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Posted to reviews@spark.apache.org by GitBox <gi...@apache.org> on 2020/09/12 07:10:05 UTC
[GitHub] [spark] c21 commented on a change in pull request #29655: [SPARK-32806][SQL] SortMergeJoin with partial hash distribution can be optimized to remove shuffle
c21 commented on a change in pull request #29655:
URL: https://github.com/apache/spark/pull/29655#discussion_r487378141
##########
File path: sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/OptimizeSortMergeJoinWithPartialHashDistribution.scala
##########
@@ -0,0 +1,115 @@
+/*
+ * 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.exchange
+
+import scala.collection.mutable
+
+import org.apache.spark.sql.catalyst.expressions.Expression
+import org.apache.spark.sql.catalyst.plans.physical.{HashPartitioning, Partitioning}
+import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.execution.{SortExec, SparkPlan}
+import org.apache.spark.sql.execution.joins.SortMergeJoinExec
+import org.apache.spark.sql.internal.SQLConf
+
+/**
+ * This rule removes shuffle for the sort merge join if the following conditions are met:
+ * - The child of ShuffleExchangeExec has HashPartitioning with the same number of partitions
+ * as the other side of join.
+ * - The child of ShuffleExchangeExec has output partitioning which has the subset of
+ * join keys on the respective join side.
+ *
+ * If the above conditions are met, shuffle can be eliminated for the sort merge join
+ * because rows are sorted before join logic is applied.
+ */
+case class OptimizeSortMergeJoinWithPartialHashDistribution(conf: SQLConf) extends Rule[SparkPlan] {
+ def apply(plan: SparkPlan): SparkPlan = {
+ if (!conf.optimizeSortMergeJoinWithPartialHashDistribution) {
+ return plan
+ }
+
+ plan.transformUp {
+ case s @ SortMergeJoinExec(_, _, _, _,
+ lSort @ SortExec(_, _,
+ ExtractShuffleExchangeExecChild(
+ lChild,
+ lChildOutputPartitioning: HashPartitioning),
+ _),
+ rSort @ SortExec(_, _,
+ ExtractShuffleExchangeExecChild(
+ rChild,
+ rChildOutputPartitioning: HashPartitioning),
+ _),
+ false) if isPartialHashDistribution(
+ s.leftKeys, lChildOutputPartitioning, s.rightKeys, rChildOutputPartitioning) =>
+ // Remove ShuffleExchangeExec.
+ s.copy(left = lSort.copy(child = lChild), right = rSort.copy(child = rChild))
+ case other => other
+ }
+ }
+
+ /*
+ * Returns true if both HashPartitioning have the same number of partitions and
+ * their partitioning expressions are a subset of their respective join keys.
+ */
+ private def isPartialHashDistribution(
+ leftKeys: Seq[Expression],
+ leftPartitioning: HashPartitioning,
+ rightKeys: Seq[Expression],
+ rightPartitioning: HashPartitioning): Boolean = {
+ val mapping = leftKeyToRightKeyMapping(leftKeys, rightKeys)
+ (leftPartitioning.numPartitions == rightPartitioning.numPartitions) &&
+ leftPartitioning.expressions.zip(rightPartitioning.expressions)
+ .forall {
+ case (le, re) => mapping.get(le.canonicalized)
+ .map(_.exists(_.semanticEquals(re)))
+ .getOrElse(false)
+ }
Review comment:
sorry if I miss anything, but I feel this might not be correct. We should make sure the `leftPartitioning.expressions` and `rightPartitioning.expressions` has same size, and the order of expressions matters, right?
`expressions` size is different, so we should not remove shuffle:
```
t1 has 1024 buckets on column (a)
t2 has 1024 buckets on columns (a, b)
SELECT *
FROM t1
JOIN t2
ON t1.a = t2.a AND t1.b = t2.b
```
`expressions` size is same, but order is wrong, so we should not remove shuffle:
```
t1 has 1024 buckets on column (a, b)
t2 has 1024 buckets on columns (b, a)
SELECT *
FROM t1
JOIN t2
ON t1.a = t2.a AND AND t1.a = t2.b AND t1.b = t2.a AND t1.b = t2.b
```
##########
File path: sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/OptimizeSortMergeJoinWithPartialHashDistribution.scala
##########
@@ -0,0 +1,115 @@
+/*
+ * 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.exchange
+
+import scala.collection.mutable
+
+import org.apache.spark.sql.catalyst.expressions.Expression
+import org.apache.spark.sql.catalyst.plans.physical.{HashPartitioning, Partitioning}
+import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.execution.{SortExec, SparkPlan}
+import org.apache.spark.sql.execution.joins.SortMergeJoinExec
+import org.apache.spark.sql.internal.SQLConf
+
+/**
+ * This rule removes shuffle for the sort merge join if the following conditions are met:
+ * - The child of ShuffleExchangeExec has HashPartitioning with the same number of partitions
+ * as the other side of join.
+ * - The child of ShuffleExchangeExec has output partitioning which has the subset of
+ * join keys on the respective join side.
+ *
+ * If the above conditions are met, shuffle can be eliminated for the sort merge join
+ * because rows are sorted before join logic is applied.
+ */
+case class OptimizeSortMergeJoinWithPartialHashDistribution(conf: SQLConf) extends Rule[SparkPlan] {
+ def apply(plan: SparkPlan): SparkPlan = {
+ if (!conf.optimizeSortMergeJoinWithPartialHashDistribution) {
+ return plan
+ }
+
+ plan.transformUp {
+ case s @ SortMergeJoinExec(_, _, _, _,
+ lSort @ SortExec(_, _,
+ ExtractShuffleExchangeExecChild(
+ lChild,
+ lChildOutputPartitioning: HashPartitioning),
+ _),
Review comment:
nit: why we can't just pattern matching `ShuffleExchangeExec(_, leftChild, _)` here? It seems to be looking simpler to me.
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