..00000000000 --- a/src/main/docbkx/upgrading.xml +++ /dev/null @@ -1,833 +0,0 @@ - - - - Upgrading - - - You cannot skip major versions upgrading. If you are upgrading from version 0.90.x to - 0.94.x, you must first go from 0.90.x to 0.92.x and then go from 0.92.x to 0.94.x. - - It may be possible to skip across versions -- for example go from 0.92.2 straight to - 0.98.0 just following the 0.96.x upgrade instructions -- but we have not tried it so - cannot say whether it works or not. - - Review , in particular the section on Hadoop version. -
- HBase version number and compatibility - HBase has two versioning schemes, pre-1.0 and post-1.0. Both are detailed below. - -
- Post 1.0 versions - Starting with 1.0.0 release, HBase uses Semantic Versioning for it release versioning. - In summary: -
- - Given a version number MAJOR.MINOR.PATCH, increment the: - - MAJOR version when you make incompatible API changes, - MINOR version when you add functionality in a backwards-compatible manner, and - PATCH version when you make backwards-compatible bug fixes. - Additional labels for pre-release and build metadata are available as extensions to the MAJOR.MINOR.PATCH format. - - -
- - -
- Compatibility Dimensions - In addition to the usual API versioning considerations HBase has other compatibility dimensions that we need to consider. - -
- Client-Server wire protocol compatibility - - Allows updating client and server out of sync. - We could only allow upgrading the server first. I.e. the server would be backward compatible to an old client, that way new APIs are OK. - Example: A user should be able to use an old client to connect to an upgraded cluster. - -
-
- Server-Server protocol compatibility - - Servers of different versions can co-exist in the same cluster. - The wire protocol between servers is compatible. - Workers for distributed tasks, such as replication and log splitting, can co-exist in the same cluster. - Dependent protocols (such as using ZK for coordination) will also not be changed. - Example: A user can perform a rolling upgrade. - -
-
- File format compatibility - - Support file formats backward and forward compatible - Example: File, ZK encoding, directory layout is upgraded automatically as part of an HBase upgrade. User can rollback to the older version and everything will continue to work. - -
-
- Client API compatibility - - Allow changing or removing existing client APIs. - An API needs to deprecated for a major version before we will change/remove it. - Example: A user using a newly deprecated api does not need to modify application code with hbase api calls until the next major version. - -
-
- Client Binary compatibility - - Old client code can run unchanged (no recompilation needed) against new jars. - Example: Old compiled client code will work unchanged with the new jars. - -
-
- Server-Side Limited API compatibility (taken from Hadoop) - - Internal APIs are marked as Stable, Evolving, or Unstable - This implies binary compatibility for coprocessors and plugins (pluggable classes, including replication) as long as these are only using marked interfaces/classes. - Example: Old compiled Coprocessor, Filter, or Plugin code will work unchanged with the new jars. - -
-
- Dependency Compatibility - - An upgrade of HBase will not require an incompatible upgrade of a dependent project, including the Java runtime. - Example: An upgrade of Hadoop will not invalidate any of the compatibilities guarantees we made. - -
-
- Operational Compatibility - - Metric changes - Behavioral changes of services - Web page APIs - -
-
- Summary - - A patch upgrade is a drop-in replacement. Any change that is not Java binary compatible would not be allowed. - A minor upgrade requires no application/client code modification. Ideally it would be a drop-in replacement but client code, coprocessors, filters, etc might have to be recompiled if new jars are used. - A major upgrade allows the HBase community to make breaking changes. - -
-
- Compatibility Matrix <footnote><para>Note that this indicates what could break, not that it will break. We will/should add specifics in our release notes.</para></footnote> - (Y means we support the compatibility. N means we can break it.) - - Compatibility Matrix - - - - - - - - - Major - Minor - Patch - - - - - Client-Server wire Compatibility - N - Y - Y - - - Server-Server Compatibility - N - Y - Y - - - File Format Compatibility - NRunning an offline upgrade tool without rollback might be needed. We will typically only support migrating data from major version X to major version X+1. - - Y - Y - - - Client API Compatibility - N - Y - Y - - - Client Binary Compatibility - N - N - Y - - - Server-Side Limited API Compatibility - - - - - - Stable - N - Y - Y - - - Evolving - N - N - Y - - - Unstable - N - N - N - - - Dependency Compatibility - N - Y - Y - - - Operational Compatibility - N - N - Y - - - -
-
- -
- HBase API surface - HBase has a lot of API points, but for the compatibility matrix above, we differentiate between Client API, Limited Private API, and Private API. HBase uses a version of - Hadoop's Interface classification. HBase's Interface classification classes can be found here. - - InterfaceAudience: captures the intended audience, possible values are Public (for end users and external projects), LimitedPrivate (for other Projects, Coprocessors or other plugin points), and Private (for internal use). - InterfaceStability: describes what types of interface changes are permitted. Possible values are Stable, Evolving, Unstable, and Deprecated. - - - -
- HBase Client API - HBase Client API consists of all the classes or methods that are marked with InterfaceAudience.Public interface. All main classes in hbase-client and dependent modules have either InterfaceAudience.Public, InterfaceAudience.LimitedPrivate, or InterfaceAudience.Private marker. Not all classes in other modules (hbase-server, etc) have the marker. If a class is not annotated with one of these, it is assumed to be a InterfaceAudience.Private class. -
- -
- HBase LimitedPrivate API - LimitedPrivate annotation comes with a set of target consumers for the interfaces. Those consumers are coprocessors, phoenix, replication endpoint implemnetations or similar. At this point, HBase only guarantees source and binary compatibility for these interfaces between patch versions. -
