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From "Damien Carol (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (HIVE-9712) Row count and data size are set to LONG.MAX when source table has 0 rows
Date Wed, 18 Feb 2015 08:56:12 GMT

     [ https://issues.apache.org/jira/browse/HIVE-9712?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Damien Carol updated HIVE-9712:
-------------------------------
    Summary: Row count and data size are set to LONG.MAX when source table has 0 rows  (was: Hive : Row count and data size are set to LONG.MAX when source table has 0 rows)

> Row count and data size are set to LONG.MAX when source table has 0 rows
> ------------------------------------------------------------------------
>
>                 Key: HIVE-9712
>                 URL: https://issues.apache.org/jira/browse/HIVE-9712
>             Project: Hive
>          Issue Type: Bug
>          Components: Physical Optimizer
>    Affects Versions: 0.14.0
>            Reporter: Mostafa Mokhtar
>            Assignee: Prasanth Jayachandran
>
> TPC-DS Q66 generates and in-efficient plan because cardinality estimate of dimension table gets set to 9223372036854775807.
> {code}
>     Map 10 
>             Map Operator Tree:
>                 TableScan
>                   alias: ship_mode
>                   filterExpr: ((sm_carrier) IN ('DIAMOND', 'AIRBORNE') and sm_ship_mode_sk is not null) (type: boolean)
>                   Statistics: Num rows: 0 Data size: 47 Basic stats: PARTIAL Column stats: COMPLETE
>                   Filter Operator
>                     predicate: ((sm_carrier) IN ('DIAMOND', 'AIRBORNE') and sm_ship_mode_sk is not null) (type: boolean)
>                     Statistics: Num rows: 9223372036854775807 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
>                     Select Operator
>                       expressions: sm_ship_mode_sk (type: int)
>                       outputColumnNames: _col0
>                       Statistics: Num rows: 9223372036854775807 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
>                       Reduce Output Operator
>                         key expressions: _col0 (type: int)
>                         sort order: +
>                         Map-reduce partition columns: _col0 (type: int)
>                         Statistics: Num rows: 9223372036854775807 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
>             Execution mode: vectorized
> {code}
> Full plan 
> {code}
> explain  
> select   
>          w_warehouse_name
>  	,w_warehouse_sq_ft
>  	,w_city
>  	,w_county
>  	,w_state
>  	,w_country
>         ,ship_carriers
>         ,year
>  	,sum(jan_sales) as jan_sales
>  	,sum(feb_sales) as feb_sales
>  	,sum(mar_sales) as mar_sales
>  	,sum(apr_sales) as apr_sales
>  	,sum(may_sales) as may_sales
>  	,sum(jun_sales) as jun_sales
>  	,sum(jul_sales) as jul_sales
>  	,sum(aug_sales) as aug_sales
>  	,sum(sep_sales) as sep_sales
>  	,sum(oct_sales) as oct_sales
>  	,sum(nov_sales) as nov_sales
>  	,sum(dec_sales) as dec_sales
>  	,sum(jan_sales/w_warehouse_sq_ft) as jan_sales_per_sq_foot
>  	,sum(feb_sales/w_warehouse_sq_ft) as feb_sales_per_sq_foot
>  	,sum(mar_sales/w_warehouse_sq_ft) as mar_sales_per_sq_foot
>  	,sum(apr_sales/w_warehouse_sq_ft) as apr_sales_per_sq_foot
>  	,sum(may_sales/w_warehouse_sq_ft) as may_sales_per_sq_foot
>  	,sum(jun_sales/w_warehouse_sq_ft) as jun_sales_per_sq_foot
>  	,sum(jul_sales/w_warehouse_sq_ft) as jul_sales_per_sq_foot
>  	,sum(aug_sales/w_warehouse_sq_ft) as aug_sales_per_sq_foot
>  	,sum(sep_sales/w_warehouse_sq_ft) as sep_sales_per_sq_foot
>  	,sum(oct_sales/w_warehouse_sq_ft) as oct_sales_per_sq_foot
>  	,sum(nov_sales/w_warehouse_sq_ft) as nov_sales_per_sq_foot
>  	,sum(dec_sales/w_warehouse_sq_ft) as dec_sales_per_sq_foot
>  	,sum(jan_net) as jan_net
>  	,sum(feb_net) as feb_net
>  	,sum(mar_net) as mar_net
>  	,sum(apr_net) as apr_net
>  	,sum(may_net) as may_net
>  	,sum(jun_net) as jun_net
>  	,sum(jul_net) as jul_net
>  	,sum(aug_net) as aug_net
>  	,sum(sep_net) as sep_net
>  	,sum(oct_net) as oct_net
>  	,sum(nov_net) as nov_net
>  	,sum(dec_net) as dec_net
>  from (
>     select 
>  	w_warehouse_name
>  	,w_warehouse_sq_ft
>  	,w_city
>  	,w_county
>  	,w_state
>  	,w_country
>  	,concat('DIAMOND', ',', 'AIRBORNE') as ship_carriers
>         ,d_year as year
>  	,sum(case when d_moy = 1 
>  		then ws_sales_price* ws_quantity else 0 end) as jan_sales
>  	,sum(case when d_moy = 2 
>  		then ws_sales_price* ws_quantity else 0 end) as feb_sales
>  	,sum(case when d_moy = 3 
>  		then ws_sales_price* ws_quantity else 0 end) as mar_sales
>  	,sum(case when d_moy = 4 
>  		then ws_sales_price* ws_quantity else 0 end) as apr_sales
>  	,sum(case when d_moy = 5 
>  		then ws_sales_price* ws_quantity else 0 end) as may_sales
>  	,sum(case when d_moy = 6 
>  		then ws_sales_price* ws_quantity else 0 end) as jun_sales
>  	,sum(case when d_moy = 7 
>  		then ws_sales_price* ws_quantity else 0 end) as jul_sales
>  	,sum(case when d_moy = 8 
>  		then ws_sales_price* ws_quantity else 0 end) as aug_sales
>  	,sum(case when d_moy = 9 
>  		then ws_sales_price* ws_quantity else 0 end) as sep_sales
>  	,sum(case when d_moy = 10 
>  		then ws_sales_price* ws_quantity else 0 end) as oct_sales
>  	,sum(case when d_moy = 11
>  		then ws_sales_price* ws_quantity else 0 end) as nov_sales
>  	,sum(case when d_moy = 12
>  		then ws_sales_price* ws_quantity else 0 end) as dec_sales
>  	,sum(case when d_moy = 1 
>  		then ws_net_paid_inc_tax * ws_quantity else 0 end) as jan_net
>  	,sum(case when d_moy = 2
>  		then ws_net_paid_inc_tax * ws_quantity else 0 end) as feb_net
>  	,sum(case when d_moy = 3 
>  		then ws_net_paid_inc_tax * ws_quantity else 0 end) as mar_net
>  	,sum(case when d_moy = 4 
>  		then ws_net_paid_inc_tax * ws_quantity else 0 end) as apr_net
>  	,sum(case when d_moy = 5 
>  		then ws_net_paid_inc_tax * ws_quantity else 0 end) as may_net
>  	,sum(case when d_moy = 6 
>  		then ws_net_paid_inc_tax * ws_quantity else 0 end) as jun_net
>  	,sum(case when d_moy = 7 
>  		then ws_net_paid_inc_tax * ws_quantity else 0 end) as jul_net
>  	,sum(case when d_moy = 8 
>  		then ws_net_paid_inc_tax * ws_quantity else 0 end) as aug_net
>  	,sum(case when d_moy = 9 
>  		then ws_net_paid_inc_tax * ws_quantity else 0 end) as sep_net
>  	,sum(case when d_moy = 10 
>  		then ws_net_paid_inc_tax * ws_quantity else 0 end) as oct_net
>  	,sum(case when d_moy = 11
>  		then ws_net_paid_inc_tax * ws_quantity else 0 end) as nov_net
>  	,sum(case when d_moy = 12
>  		then ws_net_paid_inc_tax * ws_quantity else 0 end) as dec_net
>      from
>           web_sales
>          ,warehouse
>          ,date_dim
>          ,time_dim
>  	  ,ship_mode
>      where
>             web_sales.ws_warehouse_sk =  warehouse.w_warehouse_sk
