MAPREDUCE-4807. Allow MapOutputBuffer to be pluggable. (masokan via tucu)
git-svn-id: https://svn.apache.org/repos/asf/hadoop/common/trunk@1422345 13f79535-47bb-0310-9956-ffa450edef68
This commit is contained in:
Alejandro Abdelnur
parent
803e5155d1
commit
8329fae686
@ -14,6 +14,8 @@ Trunk (Unreleased)
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MAPREDUCE-4049. Experimental api to allow for alternate shuffle plugins.
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(Avner BenHanoch via acmurthy)
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MAPREDUCE-4807. Allow MapOutputBuffer to be pluggable. (masokan via tucu)
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IMPROVEMENTS
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MAPREDUCE-3787. [Gridmix] Optimize job monitoring and STRESS mode for
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@ -0,0 +1,37 @@
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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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* distributed with this work for additional information
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* regarding copyright ownership. The ASF licenses this file
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* to you under the Apache License, Version 2.0 (the
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* "License"); you may not use this file except in compliance
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* with the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.hadoop.mapred;
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import org.apache.hadoop.classification.InterfaceAudience;
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import org.apache.hadoop.classification.InterfaceStability;
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@InterfaceAudience.LimitedPrivate({"MapReduce"})
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@InterfaceStability.Unstable
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public class IndexRecord {
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public long startOffset;
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public long rawLength;
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public long partLength;
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public IndexRecord() { }
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public IndexRecord(long startOffset, long rawLength, long partLength) {
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this.startOffset = startOffset;
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this.rawLength = rawLength;
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this.partLength = partLength;
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}
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}
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@ -0,0 +1,65 @@
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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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* distributed with this work for additional information
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* regarding copyright ownership. The ASF licenses this file
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* to you under the Apache License, Version 2.0 (the
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* "License"); you may not use this file except in compliance
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* with the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.hadoop.mapred;
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import java.io.IOException;
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import org.apache.hadoop.classification.InterfaceAudience;
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import org.apache.hadoop.classification.InterfaceStability;
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import org.apache.hadoop.mapred.Task.TaskReporter;
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@InterfaceAudience.LimitedPrivate({"MapReduce"})
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@InterfaceStability.Unstable
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public interface MapOutputCollector<K, V> {
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public void init(Context context
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) throws IOException, ClassNotFoundException;
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public void collect(K key, V value, int partition
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) throws IOException, InterruptedException;
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public void close() throws IOException, InterruptedException;
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public void flush() throws IOException, InterruptedException,
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ClassNotFoundException;
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@InterfaceAudience.LimitedPrivate({"MapReduce"})
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@InterfaceStability.Unstable
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public static class Context {
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private final MapTask mapTask;
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private final JobConf jobConf;
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private final TaskReporter reporter;
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public Context(MapTask mapTask, JobConf jobConf, TaskReporter reporter) {
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this.mapTask = mapTask;
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this.jobConf = jobConf;
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this.reporter = reporter;
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}
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public MapTask getMapTask() {
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return mapTask;
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}
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public JobConf getJobConf() {
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return jobConf;
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}
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public TaskReporter getReporter() {
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return reporter;
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}
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}
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}
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@ -56,6 +56,7 @@
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import org.apache.hadoop.mapred.IFile.Writer;
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import org.apache.hadoop.mapred.Merger.Segment;
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import org.apache.hadoop.mapred.SortedRanges.SkipRangeIterator;
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import org.apache.hadoop.mapreduce.JobContext;
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import org.apache.hadoop.mapreduce.MRJobConfig;
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import org.apache.hadoop.mapreduce.TaskAttemptContext;
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import org.apache.hadoop.mapreduce.TaskCounter;
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@ -342,6 +343,10 @@ public void run(final JobConf job, final TaskUmbilicalProtocol umbilical)
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done(umbilical, reporter);
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}
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public Progress getSortPhase() {
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return sortPhase;
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}
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@SuppressWarnings("unchecked")
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private <T> T getSplitDetails(Path file, long offset)
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throws IOException {
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@ -370,6 +375,22 @@ private <T> T getSplitDetails(Path file, long offset)
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return split;
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}
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@SuppressWarnings("unchecked")
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private <KEY, VALUE> MapOutputCollector<KEY, VALUE>
