== Physical Plan ==
TakeOrderedAndProject (45)
+- * Project (44)
   +- * BroadcastHashJoin Inner BuildRight (43)
      :- * Project (37)
      :  +- * BroadcastHashJoin Inner BuildRight (36)
      :     :- * Project (28)
      :     :  +- * Filter (27)
      :     :     +- Window (26)
      :     :        +- * Filter (25)
      :     :           +- Window (24)
      :     :              +- * Sort (23)
      :     :                 +- Exchange (22)
      :     :                    +- * HashAggregate (21)
      :     :                       +- Exchange (20)
      :     :                          +- * HashAggregate (19)
      :     :                             +- * Project (18)
      :     :                                +- * BroadcastHashJoin Inner BuildRight (17)
      :     :                                   :- * Project (12)
      :     :                                   :  +- * BroadcastHashJoin Inner BuildRight (11)
      :     :                                   :     :- * Project (9)
      :     :                                   :     :  +- * BroadcastHashJoin Inner BuildRight (8)
      :     :                                   :     :     :- * Filter (3)
      :     :                                   :     :     :  +- * ColumnarToRow (2)
      :     :                                   :     :     :     +- Scan parquet spark_catalog.default.item (1)
      :     :                                   :     :     +- BroadcastExchange (7)
      :     :                                   :     :        +- * Filter (6)
      :     :                                   :     :           +- * ColumnarToRow (5)
      :     :                                   :     :              +- Scan parquet spark_catalog.default.catalog_sales (4)
      :     :                                   :     +- ReusedExchange (10)
      :     :                                   +- BroadcastExchange (16)
      :     :                                      +- * Filter (15)
      :     :                                         +- * ColumnarToRow (14)
      :     :                                            +- Scan parquet spark_catalog.default.call_center (13)
      :     +- BroadcastExchange (35)
      :        +- * Project (34)
      :           +- Window (33)
      :              +- * Sort (32)
      :                 +- Exchange (31)
      :                    +- * HashAggregate (30)
      :                       +- ReusedExchange (29)
      +- BroadcastExchange (42)
         +- * Project (41)
            +- Window (40)
               +- * Sort (39)
                  +- ReusedExchange (38)


(1) Scan parquet spark_catalog.default.item
Output [3]: [i_item_sk#1, i_brand#2, i_category#3]
Batched: true
Location [not included in comparison]/{warehouse_dir}/item]
PushedFilters: [IsNotNull(i_item_sk), IsNotNull(i_category), IsNotNull(i_brand)]
ReadSchema: struct<i_item_sk:int,i_brand:string,i_category:string>

(2) ColumnarToRow [codegen id : 4]
Input [3]: [i_item_sk#1, i_brand#2, i_category#3]

(3) Filter [codegen id : 4]
Input [3]: [i_item_sk#1, i_brand#2, i_category#3]
Condition : ((isnotnull(i_item_sk#1) AND isnotnull(i_category#3)) AND isnotnull(i_brand#2))

(4) Scan parquet spark_catalog.default.catalog_sales
Output [4]: [cs_call_center_sk#4, cs_item_sk#5, cs_sales_price#6, cs_sold_date_sk#7]
Batched: true
Location: InMemoryFileIndex []
PartitionFilters: [isnotnull(cs_sold_date_sk#7), dynamicpruningexpression(cs_sold_date_sk#7 IN dynamicpruning#8)]
PushedFilters: [IsNotNull(cs_item_sk), IsNotNull(cs_call_center_sk)]
ReadSchema: struct<cs_call_center_sk:int,cs_item_sk:int,cs_sales_price:decimal(7,2)>

(5) ColumnarToRow [codegen id : 1]
Input [4]: [cs_call_center_sk#4, cs_item_sk#5, cs_sales_price#6, cs_sold_date_sk#7]