- -
- HBase Private API - All classes annotated with InterfaceAudience.Private or all classes that do not have the annotation are for HBase internal use only. The interfaces and method signatures can change at any point in time. If you are relying on a particular interface that is marked Private, you should open a jira to propose changing the interface to be Public or LimitedPrivate, or an interface exposed for this purpose. -
- -
-
- -
- -
- Pre 1.0 versions - - - Before the semantic versioning scheme pre-1.0, HBase tracked either Hadoop's versions (0.2x) - or 0.9x versions. If you are into the arcane, checkout our old wiki page on HBase - Versioning which tries to connect the HBase version dots. Below sections cover ONLY the - releases before 1.0. -
- Odd/Even Versioning or "Development"" Series Releases - Ahead of big releases, we have been putting up preview versions to start the - feedback cycle turning-over earlier. These "Development" Series releases, always - odd-numbered, come with no guarantees, not even regards being able to upgrade - between two sequential releases (we reserve the right to break compatibility across - "Development" Series releases). Needless to say, these releases are not for - production deploys. They are a preview of what is coming in the hope that interested - parties will take the release for a test drive and flag us early if we there are - issues we've missed ahead of our rolling a production-worthy release. - Our first "Development" Series was the 0.89 set that came out ahead of HBase - 0.90.0. HBase 0.95 is another "Development" Series that portends HBase 0.96.0. - 0.99.x is the last series in "developer preview" mode before 1.0. Afterwards, - we will be using semantic versioning naming scheme (see above). - -
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- Binary Compatibility - When we say two HBase versions are compatible, we mean that the versions are wire - and binary compatible. Compatible HBase versions means that clients can talk to - compatible but differently versioned servers. It means too that you can just swap - out the jars of one version and replace them with the jars of another, compatible - version and all will just work. Unless otherwise specified, HBase point versions are - (mostly) binary compatible. You can safely do rolling upgrades between binary compatible - versions; i.e. across point versions: e.g. from 0.94.5 to 0.94.6. See Does - compatibility between versions also mean binary compatibility? - discussion on the hbaes dev mailing list. -
- - -
-
- <firstterm>Rolling Upgrades</firstterm> - A rolling upgrade is the process by which you update the servers - in your cluster a server at a time. You can rolling upgrade across HBase versions - if they are binary or wire compatible. - See for more on what this means. - Coarsely, a rolling upgrade is a graceful stop each server, - update the software, and then restart. You do this for each server in the cluster. - Usually you upgrade the Master first and then the regionservers. - See for tools that can help use the rolling upgrade process. - - For example, in the below, hbase was symlinked to the actual hbase install. - On upgrade, before running a rolling restart over the cluser, we changed the symlink - to point at the new HBase software version and then ran - $ HADOOP_HOME=~/hadoop-2.6.0-CRC-SNAPSHOT ~/hbase/bin/rolling-restart.sh --config ~/conf_hbase - The rolling-restart script will first gracefully stop and restart the master, and then - each of the regionservers in turn. Because the symlink was changed, on restart the - server will come up using the new hbase version. Check logs for errors as the - rolling upgrade proceeds. - -
- Rolling Upgrade between versions that are Binary/Wire compatibile - Unless otherwise specified, HBase point versions are binary compatible. You can do - a between hbase point versions. - For example, you can go to 0.94.6 from 0.94.5 by doing a rolling upgrade - across the cluster replacing the 0.94.5 binary with a 0.94.6 binary. - In the minor version-particular sections below, we call out where the versions - are wire/protocol compatible and in this case, it is also possible to do a - . For example, in - , we - state that it is possible to do a rolling upgrade between hbase-0.98.x and hbase-1.0.0. -
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-
-
- Upgrading from 0.98.x to 1.0.x - In this section we first note the significant changes that come in with 1.0.0 HBase and then - we go over the upgrade process. Be sure to read the significant changes section with care - so you avoid surprises. - -
- Changes of Note! - In here we list important changes that are in 1.0.0 since 0.98.x., changes you should - be aware that will go into effect once you upgrade. -
ZooKeeper 3.4 is required in HBase 1.0.0 - See . -
-
HBase Default Ports Changed - The ports used by HBase changed. The used to be in the 600XX range. In - hbase-1.0.0 they have been moved up out of the ephemeral port range and are - 160XX instead (Master web UI was 60010 and is now 16030; the RegionServer - web UI was 60030 and is now 16030, etc). If you want to keep the old port - locations, copy the port setting configs from hbase-default.xml - into hbase-site.xml, change them back to the old values - from hbase-0.98.x era, and ensure you've distributed your configurations before - you restart. -
-
- hbase.bucketcache.percentage.in.combinedcache configuration has been REMOVED - You may have made use of this configuration if you are using BucketCache. - If NOT using BucketCache, this change does not effect you. - Its removal means that your L1 LruBlockCache is now sized - using hfile.block.cache.size -- i.e. the way you - would size the onheap L1 LruBlockCache if you were NOT doing - BucketCache -- and the BucketCache size is not whatever the - setting for hbase.bucketcache.size is. You may need to adjust - configs to get the LruBlockCache and BucketCache sizes set to - what they were in 0.98.x and previous. If you did not set this - config., its default value was 0.9. If you do nothing, your - BucketCache will increase in size by 10%. Your L1 LruBlockCache will - become hfile.block.cache.size times your java - heap size (hfile.block.cache.size is a float between 0.0 and 1.0). - To read more, see - HBASE-11520 Simplify offheap cache config by removing the confusing "hbase.bucketcache.percentage.in.combinedcache". - -
