== Physical Plan ==
VeloxColumnarToRow (82)
+- TakeOrderedAndProjectExecTransformer (81)
   +- ^ ProjectExecTransformer (79)
      +- ^ RegularHashAggregateExecTransformer (78)
         +- ^ InputIteratorTransformer (77)
            +- ColumnarExchange (75)
               +- VeloxResizeBatches (74)
                  +- ^ ProjectExecTransformer (72)
                     +- ^ FlushableHashAggregateExecTransformer (71)
                        +- ^ ProjectExecTransformer (70)
                           +- ^ BroadcastHashJoinExecTransformer Inner BuildRight (69)
                              :- ^ ProjectExecTransformer (62)
                              :  +- ^ BroadcastHashJoinExecTransformer Inner BuildRight (61)
                              :     :- ^ ProjectExecTransformer (57)
                              :     :  +- ^ BroadcastHashJoinExecTransformer Inner BuildRight (56)
                              :     :     :- ^ ProjectExecTransformer (48)
                              :     :     :  +- ^ ShuffledHashJoinExecTransformer Inner BuildRight (47)
                              :     :     :     :- ^ InputIteratorTransformer (38)
                              :     :     :     :  +- ColumnarExchange (36)
                              :     :     :     :     +- VeloxResizeBatches (35)
                              :     :     :     :        +- ^ ProjectExecTransformer (33)
                              :     :     :     :           +- ^ BroadcastHashJoinExecTransformer Inner BuildRight (32)
                              :     :     :     :              :- ^ ProjectExecTransformer (25)
                              :     :     :     :              :  +- ^ ShuffledHashJoinExecTransformer Inner BuildRight (24)
                              :     :     :     :              :     :- ^ InputIteratorTransformer (15)
                              :     :     :     :              :     :  +- ColumnarExchange (13)
                              :     :     :     :              :     :     +- VeloxResizeBatches (12)
                              :     :     :     :              :     :        +- ^ ProjectExecTransformer (10)
                              :     :     :     :              :     :           +- ^ BroadcastHashJoinExecTransformer Inner BuildRight (9)
                              :     :     :     :              :     :              :- ^ FilterExecTransformer (2)
                              :     :     :     :              :     :              :  +- ^ FileSourceScanExecTransformer parquet spark_catalog.default.web_sales (1)
                              :     :     :     :              :     :              +- ^ InputIteratorTransformer (8)
                              :     :     :     :              :     :                 +- ColumnarBroadcastExchange (6)
                              :     :     :     :              :     :                    +- ^ FilterExecTransformer (4)
                              :     :     :     :              :     :                       +- ^ FileSourceScanExecTransformer parquet spark_catalog.default.web_page (3)
                              :     :     :     :              :     +- ^ InputIteratorTransformer (23)
                              :     :     :     :              :        +- ColumnarExchange (21)
                              :     :     :     :              :           +- VeloxResizeBatches (20)
                              :     :     :     :              :              +- ^ ProjectExecTransformer (18)
                              :     :     :     :              :                 +- ^ FilterExecTransformer (17)
                              :     :     :     :              :                    +- ^ FileSourceScanExecTransformer parquet spark_catalog.default.web_returns (16)
                              :     :     :     :              +- ^ InputIteratorTransformer (31)
                              :     :     :     :                 +- ColumnarBroadcastExchange (29)
                              :     :     :     :                    +- ^ FilterExecTransformer (27)
                              :     :     :     :                       +- ^ FileSourceScanExecTransformer parquet spark_catalog.default.customer_demographics (26)
                              :     :     :     +- ^ InputIteratorTransformer (46)
                              :     :     :        +- ColumnarExchange (44)
                              :     :     :           +- VeloxResizeBatches (43)
                              :     :     :              +- ^ ProjectExecTransformer (41)
                              :     :     :                 +- ^ FilterExecTransformer (40)
                              :     :     :                    +- ^ FileSourceScanExecTransformer parquet spark_catalog.default.customer_demographics (39)
                              :     :     +- ^ InputIteratorTransformer (55)
                              :     :        +- ColumnarBroadcastExchange (53)
                              :     :           +- ^ ProjectExecTransformer (51)
                              :     :              +- ^ FilterExecTransformer (50)
                              :     :                 +- ^ FileSourceScanExecTransformer parquet spark_catalog.default.customer_address (49)
                              :     +- ^ InputIteratorTransformer (60)
                              :        +- ReusedExchange (58)
                              +- ^ InputIteratorTransformer (68)
                                 +- ColumnarBroadcastExchange (66)
                                    +- ^ FilterExecTransformer (64)
                                       +- ^ FileSourceScanExecTransformer parquet spark_catalog.default.reason (63)


(1) FileSourceScanExecTransformer parquet spark_catalog.default.web_sales
Output [7]: [ws_item_sk#1, ws_web_page_sk#2, ws_order_number#3, ws_quantity#4, ws_sales_price#5, ws_net_profit#6, ws_sold_date_sk#7]
Batched: true
Location: InMemoryFileIndex []
PartitionFilters: [isnotnull(ws_sold_date_sk#7), dynamicpruningexpression(ws_sold_date_sk#7 IN dynamicpruning#8)]
PushedFilters: [IsNotNull(ws_item_sk), IsNotNull(ws_order_number), IsNotNull(ws_web_page_sk), Or(Or(And(GreaterThanOrEqual(ws_sales_price,100.00),LessThanOrEqual(ws_sales_price,150.00)),And(GreaterThanOrEqual(ws_sales_price,50.00),LessThanOrEqual(ws_sales_price,100.00))),And(GreaterThanOrEqual(ws_sales_price,150.00),LessThanOrEqual(ws_sales_price,200.00))), Or(Or(And(GreaterThanOrEqual(ws_net_profit,100.00),LessThanOrEqual(ws_net_profit,200.00)),And(GreaterThanOrEqual(ws_net_profit,150.00),LessThanOrEqual(ws_net_profit,300.00))),And(GreaterThanOrEqual(ws_net_profit,50.00),LessThanOrEqual(ws_net_profit,250.00)))]
ReadSchema: struct<ws_item_sk:int,ws_web_page_sk:int,ws_order_number:int,ws_quantity:int,ws_sales_price:decimal(7,2),ws_net_profit:decimal(7,2)>

