http://git-wip-us.apache.org/repos/asf/hive/blob/86b18772/ql/src/test/results/clientpositive/perf/query62.q.out ---------------------------------------------------------------------- diff --git a/ql/src/test/results/clientpositive/perf/query62.q.out b/ql/src/test/results/clientpositive/perf/query62.q.out new file mode 100644 index 0000000..ae50787 --- /dev/null +++ b/ql/src/test/results/clientpositive/perf/query62.q.out @@ -0,0 +1,164 @@ +PREHOOK: query: explain +select + substr(w_warehouse_name,1,20) + ,sm_type + ,web_name + ,sum(case when (ws_ship_date_sk - ws_sold_date_sk <= 30 ) then 1 else 0 end) as `30 days` + ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 30) and + (ws_ship_date_sk - ws_sold_date_sk <= 60) then 1 else 0 end ) as `31-60 days` + ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 60) and + (ws_ship_date_sk - ws_sold_date_sk <= 90) then 1 else 0 end) as `61-90 days` + ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 90) and + (ws_ship_date_sk - ws_sold_date_sk <= 120) then 1 else 0 end) as `91-120 days` + ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 120) then 1 else 0 end) as `>120 days` +from + web_sales + ,warehouse + ,ship_mode + ,web_site + ,date_dim +where + d_month_seq between 1212 and 1212 + 11 +and ws_ship_date_sk = d_date_sk +and ws_warehouse_sk = w_warehouse_sk +and ws_ship_mode_sk = sm_ship_mode_sk +and ws_web_site_sk = web_site_sk +group by + substr(w_warehouse_name,1,20) + ,sm_type + ,web_name +order by substr(w_warehouse_name,1,20) + ,sm_type + ,web_name +limit 100 +PREHOOK: type: QUERY +POSTHOOK: query: explain +select + substr(w_warehouse_name,1,20) + ,sm_type + ,web_name + ,sum(case when (ws_ship_date_sk - ws_sold_date_sk <= 30 ) then 1 else 0 end) as `30 days` + ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 30) and + (ws_ship_date_sk - ws_sold_date_sk <= 60) then 1 else 0 end ) as `31-60 days` + ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 60) and + (ws_ship_date_sk - ws_sold_date_sk <= 90) then 1 else 0 end) as `61-90 days` + ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 90) and + (ws_ship_date_sk - ws_sold_date_sk <= 120) then 1 else 0 end) as `91-120 days` + ,sum(case when (ws_ship_date_sk - ws_sold_date_sk > 120) then 1 else 0 end) as `>120 days` +from + web_sales + ,warehouse + ,ship_mode + ,web_site + ,date_dim +where + d_month_seq between 1212 and 1212 + 11 +and ws_ship_date_sk = d_date_sk +and ws_warehouse_sk = w_warehouse_sk +and ws_ship_mode_sk = sm_ship_mode_sk +and ws_web_site_sk = web_site_sk +group by + substr(w_warehouse_name,1,20) + ,sm_type + ,web_name +order by substr(w_warehouse_name,1,20) + ,sm_type + ,web_name +limit 100 +POSTHOOK: type: QUERY +Plan optimized by CBO. + +Vertex dependency in root stage +Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 8 (SIMPLE_EDGE) +Reducer 3 <- Map 9 (SIMPLE_EDGE), Reducer 2 (SIMPLE_EDGE) +Reducer 4 <- Map 10 (SIMPLE_EDGE), Reducer 3 (SIMPLE_EDGE) +Reducer 5 <- Map 11 (SIMPLE_EDGE), Reducer 4 (SIMPLE_EDGE) +Reducer 6 <- Reducer 5 (SIMPLE_EDGE) +Reducer 7 <- Reducer 6 (SIMPLE_EDGE) + +Stage-0 + Fetch Operator + limit:-1 + Stage-1 + Reducer 7 + File Output Operator [FS_37] + Limit [LIM_35] (rows=100 width=135) + Number of rows:100 + Select Operator [SEL_34] (rows=105417161 width=135) + Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7"] + <-Reducer 6 [SIMPLE_EDGE] + SHUFFLE [RS_33] + Select Operator [SEL_32] (rows=105417161 width=135) + Output:["_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8"] + Group By Operator [GBY_31] (rows=105417161 width=135) + Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7"],aggregations:["sum(VALUE._col0)","sum(VALUE._col1)","sum(VALUE._col2)","sum(VALUE._col3)","sum(VALUE._col4)"],keys:KEY._col0, KEY._col1, KEY._col2 + <-Reducer 5 [SIMPLE_EDGE] + SHUFFLE [RS_30] + PartitionCols:_col0, _col1, _col2 + Group By Operator [GBY_29] (rows=210834322 width=135) + Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7"],aggregations:["sum(_col3)","sum(_col4)","sum(_col5)","sum(_col6)","sum(_col7)"],keys:_col0, _col1, _col2 + Select Operator [SEL_27] (rows=210834322 width=135) + Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7"] + Merge Join Operator [MERGEJOIN_60] (rows=210834322 width=135) + Conds:RS_24._col3=RS_25._col0(Inner),Output:["_col0","_col1","_col8","_col10","_col12"] + <-Map 11 [SIMPLE_EDGE] + SHUFFLE [RS_25] + PartitionCols:_col0 + Select Operator [SEL_14] (rows=1 width=0) + Output:["_col0","_col1"] + Filter Operator [FIL_56] (rows=1 width=0) + predicate:sm_ship_mode_sk is not null + TableScan [TS_12] (rows=1 width=0) + default@ship_mode,ship_mode,Tbl:PARTIAL,Col:NONE,Output:["sm_ship_mode_sk","sm_type"] + <-Reducer 4 [SIMPLE_EDGE] + SHUFFLE [RS_24] + PartitionCols:_col3 + Merge Join Operator [MERGEJOIN_59] (rows=191667562 width=135) + Conds:RS_21._col4=RS_22._col0(Inner),Output:["_col0","_col1","_col3","_col8","_col10"] + <-Map 10 [SIMPLE_EDGE] + SHUFFLE [RS_22] + PartitionCols:_col0 + Select Operator [SEL_11] (rows=27 width=1029) + Output:["_col0","_col1"] + Filter Operator [FIL_55] (rows=27 width=1029) + predicate:w_warehouse_sk is not null + TableScan [TS_9] (rows=27 width=1029) + default@warehouse,warehouse,Tbl:COMPLETE,Col:NONE,Output:["w_warehouse_sk","w_warehouse_name"] + <-Reducer 3 [SIMPLE_EDGE] + SHUFFLE [RS_21] + PartitionCols:_col4 + Merge Join Operator [MERGEJOIN_58] (rows=174243235 width=135) + Conds:RS_18._col2=RS_19._col0(Inner),Output:["_col0","_col1","_col3","_col4","_col8"] + <-Map 9 [SIMPLE_EDGE] + SHUFFLE [RS_19] + PartitionCols:_col0 + Select Operator [SEL_8] (rows=84 width=1850) + Output:["_col0","_col1"] + Filter Operator [FIL_54] (rows=84 width=1850) + predicate:web_site_sk is not null + TableScan [TS_6] (rows=84 width=1850) + default@web_site,web_site,Tbl:COMPLETE,Col:NONE,Output:["web_site_sk","web_name"] + <-Reducer 2 [SIMPLE_EDGE] + SHUFFLE [RS_18] + PartitionCols:_col2 + Merge Join Operator [MERGEJOIN_57] (rows=158402938 width=135) + Conds:RS_15._col1=RS_16._col0(Inner),Output:["_col0","_col1","_col2","_col3","_col4"] + <-Map 1 [SIMPLE_EDGE] + SHUFFLE [RS_15] + PartitionCols:_col1 + Select Operator [SEL_2] (rows=144002668 width=135) + Output:["_col0","_col1","_col2","_col3","_col4"] + Filter Operator [FIL_52] (rows=144002668 width=135) + predicate:(ws_warehouse_sk is not null and ws_ship_mode_sk is not null and ws_web_site_sk is not null and ws_ship_date_sk is not null) + TableScan [TS_0] (rows=144002668 width=135) + default@web_sales,web_sales,Tbl:COMPLETE,Col:NONE,Output:["ws_sold_date_sk","ws_ship_date_sk","ws_web_site_sk","ws_ship_mode_sk","ws_warehouse_sk"] + <-Map 8 [SIMPLE_EDGE] + SHUFFLE [RS_16] + PartitionCols:_col0 + Select Operator [SEL_5] (rows=8116 width=1119) + Output:["_col0"] + Filter Operator [FIL_53] (rows=8116 width=1119) + predicate:(d_month_seq BETWEEN 1212 AND 1223 and d_date_sk is not null) + TableScan [TS_3] (rows=73049 width=1119) + default@date_dim,date_dim,Tbl:COMPLETE,Col:NONE,Output:["d_date_sk","d_month_seq"] +
