--
-- ORCA tests
--
-- show version
SELECT count(*) from gp_opt_version();
 count 
-------
     1
(1 row)

-- Mask out Log & timestamp for orca message that has feature not supported.
-- start_matchsubs
-- m/^LOG.*\"Feature/
-- s/^LOG.*\"Feature/\"Feature/
-- end_matchsubs
-- fix the number of segments for Orca
set optimizer_segments = 3;
set optimizer_enable_master_only_queries = on;
-- master only tables
create schema orca;
create table orca.r();
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause, and no column type is suitable for a distribution key. Creating a NULL policy entry.
set allow_system_table_mods=true;
delete from gp_distribution_policy where localoid='orca.r'::regclass;
reset allow_system_table_mods;
alter table orca.r add column a int;
alter table orca.r add column b int;
insert into orca.r select i, i/3 from generate_series(1,20) i;
create table orca.s();
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause, and no column type is suitable for a distribution key. Creating a NULL policy entry.
set allow_system_table_mods=true;
delete from gp_distribution_policy where localoid='orca.s'::regclass;
reset allow_system_table_mods;
alter table orca.s add column c int;
alter table orca.s add column d int;
insert into orca.s select i, i/2 from generate_series(1,30) i;
set optimizer_log=on;
set optimizer_enable_indexjoin=on;
set optimizer_trace_fallback = on;
-- expected fall back to the planner
select sum(distinct a), count(distinct b) from orca.r;
 sum | count 
-----+-------
 210 |     7
(1 row)

select * from orca.r;
 a  | b 
----+---
  1 | 0
  2 | 0
  3 | 1
  4 | 1
  5 | 1
  6 | 2
  7 | 2
  8 | 2
  9 | 3
 10 | 3
 11 | 3
 12 | 4
 13 | 4
 14 | 4
 15 | 5
 16 | 5
 17 | 5
 18 | 6
 19 | 6
 20 | 6
(20 rows)

select * from orca.r, orca.s where r.a=s.c;
 a  | b | c  | d  
----+---+----+----
  1 | 0 |  1 |  0
  2 | 0 |  2 |  1
  3 | 1 |  3 |  1
  4 | 1 |  4 |  2
  5 | 1 |  5 |  2
  6 | 2 |  6 |  3
  7 | 2 |  7 |  3
  8 | 2 |  8 |  4
  9 | 3 |  9 |  4
 10 | 3 | 10 |  5
 11 | 3 | 11 |  5
 12 | 4 | 12 |  6
 13 | 4 | 13 |  6
 14 | 4 | 14 |  7
 15 | 5 | 15 |  7
 16 | 5 | 16 |  8
 17 | 5 | 17 |  8
 18 | 6 | 18 |  9
 19 | 6 | 19 |  9
 20 | 6 | 20 | 10
(20 rows)

select * from orca.r, orca.s where r.a<s.c+1 or r.a>s.c;
 a  | b | c  | d  
----+---+----+----
  1 | 0 |  1 |  0
  1 | 0 |  2 |  1
  1 | 0 |  3 |  1
  1 | 0 |  4 |  2
  1 | 0 |  5 |  2
  1 | 0 |  6 |  3
  1 | 0 |  7 |  3
  1 | 0 |  8 |  4
  1 | 0 |  9 |  4
  1 | 0 | 10 |  5
  1 | 0 | 11 |  5
  1 | 0 | 12 |  6
  1 | 0 | 13 |  6
  1 | 0 | 14 |  7
  1 | 0 | 15 |  7
  1 | 0 | 16 |  8
  1 | 0 | 17 |  8
  1 | 0 | 18 |  9
  1 | 0 | 19 |  9
  1 | 0 | 20 | 10
  1 | 0 | 21 | 10
  1 | 0 | 22 | 11
  1 | 0 | 23 | 11
  1 | 0 | 24 | 12
  1 | 0 | 25 | 12
  1 | 0 | 26 | 13
  1 | 0 | 27 | 13
  1 | 0 | 28 | 14
  1 | 0 | 29 | 14
  1 | 0 | 30 | 15
  2 | 0 |  1 |  0
  2 | 0 |  2 |  1
  2 | 0 |  3 |  1
  2 | 0 |  4 |  2
  2 | 0 |  5 |  2
  2 | 0 |  6 |  3
  2 | 0 |  7 |  3
  2 | 0 |  8 |  4
  2 | 0 |  9 |  4
  2 | 0 | 10 |  5
  2 | 0 | 11 |  5
  2 | 0 | 12 |  6
  2 | 0 | 13 |  6
  2 | 0 | 14 |  7
  2 | 0 | 15 |  7
  2 | 0 | 16 |  8
  2 | 0 | 17 |  8
  2 | 0 | 18 |  9
  2 | 0 | 19 |  9
  2 | 0 | 20 | 10
  2 | 0 | 21 | 10
  2 | 0 | 22 | 11
  2 | 0 | 23 | 11
  2 | 0 | 24 | 12
  2 | 0 | 25 | 12
  2 | 0 | 26 | 13
  2 | 0 | 27 | 13
  2 | 0 | 28 | 14
  2 | 0 | 29 | 14
  2 | 0 | 30 | 15
  3 | 1 |  1 |  0
  3 | 1 |  2 |  1
  3 | 1 |  3 |  1
  3 | 1 |  4 |  2
  3 | 1 |  5 |  2
  3 | 1 |  6 |  3
  3 | 1 |  7 |  3
  3 | 1 |  8 |  4
  3 | 1 |  9 |  4
  3 | 1 | 10 |  5
  3 | 1 | 11 |  5
  3 | 1 | 12 |  6
  3 | 1 | 13 |  6
  3 | 1 | 14 |  7
  3 | 1 | 15 |  7
  3 | 1 | 16 |  8
  3 | 1 | 17 |  8
  3 | 1 | 18 |  9
  3 | 1 | 19 |  9
  3 | 1 | 20 | 10
  3 | 1 | 21 | 10
  3 | 1 | 22 | 11
  3 | 1 | 23 | 11
  3 | 1 | 24 | 12
  3 | 1 | 25 | 12
  3 | 1 | 26 | 13
  3 | 1 | 27 | 13
  3 | 1 | 28 | 14
  3 | 1 | 29 | 14
  3 | 1 | 30 | 15
  4 | 1 |  1 |  0
  4 | 1 |  2 |  1
  4 | 1 |  3 |  1
  4 | 1 |  4 |  2
  4 | 1 |  5 |  2
  4 | 1 |  6 |  3
  4 | 1 |  7 |  3
  4 | 1 |  8 |  4
  4 | 1 |  9 |  4
  4 | 1 | 10 |  5
  4 | 1 | 11 |  5
  4 | 1 | 12 |  6
  4 | 1 | 13 |  6
  4 | 1 | 14 |  7
  4 | 1 | 15 |  7
  4 | 1 | 16 |  8
  4 | 1 | 17 |  8
  4 | 1 | 18 |  9
  4 | 1 | 19 |  9
  4 | 1 | 20 | 10
  4 | 1 | 21 | 10
  4 | 1 | 22 | 11
  4 | 1 | 23 | 11
  4 | 1 | 24 | 12
  4 | 1 | 25 | 12
  4 | 1 | 26 | 13
  4 | 1 | 27 | 13
  4 | 1 | 28 | 14
  4 | 1 | 29 | 14
  4 | 1 | 30 | 15
  5 | 1 |  1 |  0
  5 | 1 |  2 |  1
  5 | 1 |  3 |  1
  5 | 1 |  4 |  2
  5 | 1 |  5 |  2
  5 | 1 |  6 |  3
  5 | 1 |  7 |  3
  5 | 1 |  8 |  4
  5 | 1 |  9 |  4
  5 | 1 | 10 |  5
  5 | 1 | 11 |  5
  5 | 1 | 12 |  6
  5 | 1 | 13 |  6
  5 | 1 | 14 |  7
  5 | 1 | 15 |  7
  5 | 1 | 16 |  8
  5 | 1 | 17 |  8
  5 | 1 | 18 |  9
  5 | 1 | 19 |  9
  5 | 1 | 20 | 10
  5 | 1 | 21 | 10
  5 | 1 | 22 | 11
  5 | 1 | 23 | 11
  5 | 1 | 24 | 12
  5 | 1 | 25 | 12
  5 | 1 | 26 | 13
  5 | 1 | 27 | 13
  5 | 1 | 28 | 14
  5 | 1 | 29 | 14
  5 | 1 | 30 | 15
  6 | 2 |  1 |  0
  6 | 2 |  2 |  1
  6 | 2 |  3 |  1
  6 | 2 |  4 |  2
  6 | 2 |  5 |  2
  6 | 2 |  6 |  3
  6 | 2 |  7 |  3
  6 | 2 |  8 |  4
  6 | 2 |  9 |  4
  6 | 2 | 10 |  5
  6 | 2 | 11 |  5
  6 | 2 | 12 |  6
  6 | 2 | 13 |  6
  6 | 2 | 14 |  7
  6 | 2 | 15 |  7
  6 | 2 | 16 |  8
  6 | 2 | 17 |  8
  6 | 2 | 18 |  9
  6 | 2 | 19 |  9
  6 | 2 | 20 | 10
  6 | 2 | 21 | 10
  6 | 2 | 22 | 11
  6 | 2 | 23 | 11
  6 | 2 | 24 | 12
  6 | 2 | 25 | 12
  6 | 2 | 26 | 13
  6 | 2 | 27 | 13
  6 | 2 | 28 | 14
  6 | 2 | 29 | 14
  6 | 2 | 30 | 15
  7 | 2 |  1 |  0
  7 | 2 |  2 |  1
  7 | 2 |  3 |  1
  7 | 2 |  4 |  2
  7 | 2 |  5 |  2
  7 | 2 |  6 |  3
  7 | 2 |  7 |  3
  7 | 2 |  8 |  4
  7 | 2 |  9 |  4
  7 | 2 | 10 |  5
  7 | 2 | 11 |  5
  7 | 2 | 12 |  6
  7 | 2 | 13 |  6
  7 | 2 | 14 |  7
  7 | 2 | 15 |  7
  7 | 2 | 16 |  8
  7 | 2 | 17 |  8
  7 | 2 | 18 |  9
  7 | 2 | 19 |  9
  7 | 2 | 20 | 10
  7 | 2 | 21 | 10
  7 | 2 | 22 | 11
  7 | 2 | 23 | 11
  7 | 2 | 24 | 12
  7 | 2 | 25 | 12
  7 | 2 | 26 | 13
  7 | 2 | 27 | 13
  7 | 2 | 28 | 14
  7 | 2 | 29 | 14
  7 | 2 | 30 | 15
  8 | 2 |  1 |  0
  8 | 2 |  2 |  1
  8 | 2 |  3 |  1
  8 | 2 |  4 |  2
  8 | 2 |  5 |  2
  8 | 2 |  6 |  3
  8 | 2 |  7 |  3
  8 | 2 |  8 |  4
  8 | 2 |  9 |  4
  8 | 2 | 10 |  5
  8 | 2 | 11 |  5
  8 | 2 | 12 |  6
  8 | 2 | 13 |  6
  8 | 2 | 14 |  7
  8 | 2 | 15 |  7
  8 | 2 | 16 |  8
  8 | 2 | 17 |  8
  8 | 2 | 18 |  9
  8 | 2 | 19 |  9
  8 | 2 | 20 | 10
  8 | 2 | 21 | 10
  8 | 2 | 22 | 11
  8 | 2 | 23 | 11
  8 | 2 | 24 | 12
  8 | 2 | 25 | 12
  8 | 2 | 26 | 13
  8 | 2 | 27 | 13
  8 | 2 | 28 | 14
  8 | 2 | 29 | 14
  8 | 2 | 30 | 15
  9 | 3 |  1 |  0
  9 | 3 |  2 |  1
  9 | 3 |  3 |  1
  9 | 3 |  4 |  2
  9 | 3 |  5 |  2
  9 | 3 |  6 |  3
  9 | 3 |  7 |  3
  9 | 3 |  8 |  4
  9 | 3 |  9 |  4
  9 | 3 | 10 |  5
  9 | 3 | 11 |  5
  9 | 3 | 12 |  6
  9 | 3 | 13 |  6
  9 | 3 | 14 |  7
  9 | 3 | 15 |  7
  9 | 3 | 16 |  8
  9 | 3 | 17 |  8
  9 | 3 | 18 |  9
  9 | 3 | 19 |  9
  9 | 3 | 20 | 10
  9 | 3 | 21 | 10
  9 | 3 | 22 | 11
  9 | 3 | 23 | 11
  9 | 3 | 24 | 12
  9 | 3 | 25 | 12
  9 | 3 | 26 | 13
  9 | 3 | 27 | 13
  9 | 3 | 28 | 14
  9 | 3 | 29 | 14
  9 | 3 | 30 | 15
 10 | 3 |  1 |  0
 10 | 3 |  2 |  1
 10 | 3 |  3 |  1
 10 | 3 |  4 |  2
 10 | 3 |  5 |  2
 10 | 3 |  6 |  3
 10 | 3 |  7 |  3
 10 | 3 |  8 |  4
 10 | 3 |  9 |  4
 10 | 3 | 10 |  5
 10 | 3 | 11 |  5
 10 | 3 | 12 |  6
 10 | 3 | 13 |  6
 10 | 3 | 14 |  7
 10 | 3 | 15 |  7
 10 | 3 | 16 |  8
 10 | 3 | 17 |  8
 10 | 3 | 18 |  9
 10 | 3 | 19 |  9
 10 | 3 | 20 | 10
 10 | 3 | 21 | 10
 10 | 3 | 22 | 11
 10 | 3 | 23 | 11
 10 | 3 | 24 | 12
 10 | 3 | 25 | 12
 10 | 3 | 26 | 13
 10 | 3 | 27 | 13
 10 | 3 | 28 | 14
 10 | 3 | 29 | 14
 10 | 3 | 30 | 15
 11 | 3 |  1 |  0
 11 | 3 |  2 |  1
 11 | 3 |  3 |  1
 11 | 3 |  4 |  2
 11 | 3 |  5 |  2
 11 | 3 |  6 |  3
 11 | 3 |  7 |  3
 11 | 3 |  8 |  4
 11 | 3 |  9 |  4
 11 | 3 | 10 |  5
 11 | 3 | 11 |  5
 11 | 3 | 12 |  6
 11 | 3 | 13 |  6
 11 | 3 | 14 |  7
 11 | 3 | 15 |  7
 11 | 3 | 16 |  8
 11 | 3 | 17 |  8
 11 | 3 | 18 |  9
 11 | 3 | 19 |  9
 11 | 3 | 20 | 10
 11 | 3 | 21 | 10
 11 | 3 | 22 | 11
 11 | 3 | 23 | 11
 11 | 3 | 24 | 12
 11 | 3 | 25 | 12
 11 | 3 | 26 | 13
 11 | 3 | 27 | 13
 11 | 3 | 28 | 14
 11 | 3 | 29 | 14
 11 | 3 | 30 | 15
 12 | 4 |  1 |  0
 12 | 4 |  2 |  1
 12 | 4 |  3 |  1
 12 | 4 |  4 |  2
 12 | 4 |  5 |  2
 12 | 4 |  6 |  3
 12 | 4 |  7 |  3
 12 | 4 |  8 |  4
 12 | 4 |  9 |  4
 12 | 4 | 10 |  5
 12 | 4 | 11 |  5
 12 | 4 | 12 |  6
 12 | 4 | 13 |  6
 12 | 4 | 14 |  7
 12 | 4 | 15 |  7
 12 | 4 | 16 |  8
 12 | 4 | 17 |  8
 12 | 4 | 18 |  9
 12 | 4 | 19 |  9
 12 | 4 | 20 | 10
 12 | 4 | 21 | 10
 12 | 4 | 22 | 11
 12 | 4 | 23 | 11
 12 | 4 | 24 | 12
 12 | 4 | 25 | 12
 12 | 4 | 26 | 13
 12 | 4 | 27 | 13
 12 | 4 | 28 | 14
 12 | 4 | 29 | 14
 12 | 4 | 30 | 15
 13 | 4 |  1 |  0
 13 | 4 |  2 |  1
 13 | 4 |  3 |  1
 13 | 4 |  4 |  2
 13 | 4 |  5 |  2
 13 | 4 |  6 |  3
 13 | 4 |  7 |  3
 13 | 4 |  8 |  4
 13 | 4 |  9 |  4
 13 | 4 | 10 |  5
 13 | 4 | 11 |  5
 13 | 4 | 12 |  6
 13 | 4 | 13 |  6
 13 | 4 | 14 |  7
 13 | 4 | 15 |  7
 13 | 4 | 16 |  8
 13 | 4 | 17 |  8
 13 | 4 | 18 |  9
 13 | 4 | 19 |  9
 13 | 4 | 20 | 10
 13 | 4 | 21 | 10
 13 | 4 | 22 | 11
 13 | 4 | 23 | 11
 13 | 4 | 24 | 12
 13 | 4 | 25 | 12
 13 | 4 | 26 | 13
 13 | 4 | 27 | 13
 13 | 4 | 28 | 14
 13 | 4 | 29 | 14
 13 | 4 | 30 | 15
 14 | 4 |  1 |  0
 14 | 4 |  2 |  1
 14 | 4 |  3 |  1
 14 | 4 |  4 |  2
 14 | 4 |  5 |  2
 14 | 4 |  6 |  3
 14 | 4 |  7 |  3
 14 | 4 |  8 |  4
 14 | 4 |  9 |  4
 14 | 4 | 10 |  5
 14 | 4 | 11 |  5
 14 | 4 | 12 |  6
 14 | 4 | 13 |  6
 14 | 4 | 14 |  7
 14 | 4 | 15 |  7
 14 | 4 | 16 |  8
 14 | 4 | 17 |  8
 14 | 4 | 18 |  9
 14 | 4 | 19 |  9
 14 | 4 | 20 | 10
 14 | 4 | 21 | 10
 14 | 4 | 22 | 11
 14 | 4 | 23 | 11
 14 | 4 | 24 | 12
 14 | 4 | 25 | 12
 14 | 4 | 26 | 13
 14 | 4 | 27 | 13
 14 | 4 | 28 | 14
 14 | 4 | 29 | 14
 14 | 4 | 30 | 15
 15 | 5 |  1 |  0
 15 | 5 |  2 |  1
 15 | 5 |  3 |  1
 15 | 5 |  4 |  2
 15 | 5 |  5 |  2
 15 | 5 |  6 |  3
 15 | 5 |  7 |  3
 15 | 5 |  8 |  4
 15 | 5 |  9 |  4
 15 | 5 | 10 |  5
 15 | 5 | 11 |  5
 15 | 5 | 12 |  6
 15 | 5 | 13 |  6
 15 | 5 | 14 |  7
 15 | 5 | 15 |  7
 15 | 5 | 16 |  8
 15 | 5 | 17 |  8
 15 | 5 | 18 |  9
 15 | 5 | 19 |  9
 15 | 5 | 20 | 10
 15 | 5 | 21 | 10
 15 | 5 | 22 | 11
 15 | 5 | 23 | 11
 15 | 5 | 24 | 12
 15 | 5 | 25 | 12
 15 | 5 | 26 | 13
 15 | 5 | 27 | 13
 15 | 5 | 28 | 14
 15 | 5 | 29 | 14
 15 | 5 | 30 | 15
 16 | 5 |  1 |  0
 16 | 5 |  2 |  1
 16 | 5 |  3 |  1
 16 | 5 |  4 |  2
 16 | 5 |  5 |  2
 16 | 5 |  6 |  3
 16 | 5 |  7 |  3
 16 | 5 |  8 |  4
 16 | 5 |  9 |  4
 16 | 5 | 10 |  5
 16 | 5 | 11 |  5
 16 | 5 | 12 |  6
 16 | 5 | 13 |  6
 16 | 5 | 14 |  7
 16 | 5 | 15 |  7
 16 | 5 | 16 |  8
 16 | 5 | 17 |  8
 16 | 5 | 18 |  9
 16 | 5 | 19 |  9
 16 | 5 | 20 | 10
 16 | 5 | 21 | 10
 16 | 5 | 22 | 11
 16 | 5 | 23 | 11
 16 | 5 | 24 | 12
 16 | 5 | 25 | 12
 16 | 5 | 26 | 13
 16 | 5 | 27 | 13
 16 | 5 | 28 | 14
 16 | 5 | 29 | 14
 16 | 5 | 30 | 15
 17 | 5 |  1 |  0
 17 | 5 |  2 |  1
 17 | 5 |  3 |  1
 17 | 5 |  4 |  2
 17 | 5 |  5 |  2
 17 | 5 |  6 |  3
 17 | 5 |  7 |  3
 17 | 5 |  8 |  4
 17 | 5 |  9 |  4
 17 | 5 | 10 |  5
 17 | 5 | 11 |  5
 17 | 5 | 12 |  6
 17 | 5 | 13 |  6
 17 | 5 | 14 |  7
 17 | 5 | 15 |  7
 17 | 5 | 16 |  8
 17 | 5 | 17 |  8
 17 | 5 | 18 |  9
 17 | 5 | 19 |  9
 17 | 5 | 20 | 10
 17 | 5 | 21 | 10
 17 | 5 | 22 | 11
 17 | 5 | 23 | 11
 17 | 5 | 24 | 12
 17 | 5 | 25 | 12
 17 | 5 | 26 | 13
 17 | 5 | 27 | 13
 17 | 5 | 28 | 14
 17 | 5 | 29 | 14
 17 | 5 | 30 | 15
 18 | 6 |  1 |  0
 18 | 6 |  2 |  1
 18 | 6 |  3 |  1
 18 | 6 |  4 |  2
 18 | 6 |  5 |  2
 18 | 6 |  6 |  3
 18 | 6 |  7 |  3
 18 | 6 |  8 |  4
 18 | 6 |  9 |  4
 18 | 6 | 10 |  5
 18 | 6 | 11 |  5
 18 | 6 | 12 |  6
 18 | 6 | 13 |  6
 18 | 6 | 14 |  7
 18 | 6 | 15 |  7
 18 | 6 | 16 |  8
 18 | 6 | 17 |  8
 18 | 6 | 18 |  9
 18 | 6 | 19 |  9
 18 | 6 | 20 | 10
 18 | 6 | 21 | 10
 18 | 6 | 22 | 11
 18 | 6 | 23 | 11
 18 | 6 | 24 | 12
 18 | 6 | 25 | 12
 18 | 6 | 26 | 13
 18 | 6 | 27 | 13
 18 | 6 | 28 | 14
 18 | 6 | 29 | 14
 18 | 6 | 30 | 15
 19 | 6 |  1 |  0
 19 | 6 |  2 |  1
 19 | 6 |  3 |  1
 19 | 6 |  4 |  2
 19 | 6 |  5 |  2
 19 | 6 |  6 |  3
 19 | 6 |  7 |  3
 19 | 6 |  8 |  4
 19 | 6 |  9 |  4
 19 | 6 | 10 |  5
 19 | 6 | 11 |  5
 19 | 6 | 12 |  6
 19 | 6 | 13 |  6
 19 | 6 | 14 |  7
 19 | 6 | 15 |  7
 19 | 6 | 16 |  8
 19 | 6 | 17 |  8
 19 | 6 | 18 |  9
 19 | 6 | 19 |  9
 19 | 6 | 20 | 10
 19 | 6 | 21 | 10
 19 | 6 | 22 | 11
 19 | 6 | 23 | 11
 19 | 6 | 24 | 12
 19 | 6 | 25 | 12
 19 | 6 | 26 | 13
 19 | 6 | 27 | 13
 19 | 6 | 28 | 14
 19 | 6 | 29 | 14
 19 | 6 | 30 | 15
 20 | 6 |  1 |  0
 20 | 6 |  2 |  1
 20 | 6 |  3 |  1
 20 | 6 |  4 |  2
 20 | 6 |  5 |  2
 20 | 6 |  6 |  3
 20 | 6 |  7 |  3
 20 | 6 |  8 |  4
 20 | 6 |  9 |  4
 20 | 6 | 10 |  5
 20 | 6 | 11 |  5
 20 | 6 | 12 |  6
 20 | 6 | 13 |  6
 20 | 6 | 14 |  7
 20 | 6 | 15 |  7
 20 | 6 | 16 |  8
 20 | 6 | 17 |  8
 20 | 6 | 18 |  9
 20 | 6 | 19 |  9
 20 | 6 | 20 | 10
 20 | 6 | 21 | 10
 20 | 6 | 22 | 11
 20 | 6 | 23 | 11
 20 | 6 | 24 | 12
 20 | 6 | 25 | 12
 20 | 6 | 26 | 13
 20 | 6 | 27 | 13
 20 | 6 | 28 | 14
 20 | 6 | 29 | 14
 20 | 6 | 30 | 15
(600 rows)

select sum(r.a) from orca.r;
 sum 
-----
 210
(1 row)

select count(*) from orca.r;
 count 
-------
    20
(1 row)

select a, b from orca.r, orca.s group by a,b;
 a  | b 
----+---
 13 | 4
  9 | 3
  5 | 1
 16 | 5
 12 | 4
 17 | 5
  4 | 1
 18 | 6
 10 | 3
  8 | 2
  6 | 2
 15 | 5
 19 | 6
 14 | 4
  3 | 1
  2 | 0
 11 | 3
  7 | 2
 20 | 6
  1 | 0
(20 rows)

select r.a+1 from orca.r;
 ?column? 
----------
        2
        3
        4
        5
        6
        7
        8
        9
       10
       11
       12
       13
       14
       15
       16
       17
       18
       19
       20
       21
(20 rows)

select * from orca.r, orca.s where r.a<s.c or (r.b<s.d and r.b>s.c);
 a  | b | c  | d  
----+---+----+----
  1 | 0 |  2 |  1
  1 | 0 |  3 |  1
  1 | 0 |  4 |  2
  1 | 0 |  5 |  2
  1 | 0 |  6 |  3
  1 | 0 |  7 |  3
  1 | 0 |  8 |  4
  1 | 0 |  9 |  4
  1 | 0 | 10 |  5
  1 | 0 | 11 |  5
  1 | 0 | 12 |  6
  1 | 0 | 13 |  6
  1 | 0 | 14 |  7
  1 | 0 | 15 |  7
  1 | 0 | 16 |  8
  1 | 0 | 17 |  8
  1 | 0 | 18 |  9
  1 | 0 | 19 |  9
  1 | 0 | 20 | 10
  1 | 0 | 21 | 10
  1 | 0 | 22 | 11
  1 | 0 | 23 | 11
  1 | 0 | 24 | 12
  1 | 0 | 25 | 12
  1 | 0 | 26 | 13
  1 | 0 | 27 | 13
  1 | 0 | 28 | 14
  1 | 0 | 29 | 14
  1 | 0 | 30 | 15
  2 | 0 |  3 |  1
  2 | 0 |  4 |  2
  2 | 0 |  5 |  2
  2 | 0 |  6 |  3
  2 | 0 |  7 |  3
  2 | 0 |  8 |  4
  2 | 0 |  9 |  4
  2 | 0 | 10 |  5
  2 | 0 | 11 |  5
  2 | 0 | 12 |  6
  2 | 0 | 13 |  6
  2 | 0 | 14 |  7
  2 | 0 | 15 |  7
  2 | 0 | 16 |  8
  2 | 0 | 17 |  8
  2 | 0 | 18 |  9
  2 | 0 | 19 |  9
  2 | 0 | 20 | 10
  2 | 0 | 21 | 10
  2 | 0 | 22 | 11
  2 | 0 | 23 | 11
  2 | 0 | 24 | 12
  2 | 0 | 25 | 12
  2 | 0 | 26 | 13
  2 | 0 | 27 | 13
  2 | 0 | 28 | 14
  2 | 0 | 29 | 14
  2 | 0 | 30 | 15
  3 | 1 |  4 |  2
  3 | 1 |  5 |  2
  3 | 1 |  6 |  3
  3 | 1 |  7 |  3
  3 | 1 |  8 |  4
  3 | 1 |  9 |  4
  3 | 1 | 10 |  5
  3 | 1 | 11 |  5
  3 | 1 | 12 |  6
  3 | 1 | 13 |  6
  3 | 1 | 14 |  7
  3 | 1 | 15 |  7
  3 | 1 | 16 |  8
  3 | 1 | 17 |  8
  3 | 1 | 18 |  9
  3 | 1 | 19 |  9
  3 | 1 | 20 | 10
  3 | 1 | 21 | 10
  3 | 1 | 22 | 11
  3 | 1 | 23 | 11
  3 | 1 | 24 | 12
  3 | 1 | 25 | 12
  3 | 1 | 26 | 13
  3 | 1 | 27 | 13
  3 | 1 | 28 | 14
  3 | 1 | 29 | 14
  3 | 1 | 30 | 15
  4 | 1 |  5 |  2
  4 | 1 |  6 |  3
  4 | 1 |  7 |  3
  4 | 1 |  8 |  4
  4 | 1 |  9 |  4
  4 | 1 | 10 |  5
  4 | 1 | 11 |  5
  4 | 1 | 12 |  6
  4 | 1 | 13 |  6
  4 | 1 | 14 |  7
  4 | 1 | 15 |  7
  4 | 1 | 16 |  8
  4 | 1 | 17 |  8
  4 | 1 | 18 |  9
  4 | 1 | 19 |  9
  4 | 1 | 20 | 10
  4 | 1 | 21 | 10
  4 | 1 | 22 | 11
  4 | 1 | 23 | 11
  4 | 1 | 24 | 12
  4 | 1 | 25 | 12
  4 | 1 | 26 | 13
  4 | 1 | 27 | 13
  4 | 1 | 28 | 14
  4 | 1 | 29 | 14
  4 | 1 | 30 | 15
  5 | 1 |  6 |  3
  5 | 1 |  7 |  3
  5 | 1 |  8 |  4
  5 | 1 |  9 |  4
  5 | 1 | 10 |  5
  5 | 1 | 11 |  5
  5 | 1 | 12 |  6
  5 | 1 | 13 |  6
  5 | 1 | 14 |  7
  5 | 1 | 15 |  7
  5 | 1 | 16 |  8
  5 | 1 | 17 |  8
  5 | 1 | 18 |  9
  5 | 1 | 19 |  9
  5 | 1 | 20 | 10
  5 | 1 | 21 | 10
  5 | 1 | 22 | 11
  5 | 1 | 23 | 11
  5 | 1 | 24 | 12
  5 | 1 | 25 | 12
  5 | 1 | 26 | 13
  5 | 1 | 27 | 13
  5 | 1 | 28 | 14
  5 | 1 | 29 | 14
  5 | 1 | 30 | 15
  6 | 2 |  7 |  3
  6 | 2 |  8 |  4
  6 | 2 |  9 |  4
  6 | 2 | 10 |  5
  6 | 2 | 11 |  5
  6 | 2 | 12 |  6
  6 | 2 | 13 |  6
  6 | 2 | 14 |  7
  6 | 2 | 15 |  7
  6 | 2 | 16 |  8
  6 | 2 | 17 |  8
  6 | 2 | 18 |  9
  6 | 2 | 19 |  9
  6 | 2 | 20 | 10
  6 | 2 | 21 | 10
  6 | 2 | 22 | 11
  6 | 2 | 23 | 11
  6 | 2 | 24 | 12
  6 | 2 | 25 | 12
  6 | 2 | 26 | 13
  6 | 2 | 27 | 13
  6 | 2 | 28 | 14
  6 | 2 | 29 | 14
  6 | 2 | 30 | 15
  7 | 2 |  8 |  4
  7 | 2 |  9 |  4
  7 | 2 | 10 |  5
  7 | 2 | 11 |  5
  7 | 2 | 12 |  6
  7 | 2 | 13 |  6
  7 | 2 | 14 |  7
  7 | 2 | 15 |  7
  7 | 2 | 16 |  8
  7 | 2 | 17 |  8
  7 | 2 | 18 |  9
  7 | 2 | 19 |  9
  7 | 2 | 20 | 10
  7 | 2 | 21 | 10
  7 | 2 | 22 | 11
  7 | 2 | 23 | 11
  7 | 2 | 24 | 12
  7 | 2 | 25 | 12
  7 | 2 | 26 | 13
  7 | 2 | 27 | 13
  7 | 2 | 28 | 14
  7 | 2 | 29 | 14
  7 | 2 | 30 | 15
  8 | 2 |  9 |  4
  8 | 2 | 10 |  5
  8 | 2 | 11 |  5
  8 | 2 | 12 |  6
  8 | 2 | 13 |  6
  8 | 2 | 14 |  7
  8 | 2 | 15 |  7
  8 | 2 | 16 |  8
  8 | 2 | 17 |  8
  8 | 2 | 18 |  9
  8 | 2 | 19 |  9
  8 | 2 | 20 | 10
  8 | 2 | 21 | 10
  8 | 2 | 22 | 11
  8 | 2 | 23 | 11
  8 | 2 | 24 | 12
  8 | 2 | 25 | 12
  8 | 2 | 26 | 13
  8 | 2 | 27 | 13
  8 | 2 | 28 | 14
  8 | 2 | 29 | 14
  8 | 2 | 30 | 15
  9 | 3 | 10 |  5
  9 | 3 | 11 |  5
  9 | 3 | 12 |  6
  9 | 3 | 13 |  6
  9 | 3 | 14 |  7
  9 | 3 | 15 |  7
  9 | 3 | 16 |  8
  9 | 3 | 17 |  8
  9 | 3 | 18 |  9
  9 | 3 | 19 |  9
  9 | 3 | 20 | 10
  9 | 3 | 21 | 10
  9 | 3 | 22 | 11
  9 | 3 | 23 | 11
  9 | 3 | 24 | 12
  9 | 3 | 25 | 12
  9 | 3 | 26 | 13
  9 | 3 | 27 | 13
  9 | 3 | 28 | 14
  9 | 3 | 29 | 14
  9 | 3 | 30 | 15
 10 | 3 | 11 |  5
 10 | 3 | 12 |  6
 10 | 3 | 13 |  6
 10 | 3 | 14 |  7
 10 | 3 | 15 |  7
 10 | 3 | 16 |  8
 10 | 3 | 17 |  8
 10 | 3 | 18 |  9
 10 | 3 | 19 |  9
 10 | 3 | 20 | 10
 10 | 3 | 21 | 10
 10 | 3 | 22 | 11
 10 | 3 | 23 | 11
 10 | 3 | 24 | 12
 10 | 3 | 25 | 12
 10 | 3 | 26 | 13
 10 | 3 | 27 | 13
 10 | 3 | 28 | 14
 10 | 3 | 29 | 14
 10 | 3 | 30 | 15
 11 | 3 | 12 |  6
 11 | 3 | 13 |  6
 11 | 3 | 14 |  7
 11 | 3 | 15 |  7
 11 | 3 | 16 |  8
 11 | 3 | 17 |  8
 11 | 3 | 18 |  9
 11 | 3 | 19 |  9
 11 | 3 | 20 | 10
 11 | 3 | 21 | 10
 11 | 3 | 22 | 11
 11 | 3 | 23 | 11
 11 | 3 | 24 | 12
 11 | 3 | 25 | 12
 11 | 3 | 26 | 13
 11 | 3 | 27 | 13
 11 | 3 | 28 | 14
 11 | 3 | 29 | 14
 11 | 3 | 30 | 15
 12 | 4 | 13 |  6
 12 | 4 | 14 |  7
 12 | 4 | 15 |  7
 12 | 4 | 16 |  8
 12 | 4 | 17 |  8
 12 | 4 | 18 |  9
 12 | 4 | 19 |  9
 12 | 4 | 20 | 10
 12 | 4 | 21 | 10
 12 | 4 | 22 | 11
 12 | 4 | 23 | 11
 12 | 4 | 24 | 12
 12 | 4 | 25 | 12
 12 | 4 | 26 | 13
 12 | 4 | 27 | 13
 12 | 4 | 28 | 14
 12 | 4 | 29 | 14
 12 | 4 | 30 | 15
 13 | 4 | 14 |  7
 13 | 4 | 15 |  7
 13 | 4 | 16 |  8
 13 | 4 | 17 |  8
 13 | 4 | 18 |  9
 13 | 4 | 19 |  9
 13 | 4 | 20 | 10
 13 | 4 | 21 | 10
 13 | 4 | 22 | 11
 13 | 4 | 23 | 11
 13 | 4 | 24 | 12
 13 | 4 | 25 | 12
 13 | 4 | 26 | 13
 13 | 4 | 27 | 13
 13 | 4 | 28 | 14
 13 | 4 | 29 | 14
 13 | 4 | 30 | 15
 14 | 4 | 15 |  7
 14 | 4 | 16 |  8
 14 | 4 | 17 |  8
 14 | 4 | 18 |  9
 14 | 4 | 19 |  9
 14 | 4 | 20 | 10
 14 | 4 | 21 | 10
 14 | 4 | 22 | 11
 14 | 4 | 23 | 11
 14 | 4 | 24 | 12
 14 | 4 | 25 | 12
 14 | 4 | 26 | 13
 14 | 4 | 27 | 13
 14 | 4 | 28 | 14
 14 | 4 | 29 | 14
 14 | 4 | 30 | 15
 15 | 5 | 16 |  8
 15 | 5 | 17 |  8
 15 | 5 | 18 |  9
 15 | 5 | 19 |  9
 15 | 5 | 20 | 10
 15 | 5 | 21 | 10
 15 | 5 | 22 | 11
 15 | 5 | 23 | 11
 15 | 5 | 24 | 12
 15 | 5 | 25 | 12
 15 | 5 | 26 | 13
 15 | 5 | 27 | 13
 15 | 5 | 28 | 14
 15 | 5 | 29 | 14
 15 | 5 | 30 | 15
 16 | 5 | 17 |  8
 16 | 5 | 18 |  9
 16 | 5 | 19 |  9
 16 | 5 | 20 | 10
 16 | 5 | 21 | 10
 16 | 5 | 22 | 11
 16 | 5 | 23 | 11
 16 | 5 | 24 | 12
 16 | 5 | 25 | 12
 16 | 5 | 26 | 13
 16 | 5 | 27 | 13
 16 | 5 | 28 | 14
 16 | 5 | 29 | 14
 16 | 5 | 30 | 15
 17 | 5 | 18 |  9
 17 | 5 | 19 |  9
 17 | 5 | 20 | 10
 17 | 5 | 21 | 10
 17 | 5 | 22 | 11
 17 | 5 | 23 | 11
 17 | 5 | 24 | 12
 17 | 5 | 25 | 12
 17 | 5 | 26 | 13
 17 | 5 | 27 | 13
 17 | 5 | 28 | 14
 17 | 5 | 29 | 14
 17 | 5 | 30 | 15
 18 | 6 | 19 |  9
 18 | 6 | 20 | 10
 18 | 6 | 21 | 10
 18 | 6 | 22 | 11
 18 | 6 | 23 | 11
 18 | 6 | 24 | 12
 18 | 6 | 25 | 12
 18 | 6 | 26 | 13
 18 | 6 | 27 | 13
 18 | 6 | 28 | 14
 18 | 6 | 29 | 14
 18 | 6 | 30 | 15
 19 | 6 | 20 | 10
 19 | 6 | 21 | 10
 19 | 6 | 22 | 11
 19 | 6 | 23 | 11
 19 | 6 | 24 | 12
 19 | 6 | 25 | 12
 19 | 6 | 26 | 13
 19 | 6 | 27 | 13
 19 | 6 | 28 | 14
 19 | 6 | 29 | 14
 19 | 6 | 30 | 15
 20 | 6 | 21 | 10
 20 | 6 | 22 | 11
 20 | 6 | 23 | 11
 20 | 6 | 24 | 12
 20 | 6 | 25 | 12
 20 | 6 | 26 | 13
 20 | 6 | 27 | 13
 20 | 6 | 28 | 14
 20 | 6 | 29 | 14
 20 | 6 | 30 | 15
(390 rows)

