/* * 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 org.apache.spark._ import org.apache.spark.rdd.{CartesianPartition, CartesianRDD, RDD} import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, JoinedRow, UnsafeRow} import org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeRowJoiner import org.apache.spark.sql.execution.{BinaryExecNode, ExternalAppendOnlyUnsafeRowArray, SparkPlan} import org.apache.spark.sql.execution.metric.SQLMetrics import org.apache.spark.util.CompletionIterator /** * An optimized CartesianRDD for UnsafeRow, which will cache the rows from second child RDD, * will be much faster than building the right partition for every row in left RDD, it also * materialize the right RDD (in case of the right RDD is nondeterministic). */ class UnsafeCartesianRDD( left : RDD[UnsafeRow], right : RDD[UnsafeRow], numFieldsOfRight: Int, inMemoryBufferThreshold: Int, spillThreshold: Int) extends CartesianRDD[UnsafeRow, UnsafeRow](left.sparkContext, left, right) { override def compute(split: Partition, context: TaskContext): Iterator[(UnsafeRow, UnsafeRow)] = { val rowArray = new ExternalAppendOnlyUnsafeRowArray(inMemoryBufferThreshold, spillThreshold) val partition = split.asInstanceOf[CartesianPartition] rdd2.iterator(partition.s2, context).foreach(rowArray.add) // Create an iterator from rowArray def createIter(): Iterator[UnsafeRow] = rowArray.generateIterator() val resultIter = for (x <- rdd1.iterator(partition.s1, context); y <- createIter()) yield (x, y) CompletionIterator[(UnsafeRow, UnsafeRow), Iterator[(UnsafeRow, UnsafeRow)]]( resultIter, rowArray.clear()) } } case class CartesianProductExec( left: SparkPlan, right: SparkPlan, condition: Option[Expression]) extends BinaryExecNode { override def output: Seq[Attribute] = left.output ++ right.output override lazy val metrics = Map( "numOutputRows" -> SQLMetrics.createMetric(sparkContext, "number of output rows")) protected override def doExecute(): RDD[InternalRow] = { val numOutputRows = longMetric("numOutputRows") val leftResults = left.execute().asInstanceOf[RDD[UnsafeRow]] val rightResults = right.execute().asInstanceOf[RDD[UnsafeRow]] val pair = new UnsafeCartesianRDD( leftResults, rightResults, right.output.size, sqlContext.conf.cartesianProductExecBufferInMemoryThreshold, sqlContext.conf.cartesianProductExecBufferSpillThreshold) pair.mapPartitionsWithIndexInternal { (index, iter) => val joiner = GenerateUnsafeRowJoiner.create(left.schema, right.schema) val filtered = if (condition.isDefined) { val boundCondition = newPredicate(condition.get, left.output ++ right.output) boundCondition.initialize(index) val joined = new JoinedRow iter.filter { r => boundCondition.eval(joined(r._1, r._2)) } } else { iter } filtered.map { r => numOutputRows += 1 joiner.join(r._1, r._2) } } } }