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lixiao pushed a commit to branch branch-3.0
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The following commit(s) were added to refs/heads/branch-3.0 by this push:
new 597bbbb [SPARK-30991] Refactor AQE readers and RDDs
597bbbb is described below
commit 597bbbbb5507448980e4fadbad85ffb104808081
Author: maryannxue <[email protected]>
AuthorDate: Mon Mar 2 16:04:00 2020 -0800
[SPARK-30991] Refactor AQE readers and RDDs
### What changes were proposed in this pull request?
This PR combines `CustomShuffledRowRDD` and `LocalShuffledRowRDD` into
`ShuffledRowRDD`, and creates `CustomShuffleReaderExec` to unify and replace
all existing AQE readers: `CoalescedShuffleReaderExec`,
`LocalShuffleReaderExec` and `SkewJoinShuffleReaderExec`.
### Why are the changes needed?
To reduce code redundancy.
### Does this PR introduce any user-facing change?
No.
### How was this patch tested?
Passed existing UTs.
Closes #27742 from maryannxue/aqe-readers.
Authored-by: maryannxue <[email protected]>
Signed-off-by: gatorsmile <[email protected]>
(cherry picked from commit 473a28c1d032993c7fa515b39f2cb1e3105d65d3)
Signed-off-by: gatorsmile <[email protected]>
---
.../spark/sql/execution/ShuffledRowRDD.scala | 142 ++++++++++++---------
.../apache/spark/sql/execution/SparkPlanInfo.scala | 2 +-
.../adaptive/CustomShuffleReaderExec.scala | 81 ++++++++++++
.../execution/adaptive/CustomShuffledRowRDD.scala | 113 ----------------
.../execution/adaptive/LocalShuffledRowRDD.scala | 112 ----------------
.../adaptive/OptimizeLocalShuffleReader.scala | 88 +++----------
.../execution/adaptive/OptimizeSkewedJoin.scala | 72 ++---------
.../adaptive/ReduceNumShufflePartitions.scala | 49 ++-----
.../adaptive/ShufflePartitionsCoalescer.scala | 23 ++--
.../execution/exchange/ShuffleExchangeExec.scala | 12 +-
.../ReduceNumShufflePartitionsSuite.scala | 28 ++--
.../ShufflePartitionsCoalescerSuite.scala | 101 ++++++++++-----
.../adaptive/AdaptiveQueryExecSuite.scala | 23 ++--
13 files changed, 317 insertions(+), 529 deletions(-)
diff --git
a/sql/core/src/main/scala/org/apache/spark/sql/execution/ShuffledRowRDD.scala
b/sql/core/src/main/scala/org/apache/spark/sql/execution/ShuffledRowRDD.scala
index 4c19f95..eb02259 100644
---
a/sql/core/src/main/scala/org/apache/spark/sql/execution/ShuffledRowRDD.scala
+++
b/sql/core/src/main/scala/org/apache/spark/sql/execution/ShuffledRowRDD.scala
@@ -26,17 +26,28 @@ import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.execution.metric.{SQLMetric,
SQLShuffleReadMetricsReporter}
import org.apache.spark.sql.internal.SQLConf
+sealed trait ShufflePartitionSpec
+
+// A partition that reads data of one or more reducers, from
`startReducerIndex` (inclusive) to
+// `endReducerIndex` (exclusive).
+case class CoalescedPartitionSpec(
+ startReducerIndex: Int, endReducerIndex: Int) extends ShufflePartitionSpec
+
+// A partition that reads partial data of one reducer, from `startMapIndex`
(inclusive) to
+// `endMapIndex` (exclusive).
+case class PartialReducerPartitionSpec(
+ reducerIndex: Int, startMapIndex: Int, endMapIndex: Int) extends
ShufflePartitionSpec
+
+// A partition that reads partial data of one mapper, from `startReducerIndex`
(inclusive) to
+// `endReducerIndex` (exclusive).
+case class PartialMapperPartitionSpec(
+ mapIndex: Int, startReducerIndex: Int, endReducerIndex: Int) extends
ShufflePartitionSpec
+
/**
- * The [[Partition]] used by [[ShuffledRowRDD]]. A post-shuffle partition
- * (identified by `postShufflePartitionIndex`) contains a range of pre-shuffle
partitions
- * (`startPreShufflePartitionIndex` to `endPreShufflePartitionIndex - 1`,
inclusive).
+ * The [[Partition]] used by [[ShuffledRowRDD]].
*/
-private final class ShuffledRowRDDPartition(
- val postShufflePartitionIndex: Int,
- val startPreShufflePartitionIndex: Int,
- val endPreShufflePartitionIndex: Int) extends Partition {
- override val index: Int = postShufflePartitionIndex
-}
+private final case class ShuffledRowRDDPartition(
+ index: Int, spec: ShufflePartitionSpec) extends Partition
/**
* A dummy partitioner for use with records whose partition ids have been
pre-computed (i.e. for
@@ -94,8 +105,7 @@ class CoalescedPartitioner(val parent: Partitioner, val
partitionStartIndices: A
* interfaces / internals.
*
* This RDD takes a [[ShuffleDependency]] (`dependency`),
- * and an optional array of partition start indices as input arguments
- * (`specifiedPartitionStartIndices`).
+ * and an array of [[ShufflePartitionSpec]] as input arguments.
*
* The `dependency` has the parent RDD of this RDD, which represents the
dataset before shuffle
* (i.e. map output). Elements of this RDD are (partitionId, Row) pairs.
@@ -103,79 +113,97 @@ class CoalescedPartitioner(val parent: Partitioner, val
partitionStartIndices: A
* `dependency.partitioner` is the original partitioner used to partition
* map output, and `dependency.partitioner.numPartitions` is the number of
pre-shuffle partitions
* (i.e. the number of partitions of the map output).
- *
- * When `specifiedPartitionStartIndices` is defined,
`specifiedPartitionStartIndices.length`
- * will be the number of post-shuffle partitions. For this case, the `i`th
post-shuffle
- * partition includes `specifiedPartitionStartIndices[i]` to
- * `specifiedPartitionStartIndices[i+1] - 1` (inclusive).
- *
- * When `specifiedPartitionStartIndices` is not defined, there will be
- * `dependency.partitioner.numPartitions` post-shuffle partitions. For this
case,
- * a post-shuffle partition is created for every pre-shuffle partition.
*/
class ShuffledRowRDD(
var dependency: ShuffleDependency[Int, InternalRow, InternalRow],
metrics: Map[String, SQLMetric],
- specifiedPartitionStartIndices: Option[Array[Int]] = None)
+ partitionSpecs: Array[ShufflePartitionSpec])
extends RDD[InternalRow](dependency.rdd.context, Nil) {
+ def this(
+ dependency: ShuffleDependency[Int, InternalRow, InternalRow],
+ metrics: Map[String, SQLMetric]) = {
+ this(dependency, metrics,
+ Array.tabulate(dependency.partitioner.numPartitions)(i =>
CoalescedPartitionSpec(i, i + 1)))
+ }
+
if (SQLConf.get.fetchShuffleBlocksInBatchEnabled) {
dependency.rdd.context.setLocalProperty(
SortShuffleManager.FETCH_SHUFFLE_BLOCKS_IN_BATCH_ENABLED_KEY, "true")
}
- private[this] val numPreShufflePartitions =
dependency.partitioner.numPartitions
-
- private[this] val partitionStartIndices: Array[Int] =
specifiedPartitionStartIndices match {
- case Some(indices) => indices
- case None =>
- // When specifiedPartitionStartIndices is not defined, every
post-shuffle partition
- // corresponds to a pre-shuffle partition.
- (0 until numPreShufflePartitions).toArray
- }
-
- private[this] val part: Partitioner =
- new CoalescedPartitioner(dependency.partitioner, partitionStartIndices)
-
override def getDependencies: Seq[Dependency[_]] = List(dependency)
- override val partitioner: Option[Partitioner] = Some(part)
+ override val partitioner: Option[Partitioner] =
+ if (partitionSpecs.forall(_.isInstanceOf[CoalescedPartitionSpec])) {
+ val indices =
partitionSpecs.map(_.asInstanceOf[CoalescedPartitionSpec].startReducerIndex)
+ // TODO this check is based on assumptions of callers' behavior but is
sufficient for now.
+ if (indices.toSet.size == partitionSpecs.length) {
+ Some(new CoalescedPartitioner(dependency.partitioner, indices))
+ } else {
+ None
+ }
+ } else {
+ None
+ }
override def getPartitions: Array[Partition] = {
- assert(partitionStartIndices.length == part.numPartitions)
- Array.tabulate[Partition](partitionStartIndices.length) { i =>
- val startIndex = partitionStartIndices(i)
- val endIndex =
- if (i < partitionStartIndices.length - 1) {
- partitionStartIndices(i + 1)
- } else {
- numPreShufflePartitions
- }
- new ShuffledRowRDDPartition(i, startIndex, endIndex)
+ Array.tabulate[Partition](partitionSpecs.length) { i =>
+ ShuffledRowRDDPartition(i, partitionSpecs(i))
}
}
override def getPreferredLocations(partition: Partition): Seq[String] = {
val tracker =
SparkEnv.get.mapOutputTracker.asInstanceOf[MapOutputTrackerMaster]
- val dep = dependencies.head.asInstanceOf[ShuffleDependency[_, _, _]]
- tracker.getPreferredLocationsForShuffle(dep, partition.index)
+ partition.asInstanceOf[ShuffledRowRDDPartition].spec match {
+ case CoalescedPartitionSpec(startReducerIndex, endReducerIndex) =>
+ // TODO order by partition size.
