== Physical Plan ==
VeloxColumnarToRow (34)
+- TakeOrderedAndProjectExecTransformer (33)
   +- ^ RegularHashAggregateExecTransformer (31)
      +- ^ InputIteratorTransformer (30)
         +- ColumnarExchange (28)
            +- VeloxResizeBatches (27)
               +- ^ ProjectExecTransformer (25)
                  +- ^ FlushableHashAggregateExecTransformer (24)
                     +- ^ ProjectExecTransformer (23)
                        +- ^ BroadcastHashJoinExecTransformer Inner BuildRight (22)
                           :- ^ ProjectExecTransformer (3)
                           :  +- ^ FilterExecTransformer (2)
                           :     +- ^ FileSourceScanExecTransformer parquet spark_catalog.default.item (1)
                           +- ^ InputIteratorTransformer (21)
                              +- ColumnarBroadcastExchange (19)
                                 +- ^ ProjectExecTransformer (17)
                                    +- ^ FilterExecTransformer (16)
                                       +- ^ ProjectExecTransformer (15)
                                          +- ^ RegularHashAggregateExecTransformer (14)
                                             +- ^ InputIteratorTransformer (13)
                                                +- ColumnarExchange (11)
                                                   +- VeloxResizeBatches (10)
                                                      +- ^ ProjectExecTransformer (8)
                                                         +- ^ FlushableHashAggregateExecTransformer (7)
                                                            +- ^ ProjectExecTransformer (6)
                                                               +- ^ FilterExecTransformer (5)
                                                                  +- ^ FileSourceScanExecTransformer parquet spark_catalog.default.item (4)


(1) FileSourceScanExecTransformer parquet spark_catalog.default.item
Output [3]: [i_manufact_id#1, i_manufact#2, i_product_name#3]
Batched: true
Location: InMemoryFileIndex [{warehouse_dir}/item]
PushedFilters: [IsNotNull(i_manufact_id), GreaterThanOrEqual(i_manufact_id,738), LessThanOrEqual(i_manufact_id,778), IsNotNull(i_manufact)]
ReadSchema: struct<i_manufact_id:int,i_manufact:string,i_product_name:string>

(2) FilterExecTransformer
Input [3]: [i_manufact_id#1, i_manufact#2, i_product_name#3]
Arguments: (((isnotnull(i_manufact_id#1) AND (i_manufact_id#1 >= 738)) AND (i_manufact_id#1 <= 778)) AND isnotnull(i_manufact#2))

(3) ProjectExecTransformer
Output [2]: [i_manufact#2, i_product_name#3]
Input [3]: [i_manufact_id#1, i_manufact#2, i_product_name#3]

(4) FileSourceScanExecTransformer parquet spark_catalog.default.item
Output [5]: [i_category#4, i_manufact#5, i_size#6, i_color#7, i_units#8]
Batched: true
Location: InMemoryFileIndex [{warehouse_dir}/item]
PushedFilters: [Or(Or(And(EqualTo(i_category,Women                                             ),Or(And(And(Or(EqualTo(i_color,powder              ),EqualTo(i_color,khaki               )),Or(EqualTo(i_units,Ounce     ),EqualTo(i_units,Oz        ))),Or(EqualTo(i_size,medium              ),EqualTo(i_size,extra large         ))),And(And(Or(EqualTo(i_color,brown               ),EqualTo(i_color,honeydew            )),Or(EqualTo(i_units,Bunch     ),EqualTo(i_units,Ton       ))),Or(EqualTo(i_size,N/A                 ),EqualTo(i_size,small               ))))),And(EqualTo(i_category,Men                                               ),Or(And(And(Or(EqualTo(i_color,floral              ),EqualTo(i_color,deep                )),Or(EqualTo(i_units,N/A       ),EqualTo(i_units,Dozen     ))),Or(EqualTo(i_size,petite              ),EqualTo(i_size,large               ))),And(And(Or(EqualTo(i_color,light               ),EqualTo(i_color,cornflower          )),Or(EqualTo(i_units,Box       ),EqualTo(i_units,Pound     ))),Or(EqualTo(i_size,medium              ),EqualTo(i_size,extra large         )))))),Or(And(EqualTo(i_category,Women                                             ),Or(And(And(Or(EqualTo(i_color,midnight            ),EqualTo(i_color,snow                )),Or(EqualTo(i_units,Pallet    ),EqualTo(i_units,Gross     ))),Or(EqualTo(i_size,medium              ),EqualTo(i_size,extra large         ))),And(And(Or(EqualTo(i_color,cyan                ),EqualTo(i_color,papaya              )),Or(EqualTo(i_units,Cup       ),EqualTo(i_units,Dram      ))),Or(EqualTo(i_size,N/A                 ),EqualTo(i_size,small               ))))),And(EqualTo(i_category,Men                                               ),Or(And(And(Or(EqualTo(i_color,orange              ),EqualTo(i_color,frosted             )),Or(EqualTo(i_units,Each      ),EqualTo(i_units,Tbl       ))),Or(EqualTo(i_size,petite              ),EqualTo(i_size,large               ))),And(And(Or(EqualTo(i_color,forest              ),EqualTo(i_color,ghost               )),Or(EqualTo(i_units,Lb        ),EqualTo(i_units,Bundle    ))),Or(EqualTo(i_size,medium              ),EqualTo(i_size,extra large         ))))))), IsNotNull(i_manufact)]
ReadSchema: struct<i_category:string,i_manufact:string,i_size:string,i_color:string,i_units:string>