-
If you have your own customer filters.... - See the release notes on the issue HBASE-12068 [Branch-1] Avoid need to always do KeyValueUtil#ensureKeyValue for Filter transformCell; - be sure to follow the recommendations therein. - -
-
Distributed Log Replay - - is off by default in hbase-1.0. - Enabling it can make a big difference improving HBase MTTR. Enable this - feature if you are doing a clean stop/start when you are upgrading. - You cannot rolling upgrade on to this feature (caveat if you are running - on a version of hbase in excess of hbase-0.98.4 -- see - HBASE-12577 Disable distributed log replay by default for more). - -
-
-
- Rolling upgrade from 0.98.x to HBase 1.0.0 - From 0.96.x to 1.0.0 - You cannot do a from 0.96.x to 1.0.0 without - first doing a rolling upgrade to 0.98.x. See comment in - HBASE-11164 Document and test rolling updates from 0.98 -> 1.0 for the why. - Also because hbase-1.0.0 enables hfilev3 by default, - HBASE-9801 Change the default HFile version to V3, - and support for hfilev3 only arrives in 0.98, this is another reason you cannot rolling upgrade from hbase-0.96.x; - if the rolling upgrade stalls, the 0.96.x servers cannot open files written by the servers running the newer hbase-1.0.0 - hfilev3 writing servers. - - - There are no known issues running a from hbase-0.98.x to hbase-1.0.0. - - -
-
- Upgrading to 1.0 from 0.94 - You cannot rolling upgrade from 0.94.x to 1.x.x. You must stop your cluster, - install the 1.x.x software, run the migration described at - (substituting 1.x.x. wherever we make mention of 0.96.x in the section below), - and then restart. Be sure to upgrade your zookeeper if it is a version less than the required 3.4.x. - - -
-
- -
- Upgrading from 0.96.x to 0.98.x - A rolling upgrade from 0.96.x to 0.98.x works. The two versions are not binary - compatible. - Additional steps are required to take advantage of some of the new features of 0.98.x, - including cell visibility labels, cell ACLs, and transparent server side encryption. See - the chapter of this guide for more information. Significant - performance improvements include a change to the write ahead log threading model that - provides higher transaction throughput under high load, reverse scanners, MapReduce over - snapshot files, and striped compaction. - Clients and servers can run with 0.98.x and 0.96.x versions. However, applications may - need to be recompiled due to changes in the Java API. -
-
- Upgrading from 0.94.x to 0.98.x - A rolling upgrade from 0.94.x directly to 0.98.x does not work. The upgrade path - follows the same procedures as . Additional steps are required to use some of the new - features of 0.98.x. See for an abbreviated list of these features. -
-
- Upgrading from 0.94.x to 0.96.x - The "Singularity" - HBase 0.96.x was EOL'd, September 1st, 2014 - Do not deploy 0.96.x Deploy a 0.98.x at least. - See EOL 0.96. - - - You will have to stop your old 0.94.x cluster completely to upgrade. If you are - replicating between clusters, both clusters will have to go down to upgrade. Make sure - it is a clean shutdown. The less WAL files around, the faster the upgrade will run (the - upgrade will split any log files it finds in the filesystem as part of the upgrade - process). All clients must be upgraded to 0.96 too. - The API has changed. You will need to recompile your code against 0.96 and you may - need to adjust applications to go against new APIs (TODO: List of changes). -
- Executing the 0.96 Upgrade - - HDFS and ZooKeeper must be up! - HDFS and ZooKeeper should be up and running during the upgrade process. - - hbase-0.96.0 comes with an upgrade script. Run - $ bin/hbase upgrade to see its usage. The script - has two main modes: -check, and -execute. -
- check - The check step is run against a running 0.94 cluster. Run - it from a downloaded 0.96.x binary. The check step is - looking for the presence of HFileV1 files. These are - unsupported in hbase-0.96.0. To purge them -- have them rewritten as HFileV2 -- - you must run a compaction. - The check step prints stats at the end of its run (grep - for “Result:” in the log) printing absolute path of the tables it scanned, any - HFileV1 files found, the regions containing said files (the regions we need to - major compact to purge the HFileV1s), and any corrupted files if any found. A - corrupt file is unreadable, and so is undefined (neither HFileV1 nor HFileV2). - To run the check step, run $ bin/hbase upgrade -check. Here - is sample output: - -Tables Processed: -hdfs://localhost:41020/myHBase/.META. -hdfs://localhost:41020/myHBase/usertable -hdfs://localhost:41020/myHBase/TestTable -hdfs://localhost:41020/myHBase/t - -Count of HFileV1: 2 -HFileV1: -hdfs://localhost:41020/myHBase/usertable /fa02dac1f38d03577bd0f7e666f12812/family/249450144068442524 -hdfs://localhost:41020/myHBase/usertable /ecdd3eaee2d2fcf8184ac025555bb2af/family/249450144068442512 - -Count of corrupted files: 1 -Corrupted Files: -hdfs://localhost:41020/myHBase/usertable/fa02dac1f38d03577bd0f7e666f12812/family/1 -Count of Regions with HFileV1: 2 -Regions to Major Compact: -hdfs://localhost:41020/myHBase/usertable/fa02dac1f38d03577bd0f7e666f12812 -hdfs://localhost:41020/myHBase/usertable/ecdd3eaee2d2fcf8184ac025555bb2af - -There are some HFileV1, or corrupt files (files with incorrect major version) - - In the above sample output, there are two HFileV1 in two regions, and one - corrupt file. Corrupt files should probably be removed. The regions that have - HFileV1s need to be major compacted. To major compact, start up the hbase shell - and review how to compact an individual region. After the major compaction is - done, rerun the check step and the HFileV1s shoudl be gone, replaced by HFileV2 - instances. - By default, the check step scans the hbase root directory (defined as - hbase.rootdir in the configuration). To scan a specific directory only, pass the - -dir option. - $ bin/hbase upgrade -check -dir /myHBase/testTable - The above command would detect HFileV1s in the /myHBase/testTable directory. - Once the check step reports all the HFileV1 files have been rewritten, it is - safe to proceed with the upgrade. -
