From 2f6a040511b1c4d7517f5d253c3776a0dcdc9694 Mon Sep 17 00:00:00 2001 From: minghong Date: Fri, 29 Nov 2024 00:27:25 +0800 Subject: [PATCH] upate shape --- .../data/nereids_hint_tpcds_p0/shape/query41.out | 2 +- .../data/nereids_hint_tpcds_p0/shape/query47.out | 2 +- .../data/nereids_hint_tpcds_p0/shape/query88.out | 16 ++++++++-------- .../shape/query41.out | 2 +- .../shape/query47.out | 2 +- .../shape/query88.out | 16 ++++++++-------- .../noStatsRfPrune/query41.out | 2 +- .../noStatsRfPrune/query57.out | 2 +- .../no_stats_shape/query41.out | 2 +- .../no_stats_shape/query57.out | 2 +- .../rf_prune/query41.out | 2 +- .../rf_prune/query57.out | 2 +- .../shape/query41.out | 2 +- .../shape/query57.out | 2 +- .../shape/query41.out | 2 +- .../shape/query47.out | 2 +- .../shape/query57.out | 2 +- .../tpcds_sf100/noStatsRfPrune/query41.out | 2 +- .../tpcds_sf100/noStatsRfPrune/query57.out | 2 +- .../tpcds_sf100/no_stats_shape/query41.out | 2 +- .../tpcds_sf100/no_stats_shape/query57.out | 2 +- .../tpcds_sf100/rf_prune/query41.out | 2 +- .../tpcds_sf100/rf_prune/query57.out | 2 +- .../new_shapes_p0/tpcds_sf100/shape/query41.out | 2 +- .../new_shapes_p0/tpcds_sf100/shape/query57.out | 2 +- .../new_shapes_p0/tpcds_sf1000/shape/query41.out | 2 +- .../new_shapes_p0/tpcds_sf1000/shape/query47.out | 2 +- .../new_shapes_p0/tpcds_sf1000/shape/query88.out | 16 ++++++++-------- 28 files changed, 49 insertions(+), 49 deletions(-) diff --git a/regression-test/data/nereids_hint_tpcds_p0/shape/query41.out b/regression-test/data/nereids_hint_tpcds_p0/shape/query41.out index ea5acccd883550c..0bba60d4cdac394 100644 --- a/regression-test/data/nereids_hint_tpcds_p0/shape/query41.out +++ b/regression-test/data/nereids_hint_tpcds_p0/shape/query41.out @@ -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] diff --git a/regression-test/data/nereids_hint_tpcds_p0/shape/query47.out b/regression-test/data/nereids_hint_tpcds_p0/shape/query47.out index 5554797a7b9dc36..b1694f8470571fa 100644 --- a/regression-test/data/nereids_hint_tpcds_p0/shape/query47.out +++ b/regression-test/data/nereids_hint_tpcds_p0/shape/query47.out @@ -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] diff --git a/regression-test/data/nereids_hint_tpcds_p0/shape/query88.out b/regression-test/data/nereids_hint_tpcds_p0/shape/query88.out index 49e27f92b64a615..8f366f3d8c02025 100644 --- a/regression-test/data/nereids_hint_tpcds_p0/shape/query88.out +++ b/regression-test/data/nereids_hint_tpcds_p0/shape/query88.out @@ -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')) @@ -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')) @@ -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')) @@ -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')) @@ -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')) @@ -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')) @@ -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')) @@ -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')) diff --git a/regression-test/data/nereids_tpcds_shape_sf1000_p0/shape/query41.out b/regression-test/data/nereids_tpcds_shape_sf1000_p0/shape/query41.out index ea5acccd883550c..0bba60d4cdac394 100644 --- a/regression-test/data/nereids_tpcds_shape_sf1000_p0/shape/query41.out +++ b/regression-test/data/nereids_tpcds_shape_sf1000_p0/shape/query41.out @@ -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] diff --git a/regression-test/data/nereids_tpcds_shape_sf1000_p0/shape/query47.out b/regression-test/data/nereids_tpcds_shape_sf1000_p0/shape/query47.out index d51d48c5ab5677a..0e9f713243773a2 100644 --- a/regression-test/data/nereids_tpcds_shape_sf1000_p0/shape/query47.out +++ b/regression-test/data/nereids_tpcds_shape_sf1000_p0/shape/query47.out @@ -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] diff --git a/regression-test/data/nereids_tpcds_shape_sf1000_p0/shape/query88.out b/regression-test/data/nereids_tpcds_shape_sf1000_p0/shape/query88.out index 3c8f0335f1f415b..d016dca48db8326 100644 --- a/regression-test/data/nereids_tpcds_shape_sf1000_p0/shape/query88.out +++ b/regression-test/data/nereids_tpcds_shape_sf1000_p0/shape/query88.out @@ -23,7 +23,7 @@ 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')) @@ -43,7 +43,7 @@ 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')) @@ -63,7 +63,7 @@ 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')) @@ -83,7 +83,7 @@ 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')) @@ -103,7 +103,7 @@ 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')) @@ -123,7 +123,7 @@ 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')) @@ -143,7 +143,7 @@ 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')) @@ -163,7 +163,7 @@ 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')) diff --git a/regression-test/data/nereids_tpcds_shape_sf100_p0/noStatsRfPrune/query41.out b/regression-test/data/nereids_tpcds_shape_sf100_p0/noStatsRfPrune/query41.out index c9c1ffff4997416..3034a77fe0897a4 100644 --- a/regression-test/data/nereids_tpcds_shape_sf100_p0/noStatsRfPrune/query41.out +++ b/regression-test/data/nereids_tpcds_shape_sf100_p0/noStatsRfPrune/query41.out @@ -18,6 +18,6 @@ PhysicalResultSink ------------------------PhysicalDistribute[DistributionSpecHash] --------------------------hashAgg[LOCAL] ----------------------------PhysicalProject -------------------------------filter((((i_color IN ('aquamarine', 'blue', 'chartreuse', 'chiffon', 'dodger', 'gainsboro', 'tan', 'violet') AND i_units IN ('Bunch', 'Dozen', 'Each', 'Ounce', 'Oz', 'Pound', 'Ton', 'Tsp')) AND (((((((item.i_category = 'Women') AND i_color IN ('aquamarine', 'gainsboro')) AND i_units IN ('Dozen', 'Ounce')) AND i_size IN ('economy', 'medium')) OR ((((item.i_category = 'Women') AND i_color IN ('chiffon', 'violet')) AND i_units IN ('Pound', 'Ton')) AND i_size IN ('extra large', 'small'))) OR ((((item.i_category = 'Men') AND i_color IN ('blue', 'chartreuse')) AND i_units IN ('Each', 'Oz')) AND i_size IN ('N/A', 'large'))) OR ((((item.i_category = 'Men') AND i_color IN ('dodger', 'tan')) AND i_units IN ('Bunch', 'Tsp')) AND i_size IN ('economy', 'medium')))) OR ((i_color IN ('almond', 'blanched', 'indian', 'lime', 'peru', 'saddle', 'spring', 'tomato') AND i_units IN ('Box', 'Carton', 'Case', 'Dram', 'Gram', 'Pallet', 'Tbl', 'Unknown')) AND (((((((item.i_category = 'Women') AND i_color IN ('blanched', 'tomato')) AND i_units IN ('Case', 'Tbl')) AND i_size IN ('economy', 'medium')) OR ((((item.i_category = 'Women') AND i_color IN ('almond', 'lime')) AND