org.apache.hadoop.mapred.Reducer 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 java.util.Iterator;
import org.apache.hadoop.classification.InterfaceAudience;
import org.apache.hadoop.classification.InterfaceStability;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.io.Closeable;
/**
* Reduces a set of intermediate values which share a key to a smaller set of
* values.
*
* The number of Reducers for the job is set by the user via
* {@link JobConf#setNumReduceTasks(int)}. Reducer implementations
* can access the {@link JobConf} for the job via the
* {@link JobConfigurable#configure(JobConf)} method and initialize themselves.
* Similarly they can use the {@link Closeable#close()} method for
* de-initialization.
* Reducer has 3 primary phases:
*
* -
*
* Shuffle
*
*
Reducer is input the grouped output of a {@link Mapper}.
* In the phase the framework, for each Reducer, fetches the
* relevant partition of the output of all the Mappers, via HTTP.
*
*
*
* -
* Sort
*
*
The framework groups Reducer inputs by keys
* (since different Mappers may have output the same key) in this
* stage.
*
* The shuffle and sort phases occur simultaneously i.e. while outputs are
* being fetched they are merged.
*
* SecondarySort
*
* If equivalence rules for keys while grouping the intermediates are
* different from those for grouping keys before reduction, then one may
* specify a Comparator via
* {@link JobConf#setOutputValueGroupingComparator(Class)}.Since
* {@link JobConf#setOutputKeyComparatorClass(Class)} can be used to
* control how intermediate keys are grouped, these can be used in conjunction
* to simulate secondary sort on values.
*
*
* For example, say that you want to find duplicate web pages and tag them
* all with the url of the "best" known example. You would set up the job
* like:
*
* - Map Input Key: url
* - Map Input Value: document
* - Map Output Key: document checksum, url pagerank
* - Map Output Value: url
* - Partitioner: by checksum
* - OutputKeyComparator: by checksum and then decreasing pagerank
* - OutputValueGroupingComparator: by checksum
*
*
*
* -
* Reduce
*
*
In this phase the
* {@link #reduce(Object, Iterator, OutputCollector, Reporter)}
* method is called for each <key, (list of values)> pair in
* the grouped inputs.
* The output of the reduce task is typically written to the
* {@link FileSystem} via
* {@link OutputCollector#collect(Object, Object)}.
*
*
*
* The output of the Reducer is not re-sorted.
*
* Example:
*
* public class MyReducer<K extends WritableComparable, V extends Writable>
* extends MapReduceBase implements Reducer<K, V, K, V> {
*
* static enum MyCounters { NUM_RECORDS }
*
* private String reduceTaskId;
* private int noKeys = 0;
*
* public void configure(JobConf job) {
* reduceTaskId = job.get(JobContext.TASK_ATTEMPT_ID);
* }
*
* public void reduce(K key, Iterator<V> values,
* OutputCollector<K, V> output,
* Reporter reporter)
* throws IOException {
*
* // Process
* int noValues = 0;
* while (values.hasNext()) {
* V value = values.next();
*
* // Increment the no. of values for this key
* ++noValues;
*
* // Process the <key, value> pair (assume this takes a while)
* // ...
* // ...
*
* // Let the framework know that we are alive, and kicking!
* if ((noValues%10) == 0) {
* reporter.progress();
* }
*
* // Process some more
* // ...
* // ...
*
* // Output the <key, value>
* output.collect(key, value);
* }
*
* // Increment the no. of <key, list of values> pairs processed
* ++noKeys;
*
* // Increment counters
* reporter.incrCounter(NUM_RECORDS, 1);
*
* // Every 100 keys update application-level status
* if ((noKeys%100) == 0) {
* reporter.setStatus(reduceTaskId + " processed " + noKeys);
* }
* }
* }
*
*
* @see Mapper
* @see Partitioner
* @see Reporter
* @see MapReduceBase
*/
@InterfaceAudience.Public
@InterfaceStability.Stable
public interface Reducer extends JobConfigurable, Closeable {
/**
* Reduces values for a given key.
*
* The framework calls this method for each
* <key, (list of values)> pair in the grouped inputs.
* Output values must be of the same type as input values. Input keys must
* not be altered. The framework will reuse the key and value objects
* that are passed into the reduce, therefore the application should clone
* the objects they want to keep a copy of. In many cases, all values are
* combined into zero or one value.
*
*
* 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 a 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
*
* mapreduce.task.timeout to a high-enough value (or even zero for no
* time-outs).
*
* @param key the key.
* @param values the list of values to reduce.
* @param output to collect keys and combined values.
* @param reporter facility to report progress.
*/
void reduce(K2 key, Iterator values,
OutputCollector output, Reporter reporter)
throws IOException;
}