Exchange with bucketing












1















I have two tables with bucketing enabled.



DESCRIBE EXTENDED table1

Table |table1 | |
|Owner |user | |
|Created |Wed Nov 21 16:24:25 CST 2018 | |
|Last Access |Wed Dec 31 18:00:00 CST 1969 | |
|Type |MANAGED | |
|Provider |parquet | |
|Num Buckets |180 | |
|Bucket Columns |[`seq_id`] | |
|Sort Columns |[`seq_id`]


DESCRIBE EXTENDED table2

Table |table2 | |
|Owner |user | |
|Created |Wed Nov 21 16:15:09 CST 2018 | |
|Last Access |Wed Dec 31 18:00:00 CST 1969 | |
|Type |MANAGED | |
|Provider |parquet | |
|Num Buckets |180 | |
|Bucket Columns |[`seq_id`] | |
|Sort Columns |[`seq_id`]


Then I expect that it will let me avoid shuffling (exchange) when I join both of them.



However, exchange is there:



spark.table("table2").join(spark.table("table1"), "seq_id").explain




== Physical Plan ==
Project [seq_id#0, field1#1, ... 165 more fields]
+- SortMergeJoin [seq_id#0], [seq_id#196], Inner
:- *Sort [seq_id#0 ASC NULLS FIRST], false, 0
: +- Exchange(coordinator id: 713544719) hashpartitioning(seq_id#0, 200), coordinator[target post-shuffle partition size: 77108864]
: +- *Project [seq_id#0, field1#1, ... 73 more fields]
: +- *Filter isnotnull(seq_id#0)
: +- *FileScan parquet
test2[seq_id#0, field1#1,... 73 more fields] Batched: true, Format: Parquet, Location: InMemoryFileIndex[maprfs:/ds/hive/warehouse/test2..., PartitionFilters: , PushedFilters: [IsNotNull(seq_id)], ReadSchema: struct<seq_id:string,field1:string...
+- *Sort [seq_id#196 ASC NULLS FIRST], false, 0
+- Exchange(coordinator id: 713544719) hashpartitioning(seq_id#196, 200), coordinator[target post-shuffle partition size: 77108864]
+- *Project [line_s#195, seq_id#196, field1#197, ... 69 more fields]
+- *Filter isnotnull(seq_id#196)
+- *FileScan parquet test1[line_s#195,seq_id#196,field1#197,69 more fields] Batched: true, Format: Parquet, Location: InMemoryFileIndex[maprfs:/ds/test1..., PartitionFilters: , PushedFilters: [IsNotNull(seq_id)], ReadSchema: struct<line_s:string,seq_id:string,field1:string,...


I am using Spark 2.2.1, any idea what could be the reason that exchange still happens there?



Tables (table1 and tables2) were created as below:



spark.table("src_table1").write
.bucketBy(180, "seq_id")
.sortBy("seq_id")
.saveAsTable("table1")

spark.table("src_table2").write
.bucketBy(180, "seq_id")
.sortBy("seq_id")
.saveAsTable("table2")


Hive tables src_table1 and src_table2 are parquet format with no buckets.










share|improve this question





























    1















    I have two tables with bucketing enabled.



    DESCRIBE EXTENDED table1

    Table |table1 | |
    |Owner |user | |
    |Created |Wed Nov 21 16:24:25 CST 2018 | |
    |Last Access |Wed Dec 31 18:00:00 CST 1969 | |
    |Type |MANAGED | |
    |Provider |parquet | |
    |Num Buckets |180 | |
    |Bucket Columns |[`seq_id`] | |
    |Sort Columns |[`seq_id`]


    DESCRIBE EXTENDED table2

    Table |table2 | |
    |Owner |user | |
    |Created |Wed Nov 21 16:15:09 CST 2018 | |
    |Last Access |Wed Dec 31 18:00:00 CST 1969 | |
    |Type |MANAGED | |
    |Provider |parquet | |
    |Num Buckets |180 | |
    |Bucket Columns |[`seq_id`] | |
    |Sort Columns |[`seq_id`]


    Then I expect that it will let me avoid shuffling (exchange) when I join both of them.



