== Physical Plan ==
VeloxColumnarToRow (57)
+- TakeOrderedAndProjectExecTransformer (56)
   +- ^ ProjectExecTransformer (54)
      +- ^ BroadcastHashJoinExecTransformer Inner BuildRight (53)
         :- ^ ProjectExecTransformer (49)
         :  +- ^ BroadcastHashJoinExecTransformer Inner BuildRight (48)
         :     :- ^ ProjectExecTransformer (41)
         :     :  +- ^ ShuffledHashJoinExecTransformer Inner BuildRight (40)
         :     :     :- ^ InputIteratorTransformer (23)
         :     :     :  +- ColumnarExchange (21)
         :     :     :     +- VeloxResizeBatches (20)
         :     :     :        +- ^ ProjectExecTransformer (18)
         :     :     :           +- ^ FilterExecTransformer (17)
         :     :     :              +- ^ WindowExecTransformer (16)
         :     :     :                 +- ^ SortExecTransformer (15)
         :     :     :                    +- ^ WindowGroupLimitExecTransformer (14)
         :     :     :                       +- ^ FilterExecTransformer (13)
         :     :     :                          +- ^ ProjectExecTransformer (12)
         :     :     :                             +- ^ RegularHashAggregateExecTransformer (11)
         :     :     :                                +- ^ InputIteratorTransformer (10)
         :     :     :                                   +- ColumnarExchange (8)
         :     :     :                                      +- VeloxResizeBatches (7)
         :     :     :                                         +- ^ ProjectExecTransformer (5)
         :     :     :                                            +- ^ FlushableHashAggregateExecTransformer (4)
         :     :     :                                               +- ^ ProjectExecTransformer (3)
         :     :     :                                                  +- ^ FilterExecTransformer (2)
         :     :     :                                                     +- ^ FileSourceScanExecTransformer parquet spark_catalog.default.store_sales (1)
         :     :     +- ^ InputIteratorTransformer (39)
         :     :        +- ColumnarExchange (37)
         :     :           +- VeloxResizeBatches (36)
         :     :              +- ^ ProjectExecTransformer (34)
         :     :                 +- ^ FilterExecTransformer (33)
         :     :                    +- ^ WindowExecTransformer (32)
         :     :                       +- ^ SortExecTransformer (31)
         :     :                          +- ^ WindowGroupLimitExecTransformer (30)
         :     :                             +- ^ FilterExecTransformer (29)
         :     :                                +- ^ ProjectExecTransformer (28)
         :     :                                   +- ^ RegularHashAggregateExecTransformer (27)
         :     :                                      +- ^ InputIteratorTransformer (26)
         :     :                                         +- ReusedExchange (24)
         :     +- ^ InputIteratorTransformer (47)
         :        +- ColumnarBroadcastExchange (45)
         :           +- ^ FilterExecTransformer (43)
         :              +- ^ FileSourceScanExecTransformer parquet spark_catalog.default.item (42)
         +- ^ InputIteratorTransformer (52)
            +- ReusedExchange (50)


(1) FileSourceScanExecTransformer parquet spark_catalog.default.store_sales
Output [4]: [ss_item_sk#1, ss_store_sk#2, ss_net_profit#3, ss_sold_date_sk#4]
Batched: true
Location: CatalogFileIndex [{warehouse_dir}/store_sales]
PushedFilters: [IsNotNull(ss_store_sk), EqualTo(ss_store_sk,4)]
ReadSchema: struct<ss_item_sk:int,ss_store_sk:int,ss_net_profit:decimal(7,2)>

(2) FilterExecTransformer
Input [4]: [ss_item_sk#1, ss_store_sk#2, ss_net_profit#3, ss_sold_date_sk#4]
Arguments: (isnotnull(ss_store_sk#2) AND (ss_store_sk#2 = 4))

(3) ProjectExecTransformer
Output [2]: [ss_item_sk#1, UnscaledValue(ss_net_profit#3) AS _pre_1#5]
Input [4]: [ss_item_sk#1, ss_store_sk#2, ss_net_profit#3, ss_sold_date_sk#4]

(4) FlushableHashAggregateExecTransformer
Input [2]: [ss_item_sk#1, _pre_1#5]
Keys [1]: [ss_item_sk#1]
Functions [1]: [partial_avg(_pre_1#5)]
Aggregate Attributes [2]: [sum#6, count#7]
Results [3]: [ss_item_sk#1, sum#8, count#9]

(5) ProjectExecTransformer
Output [4]: [hash(ss_item_sk#1, 42) AS hash_partition_key#10, ss_item_sk#1, sum#8, count#9]
Input [3]: [ss_item_sk#1, sum#8, count#9]

(6) WholeStageCodegenTransformer (3)
Input [4]: [hash_partition_key#10, ss_item_sk#1, sum#8, count#9]
Arguments: false

