Fix icw test from "Cherry-Pick CUBE grouping set, predicates from subquery ..."

Author: jiaqizho

src/test/regress/expected/dpe_optimizer.out

@@ -278,6 +278,7 @@ explain (costs off, timing off, summary off, analyze) select * from pt where exi
          Extra Text: (seg1)   Hash chain length 1.0 avg, 1 max, using 2 of 262144 buckets.
          ->  Dynamic Seq Scan on pt (actual rows=8 loops=1)
                Number of partitions to scan: 6 
+               Filter: (NOT (ptid IS NULL))
                Partitions scanned:  Avg 2.0 x 3 workers.  Max 2 parts (seg0).
          ->  Hash (actual rows=2 loops=1)
                Buckets: 262144  Batches: 1  Memory Usage: 2049kB
@@ -293,7 +294,7 @@ explain (costs off, timing off, summary off, analyze) select * from pt where exi
                                              ->  Seq Scan on t (actual rows=2 loops=1)
                                                    Filter: (t1 = ('hello'::text || (tid)::text))
  Optimizer: Pivotal Optimizer (GPORCA)
-(21 rows)
+(22 rows)
 
 select * from pt where exists (select 1 from t where tid = ptid and t1 = 'hello' || tid);
  dist |   pt1   |  pt2  |    pt3    | ptid 

src/test/regress/expected/groupingsets_optimizer.out

@@ -1670,24 +1670,20 @@ explain (costs off)
                                              Sort Key: share0_ref3.b
                                              ->  Redistribute Motion 3:3  (slice2; segments: 3)
                                                    Hash Key: share0_ref3.b
-                                                   ->  Partial GroupAggregate
+                                                   ->  Streaming Partial HashAggregate
                                                          Group Key: share0_ref3.b
-                                                         ->  Sort
-                                                               Sort Key: share0_ref3.b
-                                                               ->  Shared Scan (share slice:id 2:0)
-                                 ->  GroupAggregate
+                                                         ->  Shared Scan (share slice:id 2:0)
+                                 ->  HashAggregate
                                        Group Key: share0_ref4.a
-                                       ->  Sort
-                                             Sort Key: share0_ref4.a
-                                             ->  Shared Scan (share slice:id 1:0)
+                                       ->  Shared Scan (share slice:id 1:0)
                                  ->  Result
                                        ->  Redistribute Motion 1:3  (slice3)
                                              ->  Finalize Aggregate
                                                    ->  Gather Motion 3:1  (slice4; segments: 3)
                                                          ->  Partial Aggregate
                                                                ->  Shared Scan (share slice:id 4:0)
  Optimizer: Pivotal Optimizer (GPORCA)
-(40 rows)
+(36 rows)
 
