[Hadoop源码解读]（六）MapReduce篇之MapTask类

MapTask类继承于Task类，它最主要的方法就是run()，用来执行这个Map任务。

run()首先设置一个TaskReporter并启动，然后调用JobConf的getUseNewAPI()判断是否使用New API，使用New API的设置在前面[Hadoop源码解读]（三）MapReduce篇之Job类讲到过，再调用Task继承来的initialize()方法初始化这个task，接着根据需要执行runJobCleanupTask()、runJobSetupTask()、runTaskCleanupTask()或相应的Mapper，执行Mapper时根据情况使用不同版本的MapReduce，这个版本是设置参数决定的。

  @Override
  public void run(final JobConf job, final TaskUmbilicalProtocol umbilical) 
    throws IOException, ClassNotFoundException, InterruptedException {
    this.umbilical = umbilical;

    // start thread that will handle communication with parent
    TaskReporter reporter = new TaskReporter(getProgress(), umbilical,
        jvmContext);
    reporter.startCommunicationThread();
    boolean useNewApi = job.getUseNewMapper();  //是由JobConf来的，而New API 的JobContext包含一个JobConf，Job类有
    //setUseNewAPI()方法，当Job.submit()时使用它，这样，waitForCompletion()就用submit()设置了使用New API，而此时就使用它。
    initialize(job, getJobID(), reporter, useNewApi);//一个Task的初始化工作，包括jobContext,taskContext，输出路径等，
    						     //使用的是Task.initialize()方法
 
    // check if it is a cleanupJobTask
    if (jobCleanup) {
      runJobCleanupTask(umbilical, reporter);
      return;
    }
    if (jobSetup) {
      runJobSetupTask(umbilical, reporter);
      return;
    }
    if (taskCleanup) {
      runTaskCleanupTask(umbilical, reporter);
      return;
    }

    if (useNewApi) {//根据情况使用不同的MapReduce版本执行Mapper
      runNewMapper(job, splitMetaInfo, umbilical, reporter);
    } else {
      runOldMapper(job, splitMetaInfo, umbilical, reporter);
    }
    done(umbilical, reporter);
  }

runNewMapper对应new API的MapReduce，而runOldMapper对应旧API。

runNewMapper首先创建TaskAttemptContext对象，Mapper对象，InputFormat对象，InputSplit，RecordReader；然后根据是否有Reduce task来创建不同的输出收集器NewDirectOutputCollector[没有reducer]或NewOutputCollector[有reducer]，接下来调用input.initialize()初始化RecordReader，主要是为输入做准备，设置RecordReader，输入路径等等。然后到最主要的部分：mapper.run()。这个方法就是调用前面[Hadoop源码解读]（二）MapReduce篇之Mapper类讲到的Mapper.class的run()方法。然后就是一条一条的读取K/V对，这样就衔接起来了。

 @SuppressWarnings("unchecked")
  private <INKEY,INVALUE,OUTKEY,OUTVALUE>
  void runNewMapper(final JobConf job,
                    final TaskSplitIndex splitIndex,
                    final TaskUmbilicalProtocol umbilical,
                    TaskReporter reporter
                    ) throws IOException, ClassNotFoundException,
                             InterruptedException {
    // make a task context so we can get the classes
    org.apache.hadoop.mapreduce.TaskAttemptContext taskContext =
      new org.apache.hadoop.mapreduce.TaskAttemptContext(job, getTaskID());
    // make a mapper
    org.apache.hadoop.mapreduce.Mapper<INKEY,INVALUE,OUTKEY,OUTVALUE> mapper =
      (org.apache.hadoop.mapreduce.Mapper<INKEY,INVALUE,OUTKEY,OUTVALUE>)
        ReflectionUtils.newInstance(taskContext.getMapperClass(), job);
    // make the input format
    org.apache.hadoop.mapreduce.InputFormat<INKEY,INVALUE> inputFormat =
      (org.apache.hadoop.mapreduce.InputFormat<INKEY,INVALUE>)
        ReflectionUtils.newInstance(taskContext.getInputFormatClass(), job);
    // rebuild the input split
    org.apache.hadoop.mapreduce.InputSplit split = null;
    split = getSplitDetails(new Path(splitIndex.getSplitLocation()),
        splitIndex.getStartOffset());

    org.apache.hadoop.mapreduce.RecordReader<INKEY,INVALUE> input =
      new NewTrackingRecordReader<INKEY,INVALUE>
          (split, inputFormat, reporter, job, taskContext);

    job.setBoolean("mapred.skip.on", isSkipping());
    org.apache.hadoop.mapreduce.RecordWriter output = null;
    org.apache.hadoop.mapreduce.Mapper<INKEY,INVALUE,OUTKEY,OUTVALUE>.Context 
         mapperContext = null;
    try {
      Constructor<org.apache.hadoop.mapreduce.Mapper.Context> contextConstructor =
        org.apache.hadoop.mapreduce.Mapper.Context.class.getConstructor
        (new Class[]{org.apache.hadoop.mapreduce.Mapper.class,
                     Configuration.class,
                     org.apache.hadoop.mapreduce.TaskAttemptID.class,
                     org.apache.hadoop.mapreduce.RecordReader.class,
                     org.apache.hadoop.mapreduce.RecordWriter.class,
                     org.apache.hadoop.mapreduce.OutputCommitter.class,  //
                     org.apache.hadoop.mapreduce.StatusReporter.class,
                     org.apache.hadoop.mapreduce.InputSplit.class});