(7) VeloxResizeBatches
Input [4]: [hash_partition_key#10, ss_item_sk#1, sum#8, count#9]
Arguments: 1024, 2147483647, 10485760

(8) ColumnarExchange
Input [4]: [hash_partition_key#10, ss_item_sk#1, sum#8, count#9]
Arguments: hashpartitioning(ss_item_sk#1, 1), ENSURE_REQUIREMENTS, [ss_item_sk#1, sum#8, count#9], [plan_id=1], [shuffle_writer_type=hash]

(9) InputAdapter
Input [3]: [ss_item_sk#1, sum#8, count#9]

(10) InputIteratorTransformer
Input [3]: [ss_item_sk#1, sum#8, count#9]

(11) RegularHashAggregateExecTransformer
Input [3]: [ss_item_sk#1, sum#8, count#9]
Keys [1]: [ss_item_sk#1]
Functions [1]: [avg(UnscaledValue(ss_net_profit#3))]
Aggregate Attributes [1]: [avg(UnscaledValue(ss_net_profit#3))#11]
Results [2]: [ss_item_sk#1, avg(UnscaledValue(ss_net_profit#3))#11]

(12) ProjectExecTransformer
Output [2]: [ss_item_sk#1 AS item_sk#12, cast((avg(UnscaledValue(ss_net_profit#3))#11 / 100.0) as decimal(11,6)) AS rank_col#13]
Input [2]: [ss_item_sk#1, avg(UnscaledValue(ss_net_profit#3))#11]

(13) FilterExecTransformer
Input [2]: [item_sk#12, rank_col#13]
Arguments: (isnotnull(rank_col#13) AND (cast(rank_col#13 as decimal(13,7)) > (0.9 * Subquery scalar-subquery#14, [id=#2])))

(14) WindowGroupLimitExecTransformer
Input [2]: [item_sk#12, rank_col#13]
Arguments: [rank_col#13 ASC NULLS FIRST], rank(rank_col#13), 10, GlutenFinal

(15) SortExecTransformer
Input [2]: [item_sk#12, rank_col#13]
Arguments: [rank_col#13 ASC NULLS FIRST], false, 0

(16) WindowExecTransformer
Input [2]: [item_sk#12, rank_col#13]
Arguments: [rank(rank_col#13) windowspecdefinition(rank_col#13 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS rnk#15], [rank_col#13 ASC NULLS FIRST]

(17) FilterExecTransformer
Input [3]: [item_sk#12, rank_col#13, rnk#15]
Arguments: ((rnk#15 < 11) AND isnotnull(item_sk#12))

(18) ProjectExecTransformer
Output [3]: [hash(rnk#15, 42) AS hash_partition_key#16, item_sk#12, rnk#15]
Input [3]: [item_sk#12, rank_col#13, rnk#15]

(19) WholeStageCodegenTransformer (4)
Input [3]: [hash_partition_key#16, item_sk#12, rnk#15]
Arguments: false

(20) VeloxResizeBatches
Input [3]: [hash_partition_key#16, item_sk#12, rnk#15]
Arguments: 1024, 2147483647, 10485760

(21) ColumnarExchange
Input [3]: [hash_partition_key#16, item_sk#12, rnk#15]
Arguments: hashpartitioning(rnk#15, 1), ENSURE_REQUIREMENTS, [item_sk#12, rnk#15], [plan_id=3], [shuffle_writer_type=hash]

(22) InputAdapter
Input [2]: [item_sk#12, rnk#15]

(23) InputIteratorTransformer
Input [2]: [item_sk#12, rnk#15]

(24) ReusedExchange [Reuses operator id: 8]
Output [3]: [ss_item_sk#17, sum#18, count#19]

(25) InputAdapter
Input [3]: [ss_item_sk#17, sum#18, count#19]

(26) InputIteratorTransformer
Input [3]: [ss_item_sk#17, sum#18, count#19]

(27) RegularHashAggregateExecTransformer
Input [3]: [ss_item_sk#17, sum#18, count#19]
Keys [1]: [ss_item_sk#17]
Functions [1]: [avg(UnscaledValue(ss_net_profit#20))]
Aggregate Attributes [1]: [avg(UnscaledValue(ss_net_profit#20))#21]
Results [2]: [ss_item_sk#17, avg(UnscaledValue(ss_net_profit#20))#21]

(28) ProjectExecTransformer
Output [2]: [ss_item_sk#17 AS item_sk#22, cast((avg(UnscaledValue(ss_net_profit#20))#21 / 100.0) as decimal(11,6)) AS rank_col#23]
Input [2]: [ss_item_sk#17, avg(UnscaledValue(ss_net_profit#20))#21]