select case when r.a<s.c then r.a<s.c else r.a<s.c end from orca.r, orca.s;
 case 
------
 f
 t
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 t
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 t
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(600 rows)

select case r.b<s.c when true then r.b else s.c end from orca.r, orca.s where r.a = s.d;
 c 
---
 0
 0
 0
 0
 1
 1
 1
 1
 1
 1
 2
 2
 2
 2
 2
 2
 3
 3
 3
 3
 3
 3
 4
 4
 4
 4
 4
 4
 5
(29 rows)

select * from orca.r limit 100;
 a  | b 
----+---
  1 | 0
  2 | 0
  3 | 1
  4 | 1
  5 | 1
  6 | 2
  7 | 2
  8 | 2
  9 | 3
 10 | 3
 11 | 3
 12 | 4
 13 | 4
 14 | 4
 15 | 5
 16 | 5
 17 | 5
 18 | 6
 19 | 6
 20 | 6
(20 rows)

select * from orca.r limit 10 offset 9;
 a  | b 
----+---
 10 | 3
 11 | 3
 12 | 4
 13 | 4
 14 | 4
 15 | 5
 16 | 5
 17 | 5
 18 | 6
 19 | 6
(10 rows)

select * from orca.r offset 10;
 a  | b 
----+---
 11 | 3
 12 | 4
 13 | 4
 14 | 4
 15 | 5
 16 | 5
 17 | 5
 18 | 6
 19 | 6
 20 | 6
(10 rows)

select sqrt(r.a) from orca.r;
       sqrt       
------------------
                1
  1.4142135623731
 1.73205080756888
                2
 2.23606797749979
 2.44948974278318
 2.64575131106459
 2.82842712474619
                3
 3.16227766016838
  3.3166247903554
 3.46410161513775
 3.60555127546399
 3.74165738677394
 3.87298334620742
                4
 4.12310562561766
 4.24264068711928
 4.35889894354067
 4.47213595499958
(20 rows)

select pow(r.b,r.a) from orca.r;
         pow          
----------------------
                    0
                    0
                    1
                    1
                    1
                   64
                  128
                  256
                19683
                59049
               177147
             16777216
             67108864
            268435456
          30517578125
         152587890625
         762939453125
      101559956668416
      609359740010496
 3.65615844006298e+15
(20 rows)

select b from orca.r group by b having  count(*) > 2;
 b 
---
 6
 1
 4
 2
 3
 5
(6 rows)

select b from orca.r group by b having  count(*) <= avg(a) + (select count(*) from orca.s where s.c = r.b);
 b 
---
 6
 1
 4
 2
 3
 5
(6 rows)

select sum(a) from orca.r group by b having count(*) > 2 order by b+1;
 sum 
-----
  12
  21
  30
  39
  48
  57
(6 rows)

select sum(a) from orca.r group by b having count(*) > 2 order by b+1;
 sum 
-----
  12
  21
  30
  39
  48
  57
(6 rows)

-- constants
select 0.001::numeric from orca.r;
 numeric 
---------
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
(20 rows)

select NULL::text, NULL::int from orca.r;
 text | int4 
------+------
      |     
      |     
      |     
      |     
      |     
      |     
      |     
      |     
      |     
      |     
      |     
      |     
      |     
      |     
      |     
      |     
      |     
      |     
      |     
      |     
(20 rows)

select 'helloworld'::text, 'helloworld2'::varchar from orca.r;
    text    |   varchar   
------------+-------------
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
(20 rows)

select 129::bigint, 5623::int, 45::smallint from orca.r;
 int8 | int4 | int2 
------+------+------
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
(20 rows)

select 0.001::numeric from orca.r;
 numeric 
---------
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
   0.001
(20 rows)

select NULL::text, NULL::int from orca.r;
 text | int4 
------+------
      |     
      |     
      |     
      |     
      |     
      |     
      |     
      |     
      |     
      |     
      |     
      |     
      |     
      |     
      |     
      |     
      |     
      |     
      |     
      |     
(20 rows)

select 'helloworld'::text, 'helloworld2'::varchar from orca.r;
    text    |   varchar   
------------+-------------
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
 helloworld | helloworld2
(20 rows)

select 129::bigint, 5623::int, 45::smallint from orca.r;
 int8 | int4 | int2 
------+------+------
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
  129 | 5623 |   45
(20 rows)

--  distributed tables
create table orca.foo (x1 int, x2 int, x3 int);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'x1' 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 orca.bar1 (x1 int, x2 int, x3 int) distributed randomly;
create table orca.bar2 (x1 int, x2 int, x3 int) distributed randomly;
insert into orca.foo select i,i+1,i+2 from generate_series(1,10) i;
insert into orca.bar1 select i,i+1,i+2 from generate_series(1,20) i;
insert into orca.bar2 select i,i+1,i+2 from generate_series(1,30) i;
-- produces result node
select x2 from orca.foo where x1 in (select x2 from orca.bar1);
 x2 
----
  4
  5
  6
  7
  8
  3
  9
 10
 11
(9 rows)

select 1;
 ?column? 
----------
        1
(1 row)

SELECT 1 AS one FROM orca.foo having 1 < 2;
 one 
-----
   1
(1 row)

SELECT generate_series(1,5) AS one FROM orca.foo having 1 < 2;
 one 
-----
   1
   2
   3
   4
   5
(5 rows)

SELECT 1 AS one FROM orca.foo group by x1 having 1 < 2;
 one 
-----
   1
   1
   1
   1
   1
   1
   1
   1
   1
   1
(10 rows)

SELECT x1 AS one FROM orca.foo having 1 < 2;
ERROR:  column "foo.x1" must appear in the GROUP BY clause or be used in an aggregate function
LINE 1: SELECT x1 AS one FROM orca.foo having 1 < 2;
               ^
-- distinct clause
select distinct 1, null;
 ?column? | ?column? 
----------+----------
        1 | 
(1 row)

select distinct 1, null from orca.foo;
 ?column? | ?column? 
----------+----------
        1 | 
(1 row)

select distinct 1, sum(x1) from orca.foo;
 ?column? | sum 
----------+-----
        1 |  55
(1 row)

select distinct x1, rank() over(order by x1) from (select x1 from orca.foo order by x1) x; --order none
 x1 | rank 
----+------
  1 |    1
  2 |    2
  3 |    3
  4 |    4
  5 |    5
  6 |    6
  7 |    7
  8 |    8
  9 |    9
 10 |   10
(10 rows)

select distinct x1, sum(x3) from orca.foo group by x1,x2;
 x1 | sum 
----+-----
  1 |   3
  2 |   4
  3 |   5
  4 |   6
  5 |   7
  6 |   8
  7 |   9
  8 |  10
  9 |  11
 10 |  12
(10 rows)

select distinct s from (select sum(x2) s from orca.foo group by x1) x;
 s  
----
  9
  8
 11
 10
  2
  7
  6
  4
  3
  5
(10 rows)

select * from orca.foo a where a.x1 = (select distinct sum(b.x1)+avg(b.x1) sa from orca.bar1 b group by b.x3 order by sa limit 1);
 x1 | x2 | x3 
----+----+----
  2 |  3 |  4
(1 row)

select distinct a.x1 from orca.foo a where a.x1 <= (select distinct sum(b.x1)+avg(b.x1) sa from orca.bar1 b group by b.x3 order by sa limit 1) order by 1;
 x1 
----
  1
  2
(2 rows)

select * from orca.foo a where a.x1 = (select distinct b.x1 from orca.bar1 b where b.x1=a.x1 limit 1);
 x1 | x2 | x3 
----+----+----
  1 |  2 |  3
  2 |  3 |  4
  8 |  9 | 10
  9 | 10 | 11
 10 | 11 | 12
  3 |  4 |  5
  4 |  5 |  6
  5 |  6 |  7
  6 |  7 |  8
  7 |  8 |  9
(10 rows)

-- with clause
with cte1 as (select * from orca.foo) select a.x1+1 from (select * from cte1) a group by a.x1;
 ?column? 
----------
        2
        3
        8
        7
        5
        4
        6
       10
        9
       11
(10 rows)

select count(*)+1 from orca.bar1 b where b.x1 < any (with cte1 as (select * from orca.foo) select a.x1+1 from (select * from cte1) a group by a.x1);
 ?column? 
----------
       11
(1 row)

select count(*)+1 from orca.bar1 b where b.x1 < any (with cte1 as (select * from orca.foo) select a.x1 from (select * from cte1) a group by a.x1);
 ?column? 
----------
       10
(1 row)

select count(*)+1 from orca.bar1 b where b.x1 < any (with cte1 as (select * from orca.foo) select a.x1 from cte1 a group by a.x1);
 ?column? 
----------
       10
(1 row)

with cte1 as (select * from orca.foo) select count(*)+1 from cte1 a where a.x1 < any (with cte2 as (select * from cte1 b where b.x1 > 10) select c.x1 from (select * from cte2) c group by c.x1);
 ?column? 
----------
        1
(1 row)

with cte1 as (select * from orca.foo) select count(*)+1 from cte1 a where a.x1 < any (with cte2 as (select * from cte1 b where b.x1 > 10) select c.x1+1 from (select * from cte2) c group by c.x1);
 ?column? 
----------
        1
(1 row)

with x as (select * from orca.foo) select count(*) from (select * from x) y where y.x1 <= (select count(*) from x);
 count 
-------
    10
(1 row)

with x as (select * from orca.foo) select count(*)+1 from (select * from x) y where y.x1 <= (select count(*) from x);
 ?column? 
----------
       11
(1 row)

with x as (select * from orca.foo) select count(*) from (select * from x) y where y.x1 < (with z as (select * from x) select count(*) from z);
 count 
-------
     9
(1 row)

-- outer references
select count(*)+1 from orca.foo x where x.x1 > (select count(*)+1 from orca.bar1 y where y.x1 = x.x2);
 ?column? 
----------
        9
(1 row)

select count(*)+1 from orca.foo x where x.x1 > (select count(*) from orca.bar1 y where y.x1 = x.x2);
 ?column? 
----------
       10
(1 row)

select count(*) from orca.foo x where x.x1 > (select count(*)+1 from orca.bar1 y where y.x1 = x.x2);
 count 
-------
     8
(1 row)

-- result node with one time filter and filter
explain select case when bar1.x2 = bar2.x2 then coalesce((select 1 from orca.foo where bar1.x2 = bar2.x2 and bar1.x2 = random() and foo.x2 = bar2.x2),0) else 1 end as col1, bar1.x1
from orca.bar1 inner join orca.bar2 on (bar1.x2 = bar2.x2) order by bar1.x1; 
                                                     QUERY PLAN                                                      
---------------------------------------------------------------------------------------------------------------------
 Gather Motion 3:1  (slice4; segments: 3)  (cost=72.61..72.66 rows=20 width=12)
   Merge Key: bar1.x1
   ->  Sort  (cost=72.61..72.66 rows=7 width=12)
         Sort Key: bar1.x1
         ->  Hash Join  (cost=3.85..72.17 rows=7 width=12)
               Hash Cond: bar2.x2 = bar1.x2
               ->  Redistribute Motion 3:3  (slice2; segments: 3)  (cost=0.00..3.90 rows=10 width=4)
                     Hash Key: bar2.x2
                     ->  Seq Scan on bar2  (cost=0.00..3.30 rows=10 width=4)
               ->  Hash  (cost=3.60..3.60 rows=7 width=8)
                     ->  Redistribute Motion 3:3  (slice3; segments: 3)  (cost=0.00..3.60 rows=7 width=8)
                           Hash Key: bar1.x2
                           ->  Seq Scan on bar1  (cost=0.00..3.20 rows=7 width=8)
               SubPlan 1
                 ->  Result  (cost=0.00..3.20 rows=1 width=0)
                       One-Time Filter: $0 = $1
                       ->  Result  (cost=3.20..3.21 rows=1 width=0)
                             Filter: foo.x2 = $1 AND $0::double precision = random()
                             ->  Materialize  (cost=3.20..3.21 rows=1 width=0)
                                   ->  Broadcast Motion 3:3  (slice1; segments: 3)  (cost=0.00..3.20 rows=1 width=0)
                                         ->  Seq Scan on foo  (cost=0.00..3.20 rows=1 width=0)
 Settings:  optimizer=off; optimizer_segments=3
 Optimizer status: Postgres query optimizer
(23 rows)

select case when bar1.x2 = bar2.x2 then coalesce((select 1 from orca.foo where bar1.x2 = bar2.x2 and bar1.x2 = random() and foo.x2 = bar2.x2),0) else 1 end as col1, bar1.x1
from orca.bar1 inner join orca.bar2 on (bar1.x2 = bar2.x2) order by bar1.x1; 
 col1 | x1 
------+----
    0 |  1
    0 |  2
    0 |  3
    0 |  4
    0 |  5
    0 |  6
    0 |  7
    0 |  8
    0 |  9
    0 | 10
    0 | 11
    0 | 12
    0 | 13
    0 | 14
    0 | 15
    0 | 16
    0 | 17
    0 | 18
    0 | 19
    0 | 20
(20 rows)

drop table orca.r cascade;
create table orca.r(a int, b int) distributed by (a);
create unique index r_a on orca.r(a);
create index r_b on orca.r(b);
insert into orca.r select i, i%3 from generate_series(1,20) i;
drop table orca.s;
create table orca.s(c int, d int) distributed by (c);
insert into orca.s select i%7, i%2 from generate_series(1,30) i;
analyze orca.r;
analyze orca.s;
select * from orca.r, orca.s where r.a=s.c;
 a | b | c | d 
---+---+---+---
 1 | 1 | 1 | 1
 2 | 2 | 2 | 0
 1 | 1 | 1 | 0
 2 | 2 | 2 | 1
 1 | 1 | 1 | 1
 2 | 2 | 2 | 0
 1 | 1 | 1 | 0
 2 | 2 | 2 | 1
 1 | 1 | 1 | 1
 2 | 2 | 2 | 0
 3 | 0 | 3 | 1
 4 | 1 | 4 | 0
 5 | 2 | 5 | 1
 6 | 0 | 6 | 0
 3 | 0 | 3 | 0
 4 | 1 | 4 | 1
 5 | 2 | 5 | 0
 6 | 0 | 6 | 1
 3 | 0 | 3 | 1
 4 | 1 | 4 | 0
 5 | 2 | 5 | 1
 6 | 0 | 6 | 0
 3 | 0 | 3 | 0
 4 | 1 | 4 | 1
 5 | 2 | 5 | 0
 6 | 0 | 6 | 1
(26 rows)

-- Materialize node
select * from orca.r, orca.s where r.a<s.c+1 or r.a>s.c;
 a  | b | c | d 
----+---+---+---
  1 | 1 | 3 | 1
  1 | 1 | 4 | 0
  1 | 1 | 5 | 1
  1 | 1 | 6 | 0
  1 | 1 | 3 | 0
  1 | 1 | 4 | 1
  1 | 1 | 5 | 0
  1 | 1 | 6 | 1
  1 | 1 | 3 | 1
  1 | 1 | 4 | 0
  1 | 1 | 5 | 1
  1 | 1 | 6 | 0
  1 | 1 | 3 | 0
  1 | 1 | 4 | 1
  1 | 1 | 5 | 0
  1 | 1 | 6 | 1
  2 | 2 | 3 | 1
  2 | 2 | 4 | 0
  2 | 2 | 5 | 1
  2 | 2 | 6 | 0
  2 | 2 | 3 | 0
  2 | 2 | 4 | 1
  2 | 2 | 5 | 0
  2 | 2 | 6 | 1
  2 | 2 | 3 | 1
  2 | 2 | 4 | 0
  2 | 2 | 5 | 1
  2 | 2 | 6 | 0
  2 | 2 | 3 | 0
  2 | 2 | 4 | 1
  2 | 2 | 5 | 0
  2 | 2 | 6 | 1
 13 | 1 | 3 | 1
 13 | 1 | 4 | 0
 13 | 1 | 5 | 1
 13 | 1 | 6 | 0
 13 | 1 | 3 | 0
 13 | 1 | 4 | 1
 13 | 1 | 5 | 0
 13 | 1 | 6 | 1
 13 | 1 | 3 | 1
 13 | 1 | 4 | 0
 13 | 1 | 5 | 1
 13 | 1 | 6 | 0
 13 | 1 | 3 | 0
 13 | 1 | 4 | 1
 13 | 1 | 5 | 0
 13 | 1 | 6 | 1
 14 | 2 | 3 | 1
 14 | 2 | 4 | 0
 14 | 2 | 5 | 1
 14 | 2 | 6 | 0
 14 | 2 | 3 | 0
 14 | 2 | 4 | 1
 14 | 2 | 5 | 0
 14 | 2 | 6 | 1
 14 | 2 | 3 | 1
 14 | 2 | 4 | 0
 14 | 2 | 5 | 1
 14 | 2 | 6 | 0
 14 | 2 | 3 | 0
 14 | 2 | 4 | 1
 14 | 2 | 5 | 0
 14 | 2 | 6 | 1
 15 | 0 | 3 | 1
 15 | 0 | 4 | 0
 15 | 0 | 5 | 1
 15 | 0 | 6 | 0
 15 | 0 | 3 | 0
 15 | 0 | 4 | 1
 15 | 0 | 5 | 0
 15 | 0 | 6 | 1
 15 | 0 | 3 | 1
 15 | 0 | 4 | 0
 15 | 0 | 5 | 1
 15 | 0 | 6 | 0
 15 | 0 | 3 | 0
 15 | 0 | 4 | 1
 15 | 0 | 5 | 0
 15 | 0 | 6 | 1
 16 | 1 | 3 | 1
 16 | 1 | 4 | 0
 16 | 1 | 5 | 1
 16 | 1 | 6 | 0
 16 | 1 | 3 | 0
 16 | 1 | 4 | 1
 16 | 1 | 5 | 0
 16 | 1 | 6 | 1
 16 | 1 | 3 | 1
 16 | 1 | 4 | 0
 16 | 1 | 5 | 1
 16 | 1 | 6 | 0
 16 | 1 | 3 | 0
 16 | 1 | 4 | 1
 16 | 1 | 5 | 0
 16 | 1 | 6 | 1
 17 | 2 | 3 | 1
 17 | 2 | 4 | 0
 17 | 2 | 5 | 1
 17 | 2 | 6 | 0
 17 | 2 | 3 | 0
 17 | 2 | 4 | 1
 17 | 2 | 5 | 0
 17 | 2 | 6 | 1
 17 | 2 | 3 | 1
 17 | 2 | 4 | 0
 17 | 2 | 5 | 1
 17 | 2 | 6 | 0
 17 | 2 | 3 | 0
 17 | 2 | 4 | 1
 17 | 2 | 5 | 0
 17 | 2 | 6 | 1
  3 | 0 | 3 | 1
  3 | 0 | 4 | 0
  3 | 0 | 5 | 1
  3 | 0 | 6 | 0
  3 | 0 | 3 | 0
  3 | 0 | 4 | 1
  3 | 0 | 5 | 0
  3 | 0 | 6 | 1
  3 | 0 | 3 | 1
  3 | 0 | 4 | 0
  3 | 0 | 5 | 1
  3 | 0 | 6 | 0
  3 | 0 | 3 | 0
  3 | 0 | 4 | 1
  3 | 0 | 5 | 0
  3 | 0 | 6 | 1
  4 | 1 | 3 | 1
  4 | 1 | 4 | 0
  4 | 1 | 5 | 1
  4 | 1 | 6 | 0
  4 | 1 | 3 | 0
  4 | 1 | 4 | 1
  4 | 1 | 5 | 0
  4 | 1 | 6 | 1
  4 | 1 | 3 | 1
  4 | 1 | 4 | 0
  4 | 1 | 5 | 1
  4 | 1 | 6 | 0
  4 | 1 | 3 | 0
  4 | 1 | 4 | 1
  4 | 1 | 5 | 0
  4 | 1 | 6 | 1
  5 | 2 | 3 | 1
  5 | 2 | 4 | 0
  5 | 2 | 5 | 1
  5 | 2 | 6 | 0
  5 | 2 | 3 | 0
  5 | 2 | 4 | 1
  5 | 2 | 5 | 0
  5 | 2 | 6 | 1
  5 | 2 | 3 | 1
  5 | 2 | 4 | 0
  5 | 2 | 5 | 1
  5 | 2 | 6 | 0
  5 | 2 | 3 | 0
  5 | 2 | 4 | 1
  5 | 2 | 5 | 0
  5 | 2 | 6 | 1
  6 | 0 | 3 | 1
  6 | 0 | 4 | 0
  6 | 0 | 5 | 1
  6 | 0 | 6 | 0
  6 | 0 | 3 | 0
  6 | 0 | 4 | 1
  6 | 0 | 5 | 0
  6 | 0 | 6 | 1
  6 | 0 | 3 | 1
  6 | 0 | 4 | 0
  6 | 0 | 5 | 1
  6 | 0 | 6 | 0
  6 | 0 | 3 | 0
  6 | 0 | 4 | 1
  6 | 0 | 5 | 0
  6 | 0 | 6 | 1
  7 | 1 | 3 | 1
  7 | 1 | 4 | 0
  7 | 1 | 5 | 1
  7 | 1 | 6 | 0
  7 | 1 | 3 | 0
  7 | 1 | 4 | 1
  7 | 1 | 5 | 0
  7 | 1 | 6 | 1
  7 | 1 | 3 | 1
  7 | 1 | 4 | 0
  7 | 1 | 5 | 1
  7 | 1 | 6 | 0
  7 | 1 | 3 | 0
  7 | 1 | 4 | 1
  7 | 1 | 5 | 0
  7 | 1 | 6 | 1
 18 | 0 | 3 | 1
 18 | 0 | 4 | 0
 18 | 0 | 5 | 1
 18 | 0 | 6 | 0
 18 | 0 | 3 | 0
 18 | 0 | 4 | 1
 18 | 0 | 5 | 0
 18 | 0 | 6 | 1
 18 | 0 | 3 | 1
 18 | 0 | 4 | 0
 18 | 0 | 5 | 1
 18 | 0 | 6 | 0
 18 | 0 | 3 | 0
 18 | 0 | 4 | 1
 18 | 0 | 5 | 0
 18 | 0 | 6 | 1
 19 | 1 | 3 | 1
 19 | 1 | 4 | 0
 19 | 1 | 5 | 1
 19 | 1 | 6 | 0
 19 | 1 | 3 | 0
 19 | 1 | 4 | 1
 19 | 1 | 5 | 0
 19 | 1 | 6 | 1
 19 | 1 | 3 | 1
 19 | 1 | 4 | 0
 19 | 1 | 5 | 1
 19 | 1 | 6 | 0
 19 | 1 | 3 | 0
 19 | 1 | 4 | 1
 19 | 1 | 5 | 0
 19 | 1 | 6 | 1
 20 | 2 | 3 | 1
 20 | 2 | 4 | 0
 20 | 2 | 5 | 1
 20 | 2 | 6 | 0
 20 | 2 | 3 | 0
 20 | 2 | 4 | 1
 20 | 2 | 5 | 0
 20 | 2 | 6 | 1
 20 | 2 | 3 | 1
 20 | 2 | 4 | 0
 20 | 2 | 5 | 1
 20 | 2 | 6 | 0
 20 | 2 | 3 | 0
 20 | 2 | 4 | 1
 20 | 2 | 5 | 0
 20 | 2 | 6 | 1
  8 | 2 | 3 | 1
  8 | 2 | 4 | 0
  8 | 2 | 5 | 1
  8 | 2 | 6 | 0
  8 | 2 | 3 | 0
  8 | 2 | 4 | 1
  8 | 2 | 5 | 0
  8 | 2 | 6 | 1
  8 | 2 | 3 | 1
  8 | 2 | 4 | 0
  8 | 2 | 5 | 1
  8 | 2 | 6 | 0
  8 | 2 | 3 | 0
  8 | 2 | 4 | 1
  8 | 2 | 5 | 0
  8 | 2 | 6 | 1
  9 | 0 | 3 | 1
  9 | 0 | 4 | 0
  9 | 0 | 5 | 1
  9 | 0 | 6 | 0
  9 | 0 | 3 | 0
  9 | 0 | 4 | 1
  9 | 0 | 5 | 0
  9 | 0 | 6 | 1
  9 | 0 | 3 | 1
  9 | 0 | 4 | 0
  9 | 0 | 5 | 1
  9 | 0 | 6 | 0
  9 | 0 | 3 | 0
  9 | 0 | 4 | 1
  9 | 0 | 5 | 0
  9 | 0 | 6 | 1
 10 | 1 | 3 | 1
 10 | 1 | 4 | 0
 10 | 1 | 5 | 1
 10 | 1 | 6 | 0
 10 | 1 | 3 | 0
 10 | 1 | 4 | 1
 10 | 1 | 5 | 0
 10 | 1 | 6 | 1
 10 | 1 | 3 | 1
 10 | 1 | 4 | 0
 10 | 1 | 5 | 1
 10 | 1 | 6 | 0
 10 | 1 | 3 | 0
 10 | 1 | 4 | 1
 10 | 1 | 5 | 0
 10 | 1 | 6 | 1
 11 | 2 | 3 | 1
 11 | 2 | 4 | 0
  1 | 1 | 1 | 1
  1 | 1 | 2 | 0
  1 | 1 | 0 | 1
  1 | 1 | 1 | 0
  1 | 1 | 2 | 1
  1 | 1 | 0 | 0
  1 | 1 | 1 | 1
  1 | 1 | 2 | 0
  1 | 1 | 0 | 1
  1 | 1 | 1 | 0
  1 | 1 | 2 | 1
  1 | 1 | 0 | 0
  1 | 1 | 1 | 1
  1 | 1 | 2 | 0
  2 | 2 | 1 | 1
  2 | 2 | 2 | 0
  2 | 2 | 0 | 1
  2 | 2 | 1 | 0
  2 | 2 | 2 | 1
  2 | 2 | 0 | 0
  2 | 2 | 1 | 1
  2 | 2 | 2 | 0
  2 | 2 | 0 | 1
  2 | 2 | 1 | 0
  2 | 2 | 2 | 1
  2 | 2 | 0 | 0
  2 | 2 | 1 | 1
  2 | 2 | 2 | 0
 13 | 1 | 1 | 1
 13 | 1 | 2 | 0
 13 | 1 | 0 | 1
 13 | 1 | 1 | 0
 13 | 1 | 2 | 1
 13 | 1 | 0 | 0
 13 | 1 | 1 | 1
 13 | 1 | 2 | 0
 13 | 1 | 0 | 1
 13 | 1 | 1 | 0
 13 | 1 | 2 | 1
 13 | 1 | 0 | 0
 13 | 1 | 1 | 1
 13 | 1 | 2 | 0
 14 | 2 | 1 | 1
 14 | 2 | 2 | 0
 14 | 2 | 0 | 1
 14 | 2 | 1 | 0
 14 | 2 | 2 | 1
 14 | 2 | 0 | 0
 14 | 2 | 1 | 1
 14 | 2 | 2 | 0
 14 | 2 | 0 | 1
 14 | 2 | 1 | 0
 14 | 2 | 2 | 1
 14 | 2 | 0 | 0
 14 | 2 | 1 | 1
 14 | 2 | 2 | 0
 15 | 0 | 1 | 1
 15 | 0 | 2 | 0
 15 | 0 | 0 | 1
 15 | 0 | 1 | 0
 15 | 0 | 2 | 1
 15 | 0 | 0 | 0
 15 | 0 | 1 | 1
 15 | 0 | 2 | 0
 15 | 0 | 0 | 1
 15 | 0 | 1 | 0
 15 | 0 | 2 | 1
 15 | 0 | 0 | 0
 15 | 0 | 1 | 1
 15 | 0 | 2 | 0
 16 | 1 | 1 | 1
 16 | 1 | 2 | 0
 16 | 1 | 0 | 1
 16 | 1 | 1 | 0
 16 | 1 | 2 | 1
 16 | 1 | 0 | 0
 16 | 1 | 1 | 1
 16 | 1 | 2 | 0
 16 | 1 | 0 | 1
 16 | 1 | 1 | 0
 16 | 1 | 2 | 1
 16 | 1 | 0 | 0
 16 | 1 | 1 | 1
 16 | 1 | 2 | 0
 17 | 2 | 1 | 1
 17 | 2 | 2 | 0
 17 | 2 | 0 | 1
 17 | 2 | 1 | 0
 17 | 2 | 2 | 1
 17 | 2 | 0 | 0
 17 | 2 | 1 | 1
 17 | 2 | 2 | 0
 17 | 2 | 0 | 1
 17 | 2 | 1 | 0
 17 | 2 | 2 | 1
 17 | 2 | 0 | 0
 17 | 2 | 1 | 1
 17 | 2 | 2 | 0
  3 | 0 | 1 | 1
  3 | 0 | 2 | 0
  3 | 0 | 0 | 1
  3 | 0 | 1 | 0
  3 | 0 | 2 | 1
  3 | 0 | 0 | 0
  3 | 0 | 1 | 1
  3 | 0 | 2 | 0
  3 | 0 | 0 | 1
  3 | 0 | 1 | 0
  3 | 0 | 2 | 1
  3 | 0 | 0 | 0
  3 | 0 | 1 | 1
  3 | 0 | 2 | 0
  4 | 1 | 1 | 1
  4 | 1 | 2 | 0
  4 | 1 | 0 | 1
  4 | 1 | 1 | 0
  4 | 1 | 2 | 1
  4 | 1 | 0 | 0
  4 | 1 | 1 | 1
  4 | 1 | 2 | 0
  4 | 1 | 0 | 1
  4 | 1 | 1 | 0
  4 | 1 | 2 | 1
  4 | 1 | 0 | 0
  4 | 1 | 1 | 1
  4 | 1 | 2 | 0
  5 | 2 | 1 | 1
  5 | 2 | 2 | 0
  5 | 2 | 0 | 1
  5 | 2 | 1 | 0
  5 | 2 | 2 | 1
  5 | 2 | 0 | 0
  5 | 2 | 1 | 1
  5 | 2 | 2 | 0
  5 | 2 | 0 | 1
  5 | 2 | 1 | 0
  5 | 2 | 2 | 1
  5 | 2 | 0 | 0
  5 | 2 | 1 | 1
  5 | 2 | 2 | 0
  6 | 0 | 1 | 1
  6 | 0 | 2 | 0
  6 | 0 | 0 | 1
  6 | 0 | 1 | 0
  6 | 0 | 2 | 1
  6 | 0 | 0 | 0
  6 | 0 | 1 | 1
  6 | 0 | 2 | 0
  6 | 0 | 0 | 1
  6 | 0 | 1 | 0
  6 | 0 | 2 | 1
  6 | 0 | 0 | 0
  6 | 0 | 1 | 1
  6 | 0 | 2 | 0
  7 | 1 | 1 | 1
  7 | 1 | 2 | 0
  7 | 1 | 0 | 1
  7 | 1 | 1 | 0
  7 | 1 | 2 | 1
  7 | 1 | 0 | 0
  7 | 1 | 1 | 1
  7 | 1 | 2 | 0
  7 | 1 | 0 | 1
  7 | 1 | 1 | 0
  7 | 1 | 2 | 1
  7 | 1 | 0 | 0
  7 | 1 | 1 | 1
  7 | 1 | 2 | 0
 18 | 0 | 1 | 1
 18 | 0 | 2 | 0
 18 | 0 | 0 | 1
 18 | 0 | 1 | 0
 18 | 0 | 2 | 1
 18 | 0 | 0 | 0
 18 | 0 | 1 | 1
 18 | 0 | 2 | 0
 18 | 0 | 0 | 1
 18 | 0 | 1 | 0
 18 | 0 | 2 | 1
 18 | 0 | 0 | 0
 18 | 0 | 1 | 1
 18 | 0 | 2 | 0
 19 | 1 | 1 | 1
 19 | 1 | 2 | 0
 19 | 1 | 0 | 1
 19 | 1 | 1 | 0
 19 | 1 | 2 | 1
 19 | 1 | 0 | 0
 19 | 1 | 1 | 1
 19 | 1 | 2 | 0
 19 | 1 | 0 | 1
 19 | 1 | 1 | 0
 19 | 1 | 2 | 1
 19 | 1 | 0 | 0
 19 | 1 | 1 | 1
 19 | 1 | 2 | 0
 20 | 2 | 1 | 1
 20 | 2 | 2 | 0
 20 | 2 | 0 | 1
 20 | 2 | 1 | 0
 20 | 2 | 2 | 1
 20 | 2 | 0 | 0
 20 | 2 | 1 | 1
 20 | 2 | 2 | 0
 20 | 2 | 0 | 1
 20 | 2 | 1 | 0
 20 | 2 | 2 | 1
 20 | 2 | 0 | 0
 20 | 2 | 1 | 1
 20 | 2 | 2 | 0
  8 | 2 | 1 | 1
  8 | 2 | 2 | 0
  8 | 2 | 0 | 1
  8 | 2 | 1 | 0
  8 | 2 | 2 | 1
  8 | 2 | 0 | 0
  8 | 2 | 1 | 1
  8 | 2 | 2 | 0
  8 | 2 | 0 | 1
  8 | 2 | 1 | 0
  8 | 2 | 2 | 1
  8 | 2 | 0 | 0
  8 | 2 | 1 | 1
  8 | 2 | 2 | 0
  9 | 0 | 1 | 1
  9 | 0 | 2 | 0
  9 | 0 | 0 | 1
  9 | 0 | 1 | 0
  9 | 0 | 2 | 1
  9 | 0 | 0 | 0
  9 | 0 | 1 | 1
  9 | 0 | 2 | 0
  9 | 0 | 0 | 1
  9 | 0 | 1 | 0
  9 | 0 | 2 | 1
  9 | 0 | 0 | 0
  9 | 0 | 1 | 1
  9 | 0 | 2 | 0
 10 | 1 | 1 | 1
 10 | 1 | 2 | 0
 10 | 1 | 0 | 1
 10 | 1 | 1 | 0
 10 | 1 | 2 | 1
 10 | 1 | 0 | 0
 10 | 1 | 1 | 1
 10 | 1 | 2 | 0
 10 | 1 | 0 | 1
 10 | 1 | 1 | 0
 10 | 1 | 2 | 1
 10 | 1 | 0 | 0
 10 | 1 | 1 | 1
 10 | 1 | 2 | 0
 11 | 2 | 1 | 1
 11 | 2 | 2 | 0
 11 | 2 | 0 | 1
 11 | 2 | 1 | 0
 11 | 2 | 2 | 1
 11 | 2 | 0 | 0
 11 | 2 | 1 | 1
 11 | 2 | 2 | 0
 11 | 2 | 0 | 1
 11 | 2 | 1 | 0
 11 | 2 | 2 | 1
 11 | 2 | 0 | 0
 11 | 2 | 1 | 1
 11 | 2 | 2 | 0
 12 | 0 | 1 | 1
 12 | 0 | 2 | 0
 12 | 0 | 0 | 1
 12 | 0 | 1 | 0
 12 | 0 | 2 | 1
 12 | 0 | 0 | 0
 12 | 0 | 1 | 1
 12 | 0 | 2 | 0
 12 | 0 | 0 | 1
 12 | 0 | 1 | 0
 12 | 0 | 2 | 1
 12 | 0 | 0 | 0
 12 | 0 | 1 | 1
 12 | 0 | 2 | 0
 11 | 2 | 5 | 1
 11 | 2 | 6 | 0
 11 | 2 | 3 | 0
 11 | 2 | 4 | 1
 11 | 2 | 5 | 0
 11 | 2 | 6 | 1
 11 | 2 | 3 | 1
 11 | 2 | 4 | 0
 11 | 2 | 5 | 1
 11 | 2 | 6 | 0
 11 | 2 | 3 | 0
 11 | 2 | 4 | 1
 11 | 2 | 5 | 0
 11 | 2 | 6 | 1
 12 | 0 | 3 | 1
 12 | 0 | 4 | 0
 12 | 0 | 5 | 1
 12 | 0 | 6 | 0
 12 | 0 | 3 | 0
 12 | 0 | 4 | 1
 12 | 0 | 5 | 0
 12 | 0 | 6 | 1
 12 | 0 | 3 | 1
 12 | 0 | 4 | 0
 12 | 0 | 5 | 1
 12 | 0 | 6 | 0
 12 | 0 | 3 | 0
 12 | 0 | 4 | 1
 12 | 0 | 5 | 0
 12 | 0 | 6 | 1
(600 rows)

