hbase-4939 book.xml (architecture/faq), troubleshooting.xml (created resources section)
git-svn-id: https://svn.apache.org/repos/asf/hbase/trunk@1209688 13f79535-47bb-0310-9956-ffa450edef68
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Doug Meil
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@ -1200,6 +1200,63 @@ if (!b) {
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<chapter xml:id="architecture">
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<title>Architecture</title>
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<section xml:id="arch.overview">
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<title>Overview</title>
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<section xml:id="arch.overview.nosql">
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<title>NoSQL?</title>
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<para>HBase is a type of "NoSQL" database. "NoSQL" is a general term meaning that the database isn't an RDBMS which
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supports SQL as it's primary access language, but there are many types of NoSQL databases: BerkeleyDB is an
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example of a local NoSQL database, whereas HBase is very much a distributed database. Technically speaking,
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HBase is really more a "Data Store" than "Data Base" because it lacks many of the features you find in an RDBMS,
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such as typed columns, secondary indexes, triggers, and advanced query languages, etc.
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</para>
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<para>However, HBase has many features which supports both linear and modular scaling. HBase clusters expand
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by adding RegionServers that are hosted on commodity class servers. If a cluster expands from 10 to 20
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RegionServers, for example, it doubles both in terms of storage and as well as processing capacity.
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RDBMS can scale well, but only up to a point - specifically, the size of a single database server - and for the best
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performance requires specialized hardware and storage devices. HBase features of note are:
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<itemizedlist>
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<listitem>Strongly consistent reads/writes: HBase is not an "eventually consistent" DataStore. This
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makes it very suitable for tasks such as high-speed counter aggregation. </listitem>
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<listitem>Automatic sharding: HBase tables are distributed on the cluster via regions, and regions are
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automatically split and re-distributed as your data grows.</listitem>
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<listitem>Automatic RegionServer failover</listitem>
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<listitem>Hadoop/HDFS Integration: HBase supports HDFS out of the box as it's distributed file system.</listitem>
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<listitem>MapReduce: HBase supports massively parallelized processing via MapReduce for using HBase as both
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source and sink.</listitem>
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<listitem>Java Client API: HBase supports an easy to use Java API for programmatic access.</listitem>
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<listitem>Thrift/REST API: HBase also supports Thrift and REST for non-Java front-ends.</listitem>
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<listitem>Block Cache and Bloom Filters: HBase supports a Block Cache and Bloom Filters for high volume query optimization.</listitem>
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<listitem>Operational Management: HBase provides build-in web-pages for operational insight as well as JMX metrics.</listitem>
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</itemizedlist>
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</para>
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</section>
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<section xml:id="arch.overview.when">
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<title>When Should I Use HBase?</title>
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<para>First, make sure you have enough data. HBase isn't suitable for every problem. If you have
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hundreds of millions or billions of rows, then HBase is a good candidate. If you only have a few
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thousand/million rows, then using a traditional RDBMS might be a better choice due to the
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fact that all of your data might wind up on a single node (or two) and the rest of the cluster may
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be sitting idle.
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</para>
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<para>Second, make sure you have enough hardware. Even HDFS doesn't do well with anything less than
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5 DataNodes (due to things such as HDFS block replication which has a default of 3), plus a NameNode.
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</para>
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<para>HBase can run quite well stand-alone on a laptop - but this should be considered a development
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configuration only.
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</para>
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</section>
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<section xml:id="arch.overview.hbasehdfs">
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<title>What Is The Difference Between HBase and Hadoop/HDFS?</title>
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<para><link xlink:href="http://hadoop.apache.org/hdfs/">HDFS</link> is a distributed file system that is well suited for the storage of large files.
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It's documentation states that it is not, however, a general purpose file system, and does not provide fast individual record lookups in files.
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HBase, on the other hand, is built on top of HDFS and provides fast record lookups (and updates) for large tables.
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This can sometimes be a point of conceptual confusion. HBase internally puts your data in indexed "StoreFiles" that exist
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on HDFS for high-speed lookups. See the <xref linkend="datamodel" /> and the rest of this chapter for more information on how HBase achieves its goals.
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</para>
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</section>
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</section>
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<section xml:id="arch.catalog">
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<title>Catalog Tables</title>
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@ -2000,17 +2057,7 @@ hbase> describe 't1'</programlisting>
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<qandaentry>
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<question><para>When should I use HBase?</para></question>
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<answer>
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<para>
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Anybody can download and give HBase a spin, even on a laptop. The scope of this answer is when
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would it be best to use HBase in a <emphasis>real</emphasis> deployment.