(2) FilterExecTransformer
Input [7]: [ws_item_sk#1, ws_web_page_sk#2, ws_order_number#3, ws_quantity#4, ws_sales_price#5, ws_net_profit#6, ws_sold_date_sk#7]
Arguments: ((((isnotnull(ws_item_sk#1) AND isnotnull(ws_order_number#3)) AND isnotnull(ws_web_page_sk#2)) AND ((((ws_sales_price#5 >= 100.00) AND (ws_sales_price#5 <= 150.00)) OR ((ws_sales_price#5 >= 50.00) AND (ws_sales_price#5 <= 100.00))) OR ((ws_sales_price#5 >= 150.00) AND (ws_sales_price#5 <= 200.00)))) AND ((((ws_net_profit#6 >= 100.00) AND (ws_net_profit#6 <= 200.00)) OR ((ws_net_profit#6 >= 150.00) AND (ws_net_profit#6 <= 300.00))) OR ((ws_net_profit#6 >= 50.00) AND (ws_net_profit#6 <= 250.00))))

(3) FileSourceScanExecTransformer parquet spark_catalog.default.web_page
Output [1]: [wp_web_page_sk#9]
Batched: true
Location: InMemoryFileIndex [{warehouse_dir}/web_page]
PushedFilters: [IsNotNull(wp_web_page_sk)]
ReadSchema: struct<wp_web_page_sk:int>

(4) FilterExecTransformer
Input [1]: [wp_web_page_sk#9]
Arguments: isnotnull(wp_web_page_sk#9)

(5) WholeStageCodegenTransformer (2)
Input [1]: [wp_web_page_sk#9]
Arguments: false

(6) ColumnarBroadcastExchange
Input [1]: [wp_web_page_sk#9]
Arguments: HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint)),false), [plan_id=1]

(7) InputAdapter
Input [1]: [wp_web_page_sk#9]

(8) InputIteratorTransformer
Input [1]: [wp_web_page_sk#9]

(9) BroadcastHashJoinExecTransformer
Left keys [1]: [ws_web_page_sk#2]
Right keys [1]: [wp_web_page_sk#9]
Join type: Inner
Join condition: None

(10) ProjectExecTransformer
Output [7]: [hash(ws_item_sk#1, ws_order_number#3, 42) AS hash_partition_key#10, ws_item_sk#1, ws_order_number#3, ws_quantity#4, ws_sales_price#5, ws_net_profit#6, ws_sold_date_sk#7]
Input [8]: [ws_item_sk#1, ws_web_page_sk#2, ws_order_number#3, ws_quantity#4, ws_sales_price#5, ws_net_profit#6, ws_sold_date_sk#7, wp_web_page_sk#9]

(11) WholeStageCodegenTransformer (3)
Input [7]: [hash_partition_key#10, ws_item_sk#1, ws_order_number#3, ws_quantity#4, ws_sales_price#5, ws_net_profit#6, ws_sold_date_sk#7]
Arguments: false

(12) VeloxResizeBatches
Input [7]: [hash_partition_key#10, ws_item_sk#1, ws_order_number#3, ws_quantity#4, ws_sales_price#5, ws_net_profit#6, ws_sold_date_sk#7]
Arguments: 1024, 2147483647, 10485760

(13) ColumnarExchange
Input [7]: [hash_partition_key#10, ws_item_sk#1, ws_order_number#3, ws_quantity#4, ws_sales_price#5, ws_net_profit#6, ws_sold_date_sk#7]
Arguments: hashpartitioning(ws_item_sk#1, ws_order_number#3, 1), ENSURE_REQUIREMENTS, [ws_item_sk#1, ws_order_number#3, ws_quantity#4, ws_sales_price#5, ws_net_profit#6, ws_sold_date_sk#7], [plan_id=2], [shuffle_writer_type=hash]

(14) InputAdapter
Input [6]: [ws_item_sk#1, ws_order_number#3, ws_quantity#4, ws_sales_price#5, ws_net_profit#6, ws_sold_date_sk#7]

(15) InputIteratorTransformer
Input [6]: [ws_item_sk#1, ws_order_number#3, ws_quantity#4, ws_sales_price#5, ws_net_profit#6, ws_sold_date_sk#7]

(16) FileSourceScanExecTransformer parquet spark_catalog.default.web_returns
Output [9]: [wr_item_sk#11, wr_refunded_cdemo_sk#12, wr_refunded_addr_sk#13, wr_returning_cdemo_sk#14, wr_reason_sk#15, wr_order_number#16, wr_fee#17, wr_refunded_cash#18, wr_returned_date_sk#19]
Batched: true
Location: CatalogFileIndex [{warehouse_dir}/web_returns]
PushedFilters: [IsNotNull(wr_item_sk), IsNotNull(wr_order_number), IsNotNull(wr_refunded_cdemo_sk), IsNotNull(wr_returning_cdemo_sk), IsNotNull(wr_refunded_addr_sk), IsNotNull(wr_reason_sk)]
ReadSchema: struct<wr_item_sk:int,wr_refunded_cdemo_sk:int,wr_refunded_addr_sk:int,wr_returning_cdemo_sk:int,wr_reason_sk:int,wr_order_number:int,wr_fee:decimal(7,2),wr_refunded_cash:decimal(7,2)>