http://git-wip-us.apache.org/repos/asf/hive/blob/86b18772/ql/src/test/results/clientpositive/perf/query63.q.out ---------------------------------------------------------------------- diff --git a/ql/src/test/results/clientpositive/perf/query63.q.out b/ql/src/test/results/clientpositive/perf/query63.q.out new file mode 100644 index 0000000..1f0f184 --- /dev/null +++ b/ql/src/test/results/clientpositive/perf/query63.q.out @@ -0,0 +1,143 @@ +PREHOOK: query: explain +select * +from (select i_manager_id + ,sum(ss_sales_price) sum_sales + ,avg(sum(ss_sales_price)) over (partition by i_manager_id) avg_monthly_sales + from item + ,store_sales + ,date_dim + ,store + where ss_item_sk = i_item_sk + and ss_sold_date_sk = d_date_sk + and ss_store_sk = s_store_sk + and d_month_seq in (1212,1212+1,1212+2,1212+3,1212+4,1212+5,1212+6,1212+7,1212+8,1212+9,1212+10,1212+11) + and (( i_category in ('Books','Children','Electronics') + and i_class in ('personal','portable','refernece','self-help') + and i_brand in ('scholaramalgamalg #14','scholaramalgamalg #7', + 'exportiunivamalg #9','scholaramalgamalg #9')) + or( i_category in ('Women','Music','Men') + and i_class in ('accessories','classical','fragrances','pants') + and i_brand in ('amalgimporto #1','edu packscholar #1','exportiimporto #1', + 'importoamalg #1'))) +group by i_manager_id, d_moy) tmp1 +where case when avg_monthly_sales > 0 then abs (sum_sales - avg_monthly_sales) / avg_monthly_sales else null end > 0.1 +order by i_manager_id + ,avg_monthly_sales + ,sum_sales +limit 100 +PREHOOK: type: QUERY +POSTHOOK: query: explain +select * +from (select i_manager_id + ,sum(ss_sales_price) sum_sales + ,avg(sum(ss_sales_price)) over (partition by i_manager_id) avg_monthly_sales + from item + ,store_sales + ,date_dim + ,store + where ss_item_sk = i_item_sk + and ss_sold_date_sk = d_date_sk + and ss_store_sk = s_store_sk + and d_month_seq in (1212,1212+1,1212+2,1212+3,1212+4,1212+5,1212+6,1212+7,1212+8,1212+9,1212+10,1212+11) + and (( i_category in ('Books','Children','Electronics') + and i_class in ('personal','portable','refernece','self-help') + and i_brand in ('scholaramalgamalg #14','scholaramalgamalg #7', + 'exportiunivamalg #9','scholaramalgamalg #9')) + or( i_category in ('Women','Music','Men') + and i_class in ('accessories','classical','fragrances','pants') + and i_brand in ('amalgimporto #1','edu packscholar #1','exportiimporto #1', + 'importoamalg #1'))) +group by i_manager_id, d_moy) tmp1 +where case when avg_monthly_sales > 0 then abs (sum_sales - avg_monthly_sales) / avg_monthly_sales else null end > 0.1 +order by i_manager_id + ,avg_monthly_sales + ,sum_sales +limit 100 +POSTHOOK: type: QUERY +Plan optimized by CBO. + +Vertex dependency in root stage +Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 7 (SIMPLE_EDGE) +Reducer 3 <- Map 8 (SIMPLE_EDGE), Reducer 2 (SIMPLE_EDGE) +Reducer 4 <- Map 9 (SIMPLE_EDGE), Reducer 3 (SIMPLE_EDGE) +Reducer 5 <- Reducer 4 (SIMPLE_EDGE) +Reducer 6 <- Reducer 5 (SIMPLE_EDGE) + +Stage-0 + Fetch Operator + limit:100 + Stage-1 + Reducer 6 + File Output Operator [FS_36] + Limit [LIM_35] (rows=100 width=88) + Number of rows:100 + Select Operator [SEL_34] (rows=191662559 width=88) + Output:["_col0","_col1","_col2"] + <-Reducer 5 [SIMPLE_EDGE] + SHUFFLE [RS_33] + Select Operator [SEL_30] (rows=191662559 width=88) + Output:["_col0","_col1","_col2"] + Filter Operator [FIL_46] (rows=191662559 width=88) + predicate:CASE WHEN ((avg_window_0 > 0)) THEN (((abs((_col2 - avg_window_0)) / avg_window_0) > 0.1)) ELSE (null) END + Select Operator [SEL_29] (rows=383325119 width=88) + Output:["avg_window_0","_col0","_col2"] + PTF Operator [PTF_28] (rows=383325119 width=88) + Function definitions:[{},{"name:":"windowingtablefunction","order by:":"_col0 ASC NULLS FIRST","partition by:":"_col0"}] + Select Operator [SEL_25] (rows=383325119 width=88) + Output:["_col0","_col2"] + Group By Operator [GBY_24] (rows=383325119 width=88) + Output:["_col0","_col1","_col2"],aggregations:["sum(VALUE._col0)"],keys:KEY._col0, KEY._col1 + <-Reducer 4 [SIMPLE_EDGE] + SHUFFLE [RS_23] + PartitionCols:_col0 + Group By Operator [GBY_22] (rows=766650239 width=88) + Output:["_col0","_col1","_col2"],aggregations:["sum(_col3)"],keys:_col8, _col11 + Merge Join Operator [MERGEJOIN_54] (rows=766650239 width=88) + Conds:RS_18._col2=RS_19._col0(Inner),Output:["_col3","_col8","_col11"] + <-Map 9 [SIMPLE_EDGE] + SHUFFLE [RS_19] + PartitionCols:_col0 + Select Operator [SEL_11] (rows=1704 width=1910) + Output:["_col0"] + Filter Operator [FIL_50] (rows=1704 width=1910) + predicate:s_store_sk is not null + TableScan [TS_9] (rows=1704 width=1910) + default@store,store,Tbl:COMPLETE,Col:NONE,Output:["s_store_sk"] + <-Reducer 3 [SIMPLE_EDGE] + SHUFFLE [RS_18] + PartitionCols:_col2 + Merge Join Operator [MERGEJOIN_53] (rows=696954748 width=88) + Conds:RS_15._col0=RS_16._col0(Inner),Output:["_col2","_col3","_col8","_col11"] + <-Map 8 [SIMPLE_EDGE] + SHUFFLE [RS_16] + PartitionCols:_col0 + Select Operator [SEL_8] (rows=36525 width=1119) + Output:["_col0","_col2"] + Filter Operator [FIL_49] (rows=36525 width=1119) + predicate:((d_month_seq) IN (1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223) and d_date_sk is not null) + TableScan [TS_6] (rows=73049 width=1119) + default@date_dim,date_dim,Tbl:COMPLETE,Col:NONE,Output:["d_date_sk","d_month_seq","d_moy"] + <-Reducer 2 [SIMPLE_EDGE] + SHUFFLE [RS_15] + PartitionCols:_col0 + Merge Join Operator [MERGEJOIN_52] (rows=633595212 width=88) + Conds:RS_12._col1=RS_13._col0(Inner),Output:["_col0","_col2","_col3","_col8"] + <-Map 1 [SIMPLE_EDGE] + SHUFFLE [RS_12] + PartitionCols:_col1 + Select Operator [SEL_2] (rows=575995635 width=88) + Output:["_col0","_col1","_col2","_col3"] + Filter Operator [FIL_47] (rows=575995635 width=88) + predicate:(ss_item_sk is not null and ss_sold_date_sk is not null and ss_store_sk is not null) + TableScan [TS_0] (rows=575995635 width=88) + default@store_sales,store_sales,Tbl:COMPLETE,Col:NONE,Output:["ss_sold_date_sk","ss_item_sk","ss_store_sk","ss_sales_price"] + <-Map 7 [SIMPLE_EDGE] + SHUFFLE [RS_13] + PartitionCols:_col0 + Select Operator [SEL_5] (rows=115500 width=1436) + Output:["_col0","_col4"] + Filter Operator [FIL_48] (rows=115500 width=1436) + predicate:(((i_class) IN ('personal', 'portable', 'refernece', 'self-help') or (i_class) IN ('accessories', 'classical', 'fragrances', 'pants')) and ((i_brand) IN ('scholaramalgamalg #14', 'scholaramalgamalg #7', 'exportiunivamalg #9', 'scholaramalgamalg #9') or (i_brand) IN ('amalgimporto #1', 'edu packscholar #1', 'exportiimporto #1', 'importoamalg #1')) and ((i_category) IN ('Books', 'Children', 'Electronics') or (i_category) IN ('Women', 'Music', 'Men')) and (((i_category) IN ('Books', 'Children', 'Electronics') and (i_class) IN ('personal', 'portable', 'refernece', 'self-help') and (i_brand) IN ('scholaramalgamalg #14', 'scholaramalgamalg #7', 'exportiunivamalg #9', 'scholaramalgamalg #9')) or ((i_category) IN ('Women', 'Music', 'Men') and (i_class) IN ('accessories', 'classical', 'fragrances', 'pants') and (i_brand) IN ('amalgimporto #1', 'edu packscholar #1', 'exportiimporto #1', 'importoamalg #1'))) and i_item_sk is not null) + TableScan [TS_3] (rows=462000 width=1436) + default@item,item,Tbl:COMPLETE,Col:NONE,Output:["i_item_sk","i_brand","i_class","i_category","i_manager_id"] + http://git-wip-us.apache.org/repos/asf/hive/blob/86b18772/ql/src/test/results/clientpositive/perf/query64.q.out ---------------------------------------------------------------------- diff --git a/ql/src/test/results/clientpositive/perf/query64.q.out b/ql/src/test/results/clientpositive/perf/query64.q.out index 0d2fc21..f24b14d 100644 --- a/ql/src/test/results/clientpositive/perf/query64.q.out +++ b/ql/src/test/results/clientpositive/perf/query64.q.out @@ -1,6 +1,238 @@ -PREHOOK: query: explain select cs1.product_name ,cs1.store_name ,cs1.store_zip ,cs1.b_street_number ,cs1.b_streen_name ,cs1.b_city ,cs1.b_zip ,cs1.c_street_number ,cs1.c_street_name ,cs1.c_city ,cs1.c_zip ,cs1.syear ,cs1.cnt ,cs1.s1 ,cs1.s2 ,cs1.s3 ,cs2.s1 ,cs2.s2 ,cs2.s3 ,cs2.syear ,cs2.cnt from (select i_product_name as product_name ,i_item_sk as item_sk ,s_store_name as store_name ,s_zip as store_zip ,ad1.ca_street_number as b_street_number ,ad1.ca_street_name as b_streen_name ,ad1.ca_city as b_city ,ad1.ca_zip as b_zip ,ad2.ca_street_number as c_street_number ,ad2.ca_street_name as c_street_name ,ad2.ca_city as c_city ,ad2.ca_zip as c_zip ,d1.d_year as syear ,d2.d_year as fsyear ,d3.d_year as s2year ,count(*) as cnt ,sum(ss_wholesale_cost) as s1 ,sum(ss_list_price) as s2 ,sum(ss_coupon_amt) as s3 FROM store_sales JOIN store_returns ON store_sales.ss_item_sk = store_returns.sr_item_sk and store_sales.ss_ticket_number = store_returns.sr_ticket_number JOIN customer ON store_sales.s s_customer_sk = customer.c_customer_sk JOIN date_dim d1 ON store_sales.ss_sold_date_sk = d1.d_date_sk JOIN date_dim d2 ON customer.c_first_sales_date_sk = d2.d_date_sk JOIN date_dim d3 ON customer.c_first_shipto_date_sk = d3.d_date_sk JOIN store ON store_sales.ss_store_sk = store.s_store_sk JOIN customer_demographics cd1 ON store_sales.ss_cdemo_sk= cd1.cd_demo_sk JOIN customer_demographics cd2 ON customer.c_current_cdemo_sk = cd2.cd_demo_sk JOIN promotion ON store_sales.ss_promo_sk = promotion.p_promo_sk JOIN household_demographics hd1 ON store_sales.ss_hdemo_sk = hd1.hd_demo_sk JOIN household_demographics hd2 ON customer.c_current_hdemo_sk = hd2.hd_demo_sk JOIN customer_address ad1 ON store_sales.ss_addr_sk = ad1.ca_address_sk JOIN customer_address ad2 ON customer.c_current_addr_sk = ad2.ca_address_sk JOIN income_band ib1 ON hd1.hd_income_band_sk = ib1.ib_income_band_sk JOIN income_band ib2 ON hd2.hd_income_band_sk = ib2.ib_income_band_sk JOIN item ON store_sales.ss_item_sk = item. i_item_sk JOIN (select cs_item_sk ,sum(cs_ext_list_price) as sale,sum(cr_refunded_cash+cr_reversed_charge+cr_store_credit) as refund from catalog_sales JOIN catalog_returns ON catalog_sales.cs_item_sk = catalog_returns.cr_item_sk and catalog_sales.cs_order_number = catalog_returns.cr_order_number group by cs_item_sk having sum(cs_ext_list_price)>2*sum(cr_refunded_cash+cr_reversed_charge+cr_store_credit)) cs_ui ON store_sales.ss_item_sk = cs_ui.cs_item_sk WHERE cd1.cd_marital_status <> cd2.cd_marital_status and i_color in ('maroon','burnished','dim','steel','navajo','chocolate') and i_current_price between 35 and 35 + 10 and i_current_price between 35 + 1 and 35 + 15 group by i_product_name ,i_item_sk ,s_store_name ,s_zip ,ad1.ca_street_number ,ad1.ca_street_name ,ad1.ca_city ,ad1.ca_zip ,ad2.ca_street_number ,ad2.ca_street_name ,ad2.ca_city ,ad2.ca_zip ,d1.d_year ,d2.d_year ,d3.d_year ) cs1 JOIN (select i_product_name as product_name ,i_item_sk as item_sk ,s_store_name as store_name ,s_zip as store_zip ,ad1.ca_street_number as b_street_number ,ad1.ca_street_name as b_streen_name ,ad1.ca_city as b_city ,ad1.ca_zip as b_zip ,ad2.ca_street_number as c_street_number ,ad2.ca_street_name as c_street_name ,ad2.ca_city as c_city ,ad2.ca_zip as c_zip ,d1.d_year as syear ,d2.d_year as fsyear ,d3.d_year as s2year ,count(*) as cnt ,sum(ss_wholesale_cost) as s1 ,sum(ss_list_price) as s2 ,sum(ss_coupon_amt) as s3 FROM store_sales JOIN store_returns ON store_sales.ss_item_sk = store_returns.sr_item_sk and store_sales.ss_ticket_number = store_returns.sr_ticket_number JOIN customer ON store_sales.ss_customer_sk = customer.c_customer_sk JOIN date_dim d1 ON store_sales.ss_sold_date_sk = d1.d_date_sk JOIN date_dim d2 ON customer.c_first_sales_date_sk = d2.d_date_sk JOIN