== Physical Plan ==
* ColumnarToRow (99)
+- CometTakeOrderedAndProject (98)
   +- CometHashAggregate (97)
      +- CometColumnarExchange (96)
         +- RowToColumnar (95)
            +- * HashAggregate (94)
               +- Union (93)
                  :- * HashAggregate (76)
                  :  +- * ColumnarToRow (75)
                  :     +- CometColumnarExchange (74)
                  :        +- RowToColumnar (73)
                  :           +- * HashAggregate (72)
                  :              +- Union (71)
                  :                 :- * HashAggregate (24)
                  :                 :  +- * ColumnarToRow (23)
                  :                 :     +- CometColumnarExchange (22)
                  :                 :        +- RowToColumnar (21)
                  :                 :           +- * HashAggregate (20)
                  :                 :              +- * ColumnarToRow (19)
                  :                 :                 +- CometProject (18)
                  :                 :                    +- CometBroadcastHashJoin (17)
                  :                 :                       :- CometProject (13)
                  :                 :                       :  +- CometBroadcastHashJoin (12)
                  :                 :                       :     :- CometUnion (7)
                  :                 :                       :     :  :- CometProject (3)
                  :                 :                       :     :  :  +- CometFilter (2)
                  :                 :                       :     :  :     +- CometScan parquet spark_catalog.default.store_sales (1)
                  :                 :                       :     :  +- CometProject (6)
                  :                 :                       :     :     +- CometFilter (5)
                  :                 :                       :     :        +- CometScan parquet spark_catalog.default.store_returns (4)
                  :                 :                       :     +- CometBroadcastExchange (11)
                  :                 :                       :        +- CometProject (10)
                  :                 :                       :           +- CometFilter (9)
                  :                 :                       :              +- CometScan parquet spark_catalog.default.date_dim (8)
                  :                 :                       +- CometBroadcastExchange (16)
                  :                 :                          +- CometFilter (15)
                  :                 :                             +- CometScan parquet spark_catalog.default.store (14)
                  :                 :- * HashAggregate (45)
                  :                 :  +- * ColumnarToRow (44)
                  :                 :     +- CometColumnarExchange (43)
                  :                 :        +- RowToColumnar (42)
                  :                 :           +- * HashAggregate (41)
                  :                 :              +- * ColumnarToRow (40)
                  :                 :                 +- CometProject (39)
                  :                 :                    +- CometBroadcastHashJoin (38)
                  :                 :                       :- CometProject (34)
                  :                 :                       :  +- CometBroadcastHashJoin (33)
                  :                 :                       :     :- CometUnion (31)
                  :                 :                       :     :  :- CometProject (27)
                  :                 :                       :     :  :  +- CometFilter (26)
                  :                 :                       :     :  :     +- CometScan parquet spark_catalog.default.catalog_sales (25)
                  :                 :                       :     :  +- CometProject (30)
                  :                 :                       :     :     +- CometFilter (29)
                  :                 :                       :     :        +- CometScan parquet spark_catalog.default.catalog_returns (28)
                  :                 :                       :     +- ReusedExchange (32)
                  :                 :                       +- CometBroadcastExchange (37)
                  :                 :                          +- CometFilter (36)
                  :                 :                             +- CometScan parquet spark_catalog.default.catalog_page (35)
                  :                 +- * HashAggregate (70)
                  :                    +- * ColumnarToRow (69)
                  :                       +- CometColumnarExchange (68)
                  :                          +- RowToColumnar (67)
                  :                             +- * HashAggregate (66)
                  :                                +- * ColumnarToRow (65)
                  :                                   +- CometProject (64)
                  :                                      +- CometBroadcastHashJoin (63)
                  :                                         :- CometProject (59)
                  :                                         :  +- CometBroadcastHashJoin (58)
                  :                                         :     :- CometUnion (56)
                  :                                         :     :  :- CometProject (48)
                  :                                         :     :  :  +- CometFilter (47)
                  :                                         :     :  :     +- CometScan parquet spark_catalog.default.web_sales (46)
                  :                                         :     :  +- CometProject (55)
                  :                                         :     :     +- CometBroadcastHashJoin (54)
                  :                                         :     :        :- CometBroadcastExchange (50)
                  :                                         :     :        :  +- CometScan parquet spark_catalog.default.web_returns (49)
                  :                                         :     :        +- CometProject (53)
                  :                                         :     :           +- CometFilter (52)
                  :                                         :     :              +- CometScan parquet spark_catalog.default.web_sales (51)
                  :                                         :     +- ReusedExchange (57)
                  :                                         +- CometBroadcastExchange (62)
                  :                                            +- CometFilter (61)
                  :                                               +- CometScan parquet spark_catalog.default.web_site (60)
                  :- * HashAggregate (84)
                  :  +- * ColumnarToRow (83)
                  :     +- CometColumnarExchange (82)
                  :        +- RowToColumnar (81)
                  :           +- * HashAggregate (80)
                  :              +- * HashAggregate (79)
                  :                 +- * ColumnarToRow (78)
                  :                    +- ReusedExchange (77)
                  +- * HashAggregate (92)
                     +- * ColumnarToRow (91)
                        +- CometColumnarExchange (90)
                           +- RowToColumnar (89)
                              +- * HashAggregate (88)
                                 +- * HashAggregate (87)
                                    +- * ColumnarToRow (86)
                                       +- ReusedExchange (85)