-
- execute - After the check step shows the cluster is free of HFileV1, it is safe to - proceed with the upgrade. Next is the execute step. You - must SHUTDOWN YOUR 0.94.x CLUSTER before you can run the - execute step. The execute step will not run if it - detects running HBase masters or regionservers. - HDFS and ZooKeeper should be up and running during the upgrade - process. If zookeeper is managed by HBase, then you can start zookeeper - so it is available to the upgrade by running $ - ./hbase/bin/hbase-daemon.sh start zookeeper - - - - The execute upgrade step is made of three substeps. - - - Namespaces: HBase 0.96.0 has support for namespaces. The upgrade needs - to reorder directories in the filesystem for namespaces to work. - - - ZNodes: All znodes are purged so that new ones can be written in their - place using a new protobuf'ed format and a few are migrated in place: - e.g. replication and table state znodes - - - WAL Log Splitting: If the 0.94.x cluster shutdown was not clean, we'll - split WAL logs as part of migration before we startup on 0.96.0. This - WAL splitting runs slower than the native distributed WAL splitting - because it is all inside the single upgrade process (so try and get a - clean shutdown of the 0.94.0 cluster if you can). - - - To run the execute step, make sure that first you have - copied hbase-0.96.0 binaries everywhere under servers and under clients. Make - sure the 0.94.0 cluster is down. Then do as follows: - $ bin/hbase upgrade -execute - Here is some sample output. - -Starting Namespace upgrade -Created version file at hdfs://localhost:41020/myHBase with version=7 -Migrating table testTable to hdfs://localhost:41020/myHBase/.data/default/testTable -….. -Created version file at hdfs://localhost:41020/myHBase with version=8 -Successfully completed NameSpace upgrade. -Starting Znode upgrade -…. -Successfully completed Znode upgrade - -Starting Log splitting -… -Successfully completed Log splitting - - If the output from the execute step looks good, stop the zookeeper instance - you started to do the upgrade: - $ ./hbase/bin/hbase-daemon.sh stop zookeeper - Now start up hbase-0.96.0. -
-
- Troubleshooting -
- Old Client connecting to 0.96 cluster - It will fail with an exception like the below. Upgrade. - 17:22:15 Exception in thread "main" java.lang.IllegalArgumentException: Not a host:port pair: PBUF -17:22:15 * -17:22:15 api-compat-8.ent.cloudera.com �� ���( -17:22:15 at org.apache.hadoop.hbase.util.Addressing.parseHostname(Addressing.java:60) -17:22:15 at org.apache.hadoop.hbase.ServerName.&init>(ServerName.java:101) -17:22:15 at org.apache.hadoop.hbase.ServerName.parseVersionedServerName(ServerName.java:283) -17:22:15 at org.apache.hadoop.hbase.MasterAddressTracker.bytesToServerName(MasterAddressTracker.java:77) -17:22:15 at org.apache.hadoop.hbase.MasterAddressTracker.getMasterAddress(MasterAddressTracker.java:61) -17:22:15 at org.apache.hadoop.hbase.client.HConnectionManager$HConnectionImplementation.getMaster(HConnectionManager.java:703) -17:22:15 at org.apache.hadoop.hbase.client.HBaseAdmin.&init>(HBaseAdmin.java:126) -17:22:15 at Client_4_3_0.setup(Client_4_3_0.java:716) -17:22:15 at Client_4_3_0.main(Client_4_3_0.java:63) -
-
-
- Upgrading <code>META</code> to use Protocol Buffers (Protobuf) - When you upgrade from versions prior to 0.96, META needs to be - converted to use protocol buffers. This is controlled by the configuration - option , which is set to - true by default. Therefore, by default, no action is - required on your part. - The migration is a one-time event. However, every time your cluster starts, - META is scanned to ensure that it does not need to be - converted. If you have a very large number of regions, this scan can take a long - time. Starting in 0.98.5, you can set - to - false in hbase-site.xml, to disable - this start-up scan. This should be considered an expert-level setting. -
-
- - -
- -
- Upgrading from 0.92.x to 0.94.x - We used to think that 0.92 and 0.94 were interface compatible and that you can do a - rolling upgrade between these versions but then we figured that HBASE-5357 Use builder - pattern in HColumnDescriptor changed method signatures so rather than return - void they instead return HColumnDescriptor. This will throw - java.lang.NoSuchMethodError: org.apache.hadoop.hbase.HColumnDescriptor.setMaxVersions(I)V - .... so 0.92 and 0.94 are NOT compatible. You cannot do a rolling upgrade between them.
-
- Upgrading from 0.90.x to 0.92.x - Upgrade Guide - You will find that 0.92.0 runs a little differently to 0.90.x releases. Here are a few - things to watch out for upgrading from 0.90.x to 0.92.0. - - tl;dr - If you've not patience, here are the important things to know upgrading. - - Once you upgrade, you can’t go back. - - - MSLAB is on by default. Watch that heap usage if you have a lot of - regions. - - - Distributed Log Splitting is on by default. It should make region server - failover faster. - - - There’s a separate tarball for security. - - - If -XX:MaxDirectMemorySize is set in your hbase-env.sh, it’s going to - enable the experimental off-heap cache (You may not want this). - - - - - -
- You can’t go back! - To move to 0.92.0, all you need to do is shutdown your cluster, replace your hbase - 0.90.x with hbase 0.92.0 binaries (be sure you clear out all 0.90.x instances) and - restart (You cannot do a rolling restart from 0.90.x to 0.92.x -- you must restart). - On startup, the .META. table content is rewritten removing the - table schema from the info:regioninfo column. Also, any flushes - done post first startup will write out data in the new 0.92.0 file format, HFile V2. This - means you cannot go back to 0.90.x once you’ve started HBase 0.92.0 over your HBase - data directory. -
- -