+ startReducerIndex.until(endReducerIndex).flatMap { reducerIndex =>
+ tracker.getPreferredLocationsForShuffle(dependency, reducerIndex)
+ }
+
+ case PartialReducerPartitionSpec(_, startMapIndex, endMapIndex) =>
+ tracker.getMapLocation(dependency, startMapIndex, endMapIndex)
+
+ case PartialMapperPartitionSpec(mapIndex, _, _) =>
+ tracker.getMapLocation(dependency, mapIndex, mapIndex + 1)
+ }
}
override def compute(split: Partition, context: TaskContext):
Iterator[InternalRow] = {
- val shuffledRowPartition = split.asInstanceOf[ShuffledRowRDDPartition]
val tempMetrics = context.taskMetrics().createTempShuffleReadMetrics()
// `SQLShuffleReadMetricsReporter` will update its own metrics for SQL
exchange operator,
// as well as the `tempMetrics` for basic shuffle metrics.
val sqlMetricsReporter = new SQLShuffleReadMetricsReporter(tempMetrics,
metrics)
- // The range of pre-shuffle partitions that we are fetching at here is
- // [startPreShufflePartitionIndex, endPreShufflePartitionIndex - 1].
- val reader =
- SparkEnv.get.shuffleManager.getReader(
- dependency.shuffleHandle,
- shuffledRowPartition.startPreShufflePartitionIndex,
- shuffledRowPartition.endPreShufflePartitionIndex,
- context,
- sqlMetricsReporter)
+ val reader = split.asInstanceOf[ShuffledRowRDDPartition].spec match {
+ case CoalescedPartitionSpec(startReducerIndex, endReducerIndex) =>
+ SparkEnv.get.shuffleManager.getReader(
+ dependency.shuffleHandle,
+ startReducerIndex,
+ endReducerIndex,
+ context,
+ sqlMetricsReporter)
+
+ case PartialReducerPartitionSpec(reducerIndex, startMapIndex,
endMapIndex) =>
+ SparkEnv.get.shuffleManager.getReaderForRange(
+ dependency.shuffleHandle,
+ startMapIndex,
+ endMapIndex,
+ reducerIndex,
+ reducerIndex + 1,
+ context,
+ sqlMetricsReporter)
+
+ case PartialMapperPartitionSpec(mapIndex, startReducerIndex,
endReducerIndex) =>
+ SparkEnv.get.shuffleManager.getReaderForRange(
+ dependency.shuffleHandle,
+ mapIndex,
+ mapIndex + 1,
+ startReducerIndex,
+ endReducerIndex,
+ context,
+ sqlMetricsReporter)
+ }
reader.read().asInstanceOf[Iterator[Product2[Int, InternalRow]]].map(_._2)
}
diff --git
a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlanInfo.scala
b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlanInfo.scala
index 5b72ec0..357820a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlanInfo.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlanInfo.scala
@@ -18,7 +18,7 @@
package org.apache.spark.sql.execution
import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.sql.execution.adaptive.{AdaptiveSparkPlanExec,
LocalShuffleReaderExec, QueryStageExec}
+import org.apache.spark.sql.execution.adaptive.{AdaptiveSparkPlanExec,
QueryStageExec}
import org.apache.spark.sql.execution.exchange.ReusedExchangeExec
import org.apache.spark.sql.execution.metric.SQLMetricInfo
import org.apache.spark.sql.internal.SQLConf
diff --git
a/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/CustomShuffleReaderExec.scala
b/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/CustomShuffleReaderExec.scala
new file mode 100644
index 0000000..be372bb
--- /dev/null
+++
b/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/CustomShuffleReaderExec.scala
@@ -0,0 +1,81 @@
+/*
+ * 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.adaptive
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression}
+import org.apache.spark.sql.catalyst.plans.physical.{Partitioning,
UnknownPartitioning}
+import org.apache.spark.sql.execution._
+import org.apache.spark.sql.execution.exchange.{ReusedExchangeExec,
ShuffleExchangeExec}
+
+
+/**
+ * A wrapper of shuffle query stage, which follows the given partition
arrangement.
+ *
+ * @param child It is usually `ShuffleQueryStageExec`, but can be
the shuffle exchange
+ * node during canonicalization.
+ * @param partitionSpecs The partition specs that defines the arrangement.
+ * @param description The string description of this shuffle reader.
+ */
+case class CustomShuffleReaderExec private(
+ child: SparkPlan,
+ partitionSpecs: Array[ShufflePartitionSpec],
+ description: String) extends UnaryExecNode {
+
+ override def output: Seq[Attribute] = child.output
+ override lazy val outputPartitioning: Partitioning = {
+ // If it is a local shuffle reader with one mapper per task, then the
output partitioning is
+ // the same as the plan before shuffle.
+ // TODO this check is based on assumptions of callers' behavior but is
sufficient for now.
+ if (partitionSpecs.forall(_.isInstanceOf[PartialMapperPartitionSpec]) &&
+
partitionSpecs.map(_.asInstanceOf[PartialMapperPartitionSpec].mapIndex).toSet.size
==
+ partitionSpecs.length) {
+ child match {
+ case ShuffleQueryStageExec(_, s: ShuffleExchangeExec) =>
+ s.child.outputPartitioning
+ case ShuffleQueryStageExec(_, r @ ReusedExchangeExec(_, s:
ShuffleExchangeExec)) =>
+ s.child.outputPartitioning match {
+ case e: Expression => r.updateAttr(e).asInstanceOf[Partitioning]
+ case other => other
+ }
+ case _ =>
+ throw new IllegalStateException("operating on canonicalization plan")
+ }
+ } else {
+ UnknownPartitioning(partitionSpecs.length)
+ }
+ }
+
+ override def stringArgs: Iterator[Any] = Iterator(description)
+
+ private var cachedShuffleRDD: RDD[InternalRow] = null
+
+ override protected def doExecute(): RDD[InternalRow] = {
+ if (cachedShuffleRDD == null) {
+ cachedShuffleRDD = child match {
+ case stage: ShuffleQueryStageExec =>
+ new ShuffledRowRDD(
+ stage.shuffle.shuffleDependency, stage.shuffle.readMetrics,
partitionSpecs)
+ case _ =>
+ throw new IllegalStateException("operating on canonicalization plan")
+ }
+ }
+ cachedShuffleRDD
+ }
+}
diff --git
a/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/CustomShuffledRowRDD.scala
b/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/CustomShuffledRowRDD.scala
deleted file mode 100644
index 5aba574..0000000
---
a/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/CustomShuffledRowRDD.scala
+++ /dev/null
@@ -1,113 +0,0 @@
-/*
- * 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.adaptive
-
-import org.apache.spark.{Dependency, MapOutputTrackerMaster, Partition,
ShuffleDependency, SparkEnv, TaskContext}
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.execution.metric.{SQLMetric,
SQLShuffleReadMetricsReporter}
-
-sealed trait ShufflePartitionSpec
-
-// A partition that reads data of one reducer.
-case class SinglePartitionSpec(reducerIndex: Int) extends ShufflePartitionSpec
-
-// A partition that reads data of multiple reducers, from `startReducerIndex`
(inclusive) to
-// `endReducerIndex` (exclusive).
-case class CoalescedPartitionSpec(
- startReducerIndex: Int, endReducerIndex: Int) extends ShufflePartitionSpec
-
-// A partition that reads partial data of one reducer, from `startMapIndex`
(inclusive) to
-// `endMapIndex` (exclusive).
-case class PartialPartitionSpec(
- reducerIndex: Int, startMapIndex: Int, endMapIndex: Int) extends
ShufflePartitionSpec
-
-private final case class CustomShufflePartition(
- index: Int, spec: ShufflePartitionSpec) extends Partition
-
-// TODO: merge this with `ShuffledRowRDD`, and replace `LocalShuffledRowRDD`
with this RDD.