date_dim d3 ON customer.c_first_shipto_date_sk = d3.d_date_sk JOIN store ON store_sales.ss_store_sk = store.s_store_sk JOIN customer_demographics cd1 ON store_sales.ss_cdemo_sk= cd1.cd_demo_sk JOIN customer_demogr aphics cd2 ON customer.c_current_cdemo_sk = cd2.cd_demo_sk JOIN promotion ON store_sales.ss_promo_sk = promotion.p_promo_sk JOIN household_demographics hd1 ON store_sales.ss_hdemo_sk = hd1.hd_demo_sk JOIN household_demographics hd2 ON customer.c_current_hdemo_sk = hd2.hd_demo_sk JOIN customer_address ad1 ON store_sales.ss_addr_sk = ad1.ca_address_sk JOIN customer_address ad2 ON customer.c_current_addr_sk = ad2.ca_address_sk JOIN income_band ib1 ON hd1.hd_income_band_sk = ib1.ib_income_band_sk JOIN income_band ib2 ON hd2.hd_income_band_sk = ib2.ib_income_band_sk JOIN item ON store_sales.ss_item_sk = item.i_item_sk JOIN (select cs_item_sk ,sum(cs_ext_list_price) as sale,sum(cr_refunded_cash+cr_reversed_charge+cr_store_credit) as refund from catalog_sales JOIN catalog_returns ON catalog_sales.cs_item_sk = catalog_returns.cr_item_sk and catalog_sales.cs_order_number = catalog_returns.cr_order_number group by cs_item_sk having sum(cs_ext_list_price)>2*sum(cr_refunded_cash+cr_reversed_cha rge+cr_store_credit)) cs_ui ON store_sales.ss_item_sk = cs_ui.cs_item_sk WHERE cd1.cd_marital_status <> cd2.cd_marital_status and i_color in ('maroon','burnished','dim','steel','navajo','chocolate') and i_current_price between 35 and 35 + 10 and i_current_price between 35 + 1 and 35 + 15 group by i_product_name ,i_item_sk ,s_store_name ,s_zip ,ad1.ca_street_number ,ad1.ca_street_name ,ad1.ca_city ,ad1.ca_zip ,ad2.ca_street_number ,ad2.ca_street_name ,ad2.ca_city ,ad2.ca_zip ,d1.d_year ,d2.d_year ,d3.d_year ) cs2 ON cs1.item_sk=cs2.item_sk where cs1.syear = 2000 and cs2.syear = 2000 + 1 and cs2.cnt <= cs1.cnt and cs1.store_name = cs2.store_name and cs1.store_zip = cs2.store_zip order by cs1.product_name ,cs1.store_name ,cs2.cnt +PREHOOK: query: explain +with cs_ui as + (select cs_item_sk + ,sum(cs_ext_list_price) as sale,sum(cr_refunded_cash+cr_reversed_charge+cr_store_credit) as refund + from catalog_sales + ,catalog_returns + where cs_item_sk = cr_item_sk + and cs_order_number = cr_order_number + group by cs_item_sk + having sum(cs_ext_list_price)>2*sum(cr_refunded_cash+cr_reversed_charge+cr_store_credit)), +cross_sales as + (select i_product_name product_name + ,i_item_sk item_sk + ,s_store_name store_name + ,s_zip store_zip + ,ad1.ca_street_number b_street_number + ,ad1.ca_street_name b_streen_name + ,ad1.ca_city b_city + ,ad1.ca_zip b_zip + ,ad2.ca_street_number c_street_number + ,ad2.ca_street_name c_street_name + ,ad2.ca_city c_city + ,ad2.ca_zip c_zip + ,d1.d_year as syear + ,d2.d_year as fsyear + ,d3.d_year s2year + ,count(*) cnt + ,sum(ss_wholesale_cost) s1 + ,sum(ss_list_price) s2 + ,sum(ss_coupon_amt) s3 + FROM store_sales + ,store_returns + ,cs_ui + ,date_dim d1 + ,date_dim d2 + ,date_dim d3 + ,store + ,customer + ,customer_demographics cd1 + ,customer_demographics cd2 + ,promotion + ,household_demographics hd1 + ,household_demographics hd2 + ,customer_address ad1 + ,customer_address ad2 + ,income_band ib1 + ,income_band ib2 + ,item + WHERE ss_store_sk = s_store_sk AND + ss_sold_date_sk = d1.d_date_sk AND + ss_customer_sk = c_customer_sk AND + ss_cdemo_sk= cd1.cd_demo_sk AND + ss_hdemo_sk = hd1.hd_demo_sk AND + ss_addr_sk = ad1.ca_address_sk and + ss_item_sk = i_item_sk and + ss_item_sk = sr_item_sk and + ss_ticket_number = sr_ticket_number and + ss_item_sk = cs_ui.cs_item_sk and + c_current_cdemo_sk = cd2.cd_demo_sk AND + c_current_hdemo_sk = hd2.hd_demo_sk AND + c_current_addr_sk = ad2.ca_address_sk and + c_first_sales_date_sk = d2.d_date_sk and + c_first_shipto_date_sk = d3.d_date_sk and + ss_promo_sk = p_promo_sk and + hd1.hd_income_band_sk = ib1.ib_income_band_sk and + hd2.hd_income_band_sk = ib2.ib_income_band_sk and + cd1.cd_marital_status <> cd2.cd_marital_status and + i_color in ('maroon','burnished','dim','steel','navajo','chocolate') and + i_current_price between 35 and 35 + 10 and + i_current_price between 35 + 1 and 35 + 15 +group by i_product_name + ,i_item_sk + ,s_store_name + ,s_zip + ,ad1.ca_street_number + ,ad1.ca_street_name + ,ad1.ca_city + ,ad1.ca_zip + ,ad2.ca_street_number + ,ad2.ca_street_name + ,ad2.ca_city + ,ad2.ca_zip + ,d1.d_year + ,d2.d_year + ,d3.d_year +) +select cs1.product_name + ,cs1.store_name + ,cs1.store_zip + ,cs1.b_street_number + ,cs1.b_streen_name + ,cs1.b_city + ,cs1.b_zip + ,cs1.c_street_number + ,cs1.c_street_name + ,cs1.c_city + ,cs1.c_zip + ,cs1.syear + ,cs1.cnt + ,cs1.s1 + ,cs1.s2 + ,cs1.s3 + ,cs2.s1 + ,cs2.s2 + ,cs2.s3 + ,cs2.syear + ,cs2.cnt +from cross_sales cs1,cross_sales cs2 +where cs1.item_sk=cs2.item_sk and + cs1.syear = 2000 and + cs2.syear = 2000 + 1 and + cs2.cnt <= cs1.cnt and + cs1.store_name = cs2.store_name and + cs1.store_zip = cs2.store_zip +order by cs1.product_name + ,cs1.store_name + ,cs2.cnt PREHOOK: type: QUERY -POSTHOOK: query: explain select cs1.product_name ,cs1.store_name ,cs1.store_zip ,cs1.b_street_number ,cs1.b_streen_name ,cs1.b_city ,cs1.b_zip ,cs1.c_street_number ,cs1.c_street_name ,cs1.c_city ,cs1.c_zip ,cs1.syear ,cs1.cnt ,cs1.s1 ,cs1.s2 ,cs1.s3 ,cs2.s1 ,cs2.s2 ,cs2.s3 ,cs2.syear ,cs2.cnt from (select i_product_name as product_name ,i_item_sk as item_sk ,s_store_name as store_name ,s_zip as store_zip ,ad1.ca_street_number as b_street_number ,ad1.ca_street_name as b_streen_name ,ad1.ca_city as b_city ,ad1.ca_zip as b_zip ,ad2.ca_street_number as c_street_number ,ad2.ca_street_name as c_street_name ,ad2.ca_city as c_city ,ad2.ca_zip as c_zip ,d1.d_year as syear ,d2.d_year as fsyear ,d3.d_year as s2year ,count(*) as cnt ,sum(ss_wholesale_cost) as s1 ,sum(ss_list_price) as s2 ,sum(ss_coupon_amt) as s3 FROM store_sales JOIN store_returns ON store_sales.ss_item_sk = store_returns.sr_item_sk and store_sales.ss_ticket_number = store_returns.sr_ticket_number JOIN customer ON store_sales. ss_customer_sk = customer.c_customer_sk JOIN date_dim d1 ON store_sales.ss_sold_date_sk = d1.d_date_sk JOIN date_dim d2 ON customer.c_first_sales_date_sk = d2.d_date_sk JOIN date_dim d3 ON customer.c_first_shipto_date_sk = d3.d_date_sk