>         and web_sales.ws_sold_date_sk = date_dim.d_date_sk
>         and web_sales.ws_sold_time_sk = time_dim.t_time_sk
>  	and web_sales.ws_ship_mode_sk = ship_mode.sm_ship_mode_sk
>         and d_year = 2002
>  	and t_time between 49530 and 49530+28800 
>  	and sm_carrier in ('DIAMOND','AIRBORNE')
>      group by 
>         w_warehouse_name
>  	,w_warehouse_sq_ft
>  	,w_city
>  	,w_county
>  	,w_state
>  	,w_country
>        ,d_year
>  union all
>     select 
>  	w_warehouse_name
>  	,w_warehouse_sq_ft
>  	,w_city
>  	,w_county
>  	,w_state
>  	,w_country
>         ,concat('DIAMOND', ',', 'AIRBORNE') as ship_carriers
>        ,d_year as year
>  	,sum(case when d_moy = 1 
>  		then cs_ext_sales_price* cs_quantity else 0 end) as jan_sales
>  	,sum(case when d_moy = 2 
>  		then cs_ext_sales_price* cs_quantity else 0 end) as feb_sales
>  	,sum(case when d_moy = 3 
>  		then cs_ext_sales_price* cs_quantity else 0 end) as mar_sales
>  	,sum(case when d_moy = 4 
>  		then cs_ext_sales_price* cs_quantity else 0 end) as apr_sales
>  	,sum(case when d_moy = 5 
>  		then cs_ext_sales_price* cs_quantity else 0 end) as may_sales
>  	,sum(case when d_moy = 6 
>  		then cs_ext_sales_price* cs_quantity else 0 end) as jun_sales
>  	,sum(case when d_moy = 7 
>  		then cs_ext_sales_price* cs_quantity else 0 end) as jul_sales
>  	,sum(case when d_moy = 8 
>  		then cs_ext_sales_price* cs_quantity else 0 end) as aug_sales
>  	,sum(case when d_moy = 9 
>  		then cs_ext_sales_price* cs_quantity else 0 end) as sep_sales
>  	,sum(case when d_moy = 10 
>  		then cs_ext_sales_price* cs_quantity else 0 end) as oct_sales
>  	,sum(case when d_moy = 11
>  		then cs_ext_sales_price* cs_quantity else 0 end) as nov_sales
>  	,sum(case when d_moy = 12
>  		then cs_ext_sales_price* cs_quantity else 0 end) as dec_sales
>  	,sum(case when d_moy = 1 
>  		then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as jan_net
>  	,sum(case when d_moy = 2 
>  		then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as feb_net
>  	,sum(case when d_moy = 3 
>  		then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as mar_net
>  	,sum(case when d_moy = 4 
>  		then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as apr_net
>  	,sum(case when d_moy = 5 
>  		then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as may_net
>  	,sum(case when d_moy = 6 
>  		then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as jun_net
>  	,sum(case when d_moy = 7 
>  		then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as jul_net
>  	,sum(case when d_moy = 8 
>  		then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as aug_net
>  	,sum(case when d_moy = 9 
>  		then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as sep_net
>  	,sum(case when d_moy = 10 
>  		then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as oct_net
>  	,sum(case when d_moy = 11
>  		then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as nov_net
>  	,sum(case when d_moy = 12
>  		then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as dec_net
>      from
>           catalog_sales
>          ,warehouse
>          ,date_dim
>          ,time_dim
>  	 ,ship_mode
>      where
>             catalog_sales.cs_warehouse_sk =  warehouse.w_warehouse_sk
>         and catalog_sales.cs_sold_date_sk = date_dim.d_date_sk
>         and catalog_sales.cs_sold_time_sk = time_dim.t_time_sk
>  	and catalog_sales.cs_ship_mode_sk = ship_mode.sm_ship_mode_sk
>         and d_year = 2002
>  	and t_time between 49530 AND 49530+28800 
>  	and sm_carrier in ('DIAMOND','AIRBORNE')
>      group by 
>         w_warehouse_name
>  	,w_warehouse_sq_ft
>  	,w_city
>  	,w_county
>  	,w_state
>  	,w_country
>        ,d_year
>  ) x
>  group by 
>         w_warehouse_name
>  	,w_warehouse_sq_ft
>  	,w_city
>  	,w_county
>  	,w_state
>  	,w_country
>  	,ship_carriers
>        ,year
>  order by w_warehouse_name
>  limit 100
> OK
> STAGE DEPENDENCIES:
>   Stage-1 is a root stage
>   Stage-0 depends on stages: Stage-1
> STAGE PLANS:
>   Stage: Stage-1
>     Tez
>       Edges:
>         Map 12 <- Map 15 (BROADCAST_EDGE), Map 16 (BROADCAST_EDGE)
>         Map 2 <- Map 8 (BROADCAST_EDGE), Map 9 (BROADCAST_EDGE)
>         Reducer 13 <- Map 11 (BROADCAST_EDGE), Map 12 (SIMPLE_EDGE), Map 17 (SIMPLE_EDGE)
>         Reducer 14 <- Reducer 13 (SIMPLE_EDGE), Union 5 (CONTAINS)
>         Reducer 3 <- Map 1 (BROADCAST_EDGE), Map 10 (SIMPLE_EDGE), Map 2 (SIMPLE_EDGE)
>         Reducer 4 <- Reducer 3 (SIMPLE_EDGE), Union 5 (CONTAINS)
>         Reducer 6 <- Union 5 (SIMPLE_EDGE)
>         Reducer 7 <- Reducer 6 (SIMPLE_EDGE)
>       DagName: mmokhtar_20150211222424_0df571ed-82d9-426e-9eb9-52f95f022fa1:1
>       Vertices:
>         Map 1 
>             Map Operator Tree:
>                 TableScan
>                   alias: date_dim
>                   filterExpr: ((d_year = 2002) and d_date_sk is not null) (type: boolean)
>                   Statistics: Num rows: 73049 Data size: 81741831 Basic stats: COMPLETE Column stats: COMPLETE
>                   Filter Operator
>                     predicate: ((d_year = 2002) and d_date_sk is not null) (type: boolean)
>                     Statistics: Num rows: 652 Data size: 7824 Basic stats: COMPLETE Column stats: COMPLETE
>                     Select Operator
>                       expressions: d_date_sk (type: int), d_moy (type: int)
>                       outputColumnNames: _col0, _col2
>                       Statistics: Num rows: 652 Data size: 5216 Basic stats: COMPLETE Column stats: COMPLETE
>                       Reduce Output Operator
>                         key expressions: _col0 (type: int)
>                         sort order: +
>                         Map-reduce partition columns: _col0 (type: int)
>                         Statistics: Num rows: 652 Data size: 5216 Basic stats: COMPLETE Column stats: COMPLETE
>                         value expressions: _col2 (type: int)
>             Execution mode: vectorized
>         Map 10 
>             Map Operator Tree:
>                 TableScan
>                   alias: ship_mode
>                   filterExpr: ((sm_carrier) IN ('DIAMOND', 'AIRBORNE') and sm_ship_mode_sk is not null) (type: boolean)
>                   Statistics: Num rows: 0 Data size: 47 Basic stats: PARTIAL Column stats: COMPLETE
>                   Filter Operator
>                     predicate: ((sm_carrier) IN ('DIAMOND', 'AIRBORNE') and sm_ship_mode_sk is not null) (type: boolean)
>                     Statistics: Num rows: 9223372036854775807 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
>                     Select Operator
>                       expressions: sm_ship_mode_sk (type: int)
>                       outputColumnNames: _col0
>                       Statistics: Num rows: 9223372036854775807 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
