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From "Gautam Kumar Parai (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (DRILL-4743) HashJoin's not fully parallelized in query plan
Date Mon, 25 Jul 2016 23:07:20 GMT

     [ https://issues.apache.org/jira/browse/DRILL-4743?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Gautam Kumar Parai updated DRILL-4743:
--------------------------------------
    Description: 
The underlying problem is filter selectivity under-estimate for a query with complicated predicates
e.g. deeply nested and/or predicates. This leads to under parallelization of the major fragment
doing the join. 

To really resolve this problem we need table/column statistics to correctly estimate the selectivity.
However, in the absence of statistics OR even when existing statistics are insufficient to
get a correct estimate of selectivity this will serve as a workaround.

For now, the fix is to provide options for controlling the lower and upper bounds for filter
selectivity. The user can use the options
{code} 
planner.filter.min_selectivity_estimate_factor 
{code} 

  was:
The underlying problem is filter selectivity under-estimate for a query with complicated predicates
e.g. deeply nested and/or predicates. This leads to under parallelization of the major fragment
doing the join. 

To really resolve this problem we need table/column statistics to correctly estimate the selectivity.
However, in the absence of statistics OR even when existing statistics are insufficient to
get a correct estimate of selectivity this will serve as a workaround.


> HashJoin's not fully parallelized in query plan
> -----------------------------------------------
>
>                 Key: DRILL-4743
>                 URL: https://issues.apache.org/jira/browse/DRILL-4743
>             Project: Apache Drill
>          Issue Type: Bug
>    Affects Versions: 1.5.0
>            Reporter: Gautam Kumar Parai
>            Assignee: Gautam Kumar Parai
>              Labels: doc-impacting
>             Fix For: 1.8.0
>
>
> The underlying problem is filter selectivity under-estimate for a query with complicated
predicates e.g. deeply nested and/or predicates. This leads to under parallelization of the
major fragment doing the join. 
> To really resolve this problem we need table/column statistics to correctly estimate
the selectivity. However, in the absence of statistics OR even when existing statistics are
insufficient to get a correct estimate of selectivity this will serve as a workaround.
> For now, the fix is to provide options for controlling the lower and upper bounds for
filter selectivity. The user can use the options
> {code} 
> planner.filter.min_selectivity_estimate_factor 
> {code} 



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