[jira] [Created] (HIVE-21381) Improve column pruning

["Vineet Garg (JIRA)" <ji...@apache.org>]

Vineet Garg created HIVE-21381:
----------------------------------

             Summary: Improve column pruning
                 Key: HIVE-21381
                 URL: https://issues.apache.org/jira/browse/HIVE-21381
             Project: Hive
          Issue Type: Improvement
            Reporter: Vineet Garg
            Assignee: Vineet Garg


Following query generate plan where right side of HiveSemiJoin contains HiveProject->HiveFilter->HiveProject where bottom HiveProject contain extra columns which can be pruned.

{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}

CBO Plan:
{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}

Only {{i_item_sk}} and {{$f3/count}} are used up in the plan therefore columns {{substr}} andn {{d_date}} can be removed.

Note that the above is generated with HIVE-21340 patch



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