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upate shape
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@englefly
englefly committed Nov 28, 2024
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Showing 28 changed files with 49 additions and 49 deletions.
2 changes: 1 addition & 1 deletion regression-test/data/nereids_hint_tpcds_p0/shape/query41.out
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Expand Up @@ -18,6 +18,6 @@ PhysicalResultSink
------------------------PhysicalDistribute[DistributionSpecHash]
--------------------------hashAgg[LOCAL]
----------------------------PhysicalProject
------------------------------filter((((i_color IN ('forest', 'lime', 'maroon', 'navy', 'powder', 'sky', 'slate', 'smoke') AND i_units IN ('Bunch', 'Case', 'Dozen', 'Gross', 'Lb', 'Ounce', 'Pallet', 'Pound')) AND (((((((item.i_category = 'Women') AND i_color IN ('forest', 'lime')) AND i_units IN ('Pallet', 'Pound')) AND i_size IN ('economy', 'small')) OR ((((item.i_category = 'Women') AND i_color IN ('navy', 'slate')) AND i_units IN ('Bunch', 'Gross')) AND i_size IN ('extra large', 'petite'))) OR ((((item.i_category = 'Men') AND i_color IN ('powder', 'sky')) AND i_units IN ('Dozen', 'Lb')) AND i_size IN ('N/A', 'large'))) OR ((((item.i_category = 'Men') AND i_color IN ('maroon', 'smoke')) AND i_units IN ('Case', 'Ounce')) AND i_size IN ('economy', 'small')))) OR ((i_color IN ('aquamarine', 'dark', 'firebrick', 'frosted', 'papaya', 'peach', 'plum', 'sienna') AND i_units IN ('Box', 'Bundle', 'Carton', 'Cup', 'Dram', 'Each', 'Tbl', 'Ton')) AND (((((((item.i_category = 'Women') AND i_color IN ('aquamarine', 'dark')) AND i_units IN ('Tbl', 'Ton')) AND i_size IN ('economy', 'small')) OR ((((item.i_category = 'Women') AND i_color IN ('frosted', 'plum')) AND i_units IN ('Box', 'Dram')) AND i_size IN ('extra large', 'petite'))) OR ((((item.i_category = 'Men') AND i_color IN ('papaya', 'peach')) AND i_units IN ('Bundle', 'Carton')) AND i_size IN ('N/A', 'large'))) OR ((((item.i_category = 'Men') AND i_color IN ('firebrick', 'sienna')) AND i_units IN ('Cup', 'Each')) AND i_size IN ('economy', 'small'))))) and i_category IN ('Men', 'Women') and i_size IN ('N/A', 'economy', 'extra large', 'large', 'petite', 'small'))
------------------------------filter(OR[AND[i_color IN ('forest', 'lime', 'maroon', 'navy', 'powder', 'sky', 'slate', 'smoke'),i_units IN ('Bunch', 'Case', 'Dozen', 'Gross', 'Lb', 'Ounce', 'Pallet', 'Pound'),OR[AND[(item.i_category = 'Women'),i_color IN ('forest', 'lime'),i_units IN ('Pallet', 'Pound'),i_size IN ('economy', 'small')],AND[(item.i_category = 'Women'),i_color IN ('navy', 'slate'),i_units IN ('Bunch', 'Gross'),i_size IN ('extra large', 'petite')],AND[(item.i_category = 'Men'),i_color IN ('powder', 'sky'),i_units IN ('Dozen', 'Lb'),i_size IN ('N/A', 'large')],AND[(item.i_category = 'Men'),i_color IN ('maroon', 'smoke'),i_units IN ('Case', 'Ounce'),i_size IN ('economy', 'small')]]],AND[i_color IN ('aquamarine', 'dark', 'firebrick', 'frosted', 'papaya', 'peach', 'plum', 'sienna'),i_units IN ('Box', 'Bundle', 'Carton', 'Cup', 'Dram', 'Each', 'Tbl', 'Ton'),OR[AND[(item.i_category = 'Women'),i_color IN ('aquamarine', 'dark'),i_units IN ('Tbl', 'Ton'),i_size IN ('economy', 'small')],AND[(item.i_category = 'Women'),i_color IN ('frosted', 'plum'),i_units IN ('Box', 'Dram'),i_size IN ('extra large', 'petite')],AND[(item.i_category = 'Men'),i_color IN ('papaya', 'peach'),i_units IN ('Bundle', 'Carton'),i_size IN ('N/A', 'large')],AND[(item.i_category = 'Men'),i_color IN ('firebrick', 'sienna'),i_units IN ('Cup', 'Each'),i_size IN ('economy', 'small')]]]] and i_category IN ('Men', 'Women') and i_size IN ('N/A', 'economy', 'extra large', 'large', 'petite', 'small'))
