org.apache.hadoop.mapred.Mapper Maven / Gradle / Ivy
/**
* 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.
*
* 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.
*
* The Hadoop Map-Reduce framework spawns one map task for each
* {@link InputSplit} generated by the {@link InputFormat} for the job.
* Mapper 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.
*
* The framework then calls
* {@link #map(Object, Object, OutputCollector, Reporter)}
* for each key/value pair in the InputSplit for that task.
*
* 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 Comparator via
* {@link JobConf#setOutputKeyComparatorClass(Class)}.
*
* The grouped Mapper outputs are partitioned per
* Reducer. Users can control which keys (and hence records) go to
* which Reducer by implementing a custom {@link Partitioner}.
*
*
Users can optionally specify a combiner, 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 Mapper to the Reducer.
*
*
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 JobConf.
*
* If the job has
* zero
* reduces then the output of the Mapper is directly written
* to the {@link FileSystem} without grouping by keys.
*
* Example:
*
* public class MyMapper<K extends WritableComparable, V extends Writable>
* extends MapReduceBase implements Mapper<K, V, K, V> {
*
* 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<K, V> output, Reporter reporter)
* throws IOException {
* // Process the <key, value> 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 <key, value> 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);
* }
* }
*
*
* Applications may write a custom {@link MapRunnable} to exert greater
* control on map processing e.g. multi-threaded Mappers etc.
*
* @see JobConf
* @see InputFormat
* @see Partitioner
* @see Reducer
* @see MapReduceBase
* @see MapRunnable
* @see SequenceFile
* @deprecated Use {@link org.apache.hadoop.mapreduce.Mapper} instead.
*/
@Deprecated
@InterfaceAudience.Public
@InterfaceStability.Stable
public interface Mapper extends JobConfigurable, Closeable {
/**
* Maps a single input key/value pair into an intermediate key/value pair.
*
* 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)}.
*
* Applications can use the {@link Reporter} provided to report progress
* or just indicate that they are alive. In scenarios where the application
* takes an insignificant 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
*
* mapreduce.task.timeout to a high-enough value (or even zero for no
* time-outs).
*
* @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 output, Reporter reporter)
throws IOException;
}