-class CustomShuffledRowRDD(
- var dependency: ShuffleDependency[Int, InternalRow, InternalRow],
- metrics: Map[String, SQLMetric],
- partitionSpecs: Array[ShufflePartitionSpec])
- extends RDD[InternalRow](dependency.rdd.context, Nil) {
-
- override def getDependencies: Seq[Dependency[_]] = List(dependency)
-
- override def clearDependencies() {
- super.clearDependencies()
- dependency = null
- }
-
- override def getPartitions: Array[Partition] = {
- Array.tabulate[Partition](partitionSpecs.length) { i =>
- CustomShufflePartition(i, partitionSpecs(i))
- }
- }
-
- override def getPreferredLocations(partition: Partition): Seq[String] = {
- val tracker =
SparkEnv.get.mapOutputTracker.asInstanceOf[MapOutputTrackerMaster]
- partition.asInstanceOf[CustomShufflePartition].spec match {
- case SinglePartitionSpec(reducerIndex) =>
- tracker.getPreferredLocationsForShuffle(dependency, reducerIndex)
-
- case CoalescedPartitionSpec(startReducerIndex, endReducerIndex) =>
- startReducerIndex.until(endReducerIndex).flatMap { reducerIndex =>
- tracker.getPreferredLocationsForShuffle(dependency, reducerIndex)
- }
-
- case PartialPartitionSpec(_, startMapIndex, endMapIndex) =>
- tracker.getMapLocation(dependency, startMapIndex, endMapIndex)
- }
- }
-
- override def compute(split: Partition, context: TaskContext):
Iterator[InternalRow] = {
- val tempMetrics = context.taskMetrics().createTempShuffleReadMetrics()
- // `SQLShuffleReadMetricsReporter` will update its own metrics for SQL
exchange operator,
- // as well as the `tempMetrics` for basic shuffle metrics.
- val sqlMetricsReporter = new SQLShuffleReadMetricsReporter(tempMetrics,
metrics)
- val reader = split.asInstanceOf[CustomShufflePartition].spec match {
- case SinglePartitionSpec(reducerIndex) =>
- SparkEnv.get.shuffleManager.getReader(
- dependency.shuffleHandle,
- reducerIndex,
- reducerIndex + 1,
- context,
- sqlMetricsReporter)
-
- case CoalescedPartitionSpec(startReducerIndex, endReducerIndex) =>
- SparkEnv.get.shuffleManager.getReader(
- dependency.shuffleHandle,
- startReducerIndex,
- endReducerIndex,
- context,
- sqlMetricsReporter)
-
- case PartialPartitionSpec(reducerIndex, startMapIndex, endMapIndex) =>
- SparkEnv.get.shuffleManager.getReaderForRange(
- dependency.shuffleHandle,
- startMapIndex,
- endMapIndex,
- reducerIndex,
- reducerIndex + 1,
- context,
- sqlMetricsReporter)
- }
- reader.read().asInstanceOf[Iterator[Product2[Int, InternalRow]]].map(_._2)
- }
-}
diff --git
a/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/LocalShuffledRowRDD.scala
b/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/LocalShuffledRowRDD.scala
deleted file mode 100644
index 19b78f5..0000000
---
a/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/LocalShuffledRowRDD.scala
+++ /dev/null
@@ -1,112 +0,0 @@
-/*
- * 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.adaptive
-
-import scala.collection.mutable.ArrayBuffer
-
-import org.apache.spark._
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.execution.metric.{SQLMetric,
SQLShuffleReadMetricsReporter}
-
-/**
- * The [[Partition]] used by [[LocalShuffledRowRDD]].
- * @param mapIndex the index of mapper.
- * @param startPartition the start partition ID in mapIndex mapper.
- * @param endPartition the end partition ID in mapIndex mapper.
- */
-private final class LocalShuffledRowRDDPartition(
- override val index: Int,
- val mapIndex: Int,
- val startPartition: Int,
- val endPartition: Int) extends Partition {
-}
-
-/**
- * This is a specialized version of
[[org.apache.spark.sql.execution.ShuffledRowRDD]]. This is used
- * in Spark SQL adaptive execution when a shuffle join is converted to
broadcast join at runtime
- * because the map output of one input table is small enough for broadcast.
This RDD represents the
- * data of another input table of the join that reads from shuffle. Each
partition of the RDD reads
- * the whole data from just one mapper output locally. So actually there is no
data transferred
- * from the network.
- *
- * This RDD takes a [[ShuffleDependency]] (`dependency`).
- *
- * The `dependency` has the parent RDD of this RDD, which represents the
dataset before shuffle
- * (i.e. map output). Elements of this RDD are (partitionId, Row) pairs.
- * Partition ids should be in the range [0, numPartitions - 1].
- * `dependency.partitioner.numPartitions` is the number of pre-shuffle
partitions. (i.e. the number
- * of partitions of the map output). The post-shuffle partition number is the
same to the parent
- * RDD's partition number.
- *
- * `partitionStartIndicesPerMapper` specifies how to split the shuffle blocks
of each mapper into
- * one or more partitions. For a mapper `i`, the `j`th partition includes
shuffle blocks from
- * `partitionStartIndicesPerMapper[i][j]` to
`partitionStartIndicesPerMapper[i][j+1]` (exclusive).
- */
-class LocalShuffledRowRDD(
- var dependency: ShuffleDependency[Int, InternalRow, InternalRow],
- metrics: Map[String, SQLMetric],
- partitionStartIndicesPerMapper: Array[Array[Int]])
- extends RDD[InternalRow](dependency.rdd.context, Nil) {
-
- private[this] val numReducers = dependency.partitioner.numPartitions
- private[this] val numMappers = dependency.rdd.partitions.length
-
- override def getDependencies: Seq[Dependency[_]] = List(dependency)
-
- override def getPartitions: Array[Partition] = {
- val partitions = ArrayBuffer[LocalShuffledRowRDDPartition]()
- for (mapIndex <- 0 until numMappers) {
- (partitionStartIndicesPerMapper(mapIndex) :+ numReducers).sliding(2,
1).foreach {
- case Array(start, end) =>
- partitions += new LocalShuffledRowRDDPartition(partitions.length,
mapIndex, start, end)
- }
- }
- partitions.toArray
- }
-
- override def getPreferredLocations(partition: Partition): Seq[String] = {
- val tracker =
SparkEnv.get.mapOutputTracker.asInstanceOf[MapOutputTrackerMaster]
- tracker.getMapLocation(dependency, partition.index, partition.index + 1)
- }
-
- override def compute(split: Partition, context: TaskContext):
Iterator[InternalRow] = {
- val localRowPartition = split.asInstanceOf[LocalShuffledRowRDDPartition]
- val mapIndex = localRowPartition.mapIndex
- val tempMetrics = context.taskMetrics().createTempShuffleReadMetrics()
- // `SQLShuffleReadMetricsReporter` will update its own metrics for SQL
exchange operator,
- // as well as the `tempMetrics` for basic shuffle metrics.
- val sqlMetricsReporter = new SQLShuffleReadMetricsReporter(tempMetrics,
metrics)
-
- val reader = SparkEnv.get.shuffleManager.getReaderForRange(
- dependency.shuffleHandle,
- mapIndex,
- mapIndex + 1,
- localRowPartition.startPartition,
- localRowPartition.endPartition,
- context,
- sqlMetricsReporter)
- reader.read().asInstanceOf[Iterator[Product2[Int, InternalRow]]].map(_._2)
- }
-
- override def clearDependencies() {
- super.clearDependencies()
- dependency = null
- }
-}
-
diff --git
a/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/OptimizeLocalShuffleReader.scala
b/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/OptimizeLocalShuffleReader.scala
index e95441e..e441763 100644
---
a/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/OptimizeLocalShuffleReader.scala
+++
b/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/OptimizeLocalShuffleReader.scala
@@ -17,13 +17,9 @@
package org.apache.spark.sql.execution.adaptive
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression}
-import org.apache.spark.sql.catalyst.plans.physical.{Partitioning,
UnknownPartitioning}
import org.apache.spark.sql.catalyst.rules.Rule
-import org.apache.spark.sql.execution.{SparkPlan, UnaryExecNode}
-import org.apache.spark.sql.execution.exchange.{EnsureRequirements,
ReusedExchangeExec, ShuffleExchangeExec}
+import org.apache.spark.sql.execution._
+import org.apache.spark.sql.execution.exchange.{EnsureRequirements,
ShuffleExchangeExec}
import org.apache.spark.sql.execution.joins.{BroadcastHashJoinExec, BuildLeft,
BuildRight, BuildSide}
import org.apache.spark.sql.internal.SQLConf
@@ -67,28 +63,35 @@ case class OptimizeLocalShuffleReader(conf: SQLConf)
extends Rule[SparkPlan] {
}
}
- private def createLocalReader(plan: SparkPlan): LocalShuffleReaderExec = {
+ private def createLocalReader(plan: SparkPlan): CustomShuffleReaderExec = {
plan match {
- case c @ CoalescedShuffleReaderExec(s: ShuffleQueryStageExec, _) =>
- LocalShuffleReaderExec(
- s, getPartitionStartIndices(s, Some(c.partitionStartIndices.length)))
+ case c @ CustomShuffleReaderExec(s: ShuffleQueryStageExec, _, _) =>
+ CustomShuffleReaderExec(
+ s, getPartitionSpecs(s, Some(c.partitionSpecs.length)),
LOCAL_SHUFFLE_READER_DESCRIPTION)
case s: ShuffleQueryStageExec =>
- LocalShuffleReaderExec(s, getPartitionStartIndices(s, None))
+ CustomShuffleReaderExec(s, getPartitionSpecs(s, None),
LOCAL_SHUFFLE_READER_DESCRIPTION)
}
}
// TODO: this method assumes all shuffle blocks are the same data size. We
should calculate the
// partition start indices based on block size to avoid data skew.