-- empty target list
select r.* from orca.r, orca.s where s.c=2;
 a  | b 
----+---
  8 | 2
  8 | 2
  8 | 2
  8 | 2
  8 | 2
  9 | 0
  9 | 0
  9 | 0
  9 | 0
  9 | 0
 10 | 1
 10 | 1
 10 | 1
 10 | 1
 10 | 1
 11 | 2
 11 | 2
 11 | 2
 11 | 2
 11 | 2
 12 | 0
 12 | 0
 12 | 0
 12 | 0
 12 | 0
  1 | 1
  1 | 1
  1 | 1
  1 | 1
  1 | 1
  2 | 2
  2 | 2
  2 | 2
  2 | 2
  2 | 2
 13 | 1
 13 | 1
 13 | 1
 13 | 1
 13 | 1
 14 | 2
 14 | 2
 14 | 2
 14 | 2
 14 | 2
 15 | 0
 15 | 0
 15 | 0
 15 | 0
 15 | 0
 16 | 1
 16 | 1
 16 | 1
 16 | 1
 16 | 1
 17 | 2
 17 | 2
 17 | 2
 17 | 2
 17 | 2
  3 | 0
  3 | 0
  3 | 0
  3 | 0
  3 | 0
  4 | 1
  4 | 1
  4 | 1
  4 | 1
  4 | 1
  5 | 2
  5 | 2
  5 | 2
  5 | 2
  5 | 2
  6 | 0
  6 | 0
  6 | 0
  6 | 0
  6 | 0
  7 | 1
  7 | 1
  7 | 1
  7 | 1
  7 | 1
 18 | 0
 18 | 0
 18 | 0
 18 | 0
 18 | 0
 19 | 1
 19 | 1
 19 | 1
 19 | 1
 19 | 1
 20 | 2
 20 | 2
 20 | 2
 20 | 2
 20 | 2
(100 rows)

create table orca.m();
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause, and no column type is suitable for a distribution key. Creating a NULL policy entry.
alter table orca.m add column a int;
alter table orca.m add column b int;
create table orca.m1();
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause, and no column type is suitable for a distribution key. Creating a NULL policy entry.
alter table orca.m1 add column a int;
alter table orca.m1 add column b int;
insert into orca.m select i-1, i%2 from generate_series(1,35) i;
insert into orca.m1 select i-2, i%3 from generate_series(1,25) i;
insert into orca.r values (null, 1);
-- join types
select r.a, s.c from orca.r left outer join orca.s on(r.a=s.c);
 a  | c 
----+---
  1 | 1
  1 | 1
  1 | 1
  1 | 1
  1 | 1
  2 | 2
  2 | 2
  2 | 2
  2 | 2
  2 | 2
 13 |  
 14 |  
 15 |  
 16 |  
 17 |  
  8 |  
  9 |  
 10 |  
 11 |  
 12 |  
  3 | 3
  3 | 3
  3 | 3
  3 | 3
  4 | 4
  4 | 4
  4 | 4
  4 | 4
  5 | 5
  5 | 5
  5 | 5
  5 | 5
  6 | 6
  6 | 6
  6 | 6
  6 | 6
  7 |  
 18 |  
 19 |  
 20 |  
    |  
(41 rows)

select r.a, s.c from orca.r left outer join orca.s on(r.a=s.c and r.a=r.b and s.c=s.d) order by r.a,s.c;
 a  | c 
----+---
  1 | 1
  1 | 1
  1 | 1
  2 |  
  3 |  
  4 |  
  5 |  
  6 |  
  7 |  
  8 |  
  9 |  
 10 |  
 11 |  
 12 |  
 13 |  
 14 |  
 15 |  
 16 |  
 17 |  
 18 |  
 19 |  
 20 |  
    |  
(23 rows)

select r.a, s.c from orca.r left outer join orca.s on(r.a=s.c) where s.d > 2 or s.d is null order by r.a;
 a  | c 
----+---
  7 |  
  8 |  
  9 |  
 10 |  
 11 |  
 12 |  
 13 |  
 14 |  
 15 |  
 16 |  
 17 |  
 18 |  
 19 |  
 20 |  
    |  
(15 rows)

select r.a, s.c from orca.r right outer join orca.s on(r.a=s.c);
 a | c 
---+---
 1 | 1
 2 | 2
   | 0
 1 | 1
 2 | 2
   | 0
 1 | 1
 2 | 2
   | 0
 1 | 1
 2 | 2
   | 0
 1 | 1
 2 | 2
 3 | 3
 4 | 4
 5 | 5
 6 | 6
 3 | 3
 4 | 4
 5 | 5
 6 | 6
 3 | 3
 4 | 4
 5 | 5
 6 | 6
 3 | 3
 4 | 4
 5 | 5
 6 | 6
(30 rows)

select * from orca.r where exists (select * from orca.s where s.c=r.a + 2);
 a | b 
---+---
 1 | 1
 2 | 2
 3 | 0
 4 | 1
(4 rows)

select * from orca.r where exists (select * from orca.s where s.c=r.b);
 a  | b 
----+---
  1 | 1
  2 | 2
 13 | 1
 14 | 2
 15 | 0
 16 | 1
 17 | 2
  3 | 0
  4 | 1
  5 | 2
  6 | 0
  7 | 1
 18 | 0
 19 | 1
 20 | 2
    | 1
  8 | 2
  9 | 0
 10 | 1
 11 | 2
 12 | 0
(21 rows)

select * from orca.m where m.a not in (select a from orca.m1 where a=5);
 a  | b 
----+---
  0 | 1
  1 | 0
  3 | 0
  4 | 1
  8 | 1
  9 | 0
 15 | 0
 23 | 0
 30 | 1
  2 | 1
  6 | 1
  7 | 0
 10 | 1
 11 | 0
 13 | 0
 16 | 1
 17 | 0
 18 | 1
 20 | 1
 21 | 0
 22 | 1
 24 | 1
 26 | 1
 27 | 0
 31 | 0
 32 | 1
 12 | 1
 14 | 1
 19 | 0
 25 | 0
 28 | 1
 29 | 0
 33 | 0
 34 | 1
(34 rows)

select * from orca.m where m.a not in (select a from orca.m1);
 a  | b 
----+---
 30 | 1
 25 | 0
 28 | 1
 29 | 0
 33 | 0
 34 | 1
 24 | 1
 26 | 1
 27 | 0
 31 | 0
 32 | 1
(11 rows)

select * from orca.m where m.a in (select a from orca.m1 where m1.a-1 = m.b);
 a | b 
---+---
 2 | 1
 1 | 0
(2 rows)

-- enable_hashjoin=off; enable_mergejoin=on
select 1 from orca.m, orca.m1 where m.a = m1.a and m.b!=m1.b;
 ?column? 
----------
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
(16 rows)

-- plan.qual vs hashclauses/join quals:
select * from orca.r left outer join orca.s on (r.a=s.c and r.b<s.d) where s.d is null;
 a  | b | c | d 
----+---+---+---
  1 | 1 |   |  
  2 | 2 |   |  
 13 | 1 |   |  
 14 | 2 |   |  
 15 | 0 |   |  
 16 | 1 |   |  
 17 | 2 |   |  
  8 | 2 |   |  
  9 | 0 |   |  
 10 | 1 |   |  
 11 | 2 |   |  
 12 | 0 |   |  
  4 | 1 |   |  
  5 | 2 |   |  
  7 | 1 |   |  
 18 | 0 |   |  
 19 | 1 |   |  
 20 | 2 |   |  
    | 1 |   |  
(19 rows)

-- select * from orca.r m full outer join orca.r m1 on (m.a=m1.a) where m.a is null;
-- explain Hash Join with 'IS NOT DISTINCT FROM' join condition
-- force_explain
explain  select * from orca.r, orca.s where r.a is not distinct from s.c;
                                            QUERY PLAN                                            
--------------------------------------------------------------------------------------------------
 Gather Motion 3:1  (slice2; segments: 3)  (cost=10000000000.00..10000000030.55 rows=31 width=16)
   ->  Nested Loop  (cost=10000000000.00..10000000030.55 rows=11 width=16)
         Join Filter: NOT r.a IS DISTINCT FROM s.c
         ->  Seq Scan on s  (cost=0.00..2.30 rows=10 width=8)
         ->  Materialize  (cost=0.00..4.30 rows=20 width=8)
               ->  Broadcast Motion 3:3  (slice1; segments: 3)  (cost=0.00..4.00 rows=20 width=8)
                     ->  Seq Scan on r  (cost=0.00..3.20 rows=7 width=8)
 Optimizer: Postgres query optimizer
(8 rows)

-- explain Hash Join with equality join condition
-- force_explain
explain select * from orca.r, orca.s where r.a = s.c;
                                  QUERY PLAN
------------------------------------------------------------------------------
 Gather Motion 3:1  (slice1; segments: 3)  (cost=3.45..6.20 rows=30 width=16)
   ->  Hash Join  (cost=3.45..6.20 rows=10 width=16)
         Hash Cond: s.c = r.a
         ->  Seq Scan on s  (cost=0.00..2.30 rows=10 width=8)
         ->  Hash  (cost=3.20..3.20 rows=7 width=8)
               ->  Seq Scan on r  (cost=0.00..3.20 rows=7 width=8)
 Settings:  optimizer=off; optimizer_segments=3
 Optimizer status: Postgres query optimizer
(8 rows)

-- sort
select * from orca.r join orca.s on(r.a=s.c) order by r.a, s.d;
 a | b | c | d 
---+---+---+---
 1 | 1 | 1 | 0
 1 | 1 | 1 | 0
 1 | 1 | 1 | 1
 1 | 1 | 1 | 1
 1 | 1 | 1 | 1
 2 | 2 | 2 | 0
 2 | 2 | 2 | 0
 2 | 2 | 2 | 0
 2 | 2 | 2 | 1
 2 | 2 | 2 | 1
 3 | 0 | 3 | 0
 3 | 0 | 3 | 0
 3 | 0 | 3 | 1
 3 | 0 | 3 | 1
 4 | 1 | 4 | 0
 4 | 1 | 4 | 0
 4 | 1 | 4 | 1
 4 | 1 | 4 | 1
 5 | 2 | 5 | 0
 5 | 2 | 5 | 0
 5 | 2 | 5 | 1
 5 | 2 | 5 | 1
 6 | 0 | 6 | 0
 6 | 0 | 6 | 0
 6 | 0 | 6 | 1
 6 | 0 | 6 | 1
(26 rows)

select * from orca.r join orca.s on(r.a=s.c) order by r.a, s.d limit 10;
 a | b | c | d 
---+---+---+---
 1 | 1 | 1 | 0
 1 | 1 | 1 | 0
 1 | 1 | 1 | 1
 1 | 1 | 1 | 1
 1 | 1 | 1 | 1
 2 | 2 | 2 | 0
 2 | 2 | 2 | 0
 2 | 2 | 2 | 0
 2 | 2 | 2 | 1
 2 | 2 | 2 | 1
(10 rows)

select * from orca.r join orca.s on(r.a=s.c) order by r.a + 5, s.d limit 10;
 a | b | c | d 
---+---+---+---
 1 | 1 | 1 | 0
 1 | 1 | 1 | 0
 1 | 1 | 1 | 1
 1 | 1 | 1 | 1
 1 | 1 | 1 | 1
 2 | 2 | 2 | 0
 2 | 2 | 2 | 0
 2 | 2 | 2 | 0
 2 | 2 | 2 | 1
 2 | 2 | 2 | 1
(10 rows)

-- group by
select 1 from orca.m group by a+b;
 ?column? 
----------
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
(18 rows)

-- join with const table
select * from orca.r where a = (select 1);
 a | b 
---+---
 1 | 1
(1 row)

-- union with const table
select * from ((select a as x from orca.r) union (select 1 as x )) as foo order by x;
 x  
----
  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
   
(21 rows)

insert into orca.m values (1,-1), (1,2), (1,1);
-- computed columns
select a,a,a+b from orca.m;
 a  | a  | ?column? 
----+----+----------
 12 | 12 |       13
 14 | 14 |       15
 19 | 19 |       19
 25 | 25 |       25
 28 | 28 |       29
 29 | 29 |       29
 33 | 33 |       33
 34 | 34 |       35
  1 |  1 |        0
  0 |  0 |        1
  1 |  1 |        1
  3 |  3 |        3
  4 |  4 |        5
  8 |  8 |        9
  9 |  9 |        9
 15 | 15 |       15
 23 | 23 |       23
 30 | 30 |       31
  1 |  1 |        2
  2 |  2 |        3
  5 |  5 |        5
  6 |  6 |        7
  7 |  7 |        7
 10 | 10 |       11
 11 | 11 |       11
 13 | 13 |       13
 16 | 16 |       17
 17 | 17 |       17
 18 | 18 |       19
 20 | 20 |       21
 21 | 21 |       21
 22 | 22 |       23
 24 | 24 |       25
 26 | 26 |       27
 27 | 27 |       27
 31 | 31 |       31
 32 | 32 |       33
  1 |  1 |        3
(38 rows)

select a,a+b,a+b from orca.m;
 a  | ?column? | ?column? 
----+----------+----------
 12 |       13 |       13
 14 |       15 |       15
 19 |       19 |       19
 25 |       25 |       25
 28 |       29 |       29
 29 |       29 |       29
 33 |       33 |       33
 34 |       35 |       35
  1 |        0 |        0
  0 |        1 |        1
  1 |        1 |        1
  3 |        3 |        3
  4 |        5 |        5
  8 |        9 |        9
  9 |        9 |        9
 15 |       15 |       15
 23 |       23 |       23
 30 |       31 |       31
  1 |        2 |        2
  2 |        3 |        3
  5 |        5 |        5
  6 |        7 |        7
  7 |        7 |        7
 10 |       11 |       11
 11 |       11 |       11
 13 |       13 |       13
 16 |       17 |       17
 17 |       17 |       17
 18 |       19 |       19
 20 |       21 |       21
 21 |       21 |       21
 22 |       23 |       23
 24 |       25 |       25
 26 |       27 |       27
 27 |       27 |       27
 31 |       31 |       31
 32 |       33 |       33
  1 |        3 |        3
(38 rows)

-- func expr
select * from orca.m where a=abs(b);
 a | b  
---+----
 1 |  1
 1 | -1
(2 rows)

-- grouping sets
select a,b,count(*) from orca.m group by grouping sets ((a), (a,b));
 a  | b  | count 
----+----+-------
 13 |    |     1
 12 |    |     1
 24 |  1 |     1
 16 |  1 |     1
  6 |  1 |     1
 14 |    |     1
 20 |  1 |     1
  2 |  1 |     1
 16 |    |     1
 31 |  0 |     1
 27 |  0 |     1
 13 |  0 |     1
  9 |  0 |     1
 17 |    |     1
 32 |    |     1
 17 |  0 |     1
  3 |    |     1
 34 |  1 |     1
 19 |    |     1
 11 |    |     1
  9 |    |     1
 31 |    |     1
 28 |  1 |     1
 10 |  1 |     1
 18 |  1 |     1
 21 |  0 |     1
  8 |    |     1
 32 |  1 |     1
 25 |  0 |     1
 29 |  0 |     1
  0 |    |     1
 18 |    |     1
  2 |    |     1
 14 |  1 |     1
  7 |  0 |     1
 30 |    |     1
 27 |    |     1
  5 |    |     1
  3 |  0 |     1
 11 |  0 |     1
  0 |  1 |     1
 26 |    |     1
  4 |    |     1
 34 |    |     1
 22 |  1 |     1
  1 | -1 |     1
 28 |    |     1
 19 |  0 |     1
  1 |  2 |     1
 10 |    |     1
 23 |    |     1
  1 |  1 |     1
 15 |    |     1
  8 |  1 |     1
 30 |  1 |     1
 25 |    |     1
 21 |    |     1
 33 |    |     1
 12 |  1 |     1
 20 |    |     1
 24 |    |     1
  1 |    |     4
 15 |  0 |     1
  6 |    |     1
 29 |    |     1
  7 |    |     1
  1 |  0 |     1
  5 |  0 |     1
 26 |  1 |     1
  4 |  1 |     1
 33 |  0 |     1
 22 |    |     1
 23 |  0 |     1
(73 rows)

select b,count(*) from orca.m group by grouping sets ((a), (a,b));
 b  | count 
----+-------
    |     1
  1 |     1
 -1 |     1
    |     1
  0 |     1
  2 |     1
    |     1
    |     1
  1 |     1
    |     1
  1 |     1
  1 |     1
    |     1
    |     1
    |     1
  1 |     1
    |     1
    |     1
    |     4
  0 |     1
    |     1
    |     1
    |     1
  0 |     1
  0 |     1
  1 |     1
  1 |     1
  0 |     1
    |     1
  0 |     1
    |     1
    |     1
  1 |     1
  1 |     1
  1 |     1
    |     1
  1 |     1
  1 |     1
    |     1
  0 |     1
  0 |     1
  0 |     1
  0 |     1
    |     1
  1 |     1
    |     1
  0 |     1
    |     1
    |     1
    |     1
    |     1
    |     1
  1 |     1
  1 |     1
  1 |     1
  0 |     1
    |     1
  0 |     1
  1 |     1
  0 |     1
    |     1
  1 |     1
    |     1
    |     1
  0 |     1
    |     1
    |     1
    |     1
  0 |     1
  0 |     1
  1 |     1
    |     1
    |     1
(73 rows)

select a,count(*) from orca.m group by grouping sets ((a), (a,b));
 a  | count 
----+-------
 11 |     1
  9 |     1
 31 |     1
 28 |     1
 10 |     1
 18 |     1
 21 |     1
  8 |     1
 32 |     1
 25 |     1
 29 |     1
  0 |     1
 18 |     1
  2 |     1
 14 |     1
  7 |     1
 30 |     1
 27 |     1
  5 |     1
  3 |     1
 11 |     1
  0 |     1
 26 |     1
  4 |     1
 22 |     1
 34 |     1
  1 |     1
  1 |     1
 28 |     1
 19 |     1
 10 |     1
 23 |     1
  1 |     1
 15 |     1
  8 |     1
 30 |     1
 21 |     1
 25 |     1
 33 |     1
 20 |     1
 12 |     1
 24 |     1
  1 |     4
 15 |     1
  6 |     1
  7 |     1
 29 |     1
  1 |     1
  5 |     1
 26 |     1
  4 |     1
 33 |     1
 22 |     1
 23 |     1
 13 |     1
 12 |     1
 24 |     1
 16 |     1
  6 |     1
 14 |     1
 20 |     1
  2 |     1
 16 |     1
 31 |     1
 27 |     1
 13 |     1
  9 |     1
 17 |     1
 32 |     1
 17 |     1
 34 |     1
  3 |     1
 19 |     1
(73 rows)

select a,count(*) from orca.m group by grouping sets ((a), (b));
 a  | count 
----+-------
 11 |     1
  9 |     1
 31 |     1
  8 |     1
    |    19
  0 |     1
 18 |     1
  2 |     1
 30 |     1
 27 |     1
  5 |     1
  4 |     1
 26 |     1
 34 |     1
    |     1
 28 |     1
 10 |     1
 23 |     1
 15 |     1
    |     1
 25 |     1
 21 |     1
 33 |     1
 20 |     1
 24 |     1
  1 |     4
    |    17
  6 |     1
 29 |     1
  7 |     1
 22 |     1
 13 |     1
 12 |     1
 14 |     1
 16 |     1
 17 |     1
 32 |     1
  3 |     1
 19 |     1
(39 rows)

select a,b,count(*) from orca.m group by rollup(a, b);
 a  | b  | count 
----+----+-------
 22 |  1 |     1
 34 |    |     1
  1 | -1 |     1
  1 |  2 |     1
 28 |    |     1
 19 |  0 |     1
 10 |    |     1
 23 |    |     1
  1 |  1 |     1
 15 |    |     1
  8 |  1 |     1
 30 |  1 |     1
 21 |    |     1
 25 |    |     1
 33 |    |     1
 20 |    |     1
 12 |  1 |     1
 24 |    |     1
  1 |    |     4
 15 |  0 |     1
  6 |    |     1
  7 |    |     1
 29 |    |     1
  1 |  0 |     1
  5 |  0 |     1
 26 |  1 |     1
  4 |  1 |     1
 33 |  0 |     1
 22 |    |     1
 23 |  0 |     1
 13 |    |     1
 12 |    |     1
 24 |  1 |     1
 16 |  1 |     1
  6 |  1 |     1
 14 |    |     1
 20 |  1 |     1
  2 |  1 |     1
 16 |    |     1
 31 |  0 |     1
 27 |  0 |     1
 13 |  0 |     1
  9 |  0 |     1
 17 |    |     1
 32 |    |     1
 17 |  0 |     1
  3 |    |     1
 34 |  1 |     1
 19 |    |     1
 11 |    |     1
  9 |    |     1
 31 |    |     1
 28 |  1 |     1
 10 |  1 |     1
 18 |  1 |     1
 21 |  0 |     1
  8 |    |     1
 32 |  1 |     1
 25 |  0 |     1
 29 |  0 |     1
  0 |    |     1
 18 |    |     1
  2 |    |     1
 14 |  1 |     1
  7 |  0 |     1
 30 |    |     1
 27 |    |     1
  5 |    |     1
  3 |  0 |     1
    |    |    38
 11 |  0 |     1
  0 |  1 |     1
 26 |    |     1
  4 |    |     1
(74 rows)

select a,b,count(*) from orca.m group by rollup((a),(a,b)) order by 1,2,3;
 a  | b  | count 
----+----+-------
  0 |  1 |     1
  0 |    |     1
  1 | -1 |     1
  1 |  0 |     1
  1 |  1 |     1
  1 |  2 |     1
  1 |    |     4
  2 |  1 |     1
  2 |    |     1
  3 |  0 |     1
  3 |    |     1
  4 |  1 |     1
  4 |    |     1
  5 |  0 |     1
  5 |    |     1
  6 |  1 |     1
  6 |    |     1
  7 |  0 |     1
  7 |    |     1
  8 |  1 |     1
  8 |    |     1
  9 |  0 |     1
  9 |    |     1
 10 |  1 |     1
 10 |    |     1
 11 |  0 |     1
 11 |    |     1
 12 |  1 |     1
 12 |    |     1
 13 |  0 |     1
 13 |    |     1
 14 |  1 |     1
 14 |    |     1
 15 |  0 |     1
 15 |    |     1
 16 |  1 |     1
 16 |    |     1
 17 |  0 |     1
 17 |    |     1
 18 |  1 |     1
 18 |    |     1
 19 |  0 |     1
 19 |    |     1
 20 |  1 |     1
 20 |    |     1
 21 |  0 |     1
 21 |    |     1
 22 |  1 |     1
 22 |    |     1
 23 |  0 |     1
 23 |    |     1
 24 |  1 |     1
 24 |    |     1
 25 |  0 |     1
 25 |    |     1
 26 |  1 |     1
 26 |    |     1
 27 |  0 |     1
 27 |    |     1
 28 |  1 |     1
 28 |    |     1
 29 |  0 |     1
 29 |    |     1
 30 |  1 |     1
 30 |    |     1
 31 |  0 |     1
 31 |    |     1
 32 |  1 |     1
 32 |    |     1
 33 |  0 |     1
 33 |    |     1
 34 |  1 |     1
 34 |    |     1
    |    |    38
(74 rows)

select count(*) from orca.m group by ();
 count 
-------
    38
(1 row)

select a, count(*) from orca.r group by (), a;
 a  | count 
----+-------
 14 |     1
 16 |     1
 17 |     1
  1 |     1
 15 |     1
  2 |     1
 13 |     1
  7 |     1
 19 |     1
 18 |     1
    |     1
  6 |     1
  4 |     1
 20 |     1
  3 |     1
  5 |     1
  9 |     1
 12 |     1
  8 |     1
 11 |     1
 10 |     1
(21 rows)

select a, count(*) from orca.r group by grouping sets ((),(a));
 a  | count 
----+-------
 14 |     1
  5 |     1
  9 |     1
 10 |     1
 19 |     1
 18 |     1
  1 |     1
 20 |     1
    |    21
  7 |     1
 16 |     1
 15 |     1
 11 |     1
  2 |     1
  6 |     1
    |     1
 17 |     1
  4 |     1
  8 |     1
  3 |     1
 12 |     1
 13 |     1
(22 rows)

select a, b, count(*) c from orca.r group by grouping sets ((),(a), (a,b)) order by b,a,c;
 a  | b | c  
----+---+----
  3 | 0 |  1
  6 | 0 |  1
  9 | 0 |  1
 12 | 0 |  1
 15 | 0 |  1
 18 | 0 |  1
  1 | 1 |  1
  4 | 1 |  1
  7 | 1 |  1
 10 | 1 |  1
 13 | 1 |  1
 16 | 1 |  1
 19 | 1 |  1
    | 1 |  1
  2 | 2 |  1
  5 | 2 |  1
  8 | 2 |  1
 11 | 2 |  1
 14 | 2 |  1
 17 | 2 |  1
 20 | 2 |  1
  1 |   |  1
  2 |   |  1
  3 |   |  1
  4 |   |  1
  5 |   |  1
  6 |   |  1
  7 |   |  1
  8 |   |  1
  9 |   |  1
 10 |   |  1
 11 |   |  1
 12 |   |  1
 13 |   |  1
 14 |   |  1
 15 |   |  1
 16 |   |  1
 17 |   |  1
 18 |   |  1
 19 |   |  1
 20 |   |  1
    |   |  1
    |   | 21
(43 rows)

select a, count(*) c from orca.r group by grouping sets ((),(a), (a,b)) order by b,a,c;
 a  | c  
----+----
  3 |  1
  6 |  1
  9 |  1
 12 |  1
 15 |  1
 18 |  1
  1 |  1
  4 |  1
  7 |  1
 10 |  1
 13 |  1
 16 |  1
 19 |  1
    |  1
  2 |  1
  5 |  1
  8 |  1
 11 |  1
 14 |  1
 17 |  1
 20 |  1
  1 |  1
  2 |  1
  3 |  1
  4 |  1
  5 |  1
  6 |  1
  7 |  1
  8 |  1
  9 |  1
 10 |  1
 11 |  1
 12 |  1
 13 |  1
 14 |  1
 15 |  1
 16 |  1
 17 |  1
 18 |  1
 19 |  1
 20 |  1
    |  1
    | 21
(43 rows)

select 1 from orca.r group by ();
 ?column? 
----------
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
(21 rows)

select a,1 from orca.r group by rollup(a);
 a  | ?column? 
----+----------
  1 |        1
  5 |        1
  9 |        1
 10 |        1
 14 |        1
 18 |        1
 19 |        1
  2 |        1
  6 |        1
  7 |        1
 11 |        1
 15 |        1
 16 |        1
 20 |        1
    |        1
  3 |        1
  4 |        1
  8 |        1
 12 |        1
 13 |        1
 17 |        1
    |        1
(22 rows)

-- arrays
select array[array[a,b]], array[b] from orca.r;
   array    | array 
------------+-------
 {{1,1}}    | {1}
 {{2,2}}    | {2}
 {{13,1}}   | {1}
 {{14,2}}   | {2}
 {{15,0}}   | {0}
 {{16,1}}   | {1}
 {{17,2}}   | {2}
 {{3,0}}    | {0}
 {{4,1}}    | {1}
 {{5,2}}    | {2}
 {{6,0}}    | {0}
 {{7,1}}    | {1}
 {{18,0}}   | {0}
 {{19,1}}   | {1}
 {{20,2}}   | {2}
 {{NULL,1}} | {1}
 {{8,2}}    | {2}
 {{9,0}}    | {0}
 {{10,1}}   | {1}
 {{11,2}}   | {2}
 {{12,0}}   | {0}
(21 rows)

-- setops
select a, b from orca.m union select b,a from orca.m;
 a  | b  
----+----
  0 |  1
  0 | 13
  0 | 15
  0 | 27
  0 | 29
  0 | 31
  1 |  8
  1 | 10
  1 | 12
  1 | 24
  1 | 26
  1 | 32
  3 |  0
  7 |  0
 10 |  1
 11 |  0
 14 |  1
 18 |  1
 21 |  0
 25 |  0
 28 |  1
 29 |  0
 32 |  1
  0 |  3
  0 |  5
  0 | 17
  0 | 19
  0 | 21
  0 | 33
  1 | -1
  1 |  0
  1 |  1
  1 |  2
  1 | 14
  1 | 16
  1 | 28
  1 | 30
  1 | 34
  4 |  1
  5 |  0
  8 |  1
 12 |  1
 15 |  0
 19 |  0
 22 |  1
 23 |  0
 26 |  1
 30 |  1
 33 |  0
 -1 |  1
  0 |  7
  0 |  9
  0 | 11
  0 | 23
  0 | 25
  1 |  4
  1 |  6
  1 | 18
  1 | 20
  1 | 22
  2 |  1
  6 |  1
  9 |  0
 13 |  0
 16 |  1
 17 |  0
 20 |  1
 24 |  1
 27 |  0
 31 |  0
 34 |  1
(71 rows)

SELECT a from orca.m UNION ALL select b from orca.m UNION ALL select a+b from orca.m group by 1;
 a  
----
 12
 14
 19
 25
 28
 29
 33
 34
  1
  1
  1
  0
  0
  1
  0
  0
  1
 -1
 35
 31
 17
  1
 15
 33
  2
  0
 13
 29
  0
  1
  3
  4
  8
  9
 15
 23
 30
  1
  1
  0
  0
  1
  1
  0
  0
  0
  1
  1
  7
 19
 21
  3
  5
  2
  5
  6
  7
 10
 11
 13
 16
 17
 18
 20
 21
 22
 24
 26
 27
 31
 32
  1
  1
  0
  1
  0
  1
  0
  0
  1
  0
  1
  1
  0
  1
  1
  1
  0
  0
  1
  2
  9
 27
 11
 23
 25
(96 rows)

drop table if exists orca.foo;
create table orca.foo(a int, b int, c int, d int);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'a' 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.
drop table if exists orca.bar;
NOTICE:  table "bar" does not exist, skipping
create table orca.bar(a int, b int, c int);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'a' 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.
insert into orca.foo select i, i%2, i%4, i-1 from generate_series(1,40)i;
insert into orca.bar select i, i%3, i%2 from generate_series(1,30)i;
-- distinct operation
SELECT distinct a, b from orca.foo;
 a  | b 
----+---
 16 | 0
 15 | 1
 33 | 1
 13 | 1
 30 | 0
 17 | 1
 34 | 0
 36 | 0
 35 | 1
 31 | 1
 37 | 1
 29 | 1
 28 | 0
  2 | 0
  1 | 1
 14 | 0
  5 | 1
  6 | 0
  7 | 1
  4 | 0
 22 | 0
 18 | 0
 21 | 1
 19 | 1
 38 | 0
 40 | 0
 39 | 1
 20 | 0
  3 | 1
 27 | 1
 24 | 0
 12 | 0
  8 | 0
 25 | 1
 23 | 1
 10 | 0
 11 | 1
 32 | 0
 26 | 0
  9 | 1
(40 rows)

SELECT distinct foo.a, bar.b from orca.foo, orca.bar where foo.b = bar.a;
 a  | b 
----+---
  5 | 1
 33 | 1
 19 | 1
 37 | 1
 23 | 1
  1 | 1
  9 | 1
 15 | 1
  7 | 1
 35 | 1
 39 | 1
 25 | 1
 29 | 1
 11 | 1
 27 | 1
 13 | 1
 17 | 1
 21 | 1
 31 | 1
  3 | 1
(20 rows)

SELECT distinct a, b from orca.foo;
 a  | b 
----+---
 16 | 0
 15 | 1
 33 | 1
 13 | 1
 30 | 0
 17 | 1
 34 | 0
 36 | 0
 35 | 1
 31 | 1
 37 | 1
 29 | 1
 28 | 0
  2 | 0
  1 | 1
 14 | 0
  5 | 1
  6 | 0
  7 | 1
  4 | 0
 22 | 0
 18 | 0
 21 | 1
 19 | 1
 38 | 0
 40 | 0
 39 | 1
 20 | 0
  3 | 1
 27 | 1
 24 | 0
 12 | 0
  8 | 0
 25 | 1
 23 | 1
 10 | 0
 11 | 1
 32 | 0
 26 | 0
  9 | 1
(40 rows)

