== Physical Plan ==
TakeOrderedAndProject (47)
+- * HashAggregate (46)
   +- * CometColumnarToRow (45)
      +- CometColumnarExchange (44)
         +- * HashAggregate (43)
            +- * Project (42)
               +- * BroadcastHashJoin Inner BuildRight (41)
                  :- * Project (35)
                  :  +- * BroadcastHashJoin Inner BuildRight (34)
                  :     :- * Project (28)
                  :     :  +- * Filter (27)
                  :     :     +- * BroadcastHashJoin ExistenceJoin(exists#1) BuildRight (26)
                  :     :        :- * BroadcastHashJoin ExistenceJoin(exists#2) BuildRight (19)
                  :     :        :  :- * CometColumnarToRow (12)
                  :     :        :  :  +- CometBroadcastHashJoin (11)
                  :     :        :  :     :- CometFilter (2)
                  :     :        :  :     :  +- CometScan [native_iceberg_compat] parquet spark_catalog.default.customer (1)
                  :     :        :  :     +- CometBroadcastExchange (10)
                  :     :        :  :        +- CometProject (9)
                  :     :        :  :           +- CometBroadcastHashJoin (8)
                  :     :        :  :              :- CometScan [native_iceberg_compat] parquet spark_catalog.default.store_sales (3)
                  :     :        :  :              +- CometBroadcastExchange (7)
                  :     :        :  :                 +- CometProject (6)
                  :     :        :  :                    +- CometFilter (5)
                  :     :        :  :                       +- CometScan [native_iceberg_compat] parquet spark_catalog.default.date_dim (4)
                  :     :        :  +- BroadcastExchange (18)
                  :     :        :     +- * CometColumnarToRow (17)
                  :     :        :        +- CometProject (16)
                  :     :        :           +- CometBroadcastHashJoin (15)
                  :     :        :              :- CometScan [native_iceberg_compat] parquet spark_catalog.default.web_sales (13)
                  :     :        :              +- ReusedExchange (14)
                  :     :        +- BroadcastExchange (25)
                  :     :           +- * CometColumnarToRow (24)
                  :     :              +- CometProject (23)
                  :     :                 +- CometBroadcastHashJoin (22)
                  :     :                    :- CometScan [native_iceberg_compat] parquet spark_catalog.default.catalog_sales (20)
                  :     :                    +- ReusedExchange (21)
                  :     +- BroadcastExchange (33)
                  :        +- * CometColumnarToRow (32)
                  :           +- CometProject (31)
                  :              +- CometFilter (30)
                  :                 +- CometScan [native_iceberg_compat] parquet spark_catalog.default.customer_address (29)
                  +- BroadcastExchange (40)
                     +- * CometColumnarToRow (39)
                        +- CometProject (38)
                           +- CometFilter (37)
                              +- CometScan [native_iceberg_compat] parquet spark_catalog.default.customer_demographics (36)


(1) CometScan [native_iceberg_compat] parquet spark_catalog.default.customer
Output [3]: [c_customer_sk#3, c_current_cdemo_sk#4, c_current_addr_sk#5]
Batched: true
Location [not included in comparison]/{warehouse_dir}/customer]
PushedFilters: [IsNotNull(c_current_addr_sk), IsNotNull(c_current_cdemo_sk)]
ReadSchema: struct<c_customer_sk:int,c_current_cdemo_sk:int,c_current_addr_sk:int>

(2) CometFilter
Input [3]: [c_customer_sk#3, c_current_cdemo_sk#4, c_current_addr_sk#5]
Condition : (isnotnull(c_current_addr_sk#5) AND isnotnull(c_current_cdemo_sk#4))

(3) CometScan [native_iceberg_compat] parquet spark_catalog.default.store_sales
Output [2]: [ss_customer_sk#6, ss_sold_date_sk#7]
Batched: true
Location: InMemoryFileIndex []
PartitionFilters: [isnotnull(ss_sold_date_sk#7), dynamicpruningexpression(ss_sold_date_sk#7 IN dynamicpruning#8)]
ReadSchema: struct<ss_customer_sk:int>

(4) CometScan [native_iceberg_compat] parquet spark_catalog.default.date_dim
Output [3]: [d_date_sk#9, d_year#10, d_qoy#11]
Batched: true
Location [not included in comparison]/{warehouse_dir}/date_dim]
PushedFilters: [IsNotNull(d_year), IsNotNull(d_qoy), EqualTo(d_year,2002), LessThan(d_qoy,4), IsNotNull(d_date_sk)]
ReadSchema: struct<d_date_sk:int,d_year:int,d_qoy:int>