>                       Reduce Output Operator
>                         key expressions: _col0 (type: int)
>                         sort order: +
>                         Map-reduce partition columns: _col0 (type: int)
>                         Statistics: Num rows: 9223372036854775807 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
>             Execution mode: vectorized
>         Map 11 
>             Map Operator Tree:
>                 TableScan
>                   alias: date_dim
>                   filterExpr: ((d_year = 2002) and d_date_sk is not null) (type: boolean)
>                   Statistics: Num rows: 73049 Data size: 81741831 Basic stats: COMPLETE Column stats: COMPLETE
>                   Filter Operator
>                     predicate: ((d_year = 2002) and d_date_sk is not null) (type: boolean)
>                     Statistics: Num rows: 652 Data size: 7824 Basic stats: COMPLETE Column stats: COMPLETE
>                     Select Operator
>                       expressions: d_date_sk (type: int), d_moy (type: int)
>                       outputColumnNames: _col0, _col2
>                       Statistics: Num rows: 652 Data size: 5216 Basic stats: COMPLETE Column stats: COMPLETE
>                       Reduce Output Operator
>                         key expressions: _col0 (type: int)
>                         sort order: +
>                         Map-reduce partition columns: _col0 (type: int)
>                         Statistics: Num rows: 652 Data size: 5216 Basic stats: COMPLETE Column stats: COMPLETE
>                         value expressions: _col2 (type: int)
>             Execution mode: vectorized
>         Map 12 
>             Map Operator Tree:
>                 TableScan
>                   alias: catalog_sales
>                   filterExpr: (((cs_warehouse_sk is not null and cs_sold_time_sk is not null) and cs_ship_mode_sk is not null) and cs_sold_date_sk is not null) (type: boolean)
>                   Statistics: Num rows: 286549727 Data size: 65825832570 Basic stats: COMPLETE Column stats: COMPLETE
>                   Filter Operator
>                     predicate: (((cs_warehouse_sk is not null and cs_sold_time_sk is not null) and cs_ship_mode_sk is not null) and cs_sold_date_sk is not null) (type: boolean)
>                     Statistics: Num rows: 284394646 Data size: 7948760032 Basic stats: COMPLETE Column stats: COMPLETE
>                     Select Operator
>                       expressions: cs_sold_date_sk (type: int), cs_sold_time_sk (type: int), cs_ship_mode_sk (type: int), cs_warehouse_sk (type: int), cs_quantity (type: int), cs_ext_sales_price (type: float), cs_net_paid_inc_ship_tax (type: float)
>                       outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6
>                       Statistics: Num rows: 284394646 Data size: 7948760032 Basic stats: COMPLETE Column stats: COMPLETE
>                       Map Join Operator
>                         condition map:
>                              Inner Join 0 to 1
>                         keys:
>                           0 _col3 (type: int)
>                           1 _col0 (type: int)
>                         outputColumnNames: _col0, _col1, _col2, _col4, _col5, _col6, _col8, _col9, _col10, _col11, _col12, _col13
>                         input vertices:
>                           1 Map 15
>                         Statistics: Num rows: 284394656 Data size: 142766117312 Basic stats: COMPLETE Column stats: COMPLETE
>                         Map Join Operator
>                           condition map:
>                                Inner Join 0 to 1
>                           keys:
>                             0 _col1 (type: int)
>                             1 _col0 (type: int)
>                           outputColumnNames: _col0, _col2, _col4, _col5, _col6, _col8, _col9, _col10, _col11, _col12, _col13
>                           input vertices:
>                             1 Map 16
>                           Statistics: Num rows: 142197328 Data size: 70814269344 Basic stats: COMPLETE Column stats: COMPLETE
>                           Reduce Output Operator
>                             key expressions: _col2 (type: int)
>                             sort order: +
>                             Map-reduce partition columns: _col2 (type: int)
>                             Statistics: Num rows: 142197328 Data size: 70814269344 Basic stats: COMPLETE Column stats: COMPLETE
>                             value expressions: _col0 (type: int), _col4 (type: int), _col5 (type: float), _col6 (type: float), _col8 (type: string), _col9 (type: int), _col10 (type: string), _col11 (type: string), _col12 (type: string), _col13 (type: string)
>             Execution mode: vectorized
>         Map 15 
>             Map Operator Tree:
>                 TableScan
>                   alias: warehouse
>                   filterExpr: w_warehouse_sk is not null (type: boolean)
>                   Statistics: Num rows: 6 Data size: 6166 Basic stats: COMPLETE Column stats: COMPLETE
>                   Filter Operator
>                     predicate: w_warehouse_sk is not null (type: boolean)
>                     Statistics: Num rows: 6 Data size: 2888 Basic stats: COMPLETE Column stats: COMPLETE
>                     Select Operator
>                       expressions: w_warehouse_sk (type: int), w_warehouse_name (type: string), w_warehouse_sq_ft (type: int), w_city (type: string), w_county (type: string), w_state (type: string), w_country (type: string)
>                       outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6
>                       Statistics: Num rows: 6 Data size: 2888 Basic stats: COMPLETE Column stats: COMPLETE
>                       Reduce Output Operator
>                         key expressions: _col0 (type: int)
>                         sort order: +
>                         Map-reduce partition columns: _col0 (type: int)
>                         Statistics: Num rows: 6 Data size: 2888 Basic stats: COMPLETE Column stats: COMPLETE
>                         value expressions: _col1 (type: string), _col2 (type: int), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: string)
>             Execution mode: vectorized
>         Map 16 
>             Map Operator Tree:
>                 TableScan
>                   alias: time_dim
>                   filterExpr: (t_time BETWEEN 49530 AND 78330 and t_time_sk is not null) (type: boolean)
>                   Statistics: Num rows: 86400 Data size: 40694400 Basic stats: COMPLETE Column stats: COMPLETE
>                   Filter Operator
>                     predicate: (t_time BETWEEN 49530 AND 78330 and t_time_sk is not null) (type: boolean)
>                     Statistics: Num rows: 43200 Data size: 345600 Basic stats: COMPLETE Column stats: COMPLETE