JOIN store ON store_sales.ss_store_sk = store.s_store_sk JOIN customer_demographics cd1 ON store_sales.ss_cdemo_sk= cd1.cd_demo_sk JOIN customer_demographics cd2 ON customer.c_current_cdemo_sk = cd2.cd_demo_sk JOIN promotion ON store_sales.ss_promo_sk = promotion.p_promo_sk JOIN household_demographics hd1 ON store_sales.ss_hdemo_sk = hd1.hd_demo_sk JOIN household_demographics hd2 ON customer.c_current_hdemo_sk = hd2.hd_demo_sk JOIN customer_address ad1 ON store_sales.ss_addr_sk = ad1.ca_address_sk JOIN customer_address ad2 ON customer.c_current_addr_sk = ad2.ca_address_sk JOIN income_band ib1 ON hd1.hd_income_band_sk = ib1.ib_income_band_sk JOIN income_band ib2 ON hd2.hd_income_band_sk = ib2.ib_income_band_sk JOIN item ON store_sales.ss_item_sk = item .i_item_sk JOIN (select cs_item_sk ,sum(cs_ext_list_price) as sale,sum(cr_refunded_cash+cr_reversed_charge+cr_store_credit) as refund from catalog_sales JOIN catalog_returns ON catalog_sales.cs_item_sk = catalog_returns.cr_item_sk and catalog_sales.cs_order_number = catalog_returns.cr_order_number group by cs_item_sk having sum(cs_ext_list_price)>2*sum(cr_refunded_cash+cr_reversed_charge+cr_store_credit)) cs_ui ON store_sales.ss_item_sk = cs_ui.cs_item_sk WHERE cd1.cd_marital_status <> cd2.cd_marital_status and i_color in ('maroon','burnished','dim','steel','navajo','chocolate') and i_current_price between 35 and 35 + 10 and i_current_price between 35 + 1 and 35 + 15 group by i_product_name ,i_item_sk ,s_store_name ,s_zip ,ad1.ca_street_number ,ad1.ca_street_name ,ad1.ca_city ,ad1.ca_zip ,ad2.ca_street_number ,ad2.ca_street_name ,ad2.ca_city ,ad2.ca_zip ,d1.d_year ,d2.d_year ,d3.d_year ) cs1 JOIN (select i_product_name as product_name ,i_item_sk as item_sk ,s_store_name as store_nam e ,s_zip as store_zip ,ad1.ca_street_number as b_street_number ,ad1.ca_street_name as b_streen_name ,ad1.ca_city as b_city ,ad1.ca_zip as b_zip ,ad2.ca_street_number as c_street_number ,ad2.ca_street_name as c_street_name ,ad2.ca_city as c_city ,ad2.ca_zip as c_zip ,d1.d_year as syear ,d2.d_year as fsyear ,d3.d_year as s2year ,count(*) as cnt ,sum(ss_wholesale_cost) as s1 ,sum(ss_list_price) as s2 ,sum(ss_coupon_amt) as s3 FROM store_sales JOIN store_returns ON store_sales.ss_item_sk = store_returns.sr_item_sk and store_sales.ss_ticket_number = store_returns.sr_ticket_number JOIN customer ON store_sales.ss_customer_sk = customer.c_customer_sk JOIN date_dim d1 ON store_sales.ss_sold_date_sk = d1.d_date_sk JOIN date_dim d2 ON customer.c_first_sales_date_sk = d2.d_date_sk JOIN date_dim d3 ON customer.c_first_shipto_date_sk = d3.d_date_sk JOIN store ON store_sales.ss_store_sk = store.s_store_sk JOIN customer_demographics cd1 ON store_sales.ss_cdemo_sk= cd1.cd_demo_sk JOIN customer_demog raphics cd2 ON customer.c_current_cdemo_sk = cd2.cd_demo_sk JOIN promotion ON store_sales.ss_promo_sk = promotion.p_promo_sk JOIN household_demographics hd1 ON store_sales.ss_hdemo_sk = hd1.hd_demo_sk JOIN household_demographics hd2 ON customer.c_current_hdemo_sk = hd2.hd_demo_sk JOIN customer_address ad1 ON store_sales.ss_addr_sk = ad1.ca_address_sk JOIN customer_address ad2 ON customer.c_current_addr_sk = ad2.ca_address_sk JOIN income_band ib1 ON hd1.hd_income_band_sk = ib1.ib_income_band_sk JOIN income_band ib2 ON hd2.hd_income_band_sk = ib2.ib_income_band_sk JOIN item ON store_sales.ss_item_sk = item.i_item_sk JOIN (select cs_item_sk ,sum(cs_ext_list_price) as sale,sum(cr_refunded_cash+cr_reversed_charge+cr_store_credit) as refund from catalog_sales JOIN catalog_returns ON catalog_sales.cs_item_sk = catalog_returns.cr_item_sk and catalog_sales.cs_order_number = catalog_returns.cr_order_number group by cs_item_sk having sum(cs_ext_list_price)>2*sum(cr_refunded_cash+cr_reversed_ch arge+cr_store_credit)) cs_ui ON store_sales.ss_item_sk = cs_ui.cs_item_sk WHERE cd1.cd_marital_status <> cd2.cd_marital_status and i_color in ('maroon','burnished','dim','steel','navajo','chocolate') and i_current_price between 35 and 35 + 10 and i_current_price between 35 + 1 and 35 + 15 group by i_product_name ,i_item_sk ,s_store_name ,s_zip ,ad1.ca_street_number ,ad1.ca_street_name ,ad1.ca_city ,ad1.ca_zip ,ad2.ca_street_number ,ad2.ca_street_name ,ad2.ca_city ,ad2.ca_zip ,d1.d_year ,d2.d_year ,d3.d_year ) cs2 ON cs1.item_sk=cs2.item_sk where cs1.syear = 2000 and cs2.syear = 2000 + 1 and cs2.cnt <= cs1.cnt and cs1.store_name = cs2.store_name and cs1.store_zip = cs2.store_zip order by cs1.product_name ,cs1.store_name ,cs2.cnt +POSTHOOK: query: explain +with cs_ui as + (select cs_item_sk + ,sum(cs_ext_list_price) as sale,sum(cr_refunded_cash+cr_reversed_charge+cr_store_credit) as refund + from catalog_sales + ,catalog_returns + where cs_item_sk = cr_item_sk + and cs_order_number = cr_order_number + group by cs_item_sk + having sum(cs_ext_list_price)>2*sum(cr_refunded_cash+cr_reversed_charge+cr_store_credit)), +cross_sales as + (select i_product_name product_name + ,i_item_sk item_sk + ,s_store_name store_name + ,s_zip store_zip + ,ad1.ca_street_number b_street_number + ,ad1.ca_street_name b_streen_name + ,ad1.ca_city b_city + ,ad1.ca_zip b_zip + ,ad2.ca_street_number c_street_number + ,ad2.ca_street_name c_street_name + ,ad2.ca_city c_city + ,ad2.ca_zip c_zip + ,d1.d_year as syear + ,d2.d_year as fsyear + ,d3.d_year s2year + ,count(*) cnt + ,sum(ss_wholesale_cost) s1 + ,sum(ss_list_price) s2 + ,sum(ss_coupon_amt) s3 + FROM store_sales + ,store_returns + ,cs_ui + ,date_dim d1 + ,date_dim d2 + ,date_dim d3 + ,store + ,customer + ,customer_demographics cd1 + ,customer_demographics cd2 + ,promotion + ,household_demographics hd1 + ,household_demographics hd2 + ,customer_address ad1 + ,customer_address ad2 + ,income_band ib1 + ,income_band ib2 + ,item + WHERE ss_store_sk = s_store_sk AND + ss_sold_date_sk = d1.d_date_sk AND + ss_customer_sk = c_customer_sk AND + ss_cdemo_sk= cd1.cd_demo_sk AND + ss_hdemo_sk = hd1.hd_demo_sk AND + ss_addr_sk = ad1.ca_address_sk and + ss_item_sk = i_item_sk and + ss_item_sk = sr_item_sk and + ss_ticket_number = sr_ticket_number and + ss_item_sk = cs_ui.cs_item_sk and + c_current_cdemo_sk = cd2.cd_demo_sk AND + c_current_hdemo_sk = hd2.hd_demo_sk AND + c_current_addr_sk = ad2.ca_address_sk and + c_first_sales_date_sk = d2.d_date_sk and + c_first_shipto_date_sk = d3.d_date_sk and + ss_promo_sk = p_promo_sk and + hd1.hd_income_band_sk = ib1.ib_income_band_sk and + hd2.hd_income_band_sk = ib2.ib_income_band_sk and + cd1.cd_marital_status <> cd2.cd_marital_status and + i_color in ('maroon','burnished','dim','steel','navajo','chocolate') and + i_current_price between 35 and 35 + 10 and + i_current_price between 35 + 1 and 35 + 15 +group by i_product_name + ,i_item_sk + ,s_store_name + ,s_zip + ,ad1.ca_street_number + ,ad1.ca_street_name + ,ad1.ca_city + ,ad1.ca_zip + ,ad2.ca_street_number + ,ad2.ca_street_name + ,ad2.ca_city + ,ad2.ca_zip + ,d1.d_year + ,d2.d_year + ,d3.d_year +) +select cs1.product_name + ,cs1.store_name + ,cs1.store_zip + ,cs1.b_street_number + ,cs1.b_streen_name + ,cs1.b_city + ,cs1.b_zip + ,cs1.c_street_number + ,cs1.c_street_name + ,cs1.c_city + ,cs1.c_zip + ,cs1.syear + ,cs1.cnt + ,cs1.s1 + ,cs1.s2 + ,cs1.s3 + ,cs2.s1 + ,cs2.s2 + ,cs2.s3 + ,cs2.syear + ,cs2.cnt +from cross_sales cs1,cross_sales cs2 +where cs1.item_sk=cs2.item_sk and + cs1.syear = 2000 and + cs2.syear = 2000 + 1 and + cs2.cnt <= cs1.cnt and + cs1.store_name = cs2.store_name and + cs1.store_zip = cs2.store_zip +order by cs1.product_name + ,cs1.store_name + ,cs2.cnt POSTHOOK: type: QUERY Plan optimized by CBO. @@ -161,7 +393,7 @@ Stage-0 Select Operator [SEL_130] (rows=80000000 width=860) Output:["_col0","_col1","_col2","_col3","_col4","_col5"] Filter Operator [FIL_556] (rows=80000000 width=860) - predicate:(c_customer_sk is not null and c_first_sales_date_sk is not null and c_first_shipto_date_sk is not null and c_current_cdemo_sk is not null and c_current_hdemo_sk is not null and c_current_addr_sk is not null) + predicate:(c_customer_sk is not null and c_first_shipto_date_sk is not null and c_first_sales_date_sk is not null and c_current_cdemo_sk is not null and c_current_hdemo_sk is not null and c_current_addr_sk is not null) TableScan [TS_0] (rows=80000000 width=860) default@customer,customer,Tbl:COMPLETE,Col:NONE,Output:["c_customer_sk","c_current_cdemo_sk","c_current_hdemo_sk","c_current_addr_sk","c_first_shipto_date_sk","c_first_sales_date_sk"] <-Reducer 36 [SIMPLE_EDGE] @@ -280,7 +512,7 @@ Stage-0 Select Operator [SEL_161] (rows=575995635 width=88) Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11"] Filter Operator [FIL_565] (rows=575995635 width=88) - predicate:(ss_item_sk is not null and ss_ticket_number is not null and ss_customer_sk is not null and ss_sold_date_sk is not null and ss_store_sk is not null and ss_cdemo_sk is not null and ss_promo_sk is not null and ss_hdemo_sk is not null and ss_addr_sk is not null) + predicate:(ss_item_sk is not null and ss_ticket_number is not null and ss_sold_date_sk is not null and ss_store_sk is not null and ss_customer_sk is not null and ss_cdemo_sk is not null and ss_promo_sk is not null and ss_hdemo_sk is not null and ss_addr_sk is not null) TableScan [TS_31] (rows=575995635 width=88) default@store_sales,store_sales,Tbl:COMPLETE,Col:NONE,Output:["ss_sold_date_sk","ss_item_sk","ss_customer_sk","ss_cdemo_sk","ss_hdemo_sk","ss_addr_sk","ss_store_sk","ss_promo_sk","ss_ticket_number","ss_wholesale_cost","ss_list_price","ss_coupon_amt"] <-Map 46 [SIMPLE_EDGE] @@ -470,7 +702,7 @@ Stage-0 Select Operator [SEL_33] (rows=575995635 width=88) Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col9","_col10","_col11"] Filter Operator [FIL_546] (rows=575995635 width=88) - predicate:(ss_item_sk is not null and ss_ticket_number is not null and ss_customer_sk is not null and ss_sold_date_sk is not null and ss_store_sk is not null and ss_cdemo_sk is not null and ss_promo_sk is not null and ss_hdemo_sk is not null and ss_addr_sk is not null) + predicate:(ss_item_sk is not null and ss_ticket_number is not null and ss_sold_date_sk is not null and ss_store_sk is not null and ss_customer_sk is not null and ss_cdemo_sk is not null and ss_promo_sk is not null and ss_hdemo_sk is not null and ss_addr_sk is not null) Please refer to the previous TableScan [TS_31] <-Map 46 [SIMPLE_EDGE] SHUFFLE [RS_47] @@ -598,7 +830,7 @@ Stage-0 Select Operator [SEL_2] (rows=80000000 width=860) Output:["_col0","_col1","_col2","_col3","_col4","_col5"] Filter Operator [FIL_537] (rows=80000000 width=860) - predicate:(c_customer_sk is not null and c_first_sales_date_sk is not null and c_first_shipto_date_sk is not null and c_current_cdemo_sk is not null and c_current_hdemo_sk is not null and c_current_addr_sk is not null) + predicate:(c_customer_sk is not null and c_first_shipto_date_sk is not null and c_first_sales_date_sk is not null and c_current_cdemo_sk is not null and c_current_hdemo_sk is not null and c_current_addr_sk is not null) Please refer to the previous TableScan [TS_0] <-Reducer 34 [SIMPLE_EDGE] SHUFFLE [RS_107] http://git-wip-us.apache.org/repos/asf/hive/blob/86b18772/ql/src/test/results/clientpositive/perf/query65.q.out ---------------------------------------------------------------------- diff --git a/ql/src/test/results/clientpositive/perf/query65.q.out b/ql/src/test/results/clientpositive/perf/query65.q.out index 17d80d0..b2035c2 100644 --- a/ql/src/test/results/clientpositive/perf/query65.q.out +++ b/ql/src/test/results/clientpositive/perf/query65.q.out @@ -1,77 +1,57 @@ -PREHOOK: query: explain select - s_store_name, - i_item_desc, - sc.revenue, - i_current_price, - i_wholesale_cost, - i_brand -from - store, - item, - (select - ss_store_sk, avg(revenue) as ave - from - (select - ss_store_sk, ss_item_sk, sum(ss_sales_price) as revenue - from - store_sales, date_dim - where - ss_sold_date_sk = d_date_sk - and d_month_seq between 1212 and 1212 + 11 - group by ss_store_sk , ss_item_sk) sa - group by ss_store_sk) sb, - (select - ss_store_sk, ss_item_sk, sum(ss_sales_price) as revenue - from - store_sales, date_dim - where - ss_sold_date_sk = d_date_sk - and d_month_seq between 1212 and 1212 + 11 - group by ss_store_sk , ss_item_sk) sc -where - sb.ss_store_sk = sc.ss_store_sk - and sc.revenue <= 0.1 * sb.ave - and s_store_sk = sc.ss_store_sk - and i_item_sk = sc.ss_item_sk -order by s_store_name , i_item_desc +PREHOOK: query: explain +select + s_store_name, + i_item_desc, + sc.revenue, + i_current_price, + i_wholesale_cost, + i_brand + from store, item, + (select ss_store_sk, avg(revenue) as ave + from + (select ss_store_sk, ss_item_sk, + sum(ss_sales_price) as revenue + from store_sales, date_dim + where ss_sold_date_sk = d_date_sk and d_month_seq between 1212 and 1212+11 + group by ss_store_sk, ss_item_sk) sa + group by ss_store_sk) sb, + (select ss_store_sk, ss_item_sk, sum(ss_sales_price) as revenue + from store_sales, date_dim + where ss_sold_date_sk = d_date_sk and d_month_seq between 1212 and 1212+11 + group by ss_store_sk, ss_item_sk) sc + where sb.ss_store_sk = sc.ss_store_sk and + sc.revenue <= 0.1 * sb.ave and + s_store_sk = sc.ss_store_sk and + i_item_sk = sc.ss_item_sk + order by s_store_name, i_item_desc limit 100 PREHOOK: type: QUERY -POSTHOOK: query: explain select - s_store_name, - i_item_desc, - sc.revenue, - i_current_price, - i_wholesale_cost, - i_brand -from - store, - item, - (select - ss_store_sk, avg(revenue) as ave - from - (select - ss_store_sk, ss_item_sk, sum(ss_sales_price) as revenue - from - store_sales, date_dim - where - ss_sold_date_sk = d_date_sk - and d_month_seq between 1212 and 1212 + 11 - group by ss_store_sk , ss_item_sk) sa - group by ss_store_sk) sb, - (select - ss_store_sk, ss_item_sk, sum(ss_sales_price) as revenue - from - store_sales, date_dim - where - ss_sold_date_sk = d_date_sk - and d_month_seq between 1212 and 1212 + 11 - group by ss_store_sk , ss_item_sk) sc -where - sb.ss_store_sk = sc.ss_store_sk - and sc.revenue <= 0.1 * sb.ave - and s_store_sk = sc.ss_store_sk - and i_item_sk = sc.ss_item_sk -order by s_store_name , i_item_desc +POSTHOOK: query: explain +select + s_store_name, + i_item_desc, + sc.revenue, + i_current_price, + i_wholesale_cost, + i_brand + from store, item, + (select ss_store_sk, avg(revenue) as ave + from + (select ss_store_sk, ss_item_sk, + sum(ss_sales_price) as revenue + from store_sales, date_dim + where ss_sold_date_sk = d_date_sk and d_month_seq between 1212 and 1212+11 + group by ss_store_sk, ss_item_sk) sa + group by ss_store_sk) sb, + (select ss_store_sk, ss_item_sk, sum(ss_sales_price) as revenue + from store_sales, date_dim + where ss_sold_date_sk = d_date_sk and d_month_seq between 1212 and 1212+11 + group by ss_store_sk, ss_item_sk) sc + where sb.ss_store_sk = sc.ss_store_sk and + sc.revenue <= 0.1 * sb.ave and + s_store_sk = sc.ss_store_sk and + i_item_sk = sc.ss_item_sk + order by s_store_name, i_item_desc limit 100 POSTHOOK: type: QUERY Plan optimized by CBO. http://git-wip-us.apache.org/repos/asf/hive/blob/86b18772/ql/src/test/results/clientpositive/perf/query66.q.out ---------------------------------------------------------------------- diff --git a/ql/src/test/results/clientpositive/perf/query66.q.out b/ql/src/test/results/clientpositive/perf/query66.q.out index 19cd0fb..2c74815 100644 --- a/ql/src/test/results/clientpositive/perf/query66.q.out +++ b/ql/src/test/results/clientpositive/perf/query66.q.out @@ -1,11 +1,11 @@ PREHOOK: query: explain select w_warehouse_name - ,w_warehouse_sq_ft - ,w_city - ,w_county - ,w_state - ,w_country + ,w_warehouse_sq_ft + ,w_city + ,w_county + ,w_state + ,w_country ,ship_carriers ,year ,sum(jan_sales) as jan_sales @@ -45,74 +45,74 @@ select ,sum(nov_net) as nov_net ,sum(dec_net) as dec_net from ( - select - w_warehouse_name - ,w_warehouse_sq_ft - ,w_city - ,w_county - ,w_state - ,w_country - ,concat('DIAMOND', ',', 'AIRBORNE') as ship_carriers - ,d_year as year + (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 + 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 + ,ship_mode where - web_sales.ws_warehouse_sk = warehouse.w_warehouse_sk - and web_sales.ws_sold_date_sk = date_dim.d_date_sk - and web_sales.ws_sold_time_sk = time_dim.t_time_sk - and web_sales.ws_ship_mode_sk = ship_mode.sm_ship_mode_sk + 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') @@ -124,75 +124,76 @@ select ,w_state ,w_country ,d_year + ) union all - select - w_warehouse_name - ,w_warehouse_sq_ft - ,w_city - ,w_county - ,w_state - ,w_country - ,concat('DIAMOND', ',', 'AIRBORNE') as ship_carriers + (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 + ,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 + ,ship_mode where - catalog_sales.cs_warehouse_sk = warehouse.w_warehouse_sk - and catalog_sales.cs_sold_date_sk = date_dim.d_date_sk - and catalog_sales.cs_sold_time_sk = time_dim.t_time_sk - and catalog_sales.cs_ship_mode_sk = ship_mode.sm_ship_mode_sk + 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') @@ -204,6 +205,7 @@ select ,w_state ,w_country ,d_year + ) ) x group by w_warehouse_name @@ -220,11 +222,11 @@ PREHOOK: type: QUERY POSTHOOK: query: explain select w_warehouse_name - ,w_warehouse_sq_ft - ,w_city - ,w_county - ,w_state - ,w_country + ,w_warehouse_sq_ft + ,w_city + ,w_county + ,w_state + ,w_country ,ship_carriers ,year ,sum(jan_sales) as jan_sales @@ -264,74 +266,74 @@ select ,sum(nov_net) as nov_net ,sum(dec_net) as dec_net from ( - select - w_warehouse_name - ,w_warehouse_sq_ft - ,w_city - ,w_county - ,w_state - ,w_country - ,concat('DIAMOND', ',', 'AIRBORNE') as ship_carriers - ,d_year as year + (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 + 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 + ,ship_mode where - web_sales.ws_warehouse_sk = warehouse.w_warehouse_sk - and web_sales.ws_sold_date_sk = date_dim.d_date_sk - and web_sales.ws_sold_time_sk = time_dim.t_time_sk - and web_sales.ws_ship_mode_sk = ship_mode.sm_ship_mode_sk + 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') @@ -343,75 +345,76 @@ select ,w_state ,w_country ,d_year + ) union all - select - w_warehouse_name - ,w_warehouse_sq_ft - ,w_city - ,w_county - ,w_state - ,w_country - ,concat('DIAMOND', ',', 'AIRBORNE') as ship_carriers + (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 + ,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 + ,ship_mode where - catalog_sales.cs_warehouse_sk = warehouse.w_warehouse_sk - and catalog_sales.cs_sold_date_sk = date_dim.d_date_sk - and catalog_sales.cs_sold_time_sk = time_dim.t_time_sk - and catalog_sales.cs_ship_mode_sk = ship_mode.sm_ship_mode_sk + 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') @@ -423,6 +426,7 @@ select ,w_state ,w_country ,d_year + ) ) x group by w_warehouse_name http://git-wip-us.apache.org/repos/asf/hive/blob/86b18772/ql/src/test/results/clientpositive/perf/query68.q.out ---------------------------------------------------------------------- diff --git a/ql/src/test/results/clientpositive/perf/query68.q.out b/ql/src/test/results/clientpositive/perf/query68.q.out index 84f701b..bd9b5ec 100644 --- a/ql/src/test/results/clientpositive/perf/query68.q.out +++ b/ql/src/test/results/clientpositive/perf/query68.q.out @@ -1,6 +1,84 @@ -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 = 4 or household_demographics.hd_vehicle_count= 2) and date_dim.d_year in (1998,1998+1,1998+2) and store.s_city in ('Rosedale','Bethlehem') group by ss_ticket_number ,ss_customer_sk ,ss_addr_sk,ca_city) dn ,customer ,customer_address current_addr where dn.ss_customer_sk = customer.