(5) CometFilter
Input [3]: [d_date_sk#9, d_year#10, d_qoy#11]
Condition : ((((isnotnull(d_year#10) AND isnotnull(d_qoy#11)) AND (d_year#10 = 2002)) AND (d_qoy#11 < 4)) AND isnotnull(d_date_sk#9))

(6) CometProject
Input [3]: [d_date_sk#9, d_year#10, d_qoy#11]
Arguments: [d_date_sk#9], [d_date_sk#9]

(7) CometBroadcastExchange
Input [1]: [d_date_sk#9]
Arguments: [d_date_sk#9]

(8) CometBroadcastHashJoin
Left output [2]: [ss_customer_sk#6, ss_sold_date_sk#7]
Right output [1]: [d_date_sk#9]
Arguments: [ss_sold_date_sk#7], [d_date_sk#9], Inner, BuildRight

(9) CometProject
Input [3]: [ss_customer_sk#6, ss_sold_date_sk#7, d_date_sk#9]
Arguments: [ss_customer_sk#6], [ss_customer_sk#6]

(10) CometBroadcastExchange
Input [1]: [ss_customer_sk#6]
Arguments: [ss_customer_sk#6]

(11) CometBroadcastHashJoin
Left output [3]: [c_customer_sk#3, c_current_cdemo_sk#4, c_current_addr_sk#5]
Right output [1]: [ss_customer_sk#6]
Arguments: [c_customer_sk#3], [ss_customer_sk#6], LeftSemi, BuildRight

(12) CometColumnarToRow [codegen id : 5]
Input [3]: [c_customer_sk#3, c_current_cdemo_sk#4, c_current_addr_sk#5]

(13) CometScan [native_iceberg_compat] parquet spark_catalog.default.web_sales
Output [2]: [ws_bill_customer_sk#12, ws_sold_date_sk#13]
Batched: true
Location: InMemoryFileIndex []
PartitionFilters: [isnotnull(ws_sold_date_sk#13), dynamicpruningexpression(ws_sold_date_sk#13 IN dynamicpruning#14)]
ReadSchema: struct<ws_bill_customer_sk:int>

(14) ReusedExchange [Reuses operator id: 7]
Output [1]: [d_date_sk#15]

(15) CometBroadcastHashJoin
Left output [2]: [ws_bill_customer_sk#12, ws_sold_date_sk#13]
Right output [1]: [d_date_sk#15]
Arguments: [ws_sold_date_sk#13], [d_date_sk#15], Inner, BuildRight

(16) CometProject
Input [3]: [ws_bill_customer_sk#12, ws_sold_date_sk#13, d_date_sk#15]
Arguments: [ws_bill_customer_sk#12], [ws_bill_customer_sk#12]

(17) CometColumnarToRow [codegen id : 1]
Input [1]: [ws_bill_customer_sk#12]

(18) BroadcastExchange
Input [1]: [ws_bill_customer_sk#12]
Arguments: HashedRelationBroadcastMode(List(cast(input[0, int, true] as bigint)),false), [plan_id=1]

(19) BroadcastHashJoin [codegen id : 5]
Left keys [1]: [c_customer_sk#3]
Right keys [1]: [ws_bill_customer_sk#12]
Join type: ExistenceJoin(exists#2)
Join condition: None

(20) CometScan [native_iceberg_compat] parquet spark_catalog.default.catalog_sales
Output [2]: [cs_ship_customer_sk#16, cs_sold_date_sk#17]
Batched: true
Location: InMemoryFileIndex []
PartitionFilters: [isnotnull(cs_sold_date_sk#17), dynamicpruningexpression(cs_sold_date_sk#17 IN dynamicpruning#18)]
ReadSchema: struct<cs_ship_customer_sk:int>

(21) ReusedExchange [Reuses operator id: 7]
Output [1]: [d_date_sk#19]

(22) CometBroadcastHashJoin
Left output [2]: [cs_ship_customer_sk#16, cs_sold_date_sk#17]
Right output [1]: [d_date_sk#19]
Arguments: [cs_sold_date_sk#17], [d_date_sk#19], Inner, BuildRight

(23) CometProject
Input [3]: [cs_ship_customer_sk#16, cs_sold_date_sk#17, d_date_sk#19]
Arguments: [cs_ship_customer_sk#16], [cs_ship_customer_sk#16]

(24) CometColumnarToRow [codegen id : 2]
Input [1]: [cs_ship_customer_sk#16]