i_units IN ('Box', 'Dram')) AND i_size IN ('extra large', 'small'))) OR ((((item.i_category = 'Men') AND i_color IN ('peru', 'saddle')) AND i_units IN ('Gram', 'Pallet')) AND i_size IN ('N/A', 'large'))) OR ((((item.i_category = 'Men') AND i_color IN ('indian', 'spring')) AND i_units IN ('Carton', 'Unknown')) AND i_size IN ('economy', 'medium'))))) and i_category IN ('Men', 'Women') and i_size IN ('N/A', 'economy', 'extra large', 'large', 'medium', 'small')) +------------------------------filter(OR[AND[i_color IN ('aquamarine', 'blue', 'chartreuse', 'chiffon', 'dodger', 'gainsboro', 'tan', 'violet'),i_units IN ('Bunch', 'Dozen', 'Each', 'Ounce', 'Oz', 'Pound', 'Ton', 'Tsp'),OR[AND[(item.i_category = 'Women'),i_color IN ('aquamarine', 'gainsboro'),i_units IN ('Dozen', 'Ounce'),i_size IN ('economy', 'medium')],AND[(item.i_category = 'Women'),i_color IN ('chiffon', 'violet'),i_units IN ('Pound', 'Ton'),i_size IN ('extra large', 'small')],AND[(item.i_category = 'Men'),i_color IN ('blue', 'chartreuse'),i_units IN ('Each', 'Oz'),i_size IN ('N/A', 'large')],AND[(item.i_category = 'Men'),i_color IN ('dodger', 'tan'),i_units IN ('Bunch', 'Tsp'),i_size IN ('economy', 'medium')]]],AND[i_color IN ('almond', 'blanched', 'indian', 'lime', 'peru', 'saddle', 'spring', 'tomato'),i_units IN ('Box', 'Carton', 'Case', 'Dram', 'Gram', 'Pallet', 'Tbl', 'Unknown'),OR[AND[(item.i_category = 'Women'),i_color IN ('blanched', 'tomato'),i_units IN ('Case', 'Tbl'),i_size IN ('economy', 'medium')],AND[(item.i_category = 'Women'),i_color IN ('almond', 'lime'),i_units IN ('Box', 'Dram'),i_size IN ('extra large', 'small')],AND[(item.i_category = 'Men'),i_color IN ('peru', 'saddle'),i_units IN ('Gram', 'Pallet'),i_size IN ('N/A', 'large')],AND[(item.i_category = 'Men'),i_color IN ('indian', 'spring'),i_units IN ('Carton', 'Unknown'),i_size IN ('economy', 'medium')]]]] and i_category IN ('Men', 'Women') and i_size IN ('N/A', 'economy', 'extra large', 'large', 'medium', 'small')) --------------------------------PhysicalOlapScan[item] diff --git a/regression-test/data/nereids_tpcds_shape_sf100_p0/noStatsRfPrune/query57.out b/regression-test/data/nereids_tpcds_shape_sf100_p0/noStatsRfPrune/query57.out index 842b18a4eac9e62..96d8f68090e5dea 100644 --- a/regression-test/data/nereids_tpcds_shape_sf100_p0/noStatsRfPrune/query57.out +++ b/regression-test/data/nereids_tpcds_shape_sf100_p0/noStatsRfPrune/query57.out @@ -23,7 +23,7 @@ PhysicalCteAnchor ( cteId=CTEId#0 ) ------------------------------------PhysicalProject --------------------------------------PhysicalOlapScan[item] --------------------------------PhysicalProject -----------------------------------filter((((date_dim.d_year = 1999) OR ((date_dim.d_year = 1998) AND (date_dim.d_moy = 12))) OR ((date_dim.d_year = 2000) AND (date_dim.d_moy = 1))) and d_year IN (1998, 1999, 2000)) +----------------------------------filter(OR[(date_dim.d_year = 1999),AND[(date_dim.d_year = 1998),(date_dim.d_moy = 12)],AND[(date_dim.d_year = 2000),(date_dim.d_moy = 1)]] and d_year IN (1998, 1999, 2000)) ------------------------------------PhysicalOlapScan[date_dim] ----------------------------PhysicalProject ------------------------------PhysicalOlapScan[call_center] diff --git a/regression-test/data/nereids_tpcds_shape_sf100_p0/no_stats_shape/query41.out b/regression-test/data/nereids_tpcds_shape_sf100_p0/no_stats_shape/query41.out index c9c1ffff4997416..3034a77fe0897a4 100644 --- a/regression-test/data/nereids_tpcds_shape_sf100_p0/no_stats_shape/query41.out +++ b/regression-test/data/nereids_tpcds_shape_sf100_p0/no_stats_shape/query41.out @@ -18,6 +18,6 @@ PhysicalResultSink ------------------------PhysicalDistribute[DistributionSpecHash] --------------------------hashAgg[LOCAL] ----------------------------PhysicalProject -------------------------------filter((((i_color IN ('aquamarine', 'blue', 'chartreuse', 'chiffon', 'dodger', 'gainsboro', 'tan', 'violet') AND i_units IN ('Bunch', 'Dozen', 'Each', 'Ounce', 'Oz', 'Pound', 'Ton', 'Tsp')) AND (((((((item.i_category = 'Women') AND i_color IN ('aquamarine', 'gainsboro')) AND i_units IN ('Dozen', 'Ounce')) AND i_size IN ('economy', 'medium')) OR ((((item.i_category = 'Women') AND i_color IN ('chiffon', 'violet')) AND i_units IN ('Pound', 'Ton')) AND i_size IN ('extra large', 'small'))) OR ((((item.i_category = 'Men') AND i_color IN ('blue', 'chartreuse')) AND i_units IN ('Each', 'Oz')) AND i_size IN ('N/A', 'large'))) OR ((((item.i_category = 'Men') AND i_color IN ('dodger', 'tan')) AND i_units IN ('Bunch', 'Tsp')) AND i_size IN ('economy', 'medium')))) OR ((i_color IN ('almond', 'blanched', 'indian', 'lime', 'peru', 'saddle', 'spring', 'tomato') AND i_units IN ('Box', 'Carton', 'Case', 'Dram', 'Gram', 'Pallet', 'Tbl', 'Unknown')) AND (((((((item.i_category = 'Women') AND i_color IN ('blanched', 'tomato')) AND i_units IN ('Case', 'Tbl')) AND i_size IN ('economy', 'medium')) OR ((((item.i_category = 'Women') AND i_color IN ('almond', 'lime')) AND i_units IN ('Box', 'Dram')) AND i_size IN ('extra large', 'small'))) OR ((((item.i_category = 'Men') AND i_color IN ('peru', 'saddle')) AND i_units IN ('Gram', 'Pallet')) AND i_size IN ('N/A', 'large'))) OR ((((item.i_category = 'Men') AND i_color IN ('indian', 'spring')) AND i_units IN ('Carton', 'Unknown')) AND i_size IN ('economy', 'medium'))))) and i_category IN ('Men', 'Women') and i_size IN ('N/A', 'economy', 'extra large', 'large', 'medium', 'small')) +------------------------------filter(OR[AND[i_color IN ('aquamarine', 'blue', 'chartreuse', 'chiffon', 'dodger', 'gainsboro', 'tan', 'violet'),i_units IN ('Bunch', 'Dozen', 'Each', 'Ounce', 'Oz', 'Pound', 'Ton', 'Tsp'),OR[AND[(item.i_category = 'Women'),i_color IN ('aquamarine', 'gainsboro'),i_units IN ('Dozen', 'Ounce'),i_size IN ('economy', 'medium')],AND[(item.i_category = 'Women'),i_color IN ('chiffon', 'violet'),i_units IN ('Pound', 'Ton'),i_size IN ('extra large', 'small')],AND[(item.i_category = 'Men'),i_color IN ('blue', 'chartreuse'),i_units IN ('Each', 'Oz'),i_size IN ('N/A', 'large')],AND[(item.i_category = 'Men'),i_color IN ('dodger', 'tan'),i_units IN ('Bunch', 'Tsp'),i_size IN ('economy', 'medium')]]],AND[i_color IN ('almond', 'blanched', 'indian', 'lime', 'peru', 'saddle', 'spring', 'tomato'),i_units IN ('Box', 'Carton', 'Case', 'Dram', 'Gram', 'Pallet', 'Tbl', 'Unknown'),OR[AND[(item.i_category = 'Women'),i_color IN ('blanched', 'tomato'),i_units IN ('Case', 'Tbl'),i_size IN ('economy', 'medium')],AND[(item.i_category = 'Women'),i_color IN ('almond', 