hadoop Mapper 源码

haddop Mapper 代码

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

import java.io.IOException;

import org.apache.hadoop.classification.InterfaceAudience;
import org.apache.hadoop.classification.InterfaceStability;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.io.Closeable;
import org.apache.hadoop.io.SequenceFile;
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 JobConf} for the 
 * job via the {@link JobConfigurable#configure(JobConf)} and initialize
 * themselves. Similarly they can use the {@link Closeable#close()} method for
 * de-initialization.</p>
 * 
 * <p>The framework then calls 
 * {@link #map(Object, Object, OutputCollector, Reporter)} 
 * for each key/value pair in the <code>InputSplit</code> for that task.</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 grouping by specifying
 * a <code>Comparator</code> via 
 * {@link JobConf#setOutputKeyComparatorClass(Class)}.</p>
 *
 * <p>The grouped <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 JobConf#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>The intermediate, grouped outputs are always stored in 
 * {@link SequenceFile}s. 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>JobConf</code>.</p>
 *  
 * <p>If the job has 
 * <a href="{@docRoot}/org/apache/hadoop/mapred/JobConf.html#ReducerNone">zero
 * reduces</a> then the output of the <code>Mapper</code> is directly written
 * to the {@link FileSystem} without grouping by keys.</p>
 * 
 * <p>Example:</p>
 * <p><blockquote><pre>
 *     public class MyMapper&lt;K extends WritableComparable, V extends Writable&gt; 
 *     extends MapReduceBase implements Mapper&lt;K, V, K, V&gt; {
 *     
 *       static enum MyCounters { NUM_RECORDS }
 *       
 *       private String mapTaskId;
 *       private String inputFile;
 *       private int noRecords = 0;
 *       
 *       public void configure(JobConf job) {
 *         mapTaskId = job.get(JobContext.TASK_ATTEMPT_ID);
 *         inputFile = job.get(JobContext.MAP_INPUT_FILE);
 *       }
 *       
 *       public void map(K key, V val,
 *                       OutputCollector&lt;K, V&gt; output, Reporter reporter)
 *       throws IOException {
 *         // Process the &lt;key, value&gt; pair (assume this takes a while)
 *         // ...
 *         // ...
 *         
 *         // Let the framework know that we are alive, and kicking!
 *         // reporter.progress();
 *         
 *         // Process some more
 *         // ...
 *         // ...
 *         
 *         // Increment the no. of &lt;key, value&gt; pairs processed
 *         ++noRecords;
 *
 *         // Increment counters
 *         reporter.incrCounter(NUM_RECORDS, 1);
 *        
 *         // Every 100 records update application-level status
 *         if ((noRecords%100) == 0) {
 *           reporter.setStatus(mapTaskId + " processed " + noRecords + 
 *                              " from input-file: " + inputFile); 
 *         }
 *         
 *         // Output the result
 *         output.collect(key, val);
 *       }
 *     }
 * </pre></blockquote>
 *
 * <p>Applications may write a custom {@link MapRunnable} to exert greater
 * control on map processing e.g. multi-threaded <code>Mapper</code>s etc.</p>
 * 
 * @see JobConf
 * @see InputFormat
 * @see Partitioner  
 * @see Reducer
 * @see MapReduceBase
 * @see MapRunnable
 * @see SequenceFile
 */
@InterfaceAudience.Public
@InterfaceStability.Stable
public interface Mapper<K1, V1, K2, V2> extends JobConfigurable, Closeable {
  
  /** 
   * Maps a single input key/value pair into an intermediate key/value pair.
   * 
   * <p>Output pairs need not be of the same types as input pairs.  A given 
   * input pair may map to zero or many output pairs.  Output pairs are 
   * collected with calls to 
   * {@link OutputCollector#collect(Object,Object)}.</p>
   *
   * <p>Applications can use the {@link Reporter} provided to report progress 
   * or just indicate that they are alive. In scenarios where the application 
   * takes significant amount of time to process individual key/value
   * pairs, this is crucial since the framework might assume that the task has 
   * timed-out and kill that task. The other way of avoiding this is to set 
   * <a href="{@docRoot}/../hadoop-mapreduce-client/hadoop-mapreduce-client-core/mapred-default.xml#mapreduce.task.timeout">
   * mapreduce.task.timeout</a> to a high-enough value (or even zero for no 
   * time-outs).</p>
   * 
   * @param key the input key.
   * @param value the input value.
   * @param output collects mapped keys and values.
   * @param reporter facility to report progress.
   */
  void map(K1 key, V1 value, OutputCollector<K2, V2> output, Reporter reporter)
  throws IOException;
}

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