Hadoop源码篇---解读Mapprer源码outPut输出

一。前述

上次讲完MapReduce的输入后，这次开始讲MapReduce的输出。注意MapReduce的原语很重要：

“相同”的key为一组，调用一次reduce方法，方法内迭代这一组数据进行计算！！！！！

二。代码

继续看MapTask任务。

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.task.TaskAttemptContextImpl(job, 
                                                                  getTaskID(),
                                                                  reporter);
    // 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());
    LOG.info("Processing split: " + split);

    org.apache.hadoop.mapreduce.RecordReader<INKEY,INVALUE> input =
      new NewTrackingRecordReader<INKEY,INVALUE>
        (split, inputFormat, reporter, taskContext);
    
    job.setBoolean(JobContext.SKIP_RECORDS, isSkipping());
    org.apache.hadoop.mapreduce.RecordWriter output = null;
    
    // get an output object
    if (job.getNumReduceTasks() == 0) {
      output = 
        new NewDirectOutputCollector(taskContext, job, umbilical, reporter);
    } else {
      output = new NewOutputCollector(taskContext, job, umbilical, reporter);源码解析一
    }

    org.apache.hadoop.mapreduce.MapContext<INKEY, INVALUE, OUTKEY, OUTVALUE> 
    mapContext = 
      new MapContextImpl<INKEY, INVALUE, OUTKEY, OUTVALUE>(job, getTaskID(), 
          input, output, 
          committer, 
          reporter, split);

    org.apache.hadoop.mapreduce.Mapper<INKEY,INVALUE,OUTKEY,OUTVALUE>.Context 
        mapperContext = 
          new WrappedMapper<INKEY, INVALUE, OUTKEY, OUTVALUE>().getMapContext(
              mapContext);

    try {
      input.initialize(split, mapperContext);
      mapper.run(mapperContext);
      mapPhase.complete();
      setPhase(TaskStatus.Phase.SORT);
      statusUpdate(umbilical);
      input.close();
      input = null;
      output.close(mapperContext);
      output = null;
    } finally {
      closeQuietly(input);
      closeQuietly(output, mapperContext);
    }
  }

解析一。构造OutPut对象：

 NewOutputCollector(org.apache.hadoop.mapreduce.JobContext jobContext,
                       JobConf job,
                       TaskUmbilicalProtocol umbilical,
                       TaskReporter reporter
                       ) throws IOException, ClassNotFoundException {
      collector = createSortingCollector(job, reporter);//对应解析源码1.2
      partitions = jobContext.getNumReduceTasks();//分区数等于Reduce数，分区数大于分组的概念。
      if (partitions > 1) {
        partitioner = (org.apache.hadoop.mapreduce.Partitioner<K,V>)
          ReflectionUtils.newInstance(jobContext.getPartitionerClass(), job);//对应源码1.1
      } else {
        partitioner = new org.apache.hadoop.mapreduce.Partitioner<K,V>() {
          @Override
          public int getPartition(K key, V value, int numPartitions) {
            return partitions - 1;//用户不设置时默认框架一个reduce，并且分区号为0
          }
        };
      }
    }
  @Override
    public void write(K key, V value) throws IOException, InterruptedException {
      collector.collect(key, value,
                        partitioner.getPartition(key, value, partitions));//上下文对象构造写出的值，放在collect缓存区中。
    }

解析1.1

public Class<? extends Partitioner<?,?>> getPartitionerClass()
throws ClassNotFoundException {
return (Class<? extends Partitioner<?,?>>)
conf.getClass(PARTITIONER_CLASS_ATTR, HashPartitioner.class);//当用户设置取用户的，没设置默认HashPartitioner 对应解析源码1.1.1

解析源码1.2createSortingCollector类的具体实现

 private <KEY, VALUE> MapOutputCollector<KEY, VALUE>
          createSortingCollector(JobConf job, TaskReporter reporter)
    throws IOException, ClassNotFoundException {
    MapOutputCollector.Context context =
      new MapOutputCollector.Context(this, job, reporter);

