hive-commits mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From w...@apache.org
Subject [07/17] hive git commit: HIVE-16764: Support numeric as same as decimal (Pengcheng Xiong, reviewed by Ashutosh Chauhan)
Date Wed, 31 May 2017 00:11:45 GMT
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.
 


Mime
View raw message