      // get an output object
      if (job.getNumReduceTasks() == 0) {
         output =
           new NewDirectOutputCollector(taskContext, job, umbilical, reporter);
      } else {
        output = new NewOutputCollector(taskContext, job, umbilical, reporter);
      }

      mapperContext = contextConstructor.newInstance(mapper, job, getTaskID(),
                                                     input, output, committer,
                                                     reporter, split);

      input.initialize(split, mapperContext);
      mapper.run(mapperContext);
      input.close();
      output.close(mapperContext);
    } catch (NoSuchMethodException e) {
      throw new IOException("Can't find Context constructor", e);
    } catch (InstantiationException e) {
      throw new IOException("Can't create Context", e);
    } catch (InvocationTargetException e) {
      throw new IOException("Can't invoke Context constructor", e);
    } catch (IllegalAccessException e) {
      throw new IOException("Can't invoke Context constructor", e);
    }
  }

至于运行哪个Mapper类，一般是我们用job.setMapperClass(SelectGradeMapper.class)设置的，那设置后是怎样获取的，或者默认值是什么，且看下面的追溯。

MapTask.runNewMapper()

=> (TaskAttemptContext)taskContext.getMapperClass(); //runNewMapper生成mapper时用到。

=> JobContext.getMapperClass()

=> JobConf.getClass(MAP_CLASS_ATTR,Mapper.class)

=> Configuration.getClass(name,default)

根据上面一层的调用关系，找到了默认值是Mapper.class，它的获取过程也一目了然。

再仔细看看Configuration.getClass()

  public Class<?> getClass(String name, Class<?> defaultValue) {
    String valueString = get(name);
    if (valueString == null)
      return defaultValue;
    try {
      return getClassByName(valueString);
    } catch (ClassNotFoundException e) {
      throw new RuntimeException(e);
    }
  }

它首先看是否设置了某个属性，如果设置了，就调用getClassByName获取这个属性对应的类[加载之]，否则就返回默认值。
Mapper执行完后，关闭RecordReader和OutputCollector等资源就完事了。

另外我们把关注点放在上面的runNewMapper()中的mapper.run(mapperContext)；前面对Mapper.class提到，这个mapperContext会被用于读取输入分片的K/V对和写出输出结果的K/V对。而由

      mapperContext = contextConstructor.newInstance(mapper, job, getTaskID(),
                                                     input, output, committer,
                                                     reporter, split);

可以看出，这个Context是由我们设置的mapper，RecordReader等进行配置的。

Mapper中的map方法不断使用context.write(K,V)进行输出，我们看这个函数是怎么进行的，先看Context类的层次关系：

write()方法是由TaskInputOutputContext来的：

  public void write(KEYOUT key, VALUEOUT value
                    ) throws IOException, InterruptedException {
    output.write(key, value);
  }

它调用了RecordWriter.write()，RecordWriter是一个抽象类，主要是规定了write方法。

public abstract class RecordWriter<K, V> {
  public abstract void write(K key, V value
                             ) throws IOException, InterruptedException;

  public abstract void close(TaskAttemptContext context
                             ) throws IOException, InterruptedException;
}

然后看RecordWriter的一个实现NewOutputCollector，它是MapTask的内部类：

  private class NewOutputCollector<K,V>
    extends org.apache.hadoop.mapreduce.RecordWriter<K,V> {
    private final MapOutputCollector<K,V> collector;
    private final org.apache.hadoop.mapreduce.Partitioner<K,V> partitioner;
    private final int partitions;

    @SuppressWarnings("unchecked")
    NewOutputCollector(org.apache.hadoop.mapreduce.JobContext jobContext,
                       JobConf job,
                       TaskUmbilicalProtocol umbilical,
                       TaskReporter reporter
                       ) throws IOException, ClassNotFoundException {
      collector = new MapOutputBuffer<K,V>(umbilical, job, reporter);
      partitions = jobContext.getNumReduceTasks();
      if (partitions > 0) {
        partitioner = (org.apache.hadoop.mapreduce.Partitioner<K,V>)
          ReflectionUtils.newInstance(jobContext.getPartitionerClass(), job);
      } else {
        partitioner = new org.apache.hadoop.mapreduce.Partitioner<K,V>() {
          @Override
          public int getPartition(K key, V value, int numPartitions) {
            return -1;
          }
        };
      }
    }

