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Posted to issues@spark.apache.org by "Tejas Patil (JIRA)" <ji...@apache.org> on 2017/01/08 01:38:58 UTC
[jira] [Commented] (SPARK-19122) Unnecessary shuffle+sort added if
join predicates ordering differ from bucketing and sorting order
[ https://issues.apache.org/jira/browse/SPARK-19122?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15808479#comment-15808479 ]
Tejas Patil commented on SPARK-19122:
-------------------------------------
When a `SortMergeJoinExec` node is created, the join keys in both `left` and `right` relations are extracted in their order of appearance in the query (see 0). Later, the list of keys are used as-is to define the required distribution (see 1) and ordering (see 2) for the sort merge join node. Since the ordering matters here (ie. `ClusteredDistribution(a,b) != ClusteredDistribution(b,a)`), this mismatches with the distribution and ordering of the children... thus `EnsureRequirements` ends up adding shuffle + sort.
0 : https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala#L103
1 : https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoinExec.scala#L80
2 : https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoinExec.scala#L85
> Unnecessary shuffle+sort added if join predicates ordering differ from bucketing and sorting order
> --------------------------------------------------------------------------------------------------
>
> Key: SPARK-19122
> URL: https://issues.apache.org/jira/browse/SPARK-19122
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.0.2, 2.1.0
> Reporter: Tejas Patil
>
> `table1` and `table2` are sorted and bucketed on columns `j` and `k` (in respective order)
> This is how they are generated:
> {code}
> val df = (0 until 16).map(i => (i % 8, i * 2, i.toString)).toDF("i", "j", "k").coalesce(1)
> df.write.format("org.apache.spark.sql.hive.orc.OrcFileFormat").bucketBy(8, "j", "k").sortBy("j", "k").saveAsTable("table1")
> df.write.format("org.apache.spark.sql.hive.orc.OrcFileFormat").bucketBy(8, "j", "k").sortBy("j", "k").saveAsTable("table2")
> {code}
> Now, if join predicates are specified in query in *same* order as bucketing and sort order, there is no shuffle and sort.
> {code}
> scala> hc.sql("SELECT * FROM table1 a JOIN table2 b ON a.j=b.j AND a.k=b.k").explain(true)
> == Physical Plan ==
> *SortMergeJoin [j#61, k#62], [j#100, k#101], Inner
> :- *Project [i#60, j#61, k#62]
> : +- *Filter (isnotnull(k#62) && isnotnull(j#61))
> : +- *FileScan orc default.table1[i#60,j#61,k#62] Batched: false, Format: ORC, Location: InMemoryFileIndex[file:/Users/tejasp/Desktop/dev/tp-spark/spark-warehouse/table1], PartitionFilters: [], PushedFilters: [IsNotNull(k), IsNotNull(j)], ReadSchema: struct<i:int,j:int,k:string>
> +- *Project [i#99, j#100, k#101]
> +- *Filter (isnotnull(j#100) && isnotnull(k#101))
> +- *FileScan orc default.table2[i#99,j#100,k#101] Batched: false, Format: ORC, Location: InMemoryFileIndex[file:/Users/tejasp/Desktop/dev/tp-spark/spark-warehouse/table2], PartitionFilters: [], PushedFilters: [IsNotNull(j), IsNotNull(k)], ReadSchema: struct<i:int,j:int,k:string>
> {code}
> The same query with join predicates in *different* order from bucketing and sort order leads to extra shuffle and sort being introduced
> {code}
> scala> hc.sql("SELECT * FROM table1 a JOIN table2 b ON a.k=b.k AND a.j=b.j ").explain(true)
> == Physical Plan ==
> *SortMergeJoin [k#62, j#61], [k#101, j#100], Inner
> :- *Sort [k#62 ASC NULLS FIRST, j#61 ASC NULLS FIRST], false, 0
> : +- Exchange hashpartitioning(k#62, j#61, 200)
> : +- *Project [i#60, j#61, k#62]
> : +- *Filter (isnotnull(k#62) && isnotnull(j#61))
> : +- *FileScan orc default.table1[i#60,j#61,k#62] Batched: false, Format: ORC, Location: InMemoryFileIndex[file:/spark-warehouse/table1], PartitionFilters: [], PushedFilters: [IsNotNull(k), IsNotNull(j)], ReadSchema: struct<i:int,j:int,k:string>
> +- *Sort [k#101 ASC NULLS FIRST, j#100 ASC NULLS FIRST], false, 0
> +- Exchange hashpartitioning(k#101, j#100, 200)
> +- *Project [i#99, j#100, k#101]
> +- *Filter (isnotnull(j#100) && isnotnull(k#101))
> +- *FileScan orc default.table2[i#99,j#100,k#101] Batched: false, Format: ORC, Location: InMemoryFileIndex[file:/spark-warehouse/table2], PartitionFilters: [], PushedFilters: [IsNotNull(j), IsNotNull(k)], ReadSchema: struct<i:int,j:int,k:string>
> {code}
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