(1) Scan parquet spark_catalog.default.store_sales
Output [4]: [ss_store_sk#1, ss_ext_sales_price#2, ss_net_profit#3, ss_sold_date_sk#4]
Batched: true
Location: InMemoryFileIndex []
PartitionFilters: [isnotnull(ss_sold_date_sk#4), dynamicpruningexpression(ss_sold_date_sk#4 IN dynamicpruning#5)]
PushedFilters: [IsNotNull(ss_store_sk)]
ReadSchema: struct<ss_store_sk:int,ss_ext_sales_price:decimal(7,2),ss_net_profit:decimal(7,2)>

(2) CometFilter
Input [4]: [ss_store_sk#1, ss_ext_sales_price#2, ss_net_profit#3, ss_sold_date_sk#4]
Condition : isnotnull(ss_store_sk#1)

(3) CometProject
Input [4]: [ss_store_sk#1, ss_ext_sales_price#2, ss_net_profit#3, ss_sold_date_sk#4]
Arguments: [store_sk#6, date_sk#7, sales_price#8, profit#9, return_amt#10, net_loss#11], [ss_store_sk#1 AS store_sk#6, ss_sold_date_sk#4 AS date_sk#7, ss_ext_sales_price#2 AS sales_price#8, ss_net_profit#3 AS profit#9, 0.00 AS return_amt#10, 0.00 AS net_loss#11]

(4) Scan parquet spark_catalog.default.store_returns
Output [4]: [sr_store_sk#12, sr_return_amt#13, sr_net_loss#14, sr_returned_date_sk#15]
Batched: true
Location: InMemoryFileIndex []
PartitionFilters: [isnotnull(sr_returned_date_sk#15), dynamicpruningexpression(sr_returned_date_sk#15 IN dynamicpruning#5)]
PushedFilters: [IsNotNull(sr_store_sk)]
ReadSchema: struct<sr_store_sk:int,sr_return_amt:decimal(7,2),sr_net_loss:decimal(7,2)>

(5) CometFilter
Input [4]: [sr_store_sk#12, sr_return_amt#13, sr_net_loss#14, sr_returned_date_sk#15]
Condition : isnotnull(sr_store_sk#12)

(6) CometProject
Input [4]: [sr_store_sk#12, sr_return_amt#13, sr_net_loss#14, sr_returned_date_sk#15]
Arguments: [store_sk#16, date_sk#17, sales_price#18, profit#19, return_amt#20, net_loss#21], [sr_store_sk#12 AS store_sk#16, sr_returned_date_sk#15 AS date_sk#17, 0.00 AS sales_price#18, 0.00 AS profit#19, sr_return_amt#13 AS return_amt#20, sr_net_loss#14 AS net_loss#21]

(7) CometUnion
Child 0 Input [6]: [store_sk#6, date_sk#7, sales_price#8, profit#9, return_amt#10, net_loss#11]
Child 1 Input [6]: [store_sk#16, date_sk#17, sales_price#18, profit#19, return_amt#20, net_loss#21]

(8) Scan parquet spark_catalog.default.date_dim
Output [2]: [d_date_sk#22, d_date#23]
Batched: true
Location [not included in comparison]/{warehouse_dir}/date_dim]
PushedFilters: [IsNotNull(d_date), GreaterThanOrEqual(d_date,1998-08-04), LessThanOrEqual(d_date,1998-08-18), IsNotNull(d_date_sk)]
ReadSchema: struct<d_date_sk:int,d_date:date>

(9) CometFilter
Input [2]: [d_date_sk#22, d_date#23]
Condition : (((isnotnull(d_date#23) AND (d_date#23 >= 1998-08-04)) AND (d_date#23 <= 1998-08-18)) AND isnotnull(d_date_sk#22))

(10) CometProject
Input [2]: [d_date_sk#22, d_date#23]
Arguments: [d_date_sk#22], [d_date_sk#22]

(11) CometBroadcastExchange
Input [1]: [d_date_sk#22]
Arguments: [d_date_sk#22]

(12) CometBroadcastHashJoin
Left output [6]: [store_sk#6, date_sk#7, sales_price#8, profit#9, return_amt#10, net_loss#11]
Right output [1]: [d_date_sk#22]
Arguments: [date_sk#7], [d_date_sk#22], Inner, BuildRight

(13) CometProject
Input [7]: [store_sk#6, date_sk#7, sales_price#8, profit#9, return_amt#10, net_loss#11, d_date_sk#22]
Arguments: [store_sk#6, sales_price#8, profit#9, return_amt#10, net_loss#11], [store_sk#6, sales_price#8, profit#9, return_amt#10, net_loss#11]

(14) Scan parquet spark_catalog.default.store
Output [2]: [s_store_sk#24, s_store_id#25]
Batched: true
Location [not included in comparison]/{warehouse_dir}/store]
PushedFilters: [IsNotNull(s_store_sk)]
ReadSchema: struct<s_store_sk:int,s_store_id:string>

(15) CometFilter
Input [2]: [s_store_sk#24, s_store_id#25]
Condition : isnotnull(s_store_sk#24)

(16) CometBroadcastExchange
Input [2]: [s_store_sk#24, s_store_id#25]
Arguments: [s_store_sk#24, s_store_id#25]

