[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。