>                     Select Operator
>                       expressions: t_time_sk (type: int)
>                       outputColumnNames: _col0
>                       Statistics: Num rows: 43200 Data size: 172800 Basic stats: COMPLETE Column stats: COMPLETE
>                       Reduce Output Operator
>                         key expressions: _col0 (type: int)
>                         sort order: +
>                         Map-reduce partition columns: _col0 (type: int)
>                         Statistics: Num rows: 43200 Data size: 172800 Basic stats: COMPLETE Column stats: COMPLETE
>             Execution mode: vectorized
>         Map 17 
>             Map Operator Tree:
>                 TableScan
>                   alias: ship_mode
>                   filterExpr: ((sm_carrier) IN ('DIAMOND', 'AIRBORNE') and sm_ship_mode_sk is not null) (type: boolean)
>                   Statistics: Num rows: 0 Data size: 47 Basic stats: PARTIAL Column stats: COMPLETE
>                   Filter Operator
>                     predicate: ((sm_carrier) IN ('DIAMOND', 'AIRBORNE') and sm_ship_mode_sk is not null) (type: boolean)
>                     Statistics: Num rows: 9223372036854775807 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
>                     Select Operator
>                       expressions: sm_ship_mode_sk (type: int)
>                       outputColumnNames: _col0
>                       Statistics: Num rows: 9223372036854775807 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
>                       Reduce Output Operator
>                         key expressions: _col0 (type: int)
>                         sort order: +
>                         Map-reduce partition columns: _col0 (type: int)
>                         Statistics: Num rows: 9223372036854775807 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
>             Execution mode: vectorized
>         Map 2 
>             Map Operator Tree:
>                 TableScan
>                   alias: web_sales
>                   filterExpr: (((ws_warehouse_sk is not null and ws_sold_time_sk is not null) and ws_ship_mode_sk is not null) and ws_sold_date_sk is not null) (type: boolean)
>                   Statistics: Num rows: 143966864 Data size: 33110363004 Basic stats: COMPLETE Column stats: COMPLETE
>                   Filter Operator
>                     predicate: (((ws_warehouse_sk is not null and ws_sold_time_sk is not null) and ws_ship_mode_sk is not null) and ws_sold_date_sk is not null) (type: boolean)
>                     Statistics: Num rows: 143912967 Data size: 4029131264 Basic stats: COMPLETE Column stats: COMPLETE
>                     Select Operator
>                       expressions: ws_sold_date_sk (type: int), ws_sold_time_sk (type: int), ws_ship_mode_sk (type: int), ws_warehouse_sk (type: int), ws_quantity (type: int), ws_sales_price (type: float), ws_net_paid_inc_tax (type: float)
>                       outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6
>                       Statistics: Num rows: 143912967 Data size: 4029131264 Basic stats: COMPLETE Column stats: COMPLETE
>                       Map Join Operator
>                         condition map:
>                              Inner Join 0 to 1
>                         keys:
>                           0 _col3 (type: int)
>                           1 _col0 (type: int)
>                         outputColumnNames: _col0, _col1, _col2, _col4, _col5, _col6, _col8, _col9, _col10, _col11, _col12, _col13
>                         input vertices:
>                           1 Map 8
>                         Statistics: Num rows: 143912960 Data size: 72244305920 Basic stats: COMPLETE Column stats: COMPLETE
>                         Map Join Operator
>                           condition map:
>                                Inner Join 0 to 1
>                           keys:
>                             0 _col1 (type: int)
>                             1 _col0 (type: int)
>                           outputColumnNames: _col0, _col2, _col4, _col5, _col6, _col8, _col9, _col10, _col11, _col12, _col13
>                           input vertices:
>                             1 Map 9
>                           Statistics: Num rows: 71956480 Data size: 35834327040 Basic stats: COMPLETE Column stats: COMPLETE
>                           Reduce Output Operator
>                             key expressions: _col2 (type: int)
>                             sort order: +
>                             Map-reduce partition columns: _col2 (type: int)
>                             Statistics: Num rows: 71956480 Data size: 35834327040 Basic stats: COMPLETE Column stats: COMPLETE
>                             value expressions: _col0 (type: int), _col4 (type: int), _col5 (type: float), _col6 (type: float), _col8 (type: string), _col9 (type: int), _col10 (type: string), _col11 (type: string), _col12 (type: string), _col13 (type: string)
>             Execution mode: vectorized
>         Map 8 
>             Map Operator Tree:
>                 TableScan
>                   alias: warehouse
>                   filterExpr: w_warehouse_sk is not null (type: boolean)
>                   Statistics: Num rows: 6 Data size: 6166 Basic stats: COMPLETE Column stats: COMPLETE
>                   Filter Operator
>                     predicate: w_warehouse_sk is not null (type: boolean)
>                     Statistics: Num rows: 6 Data size: 2888 Basic stats: COMPLETE Column stats: COMPLETE
>                     Select Operator
>                       expressions: w_warehouse_sk (type: int), w_warehouse_name (type: string), w_warehouse_sq_ft (type: int), w_city (type: string), w_county (type: string), w_state (type: string), w_country (type: string)
>                       outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6
>                       Statistics: Num rows: 6 Data size: 2888 Basic stats: COMPLETE Column stats: COMPLETE
>                       Reduce Output Operator
>                         key expressions: _col0 (type: int)
>                         sort order: +
>                         Map-reduce partition columns: _col0 (type: int)
>                         Statistics: Num rows: 6 Data size: 2888 Basic stats: COMPLETE Column stats: COMPLETE
>                         value expressions: _col1 (type: string), _col2 (type: int), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: string)
>             Execution mode: vectorized
>         Map 9 
>             Map Operator Tree:
>                 TableScan
>                   alias: time_dim
>                   filterExpr: (t_time BETWEEN 49530 AND 78330 and t_time_sk is not null) (type: boolean)
>                   Statistics: Num rows: 86400 Data size: 40694400 Basic stats: COMPLETE Column stats: COMPLETE
>                   Filter Operator