'lime'),i_units IN ('Box', 'Dram'),i_size IN ('extra large', 'small')],AND[(item.i_category = 'Men'),i_color IN ('peru', 'saddle'),i_units IN ('Gram', 'Pallet'),i_size IN ('N/A', 'large')],AND[(item.i_category = 'Men'),i_color IN ('indian', 'spring'),i_units IN ('Carton', 'Unknown'),i_size IN ('economy', 'medium')]]]] and i_category IN ('Men', 'Women') and i_size IN ('N/A', 'economy', 'extra large', 'large', 'medium', 'small')) --------------------------------PhysicalOlapScan[item] diff --git a/regression-test/data/nereids_tpcds_shape_sf100_p0/no_stats_shape/query57.out b/regression-test/data/nereids_tpcds_shape_sf100_p0/no_stats_shape/query57.out index e640ec24d7b3e33..88777bc1ff548d5 100644 --- a/regression-test/data/nereids_tpcds_shape_sf100_p0/no_stats_shape/query57.out +++ b/regression-test/data/nereids_tpcds_shape_sf100_p0/no_stats_shape/query57.out @@ -23,7 +23,7 @@ PhysicalCteAnchor ( cteId=CTEId#0 ) ------------------------------------PhysicalProject --------------------------------------PhysicalOlapScan[item] --------------------------------PhysicalProject -----------------------------------filter((((date_dim.d_year = 1999) OR ((date_dim.d_year = 1998) AND (date_dim.d_moy = 12))) OR ((date_dim.d_year = 2000) AND (date_dim.d_moy = 1))) and d_year IN (1998, 1999, 2000)) +----------------------------------filter(OR[(date_dim.d_year = 1999),AND[(date_dim.d_year = 1998),(date_dim.d_moy = 12)],AND[(date_dim.d_year = 2000),(date_dim.d_moy = 1)]] and d_year IN (1998, 1999, 2000)) ------------------------------------PhysicalOlapScan[date_dim] ----------------------------PhysicalProject ------------------------------PhysicalOlapScan[call_center] diff --git a/regression-test/data/nereids_tpcds_shape_sf100_p0/rf_prune/query41.out b/regression-test/data/nereids_tpcds_shape_sf100_p0/rf_prune/query41.out index c9c1ffff4997416..3034a77fe0897a4 100644 --- a/regression-test/data/nereids_tpcds_shape_sf100_p0/rf_prune/query41.out +++ b/regression-test/data/nereids_tpcds_shape_sf100_p0/rf_prune/query41.out @@ -18,6 +18,6 @@ PhysicalResultSink ------------------------PhysicalDistribute[DistributionSpecHash] --------------------------hashAgg[LOCAL] ----------------------------PhysicalProject -------------------------------filter((((i_color IN ('aquamarine', 'blue', 'chartreuse', 'chiffon', 'dodger', 'gainsboro', 'tan', 'violet') AND i_units IN ('Bunch', 'Dozen', 'Each', 'Ounce', 'Oz', 'Pound', 'Ton', 'Tsp')) AND (((((((item.i_category = 'Women') AND i_color IN ('aquamarine', 'gainsboro')) AND i_units IN ('Dozen', 'Ounce')) AND i_size IN ('economy', 'medium')) OR ((((item.i_category = 'Women') AND i_color IN ('chiffon', 'violet')) AND i_units IN ('Pound', 'Ton')) AND i_size IN ('extra large', 'small'))) OR ((((item.i_category = 'Men') AND i_color IN ('blue', 'chartreuse')) AND i_units IN ('Each', 'Oz')) AND i_size IN ('N/A', 'large'))) OR ((((item.i_category = 'Men') AND i_color IN ('dodger', 'tan')) AND i_units IN ('Bunch', 'Tsp')) AND i_size IN ('economy', 'medium')))) OR ((i_color IN ('almond', 'blanched', 'indian', 'lime', 'peru', 'saddle', 'spring', 'tomato') AND i_units IN ('Box', 'Carton', 'Case', 'Dram', 'Gram', 'Pallet', 'Tbl', 'Unknown')) AND (((((((item.i_category = 'Women') AND i_color IN ('blanched', 'tomato')) AND i_units IN ('Case', 'Tbl')) AND i_size IN ('economy', 'medium')) OR ((((item.i_category = 'Women') AND i_color IN ('almond', 'lime')) AND i_units IN ('Box', 'Dram')) AND i_size IN ('extra large', 'small'))) OR ((((item.i_category = 'Men') AND i_color IN ('peru', 'saddle')) AND i_units IN ('Gram', 'Pallet')) AND i_size IN ('N/A', 'large'))) OR ((((item.i_category = 'Men') AND i_color IN ('indian', 'spring')) AND i_units IN ('Carton', 'Unknown')) AND i_size IN ('economy', 'medium'))))) and i_category IN ('Men', 'Women') and i_size IN ('N/A', 'economy', 'extra large', 'large', 'medium', 'small')) +------------------------------filter(OR[AND[i_color IN ('aquamarine', 'blue', 'chartreuse', 'chiffon', 'dodger', 'gainsboro', 'tan', 'violet'),i_units IN ('Bunch', 'Dozen', 'Each', 'Ounce', 'Oz', 'Pound', 'Ton', 'Tsp'),OR[AND[(item.i_category = 'Women'),i_color IN ('aquamarine', 'gainsboro'),i_units IN ('Dozen', 'Ounce'),i_size IN ('economy', 'medium')],AND[(item.i_category = 'Women'),i_color IN ('chiffon', 'violet'),i_units IN ('Pound', 'Ton'),i_size IN ('extra large', 'small')],AND[(item.i_category = 'Men'),i_color IN ('blue', 'chartreuse'),i_units IN ('Each', 'Oz'),i_size IN ('N/A', 'large')],AND[(item.i_category = 'Men'),i_color IN ('dodger', 'tan'),i_units IN ('Bunch', 'Tsp'),i_size IN ('economy', 'medium')]]],AND[i_color IN ('almond', 'blanched', 'indian', 'lime', 'peru', 'saddle', 'spring', 'tomato'),i_units IN ('Box', 'Carton', 'Case', 'Dram', 'Gram', 'Pallet', 'Tbl', 'Unknown'),OR[AND[(item.i_category = 'Women'),i_color IN ('blanched', 'tomato'),i_units IN ('Case', 'Tbl'),i_size IN ('economy', 'medium')],AND[(item.i_category = 'Women'),i_color IN ('almond', 'lime'),i_units IN ('Box', 'Dram'),i_size IN ('extra large', 'small')],AND[(item.i_category = 'Men'),i_color IN ('peru', 'saddle'),i_units IN ('Gram', 'Pallet'),i_size IN ('N/A', 'large')],AND[(item.i_category = 'Men'),i_color IN ('indian', 'spring'),i_units IN ('Carton', 'Unknown'),i_size IN ('economy', 'medium')]]]] and i_category IN ('Men', 'Women') and i_size IN ('N/A', 'economy', 'extra large', 'large', 'medium', 'small')) --------------------------------PhysicalOlapScan[item] diff --git a/regression-test/data/nereids_tpcds_shape_sf100_p0/rf_prune/query57.out b/regression-test/data/nereids_tpcds_shape_sf100_p0/rf_prune/query57.out index 4a7de98eb56b504..4f23fac89cf9583 100644 --- a/regression-test/data/nereids_tpcds_shape_sf100_p0/rf_prune/query57.out +++ b/regression-test/data/nereids_tpcds_shape_sf100_p0/rf_prune/query57.out @@ -21,7 +21,7 @@ PhysicalCteAnchor ( cteId=CTEId#0 ) ------------------------------------PhysicalProject --------------------------------------PhysicalOlapScan[catalog_sales] apply RFs: RF0 ------------------------------------PhysicalProject ---------------------------------------filter((((date_dim.d_year = 1999) OR ((date_dim.d_year = 1998) AND (date_dim.d_moy = 12))) OR ((date_dim.d_year = 2000) AND (date_dim.d_moy = 1))) and d_year IN (1998, 1999, 2000)) +--------------------------------------filter(OR[(date_dim.d_year = 1999),AND[(date_dim.d_year = 1998),(date_dim.d_moy = 12)],AND[(date_dim.d_year = 2000),(date_dim.d_moy = 1)]] and d_year IN (1998, 1999, 2000)) ----------------------------------------PhysicalOlapScan[date_dim] --------------------------------PhysicalProject ----------------------------------PhysicalOlapScan[item] diff --git a/regression-test/data/nereids_tpcds_shape_sf100_p0/shape/query41.out b/regression-test/data/nereids_tpcds_shape_sf100_p0/shape/query41.out index c9c1ffff4997416..3034a77fe0897a4 100644 --- a/regression-test/data/nereids_tpcds_shape_sf100_p0/shape/query41.out +++ b/regression-test/data/nereids_tpcds_shape_sf100_p0/shape/query41.out @@ -18,6 +18,6 @@ PhysicalResultSink ------------------------PhysicalDistribute[DistributionSpecHash] --------------------------hashAgg[LOCAL] ----------------------------PhysicalProject -------------------------------filter((((i_color IN ('aquamarine', 'blue', 'chartreuse', 'chiffon', 'dodger', 'gainsboro', 'tan', 'violet') AND i_units IN ('Bunch', 'Dozen', 'Each', 'Ounce', 'Oz', 'Pound', 'Ton', 'Tsp')) AND (((((((item.i_category = 'Women') AND i_color IN ('aquamarine', 'gainsboro')) AND i_units IN ('Dozen', 'Ounce')) AND i_size IN ('economy', 'medium')) OR ((((item.i_category = 'Women') AND i_color IN ('chiffon', 'violet')) AND i_units IN ('Pound', 'Ton')) AND i_size IN ('extra large', 'small'))) OR ((((item.i_category = 'Men') AND i_color IN ('blue', 'chartreuse')) AND i_units IN ('Each', 'Oz')) AND i_size IN ('N/A', 'large'))) OR ((((item.i_category = 'Men') AND i_color IN ('dodger', 'tan')) AND i_units IN ('Bunch', 'Tsp')) AND i_size IN ('economy', 'medium')))) OR ((i_color IN ('almond', 'blanched', 'indian', 'lime', 'peru', 'saddle', 'spring', 'tomato') AND i_units IN ('Box', 'Carton', 'Case', 'Dram', 'Gram', 'Pallet', 'Tbl', 'Unknown')) AND (((((((item.i_category = 'Women') AND i_color IN ('blanched', 'tomato')) AND i_units IN ('Case', 'Tbl')) AND i_size IN ('economy', 'medium')) OR ((((item.i_category = 'Women') AND i_color IN ('almond', 'lime')) AND i_units IN ('Box', 'Dram')) AND i_size IN ('extra large', 'small'))) OR ((((item.i_category = 'Men') AND i_color IN ('peru', 'saddle')) AND i_units IN ('Gram', 'Pallet')) AND i_size IN ('N/A', 'large'))) OR ((((item.i_category = 'Men') AND i_color IN ('indian', 'spring')) AND i_units IN ('Carton', 'Unknown')) AND i_size IN ('economy', 'medium'))))) and i_category IN ('Men', 'Women') and i_size IN ('N/A', 'economy', 'extra large', 'large', 'medium', 'small')) +------------------------------filter(OR[AND[i_color IN ('aquamarine', 'blue', 'chartreuse', 'chiffon', 'dodger', 'gainsboro', 'tan', 'violet'),i_units IN ('Bunch', 'Dozen', 'Each', 'Ounce', 'Oz', 'Pound', 'Ton', 'Tsp'),OR[AND[(item.i_category = 'Women'),i_color IN ('aquamarine', 'gainsboro'),i_units IN ('Dozen', 'Ounce'),i_size IN ('economy', 'medium')],AND[(item.i_category = 'Women'),i_color IN ('chiffon', 'violet'),i_units IN ('Pound', 'Ton'),i_size IN ('extra large', 'small')],AND[(item.i_category = 'Men'),i_color IN ('blue', 'chartreuse'),i_units IN ('Each', 'Oz'),i_size IN ('N/A', 'large')],AND[(item.i_category = 'Men'),i_color IN ('dodger', 'tan'),i_units IN ('Bunch', 'Tsp'),i_size IN ('economy', 'medium')]]],AND[i_color IN ('almond', 'blanched', 'indian', 'lime', 'peru', 'saddle', 'spring', 'tomato'),i_units IN ('Box', 'Carton', 'Case', 'Dram', 'Gram', 'Pallet', 'Tbl', 'Unknown'),OR[AND[(item.i_category = 'Women'),i_color IN ('blanched', 'tomato'),i_units IN ('Case', 'Tbl'),i_size IN ('economy', 'medium')],AND[(item.i_category = 'Women'),i_color IN ('almond', 'lime'),i_units IN ('Box', 'Dram'),i_size IN ('extra large', 'small')],AND[(item.i_category = 'Men'),i_color IN ('peru', 'saddle'),i_units IN ('Gram', 'Pallet'),i_size IN ('N/A', 'large')],AND[(item.i_category = 'Men'),i_color IN ('indian', 'spring'),i_units IN ('Carton', 'Unknown'),i_size IN ('economy', 'medium')]]]] and i_category IN ('Men', 'Women') and i_size IN ('N/A', 'economy', 'extra large', 'large', 'medium', 'small')) --------------------------------PhysicalOlapScan[item] diff --git a/regression-test/data/nereids_tpcds_shape_sf100_p0/shape/query57.out b/regression-test/data/nereids_tpcds_shape_sf100_p0/shape/query57.out index a0157318139d88b..2cab4f33e1358d9 100644 --- a/regression-test/data/nereids_tpcds_shape_sf100_p0/shape/query57.out +++ b/regression-test/data/nereids_tpcds_shape_sf100_p0/shape/query57.out @@ -21,7 +21,7 @@ PhysicalCteAnchor ( cteId=CTEId#0 ) ------------------------------------PhysicalProject --------------------------------------PhysicalOlapScan[catalog_sales] apply RFs: RF0 RF1 RF2 ------------------------------------PhysicalProject ---------------------------------------filter((((date_dim.d_year = 1999) OR ((date_dim.d_year = 1998) AND (date_dim.d_moy = 12))) OR ((date_dim.d_year = 2000) AND (date_dim.d_moy = 1))) and d_year IN (1998, 1999, 2000)) +--------------------------------------filter(OR[(date_dim.d_year = 1999),AND[(date_dim.d_year = 1998),(date_dim.d_moy = 12)],AND[(date_dim.d_year = 2000),(date_dim.d_moy = 1)]] and d_year IN (1998, 1999, 2000)) ----------------------------------------PhysicalOlapScan[date_dim] --------------------------------PhysicalProject ----------------------------------PhysicalOlapScan[item] diff --git a/regression-test/data/nereids_tpcds_shape_sf10t_orc/shape/query41.out b/regression-test/data/nereids_tpcds_shape_sf10t_orc/shape/query41.out index 16a3a620b77c8ec..c99d2b2031c4eac 100644 --- a/regression-test/data/nereids_tpcds_shape_sf10t_orc/shape/query41.out +++ b/regression-test/data/nereids_tpcds_shape_sf10t_orc/shape/query41.out @@ -18,6 +18,6 @@ PhysicalResultSink ------------------------PhysicalDistribute[DistributionSpecHash] --------------------------hashAgg[LOCAL] ----------------------------PhysicalProject -------------------------------filter((((i_color IN ('black', 'chocolate', 'cornflower', 'firebrick', 'frosted', 'magenta', 'rose', 'slate') AND i_units IN ('Box', 'Bundle', 'Carton', 'Dram', 'Gross', 'Lb', 'Oz', 'Pound')) AND (((((((item.i_category = 'Women') AND i_color IN ('frosted', 'rose')) AND i_units IN ('Gross', 'Lb')) AND i_size IN ('large', 'medium')) OR ((((item.i_category = 'Women') AND i_color IN ('black', 'chocolate')) AND i_units IN ('Box', 'Dram')) AND i_size IN ('economy', 'petite'))) OR ((((item.i_category = 'Men') AND i_color IN ('magenta', 'slate')) AND i_units IN ('Bundle', 'Carton')) AND i_size IN ('N/A', 'small'))) OR ((((item.i_category = 'Men') AND i_color IN ('cornflower', 'firebrick')) AND i_units IN ('Oz', 'Pound')) AND i_size IN ('large', 'medium')))) OR ((i_color IN ('almond', 'aquamarine', 'cyan', 'lavender', 'maroon', 'papaya', 'purple', 'steel') AND i_units IN ('Bunch', 