[jira] [Work logged] (HIVE-21382) Group by keys reduction optimization - keys are not reduced in query23

["ASF GitHub Bot (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/HIVE-21382?focusedWorklogId=212055&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-212055 ]

ASF GitHub Bot logged work on HIVE-21382:
-----------------------------------------

                Author: ASF GitHub Bot
            Created on: 12/Mar/19 21:21
            Start Date: 12/Mar/19 21:21
    Worklog Time Spent: 10m 
      Work Description: vineetgarg02 commented on pull request #567: HIVE-21382: Group by keys reduction optimization - keys are not reduced in query23
URL: https://github.com/apache/hive/pull/567
 
 
   [HIVE-21382](https://issues.apache.org/jira/browse/HIVE-21382)
   Further enhancements to Group by key reduction in presence of PK/UNIQUE constraints. This patch enables reducing keys in following cases:
   * If group by keys originate from multiple tables (e.g. GBY after JOIN)
   * If group by keys contain deterministic expression 
 
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Issue Time Tracking
-------------------

            Worklog Id:     (was: 212055)
            Time Spent: 10m
    Remaining Estimate: 0h

> Group by keys reduction optimization - keys are not reduced in query23
> ----------------------------------------------------------------------
>
>                 Key: HIVE-21382
>                 URL: https://issues.apache.org/jira/browse/HIVE-21382
>             Project: Hive
>          Issue Type: Improvement
>            Reporter: Vineet Garg
>            Assignee: Vineet Garg
>            Priority: Major
>              Labels: pull-request-available
>         Attachments: HIVE-21382.1.patch, HIVE-21382.2.patch, HIVE-21382.2.patch, HIVE-21382.3.patch
>
>          Time Spent: 10m
>  Remaining Estimate: 0h
>
> {code:sql}
> explain cbo with frequent_ss_items as 
>  (select substr(i_item_desc,1,30) itemdesc,i_item_sk item_sk,d_date solddate,count(*) cnt
>   from store_sales
>       ,date_dim 
>       ,item
>   where ss_sold_date_sk = d_date_sk
>     and ss_item_sk = i_item_sk 
>     and d_year in (1999,1999+1,1999+2,1999+3)
>   group by substr(i_item_desc,1,30),i_item_sk,d_date
>   having count(*) >4)
> select  sum(sales)
>  from ((select cs_quantity*cs_list_price sales
>        from catalog_sales
>            ,date_dim 
>        where d_year = 1999 
>          and d_moy = 1 
>          and cs_sold_date_sk = d_date_sk 
>          and cs_item_sk in (select item_sk from frequent_ss_items))) subq limit 100;
> {code}
> {code:sql}
> HiveSortLimit(fetch=[100])
>   HiveProject($f0=[$0])
>     HiveAggregate(group=[{}], agg#0=[sum($0)])
>       HiveProject(sales=[*(CAST($2):DECIMAL(10, 0), $3)])
>         HiveSemiJoin(condition=[=($1, $5)], joinType=[inner])
>           HiveJoin(condition=[=($0, $4)], joinType=[inner], algorithm=[none], cost=[{2.0 rows, 0.0 cpu, 0.0 io}])
>             HiveProject(cs_sold_date_sk=[$0], cs_item_sk=[$15], cs_quantity=[$18], cs_list_price=[$20])
>               HiveFilter(condition=[IS NOT NULL($0)])
>                 HiveTableScan(table=[[perf_constraints, catalog_sales]], table:alias=[catalog_sales])
>             HiveProject(d_date_sk=[$0])
>               HiveFilter(condition=[AND(=($6, 1999), =($8, 1))])
>                 HiveTableScan(table=[[perf_constraints, date_dim]], table:alias=[date_dim])
>           HiveProject(i_item_sk=[$1])
>             HiveFilter(condition=[>($3, 4)])
>               HiveProject(substr=[$2], i_item_sk=[$1], d_date=[$0], $f3=[$3])
>                 HiveAggregate(group=[{3, 4, 5}], agg#0=[count()])
>                   HiveJoin(condition=[=($1, $4)], joinType=[inner], algorithm=[none], cost=[{2.0 rows, 0.0 cpu, 0.0 io}])
>                     HiveJoin(condition=[=($0, $2)], joinType=[inner], algorithm=[none], cost=[{2.0 rows, 0.0 cpu, 0.0 io}])
>                       HiveProject(ss_sold_date_sk=[$0], ss_item_sk=[$2])
>                         HiveFilter(condition=[IS NOT NULL($0)])
>                           HiveTableScan(table=[[perf_constraints, store_sales]], table:alias=[store_sales])
>                       HiveProject(d_date_sk=[$0], d_date=[$2])
>                         HiveFilter(condition=[IN($6, 1999, 2000, 2001, 2002)])
>                           HiveTableScan(table=[[perf_constraints, date_dim]], table:alias=[date_dim])
>                     HiveProject(i_item_sk=[$0], substr=[substr($4, 1, 30)])
>                       HiveTableScan(table=[[perf_constraints, item]], table:alias=[item])
> {code}
> Right side of HiveSemiJoin has an aggregate which could be reduce to have only {{i_item_sk}} as group by key since {{i_item_sk}} is primary key.



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