(17) FilterExecTransformer
Input [9]: [wr_item_sk#11, wr_refunded_cdemo_sk#12, wr_refunded_addr_sk#13, wr_returning_cdemo_sk#14, wr_reason_sk#15, wr_order_number#16, wr_fee#17, wr_refunded_cash#18, wr_returned_date_sk#19]
Arguments: (((((((isnotnull(wr_item_sk#11) AND isnotnull(wr_order_number#16)) AND isnotnull(wr_refunded_cdemo_sk#12)) AND isnotnull(wr_returning_cdemo_sk#14)) AND isnotnull(wr_refunded_addr_sk#13)) AND isnotnull(wr_reason_sk#15)) AND velox_might_contain(Subquery scalar-subquery#20, [id=#3], xxhash64(wr_refunded_cdemo_sk#12, 42))) AND velox_might_contain(Subquery scalar-subquery#21, [id=#4], xxhash64(wr_refunded_addr_sk#13, 42)))

(18) ProjectExecTransformer
Output [9]: [hash(wr_item_sk#11, wr_order_number#16, 42) AS hash_partition_key#22, wr_item_sk#11, wr_refunded_cdemo_sk#12, wr_refunded_addr_sk#13, wr_returning_cdemo_sk#14, wr_reason_sk#15, wr_order_number#16, wr_fee#17, wr_refunded_cash#18]
Input [9]: [wr_item_sk#11, wr_refunded_cdemo_sk#12, wr_refunded_addr_sk#13, wr_returning_cdemo_sk#14, wr_reason_sk#15, wr_order_number#16, wr_fee#17, wr_refunded_cash#18, wr_returned_date_sk#19]

(19) WholeStageCodegenTransformer (8)
Input [9]: [hash_partition_key#22, wr_item_sk#11, wr_refunded_cdemo_sk#12, wr_refunded_addr_sk#13, wr_returning_cdemo_sk#14, wr_reason_sk#15, wr_order_number#16, wr_fee#17, wr_refunded_cash#18]
Arguments: false

(20) VeloxResizeBatches
Input [9]: [hash_partition_key#22, wr_item_sk#11, wr_refunded_cdemo_sk#12, wr_refunded_addr_sk#13, wr_returning_cdemo_sk#14, wr_reason_sk#15, wr_order_number#16, wr_fee#17, wr_refunded_cash#18]
Arguments: 1024, 2147483647, 10485760

(21) ColumnarExchange
Input [9]: [hash_partition_key#22, wr_item_sk#11, wr_refunded_cdemo_sk#12, wr_refunded_addr_sk#13, wr_returning_cdemo_sk#14, wr_reason_sk#15, wr_order_number#16, wr_fee#17, wr_refunded_cash#18]
Arguments: hashpartitioning(wr_item_sk#11, wr_order_number#16, 1), ENSURE_REQUIREMENTS, [wr_item_sk#11, wr_refunded_cdemo_sk#12, wr_refunded_addr_sk#13, wr_returning_cdemo_sk#14, wr_reason_sk#15, wr_order_number#16, wr_fee#17, wr_refunded_cash#18], [plan_id=5], [shuffle_writer_type=hash]

(22) InputAdapter
Input [8]: [wr_item_sk#11, wr_refunded_cdemo_sk#12, wr_refunded_addr_sk#13, wr_returning_cdemo_sk#14, wr_reason_sk#15, wr_order_number#16, wr_fee#17, wr_refunded_cash#18]

(23) InputIteratorTransformer
Input [8]: [wr_item_sk#11, wr_refunded_cdemo_sk#12, wr_refunded_addr_sk#13, wr_returning_cdemo_sk#14, wr_reason_sk#15, wr_order_number#16, wr_fee#17, wr_refunded_cash#18]

(24) ShuffledHashJoinExecTransformer
Left keys [2]: [ws_item_sk#1, ws_order_number#3]
Right keys [2]: [wr_item_sk#11, wr_order_number#16]
Join type: Inner
Join condition: None

(25) ProjectExecTransformer
Output [10]: [ws_quantity#4, ws_sales_price#5, ws_net_profit#6, ws_sold_date_sk#7, wr_refunded_cdemo_sk#12, wr_refunded_addr_sk#13, wr_returning_cdemo_sk#14, wr_reason_sk#15, wr_fee#17, wr_refunded_cash#18]
Input [14]: [ws_item_sk#1, ws_order_number#3, ws_quantity#4, ws_sales_price#5, ws_net_profit#6, ws_sold_date_sk#7, wr_item_sk#11, wr_refunded_cdemo_sk#12, wr_refunded_addr_sk#13, wr_returning_cdemo_sk#14, wr_reason_sk#15, wr_order_number#16, wr_fee#17, wr_refunded_cash#18]

(26) FileSourceScanExecTransformer parquet spark_catalog.default.customer_demographics
Output [3]: [cd_demo_sk#23, cd_marital_status#24, cd_education_status#25]
Batched: true
Location: InMemoryFileIndex [{warehouse_dir}/customer_demographics]
PushedFilters: [IsNotNull(cd_demo_sk), IsNotNull(cd_marital_status), IsNotNull(cd_education_status), Or(Or(And(EqualTo(cd_marital_status,M),EqualTo(cd_education_status,Advanced Degree     )),And(EqualTo(cd_marital_status,S),EqualTo(cd_education_status,College             ))),And(EqualTo(cd_marital_status,W),EqualTo(cd_education_status,2 yr Degree         )))]
ReadSchema: struct<cd_demo_sk:int,cd_marital_status:string,cd_education_status:string>