(17) CometBroadcastHashJoin
Left output [5]: [store_sk#6, sales_price#8, profit#9, return_amt#10, net_loss#11]
Right output [2]: [s_store_sk#24, s_store_id#25]
Arguments: [store_sk#6], [s_store_sk#24], Inner, BuildRight

(18) CometProject
Input [7]: [store_sk#6, sales_price#8, profit#9, return_amt#10, net_loss#11, s_store_sk#24, s_store_id#25]
Arguments: [sales_price#8, profit#9, return_amt#10, net_loss#11, s_store_id#25], [sales_price#8, profit#9, return_amt#10, net_loss#11, s_store_id#25]

(19) ColumnarToRow [codegen id : 1]
Input [5]: [sales_price#8, profit#9, return_amt#10, net_loss#11, s_store_id#25]

(20) HashAggregate [codegen id : 1]
Input [5]: [sales_price#8, profit#9, return_amt#10, net_loss#11, s_store_id#25]
Keys [1]: [s_store_id#25]
Functions [4]: [partial_sum(UnscaledValue(sales_price#8)), partial_sum(UnscaledValue(return_amt#10)), partial_sum(UnscaledValue(profit#9)), partial_sum(UnscaledValue(net_loss#11))]
Aggregate Attributes [4]: [sum#26, sum#27, sum#28, sum#29]
Results [5]: [s_store_id#25, sum#30, sum#31, sum#32, sum#33]

(21) RowToColumnar
Input [5]: [s_store_id#25, sum#30, sum#31, sum#32, sum#33]

(22) CometColumnarExchange
Input [5]: [s_store_id#25, sum#30, sum#31, sum#32, sum#33]
Arguments: hashpartitioning(s_store_id#25, 5), ENSURE_REQUIREMENTS, CometColumnarShuffle, [plan_id=1]

(23) ColumnarToRow [codegen id : 2]
Input [5]: [s_store_id#25, sum#30, sum#31, sum#32, sum#33]

(24) HashAggregate [codegen id : 2]
Input [5]: [s_store_id#25, sum#30, sum#31, sum#32, sum#33]
Keys [1]: [s_store_id#25]
Functions [4]: [sum(UnscaledValue(sales_price#8)), sum(UnscaledValue(return_amt#10)), sum(UnscaledValue(profit#9)), sum(UnscaledValue(net_loss#11))]
Aggregate Attributes [4]: [sum(UnscaledValue(sales_price#8))#34, sum(UnscaledValue(return_amt#10))#35, sum(UnscaledValue(profit#9))#36, sum(UnscaledValue(net_loss#11))#37]
Results [5]: [store channel AS channel#38, concat(store, s_store_id#25) AS id#39, MakeDecimal(sum(UnscaledValue(sales_price#8))#34,17,2) AS sales#40, MakeDecimal(sum(UnscaledValue(return_amt#10))#35,17,2) AS returns#41, (MakeDecimal(sum(UnscaledValue(profit#9))#36,17,2) - MakeDecimal(sum(UnscaledValue(net_loss#11))#37,17,2)) AS profit#42]

(25) Scan parquet spark_catalog.default.catalog_sales
Output [4]: [cs_catalog_page_sk#43, cs_ext_sales_price#44, cs_net_profit#45, cs_sold_date_sk#46]
Batched: true
Location: InMemoryFileIndex []
PartitionFilters: [isnotnull(cs_sold_date_sk#46), dynamicpruningexpression(cs_sold_date_sk#46 IN dynamicpruning#47)]
PushedFilters: [IsNotNull(cs_catalog_page_sk)]
ReadSchema: struct<cs_catalog_page_sk:int,cs_ext_sales_price:decimal(7,2),cs_net_profit:decimal(7,2)>

(26) CometFilter
Input [4]: [cs_catalog_page_sk#43, cs_ext_sales_price#44, cs_net_profit#45, cs_sold_date_sk#46]
Condition : isnotnull(cs_catalog_page_sk#43)

(27) CometProject
Input [4]: [cs_catalog_page_sk#43, cs_ext_sales_price#44, cs_net_profit#45, cs_sold_date_sk#46]
Arguments: [page_sk#48, date_sk#49, sales_price#50, profit#51, return_amt#52, net_loss#53], [cs_catalog_page_sk#43 AS page_sk#48, cs_sold_date_sk#46 AS date_sk#49, cs_ext_sales_price#44 AS sales_price#50, cs_net_profit#45 AS profit#51, 0.00 AS return_amt#52, 0.00 AS net_loss#53]

(28) Scan parquet spark_catalog.default.catalog_returns
Output [4]: [cr_catalog_page_sk#54, cr_return_amount#55, cr_net_loss#56, cr_returned_date_sk#57]
Batched: true
Location: InMemoryFileIndex []
PartitionFilters: [isnotnull(cr_returned_date_sk#57), dynamicpruningexpression(cr_returned_date_sk#57 IN dynamicpruning#47)]
PushedFilters: [IsNotNull(cr_catalog_page_sk)]
ReadSchema: struct<cr_catalog_page_sk:int,cr_return_amount:decimal(7,2),cr_net_loss:decimal(7,2)>

(29) CometFilter
Input [4]: [cr_catalog_page_sk#54, cr_return_amount#55, cr_net_loss#56, cr_returned_date_sk#57]
Condition : isnotnull(cr_catalog_page_sk#54)