'Case', 'Cup', 'Each', 'Gram', 'N/A', 'Pallet', 'Tsp')) AND (((((((item.i_category = 'Women') AND i_color IN ('almond', 'steel')) AND i_units IN ('Case', 'Tsp')) AND i_size IN ('large', 'medium')) OR ((((item.i_category = 'Women') AND i_color IN ('aquamarine', 'purple')) AND i_units IN ('Bunch', 'Gram')) AND i_size IN ('economy', 'petite'))) OR ((((item.i_category = 'Men') AND i_color IN ('lavender', 'papaya')) AND i_units IN ('Cup', 'Pallet')) AND i_size IN ('N/A', 'small'))) OR ((((item.i_category = 'Men') AND i_color IN ('cyan', 'maroon')) AND i_units IN ('Each', 'N/A')) AND i_size IN ('large', 'medium'))))) and i_category IN ('Men', 'Women') and i_size IN ('N/A', 'economy', 'large', 'medium', 'petite', 'small')) +------------------------------filter(OR[AND[i_color IN ('black', 'chocolate', 'cornflower', 'firebrick', 'frosted', 'magenta', 'rose', 'slate'),i_units IN ('Box', 'Bundle', 'Carton', 'Dram', 'Gross', 'Lb', 'Oz', 'Pound'),OR[AND[(item.i_category = 'Women'),i_color IN ('frosted', 'rose'),i_units IN ('Gross', 'Lb'),i_size IN ('large', 'medium')],AND[(item.i_category = 'Women'),i_color IN ('black', 'chocolate'),i_units IN ('Box', 'Dram'),i_size IN ('economy', 'petite')],AND[(item.i_category = 'Men'),i_color IN ('magenta', 'slate'),i_units IN ('Bundle', 'Carton'),i_size IN ('N/A', 'small')],AND[(item.i_category = 'Men'),i_color IN ('cornflower', 'firebrick'),i_units IN ('Oz', 'Pound'),i_size IN ('large', 'medium')]]],AND[i_color IN ('almond', 'aquamarine', 'cyan', 'lavender', 'maroon', 'papaya', 'purple', 'steel'),i_units IN ('Bunch', 'Case', 'Cup', 'Each', 'Gram', 'N/A', 'Pallet', 'Tsp'),OR[AND[(item.i_category = 'Women'),i_color IN ('almond', 'steel'),i_units IN ('Case', 'Tsp'),i_size IN ('large', 'medium')],AND[(item.i_category = 'Women'),i_color IN ('aquamarine', 'purple'),i_units IN ('Bunch', 'Gram'),i_size IN ('economy', 'petite')],AND[(item.i_category = 'Men'),i_color IN ('lavender', 'papaya'),i_units IN ('Cup', 'Pallet'),i_size IN ('N/A', 'small')],AND[(item.i_category = 'Men'),i_color IN ('cyan', 'maroon'),i_units IN ('Each', 'N/A'),i_size IN ('large', 'medium')]]]] and i_category IN ('Men', 'Women') and i_size IN ('N/A', 'economy', 'large', 'medium', 'petite', 'small')) --------------------------------PhysicalOlapScan[item] diff --git a/regression-test/data/nereids_tpcds_shape_sf10t_orc/shape/query47.out b/regression-test/data/nereids_tpcds_shape_sf10t_orc/shape/query47.out index a3205932929569d..52c9d9bc883f666 100644 --- a/regression-test/data/nereids_tpcds_shape_sf10t_orc/shape/query47.out +++ b/regression-test/data/nereids_tpcds_shape_sf10t_orc/shape/query47.out @@ -23,7 +23,7 @@ PhysicalCteAnchor ( cteId=CTEId#0 ) ------------------------------------PhysicalProject --------------------------------------PhysicalOlapScan[item] --------------------------------PhysicalProject -----------------------------------filter((((date_dim.d_year = 1999) OR ((date_dim.d_year = 1998) AND (date_dim.d_moy = 12))) OR ((date_dim.d_year = 2000) AND (date_dim.d_moy = 1))) and d_year IN (1998, 1999, 2000)) +----------------------------------filter(OR[(date_dim.d_year = 1999),AND[(date_dim.d_year = 1998),(date_dim.d_moy = 12)],AND[(date_dim.d_year = 2000),(date_dim.d_moy = 1)]] and d_year IN (1998, 1999, 2000)) ------------------------------------PhysicalOlapScan[date_dim] ----------------------------PhysicalProject ------------------------------PhysicalOlapScan[store] diff --git a/regression-test/data/nereids_tpcds_shape_sf10t_orc/shape/query57.out b/regression-test/data/nereids_tpcds_shape_sf10t_orc/shape/query57.out index e640ec24d7b3e33..88777bc1ff548d5 100644 --- a/regression-test/data/nereids_tpcds_shape_sf10t_orc/shape/query57.out +++ b/regression-test/data/nereids_tpcds_shape_sf10t_orc/shape/query57.out @@ -23,7 +23,7 @@ PhysicalCteAnchor ( cteId=CTEId#0 ) ------------------------------------PhysicalProject --------------------------------------PhysicalOlapScan[item] --------------------------------PhysicalProject -----------------------------------filter((((date_dim.d_year = 1999) OR ((date_dim.d_year = 1998) AND (date_dim.d_moy = 12))) OR ((date_dim.d_year = 2000) AND (date_dim.d_moy = 1))) and d_year IN (1998, 1999, 2000)) +----------------------------------filter(OR[(date_dim.d_year = 1999),AND[(date_dim.d_year = 1998),(date_dim.d_moy = 12)],AND[(date_dim.d_year = 2000),(date_dim.d_moy = 1)]] and d_year IN (1998, 1999, 2000)) ------------------------------------PhysicalOlapScan[date_dim] ----------------------------PhysicalProject ------------------------------PhysicalOlapScan[call_center] diff --git a/regression-test/data/new_shapes_p0/tpcds_sf100/noStatsRfPrune/query41.out b/regression-test/data/new_shapes_p0/tpcds_sf100/noStatsRfPrune/query41.out index c9c1ffff4997416..3034a77fe0897a4 100644 --- a/regression-test/data/new_shapes_p0/tpcds_sf100/noStatsRfPrune/query41.out +++ b/regression-test/data/new_shapes_p0/tpcds_sf100/noStatsRfPrune/query41.out @@ -18,6 +18,6 @@ PhysicalResultSink ------------------------PhysicalDistribute[DistributionSpecHash] --------------------------hashAgg[LOCAL] ----------------------------PhysicalProject -------------------------------filter((((i_color IN ('aquamarine', 'blue', 'chartreuse', 'chiffon', 'dodger', 'gainsboro', 'tan', 'violet') AND i_units IN ('Bunch', 'Dozen', 'Each', 'Ounce', 'Oz', 'Pound', 'Ton', 'Tsp')) AND (((((((item.i_category = 'Women') AND i_color IN ('aquamarine', 'gainsboro')) AND i_units IN ('Dozen', 'Ounce')) AND i_size IN ('economy', 'medium')) OR ((((item.i_category = 'Women') AND i_color IN ('chiffon', 'violet')) AND i_units IN ('Pound', 'Ton')) AND i_size IN ('extra large', 'small'))) OR ((((item.i_category = 'Men') AND i_color IN ('blue', 'chartreuse')) AND i_units IN ('Each', 'Oz')) AND i_size IN ('N/A', 'large'))) OR ((((item.i_category = 'Men') AND i_color IN ('dodger', 'tan')) AND i_units IN ('Bunch', 'Tsp')) AND i_size IN ('economy', 'medium')))) OR ((i_color IN ('almond', 'blanched', 'indian', 'lime', 'peru', 'saddle', 'spring', 'tomato') AND i_units IN ('Box', 'Carton', 'Case', 'Dram', 'Gram', 'Pallet', 'Tbl', 'Unknown')) AND (((((((item.i_category = 'Women') AND i_color IN ('blanched', 'tomato')) AND i_units IN ('Case', 'Tbl')) AND i_size IN ('economy', 'medium')) OR ((((item.i_category = 'Women') AND i_color IN ('almond', 'lime')) AND i_units IN ('Box', 'Dram')) AND i_size IN ('extra large', 'small'))) OR ((((item.i_category = 'Men') AND i_color IN ('peru', 'saddle')) AND i_units IN ('Gram', 'Pallet')) AND i_size IN ('N/A', 