Spark算子执行流程详解之五

Posted 2022-11-28 亮亮-AC米兰

22.combineByKey

 

def combineByKey[C](createCombiner: V => C,     mergeValue: (C, V) => C,     mergeCombiners: (C, C) => C,     partitioner: Partitioner,     mapSideCombine: Boolean = true,     serializer: Serializer = null): 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("Default partitioner 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)) //如果分区函数相同，则不需要shuffle，只需要进行一次mapPartitions     self.mapPartitions(iter =>       val context = TaskContext.get()       new InterruptibleIterator(context, aggregator.combineValuesByKey(iter, context))     , preservesPartitioning = true)   else //否则需要进行shuffle     new ShuffledRDD[K, V, C](self, partitioner)       .setSerializer(serializer)       .setAggregator(aggregator)       .setMapSideCombine(mapSideCombine)

关注其入参：

combineByKey函数主要接受了三个函数作为参数，分别为createCombiner、mergeValue、mergeCombiners。这三个函数足以说明它究竟做了什么。理解了这三个函数，就可以很好地理解combineByKey。

要理解combineByKey()，要先理解它在处理数据时是如何处理每个元素的。由于combineByKey()会遍历分区中的所有元素，因此每个元素的键要么还没有遇到过，要么就和之前的键相同。combineByKey()的处理流程如下：

如果是一个新的元素，此时使用createCombiner()来创建那个键对应的累加器的初始值。（！注意：这个过程会在每个分区第一次出现各个键时发生，而不是在整个RDD中第一次出现一个键时发生。）

如果这是一个在处理当前分区中之前已经遇到键，此时combineByKey()使用mergeValue()将该键的累加器对应的当前值与这个新值进行合并。

由于每个分区都是独立处理的，因此对于同一个键可以有多个累加器。如果有两个或者更多的分区都有对应同一个键的累加器，就需要使用用户提供的mergeCombiners()将各个分区的结果进行合并。如果mergeCombiners为True，则在map的输出的时候提前进行一次合并，如果mergeCombiners为false，则在reduce结果的时候进行一次合并。提前进行合并的作用是为了减少shuffle读取的时候传输的数据量，提升shuffle read的速度。

先来看下ShuffleRDD里面的依赖：

class ShuffledRDD[K, V, C](     @transient var prev: RDD[_ <: Product2[K, V]],     part: Partitioner)   extends RDD[(K, C)](prev.context, Nil)    …   override def getDependencies: Seq[Dependency[_]] = //其shuffle的write和read由ShuffleDependency生成     List(new ShuffleDependency(prev, part, serializer, keyOrdering, aggregator, mapSideCombine))      …

通过向shuffleManager注册获取shuffle读写句柄，默认的shufflemanager是SortShuffleManager

class ShuffleDependency[K, V, C](     @transient _rdd: RDD[_ <: Product2[K, V]],     val partitioner: Partitioner,     val serializer: Option[Serializer] = None,     val keyOrdering: Option[Ordering[K]] = None,     val aggregator: Option[Aggregator[K, V, C]] = None,     val mapSideCombine: Boolean = false)   extends Dependency[Product2[K, V]]   override def rdd: RDD[Product2[K, V]] = _rdd.asInstanceOf[RDD[Product2[K, V]]]   val shuffleId: Int = _rdd.context.newShuffleId()   //注册shuffle句柄   val shuffleHandle: ShuffleHandle = _rdd.context.env.shuffleManager.registerShuffle(     shuffleId, _rdd.partitions.size, this)   _rdd.sparkContext.cleaner.foreach(_.registerShuffleForCleanup(this))