(29) FilterExecTransformer
Input [2]: [item_sk#22, rank_col#23]
Arguments: (isnotnull(rank_col#23) AND (cast(rank_col#23 as decimal(13,7)) > (0.9 * ReusedSubquery Subquery scalar-subquery#14, [id=#2])))

(30) WindowGroupLimitExecTransformer
Input [2]: [item_sk#22, rank_col#23]
Arguments: [rank_col#23 DESC NULLS LAST], rank(rank_col#23), 10, GlutenFinal

(31) SortExecTransformer
Input [2]: [item_sk#22, rank_col#23]
Arguments: [rank_col#23 DESC NULLS LAST], false, 0

(32) WindowExecTransformer
Input [2]: [item_sk#22, rank_col#23]
Arguments: [rank(rank_col#23) windowspecdefinition(rank_col#23 DESC NULLS LAST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS rnk#24], [rank_col#23 DESC NULLS LAST]

(33) FilterExecTransformer
Input [3]: [item_sk#22, rank_col#23, rnk#24]
Arguments: ((rnk#24 < 11) AND isnotnull(item_sk#22))

(34) ProjectExecTransformer
Output [3]: [hash(rnk#24, 42) AS hash_partition_key#25, item_sk#22, rnk#24]
Input [3]: [item_sk#22, rank_col#23, rnk#24]

(35) WholeStageCodegenTransformer (8)
Input [3]: [hash_partition_key#25, item_sk#22, rnk#24]
Arguments: false

(36) VeloxResizeBatches
Input [3]: [hash_partition_key#25, item_sk#22, rnk#24]
Arguments: 1024, 2147483647, 10485760

(37) ColumnarExchange
Input [3]: [hash_partition_key#25, item_sk#22, rnk#24]
Arguments: hashpartitioning(rnk#24, 1), ENSURE_REQUIREMENTS, [item_sk#22, rnk#24], [plan_id=4], [shuffle_writer_type=hash]

(38) InputAdapter
Input [2]: [item_sk#22, rnk#24]

(39) InputIteratorTransformer
Input [2]: [item_sk#22, rnk#24]

(40) ShuffledHashJoinExecTransformer
Left keys [1]: [rnk#15]
Right keys [1]: [rnk#24]
Join type: Inner
Join condition: None

(41) ProjectExecTransformer
Output [3]: [item_sk#12, rnk#15, item_sk#22]
Input [4]: [item_sk#12, rnk#15, item_sk#22, rnk#24]

(42) FileSourceScanExecTransformer parquet spark_catalog.default.item
Output [2]: [i_item_sk#26, i_product_name#27]
Batched: true
Location: InMemoryFileIndex [{warehouse_dir}/item]
PushedFilters: [IsNotNull(i_item_sk)]
ReadSchema: struct<i_item_sk:int,i_product_name:string>

(43) FilterExecTransformer
Input [2]: [i_item_sk#26, i_product_name#27]
Arguments: isnotnull(i_item_sk#26)

(44) WholeStageCodegenTransformer (9)
Input [2]: [i_item_sk#26, i_product_name#27]
Arguments: false

(45) ColumnarBroadcastExchange
Input [2]: [i_item_sk#26, i_product_name#27]
Arguments: HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint)),false), [plan_id=5]

(46) InputAdapter
Input [2]: [i_item_sk#26, i_product_name#27]

(47) InputIteratorTransformer
Input [2]: [i_item_sk#26, i_product_name#27]

(48) BroadcastHashJoinExecTransformer
Left keys [1]: [item_sk#12]
Right keys [1]: [i_item_sk#26]
Join type: Inner
Join condition: None

(49) ProjectExecTransformer
Output [3]: [rnk#15, item_sk#22, i_product_name#27]
Input [5]: [item_sk#12, rnk#15, item_sk#22, i_item_sk#26, i_product_name#27]

(50) ReusedExchange [Reuses operator id: 45]
Output [2]: [i_item_sk#28, i_product_name#29]

(51) InputAdapter
Input [2]: [i_item_sk#28, i_product_name#29]

(52) InputIteratorTransformer
Input [2]: [i_item_sk#28, i_product_name#29]

(53) BroadcastHashJoinExecTransformer
Left keys [1]: [item_sk#22]
Right keys [1]: [i_item_sk#28]
Join type: Inner
Join condition: None

(54) ProjectExecTransformer
Output [3]: [rnk#15, i_product_name#27 AS best_performing#30, i_product_name#29 AS worst_performing#31]
Input [5]: [rnk#15, item_sk#22, i_product_name#27, i_item_sk#28, i_product_name#29]

(55) WholeStageCodegenTransformer (11)
Input [3]: [rnk#15, best_performing#30, worst_performing#31]
Arguments: false

