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Posted to issues@spark.apache.org by "Eyal Farago (JIRA)" <ji...@apache.org> on 2018/09/14 11:38:00 UTC
[jira] [Commented] (SPARK-24410) Missing optimization for Union on
bucketed tables
[ https://issues.apache.org/jira/browse/SPARK-24410?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16614678#comment-16614678 ]
Eyal Farago commented on SPARK-24410:
-------------------------------------
[~viirya], I've opened SPARK-25203 because of your answer on August 13th.
however looking at your PR it seems you opted to solve the general case, and bucketed table is just a private case of the general case.
do you think SPARK-25203 should be closed as a duplicate of this? if so can you please add the example used there to this issue as well?
> Missing optimization for Union on bucketed tables
> -------------------------------------------------
>
> Key: SPARK-24410
> URL: https://issues.apache.org/jira/browse/SPARK-24410
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.3.0
> Reporter: Ohad Raviv
> Priority: Major
>
> A common use-case we have is of a partially aggregated table and daily increments that we need to further aggregate. we do this my unioning the two tables and aggregating again.
> we tried to optimize this process by bucketing the tables, but currently it seems that the union operator doesn't leverage the tables being bucketed (like the join operator).
> for example, for two bucketed tables a1,a2:
> {code}
> sparkSession.range(N).selectExpr(
> "id as key",
> "id % 2 as t1",
> "id % 3 as t2")
> .repartition(col("key"))
> .write
> .mode(SaveMode.Overwrite)
> .bucketBy(3, "key")
> .sortBy("t1")
> .saveAsTable("a1")
> sparkSession.range(N).selectExpr(
> "id as key",
> "id % 2 as t1",
> "id % 3 as t2")
> .repartition(col("key"))
> .write.mode(SaveMode.Overwrite)
> .bucketBy(3, "key")
> .sortBy("t1")
> .saveAsTable("a2")
> {code}
> for the join query we get the "SortMergeJoin"
> {code}
> select * from a1 join a2 on (a1.key=a2.key)
> == Physical Plan ==
> *(3) SortMergeJoin [key#24L], [key#27L], Inner
> :- *(1) Sort [key#24L ASC NULLS FIRST], false, 0
> : +- *(1) Project [key#24L, t1#25L, t2#26L]
> : +- *(1) Filter isnotnull(key#24L)
> : +- *(1) FileScan parquet default.a1[key#24L,t1#25L,t2#26L] Batched: true, Format: Parquet, Location: InMemoryFileIndex[file:/some/where/spark-warehouse/a1], PartitionFilters: [], PushedFilters: [IsNotNull(key)], ReadSchema: struct<key:bigint,t1:bigint,t2:bigint>
> +- *(2) Sort [key#27L ASC NULLS FIRST], false, 0
> +- *(2) Project [key#27L, t1#28L, t2#29L]
> +- *(2) Filter isnotnull(key#27L)
> +- *(2) FileScan parquet default.a2[key#27L,t1#28L,t2#29L] Batched: true, Format: Parquet, Location: InMemoryFileIndex[file:/some/where/spark-warehouse/a2], PartitionFilters: [], PushedFilters: [IsNotNull(key)], ReadSchema: struct<key:bigint,t1:bigint,t2:bigint>
> {code}
> but for aggregation after union we get a shuffle:
> {code}
> select key,count(*) from (select * from a1 union all select * from a2)z group by key
> == Physical Plan ==
> *(4) HashAggregate(keys=[key#25L], functions=[count(1)], output=[key#25L, count(1)#36L])
> +- Exchange hashpartitioning(key#25L, 1)
> +- *(3) HashAggregate(keys=[key#25L], functions=[partial_count(1)], output=[key#25L, count#38L])
> +- Union
> :- *(1) Project [key#25L]
> : +- *(1) FileScan parquet default.a1[key#25L] Batched: true, Format: Parquet, Location: InMemoryFileIndex[file:/some/where/spark-warehouse/a1], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<key:bigint>
> +- *(2) Project [key#28L]
> +- *(2) FileScan parquet default.a2[key#28L] Batched: true, Format: Parquet, Location: InMemoryFileIndex[file:/some/where/spark-warehouse/a2], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<key:bigint>
> {code}
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