SELECT distinct a, count(*) from orca.foo group by a;
 a  | count 
----+-------
  1 |     1
  2 |     1
  3 |     1
  4 |     1
  5 |     1
  6 |     1
  7 |     1
  8 |     1
  9 |     1
 10 |     1
 11 |     1
 12 |     1
 13 |     1
 14 |     1
 15 |     1
 16 |     1
 17 |     1
 18 |     1
 19 |     1
 20 |     1
 21 |     1
 22 |     1
 23 |     1
 24 |     1
 25 |     1
 26 |     1
 27 |     1
 28 |     1
 29 |     1
 30 |     1
 31 |     1
 32 |     1
 33 |     1
 34 |     1
 35 |     1
 36 |     1
 37 |     1
 38 |     1
 39 |     1
 40 |     1
(40 rows)

SELECT distinct foo.a, bar.b, sum(bar.c+foo.c) from orca.foo, orca.bar where foo.b = bar.a group by foo.a, bar.b;
 a  | b | sum 
----+---+-----
  1 | 1 |   2
  3 | 1 |   4
  5 | 1 |   2
  7 | 1 |   4
  9 | 1 |   2
 11 | 1 |   4
 13 | 1 |   2
 15 | 1 |   4
 17 | 1 |   2
 19 | 1 |   4
 21 | 1 |   2
 23 | 1 |   4
 25 | 1 |   2
 27 | 1 |   4
 29 | 1 |   2
 31 | 1 |   4
 33 | 1 |   2
 35 | 1 |   4
 37 | 1 |   2
 39 | 1 |   4
(20 rows)

SELECT distinct a, count(*) from orca.foo group by a;
 a  | count 
----+-------
  1 |     1
  2 |     1
  3 |     1
  4 |     1
  5 |     1
  6 |     1
  7 |     1
  8 |     1
  9 |     1
 10 |     1
 11 |     1
 12 |     1
 13 |     1
 14 |     1
 15 |     1
 16 |     1
 17 |     1
 18 |     1
 19 |     1
 20 |     1
 21 |     1
 22 |     1
 23 |     1
 24 |     1
 25 |     1
 26 |     1
 27 |     1
 28 |     1
 29 |     1
 30 |     1
 31 |     1
 32 |     1
 33 |     1
 34 |     1
 35 |     1
 36 |     1
 37 |     1
 38 |     1
 39 |     1
 40 |     1
(40 rows)

SELECT distinct foo.a, bar.b from orca.foo, orca.bar where foo.b = bar.a;
 a  | b 
----+---
 15 | 1
  7 | 1
 35 | 1
 39 | 1
 25 | 1
 29 | 1
 11 | 1
  5 | 1
 33 | 1
 19 | 1
 37 | 1
 23 | 1
  1 | 1
  9 | 1
 27 | 1
 13 | 1
 17 | 1
 21 | 1
 31 | 1
  3 | 1
(20 rows)

SELECT distinct foo.a, bar.b, sum(bar.c+foo.c) from orca.foo, orca.bar where foo.b = bar.a group by foo.a, bar.b;
 a  | b | sum 
----+---+-----
  1 | 1 |   2
  3 | 1 |   4
  5 | 1 |   2
  7 | 1 |   4
  9 | 1 |   2
 11 | 1 |   4
 13 | 1 |   2
 15 | 1 |   4
 17 | 1 |   2
 19 | 1 |   4
 21 | 1 |   2
 23 | 1 |   4
 25 | 1 |   2
 27 | 1 |   4
 29 | 1 |   2
 31 | 1 |   4
 33 | 1 |   2
 35 | 1 |   4
 37 | 1 |   2
 39 | 1 |   4
(20 rows)

SELECT distinct a, b from orca.foo;
 a  | b 
----+---
 16 | 0
 15 | 1
 33 | 1
 13 | 1
 30 | 0
 17 | 1
 34 | 0
 36 | 0
 35 | 1
 31 | 1
 37 | 1
 29 | 1
 28 | 0
  2 | 0
  1 | 1
 14 | 0
  5 | 1
  6 | 0
  7 | 1
  4 | 0
 22 | 0
 18 | 0
 21 | 1
 19 | 1
 38 | 0
 40 | 0
 39 | 1
 20 | 0
  3 | 1
 27 | 1
 24 | 0
 12 | 0
  8 | 0
 25 | 1
 23 | 1
 10 | 0
 11 | 1
 32 | 0
 26 | 0
  9 | 1
(40 rows)

SELECT distinct a, count(*) from orca.foo group by a;
 a  | count 
----+-------
  1 |     1
  2 |     1
  3 |     1
  4 |     1
  5 |     1
  6 |     1
  7 |     1
  8 |     1
  9 |     1
 10 |     1
 11 |     1
 12 |     1
 13 |     1
 14 |     1
 15 |     1
 16 |     1
 17 |     1
 18 |     1
 19 |     1
 20 |     1
 21 |     1
 22 |     1
 23 |     1
 24 |     1
 25 |     1
 26 |     1
 27 |     1
 28 |     1
 29 |     1
 30 |     1
 31 |     1
 32 |     1
 33 |     1
 34 |     1
 35 |     1
 36 |     1
 37 |     1
 38 |     1
 39 |     1
 40 |     1
(40 rows)

SELECT distinct foo.a, bar.b from orca.foo, orca.bar where foo.b = bar.a;
 a  | b 
----+---
 15 | 1
  7 | 1
 35 | 1
 39 | 1
 25 | 1
 29 | 1
 11 | 1
  5 | 1
 33 | 1
 19 | 1
 37 | 1
 23 | 1
  1 | 1
  9 | 1
 27 | 1
 13 | 1
 17 | 1
 21 | 1
 31 | 1
  3 | 1
(20 rows)

SELECT distinct foo.a, bar.b, sum(bar.c+foo.c) from orca.foo, orca.bar where foo.b = bar.a group by foo.a, bar.b;
 a  | b | sum 
----+---+-----
  1 | 1 |   2
  3 | 1 |   4
  5 | 1 |   2
  7 | 1 |   4
  9 | 1 |   2
 11 | 1 |   4
 13 | 1 |   2
 15 | 1 |   4
 17 | 1 |   2
 19 | 1 |   4
 21 | 1 |   2
 23 | 1 |   4
 25 | 1 |   2
 27 | 1 |   4
 29 | 1 |   2
 31 | 1 |   4
 33 | 1 |   2
 35 | 1 |   4
 37 | 1 |   2
 39 | 1 |   4
(20 rows)

-- window operations
select row_number() over() from orca.foo order by 1;
 row_number 
------------
          1
          2
          3
          4
          5
          6
          7
          8
          9
         10
         11
         12
         13
         14
         15
         16
         17
         18
         19
         20
         21
         22
         23
         24
         25
         26
         27
         28
         29
         30
         31
         32
         33
         34
         35
         36
         37
         38
         39
         40
(40 rows)

select rank() over(partition by b order by count(*)/sum(a)) from orca.foo group by a, b order by 1;
 rank 
------
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
    1
   20
(40 rows)

select row_number() over(order by foo.a) from orca.foo inner join orca.bar using(b) group by foo.a, bar.b, bar.a;
 row_number 
------------
          1
          2
          3
          4
          5
          6
          7
          8
          9
         10
         11
         12
         13
         14
         15
         16
         17
         18
         19
         20
         21
         22
         23
         24
         25
         26
         27
         28
         29
         30
         31
         32
         33
         34
         35
         36
         37
         38
         39
         40
         41
         42
         43
         44
         45
         46
         47
         48
         49
         50
         51
         52
         53
         54
         55
         56
         57
         58
         59
         60
         61
         62
         63
         64
         65
         66
         67
         68
         69
         70
         71
         72
         73
         74
         75
         76
         77
         78
         79
         80
         81
         82
         83
         84
         85
         86
         87
         88
         89
         90
         91
         92
         93
         94
         95
         96
         97
         98
         99
        100
        101
        102
        103
        104
        105
        106
        107
        108
        109
        110
        111
        112
        113
        114
        115
        116
        117
        118
        119
        120
        121
        122
        123
        124
        125
        126
        127
        128
        129
        130
        131
        132
        133
        134
        135
        136
        137
        138
        139
        140
        141
        142
        143
        144
        145
        146
        147
        148
        149
        150
        151
        152
        153
        154
        155
        156
        157
        158
        159
        160
        161
        162
        163
        164
        165
        166
        167
        168
        169
        170
        171
        172
        173
        174
        175
        176
        177
        178
        179
        180
        181
        182
        183
        184
        185
        186
        187
        188
        189
        190
        191
        192
        193
        194
        195
        196
        197
        198
        199
        200
        201
        202
        203
        204
        205
        206
        207
        208
        209
        210
        211
        212
        213
        214
        215
        216
        217
        218
        219
        220
        221
        222
        223
        224
        225
        226
        227
        228
        229
        230
        231
        232
        233
        234
        235
        236
        237
        238
        239
        240
        241
        242
        243
        244
        245
        246
        247
        248
        249
        250
        251
        252
        253
        254
        255
        256
        257
        258
        259
        260
        261
        262
        263
        264
        265
        266
        267
        268
        269
        270
        271
        272
        273
        274
        275
        276
        277
        278
        279
        280
        281
        282
        283
        284
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        398
        399
        400
(400 rows)

select 1+row_number() over(order by foo.a+bar.a) from orca.foo inner join orca.bar using(b);
 ?column? 
----------
        2
        3
        4
        5
        6
        7
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        9
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      400
      401
(400 rows)

select row_number() over(order by foo.a+ bar.a)/count(*) from orca.foo inner join orca.bar using(b) group by foo.a, bar.a, bar.b;
 ?column? 
----------
        1
        2
        3
        4
        5
        6
        7
        8
        9
       10
       11
       12
       13
       14
       15
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      400
(400 rows)

select count(*) over(partition by b order by a range between 1 preceding and (select count(*) from orca.bar) following) from orca.foo;
 count 
-------
    16
    16
    16
    16
    16
    15
    14
    13
    12
    11
    10
     9
     8
     7
     6
     5
     4
     3
     2
     1
    16
    16
    16
    16
    16
    15
    14
    13
    12
    11
    10
     9
     8
     7
     6
     5
     4
     3
     2
     1
(40 rows)

select a+1, rank() over(partition by b+1 order by a+1) from orca.foo order by 1, 2;
 ?column? | rank 
----------+------
        2 |    1
        3 |    1
        4 |    2
        5 |    2
        6 |    3
        7 |    3
        8 |    4
        9 |    4
       10 |    5
       11 |    5
       12 |    6
       13 |    6
       14 |    7
       15 |    7
       16 |    8
       17 |    8
       18 |    9
       19 |    9
       20 |   10
       21 |   10
       22 |   11
       23 |   11
       24 |   12
       25 |   12
       26 |   13
       27 |   13
       28 |   14
       29 |   14
       30 |   15
       31 |   15
       32 |   16
       33 |   16
       34 |   17
       35 |   17
       36 |   18
       37 |   18
       38 |   19
       39 |   19
       40 |   20
       41 |   20
(40 rows)

select a , sum(a) over (order by a range '1'::float8 preceding) from orca.r order by 1,2;
 a  | sum 
----+-----
  1 |   1
  2 |   3
  3 |   5
  4 |   7
  5 |   9
  6 |  11
  7 |  13
  8 |  15
  9 |  17
 10 |  19
 11 |  21
 12 |  23
 13 |  25
 14 |  27
 15 |  29
 16 |  31
 17 |  33
 18 |  35
 19 |  37
 20 |  39
    |    
(21 rows)

select a, b, floor(avg(b) over(order by a desc, b desc rows between unbounded preceding and unbounded following)) as avg, dense_rank() over (order by a) from orca.r order by 1,2,3,4;
 a  | b | avg | dense_rank 
----+---+-----+------------
  1 | 1 |   1 |          1
  2 | 2 |   1 |          2
  3 | 0 |   1 |          3
  4 | 1 |   1 |          4
  5 | 2 |   1 |          5
  6 | 0 |   1 |          6
  7 | 1 |   1 |          7
  8 | 2 |   1 |          8
  9 | 0 |   1 |          9
 10 | 1 |   1 |         10
 11 | 2 |   1 |         11
 12 | 0 |   1 |         12
 13 | 1 |   1 |         13
 14 | 2 |   1 |         14
 15 | 0 |   1 |         15
 16 | 1 |   1 |         16
 17 | 2 |   1 |         17
 18 | 0 |   1 |         18
 19 | 1 |   1 |         19
 20 | 2 |   1 |         20
    | 1 |   1 |         21
(21 rows)

select lead(a) over(order by a) from orca.r order by 1;
 lead 
------
    2
    3
    4
    5
    6
    7
    8
    9
   10
   11
   12
   13
   14
   15
   16
   17
   18
   19
   20
     
     
(21 rows)

select lag(c,d) over(order by c,d) from orca.s order by 1;
 lag 
-----
   0
   0
   0
   0
   1
   1
   1
   1
   1
   2
   2
   2
   2
   2
   3
   3
   3
   3
   4
   4
   4
   4
   5
   5
   5
   5
   6
   6
   6
   6
(30 rows)

select lead(c,c+d,1000) over(order by c,d) from orca.s order by 1;
 lead 
------
    0
    0
    0
    1
    1
    1
    1
    2
    2
    2
    2
    2
    3
    3
    3
    4
    4
    4
    5
    5
    5
    6
    6
    6
 1000
 1000
 1000
 1000
 1000
 1000
(30 rows)

-- cte
with x as (select a, b from orca.r)
select rank() over(partition by a, case when b = 0 then a+b end order by b asc) as rank_within_parent from x order by a desc ,case when a+b = 0 then a end ,b;
 rank_within_parent 
--------------------
                  1
                  1
                  1
                  1
                  1
                  1
                  1
                  1
                  1
                  1
                  1
                  1
                  1
                  1
                  1
                  1
                  1
                  1
                  1
                  1
                  1
(21 rows)

-- alias
select foo.d from orca.foo full join orca.bar on (foo.d = bar.a) group by d;
 d  
----
 30
 35
 14
 37
 16
 28
 31
  1
 17
 15
 36
 34
 33
  2
  0
 13
 29
  7
 19
 39
 18
  6
  4
 21
 20
 38
  3
  5
 22
 24
  9
 12
  8
 26
 27
 32
 11
 23
 25
 10
(40 rows)

select 1 as v from orca.foo full join orca.bar on (foo.d = bar.a) group by d;
 v 
---
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
 1
(40 rows)

select * from orca.r where a in (select count(*)+1 as v from orca.foo full join orca.bar on (foo.d = bar.a) group by d+r.b);
 a | b 
---+---
 2 | 2
(1 row)

select * from orca.r where r.a in (select d+r.b+1 as v from orca.foo full join orca.bar on (foo.d = bar.a) group by d+r.b) order by r.a, r.b;
 a  | b 
----+---
  3 | 0
  4 | 1
  5 | 2
  6 | 0
  7 | 1
  8 | 2
  9 | 0
 10 | 1
 11 | 2
 12 | 0
 13 | 1
 14 | 2
 15 | 0
 16 | 1
 17 | 2
 18 | 0
 19 | 1
 20 | 2
(18 rows)

drop table if exists orca.rcte;
NOTICE:  table "rcte" does not exist, skipping
create table orca.rcte(a int, b int, c int);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'a' 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.
insert into orca.rcte select i, i%2, i%3 from generate_series(1,40)i;
with x as (select * from orca.rcte where a < 10) select * from x x1, x x2;
 a | b | c | a | b | c 
---+---+---+---+---+---
 8 | 0 | 2 | 8 | 0 | 2
 8 | 0 | 2 | 9 | 1 | 0
 8 | 0 | 2 | 3 | 1 | 0
 8 | 0 | 2 | 4 | 0 | 1
 8 | 0 | 2 | 5 | 1 | 2
 8 | 0 | 2 | 6 | 0 | 0
 8 | 0 | 2 | 7 | 1 | 1
 8 | 0 | 2 | 1 | 1 | 1
 8 | 0 | 2 | 2 | 0 | 2
 9 | 1 | 0 | 8 | 0 | 2
 9 | 1 | 0 | 9 | 1 | 0
 9 | 1 | 0 | 3 | 1 | 0
 9 | 1 | 0 | 4 | 0 | 1
 9 | 1 | 0 | 5 | 1 | 2
 9 | 1 | 0 | 6 | 0 | 0
 9 | 1 | 0 | 7 | 1 | 1
 9 | 1 | 0 | 1 | 1 | 1
 9 | 1 | 0 | 2 | 0 | 2
 1 | 1 | 1 | 8 | 0 | 2
 1 | 1 | 1 | 9 | 1 | 0
 1 | 1 | 1 | 1 | 1 | 1
 1 | 1 | 1 | 2 | 0 | 2
 1 | 1 | 1 | 3 | 1 | 0
 1 | 1 | 1 | 4 | 0 | 1
 1 | 1 | 1 | 5 | 1 | 2
 1 | 1 | 1 | 6 | 0 | 0
 1 | 1 | 1 | 7 | 1 | 1
 2 | 0 | 2 | 8 | 0 | 2
 2 | 0 | 2 | 9 | 1 | 0
 2 | 0 | 2 | 1 | 1 | 1
 2 | 0 | 2 | 2 | 0 | 2
 2 | 0 | 2 | 3 | 1 | 0
 2 | 0 | 2 | 4 | 0 | 1
 2 | 0 | 2 | 5 | 1 | 2
 2 | 0 | 2 | 6 | 0 | 0
 2 | 0 | 2 | 7 | 1 | 1
 3 | 1 | 0 | 8 | 0 | 2
 3 | 1 | 0 | 9 | 1 | 0
 3 | 1 | 0 | 3 | 1 | 0
 3 | 1 | 0 | 4 | 0 | 1
 3 | 1 | 0 | 5 | 1 | 2
 3 | 1 | 0 | 6 | 0 | 0
 3 | 1 | 0 | 7 | 1 | 1
 3 | 1 | 0 | 1 | 1 | 1
 3 | 1 | 0 | 2 | 0 | 2
 4 | 0 | 1 | 8 | 0 | 2
 4 | 0 | 1 | 9 | 1 | 0
 4 | 0 | 1 | 3 | 1 | 0
 4 | 0 | 1 | 4 | 0 | 1
 4 | 0 | 1 | 5 | 1 | 2
 4 | 0 | 1 | 6 | 0 | 0
 4 | 0 | 1 | 7 | 1 | 1
 4 | 0 | 1 | 1 | 1 | 1
 4 | 0 | 1 | 2 | 0 | 2
 5 | 1 | 2 | 8 | 0 | 2
 5 | 1 | 2 | 9 | 1 | 0
 5 | 1 | 2 | 3 | 1 | 0
 5 | 1 | 2 | 4 | 0 | 1
 5 | 1 | 2 | 5 | 1 | 2
 5 | 1 | 2 | 6 | 0 | 0
 5 | 1 | 2 | 7 | 1 | 1
 5 | 1 | 2 | 1 | 1 | 1
 5 | 1 | 2 | 2 | 0 | 2
 6 | 0 | 0 | 8 | 0 | 2
 6 | 0 | 0 | 9 | 1 | 0
 6 | 0 | 0 | 3 | 1 | 0
 6 | 0 | 0 | 4 | 0 | 1
 6 | 0 | 0 | 5 | 1 | 2
 6 | 0 | 0 | 6 | 0 | 0
 6 | 0 | 0 | 7 | 1 | 1
 6 | 0 | 0 | 1 | 1 | 1
 6 | 0 | 0 | 2 | 0 | 2
 7 | 1 | 1 | 8 | 0 | 2
 7 | 1 | 1 | 9 | 1 | 0
 7 | 1 | 1 | 3 | 1 | 0
 7 | 1 | 1 | 4 | 0 | 1
 7 | 1 | 1 | 5 | 1 | 2
 7 | 1 | 1 | 6 | 0 | 0
 7 | 1 | 1 | 7 | 1 | 1
 7 | 1 | 1 | 1 | 1 | 1
 7 | 1 | 1 | 2 | 0 | 2
(81 rows)

with x as (select * from orca.rcte where a < 10) select * from x x1, x x2 where x2.a = x1.b;
 a | b | c | a | b | c 
---+---+---+---+---+---
 1 | 1 | 1 | 1 | 1 | 1
 3 | 1 | 0 | 1 | 1 | 1
 5 | 1 | 2 | 1 | 1 | 1
 7 | 1 | 1 | 1 | 1 | 1
 9 | 1 | 0 | 1 | 1 | 1
(5 rows)

with x as (select * from orca.rcte where a < 10) select a from x union all select b from x;
 a 
---
 3
 4
 5
 6
 7
 1
 0
 1
 0
 1
 8
 9
 0
 1
 1
 2
 1
 0
(18 rows)

with x as (select * from orca.rcte where a < 10) select * from x x1 where x1.b = any (select x2.a from x x2 group by x2.a);
 a | b | c 
---+---+---
 1 | 1 | 1
 3 | 1 | 0
 5 | 1 | 2
 7 | 1 | 1
 9 | 1 | 0
(5 rows)

with x as (select * from orca.rcte where a < 10) select * from x x1 where x1.b = all (select x2.a from x x2 group by x2.a);
 a | b | c 
---+---+---
(0 rows)

with x as (select * from orca.rcte where a < 10) select * from x x1, x x2, x x3 where x2.a = x1.b and x3.b = x2.b                                                                                   ;
 a | b | c | a | b | c | a | b | c 
---+---+---+---+---+---+---+---+---
 9 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1
 7 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1
 5 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1
 3 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1
 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1
 9 | 1 | 0 | 1 | 1 | 1 | 3 | 1 | 0
 7 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 0
 5 | 1 | 2 | 1 | 1 | 1 | 3 | 1 | 0
 3 | 1 | 0 | 1 | 1 | 1 | 3 | 1 | 0
 1 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 0
 9 | 1 | 0 | 1 | 1 | 1 | 5 | 1 | 2
 7 | 1 | 1 | 1 | 1 | 1 | 5 | 1 | 2
 5 | 1 | 2 | 1 | 1 | 1 | 5 | 1 | 2
 3 | 1 | 0 | 1 | 1 | 1 | 5 | 1 | 2
 1 | 1 | 1 | 1 | 1 | 1 | 5 | 1 | 2
 9 | 1 | 0 | 1 | 1 | 1 | 7 | 1 | 1
 7 | 1 | 1 | 1 | 1 | 1 | 7 | 1 | 1
 5 | 1 | 2 | 1 | 1 | 1 | 7 | 1 | 1
 3 | 1 | 0 | 1 | 1 | 1 | 7 | 1 | 1
 1 | 1 | 1 | 1 | 1 | 1 | 7 | 1 | 1
 9 | 1 | 0 | 1 | 1 | 1 | 9 | 1 | 0
 7 | 1 | 1 | 1 | 1 | 1 | 9 | 1 | 0
 5 | 1 | 2 | 1 | 1 | 1 | 9 | 1 | 0
 3 | 1 | 0 | 1 | 1 | 1 | 9 | 1 | 0
 1 | 1 | 1 | 1 | 1 | 1 | 9 | 1 | 0
(25 rows)

with x as (select * from orca.rcte where a < 10) select * from x x2 where x2.b < (select avg(b) from x x1);
 a | b | c 
---+---+---
 2 | 0 | 2
 4 | 0 | 1
 6 | 0 | 0
 8 | 0 | 2
(4 rows)

with x as (select r.a from orca.r, orca.s  where r.a < 10 and s.d < 10 and r.a = s.d) select * from x x1, x x2;
 a | a 
---+---
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
(225 rows)

with x as (select r.a from orca.r, orca.s  where r.a < 10 and s.c < 10 and r.a = s.c) select * from x x1, x x2;
 a | a 
---+---
 3 | 1
 3 | 2
 3 | 1
 3 | 2
 3 | 1
 3 | 2
 3 | 1
 3 | 2
 3 | 1
 3 | 2
 3 | 3
 3 | 4
 3 | 5
 3 | 6
 3 | 3
 3 | 4
 3 | 5
 3 | 6
 3 | 3
 3 | 4
 3 | 5
 3 | 6
 3 | 3
 3 | 4
 3 | 5
 3 | 6
 4 | 1
 4 | 2
 4 | 1
 4 | 2
 4 | 1
 4 | 2
 4 | 1
 4 | 2
 4 | 1
 4 | 2
 4 | 3
 4 | 4
 4 | 5
 4 | 6
 4 | 3
 4 | 4
 4 | 5
 4 | 6
 4 | 3
 4 | 4
 4 | 5
 4 | 6
 4 | 3
 4 | 4
 4 | 5
 4 | 6
 5 | 1
 5 | 2
 5 | 1
 5 | 2
 5 | 1
 5 | 2
 5 | 1
 5 | 2
 5 | 1
 5 | 2
 5 | 3
 5 | 4
 5 | 5
 5 | 6
 5 | 3
 5 | 4
 5 | 5
 5 | 6
 5 | 3
 5 | 4
 5 | 5
 5 | 6
 5 | 3
 5 | 4
 5 | 5
 5 | 6
 6 | 1
 6 | 2
 6 | 1
 6 | 2
 6 | 1
 6 | 2
 6 | 1
 6 | 2
 6 | 1
 6 | 2
 6 | 3
 6 | 4
 6 | 5
 6 | 6
 6 | 3
 6 | 4
 6 | 5
 6 | 6
 6 | 3
 6 | 4
 6 | 5
 6 | 6
 6 | 3
 6 | 4
 6 | 5
 6 | 6
 3 | 1
 3 | 2
 3 | 1
 3 | 2
 3 | 1
 3 | 2
 3 | 1
 3 | 2
 3 | 1
 3 | 2
 3 | 3
 3 | 4
 3 | 5
 3 | 6
 3 | 3
 3 | 4
 3 | 5
 3 | 6
 3 | 3
 3 | 4
 3 | 5
 3 | 6
 3 | 3
 3 | 4
 3 | 5
 3 | 6
 4 | 1
 4 | 2
 4 | 1
 4 | 2
 4 | 1
 4 | 2
 4 | 1
 4 | 2
 4 | 1
 4 | 2
 4 | 3
 4 | 4
 4 | 5
 4 | 6
 4 | 3
 4 | 4
 4 | 5
 4 | 6
 4 | 3
 4 | 4
 4 | 5
 4 | 6
 4 | 3
 4 | 4
 4 | 5
 4 | 6
 5 | 1
 5 | 2
 5 | 1
 5 | 2
 5 | 1
 5 | 2
 5 | 1
 5 | 2
 5 | 1
 5 | 2
 5 | 3
 5 | 4
 5 | 5
 5 | 6
 5 | 3
 5 | 4
 5 | 5
 5 | 6
 5 | 3
 5 | 4
 5 | 5
 5 | 6
 5 | 3
 5 | 4
 5 | 5
 5 | 6
 6 | 1
 6 | 2
 6 | 1
 6 | 2
 6 | 1
 6 | 2
 6 | 1
 6 | 2
 6 | 1
 6 | 2
 6 | 3
 6 | 4
 6 | 5
 6 | 6
 6 | 3
 6 | 4
 6 | 5
 6 | 6
 6 | 3
 6 | 4
 6 | 5
 6 | 6
 6 | 3
 6 | 4
 6 | 5
 6 | 6
 3 | 1
 3 | 2
 3 | 1
 3 | 2
 3 | 1
 3 | 2
 3 | 1
 3 | 2
 3 | 1
 3 | 2
 3 | 3
 3 | 4
 3 | 5
 3 | 6
 3 | 3
 3 | 4
 3 | 5
 3 | 6
 3 | 3
 3 | 4
 3 | 5
 3 | 6
 3 | 3
 3 | 4
 3 | 5
 3 | 6
 4 | 1
 4 | 2
 4 | 1
 4 | 2
 4 | 1
 4 | 2
 4 | 1
 4 | 2
 4 | 1
 4 | 2
 4 | 3
 4 | 4
 4 | 5
 4 | 6
 4 | 3
 4 | 4
 4 | 5
 4 | 6
 4 | 3
 4 | 4
 4 | 5
 4 | 6
 4 | 3
 4 | 4
 4 | 5
 4 | 6
 5 | 1
 5 | 2
 5 | 1
 5 | 2
 5 | 1
 5 | 2
 5 | 1
 5 | 2
 5 | 1
 5 | 2
 5 | 3
 5 | 4
 5 | 5
 5 | 6
 5 | 3
 5 | 4
 5 | 5
 5 | 6
 5 | 3
 5 | 4
 5 | 5
 5 | 6
 5 | 3
 5 | 4
 5 | 5
 5 | 6
 6 | 1
 6 | 2
 6 | 1
 6 | 2
 6 | 1
 6 | 2
 6 | 1
 6 | 2
 6 | 1
 6 | 2
 6 | 3
 6 | 4
 6 | 5
 6 | 6
 6 | 3
 6 | 4
 6 | 5
 6 | 6
 6 | 3
 6 | 4
 6 | 5
 6 | 6
 6 | 3
 6 | 4
 6 | 5
 6 | 6
 3 | 1
 3 | 2
 3 | 1
 3 | 2
 3 | 1
 3 | 2
 3 | 1
 3 | 2
 3 | 1
 3 | 2
 3 | 3
 3 | 4
 3 | 5
 3 | 6
 3 | 3
 3 | 4
 3 | 5
 3 | 6
 3 | 3
 3 | 4
 3 | 5
 3 | 6
 3 | 3
 3 | 4
 3 | 5
 3 | 6
 4 | 1
 4 | 2
 4 | 1
 4 | 2
 4 | 1
 4 | 2
 4 | 1
 4 | 2
 4 | 1
 4 | 2
 4 | 3
 4 | 4
 4 | 5
 4 | 6
 4 | 3
 4 | 4
 4 | 5
 4 | 6
 4 | 3
 4 | 4
 4 | 5
 4 | 6
 4 | 3
 4 | 4
 4 | 5
 4 | 6
 5 | 1
 5 | 2
 5 | 1
 5 | 2
 5 | 1
 5 | 2
 5 | 1
 5 | 2
 5 | 1
 5 | 2
 5 | 3
 5 | 4
 5 | 5
 5 | 6
 5 | 3
 5 | 4
 5 | 5
 5 | 6
 5 | 3
 5 | 4
 5 | 5
 5 | 6
 5 | 3
 5 | 4
 5 | 5
 5 | 6
 6 | 1
 6 | 2
 6 | 1
 6 | 2
 6 | 1
 6 | 2
 6 | 1
 6 | 2
 6 | 1
 6 | 2
 6 | 3
 6 | 4
 6 | 5
 6 | 6
 6 | 3
 6 | 4
 1 | 1
 1 | 2
 1 | 1
 1 | 2
 1 | 1
 1 | 2
 1 | 1
 1 | 2
 1 | 1
 1 | 2
 1 | 3
 1 | 4
 1 | 5
 1 | 6
 1 | 3
 1 | 4
 1 | 5
 1 | 6
 1 | 3
 1 | 4
 1 | 5
 1 | 6
 1 | 3
 1 | 4
 1 | 5
 1 | 6
 2 | 1
 2 | 2
 2 | 1
 2 | 2
 2 | 1
 2 | 2
 2 | 1
 2 | 2
 2 | 1
 2 | 2
 2 | 3
 2 | 4
 2 | 5
 2 | 6
 2 | 3
 2 | 4
 2 | 5
 2 | 6
 2 | 3
 2 | 4
 2 | 5
 2 | 6
 2 | 3
 2 | 4
 2 | 5
 2 | 6
 1 | 1
 1 | 2
 1 | 1
 1 | 2
 1 | 1
 1 | 2
 1 | 1
 1 | 2
 1 | 1
 1 | 2
 1 | 3
 1 | 4
 1 | 5
 1 | 6
 1 | 3
 1 | 4
 1 | 5
 1 | 6
 1 | 3
 1 | 4
 1 | 5
 1 | 6
 1 | 3
 1 | 4
 1 | 5
 1 | 6
 2 | 1
 2 | 2
 2 | 1
 2 | 2
 2 | 1
 2 | 2
 2 | 1
 2 | 2
 2 | 1
 2 | 2
 2 | 3
 2 | 4
 2 | 5
 2 | 6
 2 | 3
 2 | 4
 2 | 5
 2 | 6
 2 | 3
 2 | 4
 2 | 5
 2 | 6
 2 | 3
 2 | 4
 2 | 5
 2 | 6
 1 | 1
 1 | 2
 1 | 1
 1 | 2
 1 | 1
 1 | 2
 1 | 1
 1 | 2
 1 | 1
 1 | 2
 1 | 3
 1 | 4
 1 | 5
 1 | 6
 1 | 3
 1 | 4
 1 | 5
 1 | 6
 1 | 3
 1 | 4
 1 | 5
 1 | 6
 1 | 3
 1 | 4
 1 | 5
 1 | 6
 2 | 1
 2 | 2
 2 | 1
 2 | 2
 2 | 1
 2 | 2
 2 | 1
 2 | 2
 2 | 1
 2 | 2
 2 | 3
 2 | 4
 2 | 5
 2 | 6
 2 | 3
 2 | 4
 2 | 5
 2 | 6
 2 | 3
 2 | 4
 2 | 5
 2 | 6
 2 | 3
 2 | 4
 2 | 5
 2 | 6
 1 | 1
 1 | 2
 1 | 1
 1 | 2
 1 | 1
 1 | 2
 1 | 1
 1 | 2
 1 | 1
 1 | 2
 1 | 3
 1 | 4
 1 | 5
 1 | 6
 1 | 3
 1 | 4
 1 | 5
 1 | 6
 1 | 3
 1 | 4
 1 | 5
 1 | 6
 1 | 3
 1 | 4
 1 | 5
 1 | 6
 2 | 1
 2 | 2
 2 | 1
 2 | 2
 2 | 1
 2 | 2
 2 | 1
 2 | 2
 2 | 1
 2 | 2
 2 | 3
 2 | 4
 2 | 5
 2 | 6
 2 | 3
 2 | 4
 2 | 5
 2 | 6
 2 | 3
 2 | 4
 2 | 5
 2 | 6
 2 | 3
 2 | 4
 2 | 5
 2 | 6
 1 | 1
 1 | 2
 1 | 1
 1 | 2
 1 | 1
 1 | 2
 1 | 1
 1 | 2
 1 | 1
 1 | 2
 1 | 3
 1 | 4
 1 | 5
 1 | 6
 1 | 3
 1 | 4
 1 | 5
 1 | 6
 1 | 3
 1 | 4
 1 | 5
 1 | 6
 1 | 3
 1 | 4
 1 | 5
 1 | 6
 2 | 1
 2 | 2
 2 | 1
 2 | 2
 2 | 1
 2 | 2
 2 | 1
 2 | 2
 2 | 1
 2 | 2
 2 | 3
 2 | 4
 2 | 5
 2 | 6
 2 | 3
 2 | 4
 2 | 5
 2 | 6
 2 | 3
 2 | 4
 2 | 5
 2 | 6
 2 | 3
 2 | 4
 2 | 5
 2 | 6
 6 | 5
 6 | 6
 6 | 3
 6 | 4
 6 | 5
 6 | 6
 6 | 3
 6 | 4
 6 | 5
 6 | 6
(676 rows)

with x as (select * from orca.rcte where a < 10) (select a from x x2) union all (select max(a) from x x1);
 a 
---
 1
 2
 3
 4
 5
 6
 7
 8
 9
 9
(10 rows)

with x as (select * from orca.r) select * from x order by a;
 a  | b 
----+---
  1 | 1
  2 | 2
  3 | 0
  4 | 1
  5 | 2
  6 | 0
  7 | 1
  8 | 2
  9 | 0
 10 | 1
 11 | 2
 12 | 0
 13 | 1
 14 | 2
 15 | 0
 16 | 1
 17 | 2
 18 | 0
 19 | 1
 20 | 2
    | 1
(21 rows)