(6) Filter [codegen id : 1]
Input [4]: [cs_call_center_sk#4, cs_item_sk#5, cs_sales_price#6, cs_sold_date_sk#7]
Condition : (isnotnull(cs_item_sk#5) AND isnotnull(cs_call_center_sk#4))

(7) BroadcastExchange
Input [4]: [cs_call_center_sk#4, cs_item_sk#5, cs_sales_price#6, cs_sold_date_sk#7]
Arguments: HashedRelationBroadcastMode(List(cast(input[1, int, false] as bigint)),false), [plan_id=1]

(8) BroadcastHashJoin [codegen id : 4]
Left keys [1]: [i_item_sk#1]
Right keys [1]: [cs_item_sk#5]
Join type: Inner
Join condition: None

(9) Project [codegen id : 4]
Output [5]: [i_brand#2, i_category#3, cs_call_center_sk#4, cs_sales_price#6, cs_sold_date_sk#7]
Input [7]: [i_item_sk#1, i_brand#2, i_category#3, cs_call_center_sk#4, cs_item_sk#5, cs_sales_price#6, cs_sold_date_sk#7]

(10) ReusedExchange [Reuses operator id: 49]
Output [3]: [d_date_sk#9, d_year#10, d_moy#11]

(11) BroadcastHashJoin [codegen id : 4]
Left keys [1]: [cs_sold_date_sk#7]
Right keys [1]: [d_date_sk#9]
Join type: Inner
Join condition: None

(12) Project [codegen id : 4]
Output [6]: [i_brand#2, i_category#3, cs_call_center_sk#4, cs_sales_price#6, d_year#10, d_moy#11]
Input [8]: [i_brand#2, i_category#3, cs_call_center_sk#4, cs_sales_price#6, cs_sold_date_sk#7, d_date_sk#9, d_year#10, d_moy#11]

(13) Scan parquet spark_catalog.default.call_center
Output [2]: [cc_call_center_sk#12, cc_name#13]
Batched: true
Location [not included in comparison]/{warehouse_dir}/call_center]
PushedFilters: [IsNotNull(cc_call_center_sk), IsNotNull(cc_name)]
ReadSchema: struct<cc_call_center_sk:int,cc_name:string>

(14) ColumnarToRow [codegen id : 3]
Input [2]: [cc_call_center_sk#12, cc_name#13]

(15) Filter [codegen id : 3]
Input [2]: [cc_call_center_sk#12, cc_name#13]
Condition : (isnotnull(cc_call_center_sk#12) AND isnotnull(cc_name#13))

(16) BroadcastExchange
Input [2]: [cc_call_center_sk#12, cc_name#13]
Arguments: HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint)),false), [plan_id=2]

(17) BroadcastHashJoin [codegen id : 4]
Left keys [1]: [cs_call_center_sk#4]
Right keys [1]: [cc_call_center_sk#12]
Join type: Inner
Join condition: None

(18) Project [codegen id : 4]
Output [6]: [i_brand#2, i_category#3, cs_sales_price#6, d_year#10, d_moy#11, cc_name#13]
Input [8]: [i_brand#2, i_category#3, cs_call_center_sk#4, cs_sales_price#6, d_year#10, d_moy#11, cc_call_center_sk#12, cc_name#13]

(19) HashAggregate [codegen id : 4]
Input [6]: [i_brand#2, i_category#3, cs_sales_price#6, d_year#10, d_moy#11, cc_name#13]
Keys [5]: [i_category#3, i_brand#2, cc_name#13, d_year#10, d_moy#11]
Functions [1]: [partial_sum(UnscaledValue(cs_sales_price#6))]
Aggregate Attributes [1]: [sum#14]
Results [6]: [i_category#3, i_brand#2, cc_name#13, d_year#10, d_moy#11, sum#15]

(20) Exchange
Input [6]: [i_category#3, i_brand#2, cc_name#13, d_year#10, d_moy#11, sum#15]
Arguments: hashpartitioning(i_category#3, i_brand#2, cc_name#13, d_year#10, d_moy#11, 5), ENSURE_REQUIREMENTS, [plan_id=3]

