http://git-wip-us.apache.org/repos/asf/hive/blob/b8299551/ql/src/test/results/clientpositive/perf/tez/constraints/query66.q.out ---------------------------------------------------------------------- diff --git a/ql/src/test/results/clientpositive/perf/tez/constraints/query66.q.out b/ql/src/test/results/clientpositive/perf/tez/constraints/query66.q.out new file mode 100644 index 0000000..f82272c --- /dev/null +++ b/ql/src/test/results/clientpositive/perf/tez/constraints/query66.q.out @@ -0,0 +1,702 @@ +PREHOOK: query: 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 + ,'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 + ws_warehouse_sk = w_warehouse_sk + and ws_sold_date_sk = d_date_sk + and ws_sold_time_sk = t_time_sk + and ws_ship_mode_sk = 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 + ,'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 + cs_warehouse_sk = w_warehouse_sk + and cs_sold_date_sk = d_date_sk + and cs_sold_time_sk = t_time_sk + and cs_ship_mode_sk = 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 +PREHOOK: type: QUERY +PREHOOK: Input: default@catalog_sales +PREHOOK: Input: default@date_dim +PREHOOK: Input: default@ship_mode +PREHOOK: Input: default@time_dim +PREHOOK: Input: default@warehouse +PREHOOK: Input: default@web_sales +PREHOOK: Output: hdfs://### HDFS PATH ### +POSTHOOK: query: 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 + ,'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 + ws_warehouse_sk = w_warehouse_sk + and ws_sold_date_sk = d_date_sk + and ws_sold_time_sk = t_time_sk + and ws_ship_mode_sk = 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 + ,'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 + cs_warehouse_sk = w_warehouse_sk + and cs_sold_date_sk = d_date_sk + and cs_sold_time_sk = t_time_sk + and cs_ship_mode_sk = 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 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@catalog_sales +POSTHOOK: Input: default@date_dim +POSTHOOK: Input: default@ship_mode +POSTHOOK: Input: default@time_dim +POSTHOOK: Input: default@warehouse +POSTHOOK: Input: default@web_sales +POSTHOOK: Output: hdfs://### HDFS PATH ### +Plan optimized by CBO. + +Vertex dependency in root stage +Map 1 <- Reducer 11 (BROADCAST_EDGE), Reducer 19 (BROADCAST_EDGE), Reducer 22 (BROADCAST_EDGE) +Map 25 <- Reducer 17 (BROADCAST_EDGE), Reducer 20 (BROADCAST_EDGE), Reducer 23 (BROADCAST_EDGE) +Reducer 11 <- Map 10 (CUSTOM_SIMPLE_EDGE) +Reducer 12 <- Map 10 (SIMPLE_EDGE), Map 25 (SIMPLE_EDGE) +Reducer 13 <- Map 18 (SIMPLE_EDGE), Reducer 12 (SIMPLE_EDGE) +Reducer 14 <- Map 21 (SIMPLE_EDGE), Reducer 13 (SIMPLE_EDGE) +Reducer 15 <- Map 24 (SIMPLE_EDGE), Reducer 14 (SIMPLE_EDGE) +Reducer 16 <- Reducer 15 (SIMPLE_EDGE), Union 7 (CONTAINS) +Reducer 17 <- Map 10 (CUSTOM_SIMPLE_EDGE) +Reducer 19 <- Map 18 (CUSTOM_SIMPLE_EDGE) +Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 10 (SIMPLE_EDGE) +Reducer 20 <- Map 18 (CUSTOM_SIMPLE_EDGE) +Reducer 22 <- Map 21 (CUSTOM_SIMPLE_EDGE) +Reducer 23 <- Map 21 (CUSTOM_SIMPLE_EDGE) +Reducer 3 <- Map 18 (SIMPLE_EDGE), Reducer 2 (SIMPLE_EDGE) +Reducer 4 <- Map 21 (SIMPLE_EDGE), Reducer 3 (SIMPLE_EDGE) +Reducer 5 <- Map 24 (SIMPLE_EDGE), Reducer 4 (SIMPLE_EDGE) +Reducer 6 <- Reducer 5 (SIMPLE_EDGE), Union 7 (CONTAINS) +Reducer 8 <- Union 7 (SIMPLE_EDGE) +Reducer 9 <- Reducer 8 (SIMPLE_EDGE) + +Stage-0 + Fetch Operator + limit:-1 + Stage-1 + Reducer 9 vectorized + File Output Operator [FS_267] + Select Operator [SEL_266] (rows=100 width=4614) + Output:["_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"] + Limit [LIM_265] (rows=100 width=4510) + Number of rows:100 + Select Operator [SEL_264] (rows=2423925 width=4510) + Output:["_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"] + <-Reducer 8 [SIMPLE_EDGE] vectorized + SHUFFLE [RS_263] + Group By Operator [GBY_262] (rows=2423925 width=4510) + Output:["_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"],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, KEY._col1, KEY._col2, KEY._col3, KEY._col4, KEY._col5 + <-Union 7 [SIMPLE_EDGE] + <-Reducer 16 [CONTAINS] vectorized + Reduce Output Operator [RS_281] + PartitionCols:_col0, _col1, _col2, _col3, _col4, _col5 + Group By Operator [GBY_280] (rows=2513727 width=4510) + Output:["_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"],aggregations:["sum(_col6)","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)","sum(_col31)","sum(_col32)","sum(_col33)","sum(_col34)","sum(_col35)","sum(_col36)","sum(_col37)","sum(_col38)","sum(_col39)","sum(_col40)","sum(_col41)"],keys:_col0, _col1, _col2, _col3, _col4, _col5 + Top N Key Operator [TNK_279] (rows=2513727 width=3166) + keys:_col0, _col1, _col2, _col3, _col4, _col5,sort order:++++++,top n:100 + Select Operator [SEL_278] (rows=2513727 width=3166) + Output:["_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"] + Group By Operator [GBY_277] (rows=2513700 width=3166) + Output:["_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"],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, KEY._col1, KEY._col2, KEY._col3, KEY._col4, KEY._col5 + <-Reducer 15 [SIMPLE_EDGE] + SHUFFLE [RS_61] + PartitionCols:_col0, _col1, _col2, _col3, _col4, _col5 + Group By Operator [GBY_60] (rows=5559759 width=3166) + Output:["_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"],aggregations:["sum(_col6)","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)"],keys:_col0, _col1, _col2, _col3, _col4, _col5 + Select Operator [SEL_58] (rows=5559759 width=750) + Output:["_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"] + Merge Join Operator [MERGEJOIN_202] (rows=5559759 width=750) + Conds:RS_55._col3=RS_256._col0(Inner),Output:["_col4","_col5","_col8","_col9","_col10","_col11","_col12","_col13","_col14","_col15","_col16","_col17","_col18","_col19","_col22","_col23","_col24","_col25","_col26","_col27"] + <-Map 24 [SIMPLE_EDGE] vectorized + SHUFFLE [RS_256] + PartitionCols:_col0 + Select Operator [SEL_254] (rows=27 width=482) + Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6"] + TableScan [TS_12] (rows=27 width=482) + default@warehouse,warehouse,Tbl:COMPLETE,Col:COMPLETE,Output:["w_warehouse_sk","w_warehouse_name","w_warehouse_sq_ft","w_city","w_county","w_state","w_country"] + <-Reducer 14 [SIMPLE_EDGE] + SHUFFLE [RS_55] + PartitionCols:_col3 + Merge Join Operator [MERGEJOIN_201] (rows=5559759 width=274) + Conds:RS_52._col2=RS_243._col0(Inner),Output:["_col3","_col4","_col5","_col8","_col9","_col10","_col11","_col12","_col13","_col14","_col15","_col16","_col17","_col18","_col19"] + <-Map 21 [SIMPLE_EDGE] vectorized + SHUFFLE [RS_243] + PartitionCols:_col0 + Select Operator [SEL_240] (rows=1 width=4) + Output:["_col0"] + Filter Operator [FIL_239] (rows=1 width=88) + predicate:(sm_carrier) IN ('DIAMOND', 'AIRBORNE') + TableScan [TS_9] (rows=1 width=88) + default@ship_mode,ship_mode,Tbl:COMPLETE,Col:COMPLETE,Output:["sm_ship_mode_sk","sm_carrier"] + <-Reducer 13 [SIMPLE_EDGE] + SHUFFLE [RS_52] + PartitionCols:_col2 + Merge Join Operator [MERGEJOIN_200] (rows=11119518 width=278) + Conds:RS_49._col0=RS_231._col0(Inner),Output:["_col2","_col3","_col4","_col5","_col8","_col9","_col10","_col11","_col12","_col13","_col14","_col15","_col16","_col17","_col18","_col19"] + <-Map 18 [SIMPLE_EDGE] vectorized + SHUFFLE [RS_231] + PartitionCols:_col0 + Select Operator [SEL_228] (rows=652 width=52) + Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11","_col12"] + Filter Operator [FIL_227] (rows=652 width=12) + predicate:(d_year = 2002) + TableScan [TS_6] (rows=73049 width=12) + default@date_dim,date_dim,Tbl:COMPLETE,Col:COMPLETE,Output:["d_date_sk","d_year","d_moy"] + <-Reducer 12 [SIMPLE_EDGE] + SHUFFLE [RS_49] + PartitionCols:_col0 + Merge Join Operator [MERGEJOIN_199] (rows=31363607 width=235) + Conds:RS_276._col1=RS_219._col0(Inner),Output:["_col0","_col2","_col3","_col4","_col5"] + <-Map 10 [SIMPLE_EDGE] vectorized + SHUFFLE [RS_219] + PartitionCols:_col0 + Select Operator [SEL_216] (rows=9600 width=4) + Output:["_col0"] + Filter Operator [FIL_215] (rows=9600 width=8) + predicate:t_time BETWEEN 49530 AND 78330 + TableScan [TS_3] (rows=86400 width=8) + default@time_dim,time_dim,Tbl:COMPLETE,Col:COMPLETE,Output:["t_time_sk","t_time"] + <-Map 25 [SIMPLE_EDGE] vectorized + SHUFFLE [RS_276] + PartitionCols:_col1 + Select Operator [SEL_275] (rows=282272460 width=239) + Output:["_col0","_col1","_col2","_col3","_col4","_col5"] + Filter Operator [FIL_274] (rows=282272460 width=243) + predicate:((cs_ship_mode_sk BETWEEN DynamicValue(RS_53_ship_mode_sm_ship_mode_sk_min) AND DynamicValue(RS_53_ship_mode_sm_ship_mode_sk_max) and in_bloom_filter(cs_ship_mode_sk, DynamicValue(RS_53_ship_mode_sm_ship_mode_sk_bloom_filter))) and (cs_sold_date_sk BETWEEN DynamicValue(RS_50_date_dim_d_date_sk_min) AND DynamicValue(RS_50_date_dim_d_date_sk_max) and in_bloom_filter(cs_sold_date_sk, DynamicValue(RS_50_date_dim_d_date_sk_bloom_filter))) and (cs_sold_time_sk