(27) FilterExecTransformer
Input [3]: [cd_demo_sk#23, cd_marital_status#24, cd_education_status#25]
Arguments: (((isnotnull(cd_demo_sk#23) AND isnotnull(cd_marital_status#24)) AND isnotnull(cd_education_status#25)) AND ((((cd_marital_status#24 = M) AND (cd_education_status#25 = Advanced Degree     )) OR ((cd_marital_status#24 = S) AND (cd_education_status#25 = College             ))) OR ((cd_marital_status#24 = W) AND (cd_education_status#25 = 2 yr Degree         ))))

(28) WholeStageCodegenTransformer (9)
Input [3]: [cd_demo_sk#23, cd_marital_status#24, cd_education_status#25]
Arguments: false

(29) ColumnarBroadcastExchange
Input [3]: [cd_demo_sk#23, cd_marital_status#24, cd_education_status#25]
Arguments: HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint)),false), [plan_id=6]

(30) InputAdapter
Input [3]: [cd_demo_sk#23, cd_marital_status#24, cd_education_status#25]

(31) InputIteratorTransformer
Input [3]: [cd_demo_sk#23, cd_marital_status#24, cd_education_status#25]

(32) BroadcastHashJoinExecTransformer
Left keys [1]: [wr_refunded_cdemo_sk#12]
Right keys [1]: [cd_demo_sk#23]
Join type: Inner
Join condition: ((((((cd_marital_status#24 = M) AND (cd_education_status#25 = Advanced Degree     )) AND (ws_sales_price#5 >= 100.00)) AND (ws_sales_price#5 <= 150.00)) OR ((((cd_marital_status#24 = S) AND (cd_education_status#25 = College             )) AND (ws_sales_price#5 >= 50.00)) AND (ws_sales_price#5 <= 100.00))) OR ((((cd_marital_status#24 = W) AND (cd_education_status#25 = 2 yr Degree         )) AND (ws_sales_price#5 >= 150.00)) AND (ws_sales_price#5 <= 200.00)))

(33) ProjectExecTransformer
Output [11]: [hash(wr_returning_cdemo_sk#14, cd_marital_status#24, cd_education_status#25, 42) AS hash_partition_key#26, ws_quantity#4, ws_net_profit#6, ws_sold_date_sk#7, wr_refunded_addr_sk#13, wr_returning_cdemo_sk#14, wr_reason_sk#15, wr_fee#17, wr_refunded_cash#18, cd_marital_status#24, cd_education_status#25]
Input [13]: [ws_quantity#4, ws_sales_price#5, ws_net_profit#6, ws_sold_date_sk#7, wr_refunded_cdemo_sk#12, wr_refunded_addr_sk#13, wr_returning_cdemo_sk#14, wr_reason_sk#15, wr_fee#17, wr_refunded_cash#18, cd_demo_sk#23, cd_marital_status#24, cd_education_status#25]

(34) WholeStageCodegenTransformer (10)
Input [11]: [hash_partition_key#26, ws_quantity#4, ws_net_profit#6, ws_sold_date_sk#7, wr_refunded_addr_sk#13, wr_returning_cdemo_sk#14, wr_reason_sk#15, wr_fee#17, wr_refunded_cash#18, cd_marital_status#24, cd_education_status#25]
Arguments: false

(35) VeloxResizeBatches
Input [11]: [hash_partition_key#26, ws_quantity#4, ws_net_profit#6, ws_sold_date_sk#7, wr_refunded_addr_sk#13, wr_returning_cdemo_sk#14, wr_reason_sk#15, wr_fee#17, wr_refunded_cash#18, cd_marital_status#24, cd_education_status#25]
Arguments: 1024, 2147483647, 10485760

(36) ColumnarExchange
Input [11]: [hash_partition_key#26, ws_quantity#4, ws_net_profit#6, ws_sold_date_sk#7, wr_refunded_addr_sk#13, wr_returning_cdemo_sk#14, wr_reason_sk#15, wr_fee#17, wr_refunded_cash#18, cd_marital_status#24, cd_education_status#25]
Arguments: hashpartitioning(wr_returning_cdemo_sk#14, cd_marital_status#24, cd_education_status#25, 1), ENSURE_REQUIREMENTS, [ws_quantity#4, ws_net_profit#6, ws_sold_date_sk#7, wr_refunded_addr_sk#13, wr_returning_cdemo_sk#14, wr_reason_sk#15, wr_fee#17, wr_refunded_cash#18, cd_marital_status#24, cd_education_status#25], [plan_id=7], [shuffle_writer_type=hash]

(37) InputAdapter
Input [10]: [ws_quantity#4, ws_net_profit#6, ws_sold_date_sk#7, wr_refunded_addr_sk#13, wr_returning_cdemo_sk#14, wr_reason_sk#15, wr_fee#17, wr_refunded_cash#18, cd_marital_status#24, cd_education_status#25]

(38) InputIteratorTransformer
Input [10]: [ws_quantity#4, ws_net_profit#6, ws_sold_date_sk#7, wr_refunded_addr_sk#13, wr_returning_cdemo_sk#14, wr_reason_sk#15, wr_fee#17, wr_refunded_cash#18, cd_marital_status#24, cd_education_status#25]

(39) FileSourceScanExecTransformer parquet spark_catalog.default.customer_demographics
Output [3]: [cd_demo_sk#27, cd_marital_status#28, cd_education_status#29]
Batched: true
Location: InMemoryFileIndex [{warehouse_dir}/customer_demographics]
PushedFilters: [IsNotNull(cd_demo_sk), IsNotNull(cd_marital_status), IsNotNull(cd_education_status)]
ReadSchema: struct<cd_demo_sk:int,cd_marital_status:string,cd_education_status:string>