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createSortingCollector(JobConf job, TaskReporter reporter)
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throws IOException, ClassNotFoundException {
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MapOutputCollector<KEY, VALUE> collector
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= (MapOutputCollector<KEY, VALUE>)
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ReflectionUtils.newInstance(
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job.getClass(JobContext.MAP_OUTPUT_COLLECTOR_CLASS_ATTR,
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MapOutputBuffer.class, MapOutputCollector.class), job);
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LOG.info("Map output collector class = " + collector.getClass().getName());
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MapOutputCollector.Context context =
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new MapOutputCollector.Context(this, job, reporter);
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collector.init(context);
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return collector;
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}
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@SuppressWarnings("unchecked")
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private <INKEY,INVALUE,OUTKEY,OUTVALUE>
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void runOldMapper(final JobConf job,
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@ -392,11 +413,14 @@ void runOldMapper(final JobConf job,
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int numReduceTasks = conf.getNumReduceTasks();
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LOG.info("numReduceTasks: " + numReduceTasks);
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MapOutputCollector collector = null;
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MapOutputCollector<OUTKEY, OUTVALUE> collector = null;
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if (numReduceTasks > 0) {
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collector = new MapOutputBuffer(umbilical, job, reporter);
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collector = createSortingCollector(job, reporter);
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} else {
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collector = new DirectMapOutputCollector(umbilical, job, reporter);
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collector = new DirectMapOutputCollector<OUTKEY, OUTVALUE>();
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MapOutputCollector.Context context =
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new MapOutputCollector.Context(this, job, reporter);
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collector.init(context);
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}
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MapRunnable<INKEY,INVALUE,OUTKEY,OUTVALUE> runner =
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ReflectionUtils.newInstance(job.getMapRunnerClass(), job);
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@ -642,7 +666,7 @@ private class NewOutputCollector<K,V>
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TaskUmbilicalProtocol umbilical,
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TaskReporter reporter
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) throws IOException, ClassNotFoundException {
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collector = new MapOutputBuffer<K,V>(umbilical, job, reporter);
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collector = createSortingCollector(job, reporter);
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partitions = jobContext.getNumReduceTasks();
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if (partitions > 1) {
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partitioner = (org.apache.hadoop.mapreduce.Partitioner<K,V>)
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@ -738,17 +762,6 @@ void runNewMapper(final JobConf job,
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output.close(mapperContext);
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}
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interface MapOutputCollector<K, V> {
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public void collect(K key, V value, int partition
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) throws IOException, InterruptedException;
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public void close() throws IOException, InterruptedException;
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public void flush() throws IOException, InterruptedException,
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ClassNotFoundException;
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}
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class DirectMapOutputCollector<K, V>
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implements MapOutputCollector<K, V> {
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@ -756,14 +769,18 @@ class DirectMapOutputCollector<K, V>
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private TaskReporter reporter = null;
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private final Counters.Counter mapOutputRecordCounter;
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private final Counters.Counter fileOutputByteCounter;
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private final List<Statistics> fsStats;
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private Counters.Counter mapOutputRecordCounter;
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private Counters.Counter fileOutputByteCounter;
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private List<Statistics> fsStats;
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public DirectMapOutputCollector() {
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}
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@SuppressWarnings("unchecked")
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public DirectMapOutputCollector(TaskUmbilicalProtocol umbilical,
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JobConf job, TaskReporter reporter) throws IOException {
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this.reporter = reporter;
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public void init(MapOutputCollector.Context context
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) throws IOException, ClassNotFoundException {
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this.reporter = context.getReporter();
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JobConf job = context.getJobConf();
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String finalName = getOutputName(getPartition());
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FileSystem fs = FileSystem.get(job);
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@ -818,25 +835,27 @@ private long getOutputBytes(List<Statistics> stats) {
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}
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}
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private class MapOutputBuffer<K extends Object, V extends Object>
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@InterfaceAudience.LimitedPrivate({"MapReduce"})
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@InterfaceStability.Unstable
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public static class MapOutputBuffer<K extends Object, V extends Object>
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implements MapOutputCollector<K, V>, IndexedSortable {
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final int partitions;
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final JobConf job;
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final TaskReporter reporter;
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final Class<K> keyClass;
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final Class<V> valClass;
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final RawComparator<K> comparator;
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final SerializationFactory serializationFactory;
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final Serializer<K> keySerializer;
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final Serializer<V> valSerializer;