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</para>
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<para>First, make sure you have enough hardware. Even HDFS doesn't do well with anything less than
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5 DataNodes (due to things such as HDFS block replication which has a default of 3), plus a NameNode.
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Second, make sure you have enough data. HBase isn't suitable for every problem. If you have
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hundreds of millions or billions of rows, then HBase is a good candidate. If you only have a few
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thousand/million rows, then using a traditional RDBMS might be a better choice due to the
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fact that all of your data might wind up on a single node (or two) and the rest of the cluster may
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be sitting idle.
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<para>See the <xref linkend="arch.overview" /> in the Architecture chapter.
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</para>
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</answer>
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</qandaentry>
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@ -2031,17 +2078,6 @@ hbase> describe 't1'</programlisting>
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</para>
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</answer>
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</qandaentry>
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<qandaentry xml:id="faq.hdfs.hbase">
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<question><para>How does HBase work on top of HDFS?</para></question>
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<answer>
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<para>
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<link xlink:href="http://hadoop.apache.org/hdfs/">HDFS</link> is a distributed file system that is well suited for the storage of large files. It's documentation
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states that it is not, however, a general purpose file system, and does not provide fast individual record lookups in files.
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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.
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See the <xref linkend="datamodel" /> and <xref linkend="architecture" /> sections for more information on how HBase achieves its goals.
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</para>
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</answer>
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</qandaentry>
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</qandadiv>
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<qandadiv xml:id="faq.config"><title>Configuration</title>
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<qandaentry xml:id="faq.config.started">
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@ -2109,6 +2145,16 @@ hbase> describe 't1'</programlisting>
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</answer>
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</qandaentry>
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</qandadiv>
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<qandadiv xml:id="faq.mapreduce"><title>MapReduce</title>
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<qandaentry xml:id="faq.mapreduce.use">
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<question><para>How can I use MapReduce with HBase?</para></question>
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<answer>
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<para>
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See <xref linkend="mapreduce" />
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</para>
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</answer>
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</qandaentry>
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</qandadiv>
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<qandadiv><title>Performance and Troubleshooting</title>
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<qandaentry>
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<question><para>
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@ -196,6 +196,28 @@ export HBASE_OPTS="-XX:NewSize=64m -XX:MaxNewSize=64m <cms options from above
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</para>
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</section>
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</section>
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<section xml:id="trouble.resources">
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<title>Resources</title>
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<section xml:id="trouble.resources.lists">
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<title>Dist-Lists</title>
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<para>Sign up for the <link xlink:href="http://hbase.apache.org/mail-lists.html">HBase Dist-Lists</link> and post a question. 'Dev' is aimed at the
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community of developers actually building HBase and for features currently under development, and 'User' for generally used for questions on released
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versions of HBase.
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</para>
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</section>
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<section xml:id="trouble.resources.searchhadoop">
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<title>search-hadoop.com</title>
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<para>
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<link xlink:href="http://search-hadoop.com">search-hadoop.com</link> indexes all the mailing lists and is great for historical searches.
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</para>
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</section>
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<section xml:id="trouble.resources.jira">
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<title>JIRA</title>
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<para>
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<link xlink:href="https://issues.apache.org/jira/browse/HBASE">JIRA</link> is also really helpful when looking for Hadoop/HBase-specific issues.
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</para>
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</section>
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</section>
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<section xml:id="trouble.tools">
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<title>Tools</title>
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<section xml:id="trouble.tools.builtin">
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@ -221,12 +243,6 @@ export HBASE_OPTS="-XX:NewSize=64m -XX:MaxNewSize=64m <cms options from above
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</section>
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<section xml:id="trouble.tools.external">
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<title>External Tools</title>
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<section xml:id="trouble.tools.searchhadoop">
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<title>search-hadoop.com</title>
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<para>
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<link xlink:href="http://search-hadoop.com">search-hadoop.com</link> indexes all the mailing lists and <link xlink:href="https://issues.apache.org/jira/browse/HBASE">JIRA</link>, it’s really helpful when looking for Hadoop/HBase-specific issues.
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</para>
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</section>
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<section xml:id="trouble.tools.tail">
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<title>tail</title>
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<para>
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