(25) BroadcastExchange
Input [1]: [cs_ship_customer_sk#16]
Arguments: HashedRelationBroadcastMode(List(cast(input[0, int, true] as bigint)),false), [plan_id=2]

(26) BroadcastHashJoin [codegen id : 5]
Left keys [1]: [c_customer_sk#3]
Right keys [1]: [cs_ship_customer_sk#16]
Join type: ExistenceJoin(exists#1)
Join condition: None

(27) Filter [codegen id : 5]
Input [5]: [c_customer_sk#3, c_current_cdemo_sk#4, c_current_addr_sk#5, exists#2, exists#1]
Condition : (exists#2 OR exists#1)

(28) Project [codegen id : 5]
Output [2]: [c_current_cdemo_sk#4, c_current_addr_sk#5]
Input [5]: [c_customer_sk#3, c_current_cdemo_sk#4, c_current_addr_sk#5, exists#2, exists#1]

(29) CometScan [native_iceberg_compat] parquet spark_catalog.default.customer_address
Output [2]: [ca_address_sk#20, ca_state#21]
Batched: true
Location [not included in comparison]/{warehouse_dir}/customer_address]
PushedFilters: [IsNotNull(ca_address_sk)]
ReadSchema: struct<ca_address_sk:int,ca_state:string>

(30) CometFilter
Input [2]: [ca_address_sk#20, ca_state#21]
Condition : isnotnull(ca_address_sk#20)

(31) CometProject
Input [2]: [ca_address_sk#20, ca_state#21]
Arguments: [ca_address_sk#20, ca_state#22], [ca_address_sk#20, staticinvoke(class org.apache.spark.sql.catalyst.util.CharVarcharCodegenUtils, StringType, readSidePadding, ca_state#21, 2, true, false, true) AS ca_state#22]

(32) CometColumnarToRow [codegen id : 3]
Input [2]: [ca_address_sk#20, ca_state#22]

(33) BroadcastExchange
Input [2]: [ca_address_sk#20, ca_state#22]
Arguments: HashedRelationBroadcastMode(List(cast(input[0, int, true] as bigint)),false), [plan_id=3]

(34) BroadcastHashJoin [codegen id : 5]
Left keys [1]: [c_current_addr_sk#5]
Right keys [1]: [ca_address_sk#20]
Join type: Inner
Join condition: None

(35) Project [codegen id : 5]
Output [2]: [c_current_cdemo_sk#4, ca_state#22]
Input [4]: [c_current_cdemo_sk#4, c_current_addr_sk#5, ca_address_sk#20, ca_state#22]

(36) CometScan [native_iceberg_compat] parquet spark_catalog.default.customer_demographics
Output [6]: [cd_demo_sk#23, cd_gender#24, cd_marital_status#25, cd_dep_count#26, cd_dep_employed_count#27, cd_dep_college_count#28]
Batched: true
Location [not included in comparison]/{warehouse_dir}/customer_demographics]
PushedFilters: [IsNotNull(cd_demo_sk)]
ReadSchema: struct<cd_demo_sk:int,cd_gender:string,cd_marital_status:string,cd_dep_count:int,cd_dep_employed_count:int,cd_dep_college_count:int>

(37) CometFilter
Input [6]: [cd_demo_sk#23, cd_gender#24, cd_marital_status#25, cd_dep_count#26, cd_dep_employed_count#27, cd_dep_college_count#28]
Condition : isnotnull(cd_demo_sk#23)

(38) CometProject
Input [6]: [cd_demo_sk#23, cd_gender#24, cd_marital_status#25, cd_dep_count#26, cd_dep_employed_count#27, cd_dep_college_count#28]
Arguments: [cd_demo_sk#23, cd_gender#29, cd_marital_status#30, cd_dep_count#26, cd_dep_employed_count#27, cd_dep_college_count#28], [cd_demo_sk#23, staticinvoke(class org.apache.spark.sql.catalyst.util.CharVarcharCodegenUtils, StringType, readSidePadding, cd_gender#24, 1, true, false, true) AS cd_gender#29, staticinvoke(class org.apache.spark.sql.catalyst.util.CharVarcharCodegenUtils, StringType, readSidePadding, cd_marital_status#25, 1, true, false, true) AS cd_marital_status#30, cd_dep_count#26, cd_dep_employed_count#27, cd_dep_college_count#28]

(39) CometColumnarToRow [codegen id : 4]
Input [6]: [cd_demo_sk#23, cd_gender#29, cd_marital_status#30, cd_dep_count#26, cd_dep_employed_count#27, cd_dep_college_count#28]

