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https://issues.apache.org/jira/browse/DRILL-6118?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16440040#comment-16440040
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Bridget Bevens commented on DRILL-6118:
---------------------------------------
Documentation is updated for this JIRA:
[https://drill.apache.org/docs/parquet-filter-pushdown/]
> Handle item star columns during project / filter push down and directory
> pruning
> ----------------------------------------------------------------------------------
>
> Key: DRILL-6118
> URL: https://issues.apache.org/jira/browse/DRILL-6118
> Project: Apache Drill
> Issue Type: Improvement
> Affects Versions: 1.12.0
> Reporter: Arina Ielchiieva
> Assignee: Arina Ielchiieva
> Priority: Major
> Labels: doc-impacting, ready-to-commit
> Fix For: 1.13.0
>
>
> Project push down, filter push down and partition pruning does not work with
> dynamically expanded column with is represented as star in ITEM operator:
> _ITEM($0, 'column_name')_ where $0 is a star.
> This often occurs when view, sub-select or cte with star is issued.
> To solve this issue we can create {{DrillFilterItemStarReWriterRule}} which
> will rewrite such ITEM operator before filter push down and directory
> pruning. For project into scan push down logic will be handled separately in
> already existing rule {{DrillPushProjectIntoScanRule}}. Basically, we can
> consider the following queries the same:
> {{select col1 from t}}
> {{select col1 from (select * from t)}}
> *Use cases*
> Since item star columns where not considered during project / filter push
> down and directory pruning, push down and pruning did not happen. This was
> causing Drill to read all columns from file (when only several are needed) or
> ready all files instead. Views with star query is the most common example.
> Such behavior significantly degrades performance for item star queries
> comparing to queries without item star.
> *EXAMPLES*
> *Data set*
> will create table with three files each in dedicated sub-folder:
> {noformat}
> use dfs.tmp;
> create table `order_ctas/t1` as select cast(o_orderdate as date) as
> o_orderdate from cp.`tpch/orders.parquet` where o_orderdate between date
> '1992-01-01' and date '1992-01-03';
> create table `order_ctas/t2` as select cast(o_orderdate as date) as
> o_orderdate from cp.`tpch/orders.parquet` where o_orderdate between date
> '1992-01-04' and date '1992-01-06';
> create table `order_ctas/t3` as select cast(o_orderdate as date) as
> o_orderdate from cp.`tpch/orders.parquet` where o_orderdate between date
> '1992-01-07' and date '1992-01-09';
> {noformat}
> *Filter push down*
> {{select * from order_ctas where o_orderdate = date '1992-01-01'}} will read
> only one file
> {noformat}
> 00-00 Screen
> 00-01 Project(**=[$0])
> 00-02 Project(T1¦¦**=[$0])
> 00-03 SelectionVectorRemover
> 00-04 Filter(condition=[=($1, 1992-01-01)])
> 00-05 Project(T1¦¦**=[$0], o_orderdate=[$1])
> 00-06 Scan(groupscan=[ParquetGroupScan
> [entries=[ReadEntryWithPath [path=/tmp/order_ctas/t1/0_0_0.parquet]],
> selectionRoot=/tmp/order_ctas, numFiles=1, numRowGroups=1,
> usedMetadataFile=false, columns=[`**`]]])
> {noformat}
> {{select * from (select * from order_ctas) where o_orderdate = date
> '1992-01-01'}} will ready all three files
> {noformat}
> 00-00 Screen
> 00-01 Project(**=[$0])
> 00-02 SelectionVectorRemover
> 00-03 Filter(condition=[=(ITEM($0, 'o_orderdate'), 1992-01-01)])
> 00-04 Scan(groupscan=[ParquetGroupScan [entries=[ReadEntryWithPath
> [path=/tmp/order_ctas/t1/0_0_0.parquet], ReadEntryWithPath
> [path=/tmp/order_ctas/t2/0_0_0.parquet], ReadEntryWithPath
> [path=/tmp/order_ctas/t3/0_0_0.parquet]], selectionRoot=/tmp/order_ctas,
> numFiles=3, numRowGroups=3, usedMetadataFile=false, columns=[`**`]]])
> {noformat}
> *Directory pruning*
> {{select * from order_ctas where dir0 = 't1'}} will read data only from one
> folder
> {noformat}
> 00-00 Screen
> 00-01 Project(**=[$0])
> 00-02 Project(**=[$0])
> 00-03 Scan(groupscan=[ParquetGroupScan [entries=[ReadEntryWithPath
> [path=/tmp/order_ctas/t1/0_0_0.parquet]], selectionRoot=/tmporder_ctas,
> numFiles=1, numRowGroups=1, usedMetadataFile=false, columns=[`**`]]])
> {noformat}
> {{select * from (select * from order_ctas) where dir0 = 't1'}} will read
> content of all three folders
> {noformat}
> 00-00 Screen
> 00-01 Project(**=[$0])
> 00-02 SelectionVectorRemover
> 00-03 Filter(condition=[=(ITEM($0, 'dir0'), 't1')])
> 00-04 Scan(groupscan=[ParquetGroupScan [entries=[ReadEntryWithPath
> [path=/tmp/order_ctas/t1/0_0_0.parquet], ReadEntryWithPath
> [path=/tmp/order_ctas/t2/0_0_0.parquet], ReadEntryWithPath
> [path=/tmp/order_ctas/t3/0_0_0.parquet]], selectionRoot=/tmp/order_ctas,
> numFiles=3, numRowGroups=3, usedMetadataFile=false, columns=[`**`]]])
> {noformat}
> *Project into Scan push down*
> {{select o_orderdate, count(1) from order_ctas group by o_orderdate}} will
> ready only one column from the files
> {noformat}
> 00-00 Screen
> 00-01 Project(o_orderdate=[$0], EXPR$1=[$1])
> 00-02 HashAgg(group=[{0}], EXPR$1=[COUNT()])
> 00-03 Scan(groupscan=[ParquetGroupScan [entries=[ReadEntryWithPath
> [path=/tmp/order_ctas/t1/0_0_0.parquet], ReadEntryWithPath
> [path=/tmp/order_ctas/t2/0_0_0.parquet], ReadEntryWithPath
> [path=/tmp/order_ctas/t3/0_0_0.parquet]], selectionRoot=/tmp/order_ctas,
> numFiles=3, numRowGroups=3, usedMetadataFile=false, columns=[`o_orderdate`]]])
> {noformat}
> {{select o_orderdate, count(1) from (select * from order_ctas) group by
> o_orderdate}} will ready all columns from the files
> {noformat}
> 00-00 Screen
> 00-01 Project(col_vrchr=[$0], EXPR$1=[$1])
> 00-02 StreamAgg(group=[{0}], EXPR$1=[COUNT()])
> 00-03 Sort(sort0=[$0], dir0=[ASC])
> 00-04 Project(col_vrchr=[ITEM($0, 'o_orderdate')])
> 00-05 Scan(groupscan=[ParquetGroupScan [entries=[ReadEntryWithPath
> [path=/tmp/order_ctas/t1/0_0_0.parquet], ReadEntryWithPath
> [path=/tmp/order_ctas/t2/0_0_0.parquet], ReadEntryWithPath
> [path=/tmp/order_ctas/t3/0_0_0.parquet]], selectionRoot=/tmp/order_ctas,
> numFiles=3, numRowGroups=3, usedMetadataFile=false, columns=[`**`]]])
> {noformat}
> This Jira aims to fix all three described cases above in order to improve
> performance for queries with item star columns.
>
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