[jira] [Updated] (SPARK-24495) SortMergeJoin with duplicate keys wrong results
[
https://issues.apache.org/jira/browse/SPARK-24495?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Xiao Li updated SPARK-24495:
Priority: Blocker (was: Major)
> SortMergeJoin with duplicate keys wrong results
> ---
>
> Key: SPARK-24495
> URL: https://issues.apache.org/jira/browse/SPARK-24495
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.3.0
>Reporter: Bogdan Raducanu
>Assignee: Marco Gaido
>Priority: Blocker
> Labels: correctness
> Fix For: 2.3.2, 2.4.0
>
>
> To reproduce:
> {code:java}
> // the bug is in SortMergeJoin but the Shuffles are correct. with the default
> 200 it might split the data in such small partitions that the SortMergeJoin
> cannot return wrong results anymore
> spark.conf.set("spark.sql.shuffle.partitions", "1")
> // disable this, otherwise it would filter results before join, hiding the bug
> spark.conf.set("spark.sql.constraintPropagation.enabled", "false")
> sql("select id as a1 from range(1000)").createOrReplaceTempView("t1")
> sql("select id * 2 as b1, -id as b2 from
> range(1000)").createOrReplaceTempView("t2")
> spark.conf.set("spark.sql.autoBroadcastJoinThreshold", "-1")
> sql("""select b1, a1, b2 FROM t1 INNER JOIN t2 ON b1 = a1 AND b2 = a1""").show
> {code}
> In the results, it's expected that all columns are equal (see join condition).
> But the result is:
> {code:java}
> +---+---+---+
> | b1| a1| b2|
> +---+---+---+
> | 0| 0| 0|
> | 2| 2| -1|
> | 4| 4| -2|
> | 6| 6| -3|
> | 8| 8| -4|
>
> {code}
> I traced it to {{EnsureRequirements.reorder}} which was introduced by
> [https://github.com/apache/spark/pull/16985] and
> [https://github.com/apache/spark/pull/20041]
> It leads to an incorrect plan:
> {code:java}
> == Physical Plan ==
> *(5) Project [b1#735672L, a1#735669L, b2#735673L]
> +- *(5) SortMergeJoin [a1#735669L, a1#735669L], [b1#735672L, b1#735672L],
> Inner
>:- *(2) Sort [a1#735669L ASC NULLS FIRST, a1#735669L ASC NULLS FIRST],
> false, 0
>: +- Exchange hashpartitioning(a1#735669L, a1#735669L, 1)
>: +- *(1) Project [id#735670L AS a1#735669L]
>:+- *(1) Range (0, 1000, step=1, splits=8)
>+- *(4) Sort [b1#735672L ASC NULLS FIRST, b2#735673L ASC NULLS FIRST],
> false, 0
> +- Exchange hashpartitioning(b1#735672L, b2#735673L, 1)
> +- *(3) Project [(id#735674L * 2) AS b1#735672L, -id#735674L AS
> b2#735673L]
> +- *(3) Range (0, 1000, step=1, splits=8)
> {code}
> The SortMergeJoin keys are wrong: key b2 is missing completely.
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[jira] [Updated] (SPARK-24495) SortMergeJoin with duplicate keys wrong results
[
https://issues.apache.org/jira/browse/SPARK-24495?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Xiao Li updated SPARK-24495:
Target Version/s: 2.3.2, 2.4.0
> SortMergeJoin with duplicate keys wrong results
> ---
>
> Key: SPARK-24495
> URL: https://issues.apache.org/jira/browse/SPARK-24495
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.3.0
>Reporter: Bogdan Raducanu
>Priority: Major
> Labels: correctness
>
> To reproduce:
> {code:java}
> // the bug is in SortMergeJoin but the Shuffles are correct. with the default
> 200 it might split the data in such small partitions that the SortMergeJoin
> cannot return wrong results anymore
> spark.conf.set("spark.sql.shuffle.partitions", "1")
> // disable this, otherwise it would filter results before join, hiding the bug
> spark.conf.set("spark.sql.constraintPropagation.enabled", "false")
> sql("select id as a1 from range(1000)").createOrReplaceTempView("t1")
> sql("select id * 2 as b1, -id as b2 from
> range(1000)").createOrReplaceTempView("t2")
> spark.conf.set("spark.sql.autoBroadcastJoinThreshold", "-1")
> sql("""select b1, a1, b2 FROM t1 INNER JOIN t2 ON b1 = a1 AND b2 = a1""").show
> {code}
> In the results, it's expected that all columns are equal (see join condition).