>                     predicate: (t_time BETWEEN 49530 AND 78330 and t_time_sk is not null) (type: boolean)
>                     Statistics: Num rows: 43200 Data size: 345600 Basic stats: COMPLETE Column stats: COMPLETE
>                     Select Operator
>                       expressions: t_time_sk (type: int)
>                       outputColumnNames: _col0
>                       Statistics: Num rows: 43200 Data size: 172800 Basic stats: COMPLETE Column stats: COMPLETE
>                       Reduce Output Operator
>                         key expressions: _col0 (type: int)
>                         sort order: +
>                         Map-reduce partition columns: _col0 (type: int)
>                         Statistics: Num rows: 43200 Data size: 172800 Basic stats: COMPLETE Column stats: COMPLETE
>             Execution mode: vectorized
>         Reducer 13 
>             Reduce Operator Tree:
>               Merge Join Operator
>                 condition map:
>                      Inner Join 0 to 1
>                 keys:
>                   0 _col2 (type: int)
>                   1 _col0 (type: int)
>                 outputColumnNames: _col0, _col4, _col5, _col6, _col8, _col9, _col10, _col11, _col12, _col13
>                 Statistics: Num rows: 9223372036854775807 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
>                 Select Operator
>                   expressions: _col0 (type: int), _col10 (type: string), _col11 (type: string), _col12 (type: string), _col13 (type: string), _col4 (type: int), _col5 (type: float), _col6 (type: float), _col8 (type: string), _col9 (type: int)
>                   outputColumnNames: _col0, _col10, _col11, _col12, _col13, _col4, _col5, _col6, _col8, _col9
>                   Statistics: Num rows: 9223372036854775807 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
>                   Map Join Operator
>                     condition map:
>                          Inner Join 0 to 1
>                     keys:
>                       0 _col0 (type: int)
>                       1 _col0 (type: int)
>                     outputColumnNames: _col2, _col7, _col8, _col9, _col11, _col12, _col13, _col14, _col15, _col16
>                     input vertices:
>                       0 Map 11
>                     Statistics: Num rows: 82323356149350400 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
>                     Select Operator
>                       expressions: _col11 (type: string), _col12 (type: int), _col13 (type: string), _col14 (type: string), _col15 (type: string), _col16 (type: string), 2002 (type: int), CASE WHEN ((_col2 = 1)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 2)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 3)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 4)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 5)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 6)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 7)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 8)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 9)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 10)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 11)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 12)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 1)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 2)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 3)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 4)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 5)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 6)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 7)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 8)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 9)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 10)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 11)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 12)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float)
>                       outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, _col24, _col25, _col26, _col27, _col28, _col29, _col30
>                       Statistics: Num rows: 82323356149350400 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
>                       Group By Operator
>                         aggregations: sum(_col7), sum(_col8), sum(_col9), sum(_col10), sum(_col11), sum(_col12), sum(_col13), sum(_col14), sum(_col15), sum(_col16), sum(_col17), sum(_col18), sum(_col19), sum(_col20), sum(_col21), sum(_col22), sum(_col23), sum(_col24), sum(_col25), sum(_col26), sum(_col27), sum(_col28), sum(_col29), sum(_col30)
>                         keys: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: int)
>                         mode: hash
>                         outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, _col24, _col25, _col26, _col27, _col28, _col29, _col30
>                         Statistics: Num rows: 2147483647 Data size: 1447403978078 Basic stats: COMPLETE Column stats: COMPLETE
>                         Reduce Output Operator
>                           key expressions: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: int)
>                           sort order: +++++++
>                           Map-reduce partition columns: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: int)
>                           Statistics: Num rows: 2147483647 Data size: 1447403978078 Basic stats: COMPLETE Column stats: COMPLETE
>                           value expressions: _col7 (type: double), _col8 (type: double), _col9 (type: double), _col10 (type: double), _col11 (type: double), _col12 (type: double), _col13 (type: double), _col14 (type: double), _col15 (type: double), _col16 (type: double), _col17 (type: double), _col18 (type: double), _col19 (type: double), _col20 (type: double), _col21 (type: double), _col22 (type: double), _col23 (type: double), _col24 (type: double), _col25 (type: double), _col26 (type: double), _col27 (type: double), _col28 (type: double), _col29 (type: double), _col30 (type: double)
>         Reducer 14 
>             Reduce Operator Tree:
>               Group By Operator
>                 aggregations: sum(VALUE._col0), sum(VALUE._col1), sum(VALUE._col2), sum(VALUE._col3), sum(VALUE._col4), sum(VALUE._col5), sum(VALUE._col6), sum(VALUE._col7), sum(VALUE._col8), sum(VALUE._col9), sum(VALUE._col10), sum(VALUE._col11), sum(VALUE._col12), sum(VALUE._col13), sum(VALUE._col14), sum(VALUE._col15), sum(VALUE._col16), sum(VALUE._col17), sum(VALUE._col18), sum(VALUE._col19), sum(VALUE._col20), sum(VALUE._col21), sum(VALUE._col22), sum(VALUE._col23)
>                 keys: KEY._col0 (type: string), KEY._col1 (type: int), KEY._col2 (type: string), KEY._col3 (type: string), KEY._col4 (type: string), KEY._col5 (type: string), KEY._col6 (type: int)