-- with x as (select * from orca.rcte where a < 10) select * from x x1, x x2 where x2.a = x1.b limit 1;
-- correlated execution
select (select 1 union select 2);
ERROR:  more than one row returned by a subquery used as an expression
select (select generate_series(1,5));
ERROR:  more than one row returned by a subquery used as an expression
select (select a from orca.foo inner1 where inner1.a=outer1.a  union select b from orca.foo inner2 where inner2.b=outer1.b) from orca.foo outer1;
ERROR:  more than one row returned by a subquery used as an expression  (seg0 slice3 192.168.0.37:25432 pid=14095)
select (select generate_series(1,1)) as series;
 series 
--------
      1
(1 row)

select generate_series(1,5);
 generate_series 
-----------------
               1
               2
               3
               4
               5
(5 rows)

select a, c from orca.r, orca.s where a = any (select c) order by a, c limit 10;
 a | c 
---+---
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 2 | 2
 2 | 2
 2 | 2
 2 | 2
 2 | 2
(10 rows)

select a, c from orca.r, orca.s where a = (select c) order by a, c limit 10;
 a | c 
---+---
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 2 | 2
 2 | 2
 2 | 2
 2 | 2
 2 | 2
(10 rows)

select a, c from orca.r, orca.s where a  not in  (select c) order by a, c limit 10;
 a | c 
---+---
 1 | 0
 1 | 0
 1 | 0
 1 | 0
 1 | 2
 1 | 2
 1 | 2
 1 | 2
 1 | 2
 1 | 3
(10 rows)

select a, c from orca.r, orca.s where a  = any  (select c from orca.r) order by a, c limit 10;
 a | c 
---+---
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 1 | 1
 2 | 2
 2 | 2
 2 | 2
 2 | 2
 2 | 2
(10 rows)

select a, c from orca.r, orca.s where a  <> all (select c) order by a, c limit 10;
 a | c 
---+---
 1 | 0
 1 | 0
 1 | 0
 1 | 0
 1 | 2
 1 | 2
 1 | 2
 1 | 2
 1 | 2
 1 | 3
(10 rows)

select a, (select (select (select c from orca.s where a=c group by c))) as subq from orca.r order by a;
ERROR:  correlated subquery with skip-level correlations is not supported
with v as (select a,b from orca.r, orca.s where a=c)  select c from orca.s group by c having count(*) not in (select b from v where a=c) order by c;
 c 
---
 0
 1
 2
 3
 4
 5
 6
(7 rows)

CREATE TABLE orca.onek (
unique1 int4,
unique2 int4,
two int4,
four int4,
ten int4,
twenty int4,
hundred int4,
thousand int4,
twothousand int4,
fivethous int4,
tenthous int4,
odd int4,
even int4,
stringu1 name,
stringu2 name,
string4 name
);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'unique1' 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.
insert into orca.onek values (931,1,1,3,1,11,1,31,131,431,931,2,3,'VJAAAA','BAAAAA','HHHHxx');
insert into orca.onek values (714,2,0,2,4,14,4,14,114,214,714,8,9,'MBAAAA','CAAAAA','OOOOxx');
insert into orca.onek values (711,3,1,3,1,11,1,11,111,211,711,2,3,'JBAAAA','DAAAAA','VVVVxx');
insert into orca.onek values (883,4,1,3,3,3,3,83,83,383,883,6,7,'ZHAAAA','EAAAAA','AAAAxx');
insert into orca.onek values (439,5,1,3,9,19,9,39,39,439,439,18,19,'XQAAAA','FAAAAA','HHHHxx');
insert into orca.onek values (670,6,0,2,0,10,0,70,70,170,670,0,1,'UZAAAA','GAAAAA','OOOOxx');
insert into orca.onek values (543,7,1,3,3,3,3,43,143,43,543,6,7,'XUAAAA','HAAAAA','VVVVxx');
select ten, sum(distinct four) from orca.onek a
group by ten
having exists (select 1 from orca.onek b where sum(distinct a.four) = b.four);
 ten | sum 
-----+-----
   0 |   2
   1 |   3
   3 |   3
   4 |   2
   9 |   3
(5 rows)

-- indexes on partitioned tables
create table orca.pp(a int) partition by range(a)(partition pp1 start(1) end(10));
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'a' 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.
NOTICE:  CREATE TABLE will create partition "pp_1_prt_pp1" for table "pp"
create index pp_a on orca.pp(a);
NOTICE:  building index for child partition "pp_1_prt_pp1"
-- list partition tests
-- test homogeneous partitions
drop table if exists orca.t;
NOTICE:  table "t" does not exist, skipping
create table orca.t ( a int, b char(2), to_be_drop int, c int, d char(2), e int)
distributed by (a)
partition by list(d) (partition part1 values('a'), partition part2 values('b'));
NOTICE:  CREATE TABLE will create partition "t_1_prt_part1" for table "t"
NOTICE:  CREATE TABLE will create partition "t_1_prt_part2" for table "t"
insert into orca.t
	select i, i::char(2), i, i, case when i%2 = 0 then 'a' else 'b' end, i
	from generate_series(1,100) i;
select * from orca.t order by 1, 2, 3, 4, 5, 6 limit 4;
 a | b  | to_be_drop | c | d  | e 
---+----+------------+---+----+---
 1 | 1  |          1 | 1 | b  | 1
 2 | 2  |          2 | 2 | a  | 2
 3 | 3  |          3 | 3 | b  | 3
 4 | 4  |          4 | 4 | a  | 4
(4 rows)

alter table orca.t drop column to_be_drop;
select * from orca.t order by 1, 2, 3, 4, 5 limit 4;
 a | b  | c | d  | e 
---+----+---+----+---
 1 | 1  | 1 | b  | 1
 2 | 2  | 2 | a  | 2
 3 | 3  | 3 | b  | 3
 4 | 4  | 4 | a  | 4
(4 rows)

insert into orca.t (d, a) values('a', 0);
insert into orca.t (a, d) values(0, 'b');
select * from orca.t order by 1, 2, 3, 4, 5 limit 4;
 a | b  | c | d  | e 
---+----+---+----+---
 0 |    |   | a  |  
 0 |    |   | b  |  
 1 | 1  | 1 | b  | 1
 2 | 2  | 2 | a  | 2
(4 rows)

create table orca.multilevel_p (a int, b int)
partition by range(a)
subpartition by range(a)
subpartition template
(
subpartition sp1 start(0) end(10) every (5),
subpartition sp2 start(10) end(200) every(50)
)
(partition aa start(0) end (100) every (50),
partition bb start(100) end(200) every (50) );
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'a' 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.
NOTICE:  CREATE TABLE will create partition "multilevel_p_1_prt_aa_1" for table "multilevel_p"
NOTICE:  CREATE TABLE will create partition "multilevel_p_1_prt_aa_1_2_prt_sp1_1" for table "multilevel_p_1_prt_aa_1"
NOTICE:  CREATE TABLE will create partition "multilevel_p_1_prt_aa_1_2_prt_sp1_2" for table "multilevel_p_1_prt_aa_1"
NOTICE:  CREATE TABLE will create partition "multilevel_p_1_prt_aa_1_2_prt_sp2_1" for table "multilevel_p_1_prt_aa_1"
NOTICE:  CREATE TABLE will create partition "multilevel_p_1_prt_aa_1_2_prt_sp2_2" for table "multilevel_p_1_prt_aa_1"
NOTICE:  CREATE TABLE will create partition "multilevel_p_1_prt_aa_1_2_prt_sp2_3" for table "multilevel_p_1_prt_aa_1"
NOTICE:  CREATE TABLE will create partition "multilevel_p_1_prt_aa_1_2_prt_sp2_4" for table "multilevel_p_1_prt_aa_1"
NOTICE:  CREATE TABLE will create partition "multilevel_p_1_prt_aa_2" for table "multilevel_p"
NOTICE:  CREATE TABLE will create partition "multilevel_p_1_prt_aa_2_2_prt_sp1_1" for table "multilevel_p_1_prt_aa_2"
NOTICE:  CREATE TABLE will create partition "multilevel_p_1_prt_aa_2_2_prt_sp1_2" for table "multilevel_p_1_prt_aa_2"
NOTICE:  CREATE TABLE will create partition "multilevel_p_1_prt_aa_2_2_prt_sp2_1" for table "multilevel_p_1_prt_aa_2"
NOTICE:  CREATE TABLE will create partition "multilevel_p_1_prt_aa_2_2_prt_sp2_2" for table "multilevel_p_1_prt_aa_2"
NOTICE:  CREATE TABLE will create partition "multilevel_p_1_prt_aa_2_2_prt_sp2_3" for table "multilevel_p_1_prt_aa_2"
NOTICE:  CREATE TABLE will create partition "multilevel_p_1_prt_aa_2_2_prt_sp2_4" for table "multilevel_p_1_prt_aa_2"
NOTICE:  CREATE TABLE will create partition "multilevel_p_1_prt_bb_1" for table "multilevel_p"
NOTICE:  CREATE TABLE will create partition "multilevel_p_1_prt_bb_1_2_prt_sp1_1" for table "multilevel_p_1_prt_bb_1"
NOTICE:  CREATE TABLE will create partition "multilevel_p_1_prt_bb_1_2_prt_sp1_2" for table "multilevel_p_1_prt_bb_1"
NOTICE:  CREATE TABLE will create partition "multilevel_p_1_prt_bb_1_2_prt_sp2_1" for table "multilevel_p_1_prt_bb_1"
NOTICE:  CREATE TABLE will create partition "multilevel_p_1_prt_bb_1_2_prt_sp2_2" for table "multilevel_p_1_prt_bb_1"
NOTICE:  CREATE TABLE will create partition "multilevel_p_1_prt_bb_1_2_prt_sp2_3" for table "multilevel_p_1_prt_bb_1"
NOTICE:  CREATE TABLE will create partition "multilevel_p_1_prt_bb_1_2_prt_sp2_4" for table "multilevel_p_1_prt_bb_1"
NOTICE:  CREATE TABLE will create partition "multilevel_p_1_prt_bb_2" for table "multilevel_p"
NOTICE:  CREATE TABLE will create partition "multilevel_p_1_prt_bb_2_2_prt_sp1_1" for table "multilevel_p_1_prt_bb_2"
NOTICE:  CREATE TABLE will create partition "multilevel_p_1_prt_bb_2_2_prt_sp1_2" for table "multilevel_p_1_prt_bb_2"
NOTICE:  CREATE TABLE will create partition "multilevel_p_1_prt_bb_2_2_prt_sp2_1" for table "multilevel_p_1_prt_bb_2"
NOTICE:  CREATE TABLE will create partition "multilevel_p_1_prt_bb_2_2_prt_sp2_2" for table "multilevel_p_1_prt_bb_2"
NOTICE:  CREATE TABLE will create partition "multilevel_p_1_prt_bb_2_2_prt_sp2_3" for table "multilevel_p_1_prt_bb_2"
NOTICE:  CREATE TABLE will create partition "multilevel_p_1_prt_bb_2_2_prt_sp2_4" for table "multilevel_p_1_prt_bb_2"
insert into orca.multilevel_p values (1,1), (100,200);
select * from orca.multilevel_p;
  a  |  b  
-----+-----
   1 |   1
 100 | 200
(2 rows)

-- test appendonly
drop table if exists orca.t;
create table orca.t ( a int, b char(2), to_be_drop int, c int, d char(2), e int)
distributed by (a)
partition by list(d) (partition part1 values('a') with (appendonly=true, compresslevel=5, orientation=column), partition part2 values('b') with (appendonly=true, compresslevel=5, orientation=column));
NOTICE:  CREATE TABLE will create partition "t_1_prt_part1" for table "t"
NOTICE:  CREATE TABLE will create partition "t_1_prt_part2" for table "t"
insert into orca.t
	select i, i::char(2), i, i, case when i%2 = 0 then 'a' else 'b' end, i
	from generate_series(1,100) i;
select * from orca.t order by 1, 2, 3, 4, 5, 6 limit 4;
 a | b  | to_be_drop | c | d  | e 
---+----+------------+---+----+---
 1 | 1  |          1 | 1 | b  | 1
 2 | 2  |          2 | 2 | a  | 2
 3 | 3  |          3 | 3 | b  | 3
 4 | 4  |          4 | 4 | a  | 4
(4 rows)

alter table orca.t drop column to_be_drop;
select * from orca.t order by 1, 2, 3, 4, 5 limit 4;
 a | b  | c | d  | e 
---+----+---+----+---
 1 | 1  | 1 | b  | 1
 2 | 2  | 2 | a  | 2
 3 | 3  | 3 | b  | 3
 4 | 4  | 4 | a  | 4
(4 rows)

insert into orca.t
	select i, i::char(2), i, case when i%2 = 0 then 'a' else 'b' end, i
	from generate_series(1,100) i;
select * from orca.t order by 1, 2, 3, 4, 5 limit 4;
 a | b  | c | d  | e 
---+----+---+----+---
 1 | 1  | 1 | b  | 1
 1 | 1  | 1 | b  | 1
 2 | 2  | 2 | a  | 2
 2 | 2  | 2 | a  | 2
(4 rows)

-- test heterogeneous partitions
drop table if exists orca.t;
create table orca.t ( timest character varying(6), user_id numeric(16,0) not null, to_be_drop char(5), tag1 char(5), tag2 char(5))
distributed by (user_id)
partition by list (timest) (partition part201203 values('201203') with (appendonly=true, compresslevel=5, orientation=column), partition part201204 values('201204') with (appendonly=true, compresslevel=5, orientation=row), partition part201205 values('201205'));
NOTICE:  CREATE TABLE will create partition "t_1_prt_part201203" for table "t"
NOTICE:  CREATE TABLE will create partition "t_1_prt_part201204" for table "t"
NOTICE:  CREATE TABLE will create partition "t_1_prt_part201205" for table "t"
insert into orca.t values('201203',0,'drop', 'tag1','tag2');
alter table orca.t drop column to_be_drop;
alter table orca.t add partition part201206 values('201206') with (appendonly=true, compresslevel=5, orientation=column);
NOTICE:  CREATE TABLE will create partition "t_1_prt_part201206" for table "t"
alter table orca.t add partition part201207 values('201207') with (appendonly=true, compresslevel=5, orientation=row);
NOTICE:  CREATE TABLE will create partition "t_1_prt_part201207" for table "t"
alter table orca.t add partition part201208 values('201208');
NOTICE:  CREATE TABLE will create partition "t_1_prt_part201208" for table "t"
insert into orca.t values('201203',1,'tag1','tag2');
insert into orca.t values('201204',2,'tag1','tag2');
insert into orca.t values('201205',1,'tag1','tag2');
insert into orca.t values('201206',2,'tag1','tag2');
insert into orca.t values('201207',1,'tag1','tag2');
insert into orca.t values('201208',2,'tag1','tag2');
-- test projections
select * from orca.t order by 1,2;
 timest | user_id | tag1  | tag2  
--------+---------+-------+-------
 201203 |       0 | tag1  | tag2 
 201203 |       1 | tag1  | tag2 
 201204 |       2 | tag1  | tag2 
 201205 |       1 | tag1  | tag2 
 201206 |       2 | tag1  | tag2 
 201207 |       1 | tag1  | tag2 
 201208 |       2 | tag1  | tag2 
(7 rows)

-- test EXPLAIN support of partition selection nodes, while we're at it.
explain select * from orca.t order by 1,2;
                                        QUERY PLAN                                        
------------------------------------------------------------------------------------------
 Gather Motion 3:1  (slice1; segments: 3)  (cost=15864.38..16260.38 rows=158400 width=94)
   Merge Key: t_1_prt_part201203.timest, t_1_prt_part201203.user_id
   ->  Sort  (cost=15864.38..16260.38 rows=52800 width=94)
         Sort Key: t_1_prt_part201203.timest, t_1_prt_part201203.user_id
         ->  Append  (cost=0.00..2184.00 rows=52800 width=94)
               ->  Seq Scan on t_1_prt_part201203  (cost=0.00..364.00 rows=8800 width=94)
               ->  Seq Scan on t_1_prt_part201204  (cost=0.00..364.00 rows=8800 width=94)
               ->  Seq Scan on t_1_prt_part201205  (cost=0.00..364.00 rows=8800 width=94)
               ->  Seq Scan on t_1_prt_part201206  (cost=0.00..364.00 rows=8800 width=94)
               ->  Seq Scan on t_1_prt_part201207  (cost=0.00..364.00 rows=8800 width=94)
               ->  Seq Scan on t_1_prt_part201208  (cost=0.00..364.00 rows=8800 width=94)
 Optimizer: Postgres query optimizer
(12 rows)

select tag2, tag1 from orca.t order by 1, 2;;
 tag2  | tag1  
-------+-------
 tag2  | tag1 
 tag2  | tag1 
 tag2  | tag1 
 tag2  | tag1 
 tag2  | tag1 
 tag2  | tag1 
 tag2  | tag1 
(7 rows)

select tag1, user_id from orca.t order by 1, 2;
 tag1  | user_id 
-------+---------
 tag1  |       0
 tag1  |       1
 tag1  |       1
 tag1  |       1
 tag1  |       2
 tag1  |       2
 tag1  |       2
(7 rows)

insert into orca.t(user_id, timest, tag2) values(3, '201208','tag2');
select * from orca.t order by 1, 2;
 timest | user_id | tag1  | tag2  
--------+---------+-------+-------
 201203 |       0 | tag1  | tag2 
 201203 |       1 | tag1  | tag2 
 201204 |       2 | tag1  | tag2 
 201205 |       1 | tag1  | tag2 
 201206 |       2 | tag1  | tag2 
 201207 |       1 | tag1  | tag2 
 201208 |       2 | tag1  | tag2 
 201208 |       3 |       | tag2 
(8 rows)

-- test heterogeneous indexes with constant expression evaluation
drop table if exists orca.t_date;
NOTICE:  table "t_date" does not exist, skipping
create table orca.t_date ( timest date, user_id numeric(16,0) not null, tag1 char(5), tag2 char(5))
distributed by (user_id)
partition by list (timest) (partition part201203 values('01-03-2012'::date), partition part201204 values('01-04-2012'::date), partition part201205 values('01-05-2012'::date));
NOTICE:  CREATE TABLE will create partition "t_date_1_prt_part201203" for table "t_date"
NOTICE:  CREATE TABLE will create partition "t_date_1_prt_part201204" for table "t_date"
NOTICE:  CREATE TABLE will create partition "t_date_1_prt_part201205" for table "t_date"
create index user_id_idx on orca.t_date_1_prt_part201203(user_id);
alter table orca.t_date add partition part201206 values('01-06-2012'::date);
NOTICE:  CREATE TABLE will create partition "t_date_1_prt_part201206" for table "t_date"
alter table orca.t_date add partition part201207 values('01-07-2012'::date);
NOTICE:  CREATE TABLE will create partition "t_date_1_prt_part201207" for table "t_date"
alter table orca.t_date add partition part201208 values('01-08-2012'::date);
NOTICE:  CREATE TABLE will create partition "t_date_1_prt_part201208" for table "t_date"
insert into orca.t_date values('01-03-2012'::date,0,'tag1','tag2');
insert into orca.t_date values('01-03-2012'::date,1,'tag1','tag2');
insert into orca.t_date values('01-04-2012'::date,2,'tag1','tag2');
insert into orca.t_date values('01-05-2012'::date,1,'tag1','tag2');
insert into orca.t_date values('01-06-2012'::date,2,'tag1','tag2');
insert into orca.t_date values('01-07-2012'::date,1,'tag1','tag2');
insert into orca.t_date values('01-08-2012'::date,2,'tag1','tag2');
insert into orca.t_date values('01-03-2012'::date,2,'tag1','tag2');
insert into orca.t_date values('01-03-2012'::date,3,'tag1','tag2');
insert into orca.t_date values('01-03-2012'::date,4,'tag1','tag2');
insert into orca.t_date values('01-03-2012'::date,5,'tag1','tag2');
insert into orca.t_date values('01-03-2012'::date,6,'tag1','tag2');
insert into orca.t_date values('01-03-2012'::date,7,'tag1','tag2');
insert into orca.t_date values('01-03-2012'::date,8,'tag1','tag2');
insert into orca.t_date values('01-03-2012'::date,9,'tag1','tag2');
set optimizer_enable_partial_index=on;
set optimizer_enable_space_pruning=off;
set optimizer_enable_constant_expression_evaluation=on;
set optimizer_enumerate_plans=on;
set optimizer_plan_id = 2;
explain select * from orca.t_date where user_id=9;
                                     QUERY PLAN                                     
------------------------------------------------------------------------------------
 Gather Motion 1:1  (slice1; segments: 1)  (cost=0.00..8.19 rows=6 width=21)
   ->  Append  (cost=0.00..8.19 rows=2 width=21)
         ->  Seq Scan on t_date_1_prt_part201203  (cost=0.00..3.12 rows=1 width=20)
               Filter: user_id = 9::numeric
         ->  Seq Scan on t_date_1_prt_part201204  (cost=0.00..1.01 rows=1 width=21)
               Filter: user_id = 9::numeric
         ->  Seq Scan on t_date_1_prt_part201205  (cost=0.00..1.01 rows=1 width=21)
               Filter: user_id = 9::numeric
         ->  Seq Scan on t_date_1_prt_part201206  (cost=0.00..1.01 rows=1 width=21)
               Filter: user_id = 9::numeric
         ->  Seq Scan on t_date_1_prt_part201207  (cost=0.00..1.01 rows=1 width=21)
               Filter: user_id = 9::numeric
         ->  Seq Scan on t_date_1_prt_part201208  (cost=0.00..1.01 rows=1 width=21)
               Filter: user_id = 9::numeric
 Optimizer: Postgres query optimizer
(15 rows)

select * from orca.t_date where user_id=9;
   timest   | user_id | tag1  | tag2  
------------+---------+-------+-------
 01-03-2012 |       9 | tag1  | tag2 
(1 row)

reset optimizer_enable_space_pruning;
set optimizer_enumerate_plans=off;
set optimizer_enable_constant_expression_evaluation=off;
drop table if exists orca.t_text;
NOTICE:  table "t_text" does not exist, skipping
drop index if exists orca.user_id_idx;
create table orca.t_text ( timest date, user_id numeric(16,0) not null, tag1 char(5), tag2 char(5))
distributed by (user_id)
partition by list(tag1) (partition partgood values('good'::text), partition partbad values('bad'::text), partition partugly values('ugly'::text));
NOTICE:  CREATE TABLE will create partition "t_text_1_prt_partgood" for table "t_text"
NOTICE:  CREATE TABLE will create partition "t_text_1_prt_partbad" for table "t_text"
NOTICE:  CREATE TABLE will create partition "t_text_1_prt_partugly" for table "t_text"
create index user_id_idx on orca.t_text_1_prt_partgood(user_id);
insert into orca.t_text values('01-03-2012'::date,0,'good','tag2');
insert into orca.t_text values('01-03-2012'::date,1,'bad','tag2');
insert into orca.t_text values('01-04-2012'::date,2,'ugly','tag2');
insert into orca.t_text values('01-05-2012'::date,1,'good','tag2');
insert into orca.t_text values('01-06-2012'::date,2,'bad','tag2');
insert into orca.t_text values('01-07-2012'::date,1,'ugly','tag2');
insert into orca.t_text values('01-08-2012'::date,2,'good','tag2');
insert into orca.t_text values('01-03-2012'::date,2,'bad','tag2');
insert into orca.t_text values('01-03-2012'::date,3,'ugly','tag2');
insert into orca.t_text values('01-03-2012'::date,4,'good','tag2');
insert into orca.t_text values('01-03-2012'::date,5,'bad','tag2');
insert into orca.t_text values('01-03-2012'::date,6,'ugly','tag2');
insert into orca.t_text values('01-03-2012'::date,7,'good','tag2');
insert into orca.t_text values('01-03-2012'::date,8,'bad','tag2');
insert into orca.t_text values('01-03-2012'::date,9,'ugly','tag2');
set optimizer_enable_space_pruning=off;
set optimizer_enable_constant_expression_evaluation=on;
set optimizer_enumerate_plans=on;
set optimizer_plan_id = 2;
explain select * from orca.t_text where user_id=9;
                                    QUERY PLAN                                    
----------------------------------------------------------------------------------
 Gather Motion 1:1  (slice1; segments: 1)  (cost=0.00..7.19 rows=3 width=21)
   ->  Append  (cost=0.00..7.19 rows=1 width=21)
         ->  Seq Scan on t_text_1_prt_partgood  (cost=0.00..3.06 rows=1 width=20)
               Filter: user_id = 9::numeric
         ->  Seq Scan on t_text_1_prt_partbad  (cost=0.00..2.06 rows=1 width=21)
               Filter: user_id = 9::numeric
         ->  Seq Scan on t_text_1_prt_partugly  (cost=0.00..2.06 rows=1 width=21)
               Filter: user_id = 9::numeric
 Optimizer: Postgres query optimizer
(9 rows)

select * from orca.t_text where user_id=9;
   timest   | user_id | tag1  | tag2  
------------+---------+-------+-------
 01-03-2012 |       9 | ugly  | tag2 
(1 row)

reset optimizer_enable_space_pruning;
set optimizer_enumerate_plans=off;
set optimizer_enable_constant_expression_evaluation=off;
-- create a user defined type and only define equality on it
-- the type can be used in the partitioning list, but Orca is not able to pick a heterogenous index
-- because constraint derivation needs all comparison operators
drop type if exists orca.employee cascade;
NOTICE:  type "orca.employee" does not exist, skipping
create type orca.employee as (empid int, empname text);
create function orca.emp_equal(orca.employee, orca.employee) returns boolean
  as 'select $1.empid = $2.empid;'
  language sql
  immutable
  returns null on null input;
create operator pg_catalog.= (
        leftarg = orca.employee,
        rightarg = orca.employee,
        procedure = orca.emp_equal
);
create operator class orca.employee_op_class default for type orca.employee
  using btree as
  operator 3 =;
drop table if exists orca.t_employee;
NOTICE:  table "t_employee" does not exist, skipping
create table orca.t_employee(timest date, user_id numeric(16,0) not null, tag1 char(5), emp orca.employee)
  distributed by (user_id)
  partition by list(emp)
  (partition part1 values('(1, ''foo'')'::orca.employee), partition part2 values('(2, ''foo'')'::orca.employee));
NOTICE:  CREATE TABLE will create partition "t_employee_1_prt_part1" for table "t_employee"
NOTICE:  CREATE TABLE will create partition "t_employee_1_prt_part2" for table "t_employee"
create index user_id_idx1 on orca.t_employee_1_prt_part1(user_id);
create index user_id_idx2 on orca.t_employee_1_prt_part2(user_id);
insert into orca.t_employee values('01-03-2012'::date,0,'tag1','(1, ''foo'')'::orca.employee);
insert into orca.t_employee values('01-03-2012'::date,1,'tag1','(1, ''foo'')'::orca.employee);
insert into orca.t_employee values('01-04-2012'::date,2,'tag1','(2, ''foo'')'::orca.employee);
insert into orca.t_employee values('01-05-2012'::date,1,'tag1','(1, ''foo'')'::orca.employee);
insert into orca.t_employee values('01-06-2012'::date,2,'tag1','(2, ''foo'')'::orca.employee);
insert into orca.t_employee values('01-07-2012'::date,1,'tag1','(1, ''foo'')'::orca.employee);
insert into orca.t_employee values('01-08-2012'::date,2,'tag1','(2, ''foo'')'::orca.employee);
set optimizer_enable_constant_expression_evaluation=on;
set optimizer_enable_dynamictablescan = off;
explain select * from orca.t_employee where user_id = 2;
                                    QUERY PLAN                                     
-----------------------------------------------------------------------------------
 Gather Motion 1:1  (slice1; segments: 1)  (cost=0.00..3.09 rows=4 width=47)
   ->  Append  (cost=0.00..3.09 rows=2 width=47)
         ->  Seq Scan on t_employee_1_prt_part1  (cost=0.00..2.05 rows=1 width=46)
               Filter: user_id = 2::numeric
         ->  Seq Scan on t_employee_1_prt_part2  (cost=0.00..1.04 rows=1 width=47)
               Filter: user_id = 2::numeric
 Optimizer: Postgres query optimizer
(7 rows)

select * from orca.t_employee where user_id = 2;
   timest   | user_id | tag1  |     emp      
------------+---------+-------+--------------
 01-04-2012 |       2 | tag1  | (2," 'foo'")
 01-06-2012 |       2 | tag1  | (2," 'foo'")
 01-08-2012 |       2 | tag1  | (2," 'foo'")
(3 rows)

reset optimizer_enable_dynamictablescan;
reset optimizer_enable_constant_expression_evaluation;
reset optimizer_enable_partial_index;
-- test that constant expression evaluation works with integers
drop table if exists orca.t_ceeval_ints;
NOTICE:  table "t_ceeval_ints" does not exist, skipping
create table orca.t_ceeval_ints(user_id numeric(16,0), category_id int, tag1 char(5), tag2 char(5))
distributed by (user_id)
partition by list (category_id)
	(partition part100 values('100') , partition part101 values('101'), partition part103 values('102'));
NOTICE:  CREATE TABLE will create partition "t_ceeval_ints_1_prt_part100" for table "t_ceeval_ints"
NOTICE:  CREATE TABLE will create partition "t_ceeval_ints_1_prt_part101" for table "t_ceeval_ints"
NOTICE:  CREATE TABLE will create partition "t_ceeval_ints_1_prt_part103" for table "t_ceeval_ints"
create index user_id_ceeval_ints on orca.t_ceeval_ints_1_prt_part101(user_id);
insert into orca.t_ceeval_ints values(1, 100, 'tag1', 'tag2');
insert into orca.t_ceeval_ints values(2, 100, 'tag1', 'tag2');
insert into orca.t_ceeval_ints values(3, 100, 'tag1', 'tag2');
insert into orca.t_ceeval_ints values(4, 101, 'tag1', 'tag2');
insert into orca.t_ceeval_ints values(5, 102, 'tag1', 'tag2');
set optimizer_enable_partial_index=on;
set optimizer_enable_space_pruning=off;
set optimizer_enable_constant_expression_evaluation=on;
set optimizer_use_external_constant_expression_evaluation_for_ints = on;
set optimizer_enumerate_plans=on;
set optimizer_plan_id = 2;
explain select * from orca.t_ceeval_ints where user_id=4;
                                       QUERY PLAN                                       
----------------------------------------------------------------------------------------
 Gather Motion 1:1  (slice1; segments: 1)  (cost=0.00..4.06 rows=3 width=21)
   ->  Append  (cost=0.00..4.06 rows=1 width=21)
         ->  Seq Scan on t_ceeval_ints_1_prt_part100  (cost=0.00..2.04 rows=1 width=21)
               Filter: user_id = 4::numeric
         ->  Seq Scan on t_ceeval_ints_1_prt_part101  (cost=0.00..1.01 rows=1 width=21)
               Filter: user_id = 4::numeric
         ->  Seq Scan on t_ceeval_ints_1_prt_part103  (cost=0.00..1.01 rows=1 width=21)
               Filter: user_id = 4::numeric
 Optimizer: Postgres query optimizer
(9 rows)

select * from orca.t_ceeval_ints where user_id=4;
 user_id | category_id | tag1  | tag2  
---------+-------------+-------+-------
       4 |         101 | tag1  | tag2 
(1 row)

reset optimizer_enable_space_pruning;
reset optimizer_enumerate_plans;
reset optimizer_use_external_constant_expression_evaluation_for_ints;
reset optimizer_enable_constant_expression_evaluation;
reset optimizer_enable_partial_index;
-- test project elements in TVF
CREATE FUNCTION orca.csq_f(a int) RETURNS int AS $$ select $1 $$ LANGUAGE SQL;
CREATE TABLE orca.csq_r(a int);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'a' 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.
INSERT INTO orca.csq_r VALUES (1);
SELECT * FROM orca.csq_r WHERE a IN (SELECT * FROM orca.csq_f(orca.csq_r.a));
 a 
---
 1
(1 row)

-- test algebrization of having clause
drop table if exists orca.tab1;
NOTICE:  table "tab1" does not exist, skipping
create table orca.tab1(a int, b int, c int, d int, e int);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'a' 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.
insert into orca.tab1 values (1,2,3,4,5);
insert into orca.tab1 values (1,2,3,4,5);
insert into orca.tab1 values (1,2,3,4,5);
select b,d from orca.tab1 group by b,d having min(distinct d)>3;
 b | d 
---+---
 2 | 4
(1 row)

select b,d from orca.tab1 group by b,d having d>3;
 b | d 
---+---
 2 | 4
(1 row)

select b,d from orca.tab1 group by b,d having min(distinct d)>b;
 b | d 
---+---
 2 | 4
(1 row)

create table orca.fooh1 (a int, b int, c int);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'a' 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 orca.fooh2 (a int, b int, c int);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'a' 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.
insert into orca.fooh1 select i%4, i%3, i from generate_series(1,20) i;
insert into orca.fooh2 select i%3, i%2, i from generate_series(1,20) i;
select sum(f1.b) from orca.fooh1 f1 group by f1.a;
 sum 
-----
   6
   5
   6
   4
(4 rows)

select 1 as one, f1.a from orca.fooh1 f1 group by f1.a having sum(f1.b) > 4;
 one | a 
-----+---
   1 | 1
   1 | 2
   1 | 0
(3 rows)

select f1.a, 1 as one from orca.fooh1 f1 group by f1.a having 10 > (select f2.a from orca.fooh2 f2 group by f2.a having sum(f1.a) > count(*) order by f2.a limit 1) order by f1.a;
 a | one 
---+-----
 2 |   1
 3 |   1
(2 rows)

select 1 from orca.fooh1 f1 group by f1.a having 10 > (select f2.a from orca.fooh2 f2 group by f2.a having sum(f1.a) > count(*) order by f2.a limit 1) order by f1.a;
 ?column? 
----------
        1
        1
(2 rows)

select f1.a, 1 as one from orca.fooh1 f1 group by f1.a having 10 > (select 1 from orca.fooh2 f2 group by f2.a having sum(f1.b) > count(*) order by f2.a limit 1) order by f1.a;
 a | one 
---+-----
(0 rows)

select 1 from orca.fooh1 f1 group by f1.a having 10 > (select 1 from orca.fooh2 f2 group by f2.a having sum(f1.b) > count(*) order by f2.a limit 1) order by f1.a;
 ?column? 
----------
(0 rows)

select f1.a, 1 as one from orca.fooh1 f1 group by f1.a having 0 = (select f2.a from orca.fooh2 f2 group by f2.a having sum(f2.b) > 1 order by f2.a limit 1) order by f1.a;
 a | one 
---+-----
 0 |   1
 1 |   1
 2 |   1
 3 |   1
(4 rows)

select 1 as one from orca.fooh1 f1 group by f1.a having 0 = (select f2.a from orca.fooh2 f2 group by f2.a having sum(f2.b) > 1 order by f2.a limit 1) order by f1.a;
 one 
-----
   1
   1
   1
   1
(4 rows)

select f1.a, 1 as one from orca.fooh1 f1 group by f1.a having 0 = (select f2.a from orca.fooh2 f2 group by f2.a having sum(f2.b) > 1 order by f2.a limit 1) order by f1.a;
 a | one 
---+-----
 0 |   1
 1 |   1
 2 |   1
 3 |   1
(4 rows)

select f1.a, 1 as one from orca.fooh1 f1 group by f1.a having 0 = (select f2.a from orca.fooh2 f2 group by f2.a having sum(f2.b + f1.a) > 1 order by f2.a limit 1) order by f1.a;
 a | one 
---+-----
 0 |   1
 1 |   1
 2 |   1
 3 |   1
(4 rows)

select f1.a, 1 as one from orca.fooh1 f1 group by f1.a having 0 = (select f2.a from orca.fooh2 f2 group by f2.a having sum(f2.b + sum(f1.b)) > 1 order by f2.a limit 1) order by f1.a;
 a | one 
---+-----
 0 |   1
 1 |   1
 2 |   1
 3 |   1
(4 rows)

select f1.a, 1 as one from orca.fooh1 f1 group by f1.a having f1.a < (select f2.a from orca.fooh2 f2 group by f2.a having sum(f2.b + 1) > f1.a order by f2.a desc limit 1) order by f1.a;
 a | one 
---+-----
 0 |   1
 1 |   1
(2 rows)

select f1.a, 1 as one from orca.fooh1 f1 group by f1.a having f1.a = (select f2.a from orca.fooh2 f2 group by f2.a having sum(f2.b) + 1 > f1.a order by f2.a desc limit 1);
 a | one 
---+-----
 2 |   1
(1 row)

select f1.a, 1 as one from orca.fooh1 f1 group by f1.a having f1.a = (select f2.a from orca.fooh2 f2 group by f2.a having sum(f2.b) > 1 order by f2.a limit 1);
 a | one 
---+-----
 0 |   1
(1 row)

select sum(f1.a+1)+1 from orca.fooh1 f1 group by f1.a+1;
 ?column? 
----------
       21
       16
        6
       11
(4 rows)

select sum(f1.a+1)+sum(f1.a+1) from orca.fooh1 f1 group by f1.a+1;
 ?column? 
----------
       40
       30
       10
       20
(4 rows)

select sum(f1.a+1)+avg(f1.a+1), sum(f1.a), sum(f1.a+1) from orca.fooh1 f1 group by f1.a+1;
        ?column?        | sum | sum 
------------------------+-----+-----
    24.0000000000000000 |  15 |  20
    18.0000000000000000 |  10 |  15
 6.00000000000000000000 |   0 |   5
    12.0000000000000000 |   5 |  10
(4 rows)

create table orca.t77(C952 text) WITH (compresstype=zlib,compresslevel=2,appendonly=true,blocksize=393216,checksum=true);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'c952' 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.
insert into orca.t77 select 'text'::text;
insert into orca.t77 select 'mine'::text;
insert into orca.t77 select 'apple'::text;
insert into orca.t77 select 'orange'::text;
SELECT to_char(AVG( char_length(DT466.C952) ), '9999999.9999999'), MAX( char_length(DT466.C952) ) FROM orca.t77 DT466 GROUP BY char_length(DT466.C952);
     to_char      | max 
------------------+-----
        6.0000000 |   6
        4.0000000 |   4
        5.0000000 |   5
(3 rows)

create table orca.prod9 (sale integer, prodnm varchar,price integer);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'sale' 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.
insert into orca.prod9 values (100, 'shirts', 500);
insert into orca.prod9 values (200, 'pants',800);
insert into orca.prod9 values (300, 't-shirts', 300);
-- returning product and price using Having and Group by clause
select prodnm, price from orca.prod9 GROUP BY prodnm, price HAVING price !=300;
 prodnm | price 
--------+-------
 pants  |   800
 shirts |   500
(2 rows)

