【原創】大數據基礎之Spark（8）Spark中Join實現原理

 

spark中join有兩種，一種是RDD的join，一種是sql中的join，分別來看：

1 RDD join

org.apache.spark.rdd.PairRDDFunctions

  /**
   * Return an RDD containing all pairs of elements with matching keys in `this` and `other`. Each
   * pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in `this` and
   * (k, v2) is in `other`. Performs a hash join across the cluster.
   */
  def join[W](other: RDD[(K, W)]): RDD[(K, (V, W))] = self.withScope {
    join(other, defaultPartitioner(self, other))
  }

  /**
   * Return an RDD containing all pairs of elements with matching keys in `this` and `other`. Each
   * pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in `this` and
   * (k, v2) is in `other`. Uses the given Partitioner to partition the output RDD.
   */
  def join[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, W))] = self.withScope {
    this.cogroup(other, partitioner).flatMapValues( pair =>
      for (v <- pair._1.iterator; w <- pair._2.iterator) yield (v, w)
    )
  }

  /**
   * For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the
   * list of values for that key in `this` as well as `other`.
   */
  def cogroup[W](other: RDD[(K, W)], partitioner: Partitioner)
      : RDD[(K, (Iterable[V], Iterable[W]))] = self.withScope {
    if (partitioner.isInstanceOf[HashPartitioner] && keyClass.isArray) {
      throw new SparkException("HashPartitioner cannot partition array keys.")
    }
    val cg = new CoGroupedRDD[K](Seq(self, other), partitioner)
    cg.mapValues { case Array(vs, w1s) =>
      (vs.asInstanceOf[Iterable[V]], w1s.asInstanceOf[Iterable[W]])
    }
  }

join操做會返回CoGroupedRDD，CoGroupedRDD構造參數爲rdd數組，即多個須要join的rdd，下面看CoGroupedRDD：

org.apache.spark.rdd.CoGroupedRDD

class CoGroupedRDD[K: ClassTag](
    @transient var rdds: Seq[RDD[_ <: Product2[K, _]]],
    part: Partitioner)
  extends RDD[(K, Array[Iterable[_]])](rdds.head.context, Nil) {

  override def getDependencies: Seq[Dependency[_]] = {
    rdds.map { rdd: RDD[_] =>
      if (rdd.partitioner == Some(part)) {
        logDebug("Adding one-to-one dependency with " + rdd)
        new OneToOneDependency(rdd)
      } else {
        logDebug("Adding shuffle dependency with " + rdd)
        new ShuffleDependency[K, Any, CoGroupCombiner](
          rdd.asInstanceOf[RDD[_ <: Product2[K, _]]], part, serializer)
      }
    }
  }

  override def compute(s: Partition, context: TaskContext): Iterator[(K, Array[Iterable[_]])] = {
    val split = s.asInstanceOf[CoGroupPartition]
    val numRdds = dependencies.length

    // A list of (rdd iterator, dependency number) pairs
    val rddIterators = new ArrayBuffer[(Iterator[Product2[K, Any]], Int)]
    for ((dep, depNum) <- dependencies.zipWithIndex) dep match {
      case oneToOneDependency: OneToOneDependency[Product2[K, Any]] @unchecked =>
        val dependencyPartition = split.narrowDeps(depNum).get.split
        // Read them from the parent
        val it = oneToOneDependency.rdd.iterator(dependencyPartition, context)
        rddIterators += ((it, depNum))

      case shuffleDependency: ShuffleDependency[_, _, _] =>
        // Read map outputs of shuffle
        val it = SparkEnv.get.shuffleManager
          .getReader(shuffleDependency.shuffleHandle, split.index, split.index + 1, context)
          .read()
        rddIterators += ((it, depNum))
    }