(40) BroadcastExchange
Input [6]: [cd_demo_sk#23, cd_gender#29, cd_marital_status#30, cd_dep_count#26, cd_dep_employed_count#27, cd_dep_college_count#28]
Arguments: HashedRelationBroadcastMode(List(cast(input[0, int, true] as bigint)),false), [plan_id=4]

(41) BroadcastHashJoin [codegen id : 5]
Left keys [1]: [c_current_cdemo_sk#4]
Right keys [1]: [cd_demo_sk#23]
Join type: Inner
Join condition: None

(42) Project [codegen id : 5]
Output [6]: [ca_state#22, cd_gender#29, cd_marital_status#30, cd_dep_count#26, cd_dep_employed_count#27, cd_dep_college_count#28]
Input [8]: [c_current_cdemo_sk#4, ca_state#22, cd_demo_sk#23, cd_gender#29, cd_marital_status#30, cd_dep_count#26, cd_dep_employed_count#27, cd_dep_college_count#28]

(43) HashAggregate [codegen id : 5]
Input [6]: [ca_state#22, cd_gender#29, cd_marital_status#30, cd_dep_count#26, cd_dep_employed_count#27, cd_dep_college_count#28]
Keys [6]: [ca_state#22, cd_gender#29, cd_marital_status#30, cd_dep_count#26, cd_dep_employed_count#27, cd_dep_college_count#28]
Functions [10]: [partial_count(1), partial_avg(cd_dep_count#26), partial_max(cd_dep_count#26), partial_sum(cd_dep_count#26), partial_avg(cd_dep_employed_count#27), partial_max(cd_dep_employed_count#27), partial_sum(cd_dep_employed_count#27), partial_avg(cd_dep_college_count#28), partial_max(cd_dep_college_count#28), partial_sum(cd_dep_college_count#28)]
Aggregate Attributes [13]: [count#31, sum#32, count#33, max#34, sum#35, sum#36, count#37, max#38, sum#39, sum#40, count#41, max#42, sum#43]
Results [19]: [ca_state#22, cd_gender#29, cd_marital_status#30, cd_dep_count#26, cd_dep_employed_count#27, cd_dep_college_count#28, count#44, sum#45, count#46, max#47, sum#48, sum#49, count#50, max#51, sum#52, sum#53, count#54, max#55, sum#56]

(44) CometColumnarExchange
Input [19]: [ca_state#22, cd_gender#29, cd_marital_status#30, cd_dep_count#26, cd_dep_employed_count#27, cd_dep_college_count#28, count#44, sum#45, count#46, max#47, sum#48, sum#49, count#50, max#51, sum#52, sum#53, count#54, max#55, sum#56]
Arguments: hashpartitioning(ca_state#22, cd_gender#29, cd_marital_status#30, cd_dep_count#26, cd_dep_employed_count#27, cd_dep_college_count#28, 5), ENSURE_REQUIREMENTS, CometColumnarShuffle, [plan_id=5]

(45) CometColumnarToRow [codegen id : 6]
Input [19]: [ca_state#22, cd_gender#29, cd_marital_status#30, cd_dep_count#26, cd_dep_employed_count#27, cd_dep_college_count#28, count#44, sum#45, count#46, max#47, sum#48, sum#49, count#50, max#51, sum#52, sum#53, count#54, max#55, sum#56]

(46) HashAggregate [codegen id : 6]
Input [19]: [ca_state#22, cd_gender#29, cd_marital_status#30, cd_dep_count#26, cd_dep_employed_count#27, cd_dep_college_count#28, count#44, sum#45, count#46, max#47, sum#48, sum#49, count#50, max#51, sum#52, sum#53, count#54, max#55, sum#56]
Keys [6]: [ca_state#22, cd_gender#29, cd_marital_status#30, cd_dep_count#26, cd_dep_employed_count#27, cd_dep_college_count#28]
Functions [10]: [count(1), avg(cd_dep_count#26), max(cd_dep_count#26), sum(cd_dep_count#26), avg(cd_dep_employed_count#27), max(cd_dep_employed_count#27), sum(cd_dep_employed_count#27), avg(cd_dep_college_count#28), max(cd_dep_college_count#28), sum(cd_dep_college_count#28)]
Aggregate Attributes [10]: [count(1)#57, avg(cd_dep_count#26)#58, max(cd_dep_count#26)#59, sum(cd_dep_count#26)#60, avg(cd_dep_employed_count#27)#61, max(cd_dep_employed_count#27)#62, sum(cd_dep_employed_count#27)#63, avg(cd_dep_college_count#28)#64, max(cd_dep_college_count#28)#65, sum(cd_dep_college_count#28)#66]
Results [18]: [ca_state#22, cd_gender#29, cd_marital_status#30, cd_dep_count#26, count(1)#57 AS cnt1#67, avg(cd_dep_count#26)#58 AS avg(cd_dep_count)#68, max(cd_dep_count#26)#59 AS max(cd_dep_count)#69, sum(cd_dep_count#26)#60 AS sum(cd_dep_count)#70, cd_dep_employed_count#27, count(1)#57 AS cnt2#71, avg(cd_dep_employed_count#27)#61 AS avg(cd_dep_employed_count)#72, max(cd_dep_employed_count#27)#62 AS max(cd_dep_employed_count)#73, sum(cd_dep_employed_count#27)#63 AS sum(cd_dep_employed_count)#74, cd_dep_college_count#28, count(1)#57 AS cnt3#75, avg(cd_dep_college_count#28)#64 AS avg(cd_dep_college_count)#76, max(cd_dep_college_count#28)#65 AS max(cd_dep_college_count)#77, sum(cd_dep_college_count#28)#66 AS sum(cd_dep_college_count)#78]

