Github user gatorsmile commented on the pull request: https://github.com/apache/spark/pull/10566#issuecomment-188663070 First, will add test cases to `OuterJoinEliminationSuite` tomorrow. Second, the current fix does not cover all the possible cases. I need to get the inputs from you about the issues this PR is facing: ```scala val df = Seq((1, 2, "1"), (3, 4, "3")).toDF("int", "int2", "str").as("a") val df2 = Seq((1, 2, "1"), (5, 6, "5")).toDF("int", "int2", "str").as("b") val df3 = Seq((1, 3, "1"), (4, 6, "5")).toDF("int", "int2", "str").as("c") // Full -> Left val full2Left = df.join(df2, $"a.int" === $"b.int", "full") .join(df3, $"c.int" === $"a.int", "right").select($"a.*", $"b.*", $"c.*") ``` In the above case, the parent join condition `$"c.int" === $"a.int"` is not eligible for the current two ways we are using to decide if the predicates are null filtering. 1. The first way is based on the constraints. If the parent join is full outer, the parent join will not have any IsNotNull constraint. In the current constraint propagation, its constraints is `Set.empty[Expression]`. Thus, `$"c.int" === $"a.int"` is not eligible for using the first way. 2. The second way is based on the run-time evaluation, `canFilterOutNull`. This requires that all the attributes are from the same side. In the predicate `$"c.int" === $"a.int"`, `$"a.int"` is from the left side, but `$"c.int"` is not from the left side. (Actually, `$"c.int"` is from the other side in the parent join.) Thus, it is not eligible for the second way too. However, the parent join condition `$"c.int" === $"a.int"` is very common in the join condition. We definitely can use such predicates as null-filtering predicates. Maybe we can keep the original way as the third way, as shown in the following link: https://github.com/gatorsmile/spark/blob/d6a6e9cc31b0f7547b35cf25884135ea65b03676/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala#L798-L799 Does that look good to you? Thanks! : )
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