    val map = createExternalMap(numRdds)
    for ((it, depNum) <- rddIterators) {
      map.insertAll(it.map(pair => (pair._1, new CoGroupValue(pair._2, depNum))))
    }
    context.taskMetrics().incMemoryBytesSpilled(map.memoryBytesSpilled)
    context.taskMetrics().incDiskBytesSpilled(map.diskBytesSpilled)
    context.taskMetrics().incPeakExecutionMemory(map.peakMemoryUsedBytes)
    new InterruptibleIterator(context,
      map.iterator.asInstanceOf[Iterator[(K, Array[Iterable[_]])]])
  }

  private def createExternalMap(numRdds: Int)
    : ExternalAppendOnlyMap[K, CoGroupValue, CoGroupCombiner] = {

    val createCombiner: (CoGroupValue => CoGroupCombiner) = value => {
      val newCombiner = Array.fill(numRdds)(new CoGroup)
      newCombiner(value._2) += value._1
      newCombiner
    }
    val mergeValue: (CoGroupCombiner, CoGroupValue) => CoGroupCombiner =
      (combiner, value) => {
      combiner(value._2) += value._1
      combiner
    }
    val mergeCombiners: (CoGroupCombiner, CoGroupCombiner) => CoGroupCombiner =
      (combiner1, combiner2) => {
        var depNum = 0
        while (depNum < numRdds) {
          combiner1(depNum) ++= combiner2(depNum)
          depNum += 1
        }
        combiner1
      }
    new ExternalAppendOnlyMap[K, CoGroupValue, CoGroupCombiner](
      createCombiner, mergeValue, mergeCombiners)
  }

CoGroupedRDD首先將rdds逐個轉化爲dependency，而後將全部的dependency轉化爲rddIterators，最後經過ExternalAppendOnlyMap來實現合併；

若是rdd須要shuffle，是經過ShuffleManager實現，ShuffleManager實現類爲SortShuffleManager，shuffle過程詳見：http://www.javashuo.com/article/p-ehbhecmo-bo.html

 

附：spark中dependency結構，即常說的寬依賴、窄依賴：

org.apache.spark.Dependency

Dependency

         NarrowDependency

                  OneToOneDependency

                  RangeDependency

         ShuffleDependency

區別就是shuffle，不須要shuffle就是NarrowDependency，須要就是ShuffleDependency；

2 sql join

sql中的join有一個選擇策略：

org.apache.spark.sql.execution.SparkStrategies.JoinSelection

    def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {

      // --- BroadcastHashJoin --------------------------------------------------------------------

      case ExtractEquiJoinKeys(joinType, leftKeys, rightKeys, condition, left, right)
        if canBuildRight(joinType) && canBroadcast(right) =>
        Seq(joins.BroadcastHashJoinExec(
          leftKeys, rightKeys, joinType, BuildRight, condition, planLater(left), planLater(right)))

      case ExtractEquiJoinKeys(joinType, leftKeys, rightKeys, condition, left, right)
        if canBuildLeft(joinType) && canBroadcast(left) =>
        Seq(joins.BroadcastHashJoinExec(
          leftKeys, rightKeys, joinType, BuildLeft, condition, planLater(left), planLater(right)))

      // --- ShuffledHashJoin ---------------------------------------------------------------------

      case ExtractEquiJoinKeys(joinType, leftKeys, rightKeys, condition, left, right)
         if !conf.preferSortMergeJoin && canBuildRight(joinType) && canBuildLocalHashMap(right)
           && muchSmaller(right, left) ||
           !RowOrdering.isOrderable(leftKeys) =>
        Seq(joins.ShuffledHashJoinExec(
          leftKeys, rightKeys, joinType, BuildRight, condition, planLater(left), planLater(right)))

      case ExtractEquiJoinKeys(joinType, leftKeys, rightKeys, condition, left, right)
         if !conf.preferSortMergeJoin && canBuildLeft(joinType) && canBuildLocalHashMap(left)
           && muchSmaller(left, right) ||
           !RowOrdering.isOrderable(leftKeys) =>
        Seq(joins.ShuffledHashJoinExec(
          leftKeys, rightKeys, joinType, BuildLeft, condition, planLater(left), planLater(right)))

      // --- SortMergeJoin ------------------------------------------------------------

      case ExtractEquiJoinKeys(joinType, leftKeys, rightKeys, condition, left, right)
        if RowOrdering.isOrderable(leftKeys) =>
        joins.SortMergeJoinExec(
          leftKeys, rightKeys, joinType, condition, planLater(left), planLater(right)) :: Nil
...