-- analyze on tables with dropped attributes
create table orca.toanalyze(a int, b int);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'a' 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.
insert into orca.toanalyze values (1,1), (2,2), (3,3);
alter table orca.toanalyze drop column a;
NOTICE:  dropping a column that is part of the distribution policy forces a NULL distribution policy
analyze orca.toanalyze;
-- union
create table orca.ur (a int, b int);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'a' 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 orca.us (c int, d int);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'c' 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 orca.ut(a int);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'a' 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 orca.uu(c int, d bigint);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'c' 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.
insert into orca.ur values (1,1);
insert into orca.ur values (1,2);
insert into orca.ur values (2,1);
insert into orca.us values (1,3);
insert into orca.ut values (3);
insert into orca.uu values (1,3);
select * from (select a, a from orca.ur union select c, d from orca.us) x(g,h);
 g | h 
---+---
 1 | 1
 1 | 3
 2 | 2
(3 rows)

select * from (select a, a from orca.ur union select c, d from orca.us) x(g,h), orca.ut t where t.a = x.h;
 g | h | a 
---+---+---
 1 | 3 | 3
(1 row)

select * from (select a, a from orca.ur union select c, d from orca.uu) x(g,h), orca.ut t where t.a = x.h;
 g | h | a 
---+---+---
 1 | 3 | 3
(1 row)

select 1 AS two UNION select 2.2;
 two 
-----
   1
 2.2
(2 rows)

select 2.2 AS two UNION select 1;
 two 
-----
   1
 2.2
(2 rows)

select * from (select 2.2 AS two UNION select 1) x(a), (select 1.0 AS two UNION ALL select 1) y(a) where y.a = x.a;
 a |  a  
---+-----
 1 | 1.0
 1 |   1
(2 rows)

-- window functions inside inline CTE
CREATE TABLE orca.twf1 AS SELECT i as a, i+1 as b from generate_series(1,10)i;
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column(s) named 'a' 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 orca.twf2 AS SELECT i as c, i+1 as d from generate_series(1,10)i;
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column(s) named 'c' 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 optimizer_cte_inlining_bound=1000;
SET optimizer_cte_inlining = on;
WITH CTE(a,b) AS
(SELECT a,d FROM orca.twf1, orca.twf2 WHERE a = d),
CTE1(e,f) AS
( SELECT f1.a, rank() OVER (PARTITION BY f1.b ORDER BY CTE.a) FROM orca.twf1 f1, CTE )
SELECT * FROM CTE1,CTE WHERE CTE.a = CTE1.f and CTE.a = 2 ORDER BY 1;
 e  | f | a | b 
----+---+---+---
  1 | 2 | 2 | 2
  2 | 2 | 2 | 2
  3 | 2 | 2 | 2
  4 | 2 | 2 | 2
  5 | 2 | 2 | 2
  6 | 2 | 2 | 2
  7 | 2 | 2 | 2
  8 | 2 | 2 | 2
  9 | 2 | 2 | 2
 10 | 2 | 2 | 2
(10 rows)

SET optimizer_cte_inlining = off;
-- catalog queries
select 1 from pg_class c group by c.oid limit 1;
 ?column? 
----------
        1
(1 row)

-- CSQs
drop table if exists orca.tab1;
drop table if exists orca.tab2;
NOTICE:  table "tab2" does not exist, skipping
create table orca.tab1 (i, j) as select i,i%2 from generate_series(1,10) i;
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column(s) named 'i' 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 orca.tab2 (a, b) as select 1, 2;
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column(s) named 'a' 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.
select * from orca.tab1 where 0 < (select count(*) from generate_series(1,i)) order by 1;
 i  | j 
----+---
  1 | 1
  2 | 0
  3 | 1
  4 | 0
  5 | 1
  6 | 0
  7 | 1
  8 | 0
  9 | 1
 10 | 0
(10 rows)

select * from orca.tab1 where i > (select b from orca.tab2);
 i  | j 
----+---
  3 | 1
  4 | 0
  5 | 1
  6 | 0
  7 | 1
  8 | 0
  9 | 1
 10 | 0
(8 rows)

-- subqueries
select NULL in (select 1);
 ?column? 
----------
 
(1 row)

select 1 in (select 1);
 ?column? 
----------
 t
(1 row)

select 1 in (select 2);
 ?column? 
----------
 f
(1 row)

select NULL in (select 1/0);
ERROR:  division by zero
select 1 where 22 in (SELECT unnest(array[1,2]));
 ?column? 
----------
(0 rows)

select 1 where 22 not in (SELECT unnest(array[1,2]));
 ?column? 
----------
        1
(1 row)

select 1 where 22 in (SELECT generate_series(1,10));
 ?column? 
----------
(0 rows)

select 1 where 22 not in (SELECT generate_series(1,10));
 ?column? 
----------
        1
(1 row)

-- UDAs
CREATE FUNCTION sum_sfunc(anyelement,anyelement) returns anyelement AS 'select $1+$2' LANGUAGE SQL STRICT;
CREATE FUNCTION sum_combinefunc(anyelement,anyelement) returns anyelement AS 'select $1+$2' LANGUAGE SQL STRICT;
CREATE AGGREGATE myagg1(anyelement) (SFUNC = sum_sfunc, COMBINEFUNC = sum_combinefunc, STYPE = anyelement, INITCOND = '0');
SELECT myagg1(i) FROM orca.tab1;
 myagg1 
--------
     55
(1 row)

CREATE FUNCTION sum_sfunc2(anyelement,anyelement,anyelement) returns anyelement AS 'select $1+$2+$3' LANGUAGE SQL STRICT;
CREATE AGGREGATE myagg2(anyelement,anyelement) (SFUNC = sum_sfunc2, STYPE = anyelement, INITCOND = '0');
SELECT myagg2(i,j) FROM orca.tab1;
 myagg2 
--------
     60
(1 row)

CREATE FUNCTION gptfp(anyarray,anyelement) RETURNS anyarray AS 'select $1 || $2' LANGUAGE SQL;
CREATE FUNCTION gpffp(anyarray) RETURNS anyarray AS 'select $1' LANGUAGE SQL;
CREATE AGGREGATE myagg3(BASETYPE = anyelement, SFUNC = gptfp, STYPE = anyarray, FINALFUNC = gpffp, INITCOND = '{}');
CREATE TABLE array_table(f1 int, f2 int[], f3 text);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'f1' 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.
INSERT INTO array_table values(1,array[1],'a');
INSERT INTO array_table values(2,array[11],'b');
INSERT INTO array_table values(3,array[111],'c');
INSERT INTO array_table values(4,array[2],'a');
INSERT INTO array_table values(5,array[22],'b');
INSERT INTO array_table values(6,array[222],'c');
INSERT INTO array_table values(7,array[3],'a');
INSERT INTO array_table values(8,array[3],'b');
SELECT f3, myagg3(f1) from (select * from array_table order by f1 limit 10) as foo GROUP BY f3 ORDER BY f3;
 f3 | myagg3  
----+---------
 a  | {1,4,7}
 b  | {5,2,8}
 c  | {3,6}
(3 rows)

-- MPP-22453: wrong result in indexscan when the indexqual compares different data types
create table mpp22453(a int, d date);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'a' 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.
insert into mpp22453 values (1, '2012-01-01'), (2, '2012-01-02'), (3, '2012-12-31');
create index mpp22453_idx on mpp22453(d);
set optimizer_enable_tablescan = off;
select * from mpp22453 where d > date '2012-01-31' + interval '1 day' ;
 a |     d      
---+------------
 3 | 12-31-2012
(1 row)

select * from mpp22453 where d > '2012-02-01';
 a |     d      
---+------------
 3 | 12-31-2012
(1 row)

reset optimizer_enable_tablescan;
-- MPP-22791: SIGSEGV when querying a table with default partition only
create table mpp22791(a int, b int) partition by range(b) (default partition d);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'a' 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.
NOTICE:  CREATE TABLE will create partition "mpp22791_1_prt_d" for table "mpp22791"
insert into mpp22791 values (1, 1), (2, 2), (3, 3);
select * from mpp22791 where b > 1;
 a | b 
---+---
 2 | 2
 3 | 3
(2 rows)

select * from mpp22791 where b <= 3;
 a | b 
---+---
 1 | 1
 2 | 2
 3 | 3
(3 rows)

-- MPP-20713, MPP-20714, MPP-20738: Const table get with a filter
select 1 as x where 1 in (2, 3);
 x 
---
(0 rows)

-- MPP-23081: keys of partitioned tables
create table orca.p1(a int) partition by range(a)(partition pp1 start(1) end(10), partition pp2 start(10) end(20));
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'a' 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.
NOTICE:  CREATE TABLE will create partition "p1_1_prt_pp1" for table "p1"
NOTICE:  CREATE TABLE will create partition "p1_1_prt_pp2" for table "p1"
insert into orca.p1 select * from generate_series(2,15);
select count(*) from (select gp_segment_id,ctid,tableoid from orca.p1 group by gp_segment_id,ctid,tableoid) as foo;
 count 
-------
    14
(1 row)

-- MPP-25194: histograms on text columns are dropped in ORCA. NDVs of these histograms should be added to NDVRemain
CREATE TABLE orca.tmp_verd_s_pp_provtabs_agt_0015_extract1 (
    uid136 character(16),
    tab_nr smallint,
    prdgrp character(5),
    bg smallint,
    typ142 smallint,
    ad_gsch smallint,
    guelt_ab date,
    guelt_bis date,
    guelt_stat text,
    verarb_ab timestamp(5) without time zone,
    u_erzeugen integer,
    grenze integer,
    erstellt_am_122 timestamp(5) without time zone,
    erstellt_am_135 timestamp(5) without time zone,
    erstellt_am_134 timestamp(5) without time zone
)
WITH (appendonly=true, compresstype=zlib) DISTRIBUTED BY (uid136);
 set allow_system_table_mods=true;
 UPDATE pg_class
 SET
         relpages = 30915::int, reltuples = 7.28661e+07::real WHERE relname = 'tmp_verd_s_pp_provtabs_agt_0015_extract1' AND relnamespace = (SELECT oid FROM pg_namespace WHERE nspname = 'xvclin');
insert into pg_statistic
values (
  'orca.tmp_verd_s_pp_provtabs_agt_0015_extract1'::regclass,
  1::smallint,
  'f',
  0::real,
  17::integer,
  264682::real,
  1::smallint,
  2::smallint,
  0::smallint,
  0::smallint,
  0::smallint,
  1054::oid,
  1058::oid,
  0::oid,
  0::oid,
  0::oid,
  '{0.000161451,0.000107634,0.000107634,0.000107634,0.000107634,0.000107634,0.000107634,0.000107634,0.000107634,0.000107634,8.07255e-05,8.07255e-05,8.07255e-05,8.07255e-05,8.07255e-05,8.07255e-05,8.07255e-05,8.07255e-05,8.07255e-05,8.07255e-05,8.07255e-05,8.07255e-05,8.07255e-05,8.07255e-05,8.07255e-05}'::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  '{8X8#1F8V92A2025G,YFAXQ§UBF210PA0P,2IIVIE8V92A2025G,9§BP8F8V92A2025G,35A9EE8V92A2025G,§AJ2Z§9MA210PA0P,3NUQ3E8V92A2025G,F7ZD4F8V92A2025G,$WHHEO§§E210PA0P,Z6EATH2BE210PA0P,N7I28E8V92A2025G,YU0K$E$§9210PA0P,3TAI1ANIF210PA0P,P#H8BF8V92A2025G,VTQ$N$D§92A201SC,N7ZD4F8V92A2025G,77BP8F8V92A2025G,39XOXY78H210PA01,#2#OX6NHH210PA01,2DG1J#XZH210PA01,MFEG$E8V92A2025G,M0HKWNGND210PA0P,FSXI67NSA210PA0P,C1L77E8V92A2025G,01#21E8V92A2025G}'::bpchar[],
  '{§00§1HKZC210PA0P,1D90GE8V92A2025G,2ULZI6L0O210PA01,489G7L8$I210PA01,5RE8FF8V92A2025G,76NIRFNIF210PA0P,8KOMKE8V92A2025G,#9Y#GPSHB210PA0P,BDAJ#D8V92A2025G,CV9Z7IYVK210PA01,#EC5FE8V92A2025G,FQWY§O1XC210PA0P,H8HL4E8V92A2025G,INC5FE8V92A2025G,K4MX0XHCF210PA0P,LKE8FF8V92A2025G,N03G9UM2F210PA0P,OHJ$#GFZ9210PA0P,PXU3T1OTB210PA0P,RCUA45F1H210PA01,SU§FRY#QI210PA01,UABHMLSLK210PA01,VRBP8F8V92A2025G,X65#KZIDC210PA0P,YLFG§#A2G210PA0P,ZZG8H29OC210PA0P,ZZZDBCEVA210PA0P}'::bpchar[],
  NULL::bpchar[],
  NULL::bpchar[],
  NULL::bpchar[]
),
(
  'orca.tmp_verd_s_pp_provtabs_agt_0015_extract1'::regclass,
  2::smallint,
  'f',
  0::real,
  2::integer,
  205.116::real,
  1::smallint,
  2::smallint,
  0::smallint,
  0::smallint,
  0::smallint,
  94::oid,
  95::oid,
  0::oid,
  0::oid,
  0::oid,
  '{0.278637,0.272448,0.0303797,0.0301106,0.0249442,0.0234373,0.0231682,0.0191319,0.0169793,0.0162527,0.0142884,0.0141539,0.0125394,0.0103329,0.0098216,0.00944488,0.00850308,0.00715766,0.0066464,0.00656567,0.00591987,0.0050588,0.00454753,0.00449372,0.0044399}'::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  '{199,12,14,5,197,11,198,8,152,299,201,153,9,13,74,179,24,202,2,213,17,195,215,16,200}'::int2[],
  '{1,5,9,12,14,24,58,80,152,195,198,199,207,302,402}'::int2[],
  NULL::int2[],
  NULL::int2[],
  NULL::int2[]
),
(
  'orca.tmp_verd_s_pp_provtabs_agt_0015_extract1'::regclass,
  3::smallint,
  'f',
  0::real,
  6::integer,
  201.005::real,
  1::smallint,
  2::smallint,
  0::smallint,
  0::smallint,
  0::smallint,
  1054::oid,
  1058::oid,
  0::oid,
  0::oid,
  0::oid,
  '{0.0478164,0.0439146,0.0406856,0.0239755,0.0154186,0.0149073,0.0148804,0.0143422,0.0141808,0.0139386,0.0138848,0.0138848,0.0137502,0.0134812,0.0134004,0.0133197,0.0133197,0.013239,0.0131852,0.0130775,0.0130775,0.0130237,0.0129699,0.0129699,0.012943}'::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  '{AG023,AG032,AG999,AG022,AG--B,AG-VB,AG--C,AG-VC,AG014,AG-VA,AG036,AGT4C,AG--A,AG037,AG009,AG015,AG003,AG002,AGT3C,AG025,AG019,AGT2C,AGT1C,AG005,AG031}'::bpchar[],
  '{AG001,AG004,AG007,AG010,AG013,AG017,AG020,AG022,AG023,AG026,AG030,AG032,AG034,AG037,AG040,AG045,AG122,AG999,AG--B,AGT1C,AGT4C,AG-VB,MA017,MA--A,MK081,MKRKV}'::bpchar[],
  NULL::bpchar[],
  NULL::bpchar[],
  NULL::bpchar[]
),
(
  'orca.tmp_verd_s_pp_provtabs_agt_0015_extract1'::regclass,
  4::smallint,
  'f',
  0::real,
  2::integer,
  34::real,
  1::smallint,
  2::smallint,
  0::smallint,
  0::smallint,
  0::smallint,
  94::oid,
  95::oid,
  0::oid,
  0::oid,
  0::oid,
  '{0.1135,0.112881,0.111778,0.100288,0.0895245,0.0851384,0.0821785,0.0723569,0.0565616,0.0368646,0.0309986,0.0160375,0.00621586,0.00594677,0.0050588,0.00489734,0.00487044,0.00487044,0.00468208,0.00462826,0.00460135,0.00441299,0.00438608,0.00417081,0.00414391}'::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  '{1,3,2,7,9,4,5,16,10,6,8,77,12,11,14,13,22,23,64,61,24,51,53,15,54}'::int2[],
  '{1,2,3,4,5,6,7,9,10,14,16,17,53,98}'::int2[],
  NULL::int2[],
  NULL::int2[],
  NULL::int2[]
),
(
  'orca.tmp_verd_s_pp_provtabs_agt_0015_extract1'::regclass,
  5::smallint,
  'f',
  0::real,
  2::integer,
  1::real,
  1::smallint,
  2::smallint,
  0::smallint,
  0::smallint,
  0::smallint,
  94::oid,
  95::oid,
  0::oid,
  0::oid,
  0::oid,
  '{1}'::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  '{1}'::int2[],
  '{1}'::int2[],
  NULL::int2[],
  NULL::int2[],
  NULL::int2[]
),
(
  'orca.tmp_verd_s_pp_provtabs_agt_0015_extract1'::regclass,
  6::smallint,
  'f',
  0::real,
  2::integer,
  2::real,
  1::smallint,
  2::smallint,
  0::smallint,
  0::smallint,
  0::smallint,
  94::oid,
  95::oid,
  0::oid,
  0::oid,
  0::oid,
  '{0.850227,0.149773}'::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  '{1,2}'::int2[],
  '{1,2}'::int2[],
  NULL::int2[],
  NULL::int2[],
  NULL::int2[]
),
(
  'orca.tmp_verd_s_pp_provtabs_agt_0015_extract1'::regclass,
  7::smallint,
  'f',
  0::real,
  4::integer,
  591.134::real,
  1::smallint,
  2::smallint,
  0::smallint,
  0::smallint,
  0::smallint,
  1093::oid,
  1095::oid,
  0::oid,
  0::oid,
  0::oid,
  '{0.26042,0.0859995,0.0709308,0.0616473,0.0567231,0.0303797,0.0109787,0.0106289,0.00990232,0.00987541,0.00979469,0.00944488,0.00820709,0.00718457,0.00626968,0.00621586,0.00616204,0.00600059,0.00586605,0.00557006,0.00516643,0.00511261,0.0050857,0.0050857,0.0047628}'::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  '{1994-01-01,1997-01-01,2005-07-01,1999-01-01,2003-09-01,2000-01-01,2001-01-01,1998-10-01,2001-05-01,1999-03-01,2013-01-01,2003-01-01,2008-01-01,2004-01-01,2009-01-01,2003-02-01,1998-09-01,2000-12-01,2007-04-01,1998-08-01,1998-01-01,2003-07-01,1998-11-01,2005-02-01,1999-04-01}'::date[],
  '{1900-01-01,1994-01-01,1997-01-01,1998-05-07,1999-01-01,1999-05-01,2000-01-01,2001-03-22,2002-10-01,2003-09-01,2004-08-01,2005-07-01,2007-01-01,2008-06-01,2010-04-01,2012-07-01,2014-12-01,2015-01-01}'::date[],
  NULL::date[],
  NULL::date[],
  NULL::date[]
),
(
  'orca.tmp_verd_s_pp_provtabs_agt_0015_extract1'::regclass,
  8::smallint,
  'f',
  0::real,
  4::integer,
  474.232::real,
  1::smallint,
  2::smallint,
  0::smallint,
  0::smallint,
  0::smallint,
  1093::oid,
  1095::oid,
  0::oid,
  0::oid,
  0::oid,
  '{0.52111,0.0709308,0.0591987,0.0587412,0.0148804,0.010279,0.00990232,0.00931034,0.00791109,0.00718457,0.00688857,0.00608132,0.00557006,0.0053817,0.00503189,0.00500498,0.00457444,0.004117,0.00395555,0.00390173,0.00357883,0.00352501,0.00352501,0.00344429,0.00333665}'::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  '{9999-12-31,2005-07-01,1999-01-01,2003-09-01,2004-02-01,2001-05-01,2005-02-01,1999-03-01,1998-10-01,2000-01-01,2003-01-01,1998-09-01,1998-08-01,2008-01-01,2007-04-01,2000-12-01,1999-04-01,1998-11-01,2003-02-01,1994-01-01,2002-09-01,1999-02-01,2004-01-01,1998-07-01,2003-07-01}'::date[],
  '{1994-01-01,1998-11-01,1999-01-01,1999-04-01,2001-05-01,2003-07-01,2003-09-01,2004-02-01,2005-07-01,2007-02-01,2010-03-01,9999-12-31}'::date[],
  NULL::date[],
  NULL::date[],
  NULL::date[]
),
(
  'orca.tmp_verd_s_pp_provtabs_agt_0015_extract1'::regclass,
  9::smallint,
  'f',
  0::real,
  2::integer,
  1::real,
  1::smallint,
  2::smallint,
  0::smallint,
  0::smallint,
  0::smallint,
  98::oid,
  664::oid,
  0::oid,
  0::oid,
  0::oid,
  '{1}'::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  '{H}'::text[],
  '{H}'::text[],
  NULL::text[],
  NULL::text[],
  NULL::text[]
),
(
  'orca.tmp_verd_s_pp_provtabs_agt_0015_extract1'::regclass,
  10::smallint,
  'f',
  0::real,
  8::integer,
  1::real,
  1::smallint,
  2::smallint,
  0::smallint,
  0::smallint,
  0::smallint,
  2060::oid,
  2062::oid,
  0::oid,
  0::oid,
  0::oid,
  '{1}'::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  '{1900-01-01 00:00:00}'::timestamp[],
  '{1900-01-01 00:00:00}'::timestamp[],
  NULL::timestamp[],
  NULL::timestamp[],
  NULL::timestamp[]
),
(
  'orca.tmp_verd_s_pp_provtabs_agt_0015_extract1'::regclass,
  11::smallint,
  'f',
  1::real,
  0::integer,
  -1::real,
  0::smallint,
  0::smallint,
  0::smallint,
  0::smallint,
  0::smallint,
  0::oid,
  0::oid,
  0::oid,
  0::oid,
  0::oid,
  NULL::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  NULL::int4[],
  NULL::int4[],
  NULL::int4[],
  NULL::int4[],
  NULL::int4[]
),
(
  'orca.tmp_verd_s_pp_provtabs_agt_0015_extract1'::regclass,
  12::smallint,
  'f',
  0::real,
  4::integer,
  1::real,
  1::smallint,
  2::smallint,
  0::smallint,
  0::smallint,
  0::smallint,
  96::oid,
  97::oid,
  0::oid,
  0::oid,
  0::oid,
  '{1}'::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  '{1}'::int4[],
  '{1}'::int4[],
  NULL::int4[],
  NULL::int4[],
  NULL::int4[]
),
(
  'orca.tmp_verd_s_pp_provtabs_agt_0015_extract1'::regclass,
  13::smallint,
  'f',
  0.991927::real,
  8::integer,
  301.416::real,
  1::smallint,
  2::smallint,
  0::smallint,
  0::smallint,
  0::smallint,
  2060::oid,
  2062::oid,
  0::oid,
  0::oid,
  0::oid,
  '{8.07255e-05,2.69085e-05,2.69085e-05,2.69085e-05,2.69085e-05,2.69085e-05,2.69085e-05,2.69085e-05,2.69085e-05,2.69085e-05,2.69085e-05,2.69085e-05,2.69085e-05,2.69085e-05,2.69085e-05,2.69085e-05,2.69085e-05,2.69085e-05,2.69085e-05,2.69085e-05,2.69085e-05,2.69085e-05,2.69085e-05,2.69085e-05,2.69085e-05}'::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  '{2004-01-05 13:45:06.18894,2007-03-20 12:02:33.73888,2009-12-15 12:33:55.32684,2012-09-21 09:58:09.70321,2012-02-13 14:56:03.11625,2000-10-02 09:56:01.24836,1998-01-14 10:11:29.43055,2014-01-08 14:41:44.85935,2012-06-21 12:46:37.48899,2013-07-23 14:27:52.27322,2011-10-27 16:17:10.01694,2005-07-13 16:55:30.7964,2003-06-05 14:53:13.71932,2002-07-22 10:22:31.0967,2011-12-27 16:12:54.85765,2001-01-12 11:40:09.16207,2005-12-30 08:46:31.30943,2007-03-01 08:29:36.765,2011-06-16 09:09:43.8651,2000-12-15 14:33:29.20083,2006-04-25 13:46:46.09684,2011-06-20 16:26:23.65135,2004-01-23 12:37:06.92535,2002-03-04 10:02:08.92547,2003-08-01 10:33:57.33683}'::timestamp[],
  '{1997-12-05 10:59:43.94611,2014-11-18 08:48:18.32773}'::timestamp[],
  NULL::timestamp[],
  NULL::timestamp[],
  NULL::timestamp[]
),
(
  'orca.tmp_verd_s_pp_provtabs_agt_0015_extract1'::regclass,
  14::smallint,
  'f',
  0.329817::real,
  8::integer,
  38109.5::real,
  1::smallint,
  2::smallint,
  0::smallint,
  0::smallint,
  0::smallint,
  2060::oid,
  2062::oid,
  0::oid,
  0::oid,
  0::oid,
  '{0.0715766,0.0621317,0.00546242,0.0044399,0.000134542,8.07255e-05,8.07255e-05,8.07255e-05,8.07255e-05,8.07255e-05,8.07255e-05,8.07255e-05,8.07255e-05,8.07255e-05,8.07255e-05,8.07255e-05,8.07255e-05,8.07255e-05,8.07255e-05,8.07255e-05,8.07255e-05,8.07255e-05,8.07255e-05,8.07255e-05,8.07255e-05}'::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  '{1997-11-25 19:05:00.83798,1998-12-29 16:19:18.50226,1998-01-28 23:01:18.41289,2000-12-21 18:03:30.34549,2003-08-28 11:14:33.26306,2003-08-28 11:14:33.2622,1999-03-20 13:04:33.24015,2003-08-28 11:14:33.22312,2003-08-28 11:14:33.21933,2003-08-28 11:14:33.22082,2003-08-28 11:14:33.21425,2003-08-28 11:14:33.22336,2003-08-28 11:14:33.2092,2003-08-28 11:14:33.20251,2003-08-28 11:14:33.22145,2003-08-28 11:14:33.26235,2003-08-28 11:14:33.26525,2003-08-28 11:14:33.23714,2003-08-28 11:14:33.26667,2003-08-28 11:14:33.23978,2003-08-28 11:14:33.21527,2003-08-28 11:14:33.2227,1999-04-16 09:01:42.92689,2003-08-28 11:14:33.21846,2003-08-28 11:14:33.24725}'::timestamp[],
  '{1997-11-25 19:05:00.83798,1998-01-05 11:57:12.19545,1998-10-14 09:06:01.87217,1998-12-29 16:19:18.50226,1999-01-18 15:01:52.77062,1999-12-28 07:57:37.93632,2001-05-16 10:55:44.78317,2003-05-23 10:32:40.1846,2003-08-28 11:14:33.23985,2004-02-04 14:01:57.60942,2005-07-26 17:01:10.98951,2005-07-26 18:41:33.09864,2006-04-25 16:52:03.49003,2008-02-18 14:17:08.58924,2010-04-19 10:16:19.03194,2012-07-23 10:47:40.65789,2014-12-05 10:59:02.25493}'::timestamp[],
  NULL::timestamp[],
  NULL::timestamp[],
  NULL::timestamp[]
),
(
  'orca.tmp_verd_s_pp_provtabs_agt_0015_extract1'::regclass,
  15::smallint,
  'f',
  0.678255::real,
  8::integer,
  7167.15::real,
  1::smallint,
  2::smallint,
  0::smallint,
  0::smallint,
  0::smallint,
  2060::oid,
  2062::oid,
  0::oid,
  0::oid,
  0::oid,
  '{0.000376719,0.00034981,0.00034981,0.00034981,0.00034981,0.00034981,0.00034981,0.000322902,0.000322902,0.000322902,0.000295993,0.000295993,0.000295993,0.000295993,0.000295993,0.000295993,0.000295993,0.000295993,0.000295993,0.000295993,0.000295993,0.000295993,0.000295993,0.000269085,0.000269085}'::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  NULL::real[],
  '{2012-09-07 14:14:23.95552,2012-09-07 14:14:36.8171,2008-12-02 09:02:19.06415,2012-03-16 15:57:15.10939,1999-06-08 13:59:56.59862,1998-10-29 14:57:47.93588,1997-07-10 09:55:34.68999,2003-03-26 11:18:44.43314,2002-02-27 15:07:42.12004,2002-02-27 15:07:13.65666,2003-03-26 11:22:07.7484,2013-11-29 13:16:25.79261,2007-09-06 09:14:10.7907,1998-10-29 14:54:04.23854,2003-04-11 07:54:56.90542,2006-03-09 15:42:27.40086,2000-05-31 10:27:52.92485,2006-01-23 17:12:44.80256,2003-01-28 10:44:17.44046,2007-11-01 15:11:21.99194,2006-03-09 15:42:56.14013,2004-03-31 10:58:47.12524,1999-06-08 14:02:11.91465,1997-07-11 14:52:47.95918,1999-06-08 13:58:15.07927}'::timestamp[],
  '{1997-07-09 10:42:54.69421,1997-09-16 10:30:42.71499,1999-06-08 14:32:08.31914,2002-02-27 15:07:13.67355,2003-04-08 16:31:58.80724,2004-05-05 10:18:01.33179,2006-03-13 16:19:59.61215,2007-09-06 09:13:41.71774,2013-11-29 13:16:37.17591,2014-12-03 09:24:25.20945}'::timestamp[],
  NULL::timestamp[],
  NULL::timestamp[],
  NULL::timestamp[]
);
set optimizer_segments = 256;
select a.*,  b.guelt_ab as guelt_ab_b, b.guelt_bis as guelt_bis_b
from orca.tmp_verd_s_pp_provtabs_agt_0015_extract1 a
left outer join  orca.tmp_verd_s_pp_provtabs_agt_0015_extract1 b
 ON  a.uid136=b.uid136 and a.tab_nr=b.tab_nr and  a.prdgrp=b.prdgrp and a.bg=b.bg and a.typ142=b.typ142 and a.ad_gsch=b.ad_gsch
AND a.guelt_ab <= b.guelt_ab AND a.guelt_bis > b.guelt_ab
;
 uid136 | tab_nr | prdgrp | bg | typ142 | ad_gsch | guelt_ab | guelt_bis | guelt_stat | verarb_ab | u_erzeugen | grenze | erstellt_am_122 | erstellt_am_135 | erstellt_am_134 | guelt_ab_b | guelt_bis_b 
--------+--------+--------+----+--------+---------+----------+-----------+------------+-----------+------------+--------+-----------------+-----------------+-----------------+------------+-------------
(0 rows)

set optimizer_segments = 3;
set allow_system_table_mods=false;
-- Arrayref
drop table if exists orca.arrtest;
NOTICE:  table "arrtest" does not exist, skipping
create table orca.arrtest (
 a int2[],
 b int4[][][],
 c name[],
 d text[][]
 ) DISTRIBUTED RANDOMLY;
insert into orca.arrtest (a[1:5], b[1:1][1:2][1:2], c, d)
values ('{1,2,3,4,5}', '{{{0,0},{1,2}}}', '{}', '{}');
select a[1:3], b[1][2][1], c[1], d[1][1] FROM orca.arrtest order by 1,2,3,4;
    a    | b | c | d 
---------+---+---+---
 {1,2,3} | 1 |   | 
(1 row)

select a[b[1][2][2]] from orca.arrtest;
 a 
---
 2
(1 row)

-- MPP-20713, MPP-20714, MPP-20738: Const table get with a filter
select 1 as x where 1 in (2, 3);
 x 
---
(0 rows)

-- MPP-22918: join inner child with universal distribution
SELECT generate_series(1,10) EXCEPT SELECT 1;
 generate_series 
-----------------
               2
               3
               4
               5
               6
               7
               8
               9
              10
(9 rows)

-- MPP-23932: SetOp of const table and volatile function
SELECT generate_series(1,10) INTERSECT SELECT 1;
 generate_series 
-----------------
               1
(1 row)

SELECT generate_series(1,10) UNION SELECT 1;
 generate_series 
-----------------
               1
               2
               3
               4
               5
               6
               7
               8
               9
              10
(10 rows)