BETWEEN DynamicValue(RS_47_time_dim_t_time_sk_min) AND DynamicValue(RS_47_time_dim_t_time_sk_max) and in_bloom_filter(cs_sold_time_sk, DynamicValue(RS_47_time_dim_t_time_sk_bloom_filter))) and cs_ship_mode_sk is not null and cs_sold_date_sk is not null and cs_sold_time_sk is not null and cs_warehouse_sk is not null) + TableScan [TS_32] (rows=287989836 width=243) + default@catalog_sales,catalog_sales,Tbl:COMPLETE,Col:COMPLETE,Output:["cs_sold_date_sk","cs_sold_time_sk","cs_ship_mode_sk","cs_warehouse_sk","cs_quantity","cs_ext_sales_price","cs_net_paid_inc_ship_tax"] + <-Reducer 17 [BROADCAST_EDGE] vectorized + BROADCAST [RS_269] + Group By Operator [GBY_268] (rows=1 width=12) + Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"] + <-Map 10 [CUSTOM_SIMPLE_EDGE] vectorized + SHUFFLE [RS_224] + Group By Operator [GBY_222] (rows=1 width=12) + Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"] + Select Operator [SEL_220] (rows=9600 width=4) + Output:["_col0"] + Please refer to the previous Select Operator [SEL_216] + <-Reducer 20 [BROADCAST_EDGE] vectorized + BROADCAST [RS_271] + Group By Operator [GBY_270] (rows=1 width=12) + Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"] + <-Map 18 [CUSTOM_SIMPLE_EDGE] vectorized + SHUFFLE [RS_236] + Group By Operator [GBY_234] (rows=1 width=12) + Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"] + Select Operator [SEL_232] (rows=652 width=4) + Output:["_col0"] + Please refer to the previous Select Operator [SEL_228] + <-Reducer 23 [BROADCAST_EDGE] vectorized + BROADCAST [RS_273] + Group By Operator [GBY_272] (rows=1 width=12) + Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"] + <-Map 21 [CUSTOM_SIMPLE_EDGE] vectorized + SHUFFLE [RS_248] + Group By Operator [GBY_246] (rows=1 width=12) + Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"] + Select Operator [SEL_244] (rows=1 width=4) + Output:["_col0"] + Please refer to the previous Select Operator [SEL_240] + <-Reducer 6 [CONTAINS] vectorized + Reduce Output Operator [RS_261] + PartitionCols:_col0, _col1, _col2, _col3, _col4, _col5 + Group By Operator [GBY_260] (rows=2513727 width=4510) + Output:["_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"],aggregations:["sum(_col6)","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)","sum(_col31)","sum(_col32)","sum(_col33)","sum(_col34)","sum(_col35)","sum(_col36)","sum(_col37)","sum(_col38)","sum(_col39)","sum(_col40)","sum(_col41)"],keys:_col0, _col1, _col2, _col3, _col4, _col5 + Top N Key Operator [TNK_259] (rows=2513727 width=3166) + keys:_col0, _col1, _col2, _col3, _col4, _col5,sort order:++++++,top n:100 + Select Operator [SEL_258] (rows=2513727 width=3166) + Output:["_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"] + Group By Operator [GBY_257] (rows=27 width=3166) + Output:["_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"],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, KEY._col1, KEY._col2, KEY._col3, KEY._col4, KEY._col5 + <-Reducer 5 [SIMPLE_EDGE] + SHUFFLE [RS_29] + PartitionCols:_col0, _col1, _col2, _col3, _col4, _col5 + Group By Operator [GBY_28] (rows=27 width=3166) + Output:["_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"],aggregations:["sum(_col6)","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)"],keys:_col0, _col1, _col2, _col3, _col4, _col5 + Select Operator [SEL_26] (rows=2853684 width=750) + Output:["_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"] + Merge Join Operator [MERGEJOIN_198] (rows=2853684 width=750) + Conds:RS_23._col3=RS_255._col0(Inner),Output:["_col4","_col5","_col8","_col9","_col10","_col11","_col12","_col13","_col14","_col15","_col16","_col17","_col18","_col19","_col22","_col23","_col24","_col25","_col26","_col27"] + <-Map 24 [SIMPLE_EDGE] vectorized + SHUFFLE [RS_255] + PartitionCols:_col0 + Please refer to the previous Select Operator [SEL_254] + <-Reducer 4 [SIMPLE_EDGE] + SHUFFLE [RS_23] + PartitionCols:_col3 + Merge Join Operator [MERGEJOIN_197] (rows=2853684 width=275) + Conds:RS_20._col2=RS_241._col0(Inner),Output:["_col3","_col4","_col5","_col8","_col9","_col10","_col11","_col12","_col13","_col14","_col15","_col16","_col17","_col18","_col19"] + <-Map 21 [SIMPLE_EDGE] vectorized + SHUFFLE [RS_241] + PartitionCols:_col0 + Please refer to the previous Select Operator [SEL_240] + <-Reducer 3 [SIMPLE_EDGE] + SHUFFLE [RS_20] + PartitionCols:_col2 + Merge Join Operator [MERGEJOIN_196] (rows=5707369 