--------------------------------PhysicalOlapScan[item]

2 changes: 1 addition & 1 deletion regression-test/data/nereids_hint_tpcds_p0/shape/query47.out
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Expand Up @@ -21,7 +21,7 @@ PhysicalCteAnchor ( cteId=CTEId#0 )
------------------------------------PhysicalProject
--------------------------------------PhysicalOlapScan[store_sales] apply RFs: RF0 RF1 RF2
------------------------------------PhysicalProject
--------------------------------------filter((((date_dim.d_year = 2000) OR ((date_dim.d_year = 1999) AND (date_dim.d_moy = 12))) OR ((date_dim.d_year = 2001) AND (date_dim.d_moy = 1))) and d_year IN (1999, 2000, 2001))
--------------------------------------filter(OR[(date_dim.d_year = 2000),AND[(date_dim.d_year = 1999),(date_dim.d_moy = 12)],AND[(date_dim.d_year = 2001),(date_dim.d_moy = 1)]] and d_year IN (1999, 2000, 2001))
----------------------------------------PhysicalOlapScan[date_dim]
--------------------------------PhysicalProject
----------------------------------PhysicalOlapScan[store]
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16 changes: 8 additions & 8 deletions regression-test/data/nereids_hint_tpcds_p0/shape/query88.out
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Original file line number Diff line number Diff line change
Expand Up @@ -24,7 +24,7 @@ PhysicalResultSink
--------------------------------------filter((time_dim.t_hour = 8) and (time_dim.t_minute >= 30))
----------------------------------------PhysicalOlapScan[time_dim]
--------------------------------PhysicalProject
----------------------------------filter(((((household_demographics.hd_dep_count = 0) AND (household_demographics.hd_vehicle_count <= 2)) OR ((household_demographics.hd_dep_count = -1) AND (household_demographics.hd_vehicle_count <= 1))) OR ((household_demographics.hd_dep_count = 3) AND (household_demographics.hd_vehicle_count <= 5))) and hd_dep_count IN (-1, 0, 3))
----------------------------------filter(OR[AND[(household_demographics.hd_dep_count = 0),(household_demographics.hd_vehicle_count <= 2)],AND[(household_demographics.hd_dep_count = -1),(household_demographics.hd_vehicle_count <= 1)],AND[(household_demographics.hd_dep_count = 3),(household_demographics.hd_vehicle_count <= 5)]] and hd_dep_count IN (-1, 0, 3))
------------------------------------PhysicalOlapScan[household_demographics]
----------------------------PhysicalProject
------------------------------filter((store.s_store_name = 'ese'))
Expand All @@ -45,7 +45,7 @@ PhysicalResultSink
--------------------------------------filter((time_dim.t_hour = 9) and (time_dim.t_minute < 30))
----------------------------------------PhysicalOlapScan[time_dim]
--------------------------------PhysicalProject
----------------------------------filter(((((household_demographics.hd_dep_count = 0) AND (household_demographics.hd_vehicle_count <= 2)) OR ((household_demographics.hd_dep_count = -1) AND (household_demographics.hd_vehicle_count <= 1))) OR ((household_demographics.hd_dep_count = 3) AND (household_demographics.hd_vehicle_count <= 5))) and hd_dep_count IN (-1, 0, 3))
----------------------------------filter(OR[AND[(household_demographics.hd_dep_count = 0),(household_demographics.hd_vehicle_count <= 2)],AND[(household_demographics.hd_dep_count = -1),(household_demographics.hd_vehicle_count <= 1)],AND[(household_demographics.hd_dep_count = 3),(household_demographics.hd_vehicle_count <= 5)]] and hd_dep_count IN (-1, 0, 3))
------------------------------------PhysicalOlapScan[household_demographics]
----------------------------PhysicalProject
------------------------------filter((store.s_store_name = 'ese'))
Expand All @@ -66,7 +66,7 @@ PhysicalResultSink
------------------------------------filter((time_dim.t_hour = 9) and (time_dim.t_minute >= 30))