- private def getPartitionStartIndices(
+ private def getPartitionSpecs(
shuffleStage: ShuffleQueryStageExec,
- advisoryParallelism: Option[Int]): Array[Array[Int]] = {
+ advisoryParallelism: Option[Int]): Array[ShufflePartitionSpec] = {
val shuffleDep = shuffleStage.shuffle.shuffleDependency
val numReducers = shuffleDep.partitioner.numPartitions
val expectedParallelism = advisoryParallelism.getOrElse(numReducers)
val numMappers = shuffleDep.rdd.getNumPartitions
- Array.fill(numMappers) {
+ val splitPoints = if (numMappers == 0) {
+ Array.empty
+ } else {
equallyDivide(numReducers, math.max(1, expectedParallelism /
numMappers)).toArray
}
+ (0 until numMappers).flatMap { mapIndex =>
+ (splitPoints :+ numReducers).sliding(2).map {
+ case Array(start, end) => PartialMapperPartitionSpec(mapIndex, start,
end)
+ }
+ }.toArray
}
/**
@@ -120,6 +123,8 @@ case class OptimizeLocalShuffleReader(conf: SQLConf)
extends Rule[SparkPlan] {
object OptimizeLocalShuffleReader {
+ val LOCAL_SHUFFLE_READER_DESCRIPTION: String = "local"
+
object BroadcastJoinWithShuffleLeft {
def unapply(plan: SparkPlan): Option[(SparkPlan, BuildSide)] = plan match {
case join: BroadcastHashJoinExec if canUseLocalShuffleReader(join.left)
=>
@@ -138,60 +143,7 @@ object OptimizeLocalShuffleReader {
def canUseLocalShuffleReader(plan: SparkPlan): Boolean = plan match {
case s: ShuffleQueryStageExec => s.shuffle.canChangeNumPartitions
- case CoalescedShuffleReaderExec(s: ShuffleQueryStageExec, _) =>
s.shuffle.canChangeNumPartitions
+ case CustomShuffleReaderExec(s: ShuffleQueryStageExec, _, _) =>
s.shuffle.canChangeNumPartitions
case _ => false
}
}
-
-/**
- * A wrapper of shuffle query stage, which submits one or more reduce tasks
per mapper to read the
- * shuffle files written by one mapper. By doing this, it's very likely to
read the shuffle files
- * locally, as the shuffle files that a reduce task needs to read are in one
node.
- *
- * @param child It's usually `ShuffleQueryStageExec`, but can be the shuffle
exchange node during
- * canonicalization.
- * @param partitionStartIndicesPerMapper A mapper usually writes many shuffle
blocks, and it's
- * better to launch multiple tasks to
read shuffle blocks of
- * one mapper. This array contains the
partition start
- * indices for each mapper.
- */
-case class LocalShuffleReaderExec(
- child: SparkPlan,
- partitionStartIndicesPerMapper: Array[Array[Int]]) extends UnaryExecNode {
-
- override def output: Seq[Attribute] = child.output
-
- override lazy val outputPartitioning: Partitioning = {
- // when we read one mapper per task, then the output partitioning is the
same as the plan
- // before shuffle.
- if (partitionStartIndicesPerMapper.forall(_.length == 1)) {
- child match {
- case ShuffleQueryStageExec(_, s: ShuffleExchangeExec) =>
- s.child.outputPartitioning
- case ShuffleQueryStageExec(_, r @ ReusedExchangeExec(_, s:
ShuffleExchangeExec)) =>
- s.child.outputPartitioning match {
- case e: Expression => r.updateAttr(e).asInstanceOf[Partitioning]
- case other => other
- }
- case _ =>
- throw new IllegalStateException("operating on canonicalization plan")
- }
- } else {
- UnknownPartitioning(partitionStartIndicesPerMapper.map(_.length).sum)
- }
- }
-
- private var cachedShuffleRDD: RDD[InternalRow] = null
-
- override protected def doExecute(): RDD[InternalRow] = {
- if (cachedShuffleRDD == null) {
- cachedShuffleRDD = child match {
- case stage: ShuffleQueryStageExec =>
- stage.shuffle.createLocalShuffleRDD(partitionStartIndicesPerMapper)
- case _ =>
- throw new IllegalStateException("operating on canonicalization plan")
- }
- }
- cachedShuffleRDD
- }
-}
diff --git
a/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/OptimizeSkewedJoin.scala
b/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/OptimizeSkewedJoin.scala
index dd238fe..979fee1 100644
---
a/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/OptimizeSkewedJoin.scala
+++
b/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/OptimizeSkewedJoin.scala
@@ -23,11 +23,7 @@ import scala.collection.mutable.ArrayBuffer
import org.apache.commons.io.FileUtils
import org.apache.spark.{MapOutputStatistics, MapOutputTrackerMaster, SparkEnv}
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.Attribute
import org.apache.spark.sql.catalyst.plans._
-import org.apache.spark.sql.catalyst.plans.physical.{Partitioning,
UnknownPartitioning}
import org.apache.spark.sql.catalyst.rules.Rule
import org.apache.spark.sql.execution._
import org.apache.spark.sql.execution.exchange.{EnsureRequirements,
ShuffleExchangeExec}
@@ -221,7 +217,7 @@ case class OptimizeSkewedJoin(conf: SQLConf) extends
Rule[SparkPlan] {
getMapStartIndices(left, partitionIndex, leftTargetSize),
getNumMappers(left))
} else {
- Seq(SinglePartitionSpec(partitionIndex))
+ Seq(CoalescedPartitionSpec(partitionIndex, partitionIndex + 1))
}
val rightParts = if (isRightSkew) {
@@ -231,7 +227,7 @@ case class OptimizeSkewedJoin(conf: SQLConf) extends
Rule[SparkPlan] {
getMapStartIndices(right, partitionIndex, rightTargetSize),
getNumMappers(right))
} else {
- Seq(SinglePartitionSpec(partitionIndex))
+ Seq(CoalescedPartitionSpec(partitionIndex, partitionIndex + 1))
}
for {
@@ -259,9 +255,9 @@ case class OptimizeSkewedJoin(conf: SQLConf) extends
Rule[SparkPlan] {
logDebug("number of skewed partitions: " +
s"left ${leftSkewDesc.numPartitions}, right
${rightSkewDesc.numPartitions}")
if (leftSkewDesc.numPartitions > 0 || rightSkewDesc.numPartitions > 0) {
- val newLeft = SkewJoinShuffleReaderExec(
+ val newLeft = CustomShuffleReaderExec(
left, leftSidePartitions.toArray, leftSkewDesc.toString)
- val newRight = SkewJoinShuffleReaderExec(
+ val newRight = CustomShuffleReaderExec(
right, rightSidePartitions.toArray, rightSkewDesc.toString)
smj.copy(
left = s1.copy(child = newLeft), right = s2.copy(child = newRight),
isSkewJoin = true)
@@ -277,36 +273,21 @@ case class OptimizeSkewedJoin(conf: SQLConf) extends
Rule[SparkPlan] {
assert(nonSkewPartitionIndices.nonEmpty)
val shouldCoalesce =
conf.getConf(SQLConf.REDUCE_POST_SHUFFLE_PARTITIONS_ENABLED)
if (!shouldCoalesce || nonSkewPartitionIndices.length == 1) {
- Seq(SinglePartitionSpec(nonSkewPartitionIndices.head))
+ nonSkewPartitionIndices.map(i => CoalescedPartitionSpec(i, i + 1))
} else {
- val startIndices = ShufflePartitionsCoalescer.coalescePartitions(
+ ShufflePartitionsCoalescer.coalescePartitions(
Array(leftStats, rightStats),
firstPartitionIndex = nonSkewPartitionIndices.head,
// `lastPartitionIndex` is exclusive.
lastPartitionIndex = nonSkewPartitionIndices.last + 1,
advisoryTargetSize = conf.targetPostShuffleInputSize)
- startIndices.indices.map { i =>
- val startIndex = startIndices(i)
- val endIndex = if (i == startIndices.length - 1) {
- // `endIndex` is exclusive.
- nonSkewPartitionIndices.last + 1
- } else {
- startIndices(i + 1)
- }
- // Do not create `CoalescedPartitionSpec` if only need to read a singe
partition.
- if (startIndex + 1 == endIndex) {
- SinglePartitionSpec(startIndex)
- } else {
- CoalescedPartitionSpec(startIndex, endIndex)
- }
- }
}
}
private def createSkewPartitions(
reducerIndex: Int,
mapStartIndices: Array[Int],
- numMappers: Int): Seq[PartialPartitionSpec] = {
+ numMappers: Int): Seq[PartialReducerPartitionSpec] = {
mapStartIndices.indices.map { i =>
val startMapIndex = mapStartIndices(i)
val endMapIndex = if (i == mapStartIndices.length - 1) {
@@ -314,7 +295,7 @@ case class OptimizeSkewedJoin(conf: SQLConf) extends
Rule[SparkPlan] {
} else {
mapStartIndices(i + 1)
}
- PartialPartitionSpec(reducerIndex, startMapIndex, endMapIndex)
+ PartialReducerPartitionSpec(reducerIndex, startMapIndex, endMapIndex)
}
}
@@ -387,40 +368,3 @@ private class SkewDesc {
}
}
}
-
-/**
- * A wrapper of shuffle query stage, which follows the given partition
arrangement.