通过getwriter和getreader获取各自的读写句柄

private[spark] class SortShuffleManager(conf: SparkConf) extends ShuffleManager   private val indexShuffleBlockResolver = new IndexShuffleBlockResolver(conf)   private val shuffleMapNumber = new ConcurrentHashMap[Int, Int]()   /**    * Register a shuffle with the manager and obtain a handle for it to pass to tasks.    */   override def registerShuffle[K, V, C](       shuffleId: Int,       numMaps: Int,       dependency: ShuffleDependency[K, V, C]): ShuffleHandle =     new BaseShuffleHandle(shuffleId, numMaps, dependency)     /**    * Get a reader for a range of reduce partitions (startPartition to endPartition-1, inclusive).    * Called on executors by reduce tasks.    */   override def getReader[K, C](       handle: ShuffleHandle,       startPartition: Int,       endPartition: Int,       context: TaskContext): ShuffleReader[K, C] =     // We currently use the same block store shuffle fetcher as the hash-based shuffle.     new HashShuffleReader(       handle.asInstanceOf[BaseShuffleHandle[K, _, C]], startPartition, endPartition, context)     /** Get a writer for a given partition. Called on executors by map tasks. */   override def getWriter[K, V](handle: ShuffleHandle, mapId: Int, context: TaskContext)       : ShuffleWriter[K, V] =     val baseShuffleHandle = handle.asInstanceOf[BaseShuffleHandle[K, V, _]]     shuffleMapNumber.putIfAbsent(baseShuffleHandle.shuffleId, baseShuffleHandle.numMaps)     new SortShuffleWriter(       shuffleBlockResolver, baseShuffleHandle, mapId, context)     /** Remove a shuffle's metadata from the ShuffleManager. */   override def unregisterShuffle(shuffleId: Int): Boolean =     if (shuffleMapNumber.containsKey(shuffleId))       val numMaps = shuffleMapNumber.remove(shuffleId)       (0 until numMaps).map mapId =>         shuffleBlockResolver.removeDataByMap(shuffleId, mapId)               true     override val shuffleBlockResolver: IndexShuffleBlockResolver =     indexShuffleBlockResolver     /** Shut down this ShuffleManager. */   override def stop(): Unit =     shuffleBlockResolver.stop()

先看写句柄SortShuffleWriter：

private[spark] class SortShuffleWriter[K, V, C](     shuffleBlockResolver: IndexShuffleBlockResolver,     handle: BaseShuffleHandle[K, V, C],     mapId: Int,     context: TaskContext)   extends ShuffleWriter[K, V] with Logging   private val dep = handle.dependency   private val blockManager = SparkEnv.get.blockManager   private var sorter: ExternalSorter[K, V, _] = null   // Are we in the process of stopping? Because map tasks can call stop() with success = true   // and then call stop() with success = false if they get an exception, we want to make sure   // we don't try deleting files, etc twice.   private var stopping = false   private var mapStatus: MapStatus = null   private val writeMetrics = new ShuffleWriteMetrics()   context.taskMetrics.shuffleWriteMetrics = Some(writeMetrics)   /** Write a bunch of records to this task's output */   override def write(records: Iterator[Product2[K, V]]): Unit =     if (dep.mapSideCombine) //在map端聚合       require(dep.aggregator.isDefined, "Map-side combine without Aggregator specified!")       sorter = new ExternalSorter[K, V, C](         dep.aggregator, Some(dep.partitioner), dep.keyOrdering, dep.serializer)       sorter.insertAll(records)     else //不在map端聚合，连dep.partitioner都不传下去了，map端仅仅是按照分区函数分区罢了，其他不做任何事情       // In this case we pass neither an aggregator nor an ordering to the sorter, because we don't       // care whether the keys get sorted in each partition; that will be done on the reduce side       // if the operation being run is sortByKey.       sorter = new ExternalSorter[K, V, V](None, Some(dep.partitioner), None, dep.serializer)       sorter.insertAll(records)         // Don't bother including the time to open the merged output file in the shuffle write time,     // because it just opens a single file, so is typically too fast to measure accurately     // (see SPARK-3570).     val outputFile = shuffleBlockResolver.getDataFile(dep.shuffleId, mapId)     val blockId = ShuffleBlockId(dep.shuffleId, mapId, IndexShuffleBlockResolver.NOOP_REDUCE_ID)     val partitionLengths = sorter.writePartitionedFile(blockId, context, outputFile)     shuffleBlockResolver.writeIndexFile(dep.shuffleId, mapId, partitionLengths)     mapStatus = MapStatus(blockManager.shuffleServerId, partitionLengths)

且看ExternalSorter的insertAll：

def insertAll(records: Iterator[_ <: Product2[K, V]]): Unit =   // TODO: stop combining if we find that the reduction factor isn't high   val shouldCombine = aggregator.isDefined   if (shouldCombine) //如果在map端聚合,则需要利用mergeValue和createCombiner功能     // Combine values in-memory first using our AppendOnlyMap     val mergeValue = aggregator.get.mergeValue     val createCombiner = aggregator.get.createCombiner     var kv: Product2[K, V] = null     val update = (hadValue: Boolean, oldValue: C) =>       if (hadValue) mergeValue(oldValue, kv._2) else createCombiner(kv._2)         while (records.hasNext)       addElementsRead()       kv = records.next()       map.changeValue((getPartition(kv._1), kv._1), update)       maybeSpillCollection(usingMap = true)       else if (bypassMergeSort) //否则如果分区个数少的话，则写多个分区文件     // SPARK-4479: Also bypass buffering if merge sort is bypassed to avoid defensive copies     if (records.hasNext)       spillToPartitionFiles(         WritablePartitionedIterator.fromIterator(records.map kv =>           ((getPartition(kv._1), kv._1), kv._2.asInstanceOf[C])         )       )       else //否则如果分区个数多的话，则写一个文件，怕临时文件多     // Stick values into our buffer     while (records.hasNext)       addElementsRead()       val kv = records.next()       buffer.insert(getPartition(kv._1), kv._1, kv._2.asInstanceOf[C])       maybeSpillCollection(usingMap = false)

