== Physical Plan ==
TakeOrderedAndProject (30)
+- * Project (29)
   +- * Filter (28)
      +- Window (27)
         +- * CometColumnarToRow (26)
            +- CometSort (25)
               +- CometColumnarExchange (24)
                  +- * HashAggregate (23)
                     +- * CometColumnarToRow (22)
                        +- CometColumnarExchange (21)
                           +- * HashAggregate (20)
                              +- * Project (19)
                                 +- * BroadcastHashJoin Inner BuildRight (18)
                                    :- * Project (13)
                                    :  +- * BroadcastHashJoin Inner BuildRight (12)
                                    :     :- * Project (10)
                                    :     :  +- * BroadcastHashJoin Inner BuildRight (9)
                                    :     :     :- * CometColumnarToRow (4)
                                    :     :     :  +- CometProject (3)
                                    :     :     :     +- CometFilter (2)
                                    :     :     :        +- CometNativeScan parquet spark_catalog.default.item (1)
                                    :     :     +- BroadcastExchange (8)
                                    :     :        +- * Filter (7)
                                    :     :           +- * ColumnarToRow (6)
                                    :     :              +- Scan parquet spark_catalog.default.store_sales (5)
                                    :     +- ReusedExchange (11)
                                    +- BroadcastExchange (17)
                                       +- * CometColumnarToRow (16)
                                          +- CometFilter (15)
                                             +- CometNativeScan parquet spark_catalog.default.store (14)


(1) CometNativeScan 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: [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 : (((staticinvoke(class org.apache.spark.sql.catalyst.util.CharVarcharCodegenUtils, StringType, readSidePadding, i_category#4, 50, true, false, true) IN (Books                                             ,Electronics                                       ,Sports                                            ) AND staticinvoke(class org.apache.spark.sql.catalyst.util.CharVarcharCodegenUtils, StringType, readSidePadding, i_class#3, 50, true, false, true) IN (computers                                         ,stereo                                            ,football                                          )) OR (staticinvoke(class org.apache.spark.sql.catalyst.util.CharVarcharCodegenUtils, StringType, readSidePadding, i_category#4, 50, true, false, true) IN (Men                                               ,Jewelry                                           ,Women                                             ) AND staticinvoke(class org.apache.spark.sql.catalyst.util.CharVarcharCodegenUtils, StringType, readSidePadding, i_class#3, 50, true, false, true) IN (shirts                                            ,birdal                                            ,dresses                                           ))) AND isnotnull(i_item_sk#1))

(3) CometProject
Input [4]: [i_item_sk#1, i_brand#2, i_class#3, i_category#4]
Arguments: [i_item_sk#1, i_brand#5, i_class#6, i_category#7], [i_item_sk#1, staticinvoke(class org.apache.spark.sql.catalyst.util.CharVarcharCodegenUtils, StringType, readSidePadding, i_brand#2, 50, true, false, true) AS i_brand#5, staticinvoke(class org.apache.spark.sql.catalyst.util.CharVarcharCodegenUtils, StringType, readSidePadding, i_class#3, 50, true, false, true) AS i_class#6, staticinvoke(class org.apache.spark.sql.catalyst.util.CharVarcharCodegenUtils, StringType, readSidePadding, i_category#4, 50, true, false, true) AS i_category#7]

(4) CometColumnarToRow [codegen id : 4]
Input [4]: [i_item_sk#1, i_brand#5, i_class#6, i_category#7]

(5) Scan parquet spark_catalog.default.store_sales
Output [4]: [ss_item_sk#8, ss_store_sk#9, ss_sales_price#10, ss_sold_date_sk#11]
Batched: true
Location: InMemoryFileIndex []
PartitionFilters: [isnotnull(ss_sold_date_sk#11), dynamicpruningexpression(ss_sold_date_sk#11 IN dynamicpruning#12)]
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)>

(6) ColumnarToRow [codegen id : 1]
Input [4]: [ss_item_sk#8, ss_store_sk#9, ss_sales_price#10, ss_sold_date_sk#11]

(7) Filter [codegen id : 1]
Input [4]: [ss_item_sk#8, ss_store_sk#9, ss_sales_price#10, ss_sold_date_sk#11]
Condition : (isnotnull(ss_item_sk#8) AND isnotnull(ss_store_sk#9))