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final CombinerRunner<K,V> combinerRunner;
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final CombineOutputCollector<K, V> combineCollector;
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private int partitions;
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private JobConf job;
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private TaskReporter reporter;
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private Class<K> keyClass;
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private Class<V> valClass;
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private RawComparator<K> comparator;
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private SerializationFactory serializationFactory;
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private Serializer<K> keySerializer;
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private Serializer<V> valSerializer;
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private CombinerRunner<K,V> combinerRunner;
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private CombineOutputCollector<K, V> combineCollector;
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// Compression for map-outputs
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final CompressionCodec codec;
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private CompressionCodec codec;
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// k/v accounting
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final IntBuffer kvmeta; // metadata overlay on backing store
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private IntBuffer kvmeta; // metadata overlay on backing store
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int kvstart; // marks origin of spill metadata
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int kvend; // marks end of spill metadata
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int kvindex; // marks end of fully serialized records
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@ -860,15 +879,15 @@ private class MapOutputBuffer<K extends Object, V extends Object>
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private static final int METASIZE = NMETA * 4; // size in bytes
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// spill accounting
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final int maxRec;
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final int softLimit;
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private int maxRec;
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private int softLimit;
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boolean spillInProgress;;
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int bufferRemaining;
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volatile Throwable sortSpillException = null;
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int numSpills = 0;
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final int minSpillsForCombine;
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final IndexedSorter sorter;
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private int minSpillsForCombine;
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private IndexedSorter sorter;
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final ReentrantLock spillLock = new ReentrantLock();
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final Condition spillDone = spillLock.newCondition();
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final Condition spillReady = spillLock.newCondition();
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@ -876,12 +895,12 @@ private class MapOutputBuffer<K extends Object, V extends Object>
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volatile boolean spillThreadRunning = false;
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final SpillThread spillThread = new SpillThread();
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final FileSystem rfs;
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private FileSystem rfs;
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// Counters
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final Counters.Counter mapOutputByteCounter;
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final Counters.Counter mapOutputRecordCounter;
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final Counters.Counter fileOutputByteCounter;
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private Counters.Counter mapOutputByteCounter;
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private Counters.Counter mapOutputRecordCounter;
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private Counters.Counter fileOutputByteCounter;
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final ArrayList<SpillRecord> indexCacheList =
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new ArrayList<SpillRecord>();
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@ -889,12 +908,23 @@ private class MapOutputBuffer<K extends Object, V extends Object>
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private int indexCacheMemoryLimit;
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private static final int INDEX_CACHE_MEMORY_LIMIT_DEFAULT = 1024 * 1024;
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private MapTask mapTask;
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private MapOutputFile mapOutputFile;
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private Progress sortPhase;
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private Counters.Counter spilledRecordsCounter;
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public MapOutputBuffer() {
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}
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@SuppressWarnings("unchecked")
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public MapOutputBuffer(TaskUmbilicalProtocol umbilical, JobConf job,
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TaskReporter reporter
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) throws IOException, ClassNotFoundException {
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this.job = job;
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this.reporter = reporter;
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public void init(MapOutputCollector.Context context
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) throws IOException, ClassNotFoundException {
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job = context.getJobConf();
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reporter = context.getReporter();
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mapTask = context.getMapTask();
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mapOutputFile = mapTask.getMapOutputFile();
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sortPhase = mapTask.getSortPhase();
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spilledRecordsCounter = reporter.getCounter(TaskCounter.SPILLED_RECORDS);
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partitions = job.getNumReduceTasks();
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rfs = ((LocalFileSystem)FileSystem.getLocal(job)).getRaw();
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@ -971,7 +1001,7 @@ public MapOutputBuffer(TaskUmbilicalProtocol umbilical, JobConf job,
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if (combinerRunner != null) {
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final Counters.Counter combineOutputCounter =
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reporter.getCounter(TaskCounter.COMBINE_OUTPUT_RECORDS);
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combineCollector= new CombineOutputCollector<K,V>(combineOutputCounter, reporter, conf);
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combineCollector= new CombineOutputCollector<K,V>(combineOutputCounter, reporter, job);
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} else {
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combineCollector = null;
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}
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@ -1122,6 +1152,10 @@ public synchronized void collect(K key, V value, final int partition
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}
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}
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private TaskAttemptID getTaskID() {
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return mapTask.getTaskID();
