== Physical Plan ==
TakeOrderedAndProject (28)
+- * Project (27)
   +- * Filter (26)
      +- Window (25)
         +- * ColumnarToRow (24)
            +- CometSort (23)
               +- CometExchange (22)
                  +- CometHashAggregate (21)
                     +- CometExchange (20)
                        +- CometHashAggregate (19)
                           +- CometProject (18)
                              +- CometBroadcastHashJoin (17)
                                 :- CometProject (13)
                                 :  +- CometBroadcastHashJoin (12)
                                 :     :- CometProject (7)
                                 :     :  +- CometBroadcastHashJoin (6)
                                 :     :     :- CometFilter (2)
                                 :     :     :  +- CometScan parquet spark_catalog.default.item (1)
                                 :     :     +- CometBroadcastExchange (5)
                                 :     :        +- CometFilter (4)
                                 :     :           +- CometScan parquet spark_catalog.default.store_sales (3)
                                 :     +- 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)


(1) CometScan parquet spark_catalog.default.item
Output [4]: [i_item_sk#1, i_brand#2, i_class#3, i_category#4]
Batched: true
Location [not included in comparison]/{warehouse_dir}/item]
PushedFilters: [Or(And(In(i_category, [Books                                             ,Electronics                                       ,Sports                                            ]),In(i_class, [computers                                         ,football                                          ,stereo                                            ])),And(In(i_category, [Jewelry                                           ,Men                                               ,Women                                             ]),In(i_class, [birdal                                            ,dresses                                           ,shirts                                            ]))), IsNotNull(i_item_sk)]
ReadSchema: struct<i_item_sk:int,i_brand:string,i_class:string,i_category:string>

(2) CometFilter
Input [4]: [i_item_sk#1, i_brand#2, i_class#3, i_category#4]
Condition : (((i_category#4 IN (Books                                             ,Electronics                                       ,Sports                                            ) AND i_class#3 IN (computers                                         ,stereo                                            ,football                                          )) OR (i_category#4 IN (Men                                               ,Jewelry                                           ,Women                                             ) AND i_class#3 IN (shirts                                            ,birdal                                            ,dresses                                           ))) AND isnotnull(i_item_sk#1))

(3) CometScan parquet spark_catalog.default.store_sales
Output [4]: [ss_item_sk#5, ss_store_sk#6, ss_sales_price#7, ss_sold_date_sk#8]
Batched: true
Location: InMemoryFileIndex []
PartitionFilters: [isnotnull(ss_sold_date_sk#8)]
PushedFilters: [IsNotNull(ss_item_sk), IsNotNull(ss_store_sk)]
ReadSchema: struct<ss_item_sk:int,ss_store_sk:int,ss_sales_price:decimal(7,2)>

(4) CometFilter
Input [4]: [ss_item_sk#5, ss_store_sk#6, ss_sales_price#7, ss_sold_date_sk#8]
Condition : (isnotnull(ss_item_sk#5) AND isnotnull(ss_store_sk#6))

(5) CometBroadcastExchange
Input [4]: [ss_item_sk#5, ss_store_sk#6, ss_sales_price#7, ss_sold_date_sk#8]
Arguments: [ss_item_sk#5, ss_store_sk#6, ss_sales_price#7, ss_sold_date_sk#8]

(6) CometBroadcastHashJoin
Left output [4]: [i_item_sk#1, i_brand#2, i_class#3, i_category#4]
Right output [4]: [ss_item_sk#5, ss_store_sk#6, ss_sales_price#7, ss_sold_date_sk#8]
Arguments: [i_item_sk#1], [ss_item_sk#5], Inner, BuildRight

(7) CometProject
Input [8]: [i_item_sk#1, i_brand#2, i_class#3, i_category#4, ss_item_sk#5, ss_store_sk#6, ss_sales_price#7, ss_sold_date_sk#8]
Arguments: [i_brand#2, i_class#3, i_category#4, ss_store_sk#6, ss_sales_price#7, ss_sold_date_sk#8], [i_brand#2, i_class#3, i_category#4, ss_store_sk#6, ss_sales_price#7, ss_sold_date_sk#8]

(8) CometScan 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: [IsNotNull(d_year), EqualTo(d_year,1999), IsNotNull(d_date_sk)]
ReadSchema: struct<d_date_sk:int,d_year:int,d_moy:int>

(9) CometFilter
Input [3]: [d_date_sk#9, d_year#10, d_moy#11]
Condition : ((isnotnull(d_year#10) AND (d_year#10 = 1999)) AND isnotnull(d_date_sk#9))

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

(11) CometBroadcastExchange
Input [2]: [d_date_sk#9, d_moy#11]
Arguments: [d_date_sk#9, d_moy#11]

(12) CometBroadcastHashJoin
Left output [6]: [i_brand#2, i_class#3, i_category#4, ss_store_sk#6, ss_sales_price#7, ss_sold_date_sk#8]
Right output [2]: [d_date_sk#9, d_moy#11]
Arguments: [ss_sold_date_sk#8], [d_date_sk#9], Inner, BuildRight

(13) CometProject
Input [8]: [i_brand#2, i_class#3, i_category#4, ss_store_sk#6, ss_sales_price#7, ss_sold_date_sk#8, d_date_sk#9, d_moy#11]
Arguments: [i_brand#2, i_class#3, i_category#4, ss_store_sk#6, ss_sales_price#7, d_moy#11], [i_brand#2, i_class#3, i_category#4, ss_store_sk#6, ss_sales_price#7, d_moy#11]

