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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
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{Encoder, Row, SparkSession}
import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.plans.logical._
import org.apache.spark.sql.catalyst.plans.physical.{Partitioning, UnknownPartitioning}
import org.apache.spark.sql.execution.metric.SQLMetrics
import org.apache.spark.sql.types.DataType
import org.apache.spark.util.Utils
object RDDConversions {
def productToRowRdd[A <: Product](data: RDD[A], outputTypes: Seq[DataType]): RDD[InternalRow] = {
data.mapPartitions { iterator =>
val numColumns = outputTypes.length
val mutableRow = new GenericInternalRow(numColumns)
val converters = outputTypes.map(CatalystTypeConverters.createToCatalystConverter)
iterator.map { r =>
var i = 0
while (i < numColumns) {
mutableRow(i) = converters(i)(r.productElement(i))
i += 1
}
mutableRow
}
}
}
/**
* Convert the objects inside Row into the types Catalyst expected.
*/
def rowToRowRdd(data: RDD[Row], outputTypes: Seq[DataType]): RDD[InternalRow] = {
data.mapPartitions { iterator =>
val numColumns = outputTypes.length
val mutableRow = new GenericInternalRow(numColumns)
val converters = outputTypes.map(CatalystTypeConverters.createToCatalystConverter)
iterator.map { r =>
var i = 0
while (i < numColumns) {
mutableRow(i) = converters(i)(r(i))
i += 1
}
mutableRow
}
}
}
}
object ExternalRDD {
def apply[T: Encoder](rdd: RDD[T], session: SparkSession): LogicalPlan = {
val externalRdd = ExternalRDD(CatalystSerde.generateObjAttr[T], rdd)(session)
CatalystSerde.serialize[T](externalRdd)
}
}
/** Logical plan node for scanning data from an RDD. */
case class ExternalRDD[T](
outputObjAttr: Attribute,
rdd: RDD[T])(session: SparkSession)
extends LeafNode with ObjectProducer with MultiInstanceRelation {
override protected final def otherCopyArgs: Seq[AnyRef] = session :: Nil
override def newInstance(): ExternalRDD.this.type =
ExternalRDD(outputObjAttr.newInstance(), rdd)(session).asInstanceOf[this.type]
override protected def stringArgs: Iterator[Any] = Iterator(output)
override def computeStats(): Statistics = Statistics(
// TODO: Instead of returning a default value here, find a way to return a meaningful size
// estimate for RDDs. See PR 1238 for more discussions.
sizeInBytes = BigInt(session.sessionState.conf.defaultSizeInBytes)
)
}
/** Physical plan node for scanning data from an RDD. */
case class ExternalRDDScanExec[T](
outputObjAttr: Attribute,
rdd: RDD[T]) extends LeafExecNode with ObjectProducerExec {
override lazy val metrics = Map(
"numOutputRows" -> SQLMetrics.createMetric(sparkContext, "number of output rows"))
protected override def doExecute(): RDD[InternalRow] = {
val numOutputRows = longMetric("numOutputRows")
val outputDataType = outputObjAttr.dataType
rdd.mapPartitionsInternal { iter =>
val outputObject = ObjectOperator.wrapObjectToRow(outputDataType)
iter.map { value =>
numOutputRows += 1
outputObject(value)
}
}
}
override def simpleString: String = {
s"Scan $nodeName${output.mkString("[", ",", "]")}"
}
}
/** Logical plan node for scanning data from an RDD of InternalRow. */
case class LogicalRDD(
output: Seq[Attribute],
rdd: RDD[InternalRow],
outputPartitioning: Partitioning = UnknownPartitioning(0),
outputOrdering: Seq[SortOrder] = Nil,
override val isStreaming: Boolean = false)(session: SparkSession)
extends LeafNode with MultiInstanceRelation {
override protected final def otherCopyArgs: Seq[AnyRef] = session :: Nil
override def newInstance(): LogicalRDD.this.type = {
val rewrite = output.zip(output.map(_.newInstance())).toMap
val rewrittenPartitioning = outputPartitioning match {
case p: Expression =>
p.transform {
case e: Attribute => rewrite.getOrElse(e, e)
}.asInstanceOf[Partitioning]
case p => p
}
val rewrittenOrdering = outputOrdering.map(_.transform {
case e: Attribute => rewrite.getOrElse(e, e)
}.asInstanceOf[SortOrder])
LogicalRDD(
output.map(rewrite),
rdd,
rewrittenPartitioning,
rewrittenOrdering,
isStreaming
)(session).asInstanceOf[this.type]
}
override protected def stringArgs: Iterator[Any] = Iterator(output, isStreaming)
override def computeStats(): Statistics = Statistics(
// TODO: Instead of returning a default value here, find a way to return a meaningful size
// estimate for RDDs. See PR 1238 for more discussions.
sizeInBytes = BigInt(session.sessionState.conf.defaultSizeInBytes)
)
}
/** Physical plan node for scanning data from an RDD of InternalRow. */
case class RDDScanExec(
output: Seq[Attribute],
rdd: RDD[InternalRow],
override val nodeName: String,
override val outputPartitioning: Partitioning = UnknownPartitioning(0),
override val outputOrdering: Seq[SortOrder] = Nil) extends LeafExecNode {
override lazy val metrics = Map(
"numOutputRows" -> SQLMetrics.createMetric(sparkContext, "number of output rows"))
protected override def doExecute(): RDD[InternalRow] = {
val numOutputRows = longMetric("numOutputRows")
rdd.mapPartitionsWithIndexInternal { (index, iter) =>
val proj = UnsafeProjection.create(schema)
proj.initialize(index)
iter.map { r =>
numOutputRows += 1
proj(r)
}
}
}
override def simpleString: String = {
s"Scan $nodeName${Utils.truncatedString(output, "[", ",", "]")}"
}
}