-- warning messages for missing stats
create table foo_missing_stats(a int, b int);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'a' 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.
insert into foo_missing_stats select i, i%5 from generate_series(1,20) i;
create table bar_missing_stats(c int, d int);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'c' 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.
insert into bar_missing_stats select i, i%8 from generate_series(1,30) i;
analyze foo_missing_stats;
analyze bar_missing_stats;
select count(*) from foo_missing_stats where a = 10;
 count 
-------
     1
(1 row)

with x as (select * from foo_missing_stats) select count(*) from x x1, x x2 where x1.a = x2.a;
 count 
-------
    20
(1 row)

with x as (select * from foo_missing_stats) select count(*) from x x1, x x2 where x1.a = x2.b;
 count 
-------
    16
(1 row)

set allow_system_table_mods=true;
delete from pg_statistic where starelid='foo_missing_stats'::regclass;
delete from pg_statistic where starelid='bar_missing_stats'::regclass;
select count(*) from foo_missing_stats where a = 10;
 count 
-------
     1
(1 row)

with x as (select * from foo_missing_stats) select count(*) from x x1, x x2 where x1.a = x2.a;
 count 
-------
    20
(1 row)

with x as (select * from foo_missing_stats) select count(*) from x x1, x x2 where x1.a = x2.b;
 count 
-------
    16
(1 row)

set optimizer_print_missing_stats = off;
-- Push components of disjunctive predicates
create table cust(cid integer, firstname text, lastname text) distributed by (cid);
create table datedim(date_sk integer, year integer, moy integer) distributed by (date_sk);
create table sales(item_sk integer, ticket_number integer, cid integer, date_sk integer, type text)
  distributed by (item_sk, ticket_number);
-- create a volatile function which should not be pushed
CREATE FUNCTION plusone(integer) RETURNS integer AS $$
BEGIN
    SELECT $1 + 1;
END;
$$ LANGUAGE plpgsql volatile;
-- force_explain
set optimizer_segments = 3;
explain
select c.cid cid,
       c.firstname firstname,
       c.lastname lastname,
       d.year dyear
from cust c, sales s, datedim d
where c.cid = s.cid and s.date_sk = d.date_sk and
      ((d.year = 2001 and lower(s.type) = 't1' and plusone(d.moy) = 5) or (d.moy = 4 and upper(s.type) = 'T2'));
                                                                               QUERY PLAN                                                                               
------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 Gather Motion 3:1  (slice3; segments: 3)  (cost=2215.04..2775.38 rows=110 width=72)
   ->  Hash Join  (cost=2215.04..2775.38 rows=37 width=72)
         Hash Cond: (c.cid = s.cid)
         ->  Seq Scan on cust c  (cost=0.00..434.00 rows=11134 width=68)
         ->  Hash  (cost=2214.95..2214.95 rows=3 width=8)
               ->  Redistribute Motion 3:3  (slice2; segments: 3)  (cost=940.72..2214.95 rows=3 width=8)
                     Hash Key: s.cid
                     ->  Hash Join  (cost=940.72..2214.80 rows=3 width=8)
                           Hash Cond: (d.date_sk = s.date_sk)
                           Join Filter: (((d.year = 2001) AND (lower(s.type) = 't1'::text) AND (plusone(d.moy) = 5)) OR ((d.moy = 4) AND (upper(s.type) = 'T2'::text)))
                           ->  Seq Scan on datedim d  (cost=0.00..1268.50 rows=52 width=12)
                                 Filter: ((year = 2001) OR (moy = 4))
                           ->  Hash  (cost=939.68..939.68 rows=28 width=40)
                                 ->  Redistribute Motion 3:3  (slice1; segments: 3)  (cost=0.00..939.68 rows=28 width=40)
                                       Hash Key: s.date_sk
                                       ->  Seq Scan on sales s  (cost=0.00..938.00 rows=28 width=40)
                                             Filter: ((lower(type) = 't1'::text) OR (upper(type) = 'T2'::text))
 Planning time: 2.896 ms
 Optimizer: Postgres query optimizer
(19 rows)

reset optimizer_segments;
-- Bitmap indexes
drop table if exists orca.bm_test;
NOTICE:  table "bm_test" does not exist, skipping
create table orca.bm_test (i int, t text);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'i' 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.
insert into orca.bm_test select i % 10, (i % 10)::text  from generate_series(1, 100) i;
create index bm_test_idx on orca.bm_test using bitmap (i);
set optimizer_enable_bitmapscan=on;
explain select * from orca.bm_test where i=2 and t='2';
                                 QUERY PLAN
----------------------------------------------------------------------------
 Gather Motion 1:1  (slice1; segments: 1)  (cost=0.00..4.50 rows=4 width=6)
   ->  Seq Scan on bm_test  (cost=0.00..4.50 rows=2 width=6)
         Filter: i = 2 AND t = '2'::text
 Settings:  optimizer=off; optimizer_cte_inlining_bound=1000
 Optimizer status: Postgres query optimizer
(5 rows)

select * from orca.bm_test where i=2 and t='2';
 i | t 
---+---
 2 | 2
 2 | 2
 2 | 2
 2 | 2
 2 | 2
 2 | 2
 2 | 2
 2 | 2
 2 | 2
 2 | 2
(10 rows)

reset optimizer_enable_bitmapscan;
-- Dynamic bitmap indexes
drop table if exists orca.bm_dyn_test;
NOTICE:  table "bm_dyn_test" does not exist, skipping
create table orca.bm_dyn_test (i int, to_be_dropped char(5), j int, t text) distributed by (i) partition by list(j)
  (partition part0 values(0) with (appendonly=true, compresslevel=5, orientation=column),
   partition part1 values(1),
   partition part2 values(2) with (appendonly=true, compresslevel=5, orientation=row),
   partition part3 values(3), partition part4 values(4));
NOTICE:  CREATE TABLE will create partition "bm_dyn_test_1_prt_part0" for table "bm_dyn_test"
NOTICE:  CREATE TABLE will create partition "bm_dyn_test_1_prt_part1" for table "bm_dyn_test"
NOTICE:  CREATE TABLE will create partition "bm_dyn_test_1_prt_part2" for table "bm_dyn_test"
NOTICE:  CREATE TABLE will create partition "bm_dyn_test_1_prt_part3" for table "bm_dyn_test"
NOTICE:  CREATE TABLE will create partition "bm_dyn_test_1_prt_part4" for table "bm_dyn_test"
insert into orca.bm_dyn_test select i % 10, 'drop', i % 5, (i % 10)::text  from generate_series(1, 100) i;
create index bm_dyn_test_idx on orca.bm_dyn_test using bitmap (i);
NOTICE:  building index for child partition "bm_dyn_test_1_prt_part0"
NOTICE:  building index for child partition "bm_dyn_test_1_prt_part1"
NOTICE:  building index for child partition "bm_dyn_test_1_prt_part2"
NOTICE:  building index for child partition "bm_dyn_test_1_prt_part3"
NOTICE:  building index for child partition "bm_dyn_test_1_prt_part4"
alter table orca.bm_dyn_test drop column to_be_dropped;
alter table orca.bm_dyn_test add partition part5 values(5);
NOTICE:  CREATE TABLE will create partition "bm_dyn_test_1_prt_part5" for table "bm_dyn_test"
insert into orca.bm_dyn_test values(2, 5, '2');
set optimizer_enable_bitmapscan=on;
-- gather on 1 segment because of direct dispatch
explain select * from orca.bm_dyn_test where i=2 and t='2';
                                     QUERY PLAN                                     
------------------------------------------------------------------------------------
 Gather Motion 1:1  (slice1; segments: 1)  (cost=0.00..13.52 rows=13 width=10)
   ->  Append  (cost=0.00..13.52 rows=5 width=10)
         ->  Seq Scan on bm_dyn_test_1_prt_part0  (cost=0.00..2.30 rows=1 width=10)
               Filter: i = 2 AND t = '2'::text
         ->  Seq Scan on bm_dyn_test_1_prt_part1  (cost=0.00..2.30 rows=1 width=10)
               Filter: i = 2 AND t = '2'::text
         ->  Seq Scan on bm_dyn_test_1_prt_part2  (cost=0.00..3.30 rows=3 width=10)
               Filter: i = 2 AND t = '2'::text
         ->  Seq Scan on bm_dyn_test_1_prt_part3  (cost=0.00..2.30 rows=1 width=10)
               Filter: i = 2 AND t = '2'::text
         ->  Seq Scan on bm_dyn_test_1_prt_part4  (cost=0.00..2.30 rows=1 width=10)
               Filter: i = 2 AND t = '2'::text
         ->  Seq Scan on bm_dyn_test_1_prt_part5  (cost=0.00..1.01 rows=1 width=10)
               Filter: i = 2 AND t = '2'::text
 Optimizer: Postgres query optimizer
(15 rows)

select * from orca.bm_dyn_test where i=2 and t='2';
 i | j | t 
---+---+---
 2 | 2 | 2
 2 | 2 | 2
 2 | 2 | 2
 2 | 2 | 2
 2 | 2 | 2
 2 | 2 | 2
 2 | 2 | 2
 2 | 2 | 2
 2 | 2 | 2
 2 | 2 | 2
 2 | 5 | 2
(11 rows)

reset optimizer_enable_bitmapscan;
-- Now, create a partial index
drop table if exists orca.bm_dyn_test_onepart;
NOTICE:  table "bm_dyn_test_onepart" does not exist, skipping
create table orca.bm_dyn_test_onepart (i int, to_be_dropped char(5), j int, t text)
  distributed by (i) partition by list(j)
  (partition part0 values(0) with (appendonly=true, compresslevel=5, orientation=column),
   partition part1 values(1),
   partition part2 values(2) with (appendonly=true, compresslevel=5, orientation=row),
   partition part3 values(3), partition part4 values(4));
NOTICE:  CREATE TABLE will create partition "bm_dyn_test_onepart_1_prt_part0" for table "bm_dyn_test_onepart"
NOTICE:  CREATE TABLE will create partition "bm_dyn_test_onepart_1_prt_part1" for table "bm_dyn_test_onepart"
NOTICE:  CREATE TABLE will create partition "bm_dyn_test_onepart_1_prt_part2" for table "bm_dyn_test_onepart"
NOTICE:  CREATE TABLE will create partition "bm_dyn_test_onepart_1_prt_part3" for table "bm_dyn_test_onepart"
NOTICE:  CREATE TABLE will create partition "bm_dyn_test_onepart_1_prt_part4" for table "bm_dyn_test_onepart"
insert into orca.bm_dyn_test_onepart select i % 10, 'drop', i % 5, (i % 10)::text  from generate_series(1, 100) i;
create index bm_test_idx_part on orca.bm_dyn_test_onepart_1_prt_part2 using bitmap (i);
alter table orca.bm_dyn_test_onepart drop column to_be_dropped;
alter table orca.bm_dyn_test_onepart add partition part5 values(5);
NOTICE:  CREATE TABLE will create partition "bm_dyn_test_onepart_1_prt_part5" for table "bm_dyn_test_onepart"
insert into orca.bm_dyn_test_onepart values(2, 5, '2');
set optimizer_enable_bitmapscan=on;
set optimizer_enable_dynamictablescan = off;
-- gather on 1 segment because of direct dispatch
explain select * from orca.bm_dyn_test_onepart where i=2 and t='2';
                                         QUERY PLAN                                         
--------------------------------------------------------------------------------------------
 Gather Motion 1:1  (slice1; segments: 1)  (cost=0.00..13.52 rows=13 width=10)
   ->  Append  (cost=0.00..13.52 rows=5 width=10)
         ->  Seq Scan on bm_dyn_test_onepart_1_prt_part0  (cost=0.00..2.30 rows=1 width=10)
               Filter: i = 2 AND t = '2'::text
         ->  Seq Scan on bm_dyn_test_onepart_1_prt_part1  (cost=0.00..2.30 rows=1 width=10)
               Filter: i = 2 AND t = '2'::text
         ->  Seq Scan on bm_dyn_test_onepart_1_prt_part2  (cost=0.00..3.30 rows=3 width=10)
               Filter: i = 2 AND t = '2'::text
         ->  Seq Scan on bm_dyn_test_onepart_1_prt_part3  (cost=0.00..2.30 rows=1 width=10)
               Filter: i = 2 AND t = '2'::text
         ->  Seq Scan on bm_dyn_test_onepart_1_prt_part4  (cost=0.00..2.30 rows=1 width=10)
               Filter: i = 2 AND t = '2'::text
         ->  Seq Scan on bm_dyn_test_onepart_1_prt_part5  (cost=0.00..1.01 rows=1 width=10)
               Filter: i = 2 AND t = '2'::text
 Optimizer: Postgres query optimizer
(15 rows)

select * from orca.bm_dyn_test_onepart where i=2 and t='2';
 i | j | t 
---+---+---
 2 | 2 | 2
 2 | 2 | 2
 2 | 2 | 2
 2 | 2 | 2
 2 | 2 | 2
 2 | 2 | 2
 2 | 2 | 2
 2 | 2 | 2
 2 | 2 | 2
 2 | 2 | 2
 2 | 5 | 2
(11 rows)

reset optimizer_enable_dynamictablescan;
reset optimizer_enable_bitmapscan;
-- More BitmapTableScan & BitmapIndexScan tests
set optimizer_enable_bitmapscan=on;
create schema bm;
-- Bitmap index scan on Heterogeneous parts with dropped columns
drop table if exists bm.het_bm;
NOTICE:  table "het_bm" does not exist, skipping
create table bm.het_bm (i int, to_be_dropped char(5), j int, t text) distributed by (i) partition by list(j)
  (partition part0 values(0) with (appendonly=true, compresslevel=5, orientation=column),
   partition part1 values(1),
   partition part2 values(2) with (appendonly=true, compresslevel=5, orientation=row),
   partition part3 values(3), partition part4 values(4));
NOTICE:  CREATE TABLE will create partition "het_bm_1_prt_part0" for table "het_bm"
NOTICE:  CREATE TABLE will create partition "het_bm_1_prt_part1" for table "het_bm"
NOTICE:  CREATE TABLE will create partition "het_bm_1_prt_part2" for table "het_bm"
NOTICE:  CREATE TABLE will create partition "het_bm_1_prt_part3" for table "het_bm"
NOTICE:  CREATE TABLE will create partition "het_bm_1_prt_part4" for table "het_bm"
insert into bm.het_bm select i % 10, 'drop', i % 5, (i % 10)::text  from generate_series(1, 100) i;
create index het_bm_idx on bm.het_bm using bitmap (i);
NOTICE:  building index for child partition "het_bm_1_prt_part0"
NOTICE:  building index for child partition "het_bm_1_prt_part1"
NOTICE:  building index for child partition "het_bm_1_prt_part2"
NOTICE:  building index for child partition "het_bm_1_prt_part3"
NOTICE:  building index for child partition "het_bm_1_prt_part4"
alter table bm.het_bm drop column to_be_dropped;
alter table bm.het_bm add partition part5 values(5);
NOTICE:  CREATE TABLE will create partition "het_bm_1_prt_part5" for table "het_bm"
insert into bm.het_bm values(2, 5, '2');
select sum(i) i_sum, sum(j) j_sum, sum(t::integer) t_sum from bm.het_bm where i=2 and t='2';
 i_sum | j_sum | t_sum 
-------+-------+-------
    22 |    25 |    22
(1 row)

-- Bitmap index scan on heap parts with dropped columns
drop table if exists bm.hom_bm_heap;
NOTICE:  table "hom_bm_heap" does not exist, skipping
create table bm.hom_bm_heap (i int, to_be_dropped char(5), j int, t text) distributed by (i) partition by list(j)
  (partition part0 values(0),
   partition part1 values(1),
   partition part2 values(2),
   partition part3 values(3),
   partition part4 values(4));
NOTICE:  CREATE TABLE will create partition "hom_bm_heap_1_prt_part0" for table "hom_bm_heap"
NOTICE:  CREATE TABLE will create partition "hom_bm_heap_1_prt_part1" for table "hom_bm_heap"
NOTICE:  CREATE TABLE will create partition "hom_bm_heap_1_prt_part2" for table "hom_bm_heap"
NOTICE:  CREATE TABLE will create partition "hom_bm_heap_1_prt_part3" for table "hom_bm_heap"
NOTICE:  CREATE TABLE will create partition "hom_bm_heap_1_prt_part4" for table "hom_bm_heap"
insert into bm.hom_bm_heap select i % 10, 'drop', i % 5, (i % 10)::text  from generate_series(1, 100) i;
create index hom_bm_heap_idx on bm.hom_bm_heap using bitmap (i);
NOTICE:  building index for child partition "hom_bm_heap_1_prt_part0"
NOTICE:  building index for child partition "hom_bm_heap_1_prt_part1"
NOTICE:  building index for child partition "hom_bm_heap_1_prt_part2"
NOTICE:  building index for child partition "hom_bm_heap_1_prt_part3"
NOTICE:  building index for child partition "hom_bm_heap_1_prt_part4"
alter table bm.hom_bm_heap drop column to_be_dropped;
alter table bm.hom_bm_heap add partition part5 values(5);
NOTICE:  CREATE TABLE will create partition "hom_bm_heap_1_prt_part5" for table "hom_bm_heap"
insert into bm.hom_bm_heap values(2, 5, '2');
select sum(i) i_sum, sum(j) j_sum, sum(t::integer) t_sum from bm.hom_bm_heap where i=2 and t='2';
 i_sum | j_sum | t_sum 
-------+-------+-------
    22 |    25 |    22
(1 row)

-- Bitmap index scan on AO parts with dropped columns
drop table if exists bm.hom_bm_ao;
NOTICE:  table "hom_bm_ao" does not exist, skipping
create table bm.hom_bm_ao (i int, to_be_dropped char(5), j int, t text)
with (appendonly=true, compresslevel=5, orientation=row)
distributed by (i) partition by list(j)
  (partition part0 values(0),
   partition part1 values(1),
   partition part2 values(2),
   partition part3 values(3),
   partition part4 values(4));
NOTICE:  CREATE TABLE will create partition "hom_bm_ao_1_prt_part0" for table "hom_bm_ao"
NOTICE:  CREATE TABLE will create partition "hom_bm_ao_1_prt_part1" for table "hom_bm_ao"
NOTICE:  CREATE TABLE will create partition "hom_bm_ao_1_prt_part2" for table "hom_bm_ao"
NOTICE:  CREATE TABLE will create partition "hom_bm_ao_1_prt_part3" for table "hom_bm_ao"
NOTICE:  CREATE TABLE will create partition "hom_bm_ao_1_prt_part4" for table "hom_bm_ao"
insert into bm.hom_bm_ao select i % 10, 'drop', i % 5, (i % 10)::text  from generate_series(1, 100) i;
create index hom_bm_ao_idx on bm.hom_bm_ao using bitmap (i);
NOTICE:  building index for child partition "hom_bm_ao_1_prt_part0"
NOTICE:  building index for child partition "hom_bm_ao_1_prt_part1"
NOTICE:  building index for child partition "hom_bm_ao_1_prt_part2"
NOTICE:  building index for child partition "hom_bm_ao_1_prt_part3"
NOTICE:  building index for child partition "hom_bm_ao_1_prt_part4"
alter table bm.hom_bm_ao drop column to_be_dropped;
alter table bm.hom_bm_ao add partition part5 values(5);
NOTICE:  CREATE TABLE will create partition "hom_bm_ao_1_prt_part5" for table "hom_bm_ao"
insert into bm.hom_bm_ao values(2, 5, '2');
select sum(i) i_sum, sum(j) j_sum, sum(t::integer) t_sum from bm.hom_bm_ao where i=2 and t='2';
 i_sum | j_sum | t_sum 
-------+-------+-------
    22 |    25 |    22
(1 row)

-- Bitmap index scan on AOCO parts with dropped columns
drop table if exists bm.hom_bm_aoco;
NOTICE:  table "hom_bm_aoco" does not exist, skipping
create table bm.hom_bm_aoco (i int, to_be_dropped char(5), j int, t text)
with (appendonly=true, compresslevel=5, orientation=column)
distributed by (i) partition by list(j)
  (partition part0 values(0),
   partition part1 values(1),
   partition part2 values(2),
   partition part3 values(3),
   partition part4 values(4));
NOTICE:  CREATE TABLE will create partition "hom_bm_aoco_1_prt_part0" for table "hom_bm_aoco"
NOTICE:  CREATE TABLE will create partition "hom_bm_aoco_1_prt_part1" for table "hom_bm_aoco"
NOTICE:  CREATE TABLE will create partition "hom_bm_aoco_1_prt_part2" for table "hom_bm_aoco"
NOTICE:  CREATE TABLE will create partition "hom_bm_aoco_1_prt_part3" for table "hom_bm_aoco"
NOTICE:  CREATE TABLE will create partition "hom_bm_aoco_1_prt_part4" for table "hom_bm_aoco"
insert into bm.hom_bm_aoco select i % 10, 'drop', i % 5, (i % 10)::text  from generate_series(1, 100) i;
create index hom_bm_aoco_idx on bm.hom_bm_aoco using bitmap (i);
NOTICE:  building index for child partition "hom_bm_aoco_1_prt_part0"
NOTICE:  building index for child partition "hom_bm_aoco_1_prt_part1"
NOTICE:  building index for child partition "hom_bm_aoco_1_prt_part2"
NOTICE:  building index for child partition "hom_bm_aoco_1_prt_part3"
NOTICE:  building index for child partition "hom_bm_aoco_1_prt_part4"
alter table bm.hom_bm_aoco drop column to_be_dropped;
alter table bm.hom_bm_aoco add partition part5 values(5);
NOTICE:  CREATE TABLE will create partition "hom_bm_aoco_1_prt_part5" for table "hom_bm_aoco"
insert into bm.hom_bm_aoco values(2, 5, '2');
select sum(i) i_sum, sum(j) j_sum, sum(t::integer) t_sum from bm.hom_bm_aoco where i=2 and t='2';
 i_sum | j_sum | t_sum 
-------+-------+-------
    22 |    25 |    22
(1 row)

-- Create index before dropped column
drop table if exists bm.het_bm;
create table bm.het_bm (i int, to_be_dropped char(5), j int, t text) distributed by (i) partition by list(j)
  (partition part0 values(0) with (appendonly=true, compresslevel=5, orientation=column),
   partition part1 values(1),
   partition part2 values(2) with (appendonly=true, compresslevel=5, orientation=row),
   partition part3 values(3), partition part4 values(4));
NOTICE:  CREATE TABLE will create partition "het_bm_1_prt_part0" for table "het_bm"
NOTICE:  CREATE TABLE will create partition "het_bm_1_prt_part1" for table "het_bm"
NOTICE:  CREATE TABLE will create partition "het_bm_1_prt_part2" for table "het_bm"
NOTICE:  CREATE TABLE will create partition "het_bm_1_prt_part3" for table "het_bm"
NOTICE:  CREATE TABLE will create partition "het_bm_1_prt_part4" for table "het_bm"
insert into bm.het_bm select i % 10, 'drop', i % 5, (i % 10)::text  from generate_series(1, 100) i;
-- Create index before dropping a column
create index het_bm_i_idx on bm.het_bm using bitmap (i);
NOTICE:  building index for child partition "het_bm_1_prt_part0"
NOTICE:  building index for child partition "het_bm_1_prt_part1"
NOTICE:  building index for child partition "het_bm_1_prt_part2"
NOTICE:  building index for child partition "het_bm_1_prt_part3"
NOTICE:  building index for child partition "het_bm_1_prt_part4"
create index het_bm_j_idx on bm.het_bm using bitmap (j);
NOTICE:  building index for child partition "het_bm_1_prt_part0"
NOTICE:  building index for child partition "het_bm_1_prt_part1"
NOTICE:  building index for child partition "het_bm_1_prt_part2"
NOTICE:  building index for child partition "het_bm_1_prt_part3"
NOTICE:  building index for child partition "het_bm_1_prt_part4"
alter table bm.het_bm drop column to_be_dropped;
alter table bm.het_bm add partition part5 values(5);
NOTICE:  CREATE TABLE will create partition "het_bm_1_prt_part5" for table "het_bm"
insert into bm.het_bm values(2, 5, '2');
select sum(i) i_sum, sum(j) j_sum, sum(t::integer) t_sum from bm.het_bm where i=2 and j=2;
 i_sum | j_sum | t_sum 
-------+-------+-------
    20 |    20 |    20
(1 row)

select sum(i) i_sum, sum(j) j_sum, sum(t::integer) t_sum from bm.het_bm where i=2 and t='2';
 i_sum | j_sum | t_sum 
-------+-------+-------
    22 |    25 |    22
(1 row)

-- Drop and recreate index after we dropped column
drop index bm.het_bm_i_idx;
drop index bm.het_bm_j_idx;
create index het_bm_i_idx on bm.het_bm using bitmap (i);
NOTICE:  building index for child partition "het_bm_1_prt_part0"
NOTICE:  building index for child partition "het_bm_1_prt_part1"
NOTICE:  building index for child partition "het_bm_1_prt_part2"
NOTICE:  building index for child partition "het_bm_1_prt_part3"
NOTICE:  building index for child partition "het_bm_1_prt_part4"
NOTICE:  building index for child partition "het_bm_1_prt_part5"
create index het_bm_j_idx on bm.het_bm using bitmap (j);
NOTICE:  building index for child partition "het_bm_1_prt_part0"
NOTICE:  building index for child partition "het_bm_1_prt_part1"
NOTICE:  building index for child partition "het_bm_1_prt_part2"
NOTICE:  building index for child partition "het_bm_1_prt_part3"
NOTICE:  building index for child partition "het_bm_1_prt_part4"
NOTICE:  building index for child partition "het_bm_1_prt_part5"
select sum(i) i_sum, sum(j) j_sum, sum(t::integer) t_sum from bm.het_bm where j=2 or j=3;
 i_sum | j_sum | t_sum 
-------+-------+-------
   200 |   100 |   200
(1 row)

-- Create index after dropped column
drop table if exists bm.het_bm;
create table bm.het_bm (i int, to_be_dropped char(5), j int, t text) distributed by (i) partition by list(j)
  (partition part0 values(0) with (appendonly=true, compresslevel=5, orientation=column),
   partition part1 values(1),
   partition part2 values(2) with (appendonly=true, compresslevel=5, orientation=row),
   partition part3 values(3), partition part4 values(4));
NOTICE:  CREATE TABLE will create partition "het_bm_1_prt_part0" for table "het_bm"
NOTICE:  CREATE TABLE will create partition "het_bm_1_prt_part1" for table "het_bm"
NOTICE:  CREATE TABLE will create partition "het_bm_1_prt_part2" for table "het_bm"
NOTICE:  CREATE TABLE will create partition "het_bm_1_prt_part3" for table "het_bm"
NOTICE:  CREATE TABLE will create partition "het_bm_1_prt_part4" for table "het_bm"
insert into bm.het_bm select i % 10, 'drop', i % 5, (i % 10)::text  from generate_series(1, 100) i;
alter table bm.het_bm drop column to_be_dropped;
alter table bm.het_bm add partition part5 values(5);
NOTICE:  CREATE TABLE will create partition "het_bm_1_prt_part5" for table "het_bm"
-- Create index after dropping a column but before we insert into newly created part
create index het_bm_i_idx on bm.het_bm using bitmap (i);
NOTICE:  building index for child partition "het_bm_1_prt_part0"
NOTICE:  building index for child partition "het_bm_1_prt_part1"
NOTICE:  building index for child partition "het_bm_1_prt_part2"
NOTICE:  building index for child partition "het_bm_1_prt_part3"
NOTICE:  building index for child partition "het_bm_1_prt_part4"
NOTICE:  building index for child partition "het_bm_1_prt_part5"
create index het_bm_j_idx on bm.het_bm using bitmap (j);
NOTICE:  building index for child partition "het_bm_1_prt_part0"
NOTICE:  building index for child partition "het_bm_1_prt_part1"
NOTICE:  building index for child partition "het_bm_1_prt_part2"
NOTICE:  building index for child partition "het_bm_1_prt_part3"
NOTICE:  building index for child partition "het_bm_1_prt_part4"
NOTICE:  building index for child partition "het_bm_1_prt_part5"
insert into bm.het_bm values(2, 5, '2');
select sum(i) i_sum, sum(j) j_sum, sum(t::integer) t_sum from bm.het_bm where j=2 or j=3;
 i_sum | j_sum | t_sum 
-------+-------+-------
   200 |   100 |   200
(1 row)

-- Rescan bitmap index
drop table if exists bm.outer_tab;
NOTICE:  table "outer_tab" does not exist, skipping
create table bm.outer_tab
(
id serial,
code character varying(25),
name character varying(40)
);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'id' 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.
insert into bm.outer_tab select i % 10, i % 5, i % 2 from generate_series(1, 100) i;
set optimizer_enable_hashjoin = off;
select count(1) from bm.outer_tab;
 count 
-------
   100
(1 row)

select count(1) from bm.het_bm;
 count 
-------
   101
(1 row)

select sum(id) id_sum, sum(i) i_sum, sum(j) j_sum from bm.outer_tab as ot join bm.het_bm as het_bm on ot.id = het_bm.j
where het_bm.i between 2 and 5;
 id_sum | i_sum | j_sum 
--------+-------+-------
    950 |  1420 |   950
(1 row)

drop schema bm cascade;
NOTICE:  drop cascades to 5 other objects
DETAIL:  drop cascades to table bm.hom_bm_heap
drop cascades to append only table bm.hom_bm_ao
drop cascades to append only columnar table bm.hom_bm_aoco
drop cascades to table bm.het_bm
drop cascades to table bm.outer_tab
reset optimizer_enable_hashjoin;
reset optimizer_enable_bitmapscan;
-- End of BitmapTableScan & BitmapIndexScan tests
set optimizer_enable_constant_expression_evaluation=on;
CREATE TABLE my_tt_agg_opt (
    symbol character(16),
    event_ts bigint,
    trade_price numeric,
    trade_volume bigint
) DISTRIBUTED BY (symbol);
CREATE TABLE my_tq_agg_opt_part (
    ets bigint,
    sym character varying(16),
    bid_price numeric,
    ask_price numeric,
    end_ts bigint
) DISTRIBUTED BY (ets)
   PARTITION BY RANGE(bid_price)(PARTITION p1 START(0) END(200000),
                                 PARTITION p2 START(200000) END(900000));
NOTICE:  CREATE TABLE will create partition "my_tq_agg_opt_part_1_prt_p1" for table "my_tq_agg_opt_part"
NOTICE:  CREATE TABLE will create partition "my_tq_agg_opt_part_1_prt_p2" for table "my_tq_agg_opt_part"
CREATE INDEX my_tq_agg_opt_part_ets_end_ts_ix_1 ON my_tq_agg_opt_part_1_prt_p1 USING btree (ets, end_ts);
CREATE INDEX my_tq_agg_opt_part_ets_end_ts_ix_2 ON my_tq_agg_opt_part_1_prt_p2 USING btree (ets);
CREATE FUNCTION plusone(numeric) RETURNS numeric AS $$
BEGIN
    SELECT $1 + 1;
END;
$$ LANGUAGE plpgsql volatile;
-- start_ignore
select disable_xform('CXformInnerJoin2DynamicIndexGetApply');
                  disable_xform
--------------------------------------------------
 CXformInnerJoin2DynamicIndexGetApply is disabled
(1 row)

select disable_xform('CXformInnerJoin2HashJoin');
            disable_xform
--------------------------------------
 CXformInnerJoin2HashJoin is disabled
(1 row)

select disable_xform('CXformInnerJoin2IndexGetApply');
               disable_xform
-------------------------------------------
 CXformInnerJoin2IndexGetApply is disabled
(1 row)

select disable_xform('CXformInnerJoin2NLJoin');
           disable_xform
------------------------------------
 CXformInnerJoin2NLJoin is disabled
(1 row)
-- end_ignore
set optimizer_enable_partial_index=on;
set optimizer_enable_indexjoin=on;
-- force_explain
set optimizer_segments = 3;
EXPLAIN
SELECT (tt.event_ts / 100000) / 5 * 5 as fivemin, COUNT(*)
FROM my_tt_agg_opt tt, my_tq_agg_opt_part tq
WHERE tq.sym = tt.symbol AND
      plusone(tq.bid_price) < tt.trade_price AND
      tt.event_ts >= tq.ets AND
      tt.event_ts <  tq.end_ts
GROUP BY 1
ORDER BY 1 asc ;
                                                                QUERY PLAN
-------------------------------------------------------------------------------------------------------------------------------------------
 Gather Motion 3:1  (slice3; segments: 3)  (cost=240615.63..240618.13 rows=1000 width=16)
   Merge Key: (tt.event_ts / 100000 / 5 * 5)
   ->  Sort  (cost=240615.63..240618.13 rows=334 width=16)
         Sort Key: (tt.event_ts / 100000 / 5 * 5)
         ->  HashAggregate  (cost=240553.30..240565.80 rows=334 width=16)
               Group By: (tt.event_ts / 100000 / 5 * 5)
               ->  Redistribute Motion 3:3  (slice2; segments: 3)  (cost=240510.80..240538.30 rows=334 width=16)
                     Hash Key: (tt.event_ts / 100000 / 5 * 5)
                     ->  HashAggregate  (cost=240510.80..240518.30 rows=334 width=16)
                           Group By: tt.event_ts / 100000 / 5 * 5
                           ->  Hash Join  (cost=604.00..240341.56 rows=11283 width=8)
                                 Hash Cond: tq.sym::bpchar = tt.symbol
                                 Join Filter: tt.event_ts >= tq.ets AND tt.event_ts < tq.end_ts AND plusone(tq.bid_price) < tt.trade_price
                                 ->  Redistribute Motion 3:3  (slice1; segments: 3)  (cost=0.00..1424.00 rows=13600 width=98)
                                       Hash Key: tq.sym
                                       ->  Append  (cost=0.00..608.00 rows=13600 width=98)
                                             ->  Seq Scan on my_tq_agg_opt_part_1_prt_p1 tq  (cost=0.00..304.00 rows=6800 width=98)
                                             ->  Seq Scan on my_tq_agg_opt_part_1_prt_p2 tq_1  (cost=0.00..304.00 rows=6800 width=98)
                                 ->  Hash  (cost=324.00..324.00 rows=7467 width=108)
                                       ->  Seq Scan on my_tt_agg_opt tt  (cost=0.00..324.00 rows=7467 width=108)
 Settings:  optimizer=off; optimizer_cte_inlining_bound=1000; optimizer_segments=3
 Optimizer status: Postgres query optimizer
(22 rows)

reset optimizer_segments;
reset optimizer_enable_constant_expression_evaluation;
reset optimizer_enable_indexjoin;
reset optimizer_enable_partial_index;
-- start_ignore
select enable_xform('CXformInnerJoin2DynamicIndexGetApply');
                  enable_xform
-------------------------------------------------
 CXformInnerJoin2DynamicIndexGetApply is enabled
(1 row)

select enable_xform('CXformInnerJoin2HashJoin');
            enable_xform
-------------------------------------
 CXformInnerJoin2HashJoin is enabled
(1 row)

select enable_xform('CXformInnerJoin2IndexGetApply');
               enable_xform
------------------------------------------
 CXformInnerJoin2IndexGetApply is enabled
(1 row)

select enable_xform('CXformInnerJoin2NLJoin');
           enable_xform
-----------------------------------
 CXformInnerJoin2NLJoin is enabled
(1 row)

-- end_ignore
-- MPP-25661: IndexScan crashing for qual with reference to outer tuple
drop table if exists idxscan_outer;
NOTICE:  table "idxscan_outer" does not exist, skipping
create table idxscan_outer
(
id serial,
code character varying(25),
name character varying(40)
);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'id' 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.
drop table if exists idxscan_inner;
NOTICE:  table "idxscan_inner" does not exist, skipping
create table idxscan_inner
(
ordernum int unique,
productid int,
comment character varying(40)
);
-- Have more rows in one table, so that the planner will
-- choose to make that the outer side of the join.
insert into idxscan_outer values (1, 'a', 'aaa');
insert into idxscan_outer values (2, 'b', 'bbb');
insert into idxscan_outer values (3, 'c', 'ccc');
insert into idxscan_outer values (4, 'd', 'ddd');
insert into idxscan_outer values (5, 'e', 'eee');
insert into idxscan_outer values (6, 'f', 'fff');
insert into idxscan_outer values (7, 'g', 'ggg');
insert into idxscan_outer values (8, 'h', 'hhh');
insert into idxscan_outer values (9, 'i', 'iii');
insert into idxscan_inner values (11, 1, 'xxxx');
insert into idxscan_inner values (24, 2, 'yyyy');
insert into idxscan_inner values (13, 3, 'zzzz');
analyze idxscan_outer;
analyze idxscan_inner;
set optimizer_enable_hashjoin = off;
explain select id, comment from idxscan_outer as o join idxscan_inner as i on o.id = i.productid
where ordernum between 10 and 20;
                                             QUERY PLAN
----------------------------------------------------------------------------------------------------
 Gather Motion 3:1  (slice2; segments: 3)  (cost=2.12..5.28 rows=4 width=9)
   ->  Hash Join  (cost=2.12..5.28 rows=2 width=9)
         Hash Cond: o.id = i.productid
         ->  Seq Scan on idxscan_outer o  (cost=0.00..3.09 rows=3 width=4)
         ->  Hash  (cost=2.09..2.09 rows=1 width=9)
               ->  Redistribute Motion 3:3  (slice1; segments: 3)  (cost=0.00..2.09 rows=1 width=9)
                     Hash Key: i.productid
                     ->  Seq Scan on idxscan_inner i  (cost=0.00..2.04 rows=1 width=9)
                           Filter: ordernum >= 10 AND ordernum <= 20
 Settings:  optimizer=off; optimizer_cte_inlining_bound=1000
 Optimizer status: Postgres query optimizer
(11 rows)

select id, comment from idxscan_outer as o join idxscan_inner as i on o.id = i.productid
where ordernum between 10 and 20;
 id | comment 
----+---------
  1 | xxxx
  3 | zzzz
(2 rows)

reset optimizer_enable_hashjoin;
drop table idxscan_outer;
drop table idxscan_inner;
drop table if exists ggg;
NOTICE:  table "ggg" does not exist, skipping
set optimizer_metadata_caching=on;
create table ggg (a char(1), b char(2), d char(3));
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'a' 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.
insert into ggg values ('x', 'a', 'c');
insert into ggg values ('x', 'a');
insert into ggg values ('x');
-- MPP-25700 Fix to TO_DATE on hybrid datums during constant expression evaluation
drop table if exists orca.t3;
NOTICE:  table "t3" does not exist, skipping
create table orca.t3 (c1 timestamp without time zone);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'c1' 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.
insert into orca.t3 values  ('2015-07-03 00:00:00'::timestamp without time zone);
select to_char(c1, 'YYYY-MM-DD HH24:MI:SS') from orca.t3 where c1 = TO_DATE('2015-07-03','YYYY-MM-DD');
       to_char       
---------------------
 2015-07-03 00:00:00
(1 row)

select to_char(c1, 'YYYY-MM-DD HH24:MI:SS') from orca.t3 where c1 = '2015-07-03'::date;
       to_char       
---------------------
 2015-07-03 00:00:00
(1 row)

