Github user tejasapatil commented on a diff in the pull request:
https://github.com/apache/spark/pull/16985#discussion_r116163611
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ReorderJoinPredicates.scala
---
@@ -0,0 +1,93 @@
+/*
+ * 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 scala.collection.mutable.ArrayBuffer
+
+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.SparkPlan
+
+/**
+ * When the physical operators are created for JOIN, the ordering of join
keys is based on order
+ * in which the join keys appear in the user query. That might not match
with the output
+ * partitioning of the join node's children (thus leading to extra sort /
shuffle being
+ * introduced). This rule will change the ordering of the join keys to
match with the
+ * partitioning of the join nodes' children.
+ */
+class ReorderJoinPredicates extends Rule[SparkPlan] {
+ private def reorderJoinKeys(
+ leftKeys: Seq[Expression],
+ rightKeys: Seq[Expression],
+ leftPartitioning: Partitioning,
+ rightPartitioning: Partitioning): (Seq[Expression], Seq[Expression])
= {
+
+ def reorder(expectedOrderOfKeys: Seq[Expression],
+ currentOrderOfKeys: Seq[Expression]): (Seq[Expression],
Seq[Expression]) = {
+ val leftKeysBuffer = ArrayBuffer[Expression]()
+ val rightKeysBuffer = ArrayBuffer[Expression]()
+
+ expectedOrderOfKeys.foreach(expression => {
+ val index = currentOrderOfKeys.indexWhere(e =>
e.semanticEquals(expression))
+ leftKeysBuffer.append(leftKeys(index))
+ rightKeysBuffer.append(rightKeys(index))
+ })
+ (leftKeysBuffer, rightKeysBuffer)
+ }
+
+ if (leftKeys.forall(_.deterministic) &&
rightKeys.forall(_.deterministic)) {
+ leftPartitioning match {
+ case HashPartitioning(leftExpressions, _)
+ if leftExpressions.length == leftKeys.length &&
--- End diff --
I don't think that would be right thing to do. If child is partitioned on
`a, b, c, d`, its basically means rows are distributed over hash of `a, b, c,
d`. Lets say we have two rows with values of `a, b, c, d` as:
- row1 : 1,1,1,1 ==> hash(1,1,1,1) = x
- row2 : 1,1,2,2 ==> hash(1,1,2,2) = y
If the join key `b,a` is reordered as `a,b` and we want to avid shuffle,
that would mean that we expect the child to have same values of `a,b` in the
same partition. But if you look at row1 and row2 above, even if values of `a`
and `b` are the same, there is no guarantee that they would belong to the same
partition... as the partition is based on hash of all `a,b,c,d`.
If the join keys are a subset of the partitioning, then there needs to be a
shuffle to be done. There is only one exception to this (more of a corner case)
: https://issues.apache.org/jira/browse/SPARK-18067
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