其中conf.preferSortMergeJoin

org.apache.spark.sql.internal.SQLConf

  val PREFER_SORTMERGEJOIN = SQLConfigBuilder("spark.sql.join.preferSortMergeJoin")
    .internal()
    .doc("When true, prefer sort merge join over shuffle hash join.")
    .booleanConf
    .createWithDefault(true)

配置spark.sql.join.preferSortMergeJoin，默認爲true，便是否優先使用SortMergeJoin；

 

能夠看到join實現主要有3種，即BroadcastHashJoinExec、ShuffledHashJoinExec和SortMergeJoinExec，優先級爲

• 1 若是canBroadcast，則BroadcastHashJoinExec；
• 2 若是spark.sql.join.preferSortMergeJoin=false，則ShuffledHashJoinExec；
• 3 不然爲SortMergeJoinExec；

其中BroadcastHashJoinExec和ShuffledHashJoinExec都會用到HashJoin，先看HashJoin：

2.1 HashJoin

org.apache.spark.sql.execution.joins.HashJoin

  protected def join(
      streamedIter: Iterator[InternalRow],
      hashed: HashedRelation,
      numOutputRows: SQLMetric): Iterator[InternalRow] = {

    val joinedIter = joinType match {
      case _: InnerLike =>
        innerJoin(streamedIter, hashed)
      case LeftOuter | RightOuter =>
        outerJoin(streamedIter, hashed)
      case LeftSemi =>
        semiJoin(streamedIter, hashed)
      case LeftAnti =>
        antiJoin(streamedIter, hashed)
      case j: ExistenceJoin =>
        existenceJoin(streamedIter, hashed)
      case x =>
        throw new IllegalArgumentException(
          s"BroadcastHashJoin should not take $x as the JoinType")
    }

    val resultProj = createResultProjection
    joinedIter.map { r =>
      numOutputRows += 1
      resultProj(r)
    }
  }

  private def innerJoin(
      streamIter: Iterator[InternalRow],
      hashedRelation: HashedRelation): Iterator[InternalRow] = {
    val joinRow = new JoinedRow
    val joinKeys = streamSideKeyGenerator()
    streamIter.flatMap { srow =>
      joinRow.withLeft(srow)
      val matches = hashedRelation.get(joinKeys(srow))
      if (matches != null) {
        matches.map(joinRow.withRight(_)).filter(boundCondition)
      } else {
        Seq.empty
      }
    }
  }

這裏只貼出內關聯，即innerJoin，代碼比較簡單，注意這裏是內存操做，會在單個partition內部進行；

2.2 BroadcastHashJoinExec

org.apache.spark.sql.execution.joins.BroadcastHashJoinExec

  protected override def doExecute(): RDD[InternalRow] = {
    val numOutputRows = longMetric("numOutputRows")

    val broadcastRelation = buildPlan.executeBroadcast[HashedRelation]()
    streamedPlan.execute().mapPartitions { streamedIter =>
      val hashed = broadcastRelation.value.asReadOnlyCopy()
      TaskContext.get().taskMetrics().incPeakExecutionMemory(hashed.estimatedSize)
      join(streamedIter, hashed, numOutputRows)
    }
  }

