HADOOP-6142. Update documentation and use of harchives for relative paths added
in MAPREDUCE-739. Contributed by Mahadev Konar git-svn-id: https://svn.apache.org/repos/asf/hadoop/common/trunk@794943 13f79535-47bb-0310-9956-ffa450edef68
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@ -472,6 +472,9 @@ Trunk (unreleased changes)
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HADOOP-6099. The RPC module can be configured to not send period pings.
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The default behaviour of sending periodic pings remain unchanged. (dhruba)
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HADOOP-6142. Update documentation and use of harchives for relative paths
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added in MAPREDUCE-739. (Mahadev Konar via cdouglas)
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OPTIMIZATIONS
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HADOOP-5595. NameNode does not need to run a replicator to choose a
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@ -29,7 +29,7 @@ function print_usage(){
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echo " version print the version"
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echo " jar <jar> run a jar file"
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echo " distcp <srcurl> <desturl> copy file or directories recursively"
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echo " archive -archiveName NAME <src>* <dest> create a hadoop archive"
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echo " archive -archiveName NAME -p <parent path> <src>* <dest> create a hadoop archive"
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echo " classpath prints the class path needed to get the"
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echo " Hadoop jar and the required libraries"
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echo " daemonlog get/set the log level for each daemon"
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@ -32,26 +32,25 @@
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within the part files.
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</p>
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</section>
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<section>
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<title> How to create an archive? </title>
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<p>
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<code>Usage: hadoop archive -archiveName name <src>* <dest></code>
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<code>Usage: hadoop archive -archiveName name -p <parent> <src>* <dest></code>
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</p>
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<p>
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-archiveName is the name of the archive you would like to create.
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An example would be foo.har. The name should have a *.har extension.
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The inputs are file system pathnames which work as usual with regular
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expressions. The destination directory would contain the archive.
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The parent argument is to specify the relative path to which the files should be
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archived to. Example would be :
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</p><p><code> -p /foo/bar a/b/c e/f/g </code></p><p>
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Here /foo/bar is the parent path and a/b/c, e/f/g are relative paths to parent.
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Note that this is a Map/Reduce job that creates the archives. You would
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need a map reduce cluster to run this. The following is an example:</p>
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<p>
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<code>hadoop archive -archiveName foo.har /user/hadoop/dir1 /user/hadoop/dir2 /user/zoo/</code>
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</p><p>
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In the above example /user/hadoop/dir1 and /user/hadoop/dir2 will be
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archived in the following file system directory -- /user/zoo/foo.har.
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The sources are not changed or removed when an archive is created.
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</p>
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need a map reduce cluster to run this. For a detailed example the later sections. </p>
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<p> If you just want to archive a single directory /foo/bar then you can just use </p>
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<p><code> hadoop archive -archiveName zoo.har -p /foo/bar /outputdir </code></p>
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</section>
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<section>
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<title> How to look up files in archives? </title>
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<p>
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@ -61,20 +60,58 @@
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an error. URI for Hadoop Archives is
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</p><p><code>har://scheme-hostname:port/archivepath/fileinarchive</code></p><p>
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If no scheme is provided it assumes the underlying filesystem.
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In that case the URI would look like
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</p><p><code>
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har:///archivepath/fileinarchive</code></p>
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<p>
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Here is an example of archive. The input to the archives is /dir. The directory dir contains
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files filea, fileb. To archive /dir to /user/hadoop/foo.har, the command is
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</p>
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<p><code>hadoop archive -archiveName foo.har /dir /user/hadoop</code>
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</p><p>
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To get file listing for files in the created archive
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</p>
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<p><code>hadoop dfs -lsr har:///user/hadoop/foo.har</code></p>
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<p>To cat filea in archive -
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</p><p><code>hadoop dfs -cat har:///user/hadoop/foo.har/dir/filea</code></p>
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In that case the URI would look like </p>
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<p><code>har:///archivepath/fileinarchive</code></p>
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</section>
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</body>
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<section>
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<title> Example on creating and looking up archives </title>
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<p><code>hadoop archive -archiveName foo.har -p /user/hadoop dir1 dir2 /user/zoo </code></p>
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<p>
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The above example is creating an archive using /user/hadoop as the relative archive directory.
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The directories /user/hadoop/dir1 and /user/hadoop/dir2 will be
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archived in the following file system directory -- /user/zoo/foo.har. Archiving does not delete the input
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files. If you want to delete the input files after creating the archives (to reduce namespace), you
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will have to do it on your own.
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</p>
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<section>
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<title> Looking up files and understanding the -p option </title>
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<p> Looking up files in hadoop archives is as easy as doing an ls on the filesystem. After you have
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archived the directories /user/hadoop/dir1 and /user/hadoop/dir2 as in the exmaple above, to see all
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the files in the archives you can just run: </p>
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<p><code>hadoop dfs -lsr har:///user/zoo/foo.har/</code></p>
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<p> To understand the significance of the -p argument, lets go through the above example again. If you just do
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an ls (not lsr) on the hadoop archive using </p>
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<p><code>hadoop dfs -ls har:///user/zoo/foo.har</code></p>
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<p>The output should be:</p>
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<source>
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har:///user/zoo/foo.har/dir1
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har:///user/zoo/foo.har/dir2
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</source>
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<p> As you can recall the archives were created with the following command </p>
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<p><code>hadoop archive -archiveName foo.har -p /user/hadoop dir1 dir2 /user/zoo </code></p>
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<p> If we were to change the command to: </p>
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<p><code>hadoop archive -archiveName foo.har -p /user/ hadoop/dir1 hadoop/dir2 /user/zoo </code></p>
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<p> then a ls on the hadoop archive using </p>
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<p><code>hadoop dfs -ls har:///user/zoo/foo.har</code></p>
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<p>would give you</p>
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<source>
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har:///user/zoo/foo.har/hadoop/dir1
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har:///user/zoo/foo.har/hadoop/dir2
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</source>
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<p>
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Notice that the archived files have been archived relative to /user/ rather than /user/hadoop.
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</p>
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</section>
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</section>
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<section>
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<title> Using Hadoop Archives with Map Reduce </title>
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<p>Using Hadoop Archives in Map Reduce is as easy as specifying a different input filesystem than the default file system.
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If you have a hadoop archive stored in HDFS in /user/zoo/foo.har then for using this archive for Map Reduce input, all
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you need to specify the input directory as har:///user/zoo/foo.har. Since Hadoop Archives is exposed as a file system
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Map Reduce will be able to use all the logical input files in Hadoop Archives as input.</p>
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</section>
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</body>
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</document>
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