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18 Feb 2015 02:59:12 +0000 Date: Wed, 18 Feb 2015 02:59:12 +0000 (UTC) From: "Mostafa Mokhtar (JIRA)" To: hive-dev@hadoop.apache.org Message-ID: In-Reply-To: References: Subject: [jira] [Updated] (HIVE-9712) Hive : Row count and data size are set to LONG.MAX when source table has 0 rows MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: quoted-printable X-JIRA-FingerPrint: 30527f35849b9dde25b450d4833f0394 [ https://issues.apache.org/jira/browse/HIVE-9712?page=3Dcom.atlassian= .jira.plugin.system.issuetabpanels:all-tabpanel ] Mostafa Mokhtar updated HIVE-9712: ---------------------------------- Summary: Hive : 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 filter is applied on an estimate of 0) > Hive : 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 o= f dimension table gets set to 9223372036854775807. > {code} > Map 10=20 > Map Operator Tree: > TableScan > alias: ship_mode > filterExpr: ((sm_carrier) IN ('DIAMOND', 'AIRBORNE') an= d sm_ship_mode_sk is not null) (type: boolean) > Statistics: Num rows: 0 Data size: 47 Basic stats: PART= IAL Column stats: COMPLETE > Filter Operator > predicate: ((sm_carrier) IN ('DIAMOND', 'AIRBORNE') a= nd 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 si= ze: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE > Execution mode: vectorized > {code} > Full plan=20 > {code} > explain =20 > select =20 > w_warehouse_name > =09,w_warehouse_sq_ft > =09,w_city > =09,w_county > =09,w_state > =09,w_country > ,ship_carriers > ,year > =09,sum(jan_sales) as jan_sales > =09,sum(feb_sales) as feb_sales > =09,sum(mar_sales) as mar_sales > =09,sum(apr_sales) as apr_sales > =09,sum(may_sales) as may_sales > =09,sum(jun_sales) as jun_sales > =09,sum(jul_sales) as jul_sales > =09,sum(aug_sales) as aug_sales > =09,sum(sep_sales) as sep_sales > =09,sum(oct_sales) as oct_sales > =09,sum(nov_sales) as nov_sales > =09,sum(dec_sales) as dec_sales > =09,sum(jan_sales/w_warehouse_sq_ft) as jan_sales_per_sq_foot > =09,sum(feb_sales/w_warehouse_sq_ft) as feb_sales_per_sq_foot > =09,sum(mar_sales/w_warehouse_sq_ft) as mar_sales_per_sq_foot > =09,sum(apr_sales/w_warehouse_sq_ft) as apr_sales_per_sq_foot > =09,sum(may_sales/w_warehouse_sq_ft) as may_sales_per_sq_foot > =09,sum(jun_sales/w_warehouse_sq_ft) as jun_sales_per_sq_foot > =09,sum(jul_sales/w_warehouse_sq_ft) as jul_sales_per_sq_foot > =09,sum(aug_sales/w_warehouse_sq_ft) as aug_sales_per_sq_foot > =09,sum(sep_sales/w_warehouse_sq_ft) as sep_sales_per_sq_foot > =09,sum(oct_sales/w_warehouse_sq_ft) as oct_sales_per_sq_foot > =09,sum(nov_sales/w_warehouse_sq_ft) as nov_sales_per_sq_foot > =09,sum(dec_sales/w_warehouse_sq_ft) as dec_sales_per_sq_foot > =09,sum(jan_net) as jan_net > =09,sum(feb_net) as feb_net > =09,sum(mar_net) as mar_net > =09,sum(apr_net) as apr_net > =09,sum(may_net) as may_net > =09,sum(jun_net) as jun_net > =09,sum(jul_net) as jul_net > =09,sum(aug_net) as aug_net > =09,sum(sep_net) as sep_net > =09,sum(oct_net) as oct_net > =09,sum(nov_net) as nov_net > =09,sum(dec_net) as dec_net > from ( > select=20 > =09w_warehouse_name > =09,w_warehouse_sq_ft > =09,w_city > =09,w_county > =09,w_state > =09,w_country > =09,concat('DIAMOND', ',', 'AIRBORNE') as ship_carriers > ,d_year as year > =09,sum(case when d_moy =3D 1=20 > =09=09then ws_sales_price* ws_quantity else 0 end) as jan_sales > =09,sum(case when d_moy =3D 2=20 > =09=09then ws_sales_price* ws_quantity else 0 end) as feb_sales > =09,sum(case when d_moy =3D 3=20 > =09=09then ws_sales_price* ws_quantity else 0 end) as mar_sales > =09,sum(case when d_moy =3D 4=20 > =09=09then ws_sales_price* ws_quantity else 0 end) as apr_sales > =09,sum(case when d_moy =3D 5=20 > =09=09then ws_sales_price* ws_quantity else 0 end) as may_sales > =09,sum(case when d_moy =3D 6=20 > =09=09then ws_sales_price* ws_quantity else 0 end) as jun_sales > =09,sum(case when d_moy =3D 7=20 > =09=09then ws_sales_price* ws_quantity else 0 end) as jul_sales > =09,sum(case when d_moy =3D 8=20 > =09=09then ws_sales_price* ws_quantity else 0 end) as aug_sales > =09,sum(case when d_moy =3D 9=20 > =09=09then ws_sales_price* ws_quantity else 0 end) as sep_sales > =09,sum(case when d_moy =3D 10=20 > =09=09then ws_sales_price* ws_quantity else 0 end) as oct_sales > =09,sum(case when d_moy =3D 11 > =09=09then ws_sales_price* ws_quantity else 0 end) as nov_sales > =09,sum(case when d_moy =3D 12 > =09=09then ws_sales_price* ws_quantity else 0 end) as dec_sales > =09,sum(case when d_moy =3D 1=20 > =09=09then ws_net_paid_inc_tax * ws_quantity else 0 end) as jan_net > =09,sum(case when d_moy =3D 2 > =09=09then ws_net_paid_inc_tax * ws_quantity else 0 end) as feb_net > =09,sum(case when d_moy =3D 3=20 > =09=09then ws_net_paid_inc_tax * ws_quantity else 0 end) as mar_net > =09,sum(case when d_moy =3D 4=20 > =09=09then ws_net_paid_inc_tax * ws_quantity else 0 end) as apr_net > =09,sum(case when d_moy =3D 5=20 > =09=09then ws_net_paid_inc_tax * ws_quantity else 0 end) as may_net > =09,sum(case when d_moy =3D 6=20 > =09=09then ws_net_paid_inc_tax * ws_quantity else 0 end) as jun_net > =09,sum(case when d_moy =3D 7=20 > =09=09then ws_net_paid_inc_tax * ws_quantity else 0 end) as jul_net > =09,sum(case when d_moy =3D 8=20 > =09=09then ws_net_paid_inc_tax * ws_quantity else 0 end) as aug_net > =09,sum(case when d_moy =3D 9=20 > =09=09then ws_net_paid_inc_tax * ws_quantity else 0 end) as sep_net > =09,sum(case when d_moy =3D 10=20 > =09=09then ws_net_paid_inc_tax * ws_quantity else 0 end) as oct_net > =09,sum(case when d_moy =3D 11 > =09=09then ws_net_paid_inc_tax * ws_quantity else 0 end) as nov_net > =09,sum(case when d_moy =3D 12 > =09=09then ws_net_paid_inc_tax * ws_quantity else 0 end) as dec_net > from > web_sales > ,warehouse > ,date_dim > ,time_dim > =09 ,ship_mode > where > web_sales.ws_warehouse_sk =3D warehouse.w_warehouse_sk > and web_sales.ws_sold_date_sk =3D date_dim.d_date_sk > and web_sales.ws_sold_time_sk =3D time_dim.t_time_sk > =09and web_sales.ws_ship_mode_sk =3D ship_mode.sm_ship_mode_sk > and d_year =3D 2002 > =09and t_time between 49530 and 49530+28800=20 > =09and sm_carrier in ('DIAMOND','AIRBORNE') > group by=20 > w_warehouse_name > =09,w_warehouse_sq_ft > =09,w_city > =09,w_county > =09,w_state > =09,w_country > ,d_year > union all > select=20 > =09w_warehouse_name > =09,w_warehouse_sq_ft > =09,w_city > =09,w_county > =09,w_state > =09,w_country > ,concat('DIAMOND', ',', 'AIRBORNE') as ship_carriers > ,d_year as year > =09,sum(case when d_moy =3D 1=20 > =09=09then cs_ext_sales_price* cs_quantity else 0 end) as jan_sales > =09,sum(case when d_moy =3D 2=20 > =09=09then cs_ext_sales_price* cs_quantity else 0 end) as feb_sales > =09,sum(case when d_moy =3D 3=20 > =09=09then cs_ext_sales_price* cs_quantity else 0 end) as mar_sales > =09,sum(case when d_moy =3D 4=20 > =09=09then cs_ext_sales_price* cs_quantity else 0 end) as apr_sales > =09,sum(case when d_moy =3D 5=20 > =09=09then cs_ext_sales_price* cs_quantity else 0 end) as may_sales > =09,sum(case when d_moy =3D 6=20 > =09=09then cs_ext_sales_price* cs_quantity else 0 end) as jun_sales > =09,sum(case when d_moy =3D 7=20 > =09=09then cs_ext_sales_price* cs_quantity else 0 end) as jul_sales > =09,sum(case when d_moy =3D 8=20 > =09=09then cs_ext_sales_price* cs_quantity else 0 end) as aug_sales > =09,sum(case when d_moy =3D 9=20 > =09=09then cs_ext_sales_price* cs_quantity else 0 end) as sep_sales > =09,sum(case when d_moy =3D 10=20 > =09=09then cs_ext_sales_price* cs_quantity else 0 end) as oct_sales > =09,sum(case when d_moy =3D 11 > =09=09then cs_ext_sales_price* cs_quantity else 0 end) as nov_sales > =09,sum(case when d_moy =3D 12 > =09=09then cs_ext_sales_price* cs_quantity else 0 end) as dec_sales > =09,sum(case when d_moy =3D 1=20 > =09=09then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as jan_net > =09,sum(case when d_moy =3D 2=20 > =09=09then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as feb_net > =09,sum(case when d_moy =3D 3=20 > =09=09then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as mar_net > =09,sum(case when d_moy =3D 4=20 > =09=09then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as apr_net > =09,sum(case when d_moy =3D 5=20 > =09=09then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as may_net > =09,sum(case when d_moy =3D 6=20 > =09=09then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as jun_net > =09,sum(case when d_moy =3D 7=20 > =09=09then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as jul_net > =09,sum(case when d_moy =3D 8=20 > =09=09then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as aug_net > =09,sum(case when d_moy =3D 9=20 > =09=09then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as sep_net > =09,sum(case when d_moy =3D 10=20 > =09=09then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as oct_net > =09,sum(case when d_moy =3D 11 > =09=09then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as nov_net > =09,sum(case when d_moy =3D 12 > =09=09then cs_net_paid_inc_ship_tax * cs_quantity else 0 end) as