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Anton Okolnychyi commented on SPARK-21479: ------------------------------------------ I used the following code to investigate: {code} val inputSchema1 = StructType( StructField("col1", StringType) :: StructField("col2", IntegerType) :: Nil) val inputSchema2 = StructType( StructField("col1", StringType) :: StructField("col3", StringType) :: Nil) val rdd1 = sc.parallelize(1 to 3).map(v => Row(s"value $v", v)) val df1 = spark.createDataFrame(rdd1, inputSchema1) val rdd2 = sc.parallelize(1 to 3).map(v => Row(s"value $v", "some value")) val df2 = spark.createDataFrame(rdd2, inputSchema2) // 1st use case df1.join(df2, Seq("col1"), "right_outer").where("col2 = 2").explain(true) // 2nd use case df1.join(df2, Seq("col1"), "right_outer").where("col1 = 'value 2'").explain(true) {code} It is important to notice that the actual join type in the first case is `inner` and not `rigth_outer`. This happens due to the `EliminateOuterJoin` rule, which sees that `col2 = 2` filters out non-matching rows on the left side of the join. Once the join type is changed, the `PushPredicateThroughJoin` rule pushes `col2 = 2` to the left relation. The analyzed and optimized logical plans are: {noformat} == Analyzed Logical Plan == col1: string, col2: int, col3: string Filter (col2#3 = 2) +- Project [col1#9, col2#3, col3#10] +- Join RightOuter, (col1#2 = col1#9) :- LogicalRDD [col1#2, col2#3] +- LogicalRDD [col1#9, col3#10] == Optimized Logical Plan == Project [col1#9, col2#3, col3#10] +- Join Inner, (col1#2 = col1#9) :- Filter ((isnotnull(col2#3) && (col2#3 = 2)) && isnotnull(col1#2)) : +- LogicalRDD [col1#2, col2#3] +- Filter isnotnull(col1#9) +- LogicalRDD [col1#9, col3#10] {noformat} The second case is different. The join type stays the same (i.e., `right_outer`) and the analyzed logical plan looks like: {noformat} == Analyzed Logical Plan == col1: string, col2: int, col3: string Filter (col1#9 = value 2) +- Project [col1#9, col2#3, col3#10] +- Join RightOuter, (col1#2 = col1#9) :- LogicalRDD [col1#2, col2#3] +- LogicalRDD [col1#9, col3#10] {noformat} `col1#9` from the Filter belongs to the right relation. After `PushPredicateThroughJoin` we have: {noformat} Join RightOuter, (col1#2 = col1#9) :- LogicalRDD [col1#2, col2#3] +- Filter (isnotnull(col1#9) && (col1#9 = value 2)) +- LogicalRDD [col1#9, col3#10] {noformat} In theory, `InferFiltersFromConstraints` is capable of inferring `(col1#2 = value 2)` from `(col1#9 = value 2, col1#2 = col1#9)`. However, not in this case since the join type is `right_outer` and `InferFiltersFromConstraints` will process only constraints from the right relation (i.e., `(isnotnull(col1#9) && (col1#9 = value 2))`), which is not enough to infer `(col1#2 = value 2)`. It seems like this is done on purpose and it is expected behavior even though additional `(col1#2 = value 2)` on the left relation would be logically correct here (as far as I understand). > Outer join filter pushdown in null supplying table when condition is on one > of the joined columns > ------------------------------------------------------------------------------------------------- > > Key: SPARK-21479 > URL: https://issues.apache.org/jira/browse/SPARK-21479 > Project: Spark > Issue Type: Bug > Components: Optimizer, SQL > Affects Versions: 2.1.0, 2.1.1, 2.2.0 > Reporter: Abhijit Bhole > > Here are two different query plans - > {code:java} > df1 = spark.createDataFrame([{ "a": 1, "b" : 2}, { "a": 3, "b" : 4}]) > df2 = spark.createDataFrame([{ "a": 1, "c" : 5}, { "a": 3, "c" : 6}, { "a": > 5, "c" : 8}]) > df1.join(df2, ['a'], 'right_outer').where("b = 2").explain() > == Physical Plan == > *Project [a#16299L, b#16295L, c#16300L] > +- *SortMergeJoin [a#16294L], [a#16299L], Inner > :- *Sort [a#16294L ASC NULLS FIRST], false, 0 > : +- Exchange hashpartitioning(a#16294L, 4) > : +- *Filter ((isnotnull(b#16295L) && (b#16295L = 2)) && > isnotnull(a#16294L)) > : +- Scan ExistingRDD[a#16294L,b#16295L] > +- *Sort [a#16299L ASC NULLS FIRST], false, 0 > +- Exchange hashpartitioning(a#16299L, 4) > +- *Filter isnotnull(a#16299L) > +- Scan ExistingRDD[a#16299L,c#16300L] > df1 = spark.createDataFrame([{ "a": 1, "b" : 2}, { "a": 3, "b" : 4}]) > df2 = spark.createDataFrame([{ "a": 1, "c" : 5}, { "a": 3, "c" : 6}, { "a": > 5, "c" : 8}]) > df1.join(df2, ['a'], 'right_outer').where("a = 1").explain() > == Physical Plan == > *Project [a#16314L, b#16310L, c#16315L] > +- SortMergeJoin [a#16309L], [a#16314L], RightOuter > :- *Sort [a#16309L ASC NULLS FIRST], false, 0 > : +- Exchange hashpartitioning(a#16309L, 4) > : +- Scan ExistingRDD[a#16309L,b#16310L] > +- *Sort [a#16314L ASC NULLS FIRST], false, 0 > +- Exchange hashpartitioning(a#16314L, 4) > +- *Filter (isnotnull(a#16314L) && (a#16314L = 1)) > +- Scan ExistingRDD[a#16314L,c#16315L] > {code} > If condition on b can be pushed down on df1 then why not condition on a? -- This message was sent by Atlassian JIRA (v6.4.14#64029) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org