[SPARK-24410] Missing optimization for Union on bucketed tables

• Type: Improvement
• Status: Resolved
• Priority: Major
• Resolution: Incomplete
• Affects Version/s: 2.3.0
• Component/s: SQL
• Labels: bulk-closed
• Assignee: Unassigned
• Reporter: Ohad Raviv
• Created: 29/May/18 14:07
• Updated: 08/Oct/19 05:43
• Resolved: 08/Oct/19 05:43

Description

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:

    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")

for the join query we get the "SortMergeJoin"

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>

but for aggregation after union we get a shuffle:

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>

Issue Links

• is related to: SPARK-25203 spark sql, union all does not propagate child partitioning (when possible)
• links to: [Github] Pull Request #21498 (viirya)