(30) CometProject
Input [4]: [cr_catalog_page_sk#54, cr_return_amount#55, cr_net_loss#56, cr_returned_date_sk#57]
Arguments: [page_sk#58, date_sk#59, sales_price#60, profit#61, return_amt#62, net_loss#63], [cr_catalog_page_sk#54 AS page_sk#58, cr_returned_date_sk#57 AS date_sk#59, 0.00 AS sales_price#60, 0.00 AS profit#61, cr_return_amount#55 AS return_amt#62, cr_net_loss#56 AS net_loss#63]

(31) CometUnion
Child 0 Input [6]: [page_sk#48, date_sk#49, sales_price#50, profit#51, return_amt#52, net_loss#53]
Child 1 Input [6]: [page_sk#58, date_sk#59, sales_price#60, profit#61, return_amt#62, net_loss#63]

(32) ReusedExchange [Reuses operator id: 11]
Output [1]: [d_date_sk#64]

(33) CometBroadcastHashJoin
Left output [6]: [page_sk#48, date_sk#49, sales_price#50, profit#51, return_amt#52, net_loss#53]
Right output [1]: [d_date_sk#64]
Arguments: [date_sk#49], [d_date_sk#64], Inner, BuildRight

(34) CometProject
Input [7]: [page_sk#48, date_sk#49, sales_price#50, profit#51, return_amt#52, net_loss#53, d_date_sk#64]
Arguments: [page_sk#48, sales_price#50, profit#51, return_amt#52, net_loss#53], [page_sk#48, sales_price#50, profit#51, return_amt#52, net_loss#53]

(35) Scan parquet spark_catalog.default.catalog_page
Output [2]: [cp_catalog_page_sk#65, cp_catalog_page_id#66]
Batched: true
Location [not included in comparison]/{warehouse_dir}/catalog_page]
PushedFilters: [IsNotNull(cp_catalog_page_sk)]
ReadSchema: struct<cp_catalog_page_sk:int,cp_catalog_page_id:string>

(36) CometFilter
Input [2]: [cp_catalog_page_sk#65, cp_catalog_page_id#66]
Condition : isnotnull(cp_catalog_page_sk#65)

(37) CometBroadcastExchange
Input [2]: [cp_catalog_page_sk#65, cp_catalog_page_id#66]
Arguments: [cp_catalog_page_sk#65, cp_catalog_page_id#66]

(38) CometBroadcastHashJoin
Left output [5]: [page_sk#48, sales_price#50, profit#51, return_amt#52, net_loss#53]
Right output [2]: [cp_catalog_page_sk#65, cp_catalog_page_id#66]
Arguments: [page_sk#48], [cp_catalog_page_sk#65], Inner, BuildRight

(39) CometProject
Input [7]: [page_sk#48, sales_price#50, profit#51, return_amt#52, net_loss#53, cp_catalog_page_sk#65, cp_catalog_page_id#66]
Arguments: [sales_price#50, profit#51, return_amt#52, net_loss#53, cp_catalog_page_id#66], [sales_price#50, profit#51, return_amt#52, net_loss#53, cp_catalog_page_id#66]

(40) ColumnarToRow [codegen id : 3]
Input [5]: [sales_price#50, profit#51, return_amt#52, net_loss#53, cp_catalog_page_id#66]

(41) HashAggregate [codegen id : 3]
Input [5]: [sales_price#50, profit#51, return_amt#52, net_loss#53, cp_catalog_page_id#66]
Keys [1]: [cp_catalog_page_id#66]
Functions [4]: [partial_sum(UnscaledValue(sales_price#50)), partial_sum(UnscaledValue(return_amt#52)), partial_sum(UnscaledValue(profit#51)), partial_sum(UnscaledValue(net_loss#53))]
Aggregate Attributes [4]: [sum#67, sum#68, sum#69, sum#70]
Results [5]: [cp_catalog_page_id#66, sum#71, sum#72, sum#73, sum#74]

(42) RowToColumnar
Input [5]: [cp_catalog_page_id#66, sum#71, sum#72, sum#73, sum#74]

(43) CometColumnarExchange
Input [5]: [cp_catalog_page_id#66, sum#71, sum#72, sum#73, sum#74]
Arguments: hashpartitioning(cp_catalog_page_id#66, 5), ENSURE_REQUIREMENTS, CometColumnarShuffle, [plan_id=2]

(44) ColumnarToRow [codegen id : 4]
Input [5]: [cp_catalog_page_id#66, sum#71, sum#72, sum#73, sum#74]

(45) HashAggregate [codegen id : 4]
Input [5]: [cp_catalog_page_id#66, sum#71, sum#72, sum#73, sum#74]
Keys [1]: [cp_catalog_page_id#66]
Functions [4]: [sum(UnscaledValue(sales_price#50)), sum(UnscaledValue(return_amt#52)), sum(UnscaledValue(profit#51)), sum(UnscaledValue(net_loss#53))]
Aggregate Attributes [4]: [sum(UnscaledValue(sales_price#50))#75, sum(UnscaledValue(return_amt#52))#76, sum(UnscaledValue(profit#51))#77, sum(UnscaledValue(net_loss#53))#78]
Results [5]: [catalog channel AS channel#79, concat(catalog_page, cp_catalog_page_id#66) AS id#80, MakeDecimal(sum(UnscaledValue(sales_price#50))#75,17,2) AS sales#81, MakeDecimal(sum(UnscaledValue(return_amt#52))#76,17,2) AS returns#82, (MakeDecimal(sum(UnscaledValue(profit#51))#77,17,2) - MakeDecimal(sum(UnscaledValue(net_loss#53))#78,17,2)) AS profit#83]

