spark RDD，reduceByKey vs groupByKey

来源：cnblogs　　作者：zzzzMing　　时间：2018/10/29 9:46:33　　对本文有异议

Spark 中有两个类似的api，分别是 reduceByKey 和 groupByKey 。这两个的功能类似，但底层实现却有些不同，那么为什么要这样设计呢？我们来从源码的角度分析一下。

先看两者的调用顺序（都是使用默认的Partitioner，即defaultPartitioner）

所用 spark 版本：spark 2.1.0

先看reduceByKey

Step1

  def reduceByKey(func: (V, V) => V): RDD[(K, V)] = self.withScope {
    reduceByKey(defaultPartitioner(self), func)
  }

Setp2

  def reduceByKey(partitioner: Partitioner, func: (V, V) => V): RDD[(K, V)] = self.withScope {
    combineByKeyWithClassTag[V]((v: V) => v, func, func, partitioner)
  }

Setp3

def combineByKeyWithClassTag[C](
      createCombiner: V => C,
      mergeValue: (C, V) => C,
      mergeCombiners: (C, C) => C,
      partitioner: Partitioner,
      mapSideCombine: Boolean = true,
      serializer: Serializer = null)(implicit ct: ClassTag[C]): RDD[(K, C)] = self.withScope {
    require(mergeCombiners != null, "mergeCombiners must be defined") // required as of Spark 0.9.0
    if (keyClass.isArray) {
      if (mapSideCombine) {
        throw new SparkException("Cannot use map-side combining with array keys.")
      }
      if (partitioner.isInstanceOf[HashPartitioner]) {
        throw new SparkException("HashPartitioner cannot partition array keys.")
      }
    }
    val aggregator = new Aggregator[K, V, C](
      self.context.clean(createCombiner),
      self.context.clean(mergeValue),
      self.context.clean(mergeCombiners))
    if (self.partitioner == Some(partitioner)) {
      self.mapPartitions(iter => {
        val context = TaskContext.get()
        new InterruptibleIterator(context, aggregator.combineValuesByKey(iter, context))
      }, preservesPartitioning = true)
    } else {
      new ShuffledRDD[K, V, C](self, partitioner)
        .setSerializer(serializer)
        .setAggregator(aggregator)
        .setMapSideCombine(mapSideCombine)
    }
  }

姑且不去看方法里面的细节，我们会只要知道最后调用的是 combineByKeyWithClassTag 这个方法。这个方法有两个参数我们来重点看一下，

def combineByKeyWithClassTag[C](
      createCombiner: V => C,
      mergeValue: (C, V) => C,
      mergeCombiners: (C, C) => C,
      partitioner: Partitioner,
      mapSideCombine: Boolean = true,
      serializer: Serializer = null)

首先是 partitioner 参数，这个即是 RDD 的分区设置。除了默认的 defaultPartitioner，Spark 还提供了 RangePartitioner 和 HashPartitioner 外，此外用户也可以自定义 partitioner 。通过源码可以发现如果是 HashPartitioner 的话，那么是会抛出一个错误的。

然后是 mapSideCombine 参数，这个参数正是 reduceByKey 和 groupByKey 最大不同的地方，它决定是是否会先在节点上进行一次 Combine 操作，下面会有更具体的例子来介绍。

然后是groupByKey

Step1

  def groupByKey(): RDD[(K, Iterable[V])] = self.withScope {
    groupByKey(defaultPartitioner(self))
  }

Step2

  def groupByKey(partitioner: Partitioner): RDD[(K, Iterable[V])] = self.withScope {
    // groupByKey shouldn't use map side combine because map side combine does not
    // reduce the amount of data shuffled and requires all map side data be inserted
    // into a hash table, leading to more objects in the old gen.
    val createCombiner = (v: V) => CompactBuffer(v)
    val mergeValue = (buf: CompactBuffer[V], v: V) => buf += v
    val mergeCombiners = (c1: CompactBuffer[V], c2: CompactBuffer[V]) => c1 ++= c2
    val bufs = combineByKeyWithClassTag[CompactBuffer[V]](
      createCombiner, mergeValue, mergeCombiners, partitioner, mapSideCombine = false)
    bufs.asInstanceOf[RDD[(K, Iterable[V])]]
  }

Setp3

def combineByKeyWithClassTag[C](
      createCombiner: V => C,
      mergeValue: (C, V) => C,
      mergeCombiners: (C, C) => C,
      partitioner: Partitioner,
      mapSideCombine: Boolean = true,
      serializer: Serializer = null)(implicit ct: ClassTag[C]): RDD[(K, C)] = self.withScope {
    require(mergeCombiners != null, "mergeCombiners must be defined") // required as of Spark 0.9.0
    if (keyClass.isArray) {
      if (mapSideCombine) {
        throw new SparkException("Cannot use map-side combining with array keys.")
      }
      if (partitioner.isInstanceOf[HashPartitioner]) {
        throw new SparkException("HashPartitioner cannot partition array keys.")
      }
    }
    val aggregator = new Aggregator[K, V, C](
      self.context.clean(createCombiner),
      self.context.clean(mergeValue),
      self.context.clean(mergeCombiners))
    if (self.partitioner == Some(partitioner)) {
      self.mapPartitions(iter => {
        val context = TaskContext.get()
        new InterruptibleIterator(context, aggregator.combineValuesByKey(iter, context))
      }, preservesPartitioning = true)
    } else {
      new ShuffledRDD[K, V, C](self, partitioner)
        .setSerializer(serializer)
        .setAggregator(aggregator)
        .setMapSideCombine(mapSideCombine)
    }
  }

结合上面 reduceByKey 的调用链，可以发现最终其实都是调用 combineByKeyWithClassTag 这个方法的，但调用的参数不同。
reduceByKey的调用

combineByKeyWithClassTag[V]((v: V) => v, func, func, partitioner)

groupByKey的调用

combineByKeyWithClassTag[CompactBuffer[V]](
      createCombiner, mergeValue, mergeCombiners, partitioner, mapSideCombine = false)

正是两者不同的调用方式导致了两个方法的差别，我们分别来看

reduceByKey的泛型参数直接是[V]，而groupByKey的泛型参数是[CompactBuffer[V]]。这直接导致了 reduceByKey 和 groupByKey 的返回值不同，前者是RDD[(K, V)]，而后者是RDD[(K, Iterable[V])]
然后就是mapSideCombine = false 了，这个mapSideCombine 参数的默认是true的。这个值有什么用呢，上面也说了，这个参数的作用是控制要不要在map端进行初步合并（Combine）。可以看看下面具体的例子。