'large'))) OR ((((item.i_category = 'Men') AND i_color IN ('indian', 'spring')) AND i_units IN ('Carton', 'Unknown')) AND i_size IN ('economy', 'medium'))))) and i_category IN ('Men', 'Women') and i_size IN ('N/A', 'economy', 'extra large', 'large', 'medium', 'small')) +------------------------------filter(OR[AND[i_color IN ('aquamarine', 'blue', 'chartreuse', 'chiffon', 'dodger', 'gainsboro', 'tan', 'violet'),i_units IN ('Bunch', 'Dozen', 'Each', 'Ounce', 'Oz', 'Pound', 'Ton', 'Tsp'),OR[AND[(item.i_category = 'Women'),i_color IN ('aquamarine', 'gainsboro'),i_units IN ('Dozen', 'Ounce'),i_size IN ('economy', 'medium')],AND[(item.i_category = 'Women'),i_color IN ('chiffon', 'violet'),i_units IN ('Pound', 'Ton'),i_size IN ('extra large', 'small')],AND[(item.i_category = 'Men'),i_color IN ('blue', 'chartreuse'),i_units IN ('Each', 'Oz'),i_size IN ('N/A', 'large')],AND[(item.i_category = 'Men'),i_color IN ('dodger', 'tan'),i_units IN ('Bunch', 'Tsp'),i_size IN ('economy', 'medium')]]],AND[i_color IN ('almond', 'blanched', 'indian', 'lime', 'peru', 'saddle', 'spring', 'tomato'),i_units IN ('Box', 'Carton', 'Case', 'Dram', 'Gram', 'Pallet', 'Tbl', 'Unknown'),OR[AND[(item.i_category = 'Women'),i_color IN ('blanched', 'tomato'),i_units IN ('Case', 'Tbl'),i_size IN ('economy', 'medium')],AND[(item.i_category = 'Women'),i_color IN ('almond', 'lime'),i_units IN ('Box', 'Dram'),i_size IN ('extra large', 'small')],AND[(item.i_category = 'Men'),i_color IN ('peru', 'saddle'),i_units IN ('Gram', 'Pallet'),i_size IN ('N/A', 'large')],AND[(item.i_category = 'Men'),i_color IN ('indian', 'spring'),i_units IN ('Carton', 'Unknown'),i_size IN ('economy', 'medium')]]]] and i_category IN ('Men', 'Women') and i_size IN ('N/A', 'economy', 'extra large', 'large', 'medium', 'small')) --------------------------------PhysicalOlapScan[item] diff --git a/regression-test/data/new_shapes_p0/tpcds_sf100/noStatsRfPrune/query57.out b/regression-test/data/new_shapes_p0/tpcds_sf100/noStatsRfPrune/query57.out index 842b18a4eac9e62..96d8f68090e5dea 100644 --- a/regression-test/data/new_shapes_p0/tpcds_sf100/noStatsRfPrune/query57.out +++ b/regression-test/data/new_shapes_p0/tpcds_sf100/noStatsRfPrune/query57.out @@ -23,7 +23,7 @@ PhysicalCteAnchor ( cteId=CTEId#0 ) ------------------------------------PhysicalProject --------------------------------------PhysicalOlapScan[item] --------------------------------PhysicalProject -----------------------------------filter((((date_dim.d_year = 1999) OR ((date_dim.d_year = 1998) AND (date_dim.d_moy = 12))) OR ((date_dim.d_year = 2000) AND (date_dim.d_moy = 1))) and d_year IN (1998, 1999, 2000)) +----------------------------------filter(OR[(date_dim.d_year = 1999),AND[(date_dim.d_year = 1998),(date_dim.d_moy = 12)],AND[(date_dim.d_year = 2000),(date_dim.d_moy = 1)]] and d_year IN (1998, 1999, 2000)) ------------------------------------PhysicalOlapScan[date_dim] ----------------------------PhysicalProject ------------------------------PhysicalOlapScan[call_center] diff --git a/regression-test/data/new_shapes_p0/tpcds_sf100/no_stats_shape/query41.out b/regression-test/data/new_shapes_p0/tpcds_sf100/no_stats_shape/query41.out index c9c1ffff4997416..3034a77fe0897a4 100644 --- a/regression-test/data/new_shapes_p0/tpcds_sf100/no_stats_shape/query41.out +++ b/regression-test/data/new_shapes_p0/tpcds_sf100/no_stats_shape/query41.out @@ -18,6 +18,6 @@ PhysicalResultSink ------------------------PhysicalDistribute[DistributionSpecHash] --------------------------hashAgg[LOCAL] ----------------------------PhysicalProject -------------------------------filter((((i_color IN ('aquamarine', 'blue', 'chartreuse', 'chiffon', 'dodger', 'gainsboro', 'tan', 'violet') AND i_units IN ('Bunch', 'Dozen', 'Each', 'Ounce', 'Oz', 'Pound', 'Ton', 'Tsp')) AND (((((((item.i_category = 'Women') AND i_color IN ('aquamarine', 'gainsboro')) AND i_units IN ('Dozen', 'Ounce')) AND i_size IN ('economy', 'medium')) OR ((((item.i_category = 'Women') AND i_color IN ('chiffon', 'violet')) AND i_units IN ('Pound', 'Ton')) AND i_size IN ('extra large', 'small'))) OR ((((item.i_category = 'Men') AND i_color IN ('blue', 'chartreuse')) AND i_units IN ('Each', 'Oz')) AND i_size IN ('N/A', 'large'))) OR ((((item.i_category = 'Men') AND i_color IN ('dodger', 'tan')) AND i_units IN ('Bunch', 'Tsp')) AND i_size IN ('economy', 'medium')))) OR ((i_color IN ('almond', 'blanched', 'indian', 'lime', 'peru', 'saddle', 'spring', 'tomato') AND i_units IN ('Box', 'Carton', 'Case', 'Dram', 'Gram', 'Pallet', 'Tbl', 'Unknown')) AND (((((((item.i_category = 'Women') AND i_color IN ('blanched', 'tomato')) AND i_units IN ('Case', 'Tbl')) AND i_size IN ('economy', 'medium')) OR ((((item.i_category = 'Women') AND i_color IN ('almond', 'lime')) AND i_units IN ('Box', 'Dram')) AND i_size IN ('extra large', 'small'))) OR ((((item.i_category = 'Men') AND i_color IN ('peru', 'saddle')) AND i_units IN ('Gram', 'Pallet')) AND i_size IN ('N/A', 'large'))) OR ((((item.i_category = 'Men') AND i_color IN ('indian', 'spring')) AND i_units IN ('Carton', 'Unknown')) AND i_size IN ('economy', 'medium'))))) and i_category IN ('Men', 'Women') and i_size IN ('N/A', 'economy', 'extra large', 'large', 'medium', 'small')) +------------------------------filter(OR[AND[i_color IN ('aquamarine', 'blue', 'chartreuse', 'chiffon', 'dodger', 'gainsboro', 'tan', 'violet'),i_units IN ('Bunch', 'Dozen', 'Each', 'Ounce', 'Oz', 'Pound', 'Ton', 'Tsp'),OR[AND[(item.i_category = 'Women'),i_color IN ('aquamarine', 'gainsboro'),i_units IN ('Dozen', 'Ounce'),i_size IN ('economy', 'medium')],AND[(item.i_category = 'Women'),i_color IN ('chiffon', 'violet'),i_units IN ('Pound', 'Ton'),i_size IN ('extra large', 'small')],AND[(item.i_category = 'Men'),i_color IN ('blue', 'chartreuse'),i_units IN ('Each', 'Oz'),i_size IN ('N/A', 'large')],AND[(item.i_category = 'Men'),i_color IN ('dodger', 'tan'),i_units IN ('Bunch', 'Tsp'),i_size IN ('economy', 'medium')]]],AND[i_color IN ('almond', 'blanched', 'indian', 'lime', 'peru', 'saddle', 'spring', 'tomato'),i_units IN ('Box', 'Carton', 'Case', 'Dram', 'Gram', 'Pallet', 'Tbl', 'Unknown'),OR[AND[(item.i_category = 'Women'),i_color IN ('blanched', 'tomato'),i_units IN ('Case', 'Tbl'),i_size IN ('economy', 'medium')],AND[(item.i_category = 'Women'),i_color IN ('almond', 'lime'),i_units IN ('Box', 'Dram'),i_size IN ('extra large', 'small')],AND[(item.i_category = 'Men'),i_color IN ('peru', 'saddle'),i_units IN ('Gram', 'Pallet'),i_size IN ('N/A', 'large')],AND[(item.i_category = 'Men'),i_color