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}
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/**
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* Set the point from which meta and serialization data expand. The meta
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* indices are aligned with the buffer, so metadata never spans the ends of
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@ -1494,7 +1528,7 @@ private void checkSpillException() throws IOException {
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if (lspillException instanceof Error) {
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final String logMsg = "Task " + getTaskID() + " failed : " +
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StringUtils.stringifyException(lspillException);
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reportFatalError(getTaskID(), lspillException, logMsg);
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mapTask.reportFatalError(getTaskID(), lspillException, logMsg);
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}
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throw new IOException("Spill failed", lspillException);
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}
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@ -26,6 +26,8 @@
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import java.util.zip.CheckedOutputStream;
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import java.util.zip.Checksum;
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import org.apache.hadoop.classification.InterfaceAudience;
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import org.apache.hadoop.classification.InterfaceStability;
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import org.apache.hadoop.fs.ChecksumException;
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import org.apache.hadoop.fs.FSDataInputStream;
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import org.apache.hadoop.fs.FSDataOutputStream;
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@ -34,7 +36,9 @@
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import org.apache.hadoop.io.IOUtils;
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import org.apache.hadoop.util.PureJavaCrc32;
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class SpillRecord {
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@InterfaceAudience.LimitedPrivate({"MapReduce"})
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@InterfaceStability.Unstable
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public class SpillRecord {
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/** Backing store */
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private final ByteBuffer buf;
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@ -143,17 +147,3 @@ public void writeToFile(Path loc, JobConf job, Checksum crc)
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}
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}
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class IndexRecord {
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long startOffset;
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long rawLength;
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long partLength;
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public IndexRecord() { }
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public IndexRecord(long startOffset, long rawLength, long partLength) {
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this.startOffset = startOffset;
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this.rawLength = rawLength;
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this.partLength = partLength;
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}
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}
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@ -30,6 +30,9 @@ public interface MRJobConfig {
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public static final String MAP_CLASS_ATTR = "mapreduce.job.map.class";
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public static final String MAP_OUTPUT_COLLECTOR_CLASS_ATTR
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= "mapreduce.job.map.output.collector.class";
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public static final String COMBINE_CLASS_ATTR = "mapreduce.job.combine.class";
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public static final String REDUCE_CLASS_ATTR = "mapreduce.job.reduce.class";
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@ -938,4 +938,12 @@
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<value>jhs/_HOST@REALM.TLD</value>
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</property>
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<property>
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<name>mapreduce.job.map.output.collector.class</name>
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<value>org.apache.hadoop.mapred.MapTask$MapOutputBuffer</value>
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<description>
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It defines the MapOutputCollector implementation to use.
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</description>
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</property>
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</configuration>
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@ -0,0 +1,405 @@
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/**
|
||||
* Licensed to the Apache Software Foundation (ASF) under one
|
||||
* or more contributor license agreements. See the NOTICE file
|
||||
* distributed with this work for additional information
|
||||
* regarding copyright ownership. The ASF licenses this file
|
||||
* to you under the Apache License, Version 2.0 (the
|
||||
* "License"); you may not use this file except in compliance
|
||||
* with the License. You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
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package org.apache.hadoop.mapred;
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|
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import java.io.ByteArrayOutputStream;
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import java.io.DataOutputStream;
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import java.io.IOException;
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import java.io.OutputStream;
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import java.io.OutputStreamWriter;
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import java.io.Writer;
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import org.apache.hadoop.conf.Configuration;
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import org.apache.hadoop.fs.FSDataInputStream;
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import org.apache.hadoop.fs.FSDataOutputStream;
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import org.apache.hadoop.fs.FileSystem;
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import org.apache.hadoop.fs.FileUtil;
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import org.apache.hadoop.fs.LocalFileSystem;
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import org.apache.hadoop.fs.Path;
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import org.apache.hadoop.hdfs.MiniDFSCluster;
|
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import org.apache.hadoop.io.DataOutputBuffer;
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import org.apache.hadoop.io.LongWritable;
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import org.apache.hadoop.io.Text;
|
||||
import org.apache.hadoop.io.WritableUtils;
|
||||
|
||||
import org.apache.hadoop.io.serializer.SerializationFactory;
|
||||
import org.apache.hadoop.io.serializer.Serializer;
|
||||
|
||||
import org.apache.hadoop.mapred.Task.TaskReporter;
|
||||
|
||||
import junit.framework.TestCase;
|
||||
|
||||
@SuppressWarnings(value={"unchecked", "deprecation"})
|
||||
/**
|
||||
* This test tests the support for a merge operation in Hadoop. The input files
|
||||
* are already sorted on the key. This test implements an external
|
||||
* MapOutputCollector implementation that just copies the records to different
|
||||
* partitions while maintaining the sort order in each partition. The Hadoop
|
||||
* framework's merge on the reduce side will merge the partitions created to
|
||||
* generate the final output which is sorted on the key.