(5) FilterExecTransformer
Input [5]: [i_category#4, i_manufact#5, i_size#6, i_color#7, i_units#8]
Arguments: (((((i_category#4 = Women                                             ) AND (((((i_color#7 = powder              ) OR (i_color#7 = khaki               )) AND ((i_units#8 = Ounce     ) OR (i_units#8 = Oz        ))) AND ((i_size#6 = medium              ) OR (i_size#6 = extra large         ))) OR ((((i_color#7 = brown               ) OR (i_color#7 = honeydew            )) AND ((i_units#8 = Bunch     ) OR (i_units#8 = Ton       ))) AND ((i_size#6 = N/A                 ) OR (i_size#6 = small               ))))) OR ((i_category#4 = Men                                               ) AND (((((i_color#7 = floral              ) OR (i_color#7 = deep                )) AND ((i_units#8 = N/A       ) OR (i_units#8 = Dozen     ))) AND ((i_size#6 = petite              ) OR (i_size#6 = large               ))) OR ((((i_color#7 = light               ) OR (i_color#7 = cornflower          )) AND ((i_units#8 = Box       ) OR (i_units#8 = Pound     ))) AND ((i_size#6 = medium              ) OR (i_size#6 = extra large         )))))) OR (((i_category#4 = Women                                             ) AND (((((i_color#7 = midnight            ) OR (i_color#7 = snow                )) AND ((i_units#8 = Pallet    ) OR (i_units#8 = Gross     ))) AND ((i_size#6 = medium              ) OR (i_size#6 = extra large         ))) OR ((((i_color#7 = cyan                ) OR (i_color#7 = papaya              )) AND ((i_units#8 = Cup       ) OR (i_units#8 = Dram      ))) AND ((i_size#6 = N/A                 ) OR (i_size#6 = small               ))))) OR ((i_category#4 = Men                                               ) AND (((((i_color#7 = orange              ) OR (i_color#7 = frosted             )) AND ((i_units#8 = Each      ) OR (i_units#8 = Tbl       ))) AND ((i_size#6 = petite              ) OR (i_size#6 = large               ))) OR ((((i_color#7 = forest              ) OR (i_color#7 = ghost               )) AND ((i_units#8 = Lb        ) OR (i_units#8 = Bundle    ))) AND ((i_size#6 = medium              ) OR (i_size#6 = extra large         ))))))) AND isnotnull(i_manufact#5))

(6) ProjectExecTransformer
Output [1]: [i_manufact#5]
Input [5]: [i_category#4, i_manufact#5, i_size#6, i_color#7, i_units#8]

(7) FlushableHashAggregateExecTransformer
Input [1]: [i_manufact#5]
Keys [1]: [i_manufact#5]
Functions [1]: [partial_count(1)]
Aggregate Attributes [1]: [count#9]
Results [2]: [i_manufact#5, count#10]