- *
- * @param child It's usually `ShuffleQueryStageExec`, but can be the shuffle
exchange node during
- * canonicalization.
- * @param partitionSpecs The partition specs that defines the arrangement.
- * @param skewDesc The description of the skewed partitions.
- */
-case class SkewJoinShuffleReaderExec(
- child: SparkPlan,
- partitionSpecs: Array[ShufflePartitionSpec],
- skewDesc: String) extends UnaryExecNode {
-
- override def output: Seq[Attribute] = child.output
-
- override def outputPartitioning: Partitioning = {
- UnknownPartitioning(partitionSpecs.length)
- }
-
- override def stringArgs: Iterator[Any] = Iterator(skewDesc)
-
- private var cachedShuffleRDD: RDD[InternalRow] = null
-
- override protected def doExecute(): RDD[InternalRow] = {
- if (cachedShuffleRDD == null) {
- cachedShuffleRDD = child match {
- case stage: ShuffleQueryStageExec =>
- new CustomShuffledRowRDD(
- stage.shuffle.shuffleDependency, stage.shuffle.readMetrics,
partitionSpecs)
- case _ =>
- throw new IllegalStateException("operating on canonicalization plan")
- }
- }
- cachedShuffleRDD
- }
-}
diff --git
a/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/ReduceNumShufflePartitions.scala
b/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/ReduceNumShufflePartitions.scala
index 5bbcb14..767a4b2 100644
---
a/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/ReduceNumShufflePartitions.scala
+++
b/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/ReduceNumShufflePartitions.scala
@@ -18,12 +18,8 @@
package org.apache.spark.sql.execution.adaptive
import org.apache.spark.MapOutputStatistics
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.Attribute
-import org.apache.spark.sql.catalyst.plans.physical.{Partitioning,
UnknownPartitioning}
import org.apache.spark.sql.catalyst.rules.Rule
-import org.apache.spark.sql.execution.{ShuffledRowRDD, SparkPlan,
UnaryExecNode}
+import org.apache.spark.sql.execution.SparkPlan
import org.apache.spark.sql.internal.SQLConf
/**
@@ -31,12 +27,14 @@ import org.apache.spark.sql.internal.SQLConf
* avoid many small reduce tasks that hurt performance.
*/
case class ReduceNumShufflePartitions(conf: SQLConf) extends Rule[SparkPlan] {
+ import ReduceNumShufflePartitions._
override def apply(plan: SparkPlan): SparkPlan = {
if (!conf.reducePostShufflePartitionsEnabled) {
return plan
}
- if (!plan.collectLeaves().forall(_.isInstanceOf[QueryStageExec])) {
+ if (!plan.collectLeaves().forall(_.isInstanceOf[QueryStageExec])
+ || plan.find(_.isInstanceOf[CustomShuffleReaderExec]).isDefined) {
// If not all leaf nodes are query stages, it's not safe to reduce the
number of
// shuffle partitions, because we may break the assumption that all
children of a spark plan
// have same number of output partitions.
@@ -44,8 +42,6 @@ case class ReduceNumShufflePartitions(conf: SQLConf) extends
Rule[SparkPlan] {
}
def collectShuffleStages(plan: SparkPlan): Seq[ShuffleQueryStageExec] =
plan match {
- case _: LocalShuffleReaderExec => Nil
- case _: SkewJoinShuffleReaderExec => Nil
case stage: ShuffleQueryStageExec => Seq(stage)
case _ => plan.children.flatMap(collectShuffleStages)
}
@@ -70,7 +66,7 @@ case class ReduceNumShufflePartitions(conf: SQLConf) extends
Rule[SparkPlan] {
val distinctNumPreShufflePartitions =
validMetrics.map(stats => stats.bytesByPartitionId.length).distinct
if (validMetrics.nonEmpty && distinctNumPreShufflePartitions.length ==
1) {
- val partitionStartIndices =
ShufflePartitionsCoalescer.coalescePartitions(
+ val partitionSpecs = ShufflePartitionsCoalescer.coalescePartitions(
validMetrics.toArray,
firstPartitionIndex = 0,
lastPartitionIndex = distinctNumPreShufflePartitions.head,
@@ -83,7 +79,7 @@ case class ReduceNumShufflePartitions(conf: SQLConf) extends
Rule[SparkPlan] {
// `partitionStartIndices`, so that all the leaf shuffles in a stage
have the same
// number of output partitions.
case stage: ShuffleQueryStageExec if stageIds.contains(stage.id) =>
- CoalescedShuffleReaderExec(stage, partitionStartIndices)
+ CustomShuffleReaderExec(stage, partitionSpecs,
COALESCED_SHUFFLE_READER_DESCRIPTION)
}
} else {
plan
@@ -92,35 +88,6 @@ case class ReduceNumShufflePartitions(conf: SQLConf) extends
Rule[SparkPlan] {
}
}
-/**
- * A wrapper of shuffle query stage, which submits fewer reduce task as one
reduce task may read
- * multiple shuffle partitions. This can avoid many small reduce tasks that
hurt performance.
- *
- * @param child It's usually `ShuffleQueryStageExec`, but can be the shuffle
exchange node during
- * canonicalization.
- * @param partitionStartIndices The start partition indices for the coalesced
partitions.
- */
-case class CoalescedShuffleReaderExec(
- child: SparkPlan,
- partitionStartIndices: Array[Int]) extends UnaryExecNode {
-
- override def output: Seq[Attribute] = child.output
-
- override def outputPartitioning: Partitioning = {
- UnknownPartitioning(partitionStartIndices.length)
- }
-
- private var cachedShuffleRDD: ShuffledRowRDD = null
-
- override protected def doExecute(): RDD[InternalRow] = {
- if (cachedShuffleRDD == null) {
- cachedShuffleRDD = child match {
- case stage: ShuffleQueryStageExec =>
- stage.shuffle.createShuffledRDD(Some(partitionStartIndices))
- case _ =>
- throw new IllegalStateException("operating on canonicalization plan")
- }
- }
- cachedShuffleRDD
- }
+object ReduceNumShufflePartitions {
+ val COALESCED_SHUFFLE_READER_DESCRIPTION = "coalesced"
}
diff --git
a/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/ShufflePartitionsCoalescer.scala
b/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/ShufflePartitionsCoalescer.scala
index 18f0585..c3b8bf6 100644
---
a/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/ShufflePartitionsCoalescer.scala
+++
b/sql/core/src/main/scala/org/apache/spark/sql/execution/adaptive/ShufflePartitionsCoalescer.scala
@@ -21,11 +21,12 @@ import scala.collection.mutable.ArrayBuffer
import org.apache.spark.MapOutputStatistics
import org.apache.spark.internal.Logging
+import org.apache.spark.sql.execution.{CoalescedPartitionSpec,
ShufflePartitionSpec}
object ShufflePartitionsCoalescer extends Logging {
/**
- * Coalesce the same range of partitions (`firstPartitionIndex`` to
`lastPartitionIndex`, the
+ * Coalesce the same range of partitions (`firstPartitionIndex` to
`lastPartitionIndex`, the
* start is inclusive and the end is exclusive) from multiple shuffles. This
method assumes that
* all the shuffles have the same number of partitions, and the partitions
of same index will be
* read together by one task.
@@ -46,15 +47,17 @@ object ShufflePartitionsCoalescer extends Logging {
* - coalesced partition 2: shuffle partition 2 (size 170 MiB)
* - coalesced partition 3: shuffle partition 3 and 4 (size 50 MiB)
*
- * @return An array of partition indices which represents the coalesced
partitions. For example,
- * [0, 2, 3] means 3 coalesced partitions: [0, 2), [2, 3), [3,
lastPartitionIndex]
+ * @return An array of [[CoalescedPartitionSpec]]s. For example, if
partitions [0, 1, 2, 3, 4]
+ * split at indices [0, 2, 3], the returned partition specs will be:
+ * CoalescedPartitionSpec(0, 2), CoalescedPartitionSpec(2, 3) and
+ * CoalescedPartitionSpec(3, 5).
*/
def coalescePartitions(
mapOutputStatistics: Array[MapOutputStatistics],
firstPartitionIndex: Int,
lastPartitionIndex: Int,
advisoryTargetSize: Long,
- minNumPartitions: Int = 1): Array[Int] = {
+ minNumPartitions: Int = 1): Array[ShufflePartitionSpec] = {
// If `minNumPartitions` is very large, it is possible that we need to use
a value less than
// `advisoryTargetSize` as the target size of a coalesced task.
val totalPostShuffleInputSize =
mapOutputStatistics.map(_.bytesByPartitionId.sum).sum
@@ -82,8 +85,8 @@ object ShufflePartitionsCoalescer extends Logging {
"There should be only one distinct value of the number of shuffle
partitions " +
"among registered Exchange operators.")