--------------------------------------PhysicalOlapScan[time_dim]
------------------------------PhysicalProject
--------------------------------filter(((((household_demographics.hd_dep_count = 0) AND (household_demographics.hd_vehicle_count <= 2)) OR ((household_demographics.hd_dep_count = -1) AND (household_demographics.hd_vehicle_count <= 1))) OR ((household_demographics.hd_dep_count = 3) AND (household_demographics.hd_vehicle_count <= 5))) and hd_dep_count IN (-1, 0, 3))
--------------------------------filter(OR[AND[(household_demographics.hd_dep_count = 0),(household_demographics.hd_vehicle_count <= 2)],AND[(household_demographics.hd_dep_count = -1),(household_demographics.hd_vehicle_count <= 1)],AND[(household_demographics.hd_dep_count = 3),(household_demographics.hd_vehicle_count <= 5)]] and hd_dep_count IN (-1, 0, 3))
----------------------------------PhysicalOlapScan[household_demographics]
--------------------------PhysicalProject
----------------------------filter((store.s_store_name = 'ese'))
Expand All @@ -87,7 +87,7 @@ PhysicalResultSink
----------------------------------filter((time_dim.t_hour = 10) and (time_dim.t_minute < 30))
------------------------------------PhysicalOlapScan[time_dim]
----------------------------PhysicalProject
------------------------------filter(((((household_demographics.hd_dep_count = 0) AND (household_demographics.hd_vehicle_count <= 2)) OR ((household_demographics.hd_dep_count = -1) AND (household_demographics.hd_vehicle_count <= 1))) OR ((household_demographics.hd_dep_count = 3) AND (household_demographics.hd_vehicle_count <= 5))) and hd_dep_count IN (-1, 0, 3))
------------------------------filter(OR[AND[(household_demographics.hd_dep_count = 0),(household_demographics.hd_vehicle_count <= 2)],AND[(household_demographics.hd_dep_count = -1),(household_demographics.hd_vehicle_count <= 1)],AND[(household_demographics.hd_dep_count = 3),(household_demographics.hd_vehicle_count <= 5)]] and hd_dep_count IN (-1, 0, 3))
--------------------------------PhysicalOlapScan[household_demographics]
------------------------PhysicalProject
--------------------------filter((store.s_store_name = 'ese'))
Expand All @@ -108,7 +108,7 @@ PhysicalResultSink
--------------------------------filter((time_dim.t_hour = 10) and (time_dim.t_minute >= 30))
----------------------------------PhysicalOlapScan[time_dim]
--------------------------PhysicalProject
----------------------------filter(((((household_demographics.hd_dep_count = 0) AND (household_demographics.hd_vehicle_count <= 2)) OR ((household_demographics.hd_dep_count = -1) AND (household_demographics.hd_vehicle_count <= 1))) OR ((household_demographics.hd_dep_count = 3) AND (household_demographics.hd_vehicle_count <= 5))) and hd_dep_count IN (-1, 0, 3))
----------------------------filter(OR[AND[(household_demographics.hd_dep_count = 0),(household_demographics.hd_vehicle_count <= 2)],AND[(household_demographics.hd_dep_count = -1),(household_demographics.hd_vehicle_count <= 1)],AND[(household_demographics.hd_dep_count = 3),(household_demographics.hd_vehicle_count <= 5)]] and hd_dep_count IN (-1, 0, 3))
------------------------------PhysicalOlapScan[household_demographics]
----------------------PhysicalProject
------------------------filter((store.s_store_name = 'ese'))
Expand All @@ -129,7 +129,7 @@ PhysicalResultSink
------------------------------filter((time_dim.t_hour = 11) and (time_dim.t_minute < 30))
--------------------------------PhysicalOlapScan[time_dim]
------------------------PhysicalProject
--------------------------filter(((((household_demographics.hd_dep_count = 0) AND (household_demographics.hd_vehicle_count <= 2)) OR ((household_demographics.hd_dep_count = -1) AND (household_demographics.hd_vehicle_count <= 1))) OR ((household_demographics.hd_dep_count = 3) AND (household_demographics.hd_vehicle_count <= 5))) and hd_dep_count IN (-1, 0, 3))