dec_net > from > catalog_sales > ,warehouse > ,date_dim > ,time_dim > =09 ,ship_mode > where > catalog_sales.cs_warehouse_sk =3D warehouse.w_warehouse_sk > and catalog_sales.cs_sold_date_sk =3D date_dim.d_date_sk > and catalog_sales.cs_sold_time_sk =3D time_dim.t_time_sk > =09and catalog_sales.cs_ship_mode_sk =3D ship_mode.sm_ship_mode_sk > and d_year =3D 2002 > =09and t_time between 49530 AND 49530+28800=20 > =09and sm_carrier in ('DIAMOND','AIRBORNE') > group by=20 > w_warehouse_name > =09,w_warehouse_sq_ft > =09,w_city > =09,w_county > =09,w_state > =09,w_country > ,d_year > ) x > group by=20 > w_warehouse_name > =09,w_warehouse_sq_ft > =09,w_city > =09,w_county > =09,w_state > =09,w_country > =09,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-9eb9-52f95f022f= a1:1 > Vertices: > Map 1=20 > Map Operator Tree: > TableScan > alias: date_dim > filterExpr: ((d_year =3D 2002) and d_date_sk is not nul= l) (type: boolean) > Statistics: Num rows: 73049 Data size: 81741831 Basic s= tats: COMPLETE Column stats: COMPLETE > Filter Operator > predicate: ((d_year =3D 2002) and d_date_sk is not nu= ll) (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: in= t) > outputColumnNames: _col0, _col2 > Statistics: Num rows: 652 Data size: 5216 Basic sta= ts: 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 s= tats: COMPLETE Column stats: COMPLETE > value expressions: _col2 (type: int) > Execution mode: vectorized > Map 10=20 > Map Operator Tree: > TableScan > alias: ship_mode > filterExpr: ((sm_carrier) IN ('DIAMOND', 'AIRBORNE') an= d sm_ship_mode_sk is not null) (type: boolean) > Statistics: Num rows: 0 Data size: 47 Basic stats: PART= IAL Column stats: COMPLETE > Filter Operator > predicate: ((sm_carrier) IN ('DIAMOND', 'AIRBORNE') a= nd 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 si= ze: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE > Execution mode: vectorized > Map 11=20 > Map Operator Tree: > TableScan > alias: date_dim > filterExpr: ((d_year =3D 2002) and d_date_sk is not nul= l) (type: boolean) > Statistics: Num rows: 73049 Data size: 81741831 Basic s= tats: COMPLETE Column stats: COMPLETE > Filter Operator > predicate: ((d_year =3D 2002) and d_date_sk is not nu= ll) (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: in= t) > outputColumnNames: _col0, _col2 > Statistics: Num rows: 652 Data size: 5216 Basic sta= ts: 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 s= tats: COMPLETE Column stats: COMPLETE > value expressions: _col2 (type: int) > Execution mode: vectorized > Map 12=20 > 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 i= s 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_t= ime_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_i= nc_ship_tax (type: float) > outputColumnNames: _col0, _col1, _col2, _col3, _col= 4, _col5, _col6 > Statistics: Num rows: 284394646 Data size: 79487600= 32 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, _c= ol5, _col6, _col8, _col9, _col10, _col11, _col12, _col13 > input vertices: > 1 Map 15 > Statistics: Num rows: 284394656 Data size: 142766= 117312 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: 7081= 4269344 Basic stats: COMPLETE Column stats: COMPLETE > Reduce Output Operator > key expressions: _col2 (type: int) > sort order: + > Map-reduce partition columns: _col2 (type: in= t) > Statistics: Num rows: 142197328 Data size: 70= 814269344 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 (t= ype: string), _col13 (type: string) > Execution mode: vectorized > Map 15=20 > Map Operator Tree: > TableScan > alias: warehouse > filterExpr: w_warehouse_sk is not null (type: boolean) > Statistics: Num rows: 6 Data size: 6166 Basic stats: CO= MPLETE 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_warehous= e_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, _col= 4, _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 sta= ts: COMPLETE Column stats: COMPLETE > value expressions: _col1 (type: string), _col2 (t= ype: int), _col3 (type: string), _col4 (type: string), _col5 (type: string)= , _col6 (type: string) > Execution mode: vectorized > Map 16=20 > 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 s= tats: 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 s= tats: 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 Bas= ic stats: COMPLETE Column stats: COMPLETE > Execution mode: vectorized > Map 17=20 > Map Operator Tree: > TableScan > alias: ship_mode > filterExpr: ((sm_carrier) IN ('DIAMOND', 'AIRBORNE') an= d sm_ship_mode_sk is not null) (type: boolean) > Statistics: Num rows: 0 Data size: 47 Basic stats: PART= IAL Column stats: COMPLETE > Filter Operator > predicate: ((sm_carrier) IN ('DIAMOND', 'AIRBORNE') a= nd 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 si= ze: 9223372036854775807 Basic stats: COMPLETE Column stats: COMPLETE > Execution mode: vectorized > Map 2=20 > 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 i= s 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_t= ime_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_t= ax (type: float) > outputColumnNames: _col0, _col1, _col2, _col3, _col= 4, _col5, _col6 > Statistics: Num rows: 143912967 Data size: 40291312= 64 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, _c= ol5, _col6, _col8, _col9, _col10, _col11, _col12, _col13 > input vertices: > 1 Map 8 > Statistics: Num rows: 143912960 Data size: 722443= 05920 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: 35834= 327040 Basic stats: COMPLETE Column stats: COMPLETE > Reduce Output Operator > key expressions: _col2 (type: int) > sort order: + > Map-reduce partition columns: _col2 (type: in= t) > Statistics: Num rows: 71956480 Data size: 358= 34327040 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 (t= ype: string), _col13 (type: string) > Execution mode: vectorized > Map 8=20 > Map Operator Tree: > TableScan > alias: warehouse > filterExpr: w_warehouse_sk is not null (type: boolean) > Statistics: Num rows: 6 Data size: 6166 Basic stats: CO= MPLETE 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_warehous= e_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, _col= 4, _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 sta= ts: COMPLETE Column stats: COMPLETE > value expressions: _col1 (type: string), _col2 (t= ype: int), _col3 (type: string), _col4 (type: string), _col5 (type: string)= , _col6 (type: string) > Execution mode: vectorized > Map 9=20 > 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 s= tats: 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 s= tats: 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 Bas= ic stats: COMPLETE Column stats: COMPLETE > Execution mode: vectorized > Reducer 13=20 > 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, _co= l9, _col10, _col11, _col12, _col13 > Statistics: Num rows: 9223372036854775807 Data size: 9223= 372036854775807 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, _col1= 3, _col4, _col5, _col6, _col8, _col9 > Statistics: Num rows: 9223372036854775807 Data size: 92= 23372036854775807 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: 92= 23372036854775807 Basic stats: COMPLETE Column stats: COMPLETE > Select Operator > expressions: _col11 (type: string), _col12 (type: i= nt), _col13 (type: string), _col14 (type: string), _col15 (type: string), _= col16 (type: string), 2002 (type: int), CASE WHEN ((_col2 =3D 1)) THEN ((_c= ol8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 =3D= 2)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WH= EN ((_col2 =3D 3)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: f= loat), CASE WHEN ((_col2 =3D 4)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0= ) END (type: float), CASE WHEN ((_col2 =3D 5)) THEN ((_col8 * UDFToFloat(_c= ol7))) ELSE (0) END (type: float), CASE WHEN ((_col2 =3D 6)) THEN ((_col8 *= UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 =3D 7)) = THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((= _col2 =3D 8)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float)= , CASE WHEN ((_col2 =3D 9)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END= (type: float), CASE WHEN ((_col2 =3D 10)) THEN ((_col8 * UDFToFloat(_col7)= )) ELSE (0) END (type: float), CASE WHEN ((_col2 =3D 11)) THEN ((_col8 * UD= FToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 =3D 12)) TH= EN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_c= ol2 =3D 1)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), = CASE WHEN ((_col2 =3D 2)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (= type: float), CASE WHEN ((_col2 =3D 3)) THEN ((_col9 * UDFToFloat(_col7))) = ELSE (0) END (type: float), CASE WHEN ((_col2 =3D 4)) THEN ((_col9 * UDFToF= loat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 =3D 5)) THEN ((= _col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = =3D 6)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE= WHEN ((_col2 =3D 7)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type= : float), CASE WHEN ((_col2 =3D 8)) THEN ((_col9 * UDFToFloat(_col7))) ELSE= (0) END (type: float), CASE WHEN ((_col2 =3D 9)) THEN ((_col9 * UDFToFloat= (_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 =3D 10)) THEN ((_co= l9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 =3D = 11)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WH= EN ((_col2 =3D 12)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: = float) > outputColumnNames: _col0, _col1, _col2, _col3, _col= 4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col1= 4, _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(_col1= 5), sum(_col16), sum(_col17), sum(_col18), sum(_col19), sum(_col20), sum(_c= ol21), 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), _c= ol2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type= : string), _col6 (type: int) > mode: hash > outputColumnNames: _col0, _col1, _col2, _col3, _c= ol4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _co= l14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23= , _col24, _col25, _col26, _col27, _col28, _col29, _col30 > Statistics: Num rows: 2147483647 Data size: 14474= 03978078 Basic stats: COMPLETE Column stats: COMPLETE > Reduce Output Operator > key expressions: _col0 (type: string), _col1 (t= ype: int), _col2 (type: string), _col3 (type: string), _col4 (type: string)= , _col5 (type: string), _col6 (type: int) > sort order: +++++++ > Map-reduce partition columns: _col0 (type: stri= ng), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (= type: string), _col5 (type: string), _col6 (type: int) > Statistics: Num rows: 2147483647 Data size: 144= 7403978078 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), _co= l18 (type: double), _col19 (type: double), _col20 (type: double), _col21 (t= ype: double), _col22 (type: double), _col23 (type: double), _col24 (type: d= ouble), _col25 (type: double), _col26 (type: double), _col27 (type: double)= , _col28 (type: double), _col29 (type: double), _col30 (type: double) > Reducer 14=20 > Reduce Operator Tree: > Group By Operator > aggregations: sum(VALUE._col0), sum(VALUE._col1), sum(VAL= UE._col2), sum(VALUE._col3), sum(VALUE._col4), sum(VALUE._col5), sum(VALUE.= _col6), sum(VALUE._col7), sum(VALUE._col8), sum(VALUE._col9), sum(VALUE._co= l10), sum(VALUE._col11), sum(VALUE._col12), sum(VALUE._col13), sum(VALUE._c= ol14), 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), KE= Y._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, _co= l5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _co= l15, _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 (typ= e: string), 'DIAMOND,AIRBORNE' (type: string), _col6 (type: int), _col7 (ty= pe: double), _col8 (type: double), _col9 (type: double), _col10 (type: doub= le), _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: doubl= e), _col24 (type: double), _col25 (type: double), _col26 (type: double), _c= ol27 (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, _col= 24, _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 (t= ype: string), _col6 (type: string), _col7 (type: int), _col8 (type: double)= , _col9 (type: double), _col10 (type: double), _col11 (type: double), _col1= 2 (type: double), _col13 (type: double), _col14 (type: double), _col15 (typ= e: double), _col16 (type: double), _col17 (type: double), _col18 (type: dou= ble), _col19 (type: double), (_col8 / UDFToDouble(_col1)) (type: double), (= _col9 / UDFToDouble(_col1)) (type: double), (_col10 / UDFToDouble(_col1)) (= type: double), (_col11 / UDFToDouble(_col1)) (type: double), (_col12 / UDFT= oDouble(_col1)) (type: double), (_col13 / UDFToDouble(_col1)) (type: double= ), (_col14 / UDFToDouble(_col1)) (type: double), (_col15 / UDFToDouble(_col= 1)) (type: double), (_col16 / UDFToDouble(_col1)) (type: double), (_col17 /= UDFToDouble(_col1)) (type: double), (_col18 / UDFToDouble(_col1)) (type: d= ouble), (_col19 / UDFToDouble(_col1)) (type: double), _col20 (type: double)= , _col21 (type: double), _col22 (type: double), _col23 (type: double), _col= 24 (type: double), _col25 (type: double), _col26 (type: double), _col27 (ty= pe: double), _col28 (type: double), _col29 (type: double), _col30 (type: do= uble), _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, _c= ol24, _col25, _col26, _col27, _col28, _col29, _col30, _col31, _col32, _col3= 3, _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(_co= l22), sum(_col23), sum(_col24), sum(_col25), sum(_col26), sum(_col27), sum(= _col28), sum(_col29), sum(_col30), sum(_col31), sum(_col32), sum(_col33), s= um(_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), _col= 