- val splitPoints = ArrayBuffer[Int]()
- splitPoints += firstPartitionIndex
+ val partitionSpecs = ArrayBuffer[CoalescedPartitionSpec]()
+ var latestSplitPoint = firstPartitionIndex
var coalescedSize = 0L
var i = firstPartitionIndex
while (i < lastPartitionIndex) {
@@ -97,8 +100,9 @@ object ShufflePartitionsCoalescer extends Logging {
// If including the `totalSizeOfCurrentPartition` would exceed the
target size, then start a
// new coalesced partition.
- if (i > firstPartitionIndex && coalescedSize +
totalSizeOfCurrentPartition > targetSize) {
- splitPoints += i
+ if (i > latestSplitPoint && coalescedSize + totalSizeOfCurrentPartition
> targetSize) {
+ partitionSpecs += CoalescedPartitionSpec(latestSplitPoint, i)
+ latestSplitPoint = i
// reset postShuffleInputSize.
coalescedSize = totalSizeOfCurrentPartition
} else {
@@ -106,7 +110,8 @@ object ShufflePartitionsCoalescer extends Logging {
}
i += 1
}
+ partitionSpecs += CoalescedPartitionSpec(latestSplitPoint,
lastPartitionIndex)
- splitPoints.toArray
+ partitionSpecs.toArray
}
}
diff --git
a/sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/ShuffleExchangeExec.scala
b/sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/ShuffleExchangeExec.scala
index 4b08da0..b06742e 100644
---
a/sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/ShuffleExchangeExec.scala
+++
b/sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/ShuffleExchangeExec.scala
@@ -34,7 +34,6 @@ import org.apache.spark.sql.catalyst.expressions.{Attribute,
BoundReference, Div
import
org.apache.spark.sql.catalyst.expressions.codegen.LazilyGeneratedOrdering
import org.apache.spark.sql.catalyst.plans.physical._
import org.apache.spark.sql.execution._
-import org.apache.spark.sql.execution.adaptive.LocalShuffledRowRDD
import org.apache.spark.sql.execution.metric.{SQLMetric, SQLMetrics,
SQLShuffleReadMetricsReporter, SQLShuffleWriteMetricsReporter}
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.types.StructType
@@ -88,15 +87,6 @@ case class ShuffleExchangeExec(
writeMetrics)
}
- def createShuffledRDD(partitionStartIndices: Option[Array[Int]]):
ShuffledRowRDD = {
- new ShuffledRowRDD(shuffleDependency, readMetrics, partitionStartIndices)
- }
-
- def createLocalShuffleRDD(
- partitionStartIndicesPerMapper: Array[Array[Int]]): LocalShuffledRowRDD
= {
- new LocalShuffledRowRDD(shuffleDependency, readMetrics,
partitionStartIndicesPerMapper)
- }
-
/**
* Caches the created ShuffleRowRDD so we can reuse that.
*/
@@ -105,7 +95,7 @@ case class ShuffleExchangeExec(
protected override def doExecute(): RDD[InternalRow] = attachTree(this,
"execute") {
// Returns the same ShuffleRowRDD if this plan is used by multiple plans.
if (cachedShuffleRDD == null) {
- cachedShuffleRDD = createShuffledRDD(None)
+ cachedShuffleRDD = new ShuffledRowRDD(shuffleDependency, readMetrics)
}
cachedShuffleRDD
}
diff --git
a/sql/core/src/test/scala/org/apache/spark/sql/execution/ReduceNumShufflePartitionsSuite.scala
b/sql/core/src/test/scala/org/apache/spark/sql/execution/ReduceNumShufflePartitionsSuite.scala
index 5565a0d..a32b684 100644
---
a/sql/core/src/test/scala/org/apache/spark/sql/execution/ReduceNumShufflePartitionsSuite.scala
+++
b/sql/core/src/test/scala/org/apache/spark/sql/execution/ReduceNumShufflePartitionsSuite.scala
@@ -23,7 +23,8 @@ import org.apache.spark.{SparkConf, SparkFunSuite}
import org.apache.spark.internal.config.UI.UI_ENABLED
import org.apache.spark.sql._
import org.apache.spark.sql.execution.adaptive._
-import org.apache.spark.sql.execution.adaptive.CoalescedShuffleReaderExec
+import org.apache.spark.sql.execution.adaptive.CustomShuffleReaderExec
+import
org.apache.spark.sql.execution.adaptive.ReduceNumShufflePartitions.COALESCED_SHUFFLE_READER_DESCRIPTION
import org.apache.spark.sql.execution.exchange.ReusedExchangeExec
import org.apache.spark.sql.functions._
import org.apache.spark.sql.internal.SQLConf
@@ -107,7 +108,7 @@ class ReduceNumShufflePartitionsSuite extends SparkFunSuite
with BeforeAndAfterA
val finalPlan = agg.queryExecution.executedPlan
.asInstanceOf[AdaptiveSparkPlanExec].executedPlan
val shuffleReaders = finalPlan.collect {
- case reader: CoalescedShuffleReaderExec => reader
+ case r @ CustomShuffleReaderExec(_, _,
COALESCED_SHUFFLE_READER_DESCRIPTION) => r
}
assert(shuffleReaders.length === 1)
minNumPostShufflePartitions match {
@@ -154,7 +155,7 @@ class ReduceNumShufflePartitionsSuite extends SparkFunSuite
with BeforeAndAfterA
val finalPlan = join.queryExecution.executedPlan
.asInstanceOf[AdaptiveSparkPlanExec].executedPlan
val shuffleReaders = finalPlan.collect {
- case reader: CoalescedShuffleReaderExec => reader
+ case r @ CustomShuffleReaderExec(_, _,
COALESCED_SHUFFLE_READER_DESCRIPTION) => r
}
assert(shuffleReaders.length === 2)
minNumPostShufflePartitions match {
@@ -206,7 +207,7 @@ class ReduceNumShufflePartitionsSuite extends SparkFunSuite
with BeforeAndAfterA
val finalPlan = join.queryExecution.executedPlan
.asInstanceOf[AdaptiveSparkPlanExec].executedPlan
val shuffleReaders = finalPlan.collect {
- case reader: CoalescedShuffleReaderExec => reader
+ case r @ CustomShuffleReaderExec(_, _,
COALESCED_SHUFFLE_READER_DESCRIPTION) => r
}
assert(shuffleReaders.length === 2)
minNumPostShufflePartitions match {
@@ -258,7 +259,7 @@ class ReduceNumShufflePartitionsSuite extends SparkFunSuite
with BeforeAndAfterA
val finalPlan = join.queryExecution.executedPlan
.asInstanceOf[AdaptiveSparkPlanExec].executedPlan
val shuffleReaders = finalPlan.collect {
- case reader: CoalescedShuffleReaderExec => reader
+ case r @ CustomShuffleReaderExec(_, _,
COALESCED_SHUFFLE_READER_DESCRIPTION) => r
}
assert(shuffleReaders.length === 2)
minNumPostShufflePartitions match {
@@ -301,7 +302,7 @@ class ReduceNumShufflePartitionsSuite extends SparkFunSuite
with BeforeAndAfterA
val finalPlan = join.queryExecution.executedPlan
.asInstanceOf[AdaptiveSparkPlanExec].executedPlan
val shuffleReaders = finalPlan.collect {
- case reader: CoalescedShuffleReaderExec => reader
+ case r @ CustomShuffleReaderExec(_, _,
COALESCED_SHUFFLE_READER_DESCRIPTION) => r
}
assert(shuffleReaders.length === 0)
} finally {
@@ -329,7 +330,10 @@ class ReduceNumShufflePartitionsSuite extends
SparkFunSuite with BeforeAndAfterA
assert(finalPlan.collect {
case ShuffleQueryStageExec(_, r: ReusedExchangeExec) => r
}.length == 2)
- assert(finalPlan.collect { case p: CoalescedShuffleReaderExec => p
}.length == 3)
+ assert(
+ finalPlan.collect {
+ case p @ CustomShuffleReaderExec(_, _,
COALESCED_SHUFFLE_READER_DESCRIPTION) => p
+ }.length == 3)
// test case 2: a query stage has 2 parent stages.
@@ -377,7 +381,10 @@ class ReduceNumShufflePartitionsSuite extends
SparkFunSuite with BeforeAndAfterA
Seq(0, 1, 2).map(i => Row(i)))
val finalPlan = resultDf.queryExecution.executedPlan
.asInstanceOf[AdaptiveSparkPlanExec].executedPlan
- assert(finalPlan.collect { case p: CoalescedShuffleReaderExec => p
}.length == 0)
+ assert(
+ finalPlan.collect {
+ case p @ CustomShuffleReaderExec(_, _,
COALESCED_SHUFFLE_READER_DESCRIPTION) => p
+ }.isEmpty)
}
withSparkSession(test, 200, None)
}
@@ -395,7 +402,10 @@ class ReduceNumShufflePartitionsSuite extends
SparkFunSuite with BeforeAndAfterA
.asInstanceOf[AdaptiveSparkPlanExec].executedPlan
// As the pre-shuffle partition number are different, we will skip
reducing
// the shuffle partition numbers.