|
||||
*/
|
||||
public class TestMerge extends TestCase {
|
||||
private static final int NUM_HADOOP_DATA_NODES = 2;
|
||||
// Number of input files is same as the number of mappers.
|
||||
private static final int NUM_MAPPERS = 10;
|
||||
// Number of reducers.
|
||||
private static final int NUM_REDUCERS = 4;
|
||||
// Number of lines per input file.
|
||||
private static final int NUM_LINES = 1000;
|
||||
// Where MR job's input will reside.
|
||||
private static final Path INPUT_DIR = new Path("/testplugin/input");
|
||||
// Where output goes.
|
||||
private static final Path OUTPUT = new Path("/testplugin/output");
|
||||
|
||||
public void testMerge() throws Exception {
|
||||
MiniDFSCluster dfsCluster = null;
|
||||
MiniMRClientCluster mrCluster = null;
|
||||
FileSystem fileSystem = null;
|
||||
try {
|
||||
Configuration conf = new Configuration();
|
||||
// Start the mini-MR and mini-DFS clusters
|
||||
dfsCluster = new MiniDFSCluster(conf, NUM_HADOOP_DATA_NODES, true, null);
|
||||
fileSystem = dfsCluster.getFileSystem();
|
||||
mrCluster = MiniMRClientClusterFactory.create(this.getClass(),
|
||||
NUM_HADOOP_DATA_NODES, conf);
|
||||
// Generate input.
|
||||
createInput(fileSystem);
|
||||
// Run the test.
|
||||
runMergeTest(new JobConf(mrCluster.getConfig()), fileSystem);
|
||||
} finally {
|
||||
if (dfsCluster != null) {
|
||||
dfsCluster.shutdown();
|
||||
}
|
||||
if (mrCluster != null) {
|
||||
mrCluster.stop();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private void createInput(FileSystem fs) throws Exception {
|
||||
fs.delete(INPUT_DIR, true);
|
||||
for (int i = 0; i < NUM_MAPPERS; i++) {
|
||||
OutputStream os = fs.create(new Path(INPUT_DIR, "input_" + i + ".txt"));
|
||||
Writer writer = new OutputStreamWriter(os);
|
||||
for (int j = 0; j < NUM_LINES; j++) {
|
||||
// Create sorted key, value pairs.
|
||||
int k = j + 1;
|
||||
String formattedNumber = String.format("%09d", k);
|
||||
writer.write(formattedNumber + " " + formattedNumber + "\n");
|
||||
}
|
||||
writer.close();
|
||||
}
|
||||
}
|
||||
|
||||
private void runMergeTest(JobConf job, FileSystem fileSystem)
|
||||
throws Exception {
|
||||
// Delete any existing output.