(46) Scan parquet spark_catalog.default.web_sales
Output [4]: [ws_web_site_sk#84, ws_ext_sales_price#85, ws_net_profit#86, ws_sold_date_sk#87]
Batched: true
Location: InMemoryFileIndex []
PartitionFilters: [isnotnull(ws_sold_date_sk#87), dynamicpruningexpression(ws_sold_date_sk#87 IN dynamicpruning#88)]
PushedFilters: [IsNotNull(ws_web_site_sk)]
ReadSchema: struct<ws_web_site_sk:int,ws_ext_sales_price:decimal(7,2),ws_net_profit:decimal(7,2)>

(47) CometFilter
Input [4]: [ws_web_site_sk#84, ws_ext_sales_price#85, ws_net_profit#86, ws_sold_date_sk#87]
Condition : isnotnull(ws_web_site_sk#84)

(48) CometProject
Input [4]: [ws_web_site_sk#84, ws_ext_sales_price#85, ws_net_profit#86, ws_sold_date_sk#87]
Arguments: [wsr_web_site_sk#89, date_sk#90, sales_price#91, profit#92, return_amt#93, net_loss#94], [ws_web_site_sk#84 AS wsr_web_site_sk#89, ws_sold_date_sk#87 AS date_sk#90, ws_ext_sales_price#85 AS sales_price#91, ws_net_profit#86 AS profit#92, 0.00 AS return_amt#93, 0.00 AS net_loss#94]

(49) Scan parquet spark_catalog.default.web_returns
Output [5]: [wr_item_sk#95, wr_order_number#96, wr_return_amt#97, wr_net_loss#98, wr_returned_date_sk#99]
Batched: true
Location: InMemoryFileIndex []
PartitionFilters: [isnotnull(wr_returned_date_sk#99), dynamicpruningexpression(wr_returned_date_sk#99 IN dynamicpruning#88)]
ReadSchema: struct<wr_item_sk:int,wr_order_number:int,wr_return_amt:decimal(7,2),wr_net_loss:decimal(7,2)>

(50) CometBroadcastExchange
Input [5]: [wr_item_sk#95, wr_order_number#96, wr_return_amt#97, wr_net_loss#98, wr_returned_date_sk#99]
Arguments: [wr_item_sk#95, wr_order_number#96, wr_return_amt#97, wr_net_loss#98, wr_returned_date_sk#99]

(51) Scan parquet spark_catalog.default.web_sales
Output [4]: [ws_item_sk#100, ws_web_site_sk#101, ws_order_number#102, ws_sold_date_sk#103]
Batched: true
Location [not included in comparison]/{warehouse_dir}/web_sales]
PushedFilters: [IsNotNull(ws_item_sk), IsNotNull(ws_order_number), IsNotNull(ws_web_site_sk)]
ReadSchema: struct<ws_item_sk:int,ws_web_site_sk:int,ws_order_number:int>

(52) CometFilter
Input [4]: [ws_item_sk#100, ws_web_site_sk#101, ws_order_number#102, ws_sold_date_sk#103]
Condition : ((isnotnull(ws_item_sk#100) AND isnotnull(ws_order_number#102)) AND isnotnull(ws_web_site_sk#101))

(53) CometProject
Input [4]: [ws_item_sk#100, ws_web_site_sk#101, ws_order_number#102, ws_sold_date_sk#103]
Arguments: [ws_item_sk#100, ws_web_site_sk#101, ws_order_number#102], [ws_item_sk#100, ws_web_site_sk#101, ws_order_number#102]

(54) CometBroadcastHashJoin
Left output [5]: [wr_item_sk#95, wr_order_number#96, wr_return_amt#97, wr_net_loss#98, wr_returned_date_sk#99]
Right output [3]: [ws_item_sk#100, ws_web_site_sk#101, ws_order_number#102]
Arguments: [wr_item_sk#95, wr_order_number#96], [ws_item_sk#100, ws_order_number#102], Inner, BuildLeft

(55) CometProject
Input [8]: [wr_item_sk#95, wr_order_number#96, wr_return_amt#97, wr_net_loss#98, wr_returned_date_sk#99, ws_item_sk#100, ws_web_site_sk#101, ws_order_number#102]
Arguments: [wsr_web_site_sk#104, date_sk#105, sales_price#106, profit#107, return_amt#108, net_loss#109], [ws_web_site_sk#101 AS wsr_web_site_sk#104, wr_returned_date_sk#99 AS date_sk#105, 0.00 AS sales_price#106, 0.00 AS profit#107, wr_return_amt#97 AS return_amt#108, wr_net_loss#98 AS net_loss#109]

(56) CometUnion
Child 0 Input [6]: [wsr_web_site_sk#89, date_sk#90, sales_price#91, profit#92, return_amt#93, net_loss#94]
Child 1 Input [6]: [wsr_web_site_sk#104, date_sk#105, sales_price#106, profit#107, return_amt#108, net_loss#109]