(21) HashAggregate [codegen id : 5]
Input [6]: [i_category#3, i_brand#2, cc_name#13, d_year#10, d_moy#11, sum#15]
Keys [5]: [i_category#3, i_brand#2, cc_name#13, d_year#10, d_moy#11]
Functions [1]: [sum(UnscaledValue(cs_sales_price#6))]
Aggregate Attributes [1]: [sum(UnscaledValue(cs_sales_price#6))#16]
Results [7]: [i_category#3, i_brand#2, cc_name#13, d_year#10, d_moy#11, MakeDecimal(sum(UnscaledValue(cs_sales_price#6))#16,17,2) AS sum_sales#17, MakeDecimal(sum(UnscaledValue(cs_sales_price#6))#16,17,2) AS _w0#18]

(22) Exchange
Input [7]: [i_category#3, i_brand#2, cc_name#13, d_year#10, d_moy#11, sum_sales#17, _w0#18]
Arguments: hashpartitioning(i_category#3, i_brand#2, cc_name#13, 5), ENSURE_REQUIREMENTS, [plan_id=4]

(23) Sort [codegen id : 6]
Input [7]: [i_category#3, i_brand#2, cc_name#13, d_year#10, d_moy#11, sum_sales#17, _w0#18]
Arguments: [i_category#3 ASC NULLS FIRST, i_brand#2 ASC NULLS FIRST, cc_name#13 ASC NULLS FIRST, d_year#10 ASC NULLS FIRST, d_moy#11 ASC NULLS FIRST], false, 0

(24) Window
Input [7]: [i_category#3, i_brand#2, cc_name#13, d_year#10, d_moy#11, sum_sales#17, _w0#18]
Arguments: [rank(d_year#10, d_moy#11) windowspecdefinition(i_category#3, i_brand#2, cc_name#13, d_year#10 ASC NULLS FIRST, d_moy#11 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS rn#19], [i_category#3, i_brand#2, cc_name#13], [d_year#10 ASC NULLS FIRST, d_moy#11 ASC NULLS FIRST]

(25) Filter [codegen id : 7]
Input [8]: [i_category#3, i_brand#2, cc_name#13, d_year#10, d_moy#11, sum_sales#17, _w0#18, rn#19]
Condition : (isnotnull(d_year#10) AND (d_year#10 = 1999))

(26) Window
Input [8]: [i_category#3, i_brand#2, cc_name#13, d_year#10, d_moy#11, sum_sales#17, _w0#18, rn#19]
Arguments: [avg(_w0#18) windowspecdefinition(i_category#3, i_brand#2, cc_name#13, d_year#10, specifiedwindowframe(RowFrame, unboundedpreceding$(), unboundedfollowing$())) AS avg_monthly_sales#20], [i_category#3, i_brand#2, cc_name#13, d_year#10]

(27) Filter [codegen id : 22]
Input [9]: [i_category#3, i_brand#2, cc_name#13, d_year#10, d_moy#11, sum_sales#17, _w0#18, rn#19, avg_monthly_sales#20]
Condition : ((isnotnull(avg_monthly_sales#20) AND (avg_monthly_sales#20 > 0.000000)) AND CASE WHEN (avg_monthly_sales#20 > 0.000000) THEN ((abs((sum_sales#17 - avg_monthly_sales#20)) / avg_monthly_sales#20) > 0.1000000000000000) END)

(28) Project [codegen id : 22]
Output [8]: [i_category#3, i_brand#2, cc_name#13, d_year#10, d_moy#11, sum_sales#17, avg_monthly_sales#20, rn#19]
Input [9]: [i_category#3, i_brand#2, cc_name#13, d_year#10, d_moy#11, sum_sales#17, _w0#18, rn#19, avg_monthly_sales#20]

(29) ReusedExchange [Reuses operator id: 20]
Output [6]: [i_category#21, i_brand#22, cc_name#23, d_year#24, d_moy#25, sum#26]

