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/*
* 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, UnsafeRow}
import org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeRowJoiner
import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
import org.apache.spark.sql.execution.metric.SQLMetrics
import org.apache.spark.util.CompletionIterator
import org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter
/**
* 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).
*/
private[spark]
class UnsafeCartesianRDD(left : RDD[UnsafeRow], right : RDD[UnsafeRow], numFieldsOfRight: Int)
extends CartesianRDD[UnsafeRow, UnsafeRow](left.sparkContext, left, right) {
override def compute(split: Partition, context: TaskContext): Iterator[(UnsafeRow, UnsafeRow)] = {
// We will not sort the rows, so prefixComparator and recordComparator are null.
val sorter = UnsafeExternalSorter.create(
context.taskMemoryManager(),
SparkEnv.get.blockManager,
SparkEnv.get.serializerManager,
context,
null,
null,
1024,
SparkEnv.get.memoryManager.pageSizeBytes)
val partition = split.asInstanceOf[CartesianPartition]
for (y <- rdd2.iterator(partition.s2, context)) {
sorter.insertRecord(y.getBaseObject, y.getBaseOffset, y.getSizeInBytes, 0)
}
// Create an iterator from sorter and wrapper it as Iterator[UnsafeRow]
def createIter(): Iterator[UnsafeRow] = {
val iter = sorter.getIterator
val unsafeRow = new UnsafeRow(numFieldsOfRight)
new Iterator[UnsafeRow] {
override def hasNext: Boolean = {
iter.hasNext
}
override def next(): UnsafeRow = {
iter.loadNext()
unsafeRow.pointTo(iter.getBaseObject, iter.getBaseOffset, iter.getRecordLength)
unsafeRow
}
}
}
val resultIter =
for (x <- rdd1.iterator(partition.s1, context);
y <- createIter()) yield (x, y)
CompletionIterator[(UnsafeRow, UnsafeRow), Iterator[(UnsafeRow, UnsafeRow)]](
resultIter, sorter.cleanupResources)
}
}
case class CartesianProduct(left: SparkPlan, right: SparkPlan) extends BinaryNode {
override def output: Seq[Attribute] = left.output ++ right.output
override private[sql] lazy val metrics = Map(
"numOutputRows" -> SQLMetrics.createLongMetric(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)
pair.mapPartitionsInternal { iter =>
val joiner = GenerateUnsafeRowJoiner.create(left.schema, right.schema)
iter.map { r =>
numOutputRows += 1
joiner.join(r._1, r._2)
}
}
}
}
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