 select a, b, sum(v.x)
   from (values (1),(2)) v(x), gstest_data(v.x)
@@ -2388,23 +2384,7 @@ create table gs_group_1 as
 select g1000, g100, g10, sum(g::numeric), count(*), max(g::text) from
   (select g%1000 as g1000, g%100 as g100, g%10 as g10, g
    from generate_series(0,199999) g) s
-group by cube (g1000,g100,g10);
-NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column(s) named 'g1000' as the Greenplum Database data distribution key for this table.
-HINT:  The 'DISTRIBUTED BY' clause determines the distribution of data. Make sure column(s) chosen are the optimal data distribution key to minimize skew.
-set jit_above_cost to default;
-create table gs_group_2 as
-select g1000, g100, g10, sum(g::numeric), count(*), max(g::text) from
-  (select g/20 as g1000, g/200 as g100, g/2000 as g10, g
-   from generate_series(0,19999) g) s
-group by cube (g1000,g100,g10);
-NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column(s) named 'g1000' as the Greenplum Database data distribution key for this table.
-HINT:  The 'DISTRIBUTED BY' clause determines the distribution of data. Make sure column(s) chosen are the optimal data distribution key to minimize skew.
-create table gs_group_3 as
-select g100, g10, array_agg(g) as a, count(*) as c, max(g::text) as m from
-  (select g/200 as g100, g/2000 as g10, g
-   from generate_series(0,19999) g) s
-group by grouping sets (g100,g10);
-NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause. Creating a NULL policy entry.
+group by cube (g1000,g100,g10) distributed by (g1000);
 -- Produce results with hash aggregation.
 set enable_hashagg = true;
 set enable_sort = false;
@@ -2449,23 +2429,8 @@ create table gs_hash_1 as
 select g1000, g100, g10, sum(g::numeric), count(*), max(g::text) from
   (select g%1000 as g1000, g%100 as g100, g%10 as g10, g
    from generate_series(0,199999) g) s
-group by cube (g1000,g100,g10);
-NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column(s) named 'g1000' as the Greenplum Database data distribution key for this table.
-HINT:  The 'DISTRIBUTED BY' clause determines the distribution of data. Make sure column(s) chosen are the optimal data distribution key to minimize skew.
+group by cube (g1000,g100,g10) distributed by (g1000);
 set jit_above_cost to default;
-create table gs_hash_2 as
-select g1000, g100, g10, sum(g::numeric), count(*), max(g::text) from
-  (select g/20 as g1000, g/200 as g100, g/2000 as g10, g
-   from generate_series(0,19999) g) s
-group by cube (g1000,g100,g10);
-NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column(s) named 'g1000' as the Greenplum Database data distribution key for this table.
-HINT:  The 'DISTRIBUTED BY' clause determines the distribution of data. Make sure column(s) chosen are the optimal data distribution key to minimize skew.
-create table gs_hash_3 as
-select g100, g10, array_agg(g) as a, count(*) as c, max(g::text) as m from
-  (select g/200 as g100, g/2000 as g10, g
-   from generate_series(0,19999) g) s
-group by grouping sets (g100,g10);
-NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause. Creating a NULL policy entry.
 set enable_sort = true;
 set work_mem to default;
 WARNING:  "work_mem": setting is deprecated, and may be removed in a future release.

src/test/regress/expected/join_optimizer.out

@@ -1933,8 +1933,9 @@ where exists(select * from tenk1 b
                                              ->  Seq Scan on int4_tbl
                                        ->  Index Scan using tenk1_thous_tenthous on tenk1 tenk1_1
                                              Index Cond: (tenthous = int4_tbl.f1)
+                                             Filter: (NOT (twothousand IS NULL))
  Optimizer: Pivotal Optimizer (GPORCA)
-(18 rows)
+(19 rows)
 
 --
 -- More complicated constructs

src/test/regress/expected/window_optimizer.out

@@ -3794,11 +3794,11 @@ WINDOW fwd AS (
 INFO:  GPORCA failed to produce a plan, falling back to planner
 DETAIL:  GPDB Expression type: Query Parameter not supported in DXL
 INFO:  GPORCA failed to produce a plan, falling back to planner
-DETAIL:  GPDB Expression type: Query Parameter not supported in DXL
-INFO:  GPORCA failed to produce a plan, falling back to planner
 DETAIL:  Feature not supported: SIRV functions
 INFO:  GPORCA failed to produce a plan, falling back to planner
 DETAIL:  Feature not supported: SIRV functions
+INFO:  GPORCA failed to produce a plan, falling back to planner
+DETAIL:  GPDB Expression type: Query Parameter not supported in DXL
  eq1 | eq2 | eq3 
 -----+-----+-----
  t   | t   | t
@@ -4321,6 +4321,8 @@ EXPLAIN (costs off) SELECT * FROM pg_temp.f(2);
 (2 rows)
 
 SELECT * FROM pg_temp.f(2);
+INFO:  GPORCA failed to produce a plan, falling back to planner
+DETAIL:  GPDB Expression type: Query Parameter not supported in DXL
     f    
 ---------
  {1,2,3}