2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: = string), _col6 (type: string), _col7 (type: int) > mode: hash > outputColumnNames: _col0, _col1, _col2, _col3, _col= 4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col1= 4, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, = _col24, _col25, _col26, _col27, _col28, _col29, _col30, _col31, _col32, _co= l33, _col34, _col35, _col36, _col37, _col38, _col39, _col40, _col41, _col42= , _col43 > Reduce Output Operator > key expressions: _col0 (type: string), _col1 (typ= e: 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 (ty= pe: string), _col5 (type: string), _col6 (type: string), _col7 (type: int) > value expressions: _col8 (type: double), _col9 (t= ype: double), _col10 (type: double), _col11 (type: double), _col12 (type: d= ouble), _col13 (type: double), _col14 (type: double), _col15 (type: double)= , _col16 (type: double), _col17 (type: double), _col18 (type: double), _col= 19 (type: double), _col20 (type: double), _col21 (type: double), _col22 (ty= pe: double), _col23 (type: double), _col24 (type: double), _col25 (type: do= uble), _col26 (type: double), _col27 (type: double), _col28 (type: double),= _col29 (type: double), _col30 (type: double), _col31 (type: double), _col3= 2 (type: double), _col33 (type: double), _col34 (type: double), _col35 (typ= e: double), _col36 (type: double), _col37 (type: double), _col38 (type: dou= ble), _col39 (type: double), _col40 (type: double), _col41 (type: double), = _col42 (type: double), _col43 (type: double) > Reducer 3=20 > 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, _co= l9, _col10, _col11, _col12, _col13 > Statistics: Num rows: 9223372036854775807 Data size: 9223= 372036854775807 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, _col1= 3, _col4, _col5, _col6, _col8, _col9 > Statistics: Num rows: 9223372036854775807 Data size: 92= 23372036854775807 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: 92= 23372036854775807 Basic stats: COMPLETE Column stats: COMPLETE > Select Operator > expressions: _col11 (type: string), _col12 (type: i= nt), _col13 (type: string), _col14 (type: string), _col15 (type: string), _= col16 (type: string), 2002 (type: int), CASE WHEN ((_col2 =3D 1)) THEN ((_c= ol8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 =3D= 2)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WH= EN ((_col2 =3D 3)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: f= loat), CASE WHEN ((_col2 =3D 4)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0= ) END (type: float), CASE WHEN ((_col2 =3D 5)) THEN ((_col8 * UDFToFloat(_c= ol7))) ELSE (0) END (type: float), CASE WHEN ((_col2 =3D 6)) THEN ((_col8 *= UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 =3D 7)) = THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((= _col2 =3D 8)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float)= , CASE WHEN ((_col2 =3D 9)) THEN ((_col8 * UDFToFloat(_col7))) ELSE (0) END= (type: float), CASE WHEN ((_col2 =3D 10)) THEN ((_col8 * UDFToFloat(_col7)= )) ELSE (0) END (type: float), CASE WHEN ((_col2 =3D 11)) THEN ((_col8 * UD= FToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 =3D 12)) TH= EN ((_col8 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_c= ol2 =3D 1)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), = CASE WHEN ((_col2 =3D 2)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (= type: float), CASE WHEN ((_col2 =3D 3)) THEN ((_col9 * UDFToFloat(_col7))) = ELSE (0) END (type: float), CASE WHEN ((_col2 =3D 4)) THEN ((_col9 * UDFToF= loat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 =3D 5)) THEN ((= _col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 = =3D 6)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE= WHEN ((_col2 =3D 7)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type= : float), CASE WHEN ((_col2 =3D 8)) THEN ((_col9 * UDFToFloat(_col7))) ELSE= (0) END (type: float), CASE WHEN ((_col2 =3D 9)) THEN ((_col9 * UDFToFloat= (_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 =3D 10)) THEN ((_co= l9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WHEN ((_col2 =3D = 11)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: float), CASE WH= EN ((_col2 =3D 12)) THEN ((_col9 * UDFToFloat(_col7))) ELSE (0) END (type: = float) > outputColumnNames: _col0, _col1, _col2, _col3, _col= 4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col1= 4, _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(_col1= 5), sum(_col16), sum(_col17), sum(_col18), sum(_col19), sum(_col20), sum(_c= ol21), 