(8) BroadcastExchange
Input [4]: [ss_item_sk#8, ss_store_sk#9, ss_sales_price#10, ss_sold_date_sk#11]
Arguments: HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint)),false), [plan_id=1]

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

(10) Project [codegen id : 4]
Output [6]: [i_brand#5, i_class#6, i_category#7, ss_store_sk#9, ss_sales_price#10, ss_sold_date_sk#11]
Input [8]: [i_item_sk#1, i_brand#5, i_class#6, i_category#7, ss_item_sk#8, ss_store_sk#9, ss_sales_price#10, ss_sold_date_sk#11]

(11) ReusedExchange [Reuses operator id: 35]
Output [2]: [d_date_sk#13, d_moy#14]

(12) BroadcastHashJoin [codegen id : 4]
Left keys [1]: [ss_sold_date_sk#11]
Right keys [1]: [d_date_sk#13]
Join type: Inner
Join condition: None

(13) Project [codegen id : 4]
Output [6]: [i_brand#5, i_class#6, i_category#7, ss_store_sk#9, ss_sales_price#10, d_moy#14]
Input [8]: [i_brand#5, i_class#6, i_category#7, ss_store_sk#9, ss_sales_price#10, ss_sold_date_sk#11, d_date_sk#13, d_moy#14]

(14) CometNativeScan parquet spark_catalog.default.store
Output [3]: [s_store_sk#15, s_store_name#16, s_company_name#17]
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#15, s_store_name#16, s_company_name#17]
Condition : isnotnull(s_store_sk#15)

(16) CometColumnarToRow [codegen id : 3]
Input [3]: [s_store_sk#15, s_store_name#16, s_company_name#17]

(17) BroadcastExchange
Input [3]: [s_store_sk#15, s_store_name#16, s_company_name#17]
Arguments: HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint)),false), [plan_id=2]

(18) BroadcastHashJoin [codegen id : 4]
Left keys [1]: [ss_store_sk#9]
Right keys [1]: [s_store_sk#15]
Join type: Inner
Join condition: None

(19) Project [codegen id : 4]
Output [7]: [i_brand#5, i_class#6, i_category#7, ss_sales_price#10, d_moy#14, s_store_name#16, s_company_name#17]
Input [9]: [i_brand#5, i_class#6, i_category#7, ss_store_sk#9, ss_sales_price#10, d_moy#14, s_store_sk#15, s_store_name#16, s_company_name#17]

(20) HashAggregate [codegen id : 4]
Input [7]: [i_brand#5, i_class#6, i_category#7, ss_sales_price#10, d_moy#14, s_store_name#16, s_company_name#17]
Keys [6]: [i_category#7, i_class#6, i_brand#5, s_store_name#16, s_company_name#17, d_moy#14]
Functions [1]: [partial_sum(UnscaledValue(ss_sales_price#10))]
Aggregate Attributes [1]: [sum#18]
Results [7]: [i_category#7, i_class#6, i_brand#5, s_store_name#16, s_company_name#17, d_moy#14, sum#19]

(21) CometColumnarExchange
Input [7]: [i_category#7, i_class#6, i_brand#5, s_store_name#16, s_company_name#17, d_moy#14, sum#19]
Arguments: hashpartitioning(i_category#7, i_class#6, i_brand#5, s_store_name#16, s_company_name#17, d_moy#14, 5), ENSURE_REQUIREMENTS, CometColumnarShuffle, [plan_id=3]

(22) CometColumnarToRow [codegen id : 5]
Input [7]: [i_category#7, i_class#6, i_brand#5, s_store_name#16, s_company_name#17, d_moy#14, sum#19]

(23) HashAggregate [codegen id : 5]
Input [7]: [i_category#7, i_class#6, i_brand#5, s_store_name#16, s_company_name#17, d_moy#14, sum#19]
Keys [6]: [i_category#7, i_class#6, i_brand#5, s_store_name#16, s_company_name#17, d_moy#14]
Functions [1]: [sum(UnscaledValue(ss_sales_price#10))]
Aggregate Attributes [1]: [sum(UnscaledValue(ss_sales_price#10))#20]
Results [8]: [i_category#7, i_class#6, i_brand#5, s_store_name#16, s_company_name#17, d_moy#14, MakeDecimal(sum(UnscaledValue(ss_sales_price#10))#20,17,2) AS sum_sales#21, MakeDecimal(sum(UnscaledValue(ss_sales_price#10))#20,17,2) AS _w0#22]