(57) ReusedExchange [Reuses operator id: 11]
Output [1]: [d_date_sk#110]

(58) CometBroadcastHashJoin
Left output [6]: [wsr_web_site_sk#89, date_sk#90, sales_price#91, profit#92, return_amt#93, net_loss#94]
Right output [1]: [d_date_sk#110]
Arguments: [date_sk#90], [d_date_sk#110], Inner, BuildRight

(59) CometProject
Input [7]: [wsr_web_site_sk#89, date_sk#90, sales_price#91, profit#92, return_amt#93, net_loss#94, d_date_sk#110]
Arguments: [wsr_web_site_sk#89, sales_price#91, profit#92, return_amt#93, net_loss#94], [wsr_web_site_sk#89, sales_price#91, profit#92, return_amt#93, net_loss#94]

(60) Scan parquet spark_catalog.default.web_site
Output [2]: [web_site_sk#111, web_site_id#112]
Batched: true
Location [not included in comparison]/{warehouse_dir}/web_site]
PushedFilters: [IsNotNull(web_site_sk)]
ReadSchema: struct<web_site_sk:int,web_site_id:string>

(61) CometFilter
Input [2]: [web_site_sk#111, web_site_id#112]
Condition : isnotnull(web_site_sk#111)

(62) CometBroadcastExchange
Input [2]: [web_site_sk#111, web_site_id#112]
Arguments: [web_site_sk#111, web_site_id#112]

(63) CometBroadcastHashJoin
Left output [5]: [wsr_web_site_sk#89, sales_price#91, profit#92, return_amt#93, net_loss#94]
Right output [2]: [web_site_sk#111, web_site_id#112]
Arguments: [wsr_web_site_sk#89], [web_site_sk#111], Inner, BuildRight

(64) CometProject
Input [7]: [wsr_web_site_sk#89, sales_price#91, profit#92, return_amt#93, net_loss#94, web_site_sk#111, web_site_id#112]
Arguments: [sales_price#91, profit#92, return_amt#93, net_loss#94, web_site_id#112], [sales_price#91, profit#92, return_amt#93, net_loss#94, web_site_id#112]

(65) ColumnarToRow [codegen id : 5]
Input [5]: [sales_price#91, profit#92, return_amt#93, net_loss#94, web_site_id#112]

(66) HashAggregate [codegen id : 5]
Input [5]: [sales_price#91, profit#92, return_amt#93, net_loss#94, web_site_id#112]
Keys [1]: [web_site_id#112]
Functions [4]: [partial_sum(UnscaledValue(sales_price#91)), partial_sum(UnscaledValue(return_amt#93)), partial_sum(UnscaledValue(profit#92)), partial_sum(UnscaledValue(net_loss#94))]
Aggregate Attributes [4]: [sum#113, sum#114, sum#115, sum#116]
Results [5]: [web_site_id#112, sum#117, sum#118, sum#119, sum#120]

(67) RowToColumnar
Input [5]: [web_site_id#112, sum#117, sum#118, sum#119, sum#120]

(68) CometColumnarExchange
Input [5]: [web_site_id#112, sum#117, sum#118, sum#119, sum#120]
Arguments: hashpartitioning(web_site_id#112, 5), ENSURE_REQUIREMENTS, CometColumnarShuffle, [plan_id=3]

(69) ColumnarToRow [codegen id : 6]
Input [5]: [web_site_id#112, sum#117, sum#118, sum#119, sum#120]

(70) HashAggregate [codegen id : 6]
Input [5]: [web_site_id#112, sum#117, sum#118, sum#119, sum#120]
Keys [1]: [web_site_id#112]
Functions [4]: [sum(UnscaledValue(sales_price#91)), sum(UnscaledValue(return_amt#93)), sum(UnscaledValue(profit#92)), sum(UnscaledValue(net_loss#94))]
Aggregate Attributes [4]: [sum(UnscaledValue(sales_price#91))#121, sum(UnscaledValue(return_amt#93))#122, sum(UnscaledValue(profit#92))#123, sum(UnscaledValue(net_loss#94))#124]
Results [5]: [web channel AS channel#125, concat(web_site, web_site_id#112) AS id#126, MakeDecimal(sum(UnscaledValue(sales_price#91))#121,17,2) AS sales#127, MakeDecimal(sum(UnscaledValue(return_amt#93))#122,17,2) AS returns#128, (MakeDecimal(sum(UnscaledValue(profit#92))#123,17,2) - MakeDecimal(sum(UnscaledValue(net_loss#94))#124,17,2)) AS profit#129]

(71) Union

(72) HashAggregate [codegen id : 7]
Input [5]: [channel#38, id#39, sales#40, returns#41, profit#42]
Keys [2]: [channel#38, id#39]
Functions [3]: [partial_sum(sales#40), partial_sum(returns#41), partial_sum(profit#42)]
Aggregate Attributes [6]: [sum#130, isEmpty#131, sum#132, isEmpty#133, sum#134, isEmpty#135]
Results [8]: [channel#38, id#39, sum#136, isEmpty#137, sum#138, isEmpty#139, sum#140, isEmpty#141]

(73) RowToColumnar
Input [8]: [channel#38, id#39, sum#136, isEmpty#137, sum#138, isEmpty#139, sum#140, isEmpty#141]