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: 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 -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 = 4 or household_demographics.hd_vehicle_count= 2) and date_dim.d_year in (1998,1998+1,1998+2) and store.s_city in ('Rosedale','Bethlehem') group by ss_ticket_number ,ss_customer_sk ,ss_addr_sk,ca_city) dn ,customer ,customer_address current_addr where dn.ss_customer_sk = customer.c_customer_sk and custome r.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: 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 Plan optimized by CBO. @@ -88,7 +166,7 @@ Stage-0 Select Operator [SEL_17] (rows=7200 width=107) Output:["_col0"] Filter Operator [FIL_79] (rows=7200 width=107) - predicate:(((hd_dep_count = 4) or (hd_vehicle_count = 2)) and hd_demo_sk is not null) + predicate:(((hd_dep_count = 2) or (hd_vehicle_count = 1)) and hd_demo_sk is not null) TableScan [TS_15] (rows=7200 width=107) default@household_demographics,household_demographics,Tbl:COMPLETE,Col:NONE,Output:["hd_demo_sk","hd_dep_count","hd_vehicle_count"] <-Reducer 10 [SIMPLE_EDGE] @@ -102,7 +180,7 @@ Stage-0 Select Operator [SEL_14] (rows=852 width=1910) Output:["_col0"] Filter Operator [FIL_78] (rows=852 width=1910) - predicate:((s_city) IN ('Rosedale', 'Bethlehem') and s_store_sk is not null) + predicate:((s_city) IN ('Cedar Grove', 'Wildwood') and s_store_sk is not null) TableScan [TS_12] (rows=1704 width=1910) default@store,store,Tbl:COMPLETE,Col:NONE,Output:["s_store_sk","s_city"] <-Reducer 9 [SIMPLE_EDGE] http://git-wip-us.apache.org/repos/asf/hive/blob/86b18772/ql/src/test/results/clientpositive/perf/query69.q.out ---------------------------------------------------------------------- diff --git a/ql/src/test/results/clientpositive/perf/query69.q.out b/ql/src/test/results/clientpositive/perf/query69.q.out index 7ee80a6..a55c368 100644 --- a/ql/src/test/results/clientpositive/perf/query69.q.out +++ b/ql/src/test/results/clientpositive/perf/query69.q.out @@ -1,4 +1,5 @@ -PREHOOK: query: explain select +PREHOOK: query: explain +select cd_gender, cd_marital_status, cd_education_status, @@ -43,7 +44,8 @@ PREHOOK: query: explain select cd_credit_rating limit 100 PREHOOK: type: QUERY -POSTHOOK: query: explain select +POSTHOOK: query: explain +select cd_gender, cd_marital_status, cd_education_status, http://git-wip-us.apache.org/repos/asf/hive/blob/86b18772/ql/src/test/results/clientpositive/perf/query7.q.out ---------------------------------------------------------------------- diff --git a/ql/src/test/results/clientpositive/perf/query7.q.out b/ql/src/test/results/clientpositive/perf/query7.q.out index af77d9a..d3045d1 100644 --- a/ql/src/test/results/clientpositive/perf/query7.q.out +++ b/ql/src/test/results/clientpositive/perf/query7.q.out @@ -1,6 +1,42 @@ -PREHOOK: query: explain select i_item_id, avg(ss_quantity) agg1, avg(ss_list_price) agg2, avg(ss_coupon_amt) agg3, avg(ss_sales_price) agg4 from store_sales, customer_demographics, date_dim, item, promotion where store_sales.ss_sold_date_sk = date_dim.d_date_sk and store_sales.ss_item_sk = item.i_item_sk and store_sales.ss_cdemo_sk = customer_demographics.cd_demo_sk and store_sales.ss_promo_sk = promotion.p_promo_sk and cd_gender = 'F' and cd_marital_status = 'W' and cd_education_status = 'Primary' and (p_channel_email = 'N' or p_channel_event = 'N') and d_year = 1998 group by i_item_id order by i_item_id limit 100 +PREHOOK: query: explain +select i_item_id, + avg(ss_quantity) agg1, + avg(ss_list_price) agg2, + avg(ss_coupon_amt) agg3, + avg(ss_sales_price) agg4 + from store_sales, customer_demographics, date_dim, item, promotion + where ss_sold_date_sk = d_date_sk and + ss_item_sk = i_item_sk and + ss_cdemo_sk = cd_demo_sk and + ss_promo_sk = p_promo_sk and + cd_gender = 'F' and + cd_marital_status = 'W' and + cd_education_status = 'Primary' and + (p_channel_email = 'N' or p_channel_event = 'N') and + d_year = 1998 + group by i_item_id + order by i_item_id + limit 100 PREHOOK: type: QUERY -POSTHOOK: query: explain select i_item_id, avg(ss_quantity) agg1, avg(ss_list_price) agg2, avg(ss_coupon_amt) agg3, avg(ss_sales_price) agg4 from store_sales, customer_demographics, date_dim, item, promotion where store_sales.ss_sold_date_sk = date_dim.d_date_sk and store_sales.ss_item_sk = item.i_item_sk and store_sales.ss_cdemo_sk = customer_demographics.cd_demo_sk and store_sales.ss_promo_sk = promotion.p_promo_sk and cd_gender = 'F' and cd_marital_status = 'W' and cd_education_status = 'Primary' and (p_channel_email = 'N' or p_channel_event = 'N') and d_year = 1998 group by i_item_id order by i_item_id limit 100 +POSTHOOK: query: explain +select i_item_id, + avg(ss_quantity) agg1, + avg(ss_list_price) agg2, + avg(ss_coupon_amt) agg3, + avg(ss_sales_price) agg4 + from store_sales, customer_demographics, date_dim, item, promotion + where ss_sold_date_sk = d_date_sk and + ss_item_sk = i_item_sk and + ss_cdemo_sk = cd_demo_sk and + ss_promo_sk = p_promo_sk and + cd_gender = 'F' and + cd_marital_status = 'W' and + cd_education_status = 'Primary' and + (p_channel_email = 'N' or p_channel_event = 'N') and + d_year = 1998 + group by i_item_id + order by i_item_id + limit 100 POSTHOOK: type: QUERY Plan optimized by CBO.