接着看读句柄HashShuffleReader：

private[spark] class HashShuffleReader[K, C](     handle: BaseShuffleHandle[K, _, C],     startPartition: Int,     endPartition: Int,     context: TaskContext)   extends ShuffleReader[K, C]   require(endPartition == startPartition + 1,     "Hash shuffle currently only supports fetching one partition")   private val dep = handle.dependency   /** Read the combined key-values for this reduce task */   override def read(): Iterator[Product2[K, C]] =     val ser = Serializer.getSerializer(dep.serializer)     val iter = BlockStoreShuffleFetcher.fetch(handle.shuffleId, startPartition, context, ser)     val aggregatedIter: Iterator[Product2[K, C]] = if (dep.aggregator.isDefined)       if (dep.mapSideCombine) //如果已经在map端聚合过的话，则利用mergeCombiners即可完成数据聚合         new InterruptibleIterator(context, dep.aggregator.get.combineCombinersByKey(iter, context))       else //否则利用createCombiner和mergeValue进行聚合         new InterruptibleIterator(context, dep.aggregator.get.combineValuesByKey(iter, context))           else       require(!dep.mapSideCombine, "Map-side combine without Aggregator specified!")       // Convert the Product2s to pairs since this is what downstream RDDs currently expect       iter.asInstanceOf[Iterator[Product2[K, C]]].map(pair => (pair._1, pair._2))         // Sort the output if there is a sort ordering defined.     dep.keyOrdering match       case Some(keyOrd: Ordering[K]) =>         // Create an ExternalSorter to sort the data. Note that if spark.shuffle.spill is disabled,         // the ExternalSorter won't spill to disk.         val sorter = new ExternalSorter[K, C, C](ordering = Some(keyOrd), serializer = Some(ser))         sorter.insertAll(aggregatedIter)         context.taskMetrics.incMemoryBytesSpilled(sorter.memoryBytesSpilled)         context.taskMetrics.incDiskBytesSpilled(sorter.diskBytesSpilled)         sorter.iterator       case None =>         aggregatedIter

因此假设一下的场景combineByKey的mapSideCombine一个为false，另外一个为true的情况:

val initialScores = Array(("Fred", 88.0), ("Fred", 95.0), ("Fred", 91.0), ("Wilma", 93.0), ("Wilma", 95.0), ("Wilma", 98.0))  val d1 = sc.parallelize(initialScores)  type MVType = (Int, Double) //定义一个元组类型(科目计数器,分数)  d1.combineByKey(    score => (1, score),    (c1: MVType, newScore) => (c1._1 + 1, c1._2 + newScore),    (c1: MVType, c2: MVType) => (c1._1 + c2._1, c1._2 + c2._2),   RangePartitioner,   false ).map case (name, (num, socre)) => (name, socre / num) .collect d1.combineByKey(    score => (1, score),    (c1: MVType, newScore) => (c1._1 + 1, c1._2 + newScore),    (c1: MVType, c2: MVType) => (c1._1 + c2._1, c1._2 + c2._2), ).map case (name, (num, socre)) => (name, socre / num) .collect

mapSideCombine为false的执行流程如下：

mapSideCombine为true的执行流程如下：

可见提前在map端做聚合可以减少shuffle过程中产生的数据。

23.distinct()

去重，删除RDD中相同的元素

def distinct(): RDD[T] = withScope   distinct(partitions.length) def distinct(numPartitions: Int)(implicit ord: Ordering[T] = null): RDD[T] = withScope   map(x => (x, null)).reduceByKey((x, y) => x, numPartitions).map(_._1) def reduceByKey(func: (V, V) => V, numPartitions: Int): RDD[(K, V)] = self.withScope   reduceByKey(new HashPartitioner(numPartitions), func) def reduceByKey(partitioner: Partitioner, func: (V, V) => V): RDD[(K, V)] = self.withScope   combineByKey[V]((v: V) => v, func, func, partitioner)