- MSLAB is ON by default - In 0.92.0, the hbase.hregion.memstore.mslab.enabled - flag is set to true (See ). In 0.90.x it was false. When it is - enabled, memstores will step allocate memory in MSLAB 2MB chunks even if the - memstore has zero or just a few small elements. This is fine usually but if you had - lots of regions per regionserver in a 0.90.x cluster (and MSLAB was off), you may - find yourself OOME'ing on upgrade because the thousands of regions * number of - column families * 2MB MSLAB (at a minimum) puts your heap over the top. - Set hbase.hregion.memstore.mslab.enabled to - false or set the MSLAB size down from 2MB by setting - hbase.hregion.memstore.mslab.chunksize to something less. - -
- -
- Distributed Log Splitting is on by default - Previous, WAL logs on crash were split by the Master alone. In 0.92.0, log - splitting is done by the cluster (See See “HBASE-1364 [performance] Distributed - splitting of regionserver commit logs” or see the blog post - Apache HBase Log Splitting). - This should cut down significantly on the - amount of time it takes splitting logs and getting regions back online again. - -
- -
- Memory accounting is different now - In 0.92.0, indices and bloom filters take up residence in the same LRU - used caching blocks that come from the filesystem. In 0.90.x, the HFile v1 indices - lived outside of the LRU so they took up space even if the index was on a ‘cold’ - file, one that wasn’t being actively used. With the indices now in the LRU, you may - find you have less space for block caching. Adjust your block cache accordingly. See - the for more detail. The block size default size has been - changed in 0.92.0 from 0.2 (20 percent of heap) to 0.25. -
- - -
- On the Hadoop version to use - Run 0.92.0 on Hadoop 1.0.x (or CDH3u3 when it ships). The performance benefits are - worth making the move. Otherwise, our Hadoop prescription is as it has been; you - need an Hadoop that supports a working sync. See . - - If running on Hadoop 1.0.x (or CDH3u3), enable local read. See Practical - Caching presentation for ruminations on the performance benefits ‘going - local’ (and for how to enable local reads). -
-
- HBase 0.92.0 ships with ZooKeeper 3.4.2 - If you can, upgrade your zookeeper. If you can’t, 3.4.2 clients should work - against 3.3.X ensembles (HBase makes use of 3.4.2 API). -
-
- Online alter is off by default - In 0.92.0, we’ve added an experimental online schema alter facility (See ). Its off by default. Enable it - at your own risk. Online alter and splitting tables do not play well together so be - sure your cluster quiescent using this feature (for now). -
-
- WebUI - The webui has had a few additions made in 0.92.0. It now shows a list of the - regions currently transitioning, recent compactions/flushes, and a process list of - running processes (usually empty if all is well and requests are being handled - promptly). Other additions including requests by region, a debugging servlet dump, - etc. -
-
- Security tarball - We now ship with two tarballs; secure and insecure HBase. Documentation on how to - setup a secure HBase is on the way. -
- -
- Changes in HBase replication - 0.92.0 adds two new features: multi-slave and multi-master replication. The way to - enable this is the same as adding a new peer, so in order to have multi-master you - would just run add_peer for each cluster that acts as a master to the other slave - clusters. Collisions are handled at the timestamp level which may or may not be what - you want, this needs to be evaluated on a per use case basis. Replication is still - experimental in 0.92 and is disabled by default, run it at your own risk. -
- - -
- RegionServer now aborts if OOME - If an OOME, we now have the JVM kill -9 the regionserver process so it goes down - fast. Previous, a RegionServer might stick around after incurring an OOME limping - along in some wounded state. To disable this facility, and recommend you leave it in - place, you’d need to edit the bin/hbase file. Look for the addition of the - -XX:OnOutOfMemoryError="kill -9 %p" arguments (See [HBASE-4769] - ‘Abort - RegionServer Immediately on OOME’) -
- - -
- HFile V2 and the “Bigger, Fewer” Tendency - 0.92.0 stores data in a new format, . As HBase runs, it will move all your data from HFile v1 to - HFile v2 format. This auto-migration will run in the background as flushes and - compactions run. HFile V2 allows HBase run with larger regions/files. In fact, we - encourage that all HBasers going forward tend toward Facebook axiom #1, run with - larger, fewer regions. If you have lots of regions now -- more than 100s per host -- - you should look into setting your region size up after you move to 0.92.0 (In - 0.92.0, default size is now 1G, up from 256M), and then running online merge tool - (See “HBASE-1621 merge tool should work on online cluster, but disabled table”). - -
-
-
- Upgrading to HBase 0.90.x from 0.20.x or 0.89.x - This version of 0.90.x HBase can be started on data written by HBase 0.20.x or HBase - 0.89.x. There is no need of a migration step. HBase 0.89.x and 0.90.x does write out the - name of region directories differently -- it names them with a md5 hash of the region - name rather than a jenkins hash -- so this means that once started, there is no going - back to HBase 0.20.x. - Be sure to remove the hbase-default.xml from your - conf directory on upgrade. A 0.20.x version of this file will - have sub-optimal configurations for 0.90.x HBase. The - hbase-default.xml file is now bundled into the HBase jar and - read from there. If you would like to review the content of this file, see it in the src - tree at src/main/resources/hbase-default.xml or see . - Finally, if upgrading from 0.20.x, check your .META. schema in the - shell. In the past we would recommend that users run with a 16kb - MEMSTORE_FLUSHSIZE. Run hbase> scan '-ROOT-' in the - shell. This will output the current .META. schema. Check - MEMSTORE_FLUSHSIZE size. Is it 16kb (16384)? If so, you will need - to change this (The 'normal'/default value is 64MB (67108864)). Run the script - bin/set_meta_memstore_size.rb. This will make the necessary - edit to your .META. schema. Failure to run this change will make for - a slow cluster. See HBASE-3499 - Users upgrading to 0.90.0 need to have their .META. table updated with the - right MEMSTORE_SIZE - -