(74) CometColumnarExchange
Input [8]: [channel#38, id#39, sum#136, isEmpty#137, sum#138, isEmpty#139, sum#140, isEmpty#141]
Arguments: hashpartitioning(channel#38, id#39, 5), ENSURE_REQUIREMENTS, CometColumnarShuffle, [plan_id=4]

(75) ColumnarToRow [codegen id : 8]
Input [8]: [channel#38, id#39, sum#136, isEmpty#137, sum#138, isEmpty#139, sum#140, isEmpty#141]

(76) HashAggregate [codegen id : 8]
Input [8]: [channel#38, id#39, sum#136, isEmpty#137, sum#138, isEmpty#139, sum#140, isEmpty#141]
Keys [2]: [channel#38, id#39]
Functions [3]: [sum(sales#40), sum(returns#41), sum(profit#42)]
Aggregate Attributes [3]: [sum(sales#40)#142, sum(returns#41)#143, sum(profit#42)#144]
Results [5]: [channel#38, id#39, cast(sum(sales#40)#142 as decimal(37,2)) AS sales#145, cast(sum(returns#41)#143 as decimal(37,2)) AS returns#146, cast(sum(profit#42)#144 as decimal(38,2)) AS profit#147]

(77) ReusedExchange [Reuses operator id: 74]
Output [8]: [channel#148, id#149, sum#150, isEmpty#151, sum#152, isEmpty#153, sum#154, isEmpty#155]

(78) ColumnarToRow [codegen id : 16]
Input [8]: [channel#148, id#149, sum#150, isEmpty#151, sum#152, isEmpty#153, sum#154, isEmpty#155]

(79) HashAggregate [codegen id : 16]
Input [8]: [channel#148, id#149, sum#150, isEmpty#151, sum#152, isEmpty#153, sum#154, isEmpty#155]
Keys [2]: [channel#148, id#149]
Functions [3]: [sum(sales#156), sum(returns#157), sum(profit#158)]
Aggregate Attributes [3]: [sum(sales#156)#142, sum(returns#157)#143, sum(profit#158)#144]
Results [4]: [channel#148, sum(sales#156)#142 AS sales#159, sum(returns#157)#143 AS returns#160, sum(profit#158)#144 AS profit#161]

(80) HashAggregate [codegen id : 16]
Input [4]: [channel#148, sales#159, returns#160, profit#161]
Keys [1]: [channel#148]
Functions [3]: [partial_sum(sales#159), partial_sum(returns#160), partial_sum(profit#161)]
Aggregate Attributes [6]: [sum#162, isEmpty#163, sum#164, isEmpty#165, sum#166, isEmpty#167]
Results [7]: [channel#148, sum#168, isEmpty#169, sum#170, isEmpty#171, sum#172, isEmpty#173]

(81) RowToColumnar
Input [7]: [channel#148, sum#168, isEmpty#169, sum#170, isEmpty#171, sum#172, isEmpty#173]

(82) CometColumnarExchange
Input [7]: [channel#148, sum#168, isEmpty#169, sum#170, isEmpty#171, sum#172, isEmpty#173]
Arguments: hashpartitioning(channel#148, 5), ENSURE_REQUIREMENTS, CometColumnarShuffle, [plan_id=5]

(83) ColumnarToRow [codegen id : 17]
Input [7]: [channel#148, sum#168, isEmpty#169, sum#170, isEmpty#171, sum#172, isEmpty#173]

(84) HashAggregate [codegen id : 17]
Input [7]: [channel#148, sum#168, isEmpty#169, sum#170, isEmpty#171, sum#172, isEmpty#173]
Keys [1]: [channel#148]
Functions [3]: [sum(sales#159), sum(returns#160), sum(profit#161)]
Aggregate Attributes [3]: [sum(sales#159)#174, sum(returns#160)#175, sum(profit#161)#176]
Results [5]: [channel#148, null AS id#177, sum(sales#159)#174 AS sum(sales)#178, sum(returns#160)#175 AS sum(returns)#179, sum(profit#161)#176 AS sum(profit)#180]

(85) ReusedExchange [Reuses operator id: 74]
Output [8]: [channel#181, id#182, sum#183, isEmpty#184, sum#185, isEmpty#186, sum#187, isEmpty#188]

(86) ColumnarToRow [codegen id : 25]
Input [8]: [channel#181, id#182, sum#183, isEmpty#184, sum#185, isEmpty#186, sum#187, isEmpty#188]

(87) HashAggregate [codegen id : 25]
Input [8]: [channel#181, id#182, sum#183, isEmpty#184, sum#185, isEmpty#186, sum#187, isEmpty#188]
Keys [2]: [channel#181, id#182]
Functions [3]: [sum(sales#189), sum(returns#190), sum(profit#191)]
Aggregate Attributes [3]: [sum(sales#189)#142, sum(returns#190)#143, sum(profit#191)#144]
Results [3]: [sum(sales#189)#142 AS sales#192, sum(returns#190)#143 AS returns#193, sum(profit#191)#144 AS profit#194]