這裏會將buildPlan廣播出去，而後在streamedPlan上經過mapPartitions在1個分區內部進行join，join方法見HashJoin；

2.3 ShuffledHashJoinExec

org.apache.spark.sql.execution.joins.ShuffledHashJoinExec

  protected override def doExecute(): RDD[InternalRow] = {
    val numOutputRows = longMetric("numOutputRows")
    streamedPlan.execute().zipPartitions(buildPlan.execute()) { (streamIter, buildIter) =>
      val hashed = buildHashedRelation(buildIter)
      join(streamIter, hashed, numOutputRows)
    }
  }

join過程爲先將兩個rdd（streamedPlan和buildPlan）進行zipPartitions，而後在1個partition內部join，join方法見HashJoin；

2.4 SortMergeJoinExec

org.apache.spark.sql.execution.joins.SortMergeJoinExec

  protected override def doExecute(): RDD[InternalRow] = {
    val numOutputRows = longMetric("numOutputRows")

    left.execute().zipPartitions(right.execute()) { (leftIter, rightIter) =>
      val boundCondition: (InternalRow) => Boolean = {
        condition.map { cond =>
          newPredicate(cond, left.output ++ right.output).eval _
        }.getOrElse {
          (r: InternalRow) => true
        }
      }

      // An ordering that can be used to compare keys from both sides.
      val keyOrdering = newNaturalAscendingOrdering(leftKeys.map(_.dataType))
      val resultProj: InternalRow => InternalRow = UnsafeProjection.create(output, output)

      joinType match {
        case _: InnerLike =>
          new RowIterator {
            private[this] var currentLeftRow: InternalRow = _
            private[this] var currentRightMatches: ArrayBuffer[InternalRow] = _
            private[this] var currentMatchIdx: Int = -1
            private[this] val smjScanner = new SortMergeJoinScanner(
              createLeftKeyGenerator(),
              createRightKeyGenerator(),
              keyOrdering,
              RowIterator.fromScala(leftIter),
              RowIterator.fromScala(rightIter)
            )
            private[this] val joinRow = new JoinedRow

            if (smjScanner.findNextInnerJoinRows()) {
              currentRightMatches = smjScanner.getBufferedMatches
              currentLeftRow = smjScanner.getStreamedRow
              currentMatchIdx = 0
            }

            override def advanceNext(): Boolean = {
              while (currentMatchIdx >= 0) {
                if (currentMatchIdx == currentRightMatches.length) {
                  if (smjScanner.findNextInnerJoinRows()) {
                    currentRightMatches = smjScanner.getBufferedMatches
                    currentLeftRow = smjScanner.getStreamedRow
                    currentMatchIdx = 0
                  } else {
                    currentRightMatches = null
                    currentLeftRow = null
                    currentMatchIdx = -1
                    return false
                  }
                }
                joinRow(currentLeftRow, currentRightMatches(currentMatchIdx))
                currentMatchIdx += 1
                if (boundCondition(joinRow)) {
                  numOutputRows += 1
                  return true
                }
              }
              false
            }

            override def getRow: InternalRow = resultProj(joinRow)
          }.toScala
...

和ShuffledHashJoinExec同樣，一樣先zipPartitions，而後在1個partition內部根據joinType返回不一樣的RowIterator實現類，上邊代碼包含內關聯實現，大部分工做經過SortMergeJoinScanner實現