- assert(finalPlan.collect { case p: CoalescedShuffleReaderExec => p
}.length == 0)
+ assert(
+ finalPlan.collect {
+ case p @ CustomShuffleReaderExec(_, _,
COALESCED_SHUFFLE_READER_DESCRIPTION) => p
+ }.isEmpty)
}
withSparkSession(test, 100, None)
}
diff --git
a/sql/core/src/test/scala/org/apache/spark/sql/execution/ShufflePartitionsCoalescerSuite.scala
b/sql/core/src/test/scala/org/apache/spark/sql/execution/ShufflePartitionsCoalescerSuite.scala
index fcfde83..0befa06 100644
---
a/sql/core/src/test/scala/org/apache/spark/sql/execution/ShufflePartitionsCoalescerSuite.scala
+++
b/sql/core/src/test/scala/org/apache/spark/sql/execution/ShufflePartitionsCoalescerSuite.scala
@@ -24,7 +24,7 @@ class ShufflePartitionsCoalescerSuite extends SparkFunSuite {
private def checkEstimation(
bytesByPartitionIdArray: Array[Array[Long]],
- expectedPartitionStartIndices: Array[Int],
+ expectedPartitionStartIndices: Array[CoalescedPartitionSpec],
targetSize: Long,
minNumPartitions: Int = 1): Unit = {
val mapOutputStatistics = bytesByPartitionIdArray.zipWithIndex.map {
@@ -46,44 +46,54 @@ class ShufflePartitionsCoalescerSuite extends SparkFunSuite
{
{
// All bytes per partition are 0.
val bytesByPartitionId = Array[Long](0, 0, 0, 0, 0)
- val expectedPartitionStartIndices = Array[Int](0)
- checkEstimation(Array(bytesByPartitionId),
expectedPartitionStartIndices, targetSize)
+ val expectedPartitionSpecs = Array(CoalescedPartitionSpec(0, 5))
+ checkEstimation(Array(bytesByPartitionId), expectedPartitionSpecs,
targetSize)
}
{
// Some bytes per partition are 0 and total size is less than the target
size.
// 1 coalesced partition is expected.
val bytesByPartitionId = Array[Long](10, 0, 20, 0, 0)
- val expectedPartitionStartIndices = Array[Int](0)
- checkEstimation(Array(bytesByPartitionId),
expectedPartitionStartIndices, targetSize)
+ val expectedPartitionSpecs = Array(CoalescedPartitionSpec(0, 5))
+ checkEstimation(Array(bytesByPartitionId), expectedPartitionSpecs,
targetSize)
}
{
// 2 coalesced partitions are expected.
val bytesByPartitionId = Array[Long](10, 0, 90, 20, 0)
- val expectedPartitionStartIndices = Array[Int](0, 3)
- checkEstimation(Array(bytesByPartitionId),
expectedPartitionStartIndices, targetSize)
+ val expectedPartitionSpecs = Array(CoalescedPartitionSpec(0, 3),
CoalescedPartitionSpec(3, 5))
+ checkEstimation(Array(bytesByPartitionId), expectedPartitionSpecs,
targetSize)
}
{
// There are a few large shuffle partitions.
val bytesByPartitionId = Array[Long](110, 10, 100, 110, 0)
- val expectedPartitionStartIndices = Array[Int](0, 1, 2, 3, 4)
- checkEstimation(Array(bytesByPartitionId),
expectedPartitionStartIndices, targetSize)
+ val expectedPartitionSpecs = Array(
+ CoalescedPartitionSpec(0, 1),
+ CoalescedPartitionSpec(1, 2),
+ CoalescedPartitionSpec(2, 3),
+ CoalescedPartitionSpec(3, 4),
+ CoalescedPartitionSpec(4, 5))
+ checkEstimation(Array(bytesByPartitionId), expectedPartitionSpecs,
targetSize)
}
{
// All shuffle partitions are larger than the targeted size.
val bytesByPartitionId = Array[Long](100, 110, 100, 110, 110)
- val expectedPartitionStartIndices = Array[Int](0, 1, 2, 3, 4)
- checkEstimation(Array(bytesByPartitionId),
expectedPartitionStartIndices, targetSize)
+ val expectedPartitionSpecs = Array(
+ CoalescedPartitionSpec(0, 1),
+ CoalescedPartitionSpec(1, 2),
+ CoalescedPartitionSpec(2, 3),
+ CoalescedPartitionSpec(3, 4),
+ CoalescedPartitionSpec(4, 5))
+ checkEstimation(Array(bytesByPartitionId), expectedPartitionSpecs,
targetSize)
}
{
// The last shuffle partition is in a single coalesced partition.
val bytesByPartitionId = Array[Long](30, 30, 0, 40, 110)
- val expectedPartitionStartIndices = Array[Int](0, 4)
- checkEstimation(Array(bytesByPartitionId),
expectedPartitionStartIndices, targetSize)
+ val expectedPartitionSpecs = Array(CoalescedPartitionSpec(0, 4),
CoalescedPartitionSpec(4, 5))
+ checkEstimation(Array(bytesByPartitionId), expectedPartitionSpecs,
targetSize)
}
}
@@ -104,10 +114,10 @@ class ShufflePartitionsCoalescerSuite extends
SparkFunSuite {
// All bytes per partition are 0.
val bytesByPartitionId1 = Array[Long](0, 0, 0, 0, 0)
val bytesByPartitionId2 = Array[Long](0, 0, 0, 0, 0)
- val expectedPartitionStartIndices = Array[Int](0)
+ val expectedPartitionSpecs = Array(CoalescedPartitionSpec(0, 5))
checkEstimation(
Array(bytesByPartitionId1, bytesByPartitionId2),
- expectedPartitionStartIndices,
+ expectedPartitionSpecs,
targetSize)
}
@@ -116,10 +126,10 @@ class ShufflePartitionsCoalescerSuite extends
SparkFunSuite {
// 1 coalesced partition is expected.
val bytesByPartitionId1 = Array[Long](0, 10, 0, 20, 0)
val bytesByPartitionId2 = Array[Long](30, 0, 20, 0, 20)
- val expectedPartitionStartIndices = Array[Int](0)
+ val expectedPartitionSpecs = Array(CoalescedPartitionSpec(0, 5))
checkEstimation(
Array(bytesByPartitionId1, bytesByPartitionId2),
- expectedPartitionStartIndices,
+ expectedPartitionSpecs,
targetSize)
}
@@ -127,10 +137,13 @@ class ShufflePartitionsCoalescerSuite extends
SparkFunSuite {
// 2 coalesced partition are expected.
val bytesByPartitionId1 = Array[Long](0, 10, 0, 20, 0)
val bytesByPartitionId2 = Array[Long](30, 0, 70, 0, 30)
- val expectedPartitionStartIndices = Array[Int](0, 2, 4)
+ val expectedPartitionSpecs = Array(
+ CoalescedPartitionSpec(0, 2),
+ CoalescedPartitionSpec(2, 4),
+ CoalescedPartitionSpec(4, 5))
checkEstimation(
Array(bytesByPartitionId1, bytesByPartitionId2),
- expectedPartitionStartIndices,
+ expectedPartitionSpecs,
targetSize)
}
@@ -138,10 +151,14 @@ class ShufflePartitionsCoalescerSuite extends
SparkFunSuite {
// 4 coalesced partition are expected.
val bytesByPartitionId1 = Array[Long](0, 99, 0, 20, 0)
val bytesByPartitionId2 = Array[Long](30, 0, 70, 0, 30)
- val expectedPartitionStartIndices = Array[Int](0, 1, 2, 4)
+ val expectedPartitionSpecs = Array(
+ CoalescedPartitionSpec(0, 1),
+ CoalescedPartitionSpec(1, 2),
+ CoalescedPartitionSpec(2, 4),
+ CoalescedPartitionSpec(4, 5))
checkEstimation(
Array(bytesByPartitionId1, bytesByPartitionId2),
- expectedPartitionStartIndices,
+ expectedPartitionSpecs,
targetSize)
}
@@ -149,10 +166,14 @@ class ShufflePartitionsCoalescerSuite extends
SparkFunSuite {
// 2 coalesced partition are needed.
val bytesByPartitionId1 = Array[Long](0, 100, 0, 30, 0)
val bytesByPartitionId2 = Array[Long](30, 0, 70, 0, 30)
- val expectedPartitionStartIndices = Array[Int](0, 1, 2, 4)
+ val expectedPartitionSpecs = Array(
+ CoalescedPartitionSpec(0, 1),
+ CoalescedPartitionSpec(1, 2),
+ CoalescedPartitionSpec(2, 4),
+ CoalescedPartitionSpec(4, 5))
checkEstimation(
Array(bytesByPartitionId1, bytesByPartitionId2),
- expectedPartitionStartIndices,
+ expectedPartitionSpecs,
targetSize)
}
@@ -160,10 +181,15 @@ class ShufflePartitionsCoalescerSuite extends
SparkFunSuite {
// There are a few large shuffle partitions.