width=279) + Conds:RS_17._col0=RS_229._col0(Inner),Output:["_col2","_col3","_col4","_col5","_col8","_col9","_col10","_col11","_col12","_col13","_col14","_col15","_col16","_col17","_col18","_col19"] + <-Map 18 [SIMPLE_EDGE] vectorized + SHUFFLE [RS_229] + PartitionCols:_col0 + Please refer to the previous Select Operator [SEL_228] + <-Reducer 2 [SIMPLE_EDGE] + SHUFFLE [RS_17] + PartitionCols:_col0 + Merge Join Operator [MERGEJOIN_195] (rows=15984351 width=235) + Conds:RS_253._col1=RS_217._col0(Inner),Output:["_col0","_col2","_col3","_col4","_col5"] + <-Map 10 [SIMPLE_EDGE] vectorized + SHUFFLE [RS_217] + PartitionCols:_col0 + Please refer to the previous Select Operator [SEL_216] + <-Map 1 [SIMPLE_EDGE] vectorized + SHUFFLE [RS_253] + PartitionCols:_col1 + Select Operator [SEL_252] (rows=143859154 width=239) + Output:["_col0","_col1","_col2","_col3","_col4","_col5"] + Filter Operator [FIL_251] (rows=143859154 width=243) + predicate:((ws_ship_mode_sk BETWEEN DynamicValue(RS_21_ship_mode_sm_ship_mode_sk_min) AND DynamicValue(RS_21_ship_mode_sm_ship_mode_sk_max) and in_bloom_filter(ws_ship_mode_sk, DynamicValue(RS_21_ship_mode_sm_ship_mode_sk_bloom_filter))) and (ws_sold_date_sk BETWEEN DynamicValue(RS_18_date_dim_d_date_sk_min) AND DynamicValue(RS_18_date_dim_d_date_sk_max) and in_bloom_filter(ws_sold_date_sk, DynamicValue(RS_18_date_dim_d_date_sk_bloom_filter))) and (ws_sold_time_sk BETWEEN DynamicValue(RS_15_time_dim_t_time_sk_min) AND DynamicValue(RS_15_time_dim_t_time_sk_max) and in_bloom_filter(ws_sold_time_sk, DynamicValue(RS_15_time_dim_t_time_sk_bloom_filter))) and ws_ship_mode_sk is not null and ws_sold_date_sk is not null and ws_sold_time_sk is not null and ws_warehouse_sk is not null) + TableScan [TS_0] (rows=144002668 width=243) + default@web_sales,web_sales,Tbl:COMPLETE,Col:COMPLETE,Output:["ws_sold_date_sk","ws_sold_time_sk","ws_ship_mode_sk","ws_warehouse_sk","ws_quantity","ws_sales_price","ws_net_paid_inc_tax"] + <-Reducer 11 [BROADCAST_EDGE] vectorized + BROADCAST [RS_226] + Group By Operator [GBY_225] (rows=1 width=12) + Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"] + <-Map 10 [CUSTOM_SIMPLE_EDGE] vectorized + SHUFFLE [RS_223] + Group By Operator [GBY_221] (rows=1 width=12) + Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"] + Select Operator [SEL_218] (rows=9600 width=4) + Output:["_col0"] + Please refer to the previous Select Operator [SEL_216] + <-Reducer 19 [BROADCAST_EDGE] vectorized + BROADCAST [RS_238] + Group By Operator [GBY_237] (rows=1 width=12) + Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"] + <-Map 18 [CUSTOM_SIMPLE_EDGE] vectorized + SHUFFLE [RS_235] + Group By Operator [GBY_233] (rows=1 width=12) + Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"] + Select Operator [SEL_230] (rows=652 width=4) + Output:["_col0"] + Please refer to the previous Select Operator [SEL_228] + <-Reducer 22 [BROADCAST_EDGE] vectorized + BROADCAST [RS_250] + Group By Operator [GBY_249] (rows=1 width=12) + Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"] + <-Map 21 [CUSTOM_SIMPLE_EDGE] vectorized + SHUFFLE [RS_247] + Group By Operator [GBY_245] (rows=1 width=12) + Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"] + Select Operator [SEL_242] (rows=1 width=4) + Output:["_col0"] + Please refer to the previous Select Operator [SEL_240] +
http://git-wip-us.apache.org/repos/asf/hive/blob/b8299551/ql/src/test/results/clientpositive/perf/tez/constraints/query67.q.out ---------------------------------------------------------------------- diff --git a/ql/src/test/results/clientpositive/perf/tez/constraints/query67.q.out b/ql/src/test/results/clientpositive/perf/tez/constraints/query67.q.out new file mode 100644 index 0000000..7abc959 --- /dev/null +++ b/ql/src/test/results/clientpositive/perf/tez/constraints/query67.q.out @@ -0,0 +1,196 @@ +PREHOOK: query: explain +select * +from (select i_category + ,i_class + ,i_brand + ,i_product_name + ,d_year + ,d_qoy + ,d_moy + ,s_store_id + ,sumsales + ,rank() over (partition by i_category order by sumsales desc) rk + from (select i_category + ,i_class + ,i_brand + ,i_product_name + ,d_year + ,d_qoy + ,d_moy + ,s_store_id + ,sum(coalesce(ss_sales_price*ss_quantity,0)) sumsales + from store_sales + ,date_dim + ,store + ,item + where ss_sold_date_sk=d_date_sk + and ss_item_sk=i_item_sk + and ss_store_sk = s_store_sk + and d_month_seq between 1212 and 1212+11 + group by rollup(i_category, i_class, i_brand, i_product_name, d_year, d_qoy, d_moy,s_store_id))dw1) dw2 +where rk <= 100 +order