hadoop Mapper 源码

  • 2022-10-20
  • 浏览 (127)

haddop Mapper 代码

文件路径:/hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapreduce/Mapper.java

/**
 * 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.
 */

package org.apache.hadoop.mapreduce;

import java.io.IOException;

import org.apache.hadoop.classification.InterfaceAudience;
import org.apache.hadoop.classification.InterfaceStability;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.io.RawComparator;
import org.apache.hadoop.io.compress.CompressionCodec;

/** 
 * Maps input key/value pairs to a set of intermediate key/value pairs.  
 * 
 * <p>Maps are the individual tasks which transform input records into a 
 * intermediate records. The transformed intermediate records need not be of 
 * the same type as the input records. A given input pair may map to zero or 
 * many output pairs.</p> 
 * 
 * <p>The Hadoop Map-Reduce framework spawns one map task for each 
 * {@link InputSplit} generated by the {@link InputFormat} for the job.
 * <code>Mapper</code> implementations can access the {@link Configuration} for 
 * the job via the {@link JobContext#getConfiguration()}.
 * 
 * <p>The framework first calls 
 * {@link #setup(org.apache.hadoop.mapreduce.Mapper.Context)}, followed by
 * {@link #map(Object, Object, org.apache.hadoop.mapreduce.Mapper.Context)}
 * for each key/value pair in the <code>InputSplit</code>. Finally 
 * {@link #cleanup(org.apache.hadoop.mapreduce.Mapper.Context)} is called.</p>
 * 
 * <p>All intermediate values associated with a given output key are 
 * subsequently grouped by the framework, and passed to a {@link Reducer} to  
 * determine the final output. Users can control the sorting and grouping by 
 * specifying two key {@link RawComparator} classes.</p>
 *
 * <p>The <code>Mapper</code> outputs are partitioned per 
 * <code>Reducer</code>. Users can control which keys (and hence records) go to 
 * which <code>Reducer</code> by implementing a custom {@link Partitioner}.
 * 
 * <p>Users can optionally specify a <code>combiner</code>, via 
 * {@link Job#setCombinerClass(Class)}, to perform local aggregation of the 
 * intermediate outputs, which helps to cut down the amount of data transferred 
 * from the <code>Mapper</code> to the <code>Reducer</code>.
 * 
 * <p>Applications can specify if and how the intermediate
 * outputs are to be compressed and which {@link CompressionCodec}s are to be
 * used via the <code>Configuration</code>.</p>
 *  
 * <p>If the job has zero
 * reduces then the output of the <code>Mapper</code> is directly written
 * to the {@link OutputFormat} without sorting by keys.</p>
 * 
 * <p>Example:</p>
 * <p><blockquote><pre>
 * public class TokenCounterMapper 
 *     extends Mapper&lt;Object, Text, Text, IntWritable&gt;{
 *    
 *   private final static IntWritable one = new IntWritable(1);
 *   private Text word = new Text();
 *   
 *   public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
 *     StringTokenizer itr = new StringTokenizer(value.toString());
 *     while (itr.hasMoreTokens()) {
 *       word.set(itr.nextToken());
 *       context.write(word, one);
 *     }
 *   }
 * }
 * </pre></blockquote>
 *
 * <p>Applications may override the
 * {@link #run(org.apache.hadoop.mapreduce.Mapper.Context)} method to exert
 * greater control on map processing e.g. multi-threaded <code>Mapper</code>s 
 * etc.</p>
 * 
 * @see InputFormat
 * @see JobContext
 * @see Partitioner  
 * @see Reducer
 */
@InterfaceAudience.Public
@InterfaceStability.Stable
public class Mapper<KEYIN, VALUEIN, KEYOUT, VALUEOUT> {

  /**
   * The <code>Context</code> passed on to the {@link Mapper} implementations.
   */
  public abstract class Context
    implements MapContext<KEYIN,VALUEIN,KEYOUT,VALUEOUT> {
  }
  
  /**
   * Called once at the beginning of the task.
   */
  protected void setup(Context context
                       ) throws IOException, InterruptedException {
    // NOTHING
  }

  /**
   * Called once for each key/value pair in the input split. Most applications
   * should override this, but the default is the identity function.
   */
  @SuppressWarnings("unchecked")
  protected void map(KEYIN key, VALUEIN value, 
                     Context context) throws IOException, InterruptedException {
    context.write((KEYOUT) key, (VALUEOUT) value);
  }

  /**
   * Called once at the end of the task.
   */
  protected void cleanup(Context context
                         ) throws IOException, InterruptedException {
    // NOTHING
  }
  
  /**
   * Expert users can override this method for more complete control over the
   * execution of the Mapper.
   * @param context
   * @throws IOException
   */
  public void run(Context context) throws IOException, InterruptedException {
    setup(context);
    try {
      while (context.nextKeyValue()) {
        map(context.getCurrentKey(), context.getCurrentValue(), context);
      }
    } finally {
      cleanup(context);
    }
  }
}

相关信息

hadoop 源码目录

相关文章

hadoop Cluster 源码

hadoop ClusterMetrics 源码

hadoop ContextFactory 源码

hadoop Counter 源码

hadoop CounterGroup 源码

hadoop Counters 源码

hadoop CryptoUtils 源码

hadoop CustomJobEndNotifier 源码

hadoop FileSystemCounter 源码

hadoop ID 源码

0  赞