    However, exchange is there:



    spark.table("table2").join(spark.table("table1"), "seq_id").explain




    == Physical Plan ==
    Project [seq_id#0, field1#1, ... 165 more fields]
    +- SortMergeJoin [seq_id#0], [seq_id#196], Inner
    :- *Sort [seq_id#0 ASC NULLS FIRST], false, 0
    : +- Exchange(coordinator id: 713544719) hashpartitioning(seq_id#0, 200), coordinator[target post-shuffle partition size: 77108864]
    : +- *Project [seq_id#0, field1#1, ... 73 more fields]
    : +- *Filter isnotnull(seq_id#0)
    : +- *FileScan parquet
    test2[seq_id#0, field1#1,... 73 more fields] Batched: true, Format: Parquet, Location: InMemoryFileIndex[maprfs:/ds/hive/warehouse/test2..., PartitionFilters: , PushedFilters: [IsNotNull(seq_id)], ReadSchema: struct<seq_id:string,field1:string...
    +- *Sort [seq_id#196 ASC NULLS FIRST], false, 0
    +- Exchange(coordinator id: 713544719) hashpartitioning(seq_id#196, 200), coordinator[target post-shuffle partition size: 77108864]
    +- *Project [line_s#195, seq_id#196, field1#197, ... 69 more fields]
    +- *Filter isnotnull(seq_id#196)
    +- *FileScan parquet test1[line_s#195,seq_id#196,field1#197,69 more fields] Batched: true, Format: Parquet, Location: InMemoryFileIndex[maprfs:/ds/test1..., PartitionFilters: , PushedFilters: [IsNotNull(seq_id)], ReadSchema: struct<line_s:string,seq_id:string,field1:string,...


    I am using Spark 2.2.1, any idea what could be the reason that exchange still happens there?



    Tables (table1 and tables2) were created as below:



    spark.table("src_table1").write
    .bucketBy(180, "seq_id")
    .sortBy("seq_id")
    .saveAsTable("table1")

    spark.table("src_table2").write
    .bucketBy(180, "seq_id")
    .sortBy("seq_id")
    .saveAsTable("table2")


    Hive tables src_table1 and src_table2 are parquet format with no buckets.










    share|improve this question



























      1












      1








      1








      I have two tables with bucketing enabled.



      DESCRIBE EXTENDED table1

      Table |table1 | |
      |Owner |user | |
      |Created |Wed Nov 21 16:24:25 CST 2018 | |
      |Last Access |Wed Dec 31 18:00:00 CST 1969 | |
      |Type |MANAGED | |
      |Provider |parquet | |
      |Num Buckets |180 | |
      |Bucket Columns |[`seq_id`] | |
      |Sort Columns |[`seq_id`]


      DESCRIBE EXTENDED table2

      Table |table2 | |
      |Owner |user | |
      |Created |Wed Nov 21 16:15:09 CST 2018 | |
      |Last Access |Wed Dec 31 18:00:00 CST 1969 | |
      |Type |MANAGED | |
      |Provider |parquet | |
      |Num Buckets |180 | |
      |Bucket Columns |[`seq_id`] | |
      |Sort Columns |[`seq_id`]


      Then I expect that it will let me avoid shuffling (exchange) when I join both of them.



      However, exchange is there:



      spark.table("table2").join(spark.table("table1"), "seq_id").explain




      == Physical Plan ==
      Project [seq_id#0, field1#1, ... 165 more fields]
      +- SortMergeJoin [seq_id#0], [seq_id#196], Inner
      :- *Sort [seq_id#0 ASC NULLS FIRST], false, 0
      : +- Exchange(coordinator id: 713544719) hashpartitioning(seq_id#0, 200), coordinator[target post-shuffle partition size: 77108864]
      : +- *Project [seq_id#0, field1#1, ... 73 more fields]
      : +- *Filter isnotnull(seq_id#0)
      : +- *FileScan parquet
      test2[seq_id#0, field1#1,... 73 more fields] Batched: true, Format: Parquet, Location: InMemoryFileIndex[maprfs:/ds/hive/warehouse/test2..., PartitionFilters: , PushedFilters: [IsNotNull(seq_id)], ReadSchema: struct<seq_id:string,field1:string...
      +- *Sort [seq_id#196 ASC NULLS FIRST], false, 0
      +- Exchange(coordinator id: 713544719) hashpartitioning(seq_id#196, 200), coordinator[target post-shuffle partition size: 77108864]
      +- *Project [line_s#195, seq_id#196, field1#197, ... 69 more fields]
      +- *Filter isnotnull(seq_id#196)
      +- *FileScan parquet test1[line_s#195,seq_id#196,field1#197,69 more fields] Batched: true, Format: Parquet, Location: InMemoryFileIndex[maprfs:/ds/test1..., PartitionFilters: , PushedFilters: [IsNotNull(seq_id)], ReadSchema: struct<line_s:string,seq_id:string,field1:string,...


      I am using Spark 2.2.1, any idea what could be the reason that exchange still happens there?



      Tables (table1 and tables2) were created as below:



      spark.table("src_table1").write
      .bucketBy(180, "seq_id")
      .sortBy("seq_id")
      .saveAsTable("table1")

      spark.table("src_table2").write
      .bucketBy(180, "seq_id")
      .sortBy("seq_id")
      .saveAsTable("table2")


      Hive tables src_table1 and src_table2 are parquet format with no buckets.










      share|improve this question
















      I have two tables with bucketing enabled.