>                 mode: mergepartial
>                 outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, _col24, _col25, _col26, _col27, _col28, _col29, _col30
>                 Select Operator
>                   expressions: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), 'DIAMOND,AIRBORNE' (type: string), _col6 (type: int), _col7 (type: double), _col8 (type: double), _col9 (type: double), _col10 (type: double), _col11 (type: double), _col12 (type: double), _col13 (type: double), _col14 (type: double), _col15 (type: double), _col16 (type: double), _col17 (type: double), _col18 (type: double), _col19 (type: double), _col20 (type: double), _col21 (type: double), _col22 (type: double), _col23 (type: double), _col24 (type: double), _col25 (type: double), _col26 (type: double), _col27 (type: double), _col28 (type: double), _col29 (type: double), _col30 (type: double)
>                   outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, _col24, _col25, _col26, _col27, _col28, _col29, _col30, _col31
>                   Select Operator
>                     expressions: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: string), _col7 (type: int), _col8 (type: double), _col9 (type: double), _col10 (type: double), _col11 (type: double), _col12 (type: double), _col13 (type: double), _col14 (type: double), _col15 (type: double), _col16 (type: double), _col17 (type: double), _col18 (type: double), _col19 (type: double), (_col8 / UDFToDouble(_col1)) (type: double), (_col9 / UDFToDouble(_col1)) (type: double), (_col10 / UDFToDouble(_col1)) (type: double), (_col11 / UDFToDouble(_col1)) (type: double), (_col12 / UDFToDouble(_col1)) (type: double), (_col13 / UDFToDouble(_col1)) (type: double), (_col14 / UDFToDouble(_col1)) (type: double), (_col15 / UDFToDouble(_col1)) (type: double), (_col16 / UDFToDouble(_col1)) (type: double), (_col17 / UDFToDouble(_col1)) (type: double), (_col18 / UDFToDouble(_col1)) (type: double), (_col19 / UDFToDouble(_col1)) (type: double), _col20 (type: double), _col21 (type: double), _col22 (type: double), _col23 (type: double), _col24 (type: double), _col25 (type: double), _col26 (type: double), _col27 (type: double), _col28 (type: double), _col29 (type: double), _col30 (type: double), _col31 (type: double)
>                     outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, _col24, _col25, _col26, _col27, _col28, _col29, _col30, _col31, _col32, _col33, _col34, _col35, _col36, _col37, _col38, _col39, _col40, _col41, _col42, _col43
>                     Group By Operator
>                       aggregations: sum(_col8), sum(_col9), sum(_col10), sum(_col11), sum(_col12), sum(_col13), sum(_col14), sum(_col15), sum(_col16), sum(_col17), sum(_col18), sum(_col19), sum(_col20), sum(_col21), sum(_col22), sum(_col23), sum(_col24), sum(_col25), sum(_col26), sum(_col27), sum(_col28), sum(_col29), sum(_col30), sum(_col31), sum(_col32), sum(_col33), sum(_col34), sum(_col35), sum(_col36), sum(_col37), sum(_col38), sum(_col39), sum(_col40), sum(_col41), sum(_col42), sum(_col43)
>                       keys: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: string), _col7 (type: int)
>                       mode: hash
>                       outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, _col24, _col25, _col26, _col27, _col28, _col29, _col30, _col31, _col32, _col33, _col34, _col35, _col36, _col37, _col38, _col39, _col40, _col41, _col42, _col43
>                       Reduce Output Operator
>                         key expressions: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: string), _col7 (type: int)
>                         sort order: ++++++++
>                         Map-reduce partition columns: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: string), _col7 (type: int)
>                         value expressions: _col8 (type: double), _col9 (type: double), _col10 (type: double), _col11 (type: double), _col12 (type: double), _col13 (type: double), _col14 (type: double), _col15 (type: double), _col16 (type: double), _col17 (type: double), _col18 (type: double), _col19 (type: double), _col20 (type: double), _col21 (type: double), _col22 (type: double), _col23 (type: double), _col24 (type: double), _col25 (type: double), _col26 (type: double), _col27 (type: double), _col28 (type: double), _col29 (type: double), _col30 (type: double), _col31 (type: double), _col32 (type: double), _col33 (type: double), _col34 (type: double), _col35 (type: double), _col36 (type: double), _col37 (type: double), _col38 (type: double), _col39 (type: double), _col40 (type: double), _col41 (type: double), _col42 (type: double), _col43 (type: double)
>         Reducer 3 
>             Reduce Operator Tree:
>               Merge Join Operator
>                 condition map:
>                      Inner Join 0 to 1
>                 keys:
>                   0 _col2 (type: int)
>                   1 _col0 (type: int)
>                 outputColumnNames: _col0, _col4, _col5, _col6, _col8, _col9, _col10, _col11, _col12, _col13
>                 Statistics: Num rows: 9223372036854775807 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
>                 Select Operator
>                   expressions: _col0 (type: int), _col10 (type: string), _col11 (type: string), _col12 (type: string), _col13 (type: string), _col4 (type: int), _col5 (type: float), _col6 (type: float), _col8 (type: string), _col9 (type: int)
>                   outputColumnNames: _col0, _col10, _col11, _col12, _col13, _col4, _col5, _col6, _col8, _col9
>                   Statistics: Num rows: 9223372036854775807 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
>                   Map Join Operator
>                     condition map:
>                          Inner Join 0 to 1
>                     keys:
>                       0 _col0 (type: int)
>                       1 _col0 (type: int)
>                     outputColumnNames: _col2, _col7, _col8, _col9, _col11, _col12, _col13, _col14, _col15, _col16
>                     input vertices:
>                       0 Map 1
>                     Statistics: Num rows: 82323356149350400 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
>                     Select Operator