(40) FilterExecTransformer
Input [3]: [cd_demo_sk#27, cd_marital_status#28, cd_education_status#29]
Arguments: ((isnotnull(cd_demo_sk#27) AND isnotnull(cd_marital_status#28)) AND isnotnull(cd_education_status#29))

(41) ProjectExecTransformer
Output [4]: [hash(cd_demo_sk#27, cd_marital_status#28, cd_education_status#29, 42) AS hash_partition_key#30, cd_demo_sk#27, cd_marital_status#28, cd_education_status#29]
Input [3]: [cd_demo_sk#27, cd_marital_status#28, cd_education_status#29]

(42) WholeStageCodegenTransformer (11)
Input [4]: [hash_partition_key#30, cd_demo_sk#27, cd_marital_status#28, cd_education_status#29]
Arguments: false

(43) VeloxResizeBatches
Input [4]: [hash_partition_key#30, cd_demo_sk#27, cd_marital_status#28, cd_education_status#29]
Arguments: 1024, 2147483647, 10485760

(44) ColumnarExchange
Input [4]: [hash_partition_key#30, cd_demo_sk#27, cd_marital_status#28, cd_education_status#29]
Arguments: hashpartitioning(cd_demo_sk#27, cd_marital_status#28, cd_education_status#29, 1), ENSURE_REQUIREMENTS, [cd_demo_sk#27, cd_marital_status#28, cd_education_status#29], [plan_id=8], [shuffle_writer_type=hash]

(45) InputAdapter
Input [3]: [cd_demo_sk#27, cd_marital_status#28, cd_education_status#29]

(46) InputIteratorTransformer
Input [3]: [cd_demo_sk#27, cd_marital_status#28, cd_education_status#29]

(47) ShuffledHashJoinExecTransformer
Left keys [3]: [wr_returning_cdemo_sk#14, cd_marital_status#24, cd_education_status#25]
Right keys [3]: [cd_demo_sk#27, cd_marital_status#28, cd_education_status#29]
Join type: Inner
Join condition: None

(48) ProjectExecTransformer
Output [7]: [ws_quantity#4, ws_net_profit#6, ws_sold_date_sk#7, wr_refunded_addr_sk#13, wr_reason_sk#15, wr_fee#17, wr_refunded_cash#18]
Input [13]: [ws_quantity#4, ws_net_profit#6, ws_sold_date_sk#7, wr_refunded_addr_sk#13, wr_returning_cdemo_sk#14, wr_reason_sk#15, wr_fee#17, wr_refunded_cash#18, cd_marital_status#24, cd_education_status#25, cd_demo_sk#27, cd_marital_status#28, cd_education_status#29]

(49) FileSourceScanExecTransformer parquet spark_catalog.default.customer_address
Output [3]: [ca_address_sk#31, ca_state#32, ca_country#33]
Batched: true
Location: InMemoryFileIndex [{warehouse_dir}/customer_address]
PushedFilters: [IsNotNull(ca_country), EqualTo(ca_country,United States), IsNotNull(ca_address_sk), Or(Or(In(ca_state, [IN,NJ,OH]),In(ca_state, [CT,KY,WI])),In(ca_state, [AR,IA,LA]))]
ReadSchema: struct<ca_address_sk:int,ca_state:string,ca_country:string>

(50) FilterExecTransformer
Input [3]: [ca_address_sk#31, ca_state#32, ca_country#33]
Arguments: (((isnotnull(ca_country#33) AND (ca_country#33 = United States)) AND isnotnull(ca_address_sk#31)) AND ((ca_state#32 IN (IN,OH,NJ) OR ca_state#32 IN (WI,CT,KY)) OR ca_state#32 IN (LA,IA,AR)))

(51) ProjectExecTransformer
Output [2]: [ca_address_sk#31, ca_state#32]
Input [3]: [ca_address_sk#31, ca_state#32, ca_country#33]

(52) WholeStageCodegenTransformer (12)
Input [2]: [ca_address_sk#31, ca_state#32]
Arguments: false

(53) ColumnarBroadcastExchange
Input [2]: [ca_address_sk#31, ca_state#32]
Arguments: HashedRelationBroadcastMode(List(cast(input[0, int, true] as bigint)),false), [plan_id=9]

(54) InputAdapter
Input [2]: [ca_address_sk#31, ca_state#32]

(55) InputIteratorTransformer
Input [2]: [ca_address_sk#31, ca_state#32]

(56) BroadcastHashJoinExecTransformer
Left keys [1]: [wr_refunded_addr_sk#13]
Right keys [1]: [ca_address_sk#31]
Join type: Inner
Join condition: ((((ca_state#32 IN (IN,OH,NJ) AND (ws_net_profit#6 >= 100.00)) AND (ws_net_profit#6 <= 200.00)) OR ((ca_state#32 IN (WI,CT,KY) AND (ws_net_profit#6 >= 150.00)) AND (ws_net_profit#6 <= 300.00))) OR ((ca_state#32 IN (LA,IA,AR) AND (ws_net_profit#6 >= 50.00)) AND (ws_net_profit#6 <= 250.00)))

(57) ProjectExecTransformer
Output [5]: [ws_quantity#4, ws_sold_date_sk#7, wr_reason_sk#15, wr_fee#17, wr_refunded_cash#18]
Input [9]: [ws_quantity#4, ws_net_profit#6, ws_sold_date_sk#7, wr_refunded_addr_sk#13, wr_reason_sk#15, wr_fee#17, wr_refunded_cash#18, ca_address_sk#31, ca_state#32]