    @Override
    public void write(K key, V value) throws IOException, InterruptedException {
      collector.collect(key, value,
                        partitioner.getPartition(key, value, partitions));
    }

    @Override
    public void close(TaskAttemptContext context
                      ) throws IOException,InterruptedException {
      try {
        collector.flush();
      } catch (ClassNotFoundException cnf) {
        throw new IOException("can't find class ", cnf);
      }
      collector.close();
    }
  }

从它的write()方法，我们从context.write(K,V)追溯到了collector.collect(K,V,partition)，注意到输出需要一个Partitioner的getPartitioner()来提供当前K/V对的所属分区，因为要对K/V对分区，不同分区输出到不同Reducer，Partitioner默认是HashPartitioner，可设置，Reduce task数量决定Partition数量;

我们可以从NewOutputCollector看出NewOutputCollector就是MapOutputBuffer的封装。MapoutputBuffer是旧API中就存在了的，它很复杂，但很关键，暂且放着先，反正就是收集输出K/V对的。它实现了MapperOutputCollector接口：

  interface MapOutputCollector<K, V> {
    public void collect(K key, V value, int partition
                        ) throws IOException, InterruptedException;
    public void close() throws IOException, InterruptedException;
    public void flush() throws IOException, InterruptedException, 
                               ClassNotFoundException;
  }

这个接口告诉我们，收集器必须实现collect，close，flush方法。

看一个简单的:NewDirectOutputCollector,它在没有reduce task的时候使用，主要是从InputFormat中获取OutputFormat的RecordWriter，然后就可以用这个RecordWriter的write()方法来写出，这就与我们设置的输出格式对应起来了。

  private class NewDirectOutputCollector<K,V>
  extends org.apache.hadoop.mapreduce.RecordWriter<K,V> {
    private final org.apache.hadoop.mapreduce.RecordWriter out;

    private final TaskReporter reporter;

    private final Counters.Counter mapOutputRecordCounter;
    private final Counters.Counter fileOutputByteCounter; 
    private final Statistics fsStats;
    
    @SuppressWarnings("unchecked")
    NewDirectOutputCollector(org.apache.hadoop.mapreduce.JobContext jobContext,
        JobConf job, TaskUmbilicalProtocol umbilical, TaskReporter reporter) 
    throws IOException, ClassNotFoundException, InterruptedException {
      this.reporter = reporter;
      Statistics matchedStats = null;
      if (outputFormat instanceof org.apache.hadoop.mapreduce.lib.output.FileOutputFormat) { 
		//outputFormat是Task来的，内部类访问外部类成员变量
        matchedStats = getFsStatistics(org.apache.hadoop.mapreduce.lib.output.FileOutputFormat
            .getOutputPath(jobContext), job);
      }
      fsStats = matchedStats;
      mapOutputRecordCounter = 
        reporter.getCounter(MAP_OUTPUT_RECORDS);
      fileOutputByteCounter = reporter
          .getCounter(org.apache.hadoop.mapreduce.lib.output.FileOutputFormat.Counter.BYTES_WRITTEN);

      long bytesOutPrev = getOutputBytes(fsStats);
      out = outputFormat.getRecordWriter(taskContext); //主要是这句，获取设置的OutputputFormat里的RecordWriter
      long bytesOutCurr = getOutputBytes(fsStats);
      fileOutputByteCounter.increment(bytesOutCurr - bytesOutPrev);
    }

    @Override
    @SuppressWarnings("unchecked")
    public void write(K key, V value) 
    throws IOException, InterruptedException {
      reporter.progress();  //报告一下进度
      long bytesOutPrev = getOutputBytes(fsStats);
      out.write(key, value);//使用out收集一条记录，out是设置的OutputFormat来的。
      long bytesOutCurr = getOutputBytes(fsStats);
      fileOutputByteCounter.increment(bytesOutCurr - bytesOutPrev);  //更新输出字节数
      mapOutputRecordCounter.increment(1);      //更新输出K/V对数量
    }

    @Override
    public void close(TaskAttemptContext context) 
    throws IOException,InterruptedException {
      reporter.progress();
      if (out != null) {
        long bytesOutPrev = getOutputBytes(fsStats);
        out.close(context);
        long bytesOutCurr = getOutputBytes(fsStats);
        fileOutputByteCounter.increment(bytesOutCurr - bytesOutPrev);
      }
    }

    private long getOutputBytes(Statistics stats) {
      return stats == null ? 0 : stats.getBytesWritten();
    }
  }

另外还有一些以runOldMapper()为主导的旧MapReduce API那套，就不进行讨论了。