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From tianyi <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-4485][SQL]Add BroadcastHashOuterJoin
Date Tue, 25 Nov 2014 05:09:24 GMT
Github user tianyi commented on a diff in the pull request:

    https://github.com/apache/spark/pull/3362#discussion_r20843639
  
    --- Diff: sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
---
    @@ -0,0 +1,164 @@
    +/*
    + * Licensed to the Apache Software Foundation (ASF) under one or more
    + * contributor license agreements.  See the NOTICE file distributed with
    + * this work for additional information regarding copyright ownership.
    + * The ASF licenses this file to You under the Apache License, Version 2.0
    + * (the "License"); you may not use this file except in compliance with
    + * the License.  You may obtain a copy of the License at
    + *
    + *    http://www.apache.org/licenses/LICENSE-2.0
    + *
    + * Unless required by applicable law or agreed to in writing, software
    + * distributed under the License is distributed on an "AS IS" BASIS,
    + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    + * See the License for the specific language governing permissions and
    + * limitations under the License.
    + */
    +
    +package org.apache.spark.sql.execution.joins
    +
    +import java.util.{HashMap => JavaHashMap}
    +
    +import scala.collection.JavaConversions._
    +
    +import org.apache.spark.annotation.DeveloperApi
    +import org.apache.spark.sql.catalyst.expressions._
    +import org.apache.spark.sql.catalyst.plans.physical.{Partitioning}
    +import org.apache.spark.sql.catalyst.plans.{JoinType, LeftOuter, RightOuter}
    +import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
    +import org.apache.spark.util.collection.CompactBuffer
    +
    +
    +/**
    + * :: DeveloperApi ::
    + * Performs a hash based outer join for two child relations. When the output RDD of this
operator is
    + * being constructed, a Spark job is asynchronously started to calculate the values for
the
    + * broadcasted relation.  This data is then placed in a Spark broadcast variable.  The
streamed
    + * relation is not shuffled.
    + * Comment:In left(right) outer join only the right(left) table has an
    + * estimated physical size smaller than the user-settable threshold
    + * [[org.apache.spark.sql.SQLConf.AUTO_BROADCASTJOIN_THRESHOLD]] the planner
    + * would mark it as the broadcasted relation.
    + */
    +@DeveloperApi
    +case class BroadcastHashOuterJoin(
    +    leftKeys: Seq[Expression],
    +    rightKeys: Seq[Expression],
    +    joinType: JoinType,
    +    condition: Option[Expression],
    +    left: SparkPlan,
    +    right: SparkPlan)
    +  extends BinaryNode {
    +
    +  private val (streamedPlan, broadcastPlan) = joinType match {
    +    case LeftOuter => (left, right)
    +    case RightOuter => (right, left)
    +  }
    +
    +  private val (streamedKeys, broadcastKeys) = joinType match {
    +    case LeftOuter => (leftKeys, rightKeys)
    +    case RightOuter => (rightKeys, leftKeys)
    +  }
    +
    +  override def outputPartitioning: Partitioning = joinType match {
    +    case LeftOuter => left.outputPartitioning
    +    case RightOuter => right.outputPartitioning
    +    case x => throw new Exception(s"HashOuterJoin should not take $x as the JoinType")
    +  }
    +
    +  override def output = {
    +    joinType match {
    +      case LeftOuter =>
    +        left.output ++ right.output.map(_.withNullability(true))
    +      case RightOuter =>
    +        left.output.map(_.withNullability(true)) ++ right.output
    +      case x =>
    +        throw new Exception(s"HashOuterJoin should not take $x as the JoinType")
    +    }
    +  }
    +
    +  @transient private[this] lazy val BroadcastSideKeyGenerator: Projection =
    +    newProjection(broadcastKeys, broadcastPlan.output)
    +
    +  @transient private[this] lazy val streamSideKeyGenerator: () => MutableProjection
=
    +    newMutableProjection(streamedKeys, streamedPlan.output)
    +
    +  @transient private[this] lazy val DUMMY_LIST = Seq[Row](null)
    +  @transient private[this] lazy val EMPTY_LIST = Seq.empty[Row]
    +
    +  private[this] def buildHashTable(
    +      iter: Iterator[Row], keyGenerator: Projection): JavaHashMap[Row, CompactBuffer[Row]]
= {
    +    val hashTable = new JavaHashMap[Row, CompactBuffer[Row]]()
    +    while (iter.hasNext) {
    +      val currentRow = iter.next()
    +      val rowKey = keyGenerator(currentRow)
    +
    +      var existingMatchList = hashTable.get(rowKey)
    +      if (existingMatchList == null) {
    +        existingMatchList = new CompactBuffer[Row]()
    +        hashTable.put(rowKey, existingMatchList)
    +      }
    +
    +      existingMatchList += currentRow.copy()
    +    }
    +
    +    hashTable
    +  }
    +
    +  override def execute() = {
    +    val input: Array[Row] = broadcastPlan.execute().map(_.copy()).collect()
    +    val hashed = buildHashTable(input.iterator, BroadcastSideKeyGenerator)
    +    val broadcastHashTable = sparkContext.broadcast(hashed)
    +    val boundCondition =
    +      condition.map(newPredicate(_, left.output ++ right.output)).getOrElse((row: Row)
=> true)
    +
    +    streamedPlan.execute().mapPartitions {
    +      streamedIter =>
    +        var i = 0
    +        val joinedRow = new JoinedRow
    +        val joinKey = streamSideKeyGenerator()
    +        val broadcastNulls = new GenericMutableRow(broadcastPlan.output.size)
    +
    +        streamedIter.flatMap {
    +          streamedRow =>
    +            val rowKey = joinKey(streamedRow)
    +            val broadcastRow = broadcastHashTable.value.getOrElse(rowKey, EMPTY_LIST)
    +            var matched = false
    +            joinType match {
    +              case LeftOuter =>
    +                joinedRow.withLeft(streamedRow)
    +                (if (!rowKey.anyNull) broadcastRow.collect {
    +                  case r if (boundCondition(joinedRow.withRight(r))) =>
    +                    matched = true
    +                    joinedRow.copy
    +                } else {
    +                  Nil
    +                }) ++ DUMMY_LIST.filter(_ => !matched).map(_ => {
    +                  // DUMMY_LIST.filter(_ => !matched) is a tricky way to add additional
row,
    +                  // as we don't know whether we need to append it until finish iterating
all of the
    +                  // records in right side.
    +                  // If we didn't get any proper row, then append a single row with empty
right
    +                  joinedRow.withRight(broadcastNulls).copy
    +                })
    +              case RightOuter =>
    +                joinedRow.withRight(streamedRow)
    +                (if (!rowKey.anyNull) broadcastRow.collect {
    +                  case r if (boundCondition(joinedRow.withLeft(r))) =>
    +                    matched = true
    +                    joinedRow.copy
    +                } else {
    +                  Nil
    +                }) ++ DUMMY_LIST.filter(_ => !matched).map(_ => {
    +                  // DUMMY_LIST.filter(_ => !matched) is a tricky way to add additional
row,
    +                  // as we don't know whether we need to append it until finish iterating
all of the
    +                  // records in left side.
    +                  // If we didn't get any proper row, then append a single row with empty
left
    +                  joinedRow.withLeft(broadcastNulls).copy
    +                })
    +              case _ => Nil
    --- End diff --
    
    I think we should throw a exception here, because other types of outer join should not
use `BroadcastHashOuterJoin`


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