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https://issues.apache.org/jira/browse/SPARK-17099?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15433849#comment-15433849
]
Herman van Hovell commented on SPARK-17099:
-------------------------------------------
A small update. Disregard my previous diagnoses. This is caused by a bug in the
{{EliminateOuterJoin}} rule. This converts the Right Outer join into an Inner
join, see the following optimizer log:
{noformat}
=== Applying Rule org.apache.spark.sql.catalyst.optimizer.EliminateOuterJoin ===
Project [sum(coalesce(int_col_5, int_col_2))#34L, (coalesce(int_col_5,
int_col_2) * 2)#32]
Project [sum(coalesce(int_col_5,
int_col_2))#34L, (coalesce(int_col_5, int_col_2) * 2)#32]
+- Filter (isnotnull(sum(cast(coalesce(int_col_5#4, int_col_2#13) as
bigint))#37L) && (sum(cast(coalesce(int_col_5#4, int_col_2#13) as bigint))#37L
> cast((coalesce(int_col_5#4, int_col_2#13)#38 * 2) as bigint)))
+- Filter
(isnotnull(sum(cast(coalesce(int_col_5#4, int_col_2#13) as bigint))#37L) &&
(sum(cast(coalesce(int_col_5#4, int_col_2#13) as bigint))#37L >
cast((coalesce(int_col_5#4, int_col_2#13)#38 * 2) as bigint)))
+- Aggregate [greatest(coalesce(int_col_5#14, 109), coalesce(int_col_5#4,
-449)), coalesce(int_col_5#4, int_col_2#13)], [sum(cast(coalesce(int_col_5#4,
int_col_2#13) as bigint)) AS sum(coalesce(int_col_5, int_col_2))#34L,
(coalesce(int_col_5#4, int_col_2#13) * 2) AS (coalesce(int_col_5, int_col_2) *
2)#32, sum(cast(coalesce(int_col_5#4, int_col_2#13) as bigint)) AS
sum(cast(coalesce(int_col_5#4, int_col_2#13) as bigint))#37L,
coalesce(int_col_5#4, int_col_2#13) AS coalesce(int_col_5#4, int_col_2#13)#38]
+- Aggregate [greatest(coalesce(int_col_5#14, 109), coalesce(int_col_5#4,
-449)), coalesce(int_col_5#4, int_col_2#13)], [sum(cast(coalesce(int_col_5#4,
int_col_2#13) as bigint)) AS sum(coalesce(int_col_5, int_col_2))#34L,
(coalesce(int_col_5#4, int_col_2#13) * 2) AS (coalesce(int_col_5, int_col_2) *
2)#32, sum(cast(coalesce(int_col_5#4, int_col_2#13) as bigint)) AS
sum(cast(coalesce(int_col_5#4, int_col_2#13) as bigint))#37L,
coalesce(int_col_5#4, int_col_2#13) AS coalesce(int_col_5#4, int_col_2#13)#38]
+- Filter isnotnull(coalesce(int_col_5#4, int_col_2#13))
+- Filter
isnotnull(coalesce(int_col_5#4, int_col_2#13))
! +- Join RightOuter, (int_col_2#13 = int_col_5#4)
+- Join Inner, (int_col_2#13 =
int_col_5#4)
:- Project [value#2 AS int_col_5#4]
:- Project [value#2 AS
int_col_5#4]
: +- SerializeFromObject [input[0, int, true] AS value#2]
: +- SerializeFromObject
[input[0, int, true] AS value#2]
: +- ExternalRDD [obj#1]
: +- ExternalRDD [obj#1]
+- Project [_1#10 AS int_col_2#13, _2#11 AS int_col_5#14]
+- Project [_1#10 AS
int_col_2#13, _2#11 AS int_col_5#14]
+- SerializeFromObject [assertnotnull(input[0, scala.Tuple2,
true], top level non-flat input object)._1 AS _1#10, assertnotnull(input[0,
scala.Tuple2, true], top level non-flat input object)._2 AS _2#11]
+- SerializeFromObject
[assertnotnull(input[0, scala.Tuple2, true], top level non-flat input
object)._1 AS _1#10, assertnotnull(input[0, scala.Tuple2, true], top level
non-flat input object)._2 AS _2#11]
+- ExternalRDD [obj#9]
+- ExternalRDD [obj#9]
{noformat}
PR https://github.com/apache/spark/pull/14661 fixes this.
> Incorrect result when HAVING clause is added to group by query
> --------------------------------------------------------------
>
> Key: SPARK-17099
> URL: https://issues.apache.org/jira/browse/SPARK-17099
> Project: Spark
> Issue Type: Bug
> Affects Versions: 2.1.0
> Reporter: Josh Rosen
> Priority: Blocker
>
> Random query generation uncovered the following query which returns incorrect
> results when run on Spark SQL. This wasn't the original query uncovered by
> the generator, since I performed a bit of minimization to try to make it more
> understandable.
> With the following tables:
> {code}
> val t1 = sc.parallelize(Seq(-234, 145, 367, 975, 298)).toDF("int_col_5")
> val t2 = sc.parallelize(
> Seq(
> (-769, -244),
> (-800, -409),
> (940, 86),
> (-507, 304),
> (-367, 158))
> ).toDF("int_col_2", "int_col_5")
> t1.registerTempTable("t1")
> t2.registerTempTable("t2")
> {code}
> Run
> {code}
> SELECT
> (SUM(COALESCE(t1.int_col_5, t2.int_col_2))),
> ((COALESCE(t1.int_col_5, t2.int_col_2)) * 2)
> FROM t1
> RIGHT JOIN t2
> ON (t2.int_col_2) = (t1.int_col_5)
> GROUP BY GREATEST(COALESCE(t2.int_col_5, 109), COALESCE(t1.int_col_5, -449)),
> COALESCE(t1.int_col_5, t2.int_col_2)
> HAVING (SUM(COALESCE(t1.int_col_5, t2.int_col_2))) > ((COALESCE(t1.int_col_5,
> t2.int_col_2)) * 2)
> {code}
> In Spark SQL, this returns an empty result set, whereas Postgres returns four
> rows. However, if I omit the {{HAVING}} clause I see that the group's rows
> are being incorrectly filtered by the {{HAVING}} clause:
> {code}
> +--------------------------------------+---------------------------------------+--+
> | sum(coalesce(int_col_5, int_col_2)) | (coalesce(int_col_5, int_col_2) * 2)
> |
> +--------------------------------------+---------------------------------------+--+
> | -507 | -1014
> |
> | 940 | 1880
> |
> | -769 | -1538
> |
> | -367 | -734
> |
> | -800 | -1600
> |
> +--------------------------------------+---------------------------------------+--+
> {code}
> Based on this, the output after adding the {{HAVING}} should contain four
> rows, not zero.
> I'm not sure how to further shrink this in a straightforward way, so I'm
> opening this bug to get help in triaging further.
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