HBASE-3001 Ship dependency jars to the cluster for all jobs
git-svn-id: https://svn.apache.org/repos/asf/hbase/trunk@1000261 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Michael Stack
parent
a46c74894f
commit
17da1d0369
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@ -926,6 +926,7 @@ Release 0.21.0 - Unreleased
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HBASE-2782 QOS for META table access
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HBASE-3017 More log pruning
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HBASE-3022 Change format of enum messages in o.a.h.h.executor package
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HBASE-3001 Ship dependency jars to the cluster for all jobs
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NEW FEATURES
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HBASE-1961 HBase EC2 scripts
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@ -29,6 +29,10 @@ import org.apache.hadoop.io.Writable;
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import org.apache.hadoop.io.WritableComparable;
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import org.apache.hadoop.mapred.FileInputFormat;
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import org.apache.hadoop.mapred.JobConf;
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import org.apache.hadoop.mapred.InputFormat;
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import org.apache.hadoop.mapred.OutputFormat;
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import org.apache.hadoop.mapred.TextInputFormat;
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import org.apache.hadoop.mapred.TextOutputFormat;
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/**
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* Utility for {@link TableMap} and {@link TableReduce}
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@ -59,6 +63,11 @@ public class TableMapReduceUtil {
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job.setMapperClass(mapper);
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FileInputFormat.addInputPaths(job, table);
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job.set(TableInputFormat.COLUMN_LIST, columns);
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try {
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addDependencyJars(job);
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} catch (IOException e) {
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e.printStackTrace();
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}
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}
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/**
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@ -105,6 +114,7 @@ public class TableMapReduceUtil {
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} else if (partitioner != null) {
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job.setPartitionerClass(partitioner);
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}
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addDependencyJars(job);
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}
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/**
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@ -181,4 +191,22 @@ public class TableMapReduceUtil {
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public static void setScannerCaching(JobConf job, int batchSize) {
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job.setInt("hbase.client.scanner.caching", batchSize);
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}
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/**
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* @see org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil#addDependencyJars(Job)
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*/
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public static void addDependencyJars(JobConf job) throws IOException {
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org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil.addDependencyJars(
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job,
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org.apache.zookeeper.ZooKeeper.class,
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com.google.common.base.Function.class,
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job.getMapOutputKeyClass(),
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job.getMapOutputValueClass(),
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job.getOutputKeyClass(),
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job.getOutputValueClass(),
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job.getPartitionerClass(),
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job.getClass("mapred.input.format.class", TextInputFormat.class, InputFormat.class),
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job.getClass("mapred.output.format.class", TextOutputFormat.class, OutputFormat.class),
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job.getCombinerClass());
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}
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}
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@ -47,9 +47,6 @@ import org.apache.hadoop.io.WritableComparable;
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import org.apache.hadoop.mapreduce.Job;
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import org.apache.hadoop.util.StringUtils;
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import org.apache.hadoop.conf.Configuration;
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import org.apache.zookeeper.ZooKeeper;
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import com.google.common.base.Function;
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/**
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* Utility for {@link TableMapper} and {@link TableReducer}
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@ -81,6 +78,7 @@ public class TableMapReduceUtil {
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job.getConfiguration().set(TableInputFormat.INPUT_TABLE, table);
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job.getConfiguration().set(TableInputFormat.SCAN,
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convertScanToString(scan));
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addDependencyJars(job);
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}
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/**
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@ -192,6 +190,7 @@ public class TableMapReduceUtil {
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} else if (partitioner != null) {
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job.setPartitionerClass(partitioner);
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}
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addDependencyJars(job);
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}
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/**
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@ -246,10 +245,11 @@ public class TableMapReduceUtil {
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public static void addDependencyJars(Job job) throws IOException {
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try {
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addDependencyJars(job.getConfiguration(),
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ZooKeeper.class,
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Function.class, // Guava collections
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org.apache.zookeeper.ZooKeeper.class,
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com.google.common.base.Function.class,
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job.getMapOutputKeyClass(),
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job.getMapOutputValueClass(),
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job.getInputFormatClass(),
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job.getOutputKeyClass(),
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job.getOutputValueClass(),
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job.getOutputFormatClass(),
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@ -271,32 +271,32 @@ public class TableMapReduceUtil {
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FileSystem localFs = FileSystem.getLocal(conf);
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Set<String> jars = new HashSet<String>();
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// Add jars that are already in the tmpjars variable