(56) TakeOrderedAndProjectExecTransformer
Input [3]: [rnk#15, best_performing#30, worst_performing#31]
Arguments: 100, [rnk#15 ASC NULLS FIRST], [rnk#15, best_performing#30, worst_performing#31], 0

(57) VeloxColumnarToRow
Input [3]: [rnk#15, best_performing#30, worst_performing#31]

===== Subqueries =====

Subquery:1 Hosting operator id = 13 Hosting Expression = Subquery scalar-subquery#14, [id=#2]
VeloxColumnarToRow (71)
+- ^ ProjectExecTransformer (69)
   +- ^ RegularHashAggregateExecTransformer (68)
      +- ^ InputIteratorTransformer (67)
         +- ColumnarExchange (65)
            +- VeloxResizeBatches (64)
               +- ^ ProjectExecTransformer (62)
                  +- ^ FlushableHashAggregateExecTransformer (61)
                     +- ^ ProjectExecTransformer (60)
                        +- ^ FilterExecTransformer (59)
                           +- ^ FileSourceScanExecTransformer parquet spark_catalog.default.store_sales (58)


(58) FileSourceScanExecTransformer parquet spark_catalog.default.store_sales
Output [4]: [ss_addr_sk#32, ss_store_sk#33, ss_net_profit#34, ss_sold_date_sk#35]
Batched: true
Location: CatalogFileIndex [{warehouse_dir}/store_sales]
PushedFilters: [IsNotNull(ss_store_sk), EqualTo(ss_store_sk,4), IsNull(ss_addr_sk)]
ReadSchema: struct<ss_addr_sk:int,ss_store_sk:int,ss_net_profit:decimal(7,2)>

(59) FilterExecTransformer
Input [4]: [ss_addr_sk#32, ss_store_sk#33, ss_net_profit#34, ss_sold_date_sk#35]
Arguments: ((isnotnull(ss_store_sk#33) AND (ss_store_sk#33 = 4)) AND isnull(ss_addr_sk#32))

(60) ProjectExecTransformer
Output [2]: [ss_store_sk#33, UnscaledValue(ss_net_profit#34) AS _pre_2#36]
Input [4]: [ss_addr_sk#32, ss_store_sk#33, ss_net_profit#34, ss_sold_date_sk#35]

(61) FlushableHashAggregateExecTransformer
Input [2]: [ss_store_sk#33, _pre_2#36]
Keys [1]: [ss_store_sk#33]
Functions [1]: [partial_avg(_pre_2#36)]
Aggregate Attributes [2]: [sum#37, count#38]
Results [3]: [ss_store_sk#33, sum#39, count#40]

(62) ProjectExecTransformer
Output [4]: [hash(ss_store_sk#33, 42) AS hash_partition_key#41, ss_store_sk#33, sum#39, count#40]
Input [3]: [ss_store_sk#33, sum#39, count#40]

(63) WholeStageCodegenTransformer (1)
Input [4]: [hash_partition_key#41, ss_store_sk#33, sum#39, count#40]
Arguments: false

(64) VeloxResizeBatches
Input [4]: [hash_partition_key#41, ss_store_sk#33, sum#39, count#40]
Arguments: 1024, 2147483647, 10485760

(65) ColumnarExchange
Input [4]: [hash_partition_key#41, ss_store_sk#33, sum#39, count#40]
Arguments: hashpartitioning(ss_store_sk#33, 1), ENSURE_REQUIREMENTS, [ss_store_sk#33, sum#39, count#40], [plan_id=6], [shuffle_writer_type=hash]

(66) InputAdapter
Input [3]: [ss_store_sk#33, sum#39, count#40]

(67) InputIteratorTransformer
Input [3]: [ss_store_sk#33, sum#39, count#40]

(68) RegularHashAggregateExecTransformer
Input [3]: [ss_store_sk#33, sum#39, count#40]
Keys [1]: [ss_store_sk#33]
Functions [1]: [avg(UnscaledValue(ss_net_profit#34))]
Aggregate Attributes [1]: [avg(UnscaledValue(ss_net_profit#34))#42]
Results [2]: [ss_store_sk#33, avg(UnscaledValue(ss_net_profit#34))#42]

(69) ProjectExecTransformer
Output [1]: [cast((avg(UnscaledValue(ss_net_profit#34))#42 / 100.0) as decimal(11,6)) AS rank_col#43]
Input [2]: [ss_store_sk#33, avg(UnscaledValue(ss_net_profit#34))#42]

(70) WholeStageCodegenTransformer (2)
Input [1]: [rank_col#43]
Arguments: false

(71) VeloxColumnarToRow
Input [1]: [rank_col#43]

Subquery:2 Hosting operator id = 29 Hosting Expression = ReusedSubquery Subquery scalar-subquery#14, [id=#2]