- diff --git a/src/main/docbkx/ycsb.xml b/src/main/docbkx/ycsb.xml deleted file mode 100644 index 695614c85ab..00000000000 --- a/src/main/docbkx/ycsb.xml +++ /dev/null @@ -1,36 +0,0 @@ - - - - YCSB - YCSB: The - Yahoo! Cloud Serving Benchmark and HBase - TODO: Describe how YCSB is poor for putting up a decent cluster load. - TODO: Describe setup of YCSB for HBase. In particular, presplit your tables before you - start a run. See HBASE-4163 Create Split Strategy for YCSB Benchmark for why and a little shell - command for how to do it. - Ted Dunning redid YCSB so it's mavenized and added facility for verifying workloads. See - Ted Dunning's YCSB. - - - diff --git a/src/main/docbkx/zookeeper.xml b/src/main/docbkx/zookeeper.xml deleted file mode 100644 index 206ccf54c38..00000000000 --- a/src/main/docbkx/zookeeper.xml +++ /dev/null @@ -1,513 +0,0 @@ - - - - - ZooKeeper<indexterm> - <primary>ZooKeeper</primary> - </indexterm> - - A distributed Apache HBase installation depends on a running ZooKeeper cluster. All - participating nodes and clients need to be able to access the running ZooKeeper ensemble. Apache - HBase by default manages a ZooKeeper "cluster" for you. It will start and stop the ZooKeeper - ensemble as part of the HBase start/stop process. You can also manage the ZooKeeper ensemble - independent of HBase and just point HBase at the cluster it should use. To toggle HBase - management of ZooKeeper, use the HBASE_MANAGES_ZK variable in - conf/hbase-env.sh. This variable, which defaults to - true, tells HBase whether to start/stop the ZooKeeper ensemble servers as - part of HBase start/stop. - - When HBase manages the ZooKeeper ensemble, you can specify ZooKeeper configuration using its - native zoo.cfg file, or, the easier option is to just specify ZooKeeper - options directly in conf/hbase-site.xml. A ZooKeeper configuration option - can be set as a property in the HBase hbase-site.xml XML configuration file - by prefacing the ZooKeeper option name with hbase.zookeeper.property. For - example, the clientPort setting in ZooKeeper can be changed by setting the - hbase.zookeeper.property.clientPort property. For all default values used - by HBase, including ZooKeeper configuration, see . Look for the - hbase.zookeeper.property prefix. For the full list of ZooKeeper configurations, see ZooKeeper's - zoo.cfg. HBase does not ship with a zoo.cfg so - you will need to browse the conf directory in an appropriate ZooKeeper - download. - - You must at least list the ensemble servers in hbase-site.xml using the - hbase.zookeeper.quorum property. This property defaults to a single - ensemble member at localhost which is not suitable for a fully distributed - HBase. (It binds to the local machine only and remote clients will not be able to connect). - - How many ZooKeepers should I run? - - You can run a ZooKeeper ensemble that comprises 1 node only but in production it is - recommended that you run a ZooKeeper ensemble of 3, 5 or 7 machines; the more members an - ensemble has, the more tolerant the ensemble is of host failures. Also, run an odd number of - machines. In ZooKeeper, an even number of peers is supported, but it is normally not used - because an even sized ensemble requires, proportionally, more peers to form a quorum than an - odd sized ensemble requires. For example, an ensemble with 4 peers requires 3 to form a - quorum, while an ensemble with 5 also requires 3 to form a quorum. Thus, an ensemble of 5 - allows 2 peers to fail, and thus is more fault tolerant than the ensemble of 4, which allows - only 1 down peer. - Give each ZooKeeper server around 1GB of RAM, and if possible, its own dedicated disk (A - dedicated disk is the best thing you can do to ensure a performant ZooKeeper ensemble). For - very heavily loaded clusters, run ZooKeeper servers on separate machines from RegionServers - (DataNodes and TaskTrackers). - - - For example, to have HBase manage a ZooKeeper quorum on nodes - rs{1,2,3,4,5}.example.com, bound to port 2222 (the default is 2181) - ensure HBASE_MANAGE_ZK is commented out or set to true in - conf/hbase-env.sh and then edit conf/hbase-site.xml - and set hbase.zookeeper.property.clientPort and - hbase.zookeeper.quorum. You should also set - hbase.zookeeper.property.dataDir to other than the default as the default - has ZooKeeper persist data under /tmp which is often cleared on system - restart. In the example below we have ZooKeeper persist to - /user/local/zookeeper. - - ... - - hbase.zookeeper.property.clientPort - 2222 - Property from ZooKeeper's config zoo.cfg. - The port at which the clients will connect. - - - - hbase.zookeeper.quorum - rs1.example.com,rs2.example.com,rs3.example.com,rs4.example.com,rs5.example.com - Comma separated list of servers in the ZooKeeper Quorum. - For example, "host1.mydomain.com,host2.mydomain.com,host3.mydomain.com". - By default this is set to localhost for local and pseudo-distributed modes - of operation. For a fully-distributed setup, this should be set to a full - list of ZooKeeper quorum servers. If HBASE_MANAGES_ZK is set in hbase-env.sh - this is the list of servers which we will start/stop ZooKeeper on. - - - - hbase.zookeeper.property.dataDir - /usr/local/zookeeper - Property from ZooKeeper's config zoo.cfg. - The directory where the snapshot is stored. - - - ... - ]]> - - What verion of ZooKeeper should I use? - The newer version, the better. For example, some folks have been bitten by ZOOKEEPER-1277. If - running zookeeper 3.5+, you can ask hbase to make use of the new multi operation by enabling " in your hbase-site.xml. - - - ZooKeeper Maintenance - Be sure to set up the data dir cleaner described under Zookeeper - Maintenance else you could have 'interesting' problems a couple of months in; i.e. - zookeeper could start dropping sessions if it has to run through a directory of hundreds of - thousands of logs which is wont to do around leader reelection time -- a process rare but run - on occasion whether because a machine is dropped or happens to hiccup. - - -