(58) ReusedExchange [Reuses operator id: 87]
Output [1]: [d_date_sk#34]

(59) InputAdapter
Input [1]: [d_date_sk#34]

(60) InputIteratorTransformer
Input [1]: [d_date_sk#34]

(61) BroadcastHashJoinExecTransformer
Left keys [1]: [ws_sold_date_sk#7]
Right keys [1]: [d_date_sk#34]
Join type: Inner
Join condition: None

(62) ProjectExecTransformer
Output [4]: [ws_quantity#4, wr_reason_sk#15, wr_fee#17, wr_refunded_cash#18]
Input [6]: [ws_quantity#4, ws_sold_date_sk#7, wr_reason_sk#15, wr_fee#17, wr_refunded_cash#18, d_date_sk#34]

(63) FileSourceScanExecTransformer parquet spark_catalog.default.reason
Output [2]: [r_reason_sk#35, r_reason_desc#36]
Batched: true
Location: InMemoryFileIndex [{warehouse_dir}/reason]
PushedFilters: [IsNotNull(r_reason_sk)]
ReadSchema: struct<r_reason_sk:int,r_reason_desc:string>

(64) FilterExecTransformer
Input [2]: [r_reason_sk#35, r_reason_desc#36]
Arguments: isnotnull(r_reason_sk#35)

(65) WholeStageCodegenTransformer (14)
Input [2]: [r_reason_sk#35, r_reason_desc#36]
Arguments: false

(66) ColumnarBroadcastExchange
Input [2]: [r_reason_sk#35, r_reason_desc#36]
Arguments: HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint)),false), [plan_id=10]

(67) InputAdapter
Input [2]: [r_reason_sk#35, r_reason_desc#36]

(68) InputIteratorTransformer
Input [2]: [r_reason_sk#35, r_reason_desc#36]

(69) BroadcastHashJoinExecTransformer
Left keys [1]: [wr_reason_sk#15]
Right keys [1]: [r_reason_sk#35]
Join type: Inner
Join condition: None

(70) ProjectExecTransformer
Output [4]: [ws_quantity#4, r_reason_desc#36, UnscaledValue(wr_refunded_cash#18) AS _pre_1#37, UnscaledValue(wr_fee#17) AS _pre_2#38]
Input [6]: [ws_quantity#4, wr_reason_sk#15, wr_fee#17, wr_refunded_cash#18, r_reason_sk#35, r_reason_desc#36]

(71) FlushableHashAggregateExecTransformer
Input [4]: [ws_quantity#4, r_reason_desc#36, _pre_1#37, _pre_2#38]
Keys [1]: [r_reason_desc#36]
Functions [3]: [partial_avg(ws_quantity#4), partial_avg(_pre_1#37), partial_avg(_pre_2#38)]
Aggregate Attributes [6]: [sum#39, count#40, sum#41, count#42, sum#43, count#44]
Results [7]: [r_reason_desc#36, sum#45, count#46, sum#47, count#48, sum#49, count#50]

(72) ProjectExecTransformer
Output [8]: [hash(r_reason_desc#36, 42) AS hash_partition_key#51, r_reason_desc#36, sum#45, count#46, sum#47, count#48, sum#49, count#50]
Input [7]: [r_reason_desc#36, sum#45, count#46, sum#47, count#48, sum#49, count#50]

(73) WholeStageCodegenTransformer (15)
Input [8]: [hash_partition_key#51, r_reason_desc#36, sum#45, count#46, sum#47, count#48, sum#49, count#50]
Arguments: false

(74) VeloxResizeBatches
Input [8]: [hash_partition_key#51, r_reason_desc#36, sum#45, count#46, sum#47, count#48, sum#49, count#50]
Arguments: 1024, 2147483647, 10485760

(75) ColumnarExchange
Input [8]: [hash_partition_key#51, r_reason_desc#36, sum#45, count#46, sum#47, count#48, sum#49, count#50]
Arguments: hashpartitioning(r_reason_desc#36, 1), ENSURE_REQUIREMENTS, [r_reason_desc#36, sum#45, count#46, sum#47, count#48, sum#49, count#50], [plan_id=11], [shuffle_writer_type=hash]

(76) InputAdapter
Input [7]: [r_reason_desc#36, sum#45, count#46, sum#47, count#48, sum#49, count#50]

(77) InputIteratorTransformer
Input [7]: [r_reason_desc#36, sum#45, count#46, sum#47, count#48, sum#49, count#50]

(78) RegularHashAggregateExecTransformer
Input [7]: [r_reason_desc#36, sum#45, count#46, sum#47, count#48, sum#49, count#50]
Keys [1]: [r_reason_desc#36]
Functions [3]: [avg(ws_quantity#4), avg(UnscaledValue(wr_refunded_cash#18)), avg(UnscaledValue(wr_fee#17))]
Aggregate Attributes [3]: [avg(ws_quantity#4)#52, avg(UnscaledValue(wr_refunded_cash#18))#53, avg(UnscaledValue(wr_fee#17))#54]
Results [4]: [r_reason_desc#36, avg(ws_quantity#4)#52, avg(UnscaledValue(wr_refunded_cash#18))#53, avg(UnscaledValue(wr_fee#17))#54]

(79) ProjectExecTransformer
Output [4]: [substr(r_reason_desc#36, 1, 20) AS substr(r_reason_desc, 1, 20)#55, avg(ws_quantity#4)#52 AS avg(ws_quantity)#56, cast((avg(UnscaledValue(wr_refunded_cash#18))#53 / 100.0) as decimal(11,6)) AS avg(wr_refunded_cash)#57, cast((avg(UnscaledValue(wr_fee#17))#54 / 100.0) as decimal(11,6)) AS avg(wr_fee)#58]
Input [4]: [r_reason_desc#36, avg(ws_quantity#4)#52, avg(UnscaledValue(wr_refunded_cash#18))#53, avg(UnscaledValue(wr_fee#17))#54]

