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From "Aman Sinha (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (DRILL-4833) Union-All with a LIMIT 1 on one side does not get parallelized
Date Mon, 08 Aug 2016 05:07:20 GMT

     [ https://issues.apache.org/jira/browse/DRILL-4833?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Aman Sinha updated DRILL-4833:
------------------------------
    Assignee: Jinfeng Ni  (was: Aman Sinha)

> Union-All with a LIMIT 1 on one side does not get parallelized
> --------------------------------------------------------------
>
>                 Key: DRILL-4833
>                 URL: https://issues.apache.org/jira/browse/DRILL-4833
>             Project: Apache Drill
>          Issue Type: Bug
>          Components: Query Planning & Optimization
>    Affects Versions: 1.7.0
>            Reporter: Aman Sinha
>            Assignee: Jinfeng Ni
>
> When a Union-All has an input that is a LIMIT 1 (or some small value relative to the
slice_target), and that input is accessing Parquet files, Drill does an optimization where
a single Parquet file is read (based on the rowcount statistics in the Parquet file, we determine
that reading 1 file is sufficient).  This also means that the max width for that major fragment
is set to 1 because only 1 minor fragment is needed to read 1 row-group. 
> The net effect of this is the width of 1 is applied to the major fragment which consists
of union-all and its inputs.  This is sub-optimal because it prevents parallelization of the
other input and the union-all operator itself.  
> Here's an example query and plan that illustrates the issue: 
> {noformat}
> alter session set `planner.slice_target` = 1;
> explain plan for 
> (select c.c_nationkey, c.c_custkey, c.c_name
> from
> dfs.`/Users/asinha/data/tpchmulti/customer` c
> inner join
> dfs.`/Users/asinha/data/tpchmulti/nation`  n
> on c.c_nationkey = n.n_nationkey)
> union all
> (select c_nationkey, c_custkey, c_name
> from dfs.`/Users/asinha/data/tpchmulti/customer` c limit 1)
> +------+------+
> | text | json |
> +------+------+
> | 00-00    Screen
> 00-01      Project(c_nationkey=[$0], c_custkey=[$1], c_name=[$2])
> 00-02        Project(c_nationkey=[$0], c_custkey=[$1], c_name=[$2])
> 00-03          UnionAll(all=[true])
> 00-05            Project(c_nationkey=[$0], c_custkey=[$1], c_name=[$2])
> 00-07              HashJoin(condition=[=($0, $3)], joinType=[inner])
> 00-10                Project(c_nationkey=[$0], c_custkey=[$1], c_name=[$2])
> 00-13                  HashToRandomExchange(dist0=[[$0]])
> 01-01                    UnorderedMuxExchange
> 03-01                      Project(c_nationkey=[$0], c_custkey=[$1], c_name=[$2], E_X_P_R_H_A_S_H_F_I_E_L_D=[hash32AsDouble($0)])
> 03-02                        Scan(groupscan=[ParquetGroupScan [entries=[ReadEntryWithPath
[path=file:/Users/asinha/data/tpchmulti/customer]], selectionRoot=file:/Users/asinha/data/tpchmulti/customer,
numFiles=1, usedMetadataFile=false, columns=[`c_nationkey`, `c_custkey`, `c_name`]]])
> 00-09                Project(n_nationkey=[$0])
> 00-12                  HashToRandomExchange(dist0=[[$0]])
> 02-01                    UnorderedMuxExchange
> 04-01                      Project(n_nationkey=[$0], E_X_P_R_H_A_S_H_F_I_E_L_D=[hash32AsDouble($0)])
> 04-02                        Scan(groupscan=[ParquetGroupScan [entries=[ReadEntryWithPath
[path=file:/Users/asinha/data/tpchmulti/nation]], selectionRoot=file:/Users/asinha/data/tpchmulti/nation,
numFiles=1, usedMetadataFile=false, columns=[`n_nationkey`]]])
> 00-04            Project(c_nationkey=[$0], c_custkey=[$1], c_name=[$2])
> 00-06              SelectionVectorRemover
> 00-08                Limit(fetch=[1])
> 00-11                  Scan(groupscan=[ParquetGroupScan [entries=[ReadEntryWithPath [path=/Users/asinha/data/tpchmulti/customer/01.parquet]],
selectionRoot=file:/Users/asinha/data/tpchmulti/customer, numFiles=1, usedMetadataFile=false,
columns=[`c_nationkey`, `c_custkey`, `c_name`]]])
> {noformat}
> Note that Union-all and HashJoin are part of fragment 0 (single minor fragment) even
though they could have been parallelized.  This clearly affects performance for larger data
sets. 



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