(47) TakeOrderedAndProject
Input [18]: [ca_state#22, cd_gender#29, cd_marital_status#30, cd_dep_count#26, cnt1#67, avg(cd_dep_count)#68, max(cd_dep_count)#69, sum(cd_dep_count)#70, cd_dep_employed_count#27, cnt2#71, avg(cd_dep_employed_count)#72, max(cd_dep_employed_count)#73, sum(cd_dep_employed_count)#74, cd_dep_college_count#28, cnt3#75, avg(cd_dep_college_count)#76, max(cd_dep_college_count)#77, sum(cd_dep_college_count)#78]
Arguments: 100, [ca_state#22 ASC NULLS FIRST, cd_gender#29 ASC NULLS FIRST, cd_marital_status#30 ASC NULLS FIRST, cd_dep_count#26 ASC NULLS FIRST, cd_dep_employed_count#27 ASC NULLS FIRST, cd_dep_college_count#28 ASC NULLS FIRST], [ca_state#22, cd_gender#29, cd_marital_status#30, cd_dep_count#26, cnt1#67, avg(cd_dep_count)#68, max(cd_dep_count)#69, sum(cd_dep_count)#70, cd_dep_employed_count#27, cnt2#71, avg(cd_dep_employed_count)#72, max(cd_dep_employed_count)#73, sum(cd_dep_employed_count)#74, cd_dep_college_count#28, cnt3#75, avg(cd_dep_college_count)#76, max(cd_dep_college_count)#77, sum(cd_dep_college_count)#78]

===== Subqueries =====

Subquery:1 Hosting operator id = 3 Hosting Expression = ss_sold_date_sk#7 IN dynamicpruning#8
BroadcastExchange (52)
+- * CometColumnarToRow (51)
   +- CometProject (50)
      +- CometFilter (49)
         +- CometScan [native_iceberg_compat] parquet spark_catalog.default.date_dim (48)


(48) CometScan [native_iceberg_compat] parquet spark_catalog.default.date_dim
Output [3]: [d_date_sk#9, d_year#10, d_qoy#11]
Batched: true
Location [not included in comparison]/{warehouse_dir}/date_dim]
PushedFilters: [IsNotNull(d_year), IsNotNull(d_qoy), EqualTo(d_year,2002), LessThan(d_qoy,4), IsNotNull(d_date_sk)]
ReadSchema: struct<d_date_sk:int,d_year:int,d_qoy:int>

(49) CometFilter
Input [3]: [d_date_sk#9, d_year#10, d_qoy#11]
Condition : ((((isnotnull(d_year#10) AND isnotnull(d_qoy#11)) AND (d_year#10 = 2002)) AND (d_qoy#11 < 4)) AND isnotnull(d_date_sk#9))

(50) CometProject
Input [3]: [d_date_sk#9, d_year#10, d_qoy#11]
Arguments: [d_date_sk#9], [d_date_sk#9]

(51) CometColumnarToRow [codegen id : 1]
Input [1]: [d_date_sk#9]

(52) BroadcastExchange
Input [1]: [d_date_sk#9]
Arguments: HashedRelationBroadcastMode(List(cast(input[0, int, true] as bigint)),false), [plan_id=6]

Subquery:2 Hosting operator id = 13 Hosting Expression = ws_sold_date_sk#13 IN dynamicpruning#8

Subquery:3 Hosting operator id = 20 Hosting Expression = cs_sold_date_sk#17 IN dynamicpruning#8