--------------------------filter(OR[AND[(household_demographics.hd_dep_count = 0),(household_demographics.hd_vehicle_count <= 2)],AND[(household_demographics.hd_dep_count = -1),(household_demographics.hd_vehicle_count <= 1)],AND[(household_demographics.hd_dep_count = 3),(household_demographics.hd_vehicle_count <= 5)]] and hd_dep_count IN (-1, 0, 3))
----------------------------PhysicalOlapScan[household_demographics]
--------------------PhysicalProject
----------------------filter((store.s_store_name = 'ese'))
Expand All @@ -150,7 +150,7 @@ PhysicalResultSink
----------------------------filter((time_dim.t_hour = 11) and (time_dim.t_minute >= 30))
------------------------------PhysicalOlapScan[time_dim]
----------------------PhysicalProject
------------------------filter(((((household_demographics.hd_dep_count = 0) AND (household_demographics.hd_vehicle_count <= 2)) OR ((household_demographics.hd_dep_count = -1) AND (household_demographics.hd_vehicle_count <= 1))) OR ((household_demographics.hd_dep_count = 3) AND (household_demographics.hd_vehicle_count <= 5))) and hd_dep_count IN (-1, 0, 3))
------------------------filter(OR[AND[(household_demographics.hd_dep_count = 0),(household_demographics.hd_vehicle_count <= 2)],AND[(household_demographics.hd_dep_count = -1),(household_demographics.hd_vehicle_count <= 1)],AND[(household_demographics.hd_dep_count = 3),(household_demographics.hd_vehicle_count <= 5)]] and hd_dep_count IN (-1, 0, 3))
--------------------------PhysicalOlapScan[household_demographics]
------------------PhysicalProject
--------------------filter((store.s_store_name = 'ese'))
Expand All @@ -171,7 +171,7 @@ PhysicalResultSink
--------------------------filter((time_dim.t_hour = 12) and (time_dim.t_minute < 30))
----------------------------PhysicalOlapScan[time_dim]
--------------------PhysicalProject
----------------------filter(((((household_demographics.hd_dep_count = 0) AND (household_demographics.hd_vehicle_count <= 2)) OR ((household_demographics.hd_dep_count = -1) AND (household_demographics.hd_vehicle_count <= 1))) OR ((household_demographics.hd_dep_count = 3) AND (household_demographics.hd_vehicle_count <= 5))) and hd_dep_count IN (-1, 0, 3))
----------------------filter(OR[AND[(household_demographics.hd_dep_count = 0),(household_demographics.hd_vehicle_count <= 2)],AND[(household_demographics.hd_dep_count = -1),(household_demographics.hd_vehicle_count <= 1)],AND[(household_demographics.hd_dep_count = 3),(household_demographics.hd_vehicle_count <= 5)]] and hd_dep_count IN (-1, 0, 3))
------------------------PhysicalOlapScan[household_demographics]
----------------PhysicalProject
------------------filter((store.s_store_name = 'ese'))
Expand Down
2 changes: 1 addition & 1 deletion regression-test/data/nereids_tpcds_shape_sf1000_p0/shape/query41.out
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Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,6 @@ PhysicalResultSink
------------------------PhysicalDistribute[DistributionSpecHash]
--------------------------hashAgg[LOCAL]
----------------------------PhysicalProject