> But the result is:
> {code:java}
> +---+---+---+
> | b1| a1| b2|
> +---+---+---+
> | 0| 0| 0|
> | 2| 2| -1|
> | 4| 4| -2|
> | 6| 6| -3|
> | 8| 8| -4|
>
> {code}
> I traced it to {{EnsureRequirements.reorder}} which was introduced by
> [https://github.com/apache/spark/pull/16985] and
> [https://github.com/apache/spark/pull/20041]
> It leads to an incorrect plan:
> {code:java}
> == Physical Plan ==
> *(5) Project [b1#735672L, a1#735669L, b2#735673L]
> +- *(5) SortMergeJoin [a1#735669L, a1#735669L], [b1#735672L, b1#735672L],
> Inner
>:- *(2) Sort [a1#735669L ASC NULLS FIRST, a1#735669L ASC NULLS FIRST],
> false, 0
>: +- Exchange hashpartitioning(a1#735669L, a1#735669L, 1)
>: +- *(1) Project [id#735670L AS a1#735669L]
>:+- *(1) Range (0, 1000, step=1, splits=8)
>+- *(4) Sort [b1#735672L ASC NULLS FIRST, b2#735673L ASC NULLS FIRST],
> false, 0
> +- Exchange hashpartitioning(b1#735672L, b2#735673L, 1)
> +- *(3) Project [(id#735674L * 2) AS b1#735672L, -id#735674L AS
> b2#735673L]
> +- *(3) Range (0, 1000, step=1, splits=8)
> {code}
> The SortMergeJoin keys are wrong: key b2 is missing completely.
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[jira] [Updated] (SPARK-24495) SortMergeJoin with duplicate keys wrong results
[
https://issues.apache.org/jira/browse/SPARK-24495?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Xiao Li updated SPARK-24495:
Labels: correctness (was: wrongresults)
> SortMergeJoin with duplicate keys wrong results
> ---
>
> Key: SPARK-24495
> URL: https://issues.apache.org/jira/browse/SPARK-24495
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.3.0
>Reporter: Bogdan Raducanu
>Priority: Major
> Labels: correctness
>
> To reproduce:
> {code:java}
> // the bug is in SortMergeJoin but the Shuffles are correct. with the default
> 200 it might split the data in such small partitions that the SortMergeJoin
> cannot return wrong results anymore
> spark.conf.set("spark.sql.shuffle.partitions", "1")
> // disable this, otherwise it would filter results before join, hiding the bug
> spark.conf.set("spark.sql.constraintPropagation.enabled", "false")
> sql("select id as a1 from range(1000)").createOrReplaceTempView("t1")
> sql("select id * 2 as b1, -id as b2 from
> range(1000)").createOrReplaceTempView("t2")
> spark.conf.set("spark.sql.autoBroadcastJoinThreshold", "-1")
> sql("""select b1, a1, b2 FROM t1 INNER JOIN t2 ON b1 = a1 AND b2 = a1""").show
> {code}
> In the results, it's expected that all columns are equal (see join condition).
> But the result is:
> {code:java}
> +---+---+---+
> | b1| a1| b2|
> +---+---+---+
> | 0| 0| 0|
> | 2| 2| -1|
> | 4| 4| -2|
> | 6| 6| -3|
> | 8| 8| -4|
>
> {code}
> I traced it to {{EnsureRequirements.reorder}} which was introduced by
> [https://github.com/apache/spark/pull/16985] and
> [https://github.com/apache/spark/pull/20041]
> It leads to an incorrect plan:
> {code:java}
> == Physical Plan ==
> *(5) Project [b1#735672L, a1#735669L, b2#735673L]
> +- *(5) SortMergeJoin [a1#735669L, a1#735669L], [b1#735672L, b1#735672L],
> Inner
>:- *(2) Sort [a1#735669L ASC NULLS FIRST, a1#735669L ASC NULLS FIRST],
> false, 0
>: +- Exchange hashpartitioning(a1#735669L, a1#735669L, 1)
>: +- *(1) Project [id#735670L AS a1#735669L]
>:+- *(1) Range (0, 1000, step=1, splits=8)
>+- *(4) Sort [b1#735672L ASC NULLS FIRST, b2#735673L ASC NULLS FIRST],
> false, 0
> +- Exchange hashpartitioning(b1#735672L, b2#735673L, 1)
> +- *(3) Project [(id#735674L * 2) AS b1#735672L, -id#735674L AS
> b2#735673L]
> +- *(3) Range (0, 1000, step=1, splits=8)
> {code}
> The SortMergeJoin keys are wrong: key b2 is missing completely.
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