MapTask阶段shuffle源码分析

1. 收集阶段

在Mapper中，调用context.write(key,value)实际是调用代理NewOutPutCollector的wirte方法

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

实际调用的是MapOutPutBuffer的collect（），在进行收集前，调用partitioner来计算每个key-value的分区号

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

2. NewOutPutCollector对象的创建

@SuppressWarnings("unchecked")
  NewOutputCollector(org.apache.hadoop.mapreduce.JobContext jobContext,
            JobConf job,
            TaskUmbilicalProtocol umbilical,
            TaskReporter reporter
            ) throws IOException, ClassNotFoundException {
  // 创建实际用来收集key-value的缓存区对象
   collector = createSortingCollector(job, reporter);
  // 获取总的分区个数
   partitions = jobContext.getNumReduceTasks();
   if (partitions > 1) {
    partitioner = (org.apache.hadoop.mapreduce.Partitioner)
     ReflectionUtils.newInstance(jobContext.getPartitionerClass(), job);
   } else {
    // 默认情况，直接创建一个匿名内部类，所有的key-value都分配到0号分区
    partitioner = new org.apache.hadoop.mapreduce.Partitioner() {
     @Override
     public int getPartition(K key, V value, int numPartitions) {
      return partitions - 1;
     }
    };
   }
  }

3. 创建环形缓冲区对象

@SuppressWarnings("unchecked")
 private  MapOutputCollector
     createSortingCollector(JobConf job, TaskReporter reporter)
  throws IOException, ClassNotFoundException {
  MapOutputCollector.Context context =
   new MapOutputCollector.Context(this, job, reporter);
  // 从当前Job的配置中，获取mapreduce.job.map.output.collector.class，如果没有设置，使用MapOutputBuffer.class
  Class[] collectorClasses = job.getClasses(
   JobContext.MAP_OUTPUT_COLLECTOR_CLASS_ATTR, MapOutputBuffer.class);
  int remainingCollectors = collectorClasses.length;
  Exception lastException = null;
  for (Class clazz : collectorClasses) {
   try {
    if (!MapOutputCollector.class.isAssignableFrom(clazz)) {
     throw new IOException("Invalid output collector class: " + clazz.getName() +
      " (does not implement MapOutputCollector)");
    }
    Class subclazz =
     clazz.asSubclass(MapOutputCollector.class);
    LOG.debug("Trying map output collector class: " + subclazz.getName());
   // 创建缓冲区对象
    MapOutputCollector collector =
     ReflectionUtils.newInstance(subclazz, job);
   // 创建完缓冲区对象后，执行初始化
    collector.init(context);
    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)";
    }
    lastException = e;
    LOG.warn(msg, e);
   }
  }
  throw new IOException("Initialization of all the collectors failed. " +
   "Error in last collector was :" + lastException.getMessage(), lastException);
 }