    Class<?>[] collectorClasses = job.getClasses(
      JobContext.MAP_OUTPUT_COLLECTOR_CLASS_ATTR, MapOutputBuffer.class);
    int remainingCollectors = collectorClasses.length;
    for (Class clazz : collectorClasses) {
      try {
        if (!MapOutputCollector.class.isAssignableFrom(clazz)) {
          throw new IOException("Invalid output collector class: " + clazz.getName() +
            " (does not implement MapOutputCollector)");
        }
        Class<? extends MapOutputCollector> subclazz =
          clazz.asSubclass(MapOutputCollector.class);
        LOG.debug("Trying map output collector class: " + subclazz.getName());
        MapOutputCollector<KEY, VALUE> collector =
          ReflectionUtils.newInstance(subclazz, job);
        collector.init(context);//解析源码对应1.2.1
        LOG.info("Map output collector class = " + collector.getClass().getName());
        return collector;
      } catch (Exception e) {
        String msg = "Unable to initialize MapOutputCollector " + clazz.getName();
        if (--remainingCollectors > 0) {
          msg += " (" + remainingCollectors + " more collector(s) to try)";
        }
        LOG.warn(msg, e);
      }
    }
    throw new IOException("Unable to initialize any output collector");
  }

 解析源码1.2.1 缓冲区collect的初始化

 public void init(MapOutputCollector.Context context
                    ) throws IOException, ClassNotFoundException {
      job = context.getJobConf();
      reporter = context.getReporter();
      mapTask = context.getMapTask();
      mapOutputFile = mapTask.getMapOutputFile();
      sortPhase = mapTask.getSortPhase();
      spilledRecordsCounter = reporter.getCounter(TaskCounter.SPILLED_RECORDS);
      partitions = job.getNumReduceTasks();
      rfs = ((LocalFileSystem)FileSystem.getLocal(job)).getRaw();

      //sanity checks
      final float spillper =
        job.getFloat(JobContext.MAP_SORT_SPILL_PERCENT, (float)0.8);//缓冲区溢写阈值，
      final int sortmb = job.getInt(JobContext.IO_SORT_MB, 100);//缓冲区默认单位是100M
      indexCacheMemoryLimit = job.getInt(JobContext.INDEX_CACHE_MEMORY_LIMIT,
                                         INDEX_CACHE_MEMORY_LIMIT_DEFAULT);
      if (spillper > (float)1.0 || spillper <= (float)0.0) {
        throw new IOException("Invalid "" + JobContext.MAP_SORT_SPILL_PERCENT +
            "": " + spillper);
      }
      if ((sortmb & 0x7FF) != sortmb) {
        throw new IOException(
            "Invalid "" + JobContext.IO_SORT_MB + "": " + sortmb);
      }
      sorter = ReflectionUtils.newInstance(job.getClass("map.sort.class",
            QuickSort.class, IndexedSorter.class), job);//Map从缓冲区往磁盘写文件的时候需要排序，用的快排。
      // buffers and accounting
      int maxMemUsage = sortmb << 20;
      maxMemUsage -= maxMemUsage % METASIZE;
      kvbuffer = new byte[maxMemUsage];
      bufvoid = kvbuffer.length;
      kvmeta = ByteBuffer.wrap(kvbuffer)
         .order(ByteOrder.nativeOrder())
         .asIntBuffer();
      setEquator(0);
      bufstart = bufend = bufindex = equator;
      kvstart = kvend = kvindex;

      maxRec = kvmeta.capacity() / NMETA;
      softLimit = (int)(kvbuffer.length * spillper);
      bufferRemaining = softLimit;
      LOG.info(JobContext.IO_SORT_MB + ": " + sortmb);
      LOG.info("soft limit at " + softLimit);
      LOG.info("bufstart = " + bufstart + "; bufvoid = " + bufvoid);
      LOG.info("kvstart = " + kvstart + "; length = " + maxRec);
      comparator = job.getOutputKeyComparator();//排序所使用的比较器 见源码解析1,2.1.1
      keyClass = (Class<K>)job.getMapOutputKeyClass();
      valClass = (Class<V>)job.getMapOutputValueClass();
      serializationFactory = new SerializationFactory(job);
      keySerializer = serializationFactory.getSerializer(keyClass);
      keySerializer.open(bb);
      valSerializer = serializationFactory.getSerializer(valClass);
      valSerializer.open(bb);
// combiner
      final Counters.Counter combineInputCounter =
        reporter.getCounter(TaskCounter.COMBINE_INPUT_RECORDS);
      combinerRunner = CombinerRunner.create(job, getTaskID(), //map端的组合 
                                             combineInputCounter,
                                             reporter, null);
      if (combinerRunner != null) {
        final Counters.Counter combineOutputCounter =
          reporter.getCounter(TaskCounter.COMBINE_OUTPUT_RECORDS);
        combineCollector= new CombineOutputCollector<K,V>(combineOutputCounter, reporter, job);
      } else {
        combineCollector = null;
      }