(14) CometScan parquet spark_catalog.default.store
Output [3]: [s_store_sk#12, s_store_name#13, s_company_name#14]
Batched: true
Location [not included in comparison]/{warehouse_dir}/store]
PushedFilters: [IsNotNull(s_store_sk)]
ReadSchema: struct<s_store_sk:int,s_store_name:string,s_company_name:string>

(15) CometFilter
Input [3]: [s_store_sk#12, s_store_name#13, s_company_name#14]
Condition : isnotnull(s_store_sk#12)

(16) CometBroadcastExchange
Input [3]: [s_store_sk#12, s_store_name#13, s_company_name#14]
Arguments: [s_store_sk#12, s_store_name#13, s_company_name#14]

(17) CometBroadcastHashJoin
Left output [6]: [i_brand#2, i_class#3, i_category#4, ss_store_sk#6, ss_sales_price#7, d_moy#11]
Right output [3]: [s_store_sk#12, s_store_name#13, s_company_name#14]
Arguments: [ss_store_sk#6], [s_store_sk#12], Inner, BuildRight

(18) CometProject
Input [9]: [i_brand#2, i_class#3, i_category#4, ss_store_sk#6, ss_sales_price#7, d_moy#11, s_store_sk#12, s_store_name#13, s_company_name#14]
Arguments: [i_brand#2, i_class#3, i_category#4, ss_sales_price#7, d_moy#11, s_store_name#13, s_company_name#14], [i_brand#2, i_class#3, i_category#4, ss_sales_price#7, d_moy#11, s_store_name#13, s_company_name#14]

(19) CometHashAggregate
Input [7]: [i_brand#2, i_class#3, i_category#4, ss_sales_price#7, d_moy#11, s_store_name#13, s_company_name#14]
Keys [6]: [i_category#4, i_class#3, i_brand#2, s_store_name#13, s_company_name#14, d_moy#11]
Functions [1]: [partial_sum(UnscaledValue(ss_sales_price#7))]

(20) CometExchange
Input [7]: [i_category#4, i_class#3, i_brand#2, s_store_name#13, s_company_name#14, d_moy#11, sum#15]
Arguments: hashpartitioning(i_category#4, i_class#3, i_brand#2, s_store_name#13, s_company_name#14, d_moy#11, 5), ENSURE_REQUIREMENTS, CometNativeShuffle, [plan_id=1]

(21) CometHashAggregate
Input [7]: [i_category#4, i_class#3, i_brand#2, s_store_name#13, s_company_name#14, d_moy#11, sum#15]
Keys [6]: [i_category#4, i_class#3, i_brand#2, s_store_name#13, s_company_name#14, d_moy#11]
Functions [1]: [sum(UnscaledValue(ss_sales_price#7))]

(22) CometExchange
Input [8]: [i_category#4, i_class#3, i_brand#2, s_store_name#13, s_company_name#14, d_moy#11, sum_sales#16, _w0#17]
Arguments: hashpartitioning(i_category#4, i_brand#2, s_store_name#13, s_company_name#14, 5), ENSURE_REQUIREMENTS, CometNativeShuffle, [plan_id=2]

(23) CometSort
Input [8]: [i_category#4, i_class#3, i_brand#2, s_store_name#13, s_company_name#14, d_moy#11, sum_sales#16, _w0#17]
Arguments: [i_category#4, i_class#3, i_brand#2, s_store_name#13, s_company_name#14, d_moy#11, sum_sales#16, _w0#17], [i_category#4 ASC NULLS FIRST, i_brand#2 ASC NULLS FIRST, s_store_name#13 ASC NULLS FIRST, s_company_name#14 ASC NULLS FIRST]

(24) ColumnarToRow [codegen id : 1]
Input [8]: [i_category#4, i_class#3, i_brand#2, s_store_name#13, s_company_name#14, d_moy#11, sum_sales#16, _w0#17]

(25) Window
Input [8]: [i_category#4, i_class#3, i_brand#2, s_store_name#13, s_company_name#14, d_moy#11, sum_sales#16, _w0#17]
Arguments: [avg(_w0#17) windowspecdefinition(i_category#4, i_brand#2, s_store_name#13, s_company_name#14, specifiedwindowframe(RowFrame, unboundedpreceding$(), unboundedfollowing$())) AS avg_monthly_sales#18], [i_category#4, i_brand#2, s_store_name#13, s_company_name#14]

(26) Filter [codegen id : 2]
Input [9]: [i_category#4, i_class#3, i_brand#2, s_store_name#13, s_company_name#14, d_moy#11, sum_sales#16, _w0#17, avg_monthly_sales#18]
Condition : CASE WHEN NOT (avg_monthly_sales#18 = 0.000000) THEN ((abs((sum_sales#16 - avg_monthly_sales#18)) / avg_monthly_sales#18) > 0.1000000000000000) END

(27) Project [codegen id : 2]
Output [8]: [i_category#4, i_class#3, i_brand#2, s_store_name#13, s_company_name#14, d_moy#11, sum_sales#16, avg_monthly_sales#18]
Input [9]: [i_category#4, i_class#3, i_brand#2, s_store_name#13, s_company_name#14, d_moy#11, sum_sales#16, _w0#17, avg_monthly_sales#18]

(28) TakeOrderedAndProject
Input [8]: [i_category#4, i_class#3, i_brand#2, s_store_name#13, s_company_name#14, d_moy#11, sum_sales#16, avg_monthly_sales#18]
Arguments: 100, [(sum_sales#16 - avg_monthly_sales#18) ASC NULLS FIRST, s_store_name#13 ASC NULLS FIRST], [i_category#4, i_class#3, i_brand#2, s_store_name#13, s_company_name#14, d_moy#11, sum_sales#16, avg_monthly_sales#18]