最终调用的还是combineByKey，因为对于RDD最终的聚合类操作，其本质运算都是由combineByKey完成的。其具体的执行流程如下：

24.groupByKey

将相同key的记录聚合起来

/**  * Group the values for each key in the RDD into a single sequence. Allows controlling the  * partitioning of the resulting key-value pair RDD by passing a Partitioner.  * The ordering of elements within each group is not guaranteed, and may even differ  * each time the resulting RDD is evaluated.  *  * Note: This operation may be very expensive. If you are grouping in order to perform an  * aggregation (such as a sum or average) over each key, using [[PairRDDFunctions.aggregateByKey]]  * or [[PairRDDFunctions.reduceByKey]] will provide much better performance.  *  * Note: As currently implemented, groupByKey must be able to hold all the key-value pairs for any  * key in memory. If a key has too many values, it can result in an [[OutOfMemoryError]].  */ 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 = combineByKey[CompactBuffer[V]](     createCombiner, mergeValue, mergeCombiners, partitioner, mapSideCombine = false)   bufs.asInstanceOf[RDD[(K, Iterable[V])]]

其本质就是利用combineByKey来实现相同KEY的聚合操作，但是需要注意的一点是groupByKey不在map端聚合，因为它在map端聚合无法减少网络传输的数据量，反而会增加maptask运行消耗的java内存，进而导致GC拖慢整个计算过程。

现在假设分区函数相同和不同的情况下其groupbykey的执行流程如下：

分区函数相同：

分区函数不相同：

 

25.aggregateByKey

聚合操作，将相同的key的value聚合起来，类似于sql里面的聚合函数，可以实现求max,min,avg等操作。

/**  * Aggregate the values of each key, using given combine functions and a neutral "zero value".  * This function can return a different result type, U, than the type of the values in this RDD,  * V. Thus, we need one operation for merging a V into a U and one operation for merging two U's,  * as in scala.TraversableOnce. The former operation is used for merging values within a  * partition, and the latter is used for merging values between partitions. To avoid memory  * allocation, both of these functions are allowed to modify and return their first argument  * instead of creating a new U. / def aggregateByKey[U: ClassTag](zeroValue: U, numPartitions: Int)(seqOp: (U, V) => U,     combOp: (U, U) => U): RDD[(K, U)] = self.withScope   aggregateByKey(zeroValue, new HashPartitioner(numPartitions))(seqOp, combOp) /**  * Aggregate the values of each key, using given combine functions and a neutral "zero value".  * This function can return a different result type, U, than the type of the values in this RDD,  * V. Thus, we need one operation for merging a V into a U and one operation for merging two U's,  * as in scala.TraversableOnce. The former operation is used for merging values within a  * partition, and the latter is used for merging values between partitions. To avoid memory  * allocation, both of these functions are allowed to modify and return their first argument  * instead of creating a new U.  */ def aggregateByKey[U: ClassTag](zeroValue: U, partitioner: Partitioner)(seqOp: (U, V) => U,     combOp: (U, U) => U): RDD[(K, U)] = self.withScope   // Serialize the zero value to a byte array so that we can get a new clone of it on each key   val zeroBuffer = SparkEnv.get.serializer.newInstance().serialize(zeroValue)   val zeroArray = new Array[Byte](zeroBuffer.limit)   zeroBuffer.get(zeroArray)   lazy val cachedSerializer = SparkEnv.get.serializer.newInstance()   val createZero = () => cachedSerializer.deserialize[U](ByteBuffer.wrap(zeroArray))   // We will clean the combiner closure later in `combineByKey`   val cleanedSeqOp = self.context.clean(seqOp)   combineByKey[U]((v: V) => cleanedSeqOp(createZero(), v), cleanedSeqOp, combOp, partitioner)

首先需要和aggregate操作区别开来，aggregate里面的SeqOp和combOp都会使用zeroValue的值，而aggregateByKey的zeroValue只会在SeqOp中使用。且其mapSideCombine为true，会在map端进行聚合，假设利用aggregateByKey计算每月平均气温的操作如下：

val rdd = sc.textFile("气象数据")    val rdd2 = rdd.map(x=>x.split(" ")).map(x => (x(0).substring("从年月日中提取月"),x(1).toInt))    val zeroValue = (0,0)   val seqOp= (u:(Int, Int), v:Int) =>      (u._1 + 1, u._2 + v)            val compOp= (c1:(Int,Int),c2:(Int,Int))=>      (u1._1 + u2._1, u1._2 + u2._2)                val vdd3 = vdd2.aggregateByKey(    zeroValue ,    seqOp,    compOp  )        rdd3.foreach(x=>println(x._1 + ": average tempreture is " + x._2._2/x._2._1)

由于rdd2是从rdd转化而来，而rdd是来自文本数据，则其无分区函数，而aggregateByKey执行时是以hash分区的，那么其计算过程如下: