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Posted to issues-all@impala.apache.org by "Qifan Chen (Jira)" <ji...@apache.org> on 2021/03/23 19:20:00 UTC

[jira] [Updated] (IMPALA-10602) Intersection of multiple min/max filters when applying to common equi-join columns

     [ https://issues.apache.org/jira/browse/IMPALA-10602?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Qifan Chen updated IMPALA-10602:
--------------------------------
    Description: 
Currently, Impala generates two min/max filters from the two joins, for a test query as follows.

{quote}select straight_join  count(*)
from store_sales ss, date_dim d1, date_dim d2
where 
ss.ss_sold_time_sk = d1.d_date_sk and
ss.ss_sold_time_sk = d2.d_date_sk;{quote}

{quote}|   00:SCAN HDFS [tpcds_parquet.store_sales ss, RANDOM]                                                                                                                    |
|      HDFS partitions=1824/1824 files=1824 size=200.94MB                                                                                                                  |
|      runtime filters: RF001[min_max] -> ss.ss_sold_time_sk, RF003[min_max] -> ss.ss_sold_time_sk, RF000[bloom] -> ss.ss_sold_time_sk, RF002[bloom] -> ss.ss_sold_time_sk |
|      stored statistics:                                                                                                                                                  |
|        table: rows=2.88M size=200.94MB                                                                                                                                   |
|        partitions: 1824/1824 rows=2.88M                                                                                                                                  |
|        columns: all                                                                                                                                                      |
|      extrapolated-rows=disabled max-scan-range-rows=130.09K                                                                                                              |
|      file formats: [PARQUET]                                                                                                                                             |
|      mem-estimate=16.00MB mem-reservation=512.00KB thread-reservation=1                                                                                                  |
|      tuple-ids=0 row-size=4B cardinality=2.88M{quote}

Since the two filters are applied to the same equi-join column ss.ss_sold_time_sk, it will be more efficient to intersect RF001 and RF003 into a single filter and apply the result one instead.  Assume the range of RF001 is [10, 50] and that for RF003 is [20, 60]  then the combined filter will be [20, 50].  


  was:
Currently, Impala actually generates two min/max filters from the two joins, for a test query as follows.

{quote}select straight_join  count(*)
from store_sales ss, date_dim d1, date_dim d2
where 
ss.ss_sold_time_sk = d1.d_date_sk and
ss.ss_sold_time_sk = d2.d_date_sk;{quote}

{quote}|   00:SCAN HDFS [tpcds_parquet.store_sales ss, RANDOM]                                                                                                                    |
|      HDFS partitions=1824/1824 files=1824 size=200.94MB                                                                                                                  |
|      runtime filters: RF001[min_max] -> ss.ss_sold_time_sk, RF003[min_max] -> ss.ss_sold_time_sk, RF000[bloom] -> ss.ss_sold_time_sk, RF002[bloom] -> ss.ss_sold_time_sk |
|      stored statistics:                                                                                                                                                  |
|        table: rows=2.88M size=200.94MB                                                                                                                                   |
|        partitions: 1824/1824 rows=2.88M                                                                                                                                  |
|        columns: all                                                                                                                                                      |
|      extrapolated-rows=disabled max-scan-range-rows=130.09K                                                                                                              |
|      file formats: [PARQUET]                                                                                                                                             |
|      mem-estimate=16.00MB mem-reservation=512.00KB thread-reservation=1                                                                                                  |
|      tuple-ids=0 row-size=4B cardinality=2.88M{quote}

Since the two filters are applied to the same equi-join column ss.ss_sold_time_sk, it will be more efficient to intersect RF001 and RF003 into a single filter and apply the result one instead.  Assume the range of RF001 is [10, 50] and that for RF003 is [20, 60]  then the combined filter will be [20, 50].  


        Summary: Intersection of multiple min/max filters when applying to common equi-join columns  (was: Intersection of multiple min/max filters when applying onto common equi-join column)

> Intersection of multiple min/max filters when applying to common equi-join columns
> ----------------------------------------------------------------------------------
>
>                 Key: IMPALA-10602
>                 URL: https://issues.apache.org/jira/browse/IMPALA-10602
>             Project: IMPALA
>          Issue Type: Improvement
>            Reporter: Qifan Chen
>            Priority: Major
>
> Currently, Impala generates two min/max filters from the two joins, for a test query as follows.
> {quote}select straight_join  count(*)
> from store_sales ss, date_dim d1, date_dim d2
> where 
> ss.ss_sold_time_sk = d1.d_date_sk and
> ss.ss_sold_time_sk = d2.d_date_sk;{quote}
> {quote}|   00:SCAN HDFS [tpcds_parquet.store_sales ss, RANDOM]                                                                                                                    |
> |      HDFS partitions=1824/1824 files=1824 size=200.94MB                                                                                                                  |
> |      runtime filters: RF001[min_max] -> ss.ss_sold_time_sk, RF003[min_max] -> ss.ss_sold_time_sk, RF000[bloom] -> ss.ss_sold_time_sk, RF002[bloom] -> ss.ss_sold_time_sk |
> |      stored statistics:                                                                                                                                                  |
> |        table: rows=2.88M size=200.94MB                                                                                                                                   |
> |        partitions: 1824/1824 rows=2.88M                                                                                                                                  |
> |        columns: all                                                                                                                                                      |
> |      extrapolated-rows=disabled max-scan-range-rows=130.09K                                                                                                              |
> |      file formats: [PARQUET]                                                                                                                                             |
> |      mem-estimate=16.00MB mem-reservation=512.00KB thread-reservation=1                                                                                                  |
> |      tuple-ids=0 row-size=4B cardinality=2.88M{quote}
> Since the two filters are applied to the same equi-join column ss.ss_sold_time_sk, it will be more efficient to intersect RF001 and RF003 into a single filter and apply the result one instead.  Assume the range of RF001 is [10, 50] and that for RF003 is [20, 60]  then the combined filter will be [20, 50].  



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