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https://issues.apache.org/jira/browse/DRILL-4833?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Aman Sinha resolved DRILL-4833.
-------------------------------
Resolution: Fixed
Fix Version/s: 1.8.0
Fixed in commit 0ccc81a.
> Union-All with a small cardinality input 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: Aman Sinha
> Fix For: 1.8.0
>
>
> 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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