val bytesByPartitionId1 = Array[Long](0, 100, 40, 30, 0)
val bytesByPartitionId2 = Array[Long](30, 0, 60, 0, 110)
- val expectedPartitionStartIndices = Array[Int](0, 1, 2, 3, 4)
+ val expectedPartitionSpecs = Array(
+ CoalescedPartitionSpec(0, 1),
+ CoalescedPartitionSpec(1, 2),
+ CoalescedPartitionSpec(2, 3),
+ CoalescedPartitionSpec(3, 4),
+ CoalescedPartitionSpec(4, 5))
checkEstimation(
Array(bytesByPartitionId1, bytesByPartitionId2),
- expectedPartitionStartIndices,
+ expectedPartitionSpecs,
targetSize)
}
@@ -171,10 +197,15 @@ class ShufflePartitionsCoalescerSuite extends
SparkFunSuite {
// All pairs of shuffle partitions are larger than the targeted size.
val bytesByPartitionId1 = Array[Long](100, 100, 40, 30, 0)
val bytesByPartitionId2 = Array[Long](30, 0, 60, 70, 110)
- val expectedPartitionStartIndices = Array[Int](0, 1, 2, 3, 4)
+ val expectedPartitionSpecs = Array(
+ CoalescedPartitionSpec(0, 1),
+ CoalescedPartitionSpec(1, 2),
+ CoalescedPartitionSpec(2, 3),
+ CoalescedPartitionSpec(3, 4),
+ CoalescedPartitionSpec(4, 5))
checkEstimation(
Array(bytesByPartitionId1, bytesByPartitionId2),
- expectedPartitionStartIndices,
+ expectedPartitionSpecs,
targetSize)
}
}
@@ -188,10 +219,10 @@ class ShufflePartitionsCoalescerSuite extends
SparkFunSuite {
// the size of data is 0.
val bytesByPartitionId1 = Array[Long](0, 0, 0, 0, 0)
val bytesByPartitionId2 = Array[Long](0, 0, 0, 0, 0)
- val expectedPartitionStartIndices = Array[Int](0)
+ val expectedPartitionSpecs = Array(CoalescedPartitionSpec(0, 5))
checkEstimation(
Array(bytesByPartitionId1, bytesByPartitionId2),
- expectedPartitionStartIndices,
+ expectedPartitionSpecs,
targetSize, minNumPartitions)
}
@@ -199,10 +230,10 @@ class ShufflePartitionsCoalescerSuite extends
SparkFunSuite {
// The minimal number of coalesced partitions is enforced.
val bytesByPartitionId1 = Array[Long](10, 5, 5, 0, 20)
val bytesByPartitionId2 = Array[Long](5, 10, 0, 10, 5)
- val expectedPartitionStartIndices = Array[Int](0, 3)
+ val expectedPartitionSpecs = Array(CoalescedPartitionSpec(0, 3),
CoalescedPartitionSpec(3, 5))
checkEstimation(
Array(bytesByPartitionId1, bytesByPartitionId2),
- expectedPartitionStartIndices,
+ expectedPartitionSpecs,
targetSize, minNumPartitions)
}
@@ -210,10 +241,14 @@ class ShufflePartitionsCoalescerSuite extends
SparkFunSuite {
// The number of coalesced partitions is determined by the algorithm.
val bytesByPartitionId1 = Array[Long](10, 50, 20, 80, 20)
val bytesByPartitionId2 = Array[Long](40, 10, 0, 10, 30)
- val expectedPartitionStartIndices = Array[Int](0, 1, 3, 4)
+ val expectedPartitionSpecs = Array(
+ CoalescedPartitionSpec(0, 1),
+ CoalescedPartitionSpec(1, 3),
+ CoalescedPartitionSpec(3, 4),
+ CoalescedPartitionSpec(4, 5))
checkEstimation(
Array(bytesByPartitionId1, bytesByPartitionId2),
- expectedPartitionStartIndices,
+ expectedPartitionSpecs,
targetSize, minNumPartitions)
}
}
diff --git
a/sql/core/src/test/scala/org/apache/spark/sql/execution/adaptive/AdaptiveQueryExecSuite.scala
b/sql/core/src/test/scala/org/apache/spark/sql/execution/adaptive/AdaptiveQueryExecSuite.scala
index 64566af..17f6b29 100644
---
a/sql/core/src/test/scala/org/apache/spark/sql/execution/adaptive/AdaptiveQueryExecSuite.scala
+++
b/sql/core/src/test/scala/org/apache/spark/sql/execution/adaptive/AdaptiveQueryExecSuite.scala
@@ -22,8 +22,9 @@ import java.net.URI
import org.apache.spark.scheduler.{SparkListener, SparkListenerEvent,
SparkListenerJobStart}
import org.apache.spark.sql.QueryTest
-import org.apache.spark.sql.execution.{ReusedSubqueryExec, SparkPlan}
-import org.apache.spark.sql.execution.exchange.{BroadcastExchangeExec,
Exchange, ReusedExchangeExec, ShuffleExchangeExec}
+import org.apache.spark.sql.execution.{ReusedSubqueryExec, ShuffledRowRDD,
SparkPlan}
+import
org.apache.spark.sql.execution.adaptive.OptimizeLocalShuffleReader.LOCAL_SHUFFLE_READER_DESCRIPTION
+import org.apache.spark.sql.execution.exchange.{BroadcastExchangeExec,
Exchange, ReusedExchangeExec}
import org.apache.spark.sql.execution.joins.{BroadcastHashJoinExec,
BuildRight, SortMergeJoinExec}
import
org.apache.spark.sql.execution.ui.SparkListenerSQLAdaptiveExecutionUpdate
import org.apache.spark.sql.internal.SQLConf
@@ -110,7 +111,7 @@ class AdaptiveQueryExecSuite
}.length
val numLocalReaders = collect(plan) {
- case reader: LocalShuffleReaderExec => reader
+ case reader @ CustomShuffleReaderExec(_, _,
LOCAL_SHUFFLE_READER_DESCRIPTION) => reader
}.length
assert(numShuffles === (numLocalReaders + numShufflesWithoutLocalReader))
@@ -142,11 +143,11 @@ class AdaptiveQueryExecSuite
val bhj = findTopLevelBroadcastHashJoin(adaptivePlan)
assert(bhj.size == 1)
val localReaders = collect(adaptivePlan) {
- case reader: LocalShuffleReaderExec => reader
+ case reader @ CustomShuffleReaderExec(_, _,
LOCAL_SHUFFLE_READER_DESCRIPTION) => reader
}
assert(localReaders.length == 2)
- val localShuffleRDD0 =
localReaders(0).execute().asInstanceOf[LocalShuffledRowRDD]
- val localShuffleRDD1 =
localReaders(1).execute().asInstanceOf[LocalShuffledRowRDD]
+ val localShuffleRDD0 =
localReaders(0).execute().asInstanceOf[ShuffledRowRDD]
+ val localShuffleRDD1 =
localReaders(1).execute().asInstanceOf[ShuffledRowRDD]
// The pre-shuffle partition size is [0, 0, 0, 72, 0]
// And the partitionStartIndices is [0, 3, 4], so advisoryParallelism =
3.
// the final parallelism is
@@ -174,11 +175,11 @@ class AdaptiveQueryExecSuite
val bhj = findTopLevelBroadcastHashJoin(adaptivePlan)
assert(bhj.size == 1)
val localReaders = collect(adaptivePlan) {
- case reader: LocalShuffleReaderExec => reader
+ case reader @ CustomShuffleReaderExec(_, _,
LOCAL_SHUFFLE_READER_DESCRIPTION) => reader
}
assert(localReaders.length == 2)
- val localShuffleRDD0 =
localReaders(0).execute().asInstanceOf[LocalShuffledRowRDD]
- val localShuffleRDD1 =
localReaders(1).execute().asInstanceOf[LocalShuffledRowRDD]
+ val localShuffleRDD0 =
localReaders(0).execute().asInstanceOf[ShuffledRowRDD]
+ val localShuffleRDD1 =
localReaders(1).execute().asInstanceOf[ShuffledRowRDD]
// the final parallelism is math.max(1, numReduces / numMappers):
math.max(1, 5/2) = 2
// and the partitions length is 2 * numMappers = 4
assert(localShuffleRDD0.getPartitions.length == 4)
@@ -622,10 +623,10 @@ class AdaptiveQueryExecSuite
def checkSkewJoin(joins: Seq[SortMergeJoinExec],
expectedNumPartitions: Int): Unit = {
assert(joins.size == 1 && joins.head.isSkewJoin)
assert(joins.head.left.collect {
- case r: SkewJoinShuffleReaderExec => r
+ case r: CustomShuffleReaderExec => r
}.head.partitionSpecs.length == expectedNumPartitions)
assert(joins.head.right.collect {
- case r: SkewJoinShuffleReaderExec => r
+ case r: CustomShuffleReaderExec => r
}.head.partitionSpecs.length == expectedNumPartitions)
}
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