hadoop-reduce的示例分析

小编给大家分享一下hadoop-reduce的示例分析，相信大部分人都还不怎么了解，因此分享这篇文章给大家参考一下，希望大家阅读完这篇文章后大有收获，下面让我们一起去了解一下吧！

Map的结果，会通过partition分发到Reducer上，Reducer做完Reduce操作后，通过OutputFormat，进行输出。

* Licensed to the Apache Software Foundation (ASF) under onepackage org.apache.hadoop.mapreduce;import java.io.IOException; * Reduces a set of intermediate values which share a key to a smaller set ofpublic class Reducer {  public class Context     extends ReduceContext {    public Context(Configuration conf, TaskAttemptID taskid,                   RawKeyValueIterator input,                    Counter inputKeyCounter,                   Counter inputValueCounter,                   RecordWriter output,                   OutputCommitter committer,                   StatusReporter reporter,                   RawComparator comparator,                   Class keyClass,                   Class valueClass                   ) throws IOException, InterruptedException {      super(conf, taskid, input, inputKeyCounter, inputValueCounter,            output, committer, reporter,             comparator, keyClass, valueClass);    }  }  /**   * Called once at the start of the task.   */  protected void setup(Context context                       ) throws IOException, InterruptedException {    // NOTHING  }  /**   * This method is called once for each key. Most applications will define   * their reduce class by overriding this method. The default implementation   * is an identity function.   */  @SuppressWarnings("unchecked")  protected void reduce(KEYIN key, Iterable values, Context context                        ) throws IOException, InterruptedException {    for(VALUEIN value: values) {      context.write((KEYOUT) key, (VALUEOUT) value);    }  }  /**   * Called once at the end of the task.   */  protected void cleanup(Context context                         ) throws IOException, InterruptedException {    // NOTHING  }  /**   * Advanced application writers can use the    * {@link #run(org.apache.hadoop.mapreduce.Reducer.Context)} method to   * control how the reduce task works.   */  public void run(Context context) throws IOException, InterruptedException {    setup(context);    while (context.nextKey()) {      reduce(context.getCurrentKey(), context.getValues(), context);    }    cleanup(context);  }}

Mapper的结果，可能送到可能的Combiner做合并，Combiner在系统中并没有自己的基类，而是用Reducer作为Combiner的基类，他们对外的功能是一样的，只是使用的位置和使用时的上下文不太一样而已。

Mapper最终处理的结果对，是需要送到Reducer去合并的，合并的时候，有相同key的键/值对会送到同一个Reducer那，哪个key到哪个Reducer的分配过程，是由Partitioner规定的，它只有一个方法，输入是Map的结果对和Reducer的数目，输出则是分配的Reducer（整数编号）。系统缺省的Partitioner是HashPartitioner，它以key的Hash值对Reducer的数目取模，得到对应的Reducer。

* Licensed to the Apache Software Foundation (ASF) under onepackage org.apache.hadoop.mapreduce; * Partitions the key space.public abstract class Partitioner {    /**    * Get the partition number for a given key (hence record) given the total    * number of partitions i.e. number of reduce-tasks for the job.   *      * 
Typically a hash function on a all or a subset of the key.
   *   * @param key the key to be partioned.   * @param value the entry value.   * @param numPartitions the total number of partitions.   * @return the partition number for the key.   */  public abstract int getPartition(KEY key, VALUE value, int numPartitions);  } * Licensed to the Apache Software Foundation (ASF) under onepackage org.apache.hadoop.mapreduce.lib.partition;import org.apache.hadoop.mapreduce.Partitioner;/** Partition keys by their {@link Object#hashCode()}. */public class HashPartitioner extends Partitioner {  /** Use {@link Object#hashCode()} to partition. */  public int getPartition(K key, V value,                          int numReduceTasks) {    return (key.hashCode() & Integer.MAX_VALUE) % numReduceTasks;  }}

Reducer是所有用户定制Reducer类的基类，和Mapper类似，它也有setup，reduce，cleanup和run方法，其中setup和cleanup含义和Mapper相同，reduce是真正合并Mapper结果的地方，它的输入是key和这个key对应的所有value的一个迭代器，同时还包括Reducer的上下文。系统中定义了两个非常简单的Reducer，IntSumReducer和LongSumReducer，分别用于对整形/长整型的value求和。

* Licensed to the Apache Software Foundation (ASF) under onepackage org.apache.hadoop.mapreduce.lib.reduce;import java.io.IOException;public class IntSumReducer extends Reducer {  private IntWritable result = new IntWritable();  public void reduce(Key key, Iterable values,                      Context context) throws IOException, InterruptedException {    int sum = 0;    for (IntWritableval : values) {      sum += val.get();    }    result.set(sum);    context.write(key, result);  }}

Reduce的结果，通过Reducer.Context的方法collect输出到文件中，和输入类似，Hadoop引入了OutputFormat。OutputFormat依赖两个辅助接口：RecordWriter和OutputCommitter，来处理输出。RecordWriter提供了write方法，用于输出和close方法，用于关闭对应的输出。OutputCommitter提供了一系列方法，用户通过实现这些方法，可以定制OutputFormat生存期某些阶段需要的特殊操作。我们在TaskInputOutputContext中讨论过这些方法（明显，TaskInputOutputContext是OutputFormat和Reducer间的桥梁）。

OutputFormat和RecordWriter分别对应着InputFormat和RecordReader，系统提供了空输出NullOutputFormat（什么结果都不输出，NullOutputFormat.RecordWriter只是示例，系统中没有定义），LazyOutputFormat（没在类图中出现，不分析），FilterOutputFormat（不分析）和基于文件FileOutputFormat的SequenceFileOutputFormat和TextOutputFormat输出。

基于文件的输出FileOutputFormat利用了一些配置项配合工作，包括mapred.output.compress：是否压缩；mapred.output.compression.codec：压缩方法；mapred.output.dir：输出路径；mapred.work.output.dir：输出工作路径。FileOutputFormat还依赖于FileOutputCommitter，通过FileOutputCommitter提供一些和Job，Task相关的临时文件管理功能。如FileOutputCommitter的setupJob，会在输出路径下创建一个名为_temporary的临时目录，cleanupJob则会删除这个目录。

SequenceFileOutputFormat输出和TextOutputFormat输出分别对应输入的SequenceFileInputFormat和TextInputFormat

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