>                       expressions: _col11 (type: string), _col12 (type: int), _col13 (type: string), _col14 (type: string), _col15 (type: string), _col16 (type: string), 2002 (type: int), CASE WHEN ((_col2 = 1)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 2)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 3)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 4)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 5)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 6)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 7)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 8)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 9)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 10)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 11)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 12)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 1)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 2)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 3)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 4)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 5)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 6)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 7)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 8)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 9)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 10)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 11)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = 12)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float)
>                       outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, _col24, _col25, _col26, _col27, _col28, _col29, _col30
>                       Statistics: Num rows: 82323356149350400 Data size: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE
>                       Group By Operator
>                         aggregations: sum(_col7), sum(_col8), sum(_col9), sum(_col10), sum(_col11), sum(_col12), sum(_col13), sum(_col14), sum(_col15), sum(_col16), sum(_col17), sum(_col18), sum(_col19), sum(_col20), sum(_col21), sum(_col22), sum(_col23), sum(_col24), sum(_col25), sum(_col26), sum(_col27), sum(_col28), sum(_col29), sum(_col30)
>                         keys: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: int)
>                         mode: hash
>                         outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, _col24, _col25, _col26, _col27, _col28, _col29, _col30
>                         Statistics: Num rows: 2147483647 Data size: 1447403978078 Basic stats: COMPLETE Column stats: COMPLETE
>                         Reduce Output Operator
>                           key expressions: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: int)
>                           sort order: +++++++
>                           Map-reduce partition columns: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: int)
>                           Statistics: Num rows: 2147483647 Data size: 1447403978078 Basic stats: COMPLETE Column stats: COMPLETE
>                           value expressions: _col7 (type: double), _col8 (type: double), _col9 (type: double), _col10 (type: double), _col11 (type: double), _col12 (type: double), _col13 (type: double), _col14 (type: double), _col15 (type: double), _col16 (type: double), _col17 (type: double), _col18 (type: double), _col19 (type: double), _col20 (type: double), _col21 (type: double), _col22 (type: double), _col23 (type: double), _col24 (type: double), _col25 (type: double), _col26 (type: double), _col27 (type: double), _col28 (type: double), _col29 (type: double), _col30 (type: double)
>         Reducer 4 
>             Reduce Operator Tree:
>               Group By Operator
>                 aggregations: sum(VALUE._col0), sum(VALUE._col1), sum(VALUE._col2), sum(VALUE._col3), sum(VALUE._col4), sum(VALUE._col5), sum(VALUE._col6), sum(VALUE._col7), sum(VALUE._col8), sum(VALUE._col9), sum(VALUE._col10), sum(VALUE._col11), sum(VALUE._col12), sum(VALUE._col13), sum(VALUE._col14), sum(VALUE._col15), sum(VALUE._col16), sum(VALUE._col17), sum(VALUE._col18), sum(VALUE._col19), sum(VALUE._col20), sum(VALUE._col21), sum(VALUE._col22), sum(VALUE._col23)
>                 keys: KEY._col0 (type: string), KEY._col1 (type: int), KEY._col2 (type: string), KEY._col3 (type: string), KEY._col4 (type: string), KEY._col5 (type: string), KEY._col6 (type: int)
>                 mode: mergepartial
>                 outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, _col24, _col25, _col26, _col27, _col28, _col29, _col30
>                 Select Operator
>                   expressions: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), 'DIAMOND,AIRBORNE' (type: string), _col6 (type: int), _col7 (type: double), _col8 (type: double), _col9 (type: double), _col10 (type: double), _col11 (type: double), _col12 (type: double), _col13 (type: double), _col14 (type: double), _col15 (type: double), _col16 (type: double), _col17 (type: double), _col18 (type: double), _col19 (type: double), _col20 (type: double), _col21 (type: double), _col22 (type: double), _col23 (type: double), _col24 (type: double), _col25 (type: double), _col26 (type: double), _col27 (type: double), _col28 (type: double), _col29 (type: double), _col30 (type: double)
>                   outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, _col24, _col25, _col26, _col27, _col28, _col29, _col30, _col31
>                   Select Operator
>                     expressions: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: string), _col7 (type: int), _col8 (type: double), _col9 (type: double), _col10 (type: double), _col11 (type: double), _col12 (type: double), _col13 (type: double), _col14 (type: double), _col15 (type: double), _col16 (type: double), _col17 (type: double), _col18 (type: double), _col19 (type: double), (_col8 / UDFToDouble(_col1)) (type: double), (_col9 / UDFToDouble(_col1)) (type: double), (_col10 / UDFToDouble(_col1)) (type: double), (_col11 / UDFToDouble(_col1)) (type: double), (_col12 / UDFToDouble(_col1)) (type: double), (_col13 / UDFToDouble(_col1)) (type: double), (_col14 / UDFToDouble(_col1)) (type: double), (_col15 / UDFToDouble(_col1)) (type: double), (_col16 / UDFToDouble(_col1)) (type: double), (_col17 / UDFToDouble(_col1)) (type: double), (_col18 / UDFToDouble(_col1)) (type: double), (_col19 / UDFToDouble(_col1)) (type: double), _col20 (type: double), _col21 (type: double), _col22 (type: double), _col23 (type: double), _col24 (type: double), _col25 (type: double), _col26 (type: double), _col27 (type: double), _col28 (type: double), _col29 (type: double), _col30 (type: double), _col31 (type: double)
>                     outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, _col24, _col25, _col26, _col27, _col28, _col29, _col30, _col31, _col32, _col33, _col34, _col35, _col36, _col37, _col38, _col39, _col40, _col41, _col42, _col43
>                     Group By Operator
>                       aggregations: sum(_col8), sum(_col9), sum(_col10), sum(_col11), sum(_col12), sum(_col13), sum(_col14), sum(_col15), sum(_col16), sum(_col17), sum(_col18), sum(_col19), sum(_col20), sum(_col21), sum(_col22), sum(_col23), sum(_col24), sum(_col25), sum(_col26), sum(_col27), sum(_col28), sum(_col29), sum(_col30), sum(_col31), sum(_col32), sum(_col33), sum(_col34), sum(_col35), sum(_col36), sum(_col37), sum(_col38), sum(_col39), sum(_col40), sum(_col41), sum(_col42), sum(_col43)