- Using existing ZooKeeper ensemble - - To point HBase at an existing ZooKeeper cluster, one that is not managed by HBase, set - HBASE_MANAGES_ZK in conf/hbase-env.sh to - false - - ... - # Tell HBase whether it should manage its own instance of Zookeeper or not. - export HBASE_MANAGES_ZK=false - Next set ensemble locations and client port, if non-standard, in - hbase-site.xml, or add a suitably configured - zoo.cfg to HBase's CLASSPATH. HBase will prefer - the configuration found in zoo.cfg over any settings in - hbase-site.xml. - - When HBase manages ZooKeeper, it will start/stop the ZooKeeper servers as a part of the - regular start/stop scripts. If you would like to run ZooKeeper yourself, independent of HBase - start/stop, you would do the following - - -${HBASE_HOME}/bin/hbase-daemons.sh {start,stop} zookeeper - - - Note that you can use HBase in this manner to spin up a ZooKeeper cluster, unrelated to - HBase. Just make sure to set HBASE_MANAGES_ZK to false - if you want it to stay up across HBase restarts so that when HBase shuts down, it doesn't take - ZooKeeper down with it. - - For more information about running a distinct ZooKeeper cluster, see the ZooKeeper Getting - Started Guide. Additionally, see the ZooKeeper Wiki or the ZooKeeper - documentation for more information on ZooKeeper sizing. -
- - -
- SASL Authentication with ZooKeeper - Newer releases of Apache HBase (>= 0.92) will support connecting to a ZooKeeper Quorum - that supports SASL authentication (which is available in Zookeeper versions 3.4.0 or - later). - - This describes how to set up HBase to mutually authenticate with a ZooKeeper Quorum. - ZooKeeper/HBase mutual authentication (HBASE-2418) is required - as part of a complete secure HBase configuration (HBASE-3025). For - simplicity of explication, this section ignores additional configuration required (Secure HDFS - and Coprocessor configuration). It's recommended to begin with an HBase-managed Zookeeper - configuration (as opposed to a standalone Zookeeper quorum) for ease of learning. - -
- Operating System Prerequisites - - You need to have a working Kerberos KDC setup. For each $HOST that will - run a ZooKeeper server, you should have a principle zookeeper/$HOST. For each - such host, add a service key (using the kadmin or kadmin.local - tool's ktadd command) for zookeeper/$HOST and copy this file to - $HOST, and make it readable only to the user that will run zookeeper on - $HOST. Note the location of this file, which we will use below as - $PATH_TO_ZOOKEEPER_KEYTAB. - - Similarly, for each $HOST that will run an HBase server (master or - regionserver), you should have a principle: hbase/$HOST. For each host, add a - keytab file called hbase.keytab containing a service key for - hbase/$HOST, copy this file to $HOST, and make it readable only - to the user that will run an HBase service on $HOST. Note the location of this - file, which we will use below as $PATH_TO_HBASE_KEYTAB. - - Each user who will be an HBase client should also be given a Kerberos principal. This - principal should usually have a password assigned to it (as opposed to, as with the HBase - servers, a keytab file) which only this user knows. The client's principal's - maxrenewlife should be set so that it can be renewed enough so that the user - can complete their HBase client processes. For example, if a user runs a long-running HBase - client process that takes at most 3 days, we might create this user's principal within - kadmin with: addprinc -maxrenewlife 3days. The Zookeeper client - and server libraries manage their own ticket refreshment by running threads that wake up - periodically to do the refreshment. - - On each host that will run an HBase client (e.g. hbase shell), add the - following file to the HBase home directory's conf directory: - - -Client { - com.sun.security.auth.module.Krb5LoginModule required - useKeyTab=false - useTicketCache=true; -}; - - - We'll refer to this JAAS configuration file as $CLIENT_CONF - below. -
-
- HBase-managed Zookeeper Configuration - - On each node that will run a zookeeper, a master, or a regionserver, create a JAAS - configuration file in the conf directory of the node's HBASE_HOME - directory that looks like the following: - - -Server { - com.sun.security.auth.module.Krb5LoginModule required - useKeyTab=true - keyTab="$PATH_TO_ZOOKEEPER_KEYTAB" - storeKey=true - useTicketCache=false - principal="zookeeper/$HOST"; -}; -Client { - com.sun.security.auth.module.Krb5LoginModule required - useKeyTab=true - useTicketCache=false - keyTab="$PATH_TO_HBASE_KEYTAB" - principal="hbase/$HOST"; -}; - - - where the $PATH_TO_HBASE_KEYTAB and - $PATH_TO_ZOOKEEPER_KEYTAB files are what you created above, and - $HOST is the hostname for that node. - - The Server section will be used by the Zookeeper quorum server, while the - Client section will be used by the HBase master and regionservers. The path - to this file should be substituted for the text $HBASE_SERVER_CONF in - the hbase-env.sh listing below. - - The path to this file should be substituted for the text - $CLIENT_CONF in the hbase-env.sh listing below. - - Modify your hbase-env.sh to include the following: - - -export HBASE_OPTS="-Djava.security.auth.login.config=$CLIENT_CONF" -export HBASE_MANAGES_ZK=true -export HBASE_ZOOKEEPER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF" -export HBASE_MASTER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF" -export HBASE_REGIONSERVER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF" - - - where $HBASE_SERVER_CONF and $CLIENT_CONF are - the full paths to the JAAS configuration files created above. - - Modify your hbase-site.xml on each node that will run zookeeper, - master or regionserver to contain: - - - - hbase.zookeeper.quorum - $ZK_NODES - - - hbase.cluster.distributed - true - - - hbase.zookeeper.property.authProvider.1 - org.apache.zookeeper.server.auth.SASLAuthenticationProvider - - - hbase.zookeeper.property.kerberos.removeHostFromPrincipal - true - - - hbase.zookeeper.property.kerberos.removeRealmFromPrincipal - true - - - ]]> - - where $ZK_NODES is the comma-separated list of hostnames of the Zookeeper - Quorum hosts. - - Start your hbase cluster by running one or more of the following set of commands on the - appropriate hosts: - - -bin/hbase zookeeper start -bin/hbase master start -bin/hbase regionserver start - - -
- -