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), _c= ol2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type= : string), _col6 (type: int) > mode: hash > outputColumnNames: _col0, _col1, _col2, _col3, _c= ol4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _co= l14, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23= , _col24, _col25, _col26, _col27, _col28, _col29, _col30 > Statistics: Num rows: 2147483647 Data size: 14474= 03978078 Basic stats: COMPLETE Column stats: COMPLETE > Reduce Output Operator > key expressions: _col0 (type: string), _col1 (t= ype: int), _col2 (type: string), _col3 (type: string), _col4 (type: string)= , _col5 (type: string), _col6 (type: int) > sort order: +++++++ > Map-reduce partition columns: _col0 (type: stri= ng), _col1 (type: int), _col2 (type: string), _col3 (type: string), _col4 (= type: string), _col5 (type: string), _col6 (type: int) > Statistics: Num rows: 2147483647 Data size: 144= 7403978078 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), _co= l18 (type: double), _col19 (type: double), _col20 (type: double), _col21 (t= ype: double), _col22 (type: double), _col23 (type: double), _col24 (type: d= ouble), _col25 (type: double), _col26 (type: double), _col27 (type: double)= , _col28 (type: double), _col29 (type: double), _col30 (type: double) > Reducer 4=20 > Reduce Operator Tree: > Group By Operator > aggregations: sum(VALUE._col0), sum(VALUE._col1), sum(VAL= UE._col2), sum(VALUE._col3), sum(VALUE._col4), sum(VALUE._col5), sum(VALUE.= _col6), sum(VALUE._col7), sum(VALUE._col8), sum(VALUE._col9), sum(VALUE._co= l10), sum(VALUE._col11), sum(VALUE._col12), sum(VALUE._col13), sum(VALUE._c= ol14), 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), KE= Y._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, _co= l5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _co= l15, _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 (typ= e: string), 'DIAMOND,AIRBORNE' (type: string), _col6 (type: int), _col7 (ty= pe: double), _col8 (type: double), _col9 (type: double), _col10 (type: doub= le), _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: doubl= e), _col24 (type: double), _col25 (type: double), _col26 (type: double), _c= ol27 (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, _col= 24, _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 (t= ype: string), _col6 (type: string), _col7 (type: int), _col8 (type: double)= , _col9 (type: double), _col10 (type: double), _col11 (type: double), _col1= 2 (type: double), _col13 (type: double), _col14 (type: double), _col15 (typ= e: double), _col16 (type: double), _col17 (type: double), _col18 (type: dou= ble), _col19 (type: double), (_col8 / UDFToDouble(_col1)) (type: double), (= _col9 / UDFToDouble(_col1)) (type: double), (_col10 / UDFToDouble(_col1)) (= type: double), (_col11 / UDFToDouble(_col1)) (type: double), (_col12 / UDFT= oDouble(_col1)) (type: double), (_col13 / UDFToDouble(_col1)) (type: double= ), (_col14 / UDFToDouble(_col1)) (type: double), (_col15 / UDFToDouble(_col= 1)) (type: double), (_col16 / UDFToDouble(_col1)) (type: double), (_col17 /= UDFToDouble(_col1)) (type: double), (_col18 / UDFToDouble(_col1)) (type: d= ouble), (_col19 / UDFToDouble(_col1)) (type: double), _col20 (type: double)= , _col21 (type: double), _col22 (type: double), _col23 (type: double), _col= 24 (type: double), _col25 (type: double), _col26 (type: double), _col27 (ty= pe: double), _col28 (type: double), _col29 (type: double), _col30 (type: do= uble), _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, _c= ol24, _col25, _col26, _col27, _col28, _col29, _col30, _col31, _col32, _col3= 3, _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(_co= l22), sum(_col23), sum(_col24), sum(_col25), sum(_col26), sum(_col27), sum(= _col28), sum(_col29), sum(_col30), sum(_col31), sum(_col32), sum(_col33), s= um(_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), _col= 2 (type: string), _col3 (type: string), _col4 (type: string), _col5 (type: = string), _col6 (type: string), _col7 (type: int) > mode: hash > outputColumnNames: _col0, _col1, _col2, _col3, _col= 4, _col5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col1= 4, _col15, _col16, _col17, _col18, _col19, _col20, _col21, _col22, _col23, = _col24, _col25, _col26, _col27, _col28, _col29, _col30, _col31, _col32, _co= l33, _col34, _col35, _col36, _col37, _col38, _col39, _col40, _col41, _col42= , _col43 > Reduce Output Operator > key expressions: _col0 (type: string), _col1 (typ= e: 