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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.{AnalysisException, Row, SQLContext}
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.{LogicalPlan, Statistics}
import org.apache.spark.sql.catalyst.plans.physical.{HashPartitioning, Partitioning, UnknownPartitioning}
import org.apache.spark.sql.sources.{BaseRelation, HadoopFsRelation}
import org.apache.spark.sql.types.DataType
object RDDConversions {
def productToRowRdd[A <: Product](data: RDD[A], outputTypes: Seq[DataType]): RDD[InternalRow] = {
data.mapPartitions { iterator =>
val numColumns = outputTypes.length
val mutableRow = new GenericMutableRow(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 GenericMutableRow(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
}
}
}
}
/** Logical plan node for scanning data from an RDD. */
private[sql] case class LogicalRDD(
output: Seq[Attribute],
rdd: RDD[InternalRow])(sqlContext: SQLContext)
extends LogicalPlan with MultiInstanceRelation {
override def children: Seq[LogicalPlan] = Nil
override protected final def otherCopyArgs: Seq[AnyRef] = sqlContext :: Nil
override def newInstance(): LogicalRDD.this.type =
LogicalRDD(output.map(_.newInstance()), rdd)(sqlContext).asInstanceOf[this.type]
override def sameResult(plan: LogicalPlan): Boolean = plan match {
case LogicalRDD(_, otherRDD) => rdd.id == otherRDD.id
case _ => false
}
override def producedAttributes: AttributeSet = outputSet
@transient override lazy val statistics: 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(sqlContext.conf.defaultSizeInBytes)
)
}
/** Physical plan node for scanning data from an RDD. */
private[sql] case class PhysicalRDD(
output: Seq[Attribute],
rdd: RDD[InternalRow],
override val nodeName: String,
override val metadata: Map[String, String] = Map.empty,
isUnsafeRow: Boolean = false,
override val outputPartitioning: Partitioning = UnknownPartitioning(0))
extends LeafNode {
protected override def doExecute(): RDD[InternalRow] = {
if (isUnsafeRow) {
rdd
} else {
rdd.mapPartitionsInternal { iter =>
val proj = UnsafeProjection.create(schema)
iter.map(proj)
}
}
}
override def simpleString: String = {
val metadataEntries = for ((key, value) <- metadata.toSeq.sorted) yield s"$key: $value"
s"Scan $nodeName${output.mkString("[", ",", "]")}${metadataEntries.mkString(" ", ", ", "")}"
}
}
private[sql] object PhysicalRDD {
// Metadata keys
val INPUT_PATHS = "InputPaths"
val PUSHED_FILTERS = "PushedFilters"
def createFromDataSource(
output: Seq[Attribute],
rdd: RDD[InternalRow],
relation: BaseRelation,
metadata: Map[String, String] = Map.empty): PhysicalRDD = {
// All HadoopFsRelations output UnsafeRows
val outputUnsafeRows = relation.isInstanceOf[HadoopFsRelation]
val bucketSpec = relation match {
case r: HadoopFsRelation => r.getBucketSpec
case _ => None
}
def toAttribute(colName: String): Attribute = output.find(_.name == colName).getOrElse {
throw new AnalysisException(s"bucket column $colName not found in existing columns " +
s"(${output.map(_.name).mkString(", ")})")
}
bucketSpec.map { spec =>
val numBuckets = spec.numBuckets
val bucketColumns = spec.bucketColumnNames.map(toAttribute)
val partitioning = HashPartitioning(bucketColumns, numBuckets)
PhysicalRDD(output, rdd, relation.toString, metadata, outputUnsafeRows, partitioning)
}.getOrElse {
PhysicalRDD(output, rdd, relation.toString, metadata, outputUnsafeRows)
}
}
}
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