- External Zookeeper Configuration - Add a JAAS configuration file that looks like: - -Client { - com.sun.security.auth.module.Krb5LoginModule required - useKeyTab=true - useTicketCache=false - keyTab="$PATH_TO_HBASE_KEYTAB" - principal="hbase/$HOST"; -}; - - where the $PATH_TO_HBASE_KEYTAB is the keytab created above for - HBase services to run on this host, and $HOST is the hostname for that node. - Put this in the HBase home's configuration directory. We'll refer to this file's full - pathname as $HBASE_SERVER_CONF below. - - Modify your hbase-env.sh to include the following: - - -export HBASE_OPTS="-Djava.security.auth.login.config=$CLIENT_CONF" -export HBASE_MANAGES_ZK=false -export HBASE_MASTER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF" -export HBASE_REGIONSERVER_OPTS="-Djava.security.auth.login.config=$HBASE_SERVER_CONF" - - - - Modify your hbase-site.xml on each node that will run a master or - regionserver to contain: - - - - hbase.zookeeper.quorum - $ZK_NODES - - - hbase.cluster.distributed - true - - - ]]> - - - where $ZK_NODES is the comma-separated list of hostnames of the Zookeeper - Quorum hosts. - - Add a zoo.cfg for each Zookeeper Quorum host containing: - -authProvider.1=org.apache.zookeeper.server.auth.SASLAuthenticationProvider -kerberos.removeHostFromPrincipal=true -kerberos.removeRealmFromPrincipal=true - - Also on each of these hosts, create a JAAS configuration file containing: - -Server { - com.sun.security.auth.module.Krb5LoginModule required - useKeyTab=true - keyTab="$PATH_TO_ZOOKEEPER_KEYTAB" - storeKey=true - useTicketCache=false - principal="zookeeper/$HOST"; -}; - - where $HOST is the hostname of each Quorum host. We will refer to the full - pathname of this file as $ZK_SERVER_CONF below. - - Start your Zookeepers on each Zookeeper Quorum host with: - -SERVER_JVMFLAGS="-Djava.security.auth.login.config=$ZK_SERVER_CONF" bin/zkServer start - - - Start your HBase cluster by running one or more of the following set of commands on the - appropriate nodes: - - -bin/hbase master start -bin/hbase regionserver start - - - -
- -
- Zookeeper Server Authentication Log Output - If the configuration above is successful, you should see something similar to the - following in your Zookeeper server logs: - -11/12/05 22:43:39 INFO zookeeper.Login: successfully logged in. -11/12/05 22:43:39 INFO server.NIOServerCnxnFactory: binding to port 0.0.0.0/0.0.0.0:2181 -11/12/05 22:43:39 INFO zookeeper.Login: TGT refresh thread started. -11/12/05 22:43:39 INFO zookeeper.Login: TGT valid starting at: Mon Dec 05 22:43:39 UTC 2011 -11/12/05 22:43:39 INFO zookeeper.Login: TGT expires: Tue Dec 06 22:43:39 UTC 2011 -11/12/05 22:43:39 INFO zookeeper.Login: TGT refresh sleeping until: Tue Dec 06 18:36:42 UTC 2011 -.. -11/12/05 22:43:59 INFO auth.SaslServerCallbackHandler: - Successfully authenticated client: authenticationID=hbase/ip-10-166-175-249.us-west-1.compute.internal@HADOOP.LOCALDOMAIN; - authorizationID=hbase/ip-10-166-175-249.us-west-1.compute.internal@HADOOP.LOCALDOMAIN. -11/12/05 22:43:59 INFO auth.SaslServerCallbackHandler: Setting authorizedID: hbase -11/12/05 22:43:59 INFO server.ZooKeeperServer: adding SASL authorization for authorizationID: hbase - - -
- -
- Zookeeper Client Authentication Log Output - On the Zookeeper client side (HBase master or regionserver), you should see something - similar to the following: - -11/12/05 22:43:59 INFO zookeeper.ZooKeeper: Initiating client connection, connectString=ip-10-166-175-249.us-west-1.compute.internal:2181 sessionTimeout=180000 watcher=master:60000 -11/12/05 22:43:59 INFO zookeeper.ClientCnxn: Opening socket connection to server /10.166.175.249:2181 -11/12/05 22:43:59 INFO zookeeper.RecoverableZooKeeper: The identifier of this process is 14851@ip-10-166-175-249 -11/12/05 22:43:59 INFO zookeeper.Login: successfully logged in. -11/12/05 22:43:59 INFO client.ZooKeeperSaslClient: Client will use GSSAPI as SASL mechanism. -11/12/05 22:43:59 INFO zookeeper.Login: TGT refresh thread started. -11/12/05 22:43:59 INFO zookeeper.ClientCnxn: Socket connection established to ip-10-166-175-249.us-west-1.compute.internal/10.166.175.249:2181, initiating session -11/12/05 22:43:59 INFO zookeeper.Login: TGT valid starting at: Mon Dec 05 22:43:59 UTC 2011 -11/12/05 22:43:59 INFO zookeeper.Login: TGT expires: Tue Dec 06 22:43:59 UTC 2011 -11/12/05 22:43:59 INFO zookeeper.Login: TGT refresh sleeping until: Tue Dec 06 18:30:37 UTC 2011 -11/12/05 22:43:59 INFO zookeeper.ClientCnxn: Session establishment complete on server ip-10-166-175-249.us-west-1.compute.internal/10.166.175.249:2181, sessionid = 0x134106594320000, negotiated timeout = 180000 - -
- -
- Configuration from Scratch - - This has been tested on the current standard Amazon Linux AMI. First setup KDC and - principals as described above. Next checkout code and run a sanity check. - - -git clone git://git.apache.org/hbase.git -cd hbase -mvn clean test -Dtest=TestZooKeeperACL - - - Then configure HBase as described above. Manually edit target/cached_classpath.txt (see - below): - -bin/hbase zookeeper & -bin/hbase master & -bin/hbase regionserver & - -
- - -
- Future improvements - -
- Fix target/cached_classpath.txt - You must override the standard hadoop-core jar file from the - target/cached_classpath.txt file with the version containing the - HADOOP-7070 fix. You can use the following script to do this: - -echo `find ~/.m2 -name "*hadoop-core*7070*SNAPSHOT.jar"` ':' `cat target/cached_classpath.txt` | sed 's/ //g' > target/tmp.txt -mv target/tmp.txt target/cached_classpath.txt - -
- -
- Set JAAS configuration programmatically - - - This would avoid the need for a separate Hadoop jar that fixes HADOOP-7070. - -
- -
- Elimination of <code>kerberos.removeHostFromPrincipal</code> and - <code>kerberos.removeRealmFromPrincipal</code> - -
- -
- - -
- - - - - - diff --git a/src/main/xslt/configuration_to_docbook_section.xsl b/src/main/xslt/configuration_to_docbook_section.xsl deleted file mode 100644 index 57219abe935..00000000000 --- a/src/main/xslt/configuration_to_docbook_section.xsl +++ /dev/null @@ -1,66 +0,0 @@ - - - - - - -HBase Default Configuration - - -The documentation below is generated using the default hbase configuration file, -hbase-default.xml, as source. - - - - - - - - - - - - - - - Default - - - - - - - - - - -