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jars.addAll( conf.getStringCollection("tmpjars") );
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// Add jars containing the specified classes
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for (Class clazz : classes) {
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if (clazz == null) continue;
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String pathStr = findContainingJar(clazz);
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if (pathStr == null) {
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LOG.warn("Could not find jar for class " + clazz +
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" in order to ship it to the cluster.");
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" in order to ship it to the cluster.");
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continue;
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}
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Path path = new Path(pathStr);
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if (!localFs.exists(path)) {
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LOG.warn("Could not validate jar file " + path + " for class "
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+ clazz);
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+ clazz);
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continue;
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}
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jars.add(path.makeQualified(localFs).toString());
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}
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if (jars.isEmpty()) return;
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String tmpJars = conf.get("tmpjars");
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if (tmpJars == null) {
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tmpJars = StringUtils.arrayToString(jars.toArray(new String[0]));
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} else {
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tmpJars += "," + StringUtils.arrayToString(jars.toArray(new String[0]));
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}
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conf.set("tmpjars", tmpJars);
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conf.set("tmpjars",
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StringUtils.arrayToString(jars.toArray(new String[0])));
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}
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/**
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@ -1,5 +1,5 @@
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/*
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* Copyright 2008 The Apache Software Foundation
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* Copyright 20010 The Apache Software Foundation
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*
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* Licensed to the Apache Software Foundation (ASF) under one
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* or more contributor license agreements. See the NOTICE file
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@ -24,6 +24,7 @@ Input/OutputFormats, a table indexing MapReduce job, and utility
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<h2>Table of Contents</h2>
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<ul>
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<li><a href="#classpath">HBase, MapReduce and the CLASSPATH</a></li>
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<li><a href="#driver">Bundled HBase MapReduce Jobs</a></li>
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<li><a href="#sink">HBase as MapReduce job data source and sink</a></li>
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<li><a href="#bulk">Bulk Import writing HFiles directly</a></li>
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<li><a href="#examples">Example Code</a></li>
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@ -33,36 +34,67 @@ Input/OutputFormats, a table indexing MapReduce job, and utility
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<p>MapReduce jobs deployed to a MapReduce cluster do not by default have access
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to the HBase configuration under <code>$HBASE_CONF_DIR</code> nor to HBase classes.
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You could add <code>hbase-site.xml</code> to $HADOOP_HOME/conf and add
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hbase jars to the <code>$HADOOP_HOME/lib</code> and copy these
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changes across your cluster but a cleaner means of adding hbase configuration
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and classes to the cluster <code>CLASSPATH</code> is by uncommenting
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<code>HADOOP_CLASSPATH</code> in <code>$HADOOP_HOME/conf/hadoop-env.sh</code>
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adding hbase dependencies here. For example, here is how you would amend
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<code>hadoop-env.sh</code> adding the
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built hbase jar, zookeeper (needed by hbase client), hbase conf, and the
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<code>PerformanceEvaluation</code> class from the built hbase test jar to the
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hadoop <code>CLASSPATH</code>:
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You could add <code>hbase-site.xml</code> to
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<code>$HADOOP_HOME/conf</code> and add
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HBase jars to the <code>$HADOOP_HOME/lib</code> and copy these
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changes across your cluster (or edit conf/hadoop-env.sh and add them to the
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<code>HADOOP_CLASSPATH</code> variable) but this will pollute your
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hadoop install with HBase references; its also obnoxious requiring restart of
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the hadoop cluster before it'll notice your HBase additions.</p>
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<blockquote><pre># Extra Java CLASSPATH elements. Optional.
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# export HADOOP_CLASSPATH=
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export HADOOP_CLASSPATH=$HBASE_HOME/build/hbase-X.X.X.jar:$HBASE_HOME/build/hbase-X.X.X-test.jar:$HBASE_HOME/conf:${HBASE_HOME}/lib/zookeeper-X.X.X.jar</pre></blockquote>
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<p>As of 0.90.x, HBase will just add its dependency jars to the job
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configuration; the dependencies just need to be available on the local
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<code>CLASSPATH</code>. For example, to run the bundled HBase
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{@link RowCounter} mapreduce job against a table named <code>usertable</code>,
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type:
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<p>Expand <code>$HBASE_HOME</code> in the above appropriately to suit your
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local environment.</p>
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<blockquote><pre>
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$ HADOOP_CLASSPATH=`${HBASE_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-0.90.0.jar rowcounter usertable
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</pre></blockquote>
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<p>After copying the above change around your cluster (and restarting), this is
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how you would run the PerformanceEvaluation MR job to put up 4 clients (Presumes
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a ready mapreduce cluster):
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Expand <code>$HBASE_HOME</code> and <code>$HADOOP_HOME</code> in the above
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appropriately to suit your local environment. The content of <code>HADOOP_CLASSPATH</code>
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is set to the HBase <code>CLASSPATH</code> via backticking the command
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<code>${HBASE_HOME}/bin/hbase classpath</code>.