|
||||
fileSystem.delete(OUTPUT, true);
|
||||
job.setJobName("MergeTest");
|
||||
JobClient client = new JobClient(job);
|
||||
RunningJob submittedJob = null;
|
||||
FileInputFormat.setInputPaths(job, INPUT_DIR);
|
||||
FileOutputFormat.setOutputPath(job, OUTPUT);
|
||||
job.set("mapreduce.output.textoutputformat.separator", " ");
|
||||
job.setInputFormat(TextInputFormat.class);
|
||||
job.setMapOutputKeyClass(Text.class);
|
||||
job.setMapOutputValueClass(Text.class);
|
||||
job.setOutputKeyClass(Text.class);
|
||||
job.setOutputValueClass(Text.class);
|
||||
job.setMapperClass(MyMapper.class);
|
||||
job.setPartitionerClass(MyPartitioner.class);
|
||||
job.setOutputFormat(TextOutputFormat.class);
|
||||
job.setNumReduceTasks(NUM_REDUCERS);
|
||||
job.set(JobContext.MAP_OUTPUT_COLLECTOR_CLASS_ATTR,
|
||||
MapOutputCopier.class.getName());
|
||||
try {
|
||||
submittedJob = client.submitJob(job);
|
||||
try {
|
||||
if (! client.monitorAndPrintJob(job, submittedJob)) {
|
||||
throw new IOException("Job failed!");
|
||||
}
|
||||
} catch(InterruptedException ie) {
|
||||
Thread.currentThread().interrupt();
|
||||
}
|
||||
} catch(IOException ioe) {
|
||||
System.err.println("Job failed with: " + ioe);
|
||||
} finally {
|
||||
verifyOutput(submittedJob, fileSystem);
|
||||
}
|
||||
}
|
||||
|
||||
private void verifyOutput(RunningJob submittedJob, FileSystem fileSystem)
|
||||
throws Exception {
|
||||
FSDataInputStream dis = null;
|
||||
long numValidRecords = 0;
|
||||
long numInvalidRecords = 0;
|
||||
long numMappersLaunched = NUM_MAPPERS;
|
||||
String prevKeyValue = "000000000";
|
||||
Path[] fileList =
|
||||
FileUtil.stat2Paths(fileSystem.listStatus(OUTPUT,
|
||||
new Utils.OutputFileUtils.OutputFilesFilter()));
|
||||
for (Path outFile : fileList) {
|
||||
try {
|
||||
dis = fileSystem.open(outFile);
|
||||
String record;
|
||||
while((record = dis.readLine()) != null) {
|
||||
// Split the line into key and value.
|
||||
int blankPos = record.indexOf(" ");
|
||||
String keyString = record.substring(0, blankPos);
|
||||
String valueString = record.substring(blankPos+1);
|
||||
// Check for sorted output and correctness of record.
|
||||
if (keyString.compareTo(prevKeyValue) >= 0
|
||||
&& keyString.equals(valueString)) {
|
||||
prevKeyValue = keyString;
|
||||
numValidRecords++;
|
||||
} else {
|
||||
numInvalidRecords++;
|
||||
}
|
||||
}
|
||||
} finally {
|
||||
if (dis != null) {
|
||||
dis.close();
|
||||
dis = null;
|
||||
}
|
||||
}
|
||||
}
|
||||
// Make sure we got all input records in the output in sorted order.
|
||||
assertEquals((long)(NUM_MAPPERS*NUM_LINES), numValidRecords);
|
||||
// Make sure there is no extraneous invalid record.
|
||||
assertEquals(0, numInvalidRecords);
|
||||
}
|
||||
|
||||
/**
|
||||
* A mapper implementation that assumes that key text contains valid integers
|
||||
* in displayable form.
|
||||
*/
|
||||
public static class MyMapper extends MapReduceBase
|
||||
implements Mapper<LongWritable, Text, Text, Text> {
|
||||
private Text keyText;
|
||||
private Text valueText;
|
||||
|
||||
public MyMapper() {
|
||||
keyText = new Text();
|
||||
valueText = new Text();
|
||||
}
|
||||
|
||||
@Override
|
||||
public void map(LongWritable key, Text value,
|
||||
OutputCollector<Text, Text> output,
|
||||
Reporter reporter) throws IOException {
|
||||
String record = value.toString();
|
||||
int blankPos = record.indexOf(" ");
|
||||
keyText.set(record.substring(0, blankPos));
|
||||
valueText.set(record.substring(blankPos+1));
|
||||
output.collect(keyText, valueText);
|
||||
}
|
||||
|
||||
public void close() throws IOException {
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Partitioner implementation to make sure that output is in total sorted
|
||||
* order. We basically route key ranges to different reducers such that
|
||||
* key values monotonically increase with the partition number. For example,
|
||||
* in this test, the keys are numbers from 1 to 1000 in the form "000000001"
|
||||
* to "000001000" in each input file. The keys "000000001" to "000000250" are
|
||||
* routed to partition 0, "000000251" to "000000500" are routed to partition 1
|
||||
* and so on since we have 4 reducers.