      spillInProgress = false;
      minSpillsForCombine = job.getInt(JobContext.MAP_COMBINE_MIN_SPILLS, 3);//小文件最少是3时，会合并小文件。
      spillThread.setDaemon(true);//线程是另外一个线程负责写的 见解析源码1.2.1.2
      spillThread.setName("SpillThread");
      spillLock.lock();

总结：Mappper输出到缓冲区默认是100M，写到0.8时，会溢写！！！！这块可以调优。通过来回折半来调比如第一次调整50% 然后再80%中减小 70% 然后60%来回折半。

          Combine一定要注意，比如求平均值

 解析1,2.1.1排序比较器的实现

 public RawComparator getOutputKeyComparator() {
    Class<? extends RawComparator> theClass = getClass(
      JobContext.KEY_COMPARATOR, null, RawComparator.class);字典排序 默认
    if (theClass != null)
      return ReflectionUtils.newInstance(theClass, this);
    return WritableComparator.get(getMapOutputKeyClass().asSubclass(WritableComparable.class), this);//如果用户没有设置排序比较器，就是Key类型自己的比较器，所以Key必须实现序列化，反序列化，比较器。
  }

总结：框架默认使用Key的比较器，字典排序 默认，用户也可以覆盖Key的比较器，自定义。！！！

解析源码1.2.1.2 溢写线程做的事

protected class SpillThread extends Thread {

      @Override
      public void run() {
        spillLock.lock();
        spillThreadRunning = true;
        try {
          while (true) {
            spillDone.signal();
            while (!spillInProgress) {
              spillReady.await();
            }
            try {
              spillLock.unlock();
              sortAndSpill();//排序溢写
            } catch (Throwable t) {
              sortSpillException = t;
            } finally {
              spillLock.lock();
              if (bufend < bufstart) {
                bufvoid = kvbuffer.length;
              }
              kvstart = kvend;
              bufstart = bufend;
              spillInProgress = false;
            }
          }
        } catch (InterruptedException e) {
          Thread.currentThread().interrupt();
        } finally {
          spillLock.unlock();
          spillThreadRunning = false;
        }
      }
    }

总结：Map往缓冲区写入东西，线程把缓冲区中的内容做溢写，开始排序，溢写使用快排！！！Combine也在内存中，buffer也在内存，这些计算逻辑都在内存中，排序算法也在内存中，因为Map方法在内存中，这是第一次Combine,从Buffer产生一堆小文件的时候，然后一堆小文件在合并的时候还会执行一次Combine，这次有条件限制（小文件数量大于3）。

解析源码1.1.1

public class HashPartitioner<K, V> extends Partitioner<K, V> {

  /** Use {@link Object#hashCode()} to partition. */
  public int getPartition(K key, V value,
                          int numReduceTasks) {
    return (key.hashCode() & Integer.MAX_VALUE) % numReduceTasks;!!!
  }

return (key.hashCode() & Integer.MAX_VALUE) % numReduceTasks;!!!重要取分区的写法！！

总结1.以上源码来源于 output = new NewOutputCollector(taskContext, job, umbilical, reporter)；所以可得出在输出构造的时候需要构造一个分区器。要么是0的，要么是用户设置的，要么是默认的。
总结2.在输出构造中，有缓冲区的设置。
总结3，以上方法都是OutPut的初始化。
总结4.Map输出的K，V变成K，V,P然后写入到环形缓冲区，内存缓存区80%，然后溢写排序，（先按分区排序，然后再按Key的组排序），然后生成小文件，然后合并，用的归并算法，此时小文件已经是内部有序的，所以使用归并算法，一次io即可。

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