(8) ProjectExecTransformer
Output [3]: [hash(i_manufact#5, 42) AS hash_partition_key#11, i_manufact#5, count#10]
Input [2]: [i_manufact#5, count#10]

(9) WholeStageCodegenTransformer (1)
Input [3]: [hash_partition_key#11, i_manufact#5, count#10]
Arguments: false

(10) VeloxResizeBatches
Input [3]: [hash_partition_key#11, i_manufact#5, count#10]
Arguments: 1024, 2147483647, 10485760

(11) ColumnarExchange
Input [3]: [hash_partition_key#11, i_manufact#5, count#10]
Arguments: hashpartitioning(i_manufact#5, 1), ENSURE_REQUIREMENTS, [i_manufact#5, count#10], [plan_id=1], [shuffle_writer_type=hash]

(12) InputAdapter
Input [2]: [i_manufact#5, count#10]

(13) InputIteratorTransformer
Input [2]: [i_manufact#5, count#10]

(14) RegularHashAggregateExecTransformer
Input [2]: [i_manufact#5, count#10]
Keys [1]: [i_manufact#5]
Functions [1]: [count(1)]
Aggregate Attributes [1]: [count(1)#12]
Results [2]: [i_manufact#5, count(1)#12]

(15) ProjectExecTransformer
Output [2]: [count(1)#12 AS item_cnt#13, i_manufact#5]
Input [2]: [i_manufact#5, count(1)#12]

(16) FilterExecTransformer
Input [2]: [item_cnt#13, i_manufact#5]
Arguments: (item_cnt#13 > 0)

(17) ProjectExecTransformer
Output [1]: [i_manufact#5]
Input [2]: [item_cnt#13, i_manufact#5]

(18) WholeStageCodegenTransformer (2)
Input [1]: [i_manufact#5]
Arguments: false

(19) ColumnarBroadcastExchange
Input [1]: [i_manufact#5]
Arguments: HashedRelationBroadcastMode(List(input[0, string, true]),false), [plan_id=2]

(20) InputAdapter
Input [1]: [i_manufact#5]

(21) InputIteratorTransformer
Input [1]: [i_manufact#5]

(22) BroadcastHashJoinExecTransformer
Left keys [1]: [i_manufact#2]
Right keys [1]: [i_manufact#5]
Join type: Inner
Join condition: None

(23) ProjectExecTransformer
Output [1]: [i_product_name#3]
Input [3]: [i_manufact#2, i_product_name#3, i_manufact#5]

(24) FlushableHashAggregateExecTransformer
Input [1]: [i_product_name#3]
Keys [1]: [i_product_name#3]
Functions: []
Aggregate Attributes: []
Results [1]: [i_product_name#3]

(25) ProjectExecTransformer
Output [2]: [hash(i_product_name#3, 42) AS hash_partition_key#14, i_product_name#3]
Input [1]: [i_product_name#3]

(26) WholeStageCodegenTransformer (3)
Input [2]: [hash_partition_key#14, i_product_name#3]
Arguments: false

(27) VeloxResizeBatches
Input [2]: [hash_partition_key#14, i_product_name#3]
Arguments: 1024, 2147483647, 10485760

(28) ColumnarExchange
Input [2]: [hash_partition_key#14, i_product_name#3]
Arguments: hashpartitioning(i_product_name#3, 1), ENSURE_REQUIREMENTS, [i_product_name#3], [plan_id=3], [shuffle_writer_type=hash]

(29) InputAdapter
Input [1]: [i_product_name#3]

(30) InputIteratorTransformer
Input [1]: [i_product_name#3]

(31) RegularHashAggregateExecTransformer
Input [1]: [i_product_name#3]
Keys [1]: [i_product_name#3]
Functions: []
Aggregate Attributes: []
Results [1]: [i_product_name#3]

(32) WholeStageCodegenTransformer (4)
Input [1]: [i_product_name#3]
Arguments: false

(33) TakeOrderedAndProjectExecTransformer
Input [1]: [i_product_name#3]
Arguments: 100, [i_product_name#3 ASC NULLS FIRST], [i_product_name#3], 0

(34) VeloxColumnarToRow
Input [1]: [i_product_name#3]