|
||||
*/
|
||||
static class MyPartitioner implements Partitioner<Text, Text> {
|
||||
public MyPartitioner() {
|
||||
}
|
||||
|
||||
public void configure(JobConf job) {
|
||||
}
|
||||
|
||||
public int getPartition(Text key, Text value, int numPartitions) {
|
||||
int keyValue = 0;
|
||||
try {
|
||||
keyValue = Integer.parseInt(key.toString());
|
||||
} catch(NumberFormatException nfe) {
|
||||
keyValue = 0;
|
||||
}
|
||||
int partitionNumber = (numPartitions*(Math.max(0, keyValue-1)))/NUM_LINES;
|
||||
return partitionNumber;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Implementation of map output copier(that avoids sorting) on the map side.
|
||||
* It maintains keys in the input order within each partition created for
|
||||
* reducers.
|
||||
*/
|
||||
static class MapOutputCopier<K, V>
|
||||
implements MapOutputCollector<K, V> {
|
||||
private static final int BUF_SIZE = 128*1024;
|
||||
private MapTask mapTask;
|
||||
private JobConf jobConf;
|
||||
private TaskReporter reporter;
|
||||
private int numberOfPartitions;
|
||||
private Class<K> keyClass;
|
||||
private Class<V> valueClass;
|
||||
private KeyValueWriter<K, V> recordWriters[];
|
||||
private ByteArrayOutputStream outStreams[];
|
||||
|
||||
public MapOutputCopier() {
|
||||
}
|
||||
|
||||
@SuppressWarnings("unchecked")
|
||||
public void init(MapOutputCollector.Context context)
|
||||
throws IOException, ClassNotFoundException {
|
||||
this.mapTask = context.getMapTask();
|
||||
this.jobConf = context.getJobConf();
|
||||
this.reporter = context.getReporter();
|
||||
numberOfPartitions = jobConf.getNumReduceTasks();
|
||||
keyClass = (Class<K>)jobConf.getMapOutputKeyClass();
|
||||
valueClass = (Class<V>)jobConf.getMapOutputValueClass();
|
||||
recordWriters = new KeyValueWriter[numberOfPartitions];
|
||||
outStreams = new ByteArrayOutputStream[numberOfPartitions];
|
||||
|
||||
// Create output streams for partitions.
|
||||
for (int i = 0; i < numberOfPartitions; i++) {
|
||||
outStreams[i] = new ByteArrayOutputStream();
|
||||
recordWriters[i] = new KeyValueWriter<K, V>(jobConf, outStreams[i],
|
||||
keyClass, valueClass);
|
||||
}
|
||||
}
|
||||
|
||||
public synchronized void collect(K key, V value, int partitionNumber
|
||||
) throws IOException, InterruptedException {
|
||||
if (partitionNumber >= 0 && partitionNumber < numberOfPartitions) {
|
||||
recordWriters[partitionNumber].write(key, value);
|
||||
} else {
|
||||
throw new IOException("Invalid partition number: " + partitionNumber);
|
||||
}
|
||||
reporter.progress();
|
||||
}
|
||||
|
||||
public void close() throws IOException, InterruptedException {
|
||||
long totalSize = 0;
|
||||
for (int i = 0; i < numberOfPartitions; i++) {
|
||||
recordWriters[i].close();
|
||||
outStreams[i].close();
|
||||
totalSize += outStreams[i].size();
|
||||
}
|
||||
MapOutputFile mapOutputFile = mapTask.getMapOutputFile();
|
||||
Path finalOutput = mapOutputFile.getOutputFileForWrite(totalSize);
|
||||
Path indexPath = mapOutputFile.getOutputIndexFileForWrite(
|
||||
numberOfPartitions*mapTask.MAP_OUTPUT_INDEX_RECORD_LENGTH);
|
||||
// Copy partitions to final map output.