(80) WholeStageCodegenTransformer (16)
Input [4]: [substr(r_reason_desc, 1, 20)#55, avg(ws_quantity)#56, avg(wr_refunded_cash)#57, avg(wr_fee)#58]
Arguments: false

(81) TakeOrderedAndProjectExecTransformer
Input [4]: [substr(r_reason_desc, 1, 20)#55, avg(ws_quantity)#56, avg(wr_refunded_cash)#57, avg(wr_fee)#58]
Arguments: 100, [substr(r_reason_desc, 1, 20)#55 ASC NULLS FIRST, avg(ws_quantity)#56 ASC NULLS FIRST, avg(wr_refunded_cash)#57 ASC NULLS FIRST, avg(wr_fee)#58 ASC NULLS FIRST], [substr(r_reason_desc, 1, 20)#55, avg(ws_quantity)#56, avg(wr_refunded_cash)#57, avg(wr_fee)#58], 0

(82) VeloxColumnarToRow
Input [4]: [substr(r_reason_desc, 1, 20)#55, avg(ws_quantity)#56, avg(wr_refunded_cash)#57, avg(wr_fee)#58]

===== Subqueries =====

Subquery:1 Hosting operator id = 1 Hosting Expression = ws_sold_date_sk#7 IN dynamicpruning#8
ColumnarBroadcastExchange (87)
+- ^ ProjectExecTransformer (85)
   +- ^ FilterExecTransformer (84)
      +- ^ FileSourceScanExecTransformer parquet spark_catalog.default.date_dim (83)


(83) FileSourceScanExecTransformer parquet spark_catalog.default.date_dim
Output [2]: [d_date_sk#34, d_year#59]
Batched: true
Location: InMemoryFileIndex [{warehouse_dir}/date_dim]
PushedFilters: [IsNotNull(d_year), EqualTo(d_year,2000), IsNotNull(d_date_sk)]
ReadSchema: struct<d_date_sk:int,d_year:int>

(84) FilterExecTransformer
Input [2]: [d_date_sk#34, d_year#59]
Arguments: ((isnotnull(d_year#59) AND (d_year#59 = 2000)) AND isnotnull(d_date_sk#34))

(85) ProjectExecTransformer
Output [1]: [d_date_sk#34]
Input [2]: [d_date_sk#34, d_year#59]

(86) WholeStageCodegenTransformer (1)
Input [1]: [d_date_sk#34]
Arguments: false

(87) ColumnarBroadcastExchange
Input [1]: [d_date_sk#34]
Arguments: HashedRelationBroadcastMode(List(cast(input[0, int, true] as bigint)),false), [plan_id=12]

Subquery:2 Hosting operator id = 17 Hosting Expression = Subquery scalar-subquery#20, [id=#3]
VeloxColumnarToRow (99)
+- ^ RegularHashAggregateExecTransformer (97)
   +- ^ InputIteratorTransformer (96)
      +- ColumnarExchange (94)
         +- VeloxResizeBatches (93)
            +- ^ FlushableHashAggregateExecTransformer (91)
               +- ^ ProjectExecTransformer (90)
                  +- ^ FilterExecTransformer (89)
                     +- ^ FileSourceScanExecTransformer parquet spark_catalog.default.customer_demographics (88)


(88) FileSourceScanExecTransformer parquet spark_catalog.default.customer_demographics
Output [3]: [cd_demo_sk#23, cd_marital_status#24, cd_education_status#25]
Batched: true
Location: InMemoryFileIndex [{warehouse_dir}/customer_demographics]
PushedFilters: [IsNotNull(cd_demo_sk), IsNotNull(cd_marital_status), IsNotNull(cd_education_status), Or(Or(And(EqualTo(cd_marital_status,M),EqualTo(cd_education_status,Advanced Degree     )),And(EqualTo(cd_marital_status,S),EqualTo(cd_education_status,College             ))),And(EqualTo(cd_marital_status,W),EqualTo(cd_education_status,2 yr Degree         )))]
ReadSchema: struct<cd_demo_sk:int,cd_marital_status:string,cd_education_status:string>

(89) FilterExecTransformer
Input [3]: [cd_demo_sk#23, cd_marital_status#24, cd_education_status#25]
Arguments: (((isnotnull(cd_demo_sk#23) AND isnotnull(cd_marital_status#24)) AND isnotnull(cd_education_status#25)) AND ((((cd_marital_status#24 = M) AND (cd_education_status#25 = Advanced Degree     )) OR ((cd_marital_status#24 = S) AND (cd_education_status#25 = College             ))) OR ((cd_marital_status#24 = W) AND (cd_education_status#25 = 2 yr Degree         ))))

(90) ProjectExecTransformer
Output [1]: [xxhash64(cd_demo_sk#23, 42) AS _pre_3#60]
Input [3]: [cd_demo_sk#23, cd_marital_status#24, cd_education_status#25]

(91) FlushableHashAggregateExecTransformer
Input [1]: [_pre_3#60]
Keys: []
Functions [1]: [partial_velox_bloom_filter_agg(_pre_3#60, 159981, 2239471, 0, 0)]
Aggregate Attributes [1]: [buf#61]
Results [1]: [buf#62]

(92) WholeStageCodegenTransformer (4)
Input [1]: [buf#62]
Arguments: false

(93) VeloxResizeBatches
Input [1]: [buf#62]
Arguments: 1024, 2147483647, 10485760

