Github user JoshRosen commented on a diff in the pull request:
https://github.com/apache/spark/pull/7768#discussion_r36055073
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/optimizer/extendedOperatorOptimizations.scala
---
@@ -0,0 +1,165 @@
+/*
+ * 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.optimizer
+
+import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys
+import org.apache.spark.sql.catalyst.plans.{Inner, LeftOuter, RightOuter,
LeftSemi}
+import org.apache.spark.sql.catalyst.plans.logical.{Project, Filter, Join,
LogicalPlan}
+import org.apache.spark.sql.catalyst.rules.Rule
+
+/**
+ * An optimization rule used to insert Filters to filter out rows whose
equal join keys
+ * have at least one null values. For this kind of rows, they will not
contribute to
+ * the join results of equal joins because a null does not equal another
null. We can
+ * filter them out before shuffling join input rows. For example, we have
two tables
+ *
+ * table1(key String, value Int)
+ * "str1"|1
+ * null |2
+ *
+ * table2(key String, value Int)
+ * "str1"|3
+ * null |4
+ *
+ * For a inner equal join, the result will be
+ * "str1"|1|"str1"|3
+ *
+ * those two rows having null as the value of key will not contribute to
the result.
+ * So, we can filter them out early.
+ *
+ * This optimization rule can be disabled by setting
spark.sql.advancedOptimization to false.
+ *
+ */
+case class FilterNullsInJoinKey(
+ sqlContext: SQLContext)
+ extends Rule[LogicalPlan] {
+
+ /**
+ * Checks if we need to add a Filter operator. We will add a Filter when
+ * there is any attribute in `keys` whose corresponding attribute of
`keys`
+ * in `plan.output` is still nullable (`nullable` field is `true`).
+ */
+ private def needsFilter(keys: Seq[Expression], plan: LogicalPlan):
Boolean = {
+ val keyAttributeSet =
AttributeSet(keys.filter(_.isInstanceOf[Attribute]))
+ plan.output.filter(keyAttributeSet.contains).exists(_.nullable)
+ }
+
+ /**
+ * Adds a Filter operator to make sure that every attribute in `keys` is
non-nullable.
+ */
+ private def addFilterIfNecessary(
+ keys: Seq[Expression],
+ child: LogicalPlan): LogicalPlan = {
+ // We get all attributes from keys.
+ val attributes = keys.filter {
--- End diff --
Could just be `keys.filter(_.isInstanceOf[Attribute])`.
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