|
||||
copyPartitions(finalOutput, indexPath);
|
||||
}
|
||||
|
||||
public void flush() throws IOException, InterruptedException,
|
||||
ClassNotFoundException {
|
||||
}
|
||||
|
||||
private void copyPartitions(Path mapOutputPath, Path indexPath)
|
||||
throws IOException {
|
||||
FileSystem localFs = FileSystem.getLocal(jobConf);
|
||||
FileSystem rfs = ((LocalFileSystem)localFs).getRaw();
|
||||
FSDataOutputStream rawOutput = rfs.create(mapOutputPath, true, BUF_SIZE);
|
||||
SpillRecord spillRecord = new SpillRecord(numberOfPartitions);
|
||||
IndexRecord indexRecord = new IndexRecord();
|
||||
for (int i = 0; i < numberOfPartitions; i++) {
|
||||
indexRecord.startOffset = rawOutput.getPos();
|
||||
byte buffer[] = outStreams[i].toByteArray();
|
||||
IFileOutputStream checksumOutput = new IFileOutputStream(rawOutput);
|
||||
checksumOutput.write(buffer);
|
||||
// Write checksum.
|
||||
checksumOutput.finish();
|
||||
// Write index record
|
||||
indexRecord.rawLength = (long)buffer.length;
|
||||
indexRecord.partLength = rawOutput.getPos() - indexRecord.startOffset;
|
||||
spillRecord.putIndex(indexRecord, i);
|
||||
reporter.progress();
|
||||
}
|
||||
rawOutput.close();
|
||||
spillRecord.writeToFile(indexPath, jobConf);
|
||||
}
|
||||
}
|
||||
|
||||
static class KeyValueWriter<K, V> {
|
||||
private Class<K> keyClass;
|
||||
private Class<V> valueClass;
|
||||
private DataOutputBuffer dataBuffer;
|
||||
private Serializer<K> keySerializer;
|
||||
private Serializer<V> valueSerializer;
|
||||
private DataOutputStream outputStream;
|
||||
|
||||
public KeyValueWriter(Configuration conf, OutputStream output,
|
||||
Class<K> kyClass, Class<V> valClass
|
||||
) throws IOException {
|
||||
keyClass = kyClass;
|
||||
valueClass = valClass;
|
||||
dataBuffer = new DataOutputBuffer();
|
||||
SerializationFactory serializationFactory
|
||||
= new SerializationFactory(conf);
|
||||
keySerializer
|
||||
= (Serializer<K>)serializationFactory.getSerializer(keyClass);
|
||||
keySerializer.open(dataBuffer);
|
||||
valueSerializer
|
||||
= (Serializer<V>)serializationFactory.getSerializer(valueClass);
|
||||
valueSerializer.open(dataBuffer);
|
||||
outputStream = new DataOutputStream(output);
|
||||
}
|
||||
|
||||
public void write(K key, V value) throws IOException {
|
||||
if (key.getClass() != keyClass) {
|
||||
throw new IOException("wrong key class: "+ key.getClass()
|
||||
+" is not "+ keyClass);
|
||||
}
|
||||
if (value.getClass() != valueClass) {
|
||||
throw new IOException("wrong value class: "+ value.getClass()
|
||||
+" is not "+ valueClass);
|
||||
}
|
||||
// Append the 'key'
|
||||
keySerializer.serialize(key);
|
||||
int keyLength = dataBuffer.getLength();
|
||||
if (keyLength < 0) {
|
||||
throw new IOException("Negative key-length not allowed: " + keyLength +
|
||||
" for " + key);
|
||||
}
|
||||
// Append the 'value'
|
||||
valueSerializer.serialize(value);
|
||||
int valueLength = dataBuffer.getLength() - keyLength;
|
||||
if (valueLength < 0) {
|
||||
throw new IOException("Negative value-length not allowed: " +
|
||||
valueLength + " for " + value);
|
||||
}
|
||||
// Write the record out
|
||||
WritableUtils.writeVInt(outputStream, keyLength);
|
||||
WritableUtils.writeVInt(outputStream, valueLength);
|
||||
outputStream.write(dataBuffer.getData(), 0, dataBuffer.getLength());
|
||||
// Reset
|
||||
dataBuffer.reset();
|
||||
}
|
||||
|
||||
public void close() throws IOException {
|
||||
keySerializer.close();
|
||||
valueSerializer.close();
|
||||
WritableUtils.writeVInt(outputStream, IFile.EOF_MARKER);
|
||||
WritableUtils.writeVInt(outputStream, IFile.EOF_MARKER);
|
||||
outputStream.close();
|
||||
}
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user