org.apache.spark.sql.execution.joins.SortMergeJoinScanner

  final def findNextInnerJoinRows(): Boolean = {
    while (advancedStreamed() && streamedRowKey.anyNull) {
      // Advance the streamed side of the join until we find the next row whose join key contains
      // no nulls or we hit the end of the streamed iterator.
    }
    if (streamedRow == null) {
      // We have consumed the entire streamed iterator, so there can be no more matches.
      matchJoinKey = null
      bufferedMatches.clear()
      false
    } else if (matchJoinKey != null && keyOrdering.compare(streamedRowKey, matchJoinKey) == 0) {
      // The new streamed row has the same join key as the previous row, so return the same matches.
      true
    } else if (bufferedRow == null) {
      // The streamed row's join key does not match the current batch of buffered rows and there are
      // no more rows to read from the buffered iterator, so there can be no more matches.
      matchJoinKey = null
      bufferedMatches.clear()
      false
    } else {
      // Advance both the streamed and buffered iterators to find the next pair of matching rows.
      var comp = keyOrdering.compare(streamedRowKey, bufferedRowKey)
      do {
        if (streamedRowKey.anyNull) {
          advancedStreamed()
        } else {
          assert(!bufferedRowKey.anyNull)
          comp = keyOrdering.compare(streamedRowKey, bufferedRowKey)
          if (comp > 0) advancedBufferedToRowWithNullFreeJoinKey()
          else if (comp < 0) advancedStreamed()
        }
      } while (streamedRow != null && bufferedRow != null && comp != 0)
      if (streamedRow == null || bufferedRow == null) {
        // We have either hit the end of one of the iterators, so there can be no more matches.
        matchJoinKey = null
        bufferedMatches.clear()
        false
      } else {
        // The streamed row's join key matches the current buffered row's join, so walk through the
        // buffered iterator to buffer the rest of the matching rows.
        assert(comp == 0)
        bufferMatchingRows()
        true
      }
    }
  }

  /**
   * Advance the streamed iterator and compute the new row's join key.
   * @return true if the streamed iterator returned a row and false otherwise.
   */
  private def advancedStreamed(): Boolean = {
    if (streamedIter.advanceNext()) {
      streamedRow = streamedIter.getRow
      streamedRowKey = streamedKeyGenerator(streamedRow)
      true
    } else {
      streamedRow = null
      streamedRowKey = null
      false
    }
  }

  /**
   * Advance the buffered iterator until we find a row with join key that does not contain nulls.
   * @return true if the buffered iterator returned a row and false otherwise.
   */
  private def advancedBufferedToRowWithNullFreeJoinKey(): Boolean = {
    var foundRow: Boolean = false
    while (!foundRow && bufferedIter.advanceNext()) {
      bufferedRow = bufferedIter.getRow
      bufferedRowKey = bufferedKeyGenerator(bufferedRow)
      foundRow = !bufferedRowKey.anyNull
    }
    if (!foundRow) {
      bufferedRow = null
      bufferedRowKey = null
      false
    } else {
      true
    }
  }

  /**
   * Called when the streamed and buffered join keys match in order to buffer the matching rows.
   */
  private def bufferMatchingRows(): Unit = {
    assert(streamedRowKey != null)
    assert(!streamedRowKey.anyNull)
    assert(bufferedRowKey != null)
    assert(!bufferedRowKey.anyNull)
    assert(keyOrdering.compare(streamedRowKey, bufferedRowKey) == 0)
    // This join key may have been produced by a mutable projection, so we need to make a copy:
    matchJoinKey = streamedRowKey.copy()
    bufferedMatches.clear()
    do {
      bufferedMatches += bufferedRow.copy() // need to copy mutable rows before buffering them
      advancedBufferedToRowWithNullFreeJoinKey()
    } while (bufferedRow != null && keyOrdering.compare(streamedRowKey, bufferedRowKey) == 0)
  }

能夠看到過程和二路歸併排序Binary Merge Sort差很少；

 

附：RowIterator是一個抽象類，本質是一個接口，是一個常見的Iterator定義，以下：

org.apache.spark.sql.execution.RowIterator

abstract class RowIterator {
  /**
   * Advance this iterator by a single row. Returns `false` if this iterator has no more rows
   * and `true` otherwise. If this returns `true`, then the new row can be retrieved by calling
   * [[getRow]].
   */
  def advanceNext(): Boolean

  /**
   * Retrieve the row from this iterator. This method is idempotent. It is illegal to call this
   * method after [[advanceNext()]] has returned `false`.
   */
  def getRow: InternalRow

  /**
   * Convert this RowIterator into a [[scala.collection.Iterator]].
   */
  def toScala: Iterator[InternalRow] = new RowIteratorToScala(this)
}