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<blockquote><pre>$HADOOP_HOME/bin/hadoop org.apache.hadoop.hbase.PerformanceEvaluation sequentialWrite 4</pre></blockquote>
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<p>When the above runs, internally, the HBase jar finds its zookeeper and
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<a href="http://code.google.com/p/guava-libraries/">guava</a>,
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etc., dependencies on the passed
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</code>HADOOP_CLASSPATH</code> and adds the found jars to the mapreduce
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job configuration. See the source at
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{@link TableMapReduceUtil#addDependencyJars(org.apache.hadoop.mapreduce.Job)}
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for how this is done.
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</p>
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<p>The above may not work if you are running your HBase from its build directory;
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i.e. you've done <code>$ mvn test install</code> at
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<code>${HBASE_HOME}</code> and you are now
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trying to use this build in your mapreduce job. If you get
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<blockquote><pre>java.lang.RuntimeException: java.lang.ClassNotFoundException: org.apache.hadoop.hbase.mapreduce.RowCounter$RowCounterMapper
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...
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</pre></blockquote>
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exception thrown, try doing the following:
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<blockquote><pre>
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$ HADOOP_CLASSPATH=${HBASE_HOME}/target/hbase-0.90.0-SNAPSHOT.jar:`${HBASE_HOME}/bin/hbase classpath` ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/target/hbase-0.90.0-SNAPSHOT.jar rowcounter usertable
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</pre></blockquote>
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Notice how we preface the backtick invocation setting
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<code>HADOOP_CLASSPATH</code> with reference to the built HBase jar over in
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the <code>target</code> directory.
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</p>
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<p>Another possibility, if for example you do not have access to hadoop-env.sh or
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are unable to restart the hadoop cluster, is bundling the hbase jars into a mapreduce
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job jar adding it and its dependencies under the job jar <code>lib/</code>
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directory and the hbase conf into the job jars top-level directory.
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</a>
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<h2><a name="driver">Bundled HBase MapReduce Jobs</a></h2>
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<p>The HBase jar also serves as a Driver for some bundled mapreduce jobs. To
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learn about the bundled mapreduce jobs run:
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<blockquote><pre>
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$ ${HADOOP_HOME}/bin/hadoop jar ${HBASE_HOME}/hbase-0.90.0-SNAPSHOT.jar
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An example program must be given as the first argument.
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Valid program names are:
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copytable: Export a table from local cluster to peer cluster
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completebulkload: Complete a bulk data load.
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export: Write table data to HDFS.
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import: Import data written by Export.
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importtsv: Import data in TSV format.
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rowcounter: Count rows in HBase table
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</pre></blockquote>
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<h2><a name="sink">HBase as MapReduce job data source and sink</a></h2>
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@ -79,26 +111,26 @@ involved example, see {@link org.apache.hadoop.hbase.mapreduce.RowCounter}
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or review the <code>org.apache.hadoop.hbase.mapreduce.TestTableMapReduce</code> unit test.
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</p>
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<p>Running mapreduce jobs that have hbase as source or sink, you'll need to
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<p>Running mapreduce jobs that have HBase as source or sink, you'll need to
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specify source/sink table and column names in your configuration.</p>
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<p>Reading from hbase, the TableInputFormat asks hbase for the list of
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<p>Reading from HBase, the TableInputFormat asks HBase for the list of
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regions and makes a map-per-region or <code>mapred.map.tasks maps</code>,
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whichever is smaller (If your job only has two maps, up mapred.map.tasks
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to a number > number of regions). Maps will run on the adjacent TaskTracker
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if you are running a TaskTracer and RegionServer per node.