------------------------------filter((((i_color IN ('forest', 'lime', 'maroon', 'navy', 'powder', 'sky', 'slate', 'smoke') AND i_units IN ('Bunch', 'Case', 'Dozen', 'Gross', 'Lb', 'Ounce', 'Pallet', 'Pound')) AND (((((((item.i_category = 'Women') AND i_color IN ('forest', 'lime')) AND i_units IN ('Pallet', 'Pound')) AND i_size IN ('economy', 'small')) OR ((((item.i_category = 'Women') AND i_color IN ('navy', 'slate')) AND i_units IN ('Bunch', 'Gross')) AND i_size IN ('extra large', 'petite'))) OR ((((item.i_category = 'Men') AND i_color IN ('powder', 'sky')) AND i_units IN ('Dozen', 'Lb')) AND i_size IN ('N/A', 'large'))) OR ((((item.i_category = 'Men') AND i_color IN ('maroon', 'smoke')) AND i_units IN ('Case', 'Ounce')) AND i_size IN ('economy', 'small')))) OR ((i_color IN ('aquamarine', 'dark', 'firebrick', 'frosted', 'papaya', 'peach', 'plum', 'sienna') AND i_units IN ('Box', 'Bundle', 'Carton', 'Cup', 'Dram', 'Each', 'Tbl', 'Ton')) AND (((((((item.i_category = 'Women') AND i_color IN ('aquamarine', 'dark')) AND i_units IN ('Tbl', 'Ton')) AND i_size IN ('economy', 'small')) OR ((((item.i_category = 'Women') AND i_color IN ('frosted', 'plum')) AND i_units IN ('Box', 'Dram')) AND i_size IN ('extra large', 'petite'))) OR ((((item.i_category = 'Men') AND i_color IN ('papaya', 'peach')) AND i_units IN ('Bundle', 'Carton')) AND i_size IN ('N/A', 'large'))) OR ((((item.i_category = 'Men') AND i_color IN ('firebrick', 'sienna')) AND i_units IN ('Cup', 'Each')) AND i_size IN ('economy', 'small'))))) and i_category IN ('Men', 'Women') and i_size IN ('N/A', 'economy', 'extra large', 'large', 'petite', 'small'))
------------------------------filter(OR[AND[i_color IN ('forest', 'lime', 'maroon', 'navy', 'powder', 'sky', 'slate', 'smoke'),i_units IN ('Bunch', 'Case', 'Dozen', 'Gross', 'Lb', 'Ounce', 'Pallet', 'Pound'),OR[AND[(item.i_category = 'Women'),i_color IN ('forest', 'lime'),i_units IN ('Pallet', 'Pound'),i_size IN ('economy', 'small')],AND[(item.i_category = 'Women'),i_color IN ('navy', 'slate'),i_units IN ('Bunch', 'Gross'),i_size IN ('extra large', 'petite')],AND[(item.i_category = 'Men'),i_color IN ('powder', 'sky'),i_units IN ('Dozen', 'Lb'),i_size IN ('N/A', 'large')],AND[(item.i_category = 'Men'),i_color IN ('maroon', 'smoke'),i_units IN ('Case', 'Ounce'),i_size IN ('economy', 'small')]]],AND[i_color IN ('aquamarine', 'dark', 'firebrick', 'frosted', 'papaya', 'peach', 'plum', 'sienna'),i_units IN ('Box', 'Bundle', 'Carton', 'Cup', 'Dram', 'Each', 'Tbl', 'Ton'),OR[AND[(item.i_category = 'Women'),i_color IN ('aquamarine', 'dark'),i_units IN ('Tbl', 'Ton'),i_size IN ('economy', 'small')],AND[(item.i_category = 'Women'),i_color IN ('frosted', 'plum'),i_units IN ('Box', 'Dram'),i_size IN ('extra large', 'petite')],AND[(item.i_category = 'Men'),i_color IN ('papaya', 'peach'),i_units IN ('Bundle', 'Carton'),i_size IN ('N/A', 'large')],AND[(item.i_category = 'Men'),i_color IN ('firebrick', 'sienna'),i_units IN ('Cup', 'Each'),i_size IN ('economy', 'small')]]]] and i_category IN ('Men', 'Women') and i_size IN ('N/A', 'economy', 'extra large', 'large', 'petite', 'small'))
--------------------------------PhysicalOlapScan[item]

2 changes: 1 addition & 1 deletion regression-test/data/nereids_tpcds_shape_sf1000_p0/shape/query47.out
View file
Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,7 @@ PhysicalCteAnchor ( cteId=CTEId#0 )
------------------------------------PhysicalProject
--------------------------------------PhysicalOlapScan[store_sales] apply RFs: RF0 RF1 RF2
------------------------------------PhysicalProject
--------------------------------------filter((((date_dim.d_year = 2000) OR ((date_dim.d_year = 1999) AND (date_dim.d_moy = 12))) OR ((date_dim.d_year = 2001) AND (date_dim.d_moy = 1))) and d_year IN (1999, 2000, 2001))
--------------------------------------filter(OR[(date_dim.d_year = 2000),AND[(date_dim.d_year = 1999),(date_dim.d_moy = 12)],AND[(date_dim.d_year = 2001),(date_dim.d_moy = 1)]] and d_year IN (1999, 2000, 2001))
----------------------------------------PhysicalOlapScan[date_dim]
--------------------------------PhysicalProject
----------------------------------PhysicalOlapScan[store]
Expand Down
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