(88) HashAggregate [codegen id : 25]
Input [3]: [sales#192, returns#193, profit#194]
Keys: []
Functions [3]: [partial_sum(sales#192), partial_sum(returns#193), partial_sum(profit#194)]
Aggregate Attributes [6]: [sum#195, isEmpty#196, sum#197, isEmpty#198, sum#199, isEmpty#200]
Results [6]: [sum#201, isEmpty#202, sum#203, isEmpty#204, sum#205, isEmpty#206]

(89) RowToColumnar
Input [6]: [sum#201, isEmpty#202, sum#203, isEmpty#204, sum#205, isEmpty#206]

(90) CometColumnarExchange
Input [6]: [sum#201, isEmpty#202, sum#203, isEmpty#204, sum#205, isEmpty#206]
Arguments: SinglePartition, ENSURE_REQUIREMENTS, CometColumnarShuffle, [plan_id=6]

(91) ColumnarToRow [codegen id : 26]
Input [6]: [sum#201, isEmpty#202, sum#203, isEmpty#204, sum#205, isEmpty#206]

(92) HashAggregate [codegen id : 26]
Input [6]: [sum#201, isEmpty#202, sum#203, isEmpty#204, sum#205, isEmpty#206]
Keys: []
Functions [3]: [sum(sales#192), sum(returns#193), sum(profit#194)]
Aggregate Attributes [3]: [sum(sales#192)#207, sum(returns#193)#208, sum(profit#194)#209]
Results [5]: [null AS channel#210, null AS id#211, sum(sales#192)#207 AS sum(sales)#212, sum(returns#193)#208 AS sum(returns)#213, sum(profit#194)#209 AS sum(profit)#214]

(93) Union

(94) HashAggregate [codegen id : 27]
Input [5]: [channel#38, id#39, sales#145, returns#146, profit#147]
Keys [5]: [channel#38, id#39, sales#145, returns#146, profit#147]
Functions: []
Aggregate Attributes: []
Results [5]: [channel#38, id#39, sales#145, returns#146, profit#147]

(95) RowToColumnar
Input [5]: [channel#38, id#39, sales#145, returns#146, profit#147]

(96) CometColumnarExchange
Input [5]: [channel#38, id#39, sales#145, returns#146, profit#147]
Arguments: hashpartitioning(channel#38, id#39, sales#145, returns#146, profit#147, 5), ENSURE_REQUIREMENTS, CometColumnarShuffle, [plan_id=7]

(97) CometHashAggregate
Input [5]: [channel#38, id#39, sales#145, returns#146, profit#147]
Keys [5]: [channel#38, id#39, sales#145, returns#146, profit#147]
Functions: []

(98) CometTakeOrderedAndProject
Input [5]: [channel#38, id#39, sales#145, returns#146, profit#147]
Arguments: TakeOrderedAndProject(limit=100, orderBy=[channel#38 ASC NULLS FIRST,id#39 ASC NULLS FIRST], output=[channel#38,id#39,sales#145,returns#146,profit#147]), [channel#38, id#39, sales#145, returns#146, profit#147], 100, [channel#38 ASC NULLS FIRST, id#39 ASC NULLS FIRST], [channel#38, id#39, sales#145, returns#146, profit#147]

(99) ColumnarToRow [codegen id : 28]
Input [5]: [channel#38, id#39, sales#145, returns#146, profit#147]

===== Subqueries =====

Subquery:1 Hosting operator id = 1 Hosting Expression = ss_sold_date_sk#4 IN dynamicpruning#5
BroadcastExchange (104)
+- * ColumnarToRow (103)
   +- CometProject (102)
      +- CometFilter (101)
         +- CometScan parquet spark_catalog.default.date_dim (100)


(100) Scan parquet spark_catalog.default.date_dim
Output [2]: [d_date_sk#22, d_date#23]
Batched: true
Location [not included in comparison]/{warehouse_dir}/date_dim]
PushedFilters: [IsNotNull(d_date), GreaterThanOrEqual(d_date,1998-08-04), LessThanOrEqual(d_date,1998-08-18), IsNotNull(d_date_sk)]
ReadSchema: struct<d_date_sk:int,d_date:date>

(101) CometFilter
Input [2]: [d_date_sk#22, d_date#23]
Condition : (((isnotnull(d_date#23) AND (d_date#23 >= 1998-08-04)) AND (d_date#23 <= 1998-08-18)) AND isnotnull(d_date_sk#22))

(102) CometProject
Input [2]: [d_date_sk#22, d_date#23]
Arguments: [d_date_sk#22], [d_date_sk#22]

(103) ColumnarToRow [codegen id : 1]
Input [1]: [d_date_sk#22]

(104) BroadcastExchange
Input [1]: [d_date_sk#22]
Arguments: HashedRelationBroadcastMode(List(cast(input[0, int, true] as bigint)),false), [plan_id=8]

Subquery:2 Hosting operator id = 4 Hosting Expression = sr_returned_date_sk#15 IN dynamicpruning#5

Subquery:3 Hosting operator id = 25 Hosting Expression = cs_sold_date_sk#46 IN dynamicpruning#5

Subquery:4 Hosting operator id = 28 Hosting Expression = cr_returned_date_sk#57 IN dynamicpruning#5

Subquery:5 Hosting operator id = 46 Hosting Expression = ws_sold_date_sk#87 IN dynamicpruning#5

Subquery:6 Hosting operator id = 49 Hosting Expression = wr_returned_date_sk#99 IN dynamicpruning#5