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Writing, it may make sense to avoid the reduce step and write yourself back into
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hbase from inside your map. You'd do this when your job does not need the sort
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HBase from inside your map. You'd do this when your job does not need the sort
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and collation that mapreduce does on the map emitted data; on insert,
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hbase 'sorts' so there is no point double-sorting (and shuffling data around
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HBase 'sorts' so there is no point double-sorting (and shuffling data around
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your mapreduce cluster) unless you need to. If you do not need the reduce,
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you might just have your map emit counts of records processed just so the
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framework's report at the end of your job has meaning or set the number of
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reduces to zero and use TableOutputFormat. See example code
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below. If running the reduce step makes sense in your case, its usually better
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to have lots of reducers so load is spread across the hbase cluster.</p>
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to have lots of reducers so load is spread across the HBase cluster.</p>
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<p>There is also a new hbase partitioner that will run as many reducers as
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<p>There is also a new HBase partitioner that will run as many reducers as
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currently existing regions. The
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{@link org.apache.hadoop.hbase.mapreduce.HRegionPartitioner} is suitable
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when your table is large and your upload is not such that it will greatly
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@ -110,54 +142,10 @@ partitioner.
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<p>If importing into a new table, its possible to by-pass the HBase API
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and write your content directly to the filesystem properly formatted as
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HBase data files (HFiles). Your import will run faster, perhaps an order of
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magnitude faster if not more.
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magnitude faster if not more. For more on how this mechanism works, see
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<a href="http://hbase.apache.org/docs/current/bulk-loads.html">Bulk Loads</code>
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documentation.
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</p>
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<p>You will need to write a MapReduce job. The map task will know how to
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pull from your data source. Your reduce task will need to be hooked up to
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{@link org.apache.hadoop.hbase.mapreduce.HFileOutputFormat}. It expects to receive a row id and a value.
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The row id must be formatted as a {@link org.apache.hadoop.hbase.io.ImmutableBytesWritable} and the
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value as a {@link org.apache.hadoop.hbase.KeyValue} (A KeyValue holds the value for a cell and
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its coordinates; row/family/qualifier/timestamp, etc.). Note that you must
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specify a timestamp when you create the KeyValue in your map task
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otherwise the KeyValue will be created with the default LATEST_TIMESTAMP (Long.MAX_VALUE).
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Use System.currentTimeMillis() if your data does not inherently bear a timestamp.
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Your reduce task
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will also need to emit the KeyValues in order. See {@link org.apache.hadoop.hbase.mapreduce.KeyValueSortReducer}
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for an example reducer that emits KeyValues in order.
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</p>
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<p>Most importantly, you will also need to ensure that your MapReduce job
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ensures a total ordering among all keys. MapReduce by default distributes
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keys among reducers using a Partitioner that hashes on the map task output
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key: i.e. the reducer a key ends up in is by default determined as follows
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<code> (key.hashCode() & Integer.MAX_VALUE) % numReduceTasks</code>.
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Keys are sorted by the MapReduce framework before they are passed to the reducer
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BUT the sort is scoped to the particular reducer. Its not a global sort.
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Given the default hash Partitioner, if the keys were 0-4 (inclusive), and you
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had configured two reducers, reducer 0 would have get keys 0, 2 and 4 whereas
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reducer 1 would get keys 1 and 3 (in order). For your bulk import to work,
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the keys need to be ordered so reducer 0 gets keys 0-2 and reducer 1 gets keys
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3-4 (See TotalOrderPartitioner up in hadoop for more on what this means. See
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how it runs a sampler step first. You may need to write one of these).
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To achieve total ordering, you will likely need to write a Partitioner
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that is intimate with your tables key namespace and that knows how
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to distribute keys among the reducers so a total order is maintained. If your
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keys are distributed with some regularity across a defined key space -- i.e.
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you know the start and end keys -- then the
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{@link org.apache.hadoop.hbase.mapreduce.SimpleTotalOrderPartitioner}
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may be all you need.
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</p>
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<p>See org.apache.hadoop.hbase.mapreduce.TestHFileOutputFormat for an example
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that puts together {@link org.apache.hadoop.hbase.mapreduce.KeyValueSortReducer},
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{@link org.apache.hadoop.hbase.mapreduce.SimpleTotalOrderPartitioner}, and
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{@link org.apache.hadoop.hbase.mapreduce.HFileOutputFormat}.</p>
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<p>HFileOutputFormat writes HFiles. When your MapReduce file finishes, in your
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output directory you will have many HFiles. Run the script <code>bin/loadtable.rb</code>
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to move the files from the MapReduce output directory under hbase. See head of script
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for how to run it. This script
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also adds the new table data to the hbase catalog tables. When the script completes,
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on the next run of the hbase metascanner -- it usually runs every minute -- your
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new table should be visible and populated.</p>
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<h2><a name="examples">Example Code</a></h2>
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<h3>Sample Row Counter</h3>
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