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 (ty= pe: string), _col5 (type: string), _col6 (type: string), _col7 (type: int) > value expressions: _col8 (type: double), _col9 (t= ype: double), _col10 (type: double), _col11 (type: double), _col12 (type: d= ouble), _col13 (type: double), _col14 (type: double), _col15 (type: double)= , _col16 (type: double), _col17 (type: double), _col18 (type: double), _col= 19 (type: double), _col20 (type: double), _col21 (type: double), _col22 (ty= pe: double), _col23 (type: double), _col24 (type: double), _col25 (type: do= uble), _col26 (type: double), _col27 (type: double), _col28 (type: double),= _col29 (type: double), _col30 (type: double), _col31 (type: double), _col3= 2 (type: double), _col33 (type: double), _col34 (type: double), _col35 (typ= e: double), _col36 (type: double), _col37 (type: double), _col38 (type: dou= ble), _col39 (type: double), _col40 (type: double), _col41 (type: double), = _col42 (type: double), _col43 (type: double) > Reducer 6=20 > Reduce Operator Tree: > Group By Operator > aggregations: sum(VALUE._col0), sum(VALUE._col1), sum(VAL= UE._col2), sum(VALUE._col3), sum(VALUE._col4), sum(VALUE._col5), sum(VALUE.= _col6), sum(VALUE._col7), sum(VALUE._col8), sum(VALUE._col9), sum(VALUE._co= l10), sum(VALUE._col11), sum(VALUE._col12), sum(VALUE._col13), sum(VALUE._c= ol14), 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(VALU= E._col30), sum(VALUE._col31), sum(VALUE._col32), sum(VALUE._col33), sum(VAL= UE._col34), sum(VALUE._col35) > keys: KEY._col0 (type: string), KEY._col1 (type: int), KE= Y._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, _co= l5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _co= l15, _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, _col= 43 > Statistics: Num rows: 1 Data size: 288 Basic stats: COMPL= ETE Column stats: COMPLETE > Reduce Output Operator > key expressions: _col0 (type: string) > sort order: + > Statistics: Num rows: 1 Data size: 288 Basic stats: COM= PLETE Column stats: COMPLETE > TopN Hash Memory Usage: 0.04 > value expressions: _col1 (type: int), _col2 (type: stri= ng), _col3 (type: string), _col4 (type: string), _col5 (type: string), _col= 6 (type: string), _col7 (type: int), _col8 (type: double), _col9 (type: dou= ble), _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: doub= le), _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: doubl= e), _col36 (type: double), _col37 (type: double), _col38 (type: double), _c= ol39 (type: double), _col40 (type: double), _col41 (type: double), _col42 (= type: double), _col43 (type: double) > Reducer 7=20 > Reduce Operator Tree: > Select Operator > expressions: KEY.reducesinkkey0 (type: string), VALUE._co= l0 (type: int), VALUE._col1 (type: string), VALUE._col2 (type: string), VAL= UE._col3 (type: string), VALUE._col4 (type: string), VALUE._col5 (type: str= ing), VALUE._col6 (type: int), VALUE._col7 (type: double), VALUE._col8 (typ= e: double), VALUE._col9 (type: double), VALUE._col10 (type: double), VALUE.= _col11 (type: double), VALUE._col12 (type: double), VALUE._col13 (type: dou= ble), VALUE._col14 (type: double), VALUE._col15 (type: double), VALUE._col1= 6 (type: double), VALUE._col17 (type: double), VALUE._col18 (type: double),= VALUE._col19 (type: double), VALUE._col20 (type: double), VALUE._col21 (ty= pe: double), VALUE._col22 (type: double), VALUE._col23 (type: double), VALU= E._col24 (type: double), VALUE._col25 (type: double), VALUE._col26 (type: d= ouble), VALUE._col27 (type: double), VALUE._col28 (type: double), VALUE._co= l29 (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), VA= LUE._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, _co= l5, _col6, _col7, _col8, _col9, _col10, _col11, _col12, _col13, _col14, _co= l15, _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, _col= 43 > Statistics: Num rows: 1 Data size: 288 Basic stats: COMPL= ETE Column stats: COMPLETE > Limit > Number of rows: 100 > Statistics: Num rows: 1 Data size: 288 Basic stats: COM= PLETE Column stats: COMPLETE > File Output Operator > compressed: false > Statistics: Num rows: 1 Data size: 288 Basic stats: C= OMPLETE Column stats: COMPLETE > table: > input format: org.apache.hadoop.mapred.TextInputF= ormat > output format: org.apache.hadoop.hive.ql.io.HiveI= gnoreKeyTextOutputFormat > serde: org.apache.hadoop.hive.serde2.lazy.LazySim= pleSerDe > Union 5=20 > Vertex: Union 5 > Stage: Stage-0 > Fetch Operator > limit: 100 > Processor Tree: > ListSink > {code} -- This message was sent by Atlassian JIRA (v6.3.4#6332)