(30) HashAggregate [codegen id : 12]
Input [6]: [i_category#21, i_brand#22, cc_name#23, d_year#24, d_moy#25, sum#26]
Keys [5]: [i_category#21, i_brand#22, cc_name#23, d_year#24, d_moy#25]
Functions [1]: [sum(UnscaledValue(cs_sales_price#27))]
Aggregate Attributes [1]: [sum(UnscaledValue(cs_sales_price#27))#16]
Results [6]: [i_category#21, i_brand#22, cc_name#23, d_year#24, d_moy#25, MakeDecimal(sum(UnscaledValue(cs_sales_price#27))#16,17,2) AS sum_sales#28]

(31) Exchange
Input [6]: [i_category#21, i_brand#22, cc_name#23, d_year#24, d_moy#25, sum_sales#28]
Arguments: hashpartitioning(i_category#21, i_brand#22, cc_name#23, 5), ENSURE_REQUIREMENTS, [plan_id=5]

(32) Sort [codegen id : 13]
Input [6]: [i_category#21, i_brand#22, cc_name#23, d_year#24, d_moy#25, sum_sales#28]
Arguments: [i_category#21 ASC NULLS FIRST, i_brand#22 ASC NULLS FIRST, cc_name#23 ASC NULLS FIRST, d_year#24 ASC NULLS FIRST, d_moy#25 ASC NULLS FIRST], false, 0

(33) Window
Input [6]: [i_category#21, i_brand#22, cc_name#23, d_year#24, d_moy#25, sum_sales#28]
Arguments: [rank(d_year#24, d_moy#25) windowspecdefinition(i_category#21, i_brand#22, cc_name#23, d_year#24 ASC NULLS FIRST, d_moy#25 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS rn#29], [i_category#21, i_brand#22, cc_name#23], [d_year#24 ASC NULLS FIRST, d_moy#25 ASC NULLS FIRST]

(34) Project [codegen id : 14]
Output [5]: [i_category#21, i_brand#22, cc_name#23, sum_sales#28, rn#29]
Input [7]: [i_category#21, i_brand#22, cc_name#23, d_year#24, d_moy#25, sum_sales#28, rn#29]

(35) BroadcastExchange
Input [5]: [i_category#21, i_brand#22, cc_name#23, sum_sales#28, rn#29]
Arguments: HashedRelationBroadcastMode(List(input[0, string, true], input[1, string, true], input[2, string, true], (input[4, int, false] + 1)),false), [plan_id=6]

(36) BroadcastHashJoin [codegen id : 22]
Left keys [4]: [i_category#3, i_brand#2, cc_name#13, rn#19]
Right keys [4]: [i_category#21, i_brand#22, cc_name#23, (rn#29 + 1)]
Join type: Inner
Join condition: None

(37) Project [codegen id : 22]
Output [9]: [i_category#3, i_brand#2, cc_name#13, d_year#10, d_moy#11, sum_sales#17, avg_monthly_sales#20, rn#19, sum_sales#28]
Input [13]: [i_category#3, i_brand#2, cc_name#13, d_year#10, d_moy#11, sum_sales#17, avg_monthly_sales#20, rn#19, i_category#21, i_brand#22, cc_name#23, sum_sales#28, rn#29]

(38) ReusedExchange [Reuses operator id: 31]
Output [6]: [i_category#30, i_brand#31, cc_name#32, d_year#33, d_moy#34, sum_sales#35]

(39) Sort [codegen id : 20]
Input [6]: [i_category#30, i_brand#31, cc_name#32, d_year#33, d_moy#34, sum_sales#35]
Arguments: [i_category#30 ASC NULLS FIRST, i_brand#31 ASC NULLS FIRST, cc_name#32 ASC NULLS FIRST, d_year#33 ASC NULLS FIRST, d_moy#34 ASC NULLS FIRST], false, 0