3. MapOutPutBuffer的初始化   环形缓冲区对象

@SuppressWarnings("unchecked")
  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);
   // 获取分区总个数，取决于ReduceTask的数量
   partitions = job.getNumReduceTasks();
   rfs = ((LocalFileSystem)FileSystem.getLocal(job)).getRaw();
   //sanity checks
   // 从当前配置中，获取mapreduce.map.sort.spill.percent，如果没有设置，就是0.8
   final float spillper =
    job.getFloat(JobContext.MAP_SORT_SPILL_PERCENT, (float)0.8);
   // 获取mapreduce.task.io.sort.mb，如果没设置，就是100MB
   final int sortmb = job.getInt(JobContext.IO_SORT_MB, 100);
   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);
   }
// 在溢写前，对key-value排序，采用的排序器，使用快速排序，只排索引
   sorter = ReflectionUtils.newInstance(job.getClass("map.sort.class",
      QuickSort.class, IndexedSorter.class), job);
   // buffers and accounting
   int maxMemUsage = sortmb << 20;
   maxMemUsage -= maxMemUsage % METASIZE;
   // 存放key-value
   kvbuffer = new byte[maxMemUsage];
   bufvoid = kvbuffer.length;
  // 存储key-value的属性信息，分区号，索引等
   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);
   // k/v serialization
    // 获取快速排序的Key的比较器，排序只按照key进行排序！
   comparator = job.getOutputKeyComparator();
  // 获取key-value的序列化器
   keyClass = (Class)job.getMapOutputKeyClass();
   valClass = (Class)job.getMapOutputValueClass();
   serializationFactory = new SerializationFactory(job);
   keySerializer = serializationFactory.getSerializer(keyClass);
   keySerializer.open(bb);
   valSerializer = serializationFactory.getSerializer(valClass);
   valSerializer.open(bb);
   // output counters
   mapOutputByteCounter = reporter.getCounter(TaskCounter.MAP_OUTPUT_BYTES);
   mapOutputRecordCounter =
    reporter.getCounter(TaskCounter.MAP_OUTPUT_RECORDS);
   fileOutputByteCounter = reporter
     .getCounter(TaskCounter.MAP_OUTPUT_MATERIALIZED_BYTES);
   // 溢写到磁盘，可以使用一个压缩格式！ 获取指定的压缩编解码器
   // compression
   if (job.getCompressMapOutput()) {
    Class codecClass =
     job.getMapOutputCompressorClass(DefaultCodec.class);
    codec = ReflectionUtils.newInstance(codecClass, job);
   } else {
    codec = null;
   }
   // 获取Combiner组件
   // combiner
   final Counters.Counter combineInputCounter =
    reporter.getCounter(TaskCounter.COMBINE_INPUT_RECORDS);
   combinerRunner = CombinerRunner.create(job, getTaskID(),
                       combineInputCounter,
                       reporter, null);
   if (combinerRunner != null) {
    final Counters.Counter combineOutputCounter =
     reporter.getCounter(TaskCounter.COMBINE_OUTPUT_RECORDS);
    combineCollector= new CombineOutputCollector(combineOutputCounter, reporter, job);
   } else {
    combineCollector = null;
   }
   spillInProgress = false;
   minSpillsForCombine = job.getInt(JobContext.MAP_COMBINE_MIN_SPILLS, 3);
   // 设置溢写线程在后台运行，溢写是在后台运行另外一个溢写线程！和收集是两个线程！
   spillThread.setDaemon(true);
   spillThread.setName("SpillThread");
   spillLock.lock();
   try {
   // 启动线程
    spillThread.start();
    while (!spillThreadRunning) {
     spillDone.await();
    }
   } catch (InterruptedException e) {
    throw new IOException("Spill thread failed to initialize", e);
   } finally {
    spillLock.unlock();
   }
   if (sortSpillException != null) {
    throw new IOException("Spill thread failed to initialize",
      sortSpillException);
   }
  }

4. Paritionner的获取

从配置中读取mapreduce.job.partitioner.class，如果没有指定，采用HashPartitioner.class

如果reduceTask > 1， 还没有设置分区组件，使用HashPartitioner

@SuppressWarnings("unchecked")
 public Class> getPartitionerClass()
   throws ClassNotFoundException {
  return (Class>)
   conf.getClass(PARTITIONER_CLASS_ATTR, HashPartitioner.class);
 }

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;
 }
}

分区号的限制：0 <= 分区号 < 总的分区数(reduceTask的个数)

if (partition < 0 || partition >= partitions) {
    throw new IOException("Illegal partition for " + key + " (" +
      partition + ")");
   }

5.MapTask shuffle的流程

              ①在map()调用context.write()

              ②调用MapoutPutBuffer的collect()

•                             调用分区组件Partitionner计算当前这组key-value的分区号

              ③将当前key-value收集到MapOutPutBuffer中

•                             如果超过溢写的阀值，在后台启动溢写线程，来进行溢写！

              ④溢写前，先根据分区号，将相同分区号的key-value，采用快速排序算法，进行排序！

•                             排序并不在内存中移动key-value，而是记录排序后key-value的有序索引！

              ⑤ 开始溢写，按照排序后有序的索引，将文件写入到一个临时的溢写文件中

•                             如果没有定义Combiner，直接溢写！
•                             如果定义了Combiner，使用CombinerRunner.conbine（）对key-value处理后再次溢写！

              ⑥多次溢写后，每次溢写都会产生一个临时文件

              ⑦最后，执行一次flush()，将剩余的key-value进行溢写

              ⑧MergeParts: 将多次溢写的结果，保存为一个总的文件！

•                      在合并为一个总的文件前，会执行归并排序，保证合并后的文件，各个分区也是有序的！
•                      如果定义了Conbiner，Conbiner会再次运行（前提是溢写的文件个数大于3）！
•                      否则，就直接溢写！

              ⑨最终保证生成一个最终的文件，这个文件根据总区号，分为若干部分，每个部分的key-value都已经排好序，等待ReduceTask来拷贝相应分区的数据

6. Combiner

combiner其实就是Reducer类型：

Class> cls =
    (Class>) job.getCombinerClass();

Combiner的运行时机：

MapTask：

•               ①每次溢写前，如果指定了Combiner，会运行
•               ②将多个溢写片段，进行合并为一个最终的文件时，也会运行Combiner，前提是片段数>=3

ReduceTask:

              ③reduceTask在运行时，需要启动shuffle进程拷贝MapTask产生的数据！

•                      数据在copy后，进入shuffle工作的内存，在内存中进行merge和sort！
•                      数据过多，内部不够，将部分数据溢写在磁盘！
•                      如果有溢写的过程，那么combiner会再次运行！

①一定会运行，②，③需要条件！

总结

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