by i_category + ,i_class + ,i_brand + ,i_product_name + ,d_year + ,d_qoy + ,d_moy + ,s_store_id + ,sumsales + ,rk +limit 100 +PREHOOK: type: QUERY +PREHOOK: Input: default@date_dim +PREHOOK: Input: default@item +PREHOOK: Input: default@store +PREHOOK: Input: default@store_sales +PREHOOK: Output: hdfs://### HDFS PATH ### +POSTHOOK: query: explain +select * +from (select i_category + ,i_class + ,i_brand + ,i_product_name + ,d_year + ,d_qoy + ,d_moy + ,s_store_id + ,sumsales + ,rank() over (partition by i_category order by sumsales desc) rk + from (select i_category + ,i_class + ,i_brand + ,i_product_name + ,d_year + ,d_qoy + ,d_moy + ,s_store_id + ,sum(coalesce(ss_sales_price*ss_quantity,0)) sumsales + from store_sales + ,date_dim + ,store + ,item + where ss_sold_date_sk=d_date_sk + and ss_item_sk=i_item_sk + and ss_store_sk = s_store_sk + and d_month_seq between 1212 and 1212+11 + group by rollup(i_category, i_class, i_brand, i_product_name, d_year, d_qoy, d_moy,s_store_id))dw1) dw2 +where rk <= 100 +order by i_category + ,i_class + ,i_brand + ,i_product_name + ,d_year + ,d_qoy + ,d_moy + ,s_store_id + ,sumsales + ,rk +limit 100 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@date_dim +POSTHOOK: Input: default@item +POSTHOOK: Input: default@store +POSTHOOK: Input: default@store_sales +POSTHOOK: Output: hdfs://### HDFS PATH ### +Plan optimized by CBO. + +Vertex dependency in root stage +Map 1 <- Reducer 9 (BROADCAST_EDGE) +Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 8 (SIMPLE_EDGE) +Reducer 3 <- Map 10 (SIMPLE_EDGE), Reducer 2 (SIMPLE_EDGE) +Reducer 4 <- Map 11 (SIMPLE_EDGE), Reducer 3 (SIMPLE_EDGE) +Reducer 5 <- Reducer 4 (SIMPLE_EDGE) +Reducer 6 <- Reducer 5 (SIMPLE_EDGE) +Reducer 7 <- Reducer 6 (SIMPLE_EDGE) +Reducer 9 <- Map 8 (CUSTOM_SIMPLE_EDGE) + +Stage-0 + Fetch Operator + limit:100 + Stage-1 + Reducer 7 vectorized + File Output Operator [FS_107] + Limit [LIM_106] (rows=100 width=617) + Number of rows:100 + Select Operator [SEL_105] (rows=273593580 width=617) + Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9"] + <-Reducer 6 [SIMPLE_EDGE] vectorized + SHUFFLE [RS_104] + Select Operator [SEL_103] (rows=273593580 width=617) + Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9"] + Filter Operator [FIL_102] (rows=273593580 width=613) + predicate:(rank_window_0 <= 100) + PTF Operator [PTF_101] (rows=820780740 width=613) + Function definitions:[{},{"name:":"windowingtablefunction","order by:":"_col8 DESC NULLS LAST","partition by:":"_col2"}] + Select Operator [SEL_100] (rows=820780740 width=613) + Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8"] + <-Reducer 5 [SIMPLE_EDGE] vectorized + SHUFFLE [RS_99] + PartitionCols:_col2 + Select Operator [SEL_98] (rows=820780740 width=613) + Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8"] + Group By Operator [GBY_97] (rows=820780740 width=621) + Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col9"],aggregations:["sum(VALUE._col0)"],keys:KEY._col0, KEY._col1, KEY._col2, KEY._col3, KEY._col4, KEY._col5, KEY._col6, KEY._col7, KEY._col8 + <-Reducer 4 [SIMPLE_EDGE] + SHUFFLE [RS_21] + PartitionCols:_col0, _col1, _col2, _col3, _col4, _col5, _col6, _col7, _col8 + Group By Operator [GBY_20] (rows=820780740 width=621) + Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9"],aggregations:["sum(_col3)"],keys:_col11, _col12, _col13, _col14, _col5, _col6, _col7, _col9, 0L + Merge Join Operator [MERGEJOIN_81] (rows=91197860 width=613) + Conds:RS_16._col1=RS_96._col0(Inner),Output:["_col3","_col5","_col6","_col7","_col9","_col11","_col12","_col13","_col14"] + <-Map 11 [SIMPLE_EDGE] vectorized + SHUFFLE [RS_96] + PartitionCols:_col0 + Select Operator [SEL_95] (rows=462000 width=393) + Output:["_col0","_col1","_col2","_col3","_col4"] + TableScan [TS_8] (rows=462000 width=393) + default@item,item,Tbl:COMPLETE,Col:COMPLETE,Output:["i_item_sk","i_brand","i_class","i_category","i_product_name"] + <-Reducer 3 [SIMPLE_EDGE] + SHUFFLE [RS_16] + PartitionCols:_col1 + Merge Join Operator [MERGEJOIN_80] (rows=91197860 width=228) + Conds:RS_13._col2=RS_94._col0(Inner),Output:["_col1","_col3","_col5","_col6","_col7","_col9"] + <-Map 10 [SIMPLE_EDGE] vectorized + SHUFFLE [RS_94] + PartitionCols:_col0 + Select Operator [SEL_93] (rows=1704 width=104) + Output:["_col0","_col1"] + TableScan [TS_6] (rows=1704 width=104) + default@store,store,Tbl:COMPLETE,Col:COMPLETE,Output:["s_store_sk","s_store_id"] + <-Reducer 2 [SIMPLE_EDGE] + SHUFFLE [RS_13] + PartitionCols:_col2 + Merge Join Operator [MERGEJOIN_79] (rows=91197860 width=130) + Conds:RS_92._col0=RS_84._col0(Inner),Output:["_col1","_col2","_col3","_col5","_col6","_col7"] + <-Map 8 [SIMPLE_EDGE] vectorized + PARTITION_ONLY_SHUFFLE [RS_84] + PartitionCols:_col0 + Select Operator [SEL_83] (rows=317 width=16) + Output:["_col0","_col1","_col2","_col3"] + Filter Operator [FIL_82] (rows=317 width=20) + predicate:d_month_seq BETWEEN 1212 AND 1223 + TableScan [TS_3] (rows=73049 width=20) + default@date_dim,date_dim,Tbl:COMPLETE,Col:COMPLETE,Output:["d_date_sk","d_month_seq","d_year","d_moy","d_qoy"] + <-Map 1 [SIMPLE_EDGE] vectorized + SHUFFLE [RS_92] + PartitionCols:_col0 + Select Operator [SEL_91] (rows=525329897 width=123) + Output:["_col0","_col1","_col2","_col3"] + Filter Operator [FIL_90] (rows=525329897 width=122) + predicate:((ss_sold_date_sk BETWEEN DynamicValue(RS_11_date_dim_d_date_sk_min) AND DynamicValue(RS_11_date_dim_d_date_sk_max) and in_bloom_filter(ss_sold_date_sk, DynamicValue(RS_11_date_dim_d_date_sk_bloom_filter))) and ss_sold_date_sk is not null and ss_store_sk is not null) + TableScan [TS_0] (rows=575995635 width=122) + default@store_sales,store_sales,Tbl:COMPLETE,Col:COMPLETE,Output:["ss_sold_date_sk","ss_item_sk","ss_store_sk","ss_quantity","ss_sales_price"] + <-Reducer 9 [BROADCAST_EDGE] vectorized + BROADCAST [RS_89] + Group By Operator [GBY_88] (rows=1 width=12) + Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"] + <-Map 8 [CUSTOM_SIMPLE_EDGE] vectorized + PARTITION_ONLY_SHUFFLE [RS_87] + Group By Operator [GBY_86] (rows=1 width=12) + Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"] + Select Operator [SEL_85] (rows=317 width=4) + Output:["_col0"] + Please refer to the previous Select Operator [SEL_83] + http://git-wip-us.apache.org/repos/asf/hive/blob/b8299551/ql/src/test/results/clientpositive/perf/tez/constraints/query68.q.out ---------------------------------------------------------------------- diff --git a/ql/src/test/results/clientpositive/perf/tez/constraints/query68.q.out b/ql/src/test/results/clientpositive/perf/tez/constraints/query68.q.out new file mode 100644 index 0000000..2188af5 --- /dev/null +++ b/ql/src/test/results/clientpositive/perf/tez/constraints/query68.q.out @@ -0,0 +1,254 @@ +PREHOOK: query: explain +select c_last_name + ,c_first_name + ,ca_city + ,bought_city + ,ss_ticket_number + ,extended_price + ,extended_tax + ,list_price + from (select ss_ticket_number + ,ss_customer_sk + ,ca_city bought_city + ,sum(ss_ext_sales_price) extended_price + ,sum(ss_ext_list_price) list_price + ,sum(ss_ext_tax) extended_tax + from store_sales + ,date_dim + ,store + ,household_demographics + ,customer_address + where store_sales.ss_sold_date_sk = date_dim.d_date_sk + and store_sales.ss_store_sk = store.s_store_sk + and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk + and store_sales.ss_addr_sk = customer_address.ca_address_sk + and date_dim.d_dom between 1 and 2 + and (household_demographics.hd_dep_count = 2 or + household_demographics.hd_vehicle_count= 1) + and date_dim.d_year in (1998,1998+1,1998+2) + and store.s_city in ('Cedar Grove','Wildwood') + group by ss_ticket_number + ,ss_customer_sk + ,ss_addr_sk,ca_city) dn + ,customer + ,customer_address current_addr + where ss_customer_sk = c_customer_sk + and customer.c_current_addr_sk = current_addr.ca_address_sk + and current_addr.ca_city <> bought_city + order by c_last_name + ,ss_ticket_number + limit 100 +PREHOOK: type: QUERY +PREHOOK: Input: default@customer +PREHOOK: Input: default@customer_address +PREHOOK: Input: default@date_dim +PREHOOK: Input: default@household_demographics +PREHOOK: Input: default@store +PREHOOK: Input: default@store_sales +PREHOOK: Output: hdfs://### HDFS PATH ### +POSTHOOK: query: explain +select c_last_name + ,c_first_name + ,ca_city + ,bought_city + ,ss_ticket_number + ,extended_price + ,extended_tax + ,list_price + from (select ss_ticket_number + ,ss_customer_sk + ,ca_city bought_city + ,sum(ss_ext_sales_price) extended_price + ,sum(ss_ext_list_price) list_price + ,sum(ss_ext_tax) extended_tax + from store_sales + ,date_dim + ,store + ,household_demographics + ,customer_address + where store_sales.ss_sold_date_sk = date_dim.d_date_sk + and store_sales.ss_store_sk = store.s_store_sk + and store_sales.ss_hdemo_sk = household_demographics.hd_demo_sk + and store_sales.ss_addr_sk = customer_address.ca_address_sk + and date_dim.d_dom between 1 and 2 + and (household_demographics.hd_dep_count = 2 or + household_demographics.hd_vehicle_count= 1) + and date_dim.d_year in (1998,1998+1,1998+2) + and store.s_city in ('Cedar Grove','Wildwood') + group by ss_ticket_number + ,ss_customer_sk + ,ss_addr_sk,ca_city) dn + ,customer + ,customer_address current_addr + where ss_customer_sk = c_customer_sk + and customer.c_current_addr_sk = current_addr.ca_address_sk + and current_addr.ca_city <> bought_city + order by c_last_name + ,ss_ticket_number + limit 100 +POSTHOOK: type: QUERY +POSTHOOK: Input: default@customer +POSTHOOK: Input: default@customer_address +POSTHOOK: Input: default@date_dim +POSTHOOK: Input: default@household_demographics +POSTHOOK: Input: default@store +POSTHOOK: Input: default@store_sales +POSTHOOK: Output: hdfs://### HDFS PATH ### +Plan optimized by CBO. + +Vertex dependency in root stage +Map 8 <- Reducer 13 (BROADCAST_EDGE), Reducer 15 (BROADCAST_EDGE), Reducer 17 (BROADCAST_EDGE) +Reducer 10 <- Map 14 (SIMPLE_EDGE), Reducer 9 (SIMPLE_EDGE) +Reducer 11 <- Map 16 (SIMPLE_EDGE), Reducer 10 (SIMPLE_EDGE) +Reducer 13 <- Map 12 (CUSTOM_SIMPLE_EDGE) +Reducer 15 <- Map 14 (CUSTOM_SIMPLE_EDGE) +Reducer 17 <- Map 16 (CUSTOM_SIMPLE_EDGE) +Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 5 (SIMPLE_EDGE) +Reducer 3 <- Reducer 2 (SIMPLE_EDGE), Reducer 7 (SIMPLE_EDGE) +Reducer 4 <- Reducer 3 (SIMPLE_EDGE) +Reducer 6 <- Map 5 (SIMPLE_EDGE), Reducer 11 (SIMPLE_EDGE) +Reducer 7 <- Reducer 6 (SIMPLE_EDGE) +Reducer 9 <- Map 12 (SIMPLE_EDGE), Map 8 (SIMPLE_EDGE) + +Stage-0 + Fetch Operator + limit:100 + Stage-1 + Reducer 4 vectorized + File Output Operator [FS_182] + Limit [LIM_181] (rows=100 width=706) + Number of rows:100 + Select Operator [SEL_180] (rows=4418634 width=706) + Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7"] + <-Reducer 3 [SIMPLE_EDGE] + SHUFFLE [RS_44] + Select Operator [SEL_43] (rows=4418634 width=706) + Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7"] + Filter Operator [FIL_42] (rows=4418634 width=706) + predicate:(_col5 <> _col8) + Merge Join Operator [MERGEJOIN_143] (rows=4418634 width=706) + Conds:RS_39._col0=RS_179._col1(Inner),Output:["_col2","_col3","_col5","_col6","_col8","_col9","_col10","_col11"] + <-Reducer 2 [SIMPLE_EDGE] + SHUFFLE [RS_39] + PartitionCols:_col0 + Merge Join Operator [MERGEJOIN_138] (rows=80000000 width=277) + Conds:RS_146._col1=RS_148._col0(Inner),Output:["_col0","_col2","_col3","_col5"] + <-Map 5 [SIMPLE_EDGE] vectorized + SHUFFLE [RS_148] + PartitionCols:_col0 + Select Operator [SEL_147] (rows=40000000 width=97) + Output:["_col0","_col1"] + TableScan [TS_3] (rows=40000000 width=97) + default@customer_address,current_addr,Tbl:COMPLETE,Col:COMPLETE,Output:["ca_address_sk","ca_city"] + <-Map 1 [SIMPLE_EDGE] vectorized + SHUFFLE [RS_146] + PartitionCols:_col1 + Select Operator [SEL_145] (rows=80000000 width=188) + Output:["_col0","_col1","_col2","_col3"] + Filter Operator [FIL_144] (rows=80000000 width=188) + predicate:c_current_addr_sk is not null + TableScan [TS_0] (rows=80000000 width=188) + default@customer,customer,Tbl:COMPLETE,Col:COMPLETE,Output:["c_customer_sk","c_current_addr_sk","c_first_name","c_last_name"] + <-Reducer 7 [SIMPLE_EDGE] vectorized + SHUFFLE [RS_179] + PartitionCols:_col1 + Select Operator [SEL_178] (rows=4418634 width=433) + Output:["_col0","_col1","_col2","_col3","_col4","_col5"] + Group By Operator [GBY_177] (rows=4418634 width=433) + Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6"],aggregations:["sum(VALUE._col0)","sum(VALUE._col1)","sum(VALUE._col2)"],keys:KEY._col0, KEY._col1, KEY._col2, KEY._col3 + <-Reducer 6 [SIMPLE_EDGE] + SHUFFLE [RS_33] + PartitionCols:_col0, _col1, _col2, _col3 + Group By Operator [GBY_32] (rows=4418634 width=433) + Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6"],aggregations:["sum(_col6)","sum(_col7)","sum(_col8)"],keys:_col1, _col13, _col3, _col5 + Merge Join Operator [MERGEJOIN_142] (rows=4418634 width=97) + Conds:RS_28._col3=RS_149._col0(Inner),Output:["_col1","_col3","_col5","_col6","_col7","_col8","_col13"] + <-Map 5 [SIMPLE_EDGE] vectorized + SHUFFLE [RS_149] + PartitionCols:_col0 + Please refer to the previous Select Operator [SEL_147] + <-Reducer 11 [SIMPLE_EDGE] + SHUFFLE [RS_28] + PartitionCols:_col3 + Merge Join Operator [MERGEJOIN_141] (rows=4418634 width=4) + Conds:RS_25._col2=RS_168._col0(Inner),Output:["_col1","_col3","_col5","_col6","_col7","_col8"] + <-Map 16 [SIMPLE_EDGE] vectorized + PARTITION_ONLY_SHUFFLE [RS_168] + PartitionCols:_col0 + Select Operator [SEL_167] (rows=1855 width=4) + Output:["_col0"] + Filter Operator [FIL_166] (rows=1855 width=12) + predicate:((hd_dep_count = 2) or (hd_vehicle_count = 1)) + TableScan [TS_14] (rows=7200 width=12) + default@household_demographics,household_demographics,Tbl:COMPLETE,Col:COMPLETE,Output:["hd_demo_sk","hd_dep_count","hd_vehicle_count"] + <-Reducer 10 [SIMPLE_EDGE] + SHUFFLE [RS_25] + PartitionCols:_col2 + Merge Join Operator [MERGEJOIN_140] (rows=17150490 width=4) + Conds:RS_22._col4=RS_160._col0(Inner),Output:["_col1","_col2","_col3","_col5","_col6","_col7","_col8"] + <-Map 14 [SIMPLE_EDGE] vectorized + PARTITION_ONLY_SHUFFLE [RS_160] + PartitionCols:_col0 + Select Operator [SEL_159] (rows=85 width=4) + Output:["_col0"] + Filter Operator [FIL_158] (rows=85 width=97) + predicate:(s_city) IN ('Cedar Grove', 'Wildwood') + TableScan [TS_11] (rows=1704 width=97) + default@store,store,Tbl:COMPLETE,Col:COMPLETE,Output:["s_store_sk","s_city"] + <-Reducer 9 [SIMPLE_EDGE] + SHUFFLE [RS_22] + PartitionCols:_col4 + Merge Join Operator [MERGEJOIN_139] (rows=42598570 width=185) + Conds:RS_176._col0=RS_152._col0(Inner),Output:["_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8"] + <-Map 12 [SIMPLE_EDGE] vectorized + SHUFFLE [RS_152] + PartitionCols:_col0 + Select Operator [SEL_151] (rows=170 width=4) + Output:["_col0"] + Filter Operator [FIL_150] (rows=170 width=12) + predicate:((d_year) IN (1998, 1999, 2000) and d_dom BETWEEN 1 AND 2) + TableScan [TS_8] (rows=73049 width=12) + default@date_dim,date_dim,Tbl:COMPLETE,Col:COMPLETE,Output:["d_date_sk","d_year","d_dom"] + <-Map 8 [SIMPLE_EDGE] vectorized + SHUFFLE [RS_176] + PartitionCols:_col0 + Select Operator [SEL_175] (rows=457565061 width=343) + Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8"] + Filter Operator [FIL_174] (rows=457565061 width=343) + predicate:((ss_hdemo_sk BETWEEN DynamicValue(RS_26_household_demographics_hd_demo_sk_min) AND DynamicValue(RS_26_household_demographics_hd_demo_sk_max) and in_bloom_filter(ss_hdemo_sk, DynamicValue(RS_26_household_demographics_hd_demo_sk_bloom_filter))) and (ss_sold_date_sk BETWEEN DynamicValue(RS_20_date_dim_d_date_sk_min) AND DynamicValue(RS_20_date_dim_d_date_sk_max) and in_bloom_filter(ss_sold_date_sk, DynamicValue(RS_20_date_dim_d_date_sk_bloom_filter))) and (ss_store_sk BETWEEN DynamicValue(RS_23_store_s_store_sk_min) AND DynamicValue(RS_23_store_s_store_sk_max) and in_bloom_filter(ss_store_sk, DynamicValue(RS_23_store_s_store_sk_bloom_filter))) and ss_addr_sk is not null and ss_customer_sk is not null and ss_hdemo_sk is not null and ss_sold_date_sk is not null and ss_store_sk is not null) + TableScan [TS_5] (rows=575995635 width=343) + default@store_sales,store_sales,Tbl:COMPLETE,Col:COMPLETE,Output:["ss_sold_date_sk","ss_customer_sk","ss_hdemo_sk","ss_addr_sk","ss_store_sk","ss_ticket_number","ss_ext_sales_price","ss_ext_list_price","ss_ext_tax"] + <-Reducer 13 [BROADCAST_EDGE] vectorized + BROADCAST [RS_157] + Group By Operator [GBY_156] (rows=1 width=12) + Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"] + <-Map 12 [CUSTOM_SIMPLE_EDGE] vectorized + SHUFFLE [RS_155] + Group By Operator [GBY_154] (rows=1 width=12) + Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"] + Select Operator [SEL_153] (rows=170 width=4) + Output:["_col0"] + Please refer to the previous Select Operator [SEL_151] + <-Reducer 15 [BROADCAST_EDGE] vectorized + BROADCAST [RS_165] + Group By Operator [GBY_164] (rows=1 width=12) + Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"] + <-Map 14 [CUSTOM_SIMPLE_EDGE] vectorized + PARTITION_ONLY_SHUFFLE [RS_163] + Group By Operator [GBY_162] (rows=1 width=12) + Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"] + Select Operator [SEL_161] (rows=85 width=4) + Output:["_col0"] + Please refer to the previous Select Operator [SEL_159] + <-Reducer 17 [BROADCAST_EDGE] vectorized + BROADCAST [RS_173] + Group By Operator [GBY_172] (rows=1 width=12) + Output:["_col0","_col1","_col2"],aggregations:["min(VALUE._col0)","max(VALUE._col1)","bloom_filter(VALUE._col2, expectedEntries=1000000)"] + <-Map 16 [CUSTOM_SIMPLE_EDGE] vectorized + PARTITION_ONLY_SHUFFLE [RS_171] + Group By Operator [GBY_170] (rows=1 width=12) + Output:["_col0","_col1","_col2"],aggregations:["min(_col0)","max(_col0)","bloom_filter(_col0, expectedEntries=1000000)"] + Select Operator [SEL_169] (rows=1855 width=4) + Output:["_col0"] + Please refer to the previous Select Operator [SEL_167] +