IN ('indian', 'spring'),i_units IN ('Carton', 'Unknown'),i_size IN ('economy', 'medium')]]]] and i_category IN ('Men', 'Women') and i_size IN ('N/A', 'economy', 'extra large', 'large', 'medium', 'small')) --------------------------------PhysicalOlapScan[item] diff --git a/regression-test/data/new_shapes_p0/tpcds_sf100/no_stats_shape/query57.out b/regression-test/data/new_shapes_p0/tpcds_sf100/no_stats_shape/query57.out index e640ec24d7b3e33..88777bc1ff548d5 100644 --- a/regression-test/data/new_shapes_p0/tpcds_sf100/no_stats_shape/query57.out +++ b/regression-test/data/new_shapes_p0/tpcds_sf100/no_stats_shape/query57.out @@ -23,7 +23,7 @@ PhysicalCteAnchor ( cteId=CTEId#0 ) ------------------------------------PhysicalProject --------------------------------------PhysicalOlapScan[item] --------------------------------PhysicalProject -----------------------------------filter((((date_dim.d_year = 1999) OR ((date_dim.d_year = 1998) AND (date_dim.d_moy = 12))) OR ((date_dim.d_year = 2000) AND (date_dim.d_moy = 1))) and d_year IN (1998, 1999, 2000)) +----------------------------------filter(OR[(date_dim.d_year = 1999),AND[(date_dim.d_year = 1998),(date_dim.d_moy = 12)],AND[(date_dim.d_year = 2000),(date_dim.d_moy = 1)]] and d_year IN (1998, 1999, 2000)) ------------------------------------PhysicalOlapScan[date_dim] ----------------------------PhysicalProject ------------------------------PhysicalOlapScan[call_center] diff --git a/regression-test/data/new_shapes_p0/tpcds_sf100/rf_prune/query41.out b/regression-test/data/new_shapes_p0/tpcds_sf100/rf_prune/query41.out index c9c1ffff4997416..3034a77fe0897a4 100644 --- a/regression-test/data/new_shapes_p0/tpcds_sf100/rf_prune/query41.out +++ b/regression-test/data/new_shapes_p0/tpcds_sf100/rf_prune/query41.out @@ -18,6 +18,6 @@ PhysicalResultSink ------------------------PhysicalDistribute[DistributionSpecHash] --------------------------hashAgg[LOCAL] ----------------------------PhysicalProject -------------------------------filter((((i_color IN ('aquamarine', 'blue', 'chartreuse', 'chiffon', 'dodger', 'gainsboro', 'tan', 'violet') AND i_units IN ('Bunch', 'Dozen', 'Each', 'Ounce', 'Oz', 'Pound', 'Ton', 'Tsp')) AND (((((((item.i_category = 'Women') AND i_color IN ('aquamarine', 'gainsboro')) AND i_units IN ('Dozen', 'Ounce')) AND i_size IN ('economy', 'medium')) OR ((((item.i_category = 'Women') AND i_color IN ('chiffon', 'violet')) AND i_units IN ('Pound', 'Ton')) AND i_size IN ('extra large', 'small'))) OR ((((item.i_category = 'Men') AND i_color IN ('blue', 'chartreuse')) AND i_units IN ('Each', 'Oz')) AND i_size IN ('N/A', 'large'))) OR ((((item.i_category = 'Men') AND i_color IN ('dodger', 'tan')) AND i_units IN ('Bunch', 'Tsp')) AND i_size IN ('economy', 'medium')))) OR ((i_color IN ('almond', 'blanched', 'indian', 'lime', 'peru', 'saddle', 'spring', 'tomato') AND i_units IN ('Box', 'Carton', 'Case', 'Dram', 'Gram', 'Pallet', 'Tbl', 'Unknown')) AND (((((((item.i_category = 'Women') AND i_color IN ('blanched', 'tomato')) AND i_units IN ('Case', 'Tbl')) AND i_size IN ('economy', 'medium')) OR ((((item.i_category = 'Women') AND i_color IN ('almond', 'lime')) AND i_units IN ('Box', 'Dram')) AND i_size IN ('extra large', 'small'))) OR ((((item.i_category = 'Men') AND i_color IN ('peru', 'saddle')) AND i_units IN ('Gram', 'Pallet')) AND i_size IN ('N/A', 'large'))) OR ((((item.i_category = 'Men') AND i_color IN ('indian', 'spring')) AND i_units IN ('Carton', 'Unknown')) AND i_size IN ('economy', 'medium'))))) and i_category IN ('Men', 'Women') and i_size IN ('N/A', 'economy', 'extra large', 'large', 'medium', 'small')) +------------------------------filter(OR[AND[i_color IN ('aquamarine', 'blue', 'chartreuse', 'chiffon', 'dodger', 'gainsboro', 'tan', 'violet'),i_units IN ('Bunch', 'Dozen', 'Each', 'Ounce', 'Oz', 'Pound', 'Ton', 'Tsp'),OR[AND[(item.i_category = 'Women'),i_color IN ('aquamarine', 'gainsboro'),i_units IN ('Dozen', 'Ounce'),i_size IN ('economy', 'medium')],AND[(item.i_category = 'Women'),i_color IN ('chiffon', 'violet'),i_units IN ('Pound', 'Ton'),i_size IN ('extra large', 'small')],AND[(item.i_category = 'Men'),i_color IN ('blue', 'chartreuse'),i_units IN ('Each', 'Oz'),i_size IN ('N/A', 'large')],AND[(item.i_category = 'Men'),i_color IN ('dodger', 'tan'),i_units IN ('Bunch', 'Tsp'),i_size IN ('economy', 'medium')]]],AND[i_color IN ('almond', 'blanched', 'indian', 'lime', 'peru', 'saddle', 'spring', 'tomato'),i_units IN ('Box', 'Carton', 'Case', 'Dram', 'Gram', 'Pallet', 'Tbl', 'Unknown'),OR[AND[(item.i_category = 'Women'),i_color IN ('blanched', 'tomato'),i_units IN ('Case', 'Tbl'),i_size IN ('economy', 'medium')],AND[(item.i_category = 'Women'),i_color IN ('almond', 'lime'),i_units IN ('Box', 'Dram'),i_size IN ('extra large', 'small')],AND[(item.i_category = 'Men'),i_color IN ('peru', 'saddle'),i_units IN ('Gram', 'Pallet'),i_size IN ('N/A', 'large')],AND[(item.i_category = 'Men'),i_color IN ('indian', 'spring'),i_units IN ('Carton', 'Unknown'),i_size IN ('economy', 'medium')]]]] and i_category IN ('Men', 'Women') and i_size IN ('N/A', 'economy', 'extra large', 'large', 'medium', 'small')) --------------------------------PhysicalOlapScan[item] diff --git a/regression-test/data/new_shapes_p0/tpcds_sf100/rf_prune/query57.out b/regression-test/data/new_shapes_p0/tpcds_sf100/rf_prune/query57.out index 4a7de98eb56b504..4f23fac89cf9583 100644 --- a/regression-test/data/new_shapes_p0/tpcds_sf100/rf_prune/query57.out +++ b/regression-test/data/new_shapes_p0/tpcds_sf100/rf_prune/query57.out @@ -21,7 +21,7 @@ PhysicalCteAnchor ( cteId=CTEId#0 ) ------------------------------------PhysicalProject --------------------------------------PhysicalOlapScan[catalog_sales] apply RFs: RF0 ------------------------------------PhysicalProject ---------------------------------------filter((((date_dim.d_year = 1999) OR ((date_dim.d_year = 1998) AND (date_dim.d_moy = 12))) OR ((date_dim.d_year = 2000) AND (date_dim.d_moy = 1))) and d_year IN (1998, 1999, 2000)) +--------------------------------------filter(OR[(date_dim.d_year = 1999),AND[(date_dim.d_year = 1998),(date_dim.d_moy = 12)],AND[(date_dim.d_year = 2000),(date_dim.d_moy = 1)]] and d_year IN (1998, 1999, 2000)) ----------------------------------------PhysicalOlapScan[date_dim] --------------------------------PhysicalProject ----------------------------------PhysicalOlapScan[item] diff --git a/regression-test/data/new_shapes_p0/tpcds_sf100/shape/query41.out b/regression-test/data/new_shapes_p0/tpcds_sf100/shape/query41.out