(40) Window
Input [6]: [i_category#30, i_brand#31, cc_name#32, d_year#33, d_moy#34, sum_sales#35]
Arguments: [rank(d_year#33, d_moy#34) windowspecdefinition(i_category#30, i_brand#31, cc_name#32, d_year#33 ASC NULLS FIRST, d_moy#34 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS rn#36], [i_category#30, i_brand#31, cc_name#32], [d_year#33 ASC NULLS FIRST, d_moy#34 ASC NULLS FIRST]

(41) Project [codegen id : 21]
Output [5]: [i_category#30, i_brand#31, cc_name#32, sum_sales#35, rn#36]
Input [7]: [i_category#30, i_brand#31, cc_name#32, d_year#33, d_moy#34, sum_sales#35, rn#36]

(42) BroadcastExchange
Input [5]: [i_category#30, i_brand#31, cc_name#32, sum_sales#35, rn#36]
Arguments: HashedRelationBroadcastMode(List(input[0, string, true], input[1, string, true], input[2, string, true], (input[4, int, false] - 1)),false), [plan_id=7]

(43) BroadcastHashJoin [codegen id : 22]
Left keys [4]: [i_category#3, i_brand#2, cc_name#13, rn#19]
Right keys [4]: [i_category#30, i_brand#31, cc_name#32, (rn#36 - 1)]
Join type: Inner
Join condition: None

(44) Project [codegen id : 22]
Output [8]: [i_category#3, i_brand#2, d_year#10, d_moy#11, avg_monthly_sales#20, sum_sales#17, sum_sales#28 AS psum#37, sum_sales#35 AS nsum#38]
Input [14]: [i_category#3, i_brand#2, cc_name#13, d_year#10, d_moy#11, sum_sales#17, avg_monthly_sales#20, rn#19, sum_sales#28, i_category#30, i_brand#31, cc_name#32, sum_sales#35, rn#36]

(45) TakeOrderedAndProject
Input [8]: [i_category#3, i_brand#2, d_year#10, d_moy#11, avg_monthly_sales#20, sum_sales#17, psum#37, nsum#38]
Arguments: 100, [(sum_sales#17 - avg_monthly_sales#20) ASC NULLS FIRST, d_year#10 ASC NULLS FIRST], [i_category#3, i_brand#2, d_year#10, d_moy#11, avg_monthly_sales#20, sum_sales#17, psum#37, nsum#38]

===== Subqueries =====

Subquery:1 Hosting operator id = 4 Hosting Expression = cs_sold_date_sk#7 IN dynamicpruning#8
BroadcastExchange (49)
+- * Filter (48)
   +- * ColumnarToRow (47)
      +- Scan parquet spark_catalog.default.date_dim (46)


(46) Scan parquet spark_catalog.default.date_dim
Output [3]: [d_date_sk#9, d_year#10, d_moy#11]
Batched: true
Location [not included in comparison]/{warehouse_dir}/date_dim]
PushedFilters: [Or(Or(EqualTo(d_year,1999),And(EqualTo(d_year,1998),EqualTo(d_moy,12))),And(EqualTo(d_year,2000),EqualTo(d_moy,1))), IsNotNull(d_date_sk)]
ReadSchema: struct<d_date_sk:int,d_year:int,d_moy:int>

(47) ColumnarToRow [codegen id : 1]
Input [3]: [d_date_sk#9, d_year#10, d_moy#11]

(48) Filter [codegen id : 1]
Input [3]: [d_date_sk#9, d_year#10, d_moy#11]
Condition : ((((d_year#10 = 1999) OR ((d_year#10 = 1998) AND (d_moy#11 = 12))) OR ((d_year#10 = 2000) AND (d_moy#11 = 1))) AND isnotnull(d_date_sk#9))

(49) BroadcastExchange
Input [3]: [d_date_sk#9, d_year#10, d_moy#11]
Arguments: HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint)),false), [plan_id=8]