      DESCRIBE EXTENDED table1

      Table |table1 | |
      |Owner |user | |
      |Created |Wed Nov 21 16:24:25 CST 2018 | |
      |Last Access |Wed Dec 31 18:00:00 CST 1969 | |
      |Type |MANAGED | |
      |Provider |parquet | |
      |Num Buckets |180 | |
      |Bucket Columns |[`seq_id`] | |
      |Sort Columns |[`seq_id`]


      DESCRIBE EXTENDED table2

      Table |table2 | |
      |Owner |user | |
      |Created |Wed Nov 21 16:15:09 CST 2018 | |
      |Last Access |Wed Dec 31 18:00:00 CST 1969 | |
      |Type |MANAGED | |
      |Provider |parquet | |
      |Num Buckets |180 | |
      |Bucket Columns |[`seq_id`] | |
      |Sort Columns |[`seq_id`]


      Then I expect that it will let me avoid shuffling (exchange) when I join both of them.



      However, exchange is there:



      spark.table("table2").join(spark.table("table1"), "seq_id").explain




      == Physical Plan ==
      Project [seq_id#0, field1#1, ... 165 more fields]
      +- SortMergeJoin [seq_id#0], [seq_id#196], Inner
      :- *Sort [seq_id#0 ASC NULLS FIRST], false, 0
      : +- Exchange(coordinator id: 713544719) hashpartitioning(seq_id#0, 200), coordinator[target post-shuffle partition size: 77108864]
      : +- *Project [seq_id#0, field1#1, ... 73 more fields]
      : +- *Filter isnotnull(seq_id#0)
      : +- *FileScan parquet
      test2[seq_id#0, field1#1,... 73 more fields] Batched: true, Format: Parquet, Location: InMemoryFileIndex[maprfs:/ds/hive/warehouse/test2..., PartitionFilters: , PushedFilters: [IsNotNull(seq_id)], ReadSchema: struct<seq_id:string,field1:string...
      +- *Sort [seq_id#196 ASC NULLS FIRST], false, 0
      +- Exchange(coordinator id: 713544719) hashpartitioning(seq_id#196, 200), coordinator[target post-shuffle partition size: 77108864]
      +- *Project [line_s#195, seq_id#196, field1#197, ... 69 more fields]
      +- *Filter isnotnull(seq_id#196)
      +- *FileScan parquet test1[line_s#195,seq_id#196,field1#197,69 more fields] Batched: true, Format: Parquet, Location: InMemoryFileIndex[maprfs:/ds/test1..., PartitionFilters: , PushedFilters: [IsNotNull(seq_id)], ReadSchema: struct<line_s:string,seq_id:string,field1:string,...


      I am using Spark 2.2.1, any idea what could be the reason that exchange still happens there?



      Tables (table1 and tables2) were created as below:



      spark.table("src_table1").write
      .bucketBy(180, "seq_id")
      .sortBy("seq_id")
      .saveAsTable("table1")

      spark.table("src_table2").write
      .bucketBy(180, "seq_id")
      .sortBy("seq_id")
      .saveAsTable("table2")


      Hive tables src_table1 and src_table2 are parquet format with no buckets.







      apache-spark apache-spark-sql






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      share|improve this question













      share|improve this question




      share|improve this question








      edited Nov 22 '18 at 0:16









      user10465355

      2,1192521




      2,1192521










      asked Nov 21 '18 at 23:27









      Tomasz KrolTomasz Krol

      241214




      241214
























          1 Answer
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          0














          Seems like adaptive query execution enabled (spark.sql.adaptive.enabled=true) was the problem. After disabling this, exchange is not there anymore. Need to dig more, why it happens.






          share|improve this answer
























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            1 Answer
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            active

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            1 Answer
            1






            active

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            active

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            0














            Seems like adaptive query execution enabled (spark.sql.adaptive.enabled=true) was the problem. After disabling this, exchange is not there anymore. Need to dig more, why it happens.






            share|improve this answer




























              0














              Seems like adaptive query execution enabled (spark.sql.adaptive.enabled=true) was the problem. After disabling this, exchange is not there anymore. Need to dig more, why it happens.






              share|improve this answer


























                0












                0








                0







                Seems like adaptive query execution enabled (spark.sql.adaptive.enabled=true) was the problem. After disabling this, exchange is not there anymore. Need to dig more, why it happens.






                share|improve this answer













                Seems like adaptive query execution enabled (spark.sql.adaptive.enabled=true) was the problem. After disabling this, exchange is not there anymore. Need to dig more, why it happens.







                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered Nov 22 '18 at 23:19









                Tomasz KrolTomasz Krol

                241214




                241214
































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