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!
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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 one package org.apache.hadoop.mapreduce; import java.io.IOException; * Reduces a set of intermediate values which share a key to a smaller set of public 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
* Licensed to the Apache Software Foundation (ASF) under one package 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 thekey
. */ public abstract int getPartition(KEY key, VALUE value, int numPartitions); } * Licensed to the Apache Software Foundation (ASF) under one package org.apache.hadoop.mapreduce.lib.partition; import org.apache.hadoop.mapreduce.Partitioner; /** Partition keys by their {@link Object#hashCode()}. */ public class HashPartitionerextends 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 one package org.apache.hadoop.mapreduce.lib.reduce; import java.io.IOException; public class IntSumReducerextends Reducer { private IntWritable result = new IntWritable(); public void reduce(Key key, Iterable values, Context context) throws IOException, InterruptedException { int sum = 0; for (IntWritable val : 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
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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文章标题:hadoop-reduce的示例分析
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