(94) ColumnarExchange
Input [1]: [buf#62]
Arguments: SinglePartition, ENSURE_REQUIREMENTS, [plan_id=13], [shuffle_writer_type=hash]

(95) InputAdapter
Input [1]: [buf#62]

(96) InputIteratorTransformer
Input [1]: [buf#62]

(97) RegularHashAggregateExecTransformer
Input [1]: [buf#62]
Keys: []
Functions [1]: [velox_bloom_filter_agg(xxhash64(cd_demo_sk#23, 42), 159981, 2239471, 0, 0)]
Aggregate Attributes [1]: [bloom_filter_agg(xxhash64(cd_demo_sk#23, 42), 159981, 2239471, 0, 0)#63]
Results [1]: [bloom_filter_agg(xxhash64(cd_demo_sk#23, 42), 159981, 2239471, 0, 0)#63 AS bloomFilter#64]

(98) WholeStageCodegenTransformer (5)
Input [1]: [bloomFilter#64]
Arguments: false

(99) VeloxColumnarToRow
Input [1]: [bloomFilter#64]

Subquery:3 Hosting operator id = 17 Hosting Expression = Subquery scalar-subquery#21, [id=#4]
VeloxColumnarToRow (111)
+- ^ RegularHashAggregateExecTransformer (109)
   +- ^ InputIteratorTransformer (108)
      +- ColumnarExchange (106)
         +- VeloxResizeBatches (105)
            +- ^ FlushableHashAggregateExecTransformer (103)
               +- ^ ProjectExecTransformer (102)
                  +- ^ FilterExecTransformer (101)
                     +- ^ FileSourceScanExecTransformer parquet spark_catalog.default.customer_address (100)


(100) FileSourceScanExecTransformer parquet spark_catalog.default.customer_address
Output [3]: [ca_address_sk#31, ca_state#32, ca_country#33]
Batched: true
Location: InMemoryFileIndex [{warehouse_dir}/customer_address]
PushedFilters: [IsNotNull(ca_country), EqualTo(ca_country,United States), IsNotNull(ca_address_sk), Or(Or(In(ca_state, [IN,NJ,OH]),In(ca_state, [CT,KY,WI])),In(ca_state, [AR,IA,LA]))]
ReadSchema: struct<ca_address_sk:int,ca_state:string,ca_country:string>

(101) FilterExecTransformer
Input [3]: [ca_address_sk#31, ca_state#32, ca_country#33]
Arguments: (((isnotnull(ca_country#33) AND (ca_country#33 = United States)) AND isnotnull(ca_address_sk#31)) AND ((ca_state#32 IN (IN,OH,NJ) OR ca_state#32 IN (WI,CT,KY)) OR ca_state#32 IN (LA,IA,AR)))

(102) ProjectExecTransformer
Output [1]: [xxhash64(ca_address_sk#31, 42) AS _pre_4#65]
Input [3]: [ca_address_sk#31, ca_state#32, ca_country#33]

(103) FlushableHashAggregateExecTransformer
Input [1]: [_pre_4#65]
Keys: []
Functions [1]: [partial_velox_bloom_filter_agg(_pre_4#65, 152837, 2153999, 0, 0)]
Aggregate Attributes [1]: [buf#66]
Results [1]: [buf#67]

(104) WholeStageCodegenTransformer (6)
Input [1]: [buf#67]
Arguments: false

(105) VeloxResizeBatches
Input [1]: [buf#67]
Arguments: 1024, 2147483647, 10485760

(106) ColumnarExchange
Input [1]: [buf#67]
Arguments: SinglePartition, ENSURE_REQUIREMENTS, [plan_id=14], [shuffle_writer_type=hash]

(107) InputAdapter
Input [1]: [buf#67]

(108) InputIteratorTransformer
Input [1]: [buf#67]

(109) RegularHashAggregateExecTransformer
Input [1]: [buf#67]
Keys: []
Functions [1]: [velox_bloom_filter_agg(xxhash64(ca_address_sk#31, 42), 152837, 2153999, 0, 0)]
Aggregate Attributes [1]: [bloom_filter_agg(xxhash64(ca_address_sk#31, 42), 152837, 2153999, 0, 0)#68]
Results [1]: [bloom_filter_agg(xxhash64(ca_address_sk#31, 42), 152837, 2153999, 0, 0)#68 AS bloomFilter#69]

(110) WholeStageCodegenTransformer (7)
Input [1]: [bloomFilter#69]
Arguments: false

(111) VeloxColumnarToRow
Input [1]: [bloomFilter#69]

Subquery:4 Hosting operator id = 16 Hosting Expression = Subquery scalar-subquery#20, [id=#3]
VeloxColumnarToRow (99)
+- ^ RegularHashAggregateExecTransformer (97)
   +- ^ InputIteratorTransformer (96)
      +- ColumnarExchange (94)
         +- VeloxResizeBatches (93)
            +- ^ FlushableHashAggregateExecTransformer (91)
               +- ^ ProjectExecTransformer (90)
                  +- ^ FilterExecTransformer (89)
                     +- ^ FileSourceScanExecTransformer parquet spark_catalog.default.customer_demographics (88)


Subquery:5 Hosting operator id = 16 Hosting Expression = Subquery scalar-subquery#21, [id=#4]
VeloxColumnarToRow (111)
+- ^ RegularHashAggregateExecTransformer (109)
   +- ^ InputIteratorTransformer (108)
      +- ColumnarExchange (106)
         +- VeloxResizeBatches (105)
            +- ^ FlushableHashAggregateExecTransformer (103)
               +- ^ ProjectExecTransformer (102)
                  +- ^ FilterExecTransformer (101)
                     +- ^ FileSourceScanExecTransformer parquet spark_catalog.default.customer_address (100)