index c9c1ffff4997416..3034a77fe0897a4 100644 --- a/regression-test/data/new_shapes_p0/tpcds_sf100/shape/query41.out +++ b/regression-test/data/new_shapes_p0/tpcds_sf100/shape/query41.out @@ -18,6 +18,6 @@ PhysicalResultSink ------------------------PhysicalDistribute[DistributionSpecHash] --------------------------hashAgg[LOCAL] ----------------------------PhysicalProject -------------------------------filter((((i_color IN ('aquamarine', 'blue', 'chartreuse', 'chiffon', 'dodger', 'gainsboro', 'tan', 'violet') AND i_units IN ('Bunch', 'Dozen', 'Each', 'Ounce', 'Oz', 'Pound', 'Ton', 'Tsp')) AND (((((((item.i_category = 'Women') AND i_color IN ('aquamarine', 'gainsboro')) AND i_units IN ('Dozen', 'Ounce')) AND i_size IN ('economy', 'medium')) OR ((((item.i_category = 'Women') AND i_color IN ('chiffon', 'violet')) AND i_units IN ('Pound', 'Ton')) AND i_size IN ('extra large', 'small'))) OR ((((item.i_category = 'Men') AND i_color IN ('blue', 'chartreuse')) AND i_units IN ('Each', 'Oz')) AND i_size IN ('N/A', 'large'))) OR ((((item.i_category = 'Men') AND i_color IN ('dodger', 'tan')) AND i_units IN ('Bunch', 'Tsp')) AND i_size IN ('economy', 'medium')))) OR ((i_color IN ('almond', 'blanched', 'indian', 'lime', 'peru', 'saddle', 'spring', 'tomato') AND i_units IN ('Box', 'Carton', 'Case', 'Dram', 'Gram', 'Pallet', 'Tbl', 'Unknown')) AND (((((((item.i_category = 'Women') AND i_color IN ('blanched', 'tomato')) AND i_units IN ('Case', 'Tbl')) AND i_size IN ('economy', 'medium')) OR ((((item.i_category = 'Women') AND i_color IN ('almond', 'lime')) AND i_units IN ('Box', 'Dram')) AND i_size IN ('extra large', 'small'))) OR ((((item.i_category = 'Men') AND i_color IN ('peru', 'saddle')) AND i_units IN ('Gram', 'Pallet')) AND i_size IN ('N/A', 'large'))) OR ((((item.i_category = 'Men') AND i_color IN ('indian', 'spring')) AND i_units IN ('Carton', 'Unknown')) AND i_size IN ('economy', 'medium'))))) and i_category IN ('Men', 'Women') and i_size IN ('N/A', 'economy', 'extra large', 'large', 'medium', 'small')) +------------------------------filter(OR[AND[i_color IN ('aquamarine', 'blue', 'chartreuse', 'chiffon', 'dodger', 'gainsboro', 'tan', 'violet'),i_units IN ('Bunch', 'Dozen', 'Each', 'Ounce', 'Oz', 'Pound', 'Ton', 'Tsp'),OR[AND[(item.i_category = 'Women'),i_color IN ('aquamarine', 'gainsboro'),i_units IN ('Dozen', 'Ounce'),i_size IN ('economy', 'medium')],AND[(item.i_category = 'Women'),i_color IN ('chiffon', 'violet'),i_units IN ('Pound', 'Ton'),i_size IN ('extra large', 'small')],AND[(item.i_category = 'Men'),i_color IN ('blue', 'chartreuse'),i_units IN ('Each', 'Oz'),i_size IN ('N/A', 'large')],AND[(item.i_category = 'Men'),i_color IN ('dodger', 'tan'),i_units IN ('Bunch', 'Tsp'),i_size IN ('economy', 'medium')]]],AND[i_color IN ('almond', 'blanched', 'indian', 'lime', 'peru', 'saddle', 'spring', 'tomato'),i_units IN ('Box', 'Carton', 'Case', 'Dram', 'Gram', 'Pallet', 'Tbl', 'Unknown'),OR[AND[(item.i_category = 'Women'),i_color IN ('blanched', 'tomato'),i_units IN ('Case', 'Tbl'),i_size IN ('economy', 'medium')],AND[(item.i_category = 'Women'),i_color IN ('almond', 'lime'),i_units IN ('Box', 'Dram'),i_size IN ('extra large', 'small')],AND[(item.i_category = 'Men'),i_color IN ('peru', 'saddle'),i_units IN ('Gram', 'Pallet'),i_size IN ('N/A', 'large')],AND[(item.i_category = 'Men'),i_color IN ('indian', 'spring'),i_units IN ('Carton', 'Unknown'),i_size IN ('economy', 'medium')]]]] and i_category IN ('Men', 'Women') and i_size IN ('N/A', 'economy', 'extra large', 'large', 'medium', 'small')) --------------------------------PhysicalOlapScan[item] diff --git a/regression-test/data/new_shapes_p0/tpcds_sf100/shape/query57.out b/regression-test/data/new_shapes_p0/tpcds_sf100/shape/query57.out index a0157318139d88b..2cab4f33e1358d9 100644 --- a/regression-test/data/new_shapes_p0/tpcds_sf100/shape/query57.out +++ b/regression-test/data/new_shapes_p0/tpcds_sf100/shape/query57.out @@ -21,7 +21,7 @@ PhysicalCteAnchor ( cteId=CTEId#0 ) ------------------------------------PhysicalProject --------------------------------------PhysicalOlapScan[catalog_sales] apply RFs: RF0 RF1 RF2 ------------------------------------PhysicalProject ---------------------------------------filter((((date_dim.d_year = 1999) OR ((date_dim.d_year = 1998) AND (date_dim.d_moy = 12))) OR ((date_dim.d_year = 2000) AND (date_dim.d_moy = 1))) and d_year IN (1998, 1999, 2000)) +--------------------------------------filter(OR[(date_dim.d_year = 1999),AND[(date_dim.d_year = 1998),(date_dim.d_moy = 12)],AND[(date_dim.d_year = 2000),(date_dim.d_moy = 1)]] and d_year IN (1998, 1999, 2000)) ----------------------------------------PhysicalOlapScan[date_dim] --------------------------------PhysicalProject ----------------------------------PhysicalOlapScan[item] diff --git a/regression-test/data/new_shapes_p0/tpcds_sf1000/shape/query41.out b/regression-test/data/new_shapes_p0/tpcds_sf1000/shape/query41.out index ea5acccd883550c..0bba60d4cdac394 100644 --- a/regression-test/data/new_shapes_p0/tpcds_sf1000/shape/query41.out +++ b/regression-test/data/new_shapes_p0/tpcds_sf1000/shape/query41.out @@ -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] diff --git a/regression-test/data/new_shapes_p0/tpcds_sf1000/shape/query47.out b/regression-test/data/new_shapes_p0/tpcds_sf1000/shape/query47.out index d51d48c5ab5677a..0e9f713243773a2 100644 --- a/regression-test/data/new_shapes_p0/tpcds_sf1000/shape/query47.out +++ b/regression-test/data/new_shapes_p0/tpcds_sf1000/shape/query47.out @@ -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] diff --git a/regression-test/data/new_shapes_p0/tpcds_sf1000/shape/query88.out b/regression-test/data/new_shapes_p0/tpcds_sf1000/shape/query88.out index 3c8f0335f1f415b..d016dca48db8326 100644 --- a/regression-test/data/new_shapes_p0/tpcds_sf1000/shape/query88.out +++ b/regression-test/data/new_shapes_p0/tpcds_sf1000/shape/query88.out @@ -23,7 +23,7 @@ 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')) @@ -43,7 +43,7 @@ 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')) @@ -63,7 +63,7 @@ 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')) @@ -83,7 +83,7 @@ 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')) @@ -103,7 +103,7 @@ 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')) @@ -123,7 +123,7 @@ 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')) @@ -143,7 +143,7 @@ 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')) @@ -163,7 +163,7 @@ 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'))