>                       keys: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: string), _col7 (type: int)
>                       mode: hash
>                       outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, _col24, _col25, _col26, _col27, _col28, _col29, _col30, _col31, _col32, _col33, _col34, _col35, _col36, _col37, _col38, _col39, _col40, _col41, _col42, _col43
>                       Reduce Output Operator
>                         key expressions: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: string), _col7 (type: int)
>                         sort order: ++++++++
>                         Map-reduce partition columns: _col0 (type: string), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: string), _col7 (type: int)
>                         value expressions: _col8 (type: double), _col9 (type: double), _col10 (type: double), _col11 (type: double), _col12 (type: double), _col13 (type: double), _col14 (type: double), _col15 (type: double), _col16 (type: double), _col17 (type: double), _col18 (type: double), _col19 (type: double), _col20 (type: double), _col21 (type: double), _col22 (type: double), _col23 (type: double), _col24 (type: double), _col25 (type: double), _col26 (type: double), _col27 (type: double), _col28 (type: double), _col29 (type: double), _col30 (type: double), _col31 (type: double), _col32 (type: double), _col33 (type: double), _col34 (type: double), _col35 (type: double), _col36 (type: double), _col37 (type: double), _col38 (type: double), _col39 (type: double), _col40 (type: double), _col41 (type: double), _col42 (type: double), _col43 (type: double)
>         Reducer 6 
>             Reduce Operator Tree:
>               Group By Operator
>                 aggregations: sum(VALUE._col0), sum(VALUE._col1), sum(VALUE._col2), sum(VALUE._col3), sum(VALUE._col4), sum(VALUE._col5), sum(VALUE._col6), sum(VALUE._col7), sum(VALUE._col8), sum(VALUE._col9), sum(VALUE._col10), sum(VALUE._col11), sum(VALUE._col12), sum(VALUE._col13), sum(VALUE._col14), sum(VALUE._col15), sum(VALUE._col16), sum(VALUE._col17), sum(VALUE._col18), sum(VALUE._col19), sum(VALUE._col20), sum(VALUE._col21), sum(VALUE._col22), sum(VALUE._col23), sum(VALUE._col24), sum(VALUE._col25), sum(VALUE._col26), sum(VALUE._col27), sum(VALUE._col28), sum(VALUE._col29), sum(VALUE._col30), sum(VALUE._col31), sum(VALUE._col32), sum(VALUE._col33), sum(VALUE._col34), sum(VALUE._col35)
>                 keys: KEY._col0 (type: string), KEY._col1 (type: int), KEY._col2 (type: string), KEY._col3 (type: string), KEY._col4 (type: string), KEY._col5 (type: string), KEY._col6 (type: string), KEY._col7 (type: int)
>                 mode: mergepartial
>                 outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, _col24, _col25, _col26, _col27, _col28, _col29, _col30, _col31, _col32, _col33, _col34, _col35, _col36, _col37, _col38, _col39, _col40, _col41, _col42, _col43
>                 Statistics: Num rows: 1 Data size: 288 Basic stats: COMPLETE Column stats: COMPLETE
>                 Reduce Output Operator
>                   key expressions: _col0 (type: string)
>                   sort order: +
>                   Statistics: Num rows: 1 Data size: 288 Basic stats: COMPLETE Column stats: COMPLETE
>                   TopN Hash Memory Usage: 0.04
>                   value expressions: _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col6 (type: string), _col7 (type: int), _col8 (type: double), _col9 (type: double), _col10 (type: double), _col11 (type: double), _col12 (type: double), _col13 (type: double), _col14 (type: double), _col15 (type: double), _col16 (type: double), _col17 (type: double), _col18 (type: double), _col19 (type: double), _col20 (type: double), _col21 (type: double), _col22 (type: double), _col23 (type: double), _col24 (type: double), _col25 (type: double), _col26 (type: double), _col27 (type: double), _col28 (type: double), _col29 (type: double), _col30 (type: double), _col31 (type: double), _col32 (type: double), _col33 (type: double), _col34 (type: double), _col35 (type: double), _col36 (type: double), _col37 (type: double), _col38 (type: double), _col39 (type: double), _col40 (type: double), _col41 (type: double), _col42 (type: double), _col43 (type: double)
>         Reducer 7 
>             Reduce Operator Tree:
>               Select Operator
>                 expressions: KEY.reducesinkkey0 (type: string), VALUE._col0 (type: int), VALUE._col1 (type: string), VALUE._col2 (type: string), VALUE._col3 (type: string), VALUE._col4 (type: string), VALUE._col5 (type: string), VALUE._col6 (type: int), VALUE._col7 (type: double), VALUE._col8 (type: double), VALUE._col9 (type: double), VALUE._col10 (type: double), VALUE._col11 (type: double), VALUE._col12 (type: double), VALUE._col13 (type: double), VALUE._col14 (type: double), VALUE._col15 (type: double), VALUE._col16 (type: double), VALUE._col17 (type: double), VALUE._col18 (type: double), VALUE._col19 (type: double), VALUE._col20 (type: double), VALUE._col21 (type: double), VALUE._col22 (type: double), VALUE._col23 (type: double), VALUE._col24 (type: double), VALUE._col25 (type: double), VALUE._col26 (type: double), VALUE._col27 (type: double), VALUE._col28 (type: double), VALUE._col29 (type: double), VALUE._col30 (type: double), VALUE._col31 (type: double), VALUE._col32 (type: double), VALUE._col33 (type: double), VALUE._col34 (type: double), VALUE._col35 (type: double), VALUE._col36 (type: double), VALUE._col37 (type: double), VALUE._col38 (type: double), VALUE._col39 (type: double), VALUE._col40 (type: double), VALUE._col41 (type: double), VALUE._col42 (type: double)
>                 outputColumnNames: _col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, _col24, _col25, _col26, _col27, _col28, _col29, _col30, _col31, _col32, _col33, _col34, _col35, _col36, _col37, _col38, _col39, _col40, _col41, _col42, _col43
>                 Statistics: Num rows: 1 Data size: 288 Basic stats: COMPLETE Column stats: COMPLETE
>                 Limit
>                   Number of rows: 100
>                   Statistics: Num rows: 1 Data size: 288 Basic stats: COMPLETE Column stats: COMPLETE
>                   File Output Operator
>                     compressed: false
>                     Statistics: Num rows: 1 Data size: 288 Basic stats: COMPLETE Column stats: COMPLETE
>                     table:
>                         input format: org.apache.hadoop.mapred.TextInputFormat
>                         output format: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat
>                         serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
>         Union 5 
>             Vertex: Union 5
>   Stage: Stage-0
>     Fetch Operator
>       limit: 100
>       Processor Tree:
>         ListSink
> {code}



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