(24) CometColumnarExchange
Input [8]: [i_category#7, i_class#6, i_brand#5, s_store_name#16, s_company_name#17, d_moy#14, sum_sales#21, _w0#22]
Arguments: hashpartitioning(i_category#7, i_brand#5, s_store_name#16, s_company_name#17, 5), ENSURE_REQUIREMENTS, CometColumnarShuffle, [plan_id=4]

(25) CometSort
Input [8]: [i_category#7, i_class#6, i_brand#5, s_store_name#16, s_company_name#17, d_moy#14, sum_sales#21, _w0#22]
Arguments: [i_category#7, i_class#6, i_brand#5, s_store_name#16, s_company_name#17, d_moy#14, sum_sales#21, _w0#22], [i_category#7 ASC NULLS FIRST, i_brand#5 ASC NULLS FIRST, s_store_name#16 ASC NULLS FIRST, s_company_name#17 ASC NULLS FIRST]

(26) CometColumnarToRow [codegen id : 6]
Input [8]: [i_category#7, i_class#6, i_brand#5, s_store_name#16, s_company_name#17, d_moy#14, sum_sales#21, _w0#22]

(27) Window
Input [8]: [i_category#7, i_class#6, i_brand#5, s_store_name#16, s_company_name#17, d_moy#14, sum_sales#21, _w0#22]
Arguments: [avg(_w0#22) windowspecdefinition(i_category#7, i_brand#5, s_store_name#16, s_company_name#17, specifiedwindowframe(RowFrame, unboundedpreceding$(), unboundedfollowing$())) AS avg_monthly_sales#23], [i_category#7, i_brand#5, s_store_name#16, s_company_name#17]

(28) Filter [codegen id : 7]
Input [9]: [i_category#7, i_class#6, i_brand#5, s_store_name#16, s_company_name#17, d_moy#14, sum_sales#21, _w0#22, avg_monthly_sales#23]
Condition : CASE WHEN NOT (avg_monthly_sales#23 = 0.000000) THEN ((abs((sum_sales#21 - avg_monthly_sales#23)) / avg_monthly_sales#23) > 0.1000000000000000) END

(29) Project [codegen id : 7]
Output [8]: [i_category#7, i_class#6, i_brand#5, s_store_name#16, s_company_name#17, d_moy#14, sum_sales#21, avg_monthly_sales#23]
Input [9]: [i_category#7, i_class#6, i_brand#5, s_store_name#16, s_company_name#17, d_moy#14, sum_sales#21, _w0#22, avg_monthly_sales#23]

(30) TakeOrderedAndProject
Input [8]: [i_category#7, i_class#6, i_brand#5, s_store_name#16, s_company_name#17, d_moy#14, sum_sales#21, avg_monthly_sales#23]
Arguments: 100, [(sum_sales#21 - avg_monthly_sales#23) ASC NULLS FIRST, s_store_name#16 ASC NULLS FIRST], [i_category#7, i_class#6, i_brand#5, s_store_name#16, s_company_name#17, d_moy#14, sum_sales#21, avg_monthly_sales#23]

===== Subqueries =====

Subquery:1 Hosting operator id = 5 Hosting Expression = ss_sold_date_sk#11 IN dynamicpruning#12
BroadcastExchange (35)
+- * CometColumnarToRow (34)
   +- CometProject (33)
      +- CometFilter (32)
         +- CometNativeScan parquet spark_catalog.default.date_dim (31)


(31) CometNativeScan parquet spark_catalog.default.date_dim
Output [3]: [d_date_sk#13, d_year#24, d_moy#14]
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>

(32) CometFilter
Input [3]: [d_date_sk#13, d_year#24, d_moy#14]
Condition : ((isnotnull(d_year#24) AND (d_year#24 = 1999)) AND isnotnull(d_date_sk#13))

(33) CometProject
Input [3]: [d_date_sk#13, d_year#24, d_moy#14]
Arguments: [d_date_sk#13, d_moy#14], [d_date_sk#13, d_moy#14]

(34) CometColumnarToRow [codegen id : 1]
Input [2]: [d_date_sk#13, d_moy#14]

(35) BroadcastExchange
Input [2]: [d_date_sk#13, d_moy#14]
Arguments: HashedRelationBroadcastMode(List(cast(input[0, int, true] as bigint)),false), [plan_id=5]


