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Posted to dev@hive.apache.org by "Eric Hanson (JIRA)" <ji...@apache.org> on 2013/06/11 01:50:20 UTC

[jira] [Updated] (HIVE-4701) Optimize filter Column IN ( list-of-constants ) for vectorized execution

     [ https://issues.apache.org/jira/browse/HIVE-4701?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Eric Hanson updated HIVE-4701:
------------------------------

    Description: 
OR filters have been optimized to run with vectorized query execution. IN filters of the form "Column IN (list-of-constants)" are a special case of OR. However, IN does not vectorized currently.

E.g. 

select ddate, count\(\*\) from factsqlengineam_vec_orc where ddate = "2012-05-19 00:00:00" OR ddate = "2012-05-20 00:00:00" or ddate = "2012-05-21 00:00:00" group by ddate;

takes about 23 seconds of CPU and 

select ddate, count\(\*\) from factsqlengineam_vec_orc where ddate IN ("2012-05-19 00:00:00", "2012-05-20 00:00:00", "2012-05-21 00:00:00") group by ddate;

takes about 153 seconds of CPU.

A simple fix may be that for short IN lists (say <= 64 elements) we turn them into OR by manipulating the query tree before planning whether vectorization can be used.

A more complex fix that covers more cases would be to turn longer IN lists into a join so when we eventually support vectorized joins it will be fast.

An intermediate approach might be to implement a special IN filter operator that stores the constant values in a sorted array or high-performance hash table (like Cuckoo hashing).



  was:
OR filters have been optimized to run with vectorized query execution. IN filters of the form "Column IN (list-of-constants)" are a special case of OR. However, IN does not vectorized currently.

E.g. 

select ddate, count(*) from factsqlengineam_vec_orc where ddate = "2012-05-19 00:00:00" OR ddate = "2012-05-20 00:00:00" or ddate = "2012-05-21 00:00:00" group by ddate;

takes about 23 seconds of CPU and 

select ddate, count(*) from factsqlengineam_vec_orc where ddate IN ("2012-05-19 00:00:00", "2012-05-20 00:00:00", "2012-05-21 00:00:00") group by ddate;

takes about 153 seconds of CPU.

A simple fix may be that for short IN lists (say <= 64 elements) we turn them into OR by manipulating the query tree before planning whether vectorization can be used.

A more complex fix that covers more cases would be to turn longer IN lists into a join so when we eventually support vectorized joins it will be fast.

An intermediate approach might be to implement a special IN filter operator that stores the constant values in a sorted array or high-performance hash table (like Cuckoo hashing).



    
> Optimize filter Column IN ( list-of-constants ) for vectorized execution
> ------------------------------------------------------------------------
>
>                 Key: HIVE-4701
>                 URL: https://issues.apache.org/jira/browse/HIVE-4701
>             Project: Hive
>          Issue Type: Sub-task
>            Reporter: Eric Hanson
>
> OR filters have been optimized to run with vectorized query execution. IN filters of the form "Column IN (list-of-constants)" are a special case of OR. However, IN does not vectorized currently.
> E.g. 
> select ddate, count\(\*\) from factsqlengineam_vec_orc where ddate = "2012-05-19 00:00:00" OR ddate = "2012-05-20 00:00:00" or ddate = "2012-05-21 00:00:00" group by ddate;
> takes about 23 seconds of CPU and 
> select ddate, count\(\*\) from factsqlengineam_vec_orc where ddate IN ("2012-05-19 00:00:00", "2012-05-20 00:00:00", "2012-05-21 00:00:00") group by ddate;
> takes about 153 seconds of CPU.
> A simple fix may be that for short IN lists (say <= 64 elements) we turn them into OR by manipulating the query tree before planning whether vectorization can be used.
> A more complex fix that covers more cases would be to turn longer IN lists into a join so when we eventually support vectorized joins it will be fast.
> An intermediate approach might be to implement a special IN filter operator that stores the constant values in a sorted array or high-performance hash table (like Cuckoo hashing).

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