-- MPP-25806: multi-column index
create table orca.index_test (a int, b int, c int, d int, e int, constraint index_test_pkey PRIMARY KEY (a, b, c, d));
insert into orca.index_test select i,i%2,i%3,i%4,i%5 from generate_series(1,100) i;
-- force_explain
explain select * from orca.index_test where a = 5;
                                         QUERY PLAN
--------------------------------------------------------------------------------------------
 Gather Motion 3:1  (slice1; segments: 3)  (cost=0.00..4.25 rows=1 width=20)
   ->  Seq Scan on index_test  (cost=0.00..4.25 rows=1 width=20)
         Filter: a = 5
 Settings:  optimizer=off; optimizer_cte_inlining_bound=1000; optimizer_metadata_caching=on
 Optimizer status: Postgres query optimizer
(5 rows)

-- force_explain
explain select * from orca.index_test where c = 5;
                                         QUERY PLAN
--------------------------------------------------------------------------------------------
 Gather Motion 3:1  (slice1; segments: 3)  (cost=0.00..4.25 rows=1 width=20)
   ->  Seq Scan on index_test  (cost=0.00..4.25 rows=1 width=20)
         Filter: c = 5
 Settings:  optimizer=off; optimizer_cte_inlining_bound=1000; optimizer_metadata_caching=on
 Optimizer status: Postgres query optimizer
(5 rows)

-- force_explain
explain select * from orca.index_test where a = 5 and c = 5;
                                         QUERY PLAN
--------------------------------------------------------------------------------------------
 Gather Motion 3:1  (slice1; segments: 3)  (cost=0.00..4.50 rows=1 width=20)
   ->  Seq Scan on index_test  (cost=0.00..4.50 rows=1 width=20)
         Filter: a = 5 AND c = 5
 Settings:  optimizer=off; optimizer_cte_inlining_bound=1000; optimizer_metadata_caching=on
 Optimizer status: Postgres query optimizer
(5 rows)

-- renaming columns
select * from (values (2),(null)) v(k);
 k 
---
 2
  
(2 rows)

-- Checking if ORCA correctly populates canSetTag in PlannedStmt for multiple statements because of rules
drop table if exists can_set_tag_target;
NOTICE:  table "can_set_tag_target" does not exist, skipping
create table can_set_tag_target
(
	x int,
	y int,
	z char
);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'x' 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.
drop table if exists can_set_tag_audit;
NOTICE:  table "can_set_tag_audit" does not exist, skipping
create table can_set_tag_audit
(
	t timestamp without time zone,
	x int,
	y int,
	z char
);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 't' 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 rule can_set_tag_audit_update AS
    ON UPDATE TO can_set_tag_target DO  INSERT INTO can_set_tag_audit (t, x, y, z)
  VALUES (now(), old.x, old.y, old.z);
insert into can_set_tag_target select i, i + 1, i + 2 from generate_series(1,2) as i;
create role unpriv;
NOTICE:  resource queue required -- using default resource queue "pg_default"
grant all on can_set_tag_target to unpriv;
grant all on can_set_tag_audit to unpriv;
set role unpriv;
show optimizer;
 optimizer 
-----------
 off
(1 row)

update can_set_tag_target set y = y + 1;
select count(1) from can_set_tag_audit;
 count 
-------
     2
(1 row)

reset role;
revoke all on can_set_tag_target from unpriv;
revoke all on can_set_tag_audit from unpriv;
drop role unpriv;
drop table can_set_tag_target;
drop table can_set_tag_audit;
-- start_ignore
create language plpythonu;
-- end_ignore
-- Checking if ORCA uses parser's canSetTag for CREATE TABLE AS SELECT
create or replace function canSetTag_Func(x int) returns int as $$
    if (x is None):
        return 0
    else:
        return x * 3
$$ language plpythonu;
create table canSetTag_input_data (domain integer, class integer, attr text, value integer)
   distributed by (domain);
insert into canSetTag_input_data values(1, 1, 'A', 1);
insert into canSetTag_input_data values(2, 1, 'A', 0);
insert into canSetTag_input_data values(3, 0, 'B', 1);
create table canSetTag_bug_table as
SELECT attr, class, (select canSetTag_Func(count(distinct class)::int) from canSetTag_input_data)
   as dclass FROM canSetTag_input_data GROUP BY attr, class distributed by (attr);
drop function canSetTag_Func(x int);
drop table canSetTag_bug_table;
drop table canSetTag_input_data;
-- Test B-Tree index scan with in list
CREATE TABLE btree_test as SELECT * FROM generate_series(1,100) as a distributed randomly;
CREATE INDEX btree_test_index ON btree_test(a);
EXPLAIN SELECT * FROM btree_test WHERE a in (1, 47);
                                         QUERY PLAN
--------------------------------------------------------------------------------------------
 Gather Motion 3:1  (slice1; segments: 3)  (cost=0.00..4.25 rows=2 width=4)
   ->  Seq Scan on btree_test  (cost=0.00..4.25 rows=1 width=4)
         Filter: a = ANY ('{1,47}'::integer[])
 Settings:  optimizer=off; optimizer_cte_inlining_bound=1000; optimizer_metadata_caching=on
 Optimizer status: Postgres query optimizer
(5 rows)

EXPLAIN SELECT * FROM btree_test WHERE a in ('2', 47);
                                         QUERY PLAN
--------------------------------------------------------------------------------------------
 Gather Motion 3:1  (slice1; segments: 3)  (cost=0.00..4.25 rows=2 width=4)
   ->  Seq Scan on btree_test  (cost=0.00..4.25 rows=1 width=4)
         Filter: a = ANY ('{2,47}'::integer[])
 Settings:  optimizer=off; optimizer_cte_inlining_bound=1000; optimizer_metadata_caching=on
 Optimizer status: Postgres query optimizer
(5 rows)

EXPLAIN SELECT * FROM btree_test WHERE a in ('1', '2');
                                         QUERY PLAN
--------------------------------------------------------------------------------------------
 Gather Motion 3:1  (slice1; segments: 3)  (cost=0.00..4.25 rows=2 width=4)
   ->  Seq Scan on btree_test  (cost=0.00..4.25 rows=1 width=4)
         Filter: a = ANY ('{1,2}'::integer[])
 Settings:  optimizer=off; optimizer_cte_inlining_bound=1000; optimizer_metadata_caching=on
 Optimizer status: Postgres query optimizer
(5 rows)

EXPLAIN SELECT * FROM btree_test WHERE a in ('1', '2', 47);
                                         QUERY PLAN
--------------------------------------------------------------------------------------------
 Gather Motion 3:1  (slice1; segments: 3)  (cost=0.00..4.38 rows=3 width=4)
   ->  Seq Scan on btree_test  (cost=0.00..4.38 rows=1 width=4)
         Filter: a = ANY ('{1,2,47}'::integer[])
 Settings:  optimizer=off; optimizer_cte_inlining_bound=1000; optimizer_metadata_caching=on
 Optimizer status: Postgres query optimizer
(5 rows)

-- Test Bitmap index scan with in list
CREATE TABLE bitmap_test as SELECT * FROM generate_series(1,100) as a distributed randomly;
CREATE INDEX bitmap_index ON bitmap_test USING BITMAP(a);
EXPLAIN SELECT * FROM bitmap_test WHERE a in (1);
                                         QUERY PLAN
--------------------------------------------------------------------------------------------
 Gather Motion 3:1  (slice1; segments: 3)  (cost=0.00..4.25 rows=1 width=4)
   ->  Seq Scan on bitmap_test  (cost=0.00..4.25 rows=1 width=4)
         Filter: a = 1
 Settings:  optimizer=off; optimizer_cte_inlining_bound=1000; optimizer_metadata_caching=on
 Optimizer status: Postgres query optimizer
(5 rows)

EXPLAIN SELECT * FROM bitmap_test WHERE a in (1, 47);
                                         QUERY PLAN
--------------------------------------------------------------------------------------------
 Gather Motion 3:1  (slice1; segments: 3)  (cost=0.00..4.25 rows=2 width=4)
   ->  Seq Scan on bitmap_test  (cost=0.00..4.25 rows=1 width=4)
         Filter: a = ANY ('{1,47}'::integer[])
 Settings:  optimizer=off; optimizer_cte_inlining_bound=1000; optimizer_metadata_caching=on
 Optimizer status: Postgres query optimizer
(5 rows)

EXPLAIN SELECT * FROM bitmap_test WHERE a in ('2', 47);
                                         QUERY PLAN
--------------------------------------------------------------------------------------------
 Gather Motion 3:1  (slice1; segments: 3)  (cost=0.00..4.25 rows=2 width=4)
   ->  Seq Scan on bitmap_test  (cost=0.00..4.25 rows=1 width=4)
         Filter: a = ANY ('{2,47}'::integer[])
 Settings:  optimizer=off; optimizer_cte_inlining_bound=1000; optimizer_metadata_caching=on
 Optimizer status: Postgres query optimizer
(5 rows)

EXPLAIN SELECT * FROM bitmap_test WHERE a in ('1', '2');
                                         QUERY PLAN
--------------------------------------------------------------------------------------------
 Gather Motion 3:1  (slice1; segments: 3)  (cost=0.00..4.25 rows=2 width=4)
   ->  Seq Scan on bitmap_test  (cost=0.00..4.25 rows=1 width=4)
         Filter: a = ANY ('{1,2}'::integer[])
 Settings:  optimizer=off; optimizer_cte_inlining_bound=1000; optimizer_metadata_caching=on
 Optimizer status: Postgres query optimizer
(5 rows)

EXPLAIN SELECT * FROM bitmap_test WHERE a in ('1', '2', 47);
                                         QUERY PLAN
--------------------------------------------------------------------------------------------
 Gather Motion 3:1  (slice1; segments: 3)  (cost=0.00..4.38 rows=3 width=4)
   ->  Seq Scan on bitmap_test  (cost=0.00..4.38 rows=1 width=4)
         Filter: a = ANY ('{1,2,47}'::integer[])
 Settings:  optimizer=off; optimizer_cte_inlining_bound=1000; optimizer_metadata_caching=on
 Optimizer status: Postgres query optimizer
(5 rows)

-- Test Logging for unsupported features in ORCA
-- start_ignore
drop table if exists foo;
NOTICE:  table "foo" does not exist, skipping
-- end_ignore
create table foo(a int, b int) distributed by (a);
-- The amount of log messages you get depends on a lot of options, but any
-- difference in the output will make the test fail. Disable log_statement
-- and log_min_duration_statement, they are the most obvious ones.
set log_statement='none';
set log_min_duration_statement=-1;
set client_min_messages='log';
explain select count(*) from foo group by cube(a,b);
                                                                   QUERY PLAN                                                                   
------------------------------------------------------------------------------------------------------------------------------------------------
 Gather Motion 3:1  (slice3; segments: 3)  (cost=10128.79..20306.02 rows=40897 width=28)
   ->  Append  (cost=10128.79..20306.02 rows=13633 width=28)
         ->  HashAggregate  (cost=10128.79..10408.61 rows=9328 width=28)
               Group Key: "rollup".unnamed_attr_2, "rollup".unnamed_attr_1, "rollup"."grouping", "rollup"."group_id"
               ->  Subquery Scan on "rollup"  (cost=8552.10..10074.98 rows=1435 width=28)
                     ->  Redistribute Motion 3:3  (slice1; segments: 3)  (cost=8552.10..10031.93 rows=1435 width=28)
                           Hash Key: rollup_1.unnamed_attr_2, rollup_1.unnamed_attr_1, (Grouping), group_id()
                           ->  GroupAggregate  (cost=8552.10..9945.83 rows=1435 width=28)
                                 Group Key: rollup_1."grouping", rollup_1."group_id"
                                 ->  Subquery Scan on rollup_1  (cost=8552.10..9822.06 rows=2153 width=28)
                                       ->  GroupAggregate  (cost=8552.10..9757.49 rows=2153 width=28)
                                             Group Key: rollup_2.unnamed_attr_2, rollup_2."grouping", rollup_2."group_id"
                                             ->  Subquery Scan on rollup_2  (cost=8552.10..9585.30 rows=2870 width=28)
                                                   ->  GroupAggregate  (cost=8552.10..9499.20 rows=2870 width=28)
                                                         Group Key: b, a
                                                         ->  Sort  (cost=8552.10..8767.35 rows=28700 width=8)
                                                               Sort Key: b, a
                                                               ->  Shared Scan (share slice:id 1:0)  (cost=1391.50..1494.60 rows=28700 width=8)
                                                                     ->  Materialize  (cost=0.00..1391.50 rows=28700 width=8)
                                                                           ->  Seq Scan on foo  (cost=0.00..961.00 rows=28700 width=8)
         ->  HashAggregate  (cost=9639.11..9768.26 rows=4305 width=28)
               Group Key: rollup_3.unnamed_attr_1, rollup_3.unnamed_attr_2, rollup_3."grouping", rollup_3."group_id"
               ->  Subquery Scan on rollup_3  (cost=8552.10..9585.30 rows=1435 width=28)
                     ->  Redistribute Motion 3:3  (slice2; segments: 3)  (cost=8552.10..9542.25 rows=1435 width=28)
                           Hash Key: a, b, (Grouping), group_id()
                           ->  GroupAggregate  (cost=8552.10..9456.15 rows=1435 width=28)
                                 Group Key: a
                                 ->  Sort  (cost=8552.10..8767.35 rows=28700 width=8)
                                       Sort Key: a, b
                                       ->  Shared Scan (share slice:id 2:0)  (cost=1391.50..1494.60 rows=28700 width=8)
 Optimizer: Postgres query optimizer
(31 rows)

reset client_min_messages;
reset log_statement;
reset log_min_duration_statement;
-- TVF accepts ANYENUM, ANYELEMENT returns ANYENUM, ANYARRAY
CREATE TYPE rainbow AS ENUM('red','yellow','blue');
CREATE FUNCTION func_enum_element(ANYENUM, ANYELEMENT) RETURNS TABLE(a ANYENUM, b ANYARRAY)
AS $$ SELECT $1, ARRAY[$2] $$ LANGUAGE SQL STABLE;
SELECT * FROM func_enum_element('red'::rainbow, 'blue'::rainbow::anyelement);
  a  |   b    
-----+--------
 red | {blue}
(1 row)

DROP FUNCTION IF EXISTS func_enum_element(ANYENUM, ANYELEMENT);
-- TVF accepts ANYELEMENT, ANYARRAY returns ANYELEMENT, ANYENUM
CREATE FUNCTION func_element_array(ANYELEMENT, ANYARRAY) RETURNS TABLE(a ANYELEMENT, b ANYENUM)
AS $$ SELECT $1, $2[1]$$ LANGUAGE SQL STABLE;
SELECT * FROM func_element_array('red'::rainbow, ARRAY['blue'::rainbow]);
  a  |  b   
-----+------
 red | blue
(1 row)

DROP FUNCTION IF EXISTS func_element_array(ANYELEMENT, ANYARRAY);
-- TVF accepts ANYARRAY, ANYENUM returns ANYELEMENT
CREATE FUNCTION func_element_array(ANYARRAY, ANYENUM) RETURNS TABLE(a ANYELEMENT, b ANYELEMENT)
AS $$ SELECT $1[1], $2 $$ LANGUAGE SQL STABLE;
SELECT * FROM func_element_array(ARRAY['blue'::rainbow], 'blue'::rainbow);
  a   |  b   
------+------
 blue | blue
(1 row)

DROP FUNCTION IF EXISTS func_element_array(ANYARRAY, ANYENUM);
-- TVF accepts ANYARRAY argument returns ANYELEMENT, ANYENUM
CREATE FUNCTION func_array(ANYARRAY) RETURNS TABLE(a ANYELEMENT, b ANYENUM)
AS $$ SELECT $1[1], $1[2] $$ LANGUAGE SQL STABLE;
SELECT * FROM func_array(ARRAY['blue'::rainbow, 'yellow'::rainbow]);
  a   |   b    
------+--------
 blue | yellow
(1 row)

DROP FUNCTION IF EXISTS func_array(ANYARRAY);
-- TVF accepts ANYELEMENT, VARIADIC ARRAY returns ANYARRAY
CREATE FUNCTION func_element_variadic(ANYELEMENT, VARIADIC ANYARRAY) RETURNS TABLE(a ANYARRAY)
AS $$ SELECT array_prepend($1, $2); $$ LANGUAGE SQL STABLE;
SELECT * FROM func_element_variadic(1.1, 1.1, 2.2, 3.3);
         a         
-------------------
 {1.1,1.1,2.2,3.3}
(1 row)

DROP FUNCTION IF EXISTS func_element_variadic(ANYELEMENT, VARIADIC ANYARRAY);
-- TVF accepts ANYNONARRAY returns ANYNONARRAY
CREATE FUNCTION func_nonarray(ANYNONARRAY) RETURNS TABLE(a ANYNONARRAY)
AS $$ SELECT $1; $$ LANGUAGE SQL STABLE;
SELECT * FROM func_nonarray(5);
 a 
---
 5
(1 row)

DROP FUNCTION IF EXISTS func_nonarray(ANYNONARRAY);
-- TVF accepts ANYNONARRAY, ANYENUM returns ANYARRAY
CREATE FUNCTION func_nonarray_enum(ANYNONARRAY, ANYENUM) RETURNS TABLE(a ANYARRAY)
AS $$ SELECT ARRAY[$1, $2]; $$ LANGUAGE SQL STABLE;
SELECT * FROM func_nonarray_enum('blue'::rainbow, 'red'::rainbow);
     a      
------------
 {blue,red}
(1 row)

DROP FUNCTION IF EXISTS func_nonarray_enum(ANYNONARRAY, ANYENUM);
-- TVF accepts ANYARRAY, ANYNONARRAY, ANYENUM returns ANYNONARRAY
CREATE FUNCTION func_array_nonarray_enum(ANYARRAY, ANYNONARRAY, ANYENUM) RETURNS TABLE(a ANYNONARRAY)
AS $$ SELECT $1[1]; $$ LANGUAGE SQL STABLE;
SELECT * FROM func_array_nonarray_enum(ARRAY['blue'::rainbow, 'red'::rainbow], 'red'::rainbow, 'yellow'::rainbow);
  a   
------
 blue
(1 row)

DROP FUNCTION IF EXISTS func_array_nonarray_enum(ANYARRAY, ANYNONARRAY, ANYENUM);
-- start_ignore
drop table foo;
-- end_ignore
-- Test GPDB Expr (T_ArrayCoerceExpr) conversion to Scalar Array Coerce Expr
-- start_ignore
create table foo (a int, b character varying(10));
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'a' 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.
-- end_ignore
-- Query should not fallback to planner
explain select * from foo where b in ('1', '2');
                                         QUERY PLAN
--------------------------------------------------------------------------------------------
 Gather Motion 3:1  (slice1; segments: 3)  (cost=0.00..667.50 rows=91 width=42)
   ->  Seq Scan on foo  (cost=0.00..667.50 rows=31 width=42)
         Filter: b::text = ANY ('{1,2}'::text[])
 Settings:  optimizer=off; optimizer_cte_inlining_bound=1000; optimizer_metadata_caching=on
 Optimizer status: Postgres query optimizer
(5 rows)

set optimizer_enable_ctas = off;
set log_statement='none';
set log_min_duration_statement=-1;
set client_min_messages='log';
create table foo_ctas(a) as (select generate_series(1,10)) distributed by (a);
reset client_min_messages;
reset log_min_duration_statement;
reset log_statement;
reset optimizer_enable_ctas;
-- Test to ensure that ORCA produces correct results for a query with an Agg on top of LOJ
-- start_ignore
create table input_tab1 (a int, b int);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'a' 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 input_tab2 (c int, d int);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'c' 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.
insert into input_tab1 values (1, 1);
insert into input_tab1 values (NULL, NULL);
set optimizer_force_multistage_agg = off;
set optimizer_force_three_stage_scalar_dqa = off;
-- end_ignore
explain (costs off) select count(*), t2.c from input_tab1 t1 left join input_tab2 t2 on t1.a = t2.c group by t2.c;
                          QUERY PLAN                           
---------------------------------------------------------------
 Gather Motion 3:1  (slice2; segments: 3)
   ->  HashAggregate
         Group Key: t2.c
         ->  Redistribute Motion 3:3  (slice1; segments: 3)
               Hash Key: t2.c
               ->  HashAggregate
                     Group Key: t2.c
                     ->  Hash Right Join
                           Hash Cond: (t2.c = t1.a)
                           ->  Seq Scan on input_tab2 t2
                           ->  Hash
                                 ->  Seq Scan on input_tab1 t1
 Optimizer: Postgres query optimizer
(13 rows)

select count(*), t2.c from input_tab1 t1 left join input_tab2 t2 on t1.a = t2.c group by t2.c;
 count | c 
-------+---
     2 |  
(1 row)

-- start_ignore
reset optimizer_force_multistage_agg;
reset optimizer_force_three_stage_scalar_dqa;
-- end_ignore
--
-- Test to ensure orca produces correct equivalence class for an alias projected by a LOJ and thus producing correct results.
-- Previously, orca produced an incorrect filter (cd2 = cd) on top of LOJ which led to incorrect results as column 'cd' is
-- produced by a nullable side of LOJ (tab2).
--
-- start_ignore
CREATE TABLE tab_1 (id VARCHAR(32)) DISTRIBUTED RANDOMLY;
INSERT INTO tab_1 VALUES('qwert'), ('vbn');
CREATE TABLE tab_2(key VARCHAR(200) NOT NULL, id VARCHAR(32) NOT NULL, cd VARCHAR(2) NOT NULL) DISTRIBUTED BY(key);
INSERT INTO tab_2 VALUES('abc', 'rew', 'dr');
INSERT INTO tab_2 VALUES('tyu', 'rer', 'fd');
CREATE TABLE tab_3 (region TEXT, code TEXT) DISTRIBUTED RANDOMLY;
INSERT INTO tab_3 VALUES('cvb' ,'tyu');
INSERT INTO tab_3 VALUES('hjj' ,'xyz');
-- end_ignore
EXPLAIN SELECT Count(*)
FROM   (SELECT *
        FROM   (SELECT tab_2.cd AS CD1,
                       tab_2.cd AS CD2
                FROM   tab_1
                       LEFT JOIN tab_2
                              ON tab_1.id = tab_2.id) f
        UNION ALL
        SELECT region,
               code
        FROM   tab_3)a;
                                                         QUERY PLAN                                                         
----------------------------------------------------------------------------------------------------------------------------
 Aggregate  (cost=3.31..3.32 rows=1 width=8)
   ->  Gather Motion 3:1  (slice3; segments: 3)  (cost=3.25..3.30 rows=1 width=8)
         ->  Aggregate  (cost=3.25..3.26 rows=1 width=8)
               ->  Subquery Scan on a  (cost=1.04..3.24 rows=2 width=0)
                     ->  Append  (cost=1.04..3.20 rows=2 width=4)
                           ->  Hash Left Join  (cost=1.04..2.14 rows=2 width=3)
                                 Hash Cond: tab_1.id::text = tab_2.id::text
                                 ->  Redistribute Motion 3:3  (slice1; segments: 3)  (cost=0.00..1.06 rows=1 width=5)
                                       Hash Key: tab_1.id
                                       ->  Seq Scan on tab_1  (cost=0.00..1.02 rows=1 width=5)
                                 ->  Hash  (cost=1.03..1.03 rows=1 width=7)
                                       ->  Redistribute Motion 3:3  (slice2; segments: 3)  (cost=0.00..1.03 rows=1 width=7)
                                             Hash Key: tab_2.id
                                             ->  Seq Scan on tab_2  (cost=0.00..1.01 rows=1 width=7)
                           ->  Subquery Scan on "*SELECT* 2"  (cost=0.00..1.02 rows=1 width=8)
                                 ->  Seq Scan on tab_3  (cost=0.00..1.01 rows=1 width=8)
 Optimizer: Postgres query optimizer
(17 rows)

SELECT Count(*)
FROM   (SELECT *
        FROM   (SELECT tab_2.cd AS CD1,
                       tab_2.cd AS CD2
                FROM   tab_1
                       LEFT JOIN tab_2
                              ON tab_1.id = tab_2.id) f
        UNION ALL
        SELECT region,
               code
        FROM   tab_3)a;
 count 
-------
     4
(1 row)

--
-- Test to ensure that ORCA produces correct results with both blocking and
-- streaming materialze as controlled by optimizer_enable_streaming_material
-- GUC.
--
-- start_ignore
create table t_outer (c1 integer);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'c1' 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 t_inner (c2 integer);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'c2' 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.
insert into t_outer values (generate_series (1,10));
insert into t_inner values (generate_series (1,300));
-- end_ignore
set optimizer_enable_streaming_material = on;
select c1 from t_outer where not c1 =all (select c2 from t_inner);
 c1 
----
  8
  9
 10
  1
  2
  3
  4
  5
  6
  7
(10 rows)

set optimizer_enable_streaming_material = off;
select c1 from t_outer where not c1 =all (select c2 from t_inner);
 c1 
----
  8
  9
 10
  1
  2
  3
  4
  5
  6
  7
(10 rows)

reset optimizer_enable_streaming_material;
--
-- Test to ensure sane behavior when DML queries are optimized by ORCA by
-- enforcing a non-master gather motion, controlled by
-- optimizer_enable_gather_on_segment_for_DML GUC
--
--
-- CTAS with global-local aggregation
--
-- start_ignore
create table test1 (a int, b int);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'a' 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.
insert into test1 select generate_series(1,100),generate_series(1,100);
-- end_ignore
create table t_new as select avg(a) from test1 join (select i from unnest(array[1,2,3]) i) t on (test1.a = t.i);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column(s) named 'avg' 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.
select * from t_new;
        avg         
--------------------
 2.0000000000000000
(1 row)

-- start_ignore
drop table t_new;
set optimizer_enable_gather_on_segment_for_DML=off;
-- end_ignore
create table t_new as select avg(a) from test1 join (select i from unnest(array[1,2,3]) i) t on (test1.a = t.i);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column(s) named 'avg' 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.
select * from t_new;
        avg         
--------------------
 2.0000000000000000
(1 row)

-- start_ignore
reset optimizer_enable_gather_on_segment_for_DML;
-- end_ignore
--
-- Insert with outer references in the subquery
--
-- start_ignore
create table x_tab(a int);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'a' 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 y_tab(a int);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'a' 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 z_tab(a int);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'a' 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.
insert into x_tab values(1);
insert into y_tab values(0);
insert into z_tab values(1);
-- end_ignore
insert into x_tab select * from x_tab where exists (select * from x_tab where x_tab.a = (select x_tab.a + y_tab.a from y_tab));
select * from x_tab;
 a 
---
 1
 1
(2 rows)

--
-- Insert with Union All with an universal child
--
insert into y_tab select 1 union all select a from x_tab limit 10;
select * from y_tab;
 a 
---
 1
 1
 1
 0
(4 rows)

--
-- Insert with a function containing a SQL
--
create or replace function test_func_pg_stats()
returns integer
as $$ declare cnt int; begin execute 'select count(*) from pg_statistic' into cnt; return cnt; end $$
language plpgsql volatile READS SQL DATA;
insert into y_tab select test_func_pg_stats() from x_tab limit 2;
select count(*) from y_tab;
 count 
-------
     6
(1 row)

--
-- Delete with Hash Join with a universal child
--
delete from x_tab where exists (select z_tab.a from z_tab join (select 1 as g) as tab on z_tab.a = tab.g);
select * from x_tab;
 a 
---
(0 rows)

-- start_ignore
drop table bar;
-- end_ignore
-- TVF with a subplan that generates an RTABLE entry
create table bar(name text);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'name' 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.
insert into bar values('person');
select * from unnest((select string_to_array(name, ',') from bar)) as a;
   a    
--------
 person
(1 row)

-- Query should not fall back to planner and handle implicit cast properly
CREATE TYPE myint;
CREATE FUNCTION myintout(myint) RETURNS cstring AS 'int4out' LANGUAGE INTERNAL STRICT IMMUTABLE;
NOTICE:  argument type myint is only a shell
CREATE FUNCTION myintin(cstring) RETURNS myint AS 'int4in' LANGUAGE INTERNAL STRICT IMMUTABLE;
NOTICE:  return type myint is only a shell
CREATE TYPE myint
(
	INPUT=myintin,
	OUTPUT=myintout,
	INTERNALLENGTH=4,
	PASSEDBYVALUE
);
CREATE FUNCTION myint_int8(myint) RETURNS int8 AS 'int48' LANGUAGE INTERNAL STRICT IMMUTABLE;
CREATE CAST (myint AS int8) WITH FUNCTION myint_int8(myint) AS IMPLICIT;
CREATE TABLE csq_cast_param_outer (a int, b myint);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'a' 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.
INSERT INTO csq_cast_param_outer VALUES
	(1, '42'),
	(2, '12');
CREATE TABLE csq_cast_param_inner (c int, d myint);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'c' 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.
INSERT INTO csq_cast_param_inner VALUES
	(11, '11'),
	(101, '12');
EXPLAIN SELECT a FROM csq_cast_param_outer WHERE b in (SELECT CASE WHEN a > 1 THEN d ELSE '42' END FROM csq_cast_param_inner);
                                                   QUERY PLAN
-----------------------------------------------------------------------------------------------------------------
 Gather Motion 3:1  (slice2; segments: 3)  (cost=1.11..2.49 rows=4 width=4)
   ->  Nested Loop Semi Join  (cost=1.11..2.49 rows=2 width=4)
         Join Filter: CASE WHEN csq_cast_param_outer.a > 1 THEN csq_cast_param_inner.d ELSE '42'::myint END::bigint = csq_cast_param_outer.b::bigint
         ->  Seq Scan on csq_cast_param_outer  (cost=0.00..1.02 rows=1 width=8)
         ->  Materialize  (cost=1.11..1.17 rows=2 width=4)
               ->  Broadcast Motion 3:3  (slice1; segments: 3)  (cost=0.00..1.10 rows=2 width=4)
                     ->  Seq Scan on csq_cast_param_inner  (cost=0.00..1.02 rows=1 width=4)
 Settings:  optimizer=off; optimizer_cte_inlining_bound=1000; optimizer_metadata_caching=on
 Optimizer status: Postgres query optimizer
(9 rows)

SELECT a FROM csq_cast_param_outer WHERE b in (SELECT CASE WHEN a > 1 THEN d ELSE '42' END FROM csq_cast_param_inner);
 a 
---
 1
 2
(2 rows)

DROP CAST (myint as int8);
CREATE FUNCTION myint_numeric(myint) RETURNS numeric AS 'int4_numeric' LANGUAGE INTERNAL STRICT IMMUTABLE;
CREATE CAST (myint AS numeric) WITH FUNCTION myint_numeric(myint) AS IMPLICIT;
EXPLAIN SELECT a FROM csq_cast_param_outer WHERE b in (SELECT CASE WHEN a > 1 THEN d ELSE '42' END FROM csq_cast_param_inner);
                                                    QUERY PLAN
-------------------------------------------------------------------------------------------------------------------
 Gather Motion 3:1  (slice2; segments: 3)  (cost=1.11..2.49 rows=4 width=4)
   ->  Nested Loop Semi Join  (cost=1.11..2.49 rows=2 width=4)
         Join Filter: CASE WHEN csq_cast_param_outer.a > 1 THEN csq_cast_param_inner.d ELSE '42'::myint END::numeric = csq_cast_param_outer.b::numeric
         ->  Seq Scan on csq_cast_param_outer  (cost=0.00..1.02 rows=1 width=8)
         ->  Materialize  (cost=1.11..1.17 rows=2 width=4)
               ->  Broadcast Motion 3:3  (slice1; segments: 3)  (cost=0.00..1.10 rows=2 width=4)
                     ->  Seq Scan on csq_cast_param_inner  (cost=0.00..1.02 rows=1 width=4)
 Settings:  optimizer=off; optimizer_cte_inlining_bound=1000; optimizer_metadata_caching=on
 Optimizer status: Postgres query optimizer
(9 rows)

SELECT a FROM csq_cast_param_outer WHERE b in (SELECT CASE WHEN a > 1 THEN d ELSE '42' END FROM csq_cast_param_inner);
 a 
---
 1
 2
(2 rows)

SELECT a FROM ggg WHERE a IN (NULL, 'x');
 a 
---
 x
 x
 x
(3 rows)

EXPLAIN SELECT a FROM ggg WHERE a NOT IN (NULL, '');
                                 QUERY PLAN                                 
----------------------------------------------------------------------------
 Gather Motion 3:1  (slice1; segments: 3)  (cost=0.00..1.01 rows=1 width=2)
   ->  Seq Scan on ggg  (cost=0.00..1.01 rows=1 width=2)
         Filter: a <> ALL ('{NULL,""}'::bpchar[])
 Optimizer: Postgres query optimizer
(4 rows)

EXPLAIN SELECT a FROM ggg WHERE a IN (NULL, 'x');
                                 QUERY PLAN                                 
----------------------------------------------------------------------------
 Gather Motion 3:1  (slice1; segments: 3)  (cost=0.00..1.01 rows=1 width=2)
   ->  Seq Scan on ggg  (cost=0.00..1.01 rows=1 width=2)
         Filter: (a = ANY ('{NULL,x}'::bpchar[]))
 Planning time: 0.038 ms
 Optimizer: Postgres query optimizer
(5 rows)

-- result node with one time filter and filter
CREATE TABLE onetimefilter1 (a int, b int);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'a' 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 onetimefilter2 (a int, b int);
NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'a' 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.
INSERT INTO onetimefilter1 SELECT i, i FROM generate_series(1,10)i;
INSERT INTO onetimefilter2 SELECT i, i FROM generate_series(1,10)i;
ANALYZE onetimefilter1;
ANALYZE onetimefilter2;
EXPLAIN WITH abc AS (SELECT onetimefilter1.a, onetimefilter1.b FROM onetimefilter1, onetimefilter2 WHERE onetimefilter1.a=onetimefilter2.a) SELECT (SELECT 1 FROM abc WHERE f1.b = f2.b LIMIT 1), COALESCE((SELECT 2 FROM abc WHERE f1.a=random() AND f1.a=2), 0), (SELECT b FROM abc WHERE b=f1.b) FROM onetimefilter1 f1, onetimefilter2 f2 WHERE f1.b = f2.b;
                                                               QUERY PLAN                                                               
----------------------------------------------------------------------------------------------------------------------------------------
 Gather Motion 3:1  (slice6; segments: 3)  (cost=3.43..12.31 rows=10 width=12)
   ->  Hash Join  (cost=3.43..12.31 rows=4 width=12)
         Hash Cond: (f1.b = f2.b)
         ->  Redistribute Motion 3:3  (slice4; segments: 3)  (cost=0.00..3.30 rows=4 width=8)
               Hash Key: f1.b
               ->  Seq Scan on onetimefilter1 f1  (cost=0.00..3.10 rows=4 width=8)
         ->  Hash  (cost=3.30..3.30 rows=4 width=4)
               ->  Redistribute Motion 3:3  (slice5; segments: 3)  (cost=0.00..3.30 rows=4 width=4)
                     Hash Key: f2.b
                     ->  Seq Scan on onetimefilter2 f2  (cost=0.00..3.10 rows=4 width=4)
         SubPlan 1  (slice6; segments: 3)
           ->  Limit  (cost=0.00..0.04 rows=1 width=0)
                 ->  Limit  (cost=0.00..0.02 rows=1 width=0)
                       ->  Result  (cost=0.00..0.20 rows=4 width=0)
                             One-Time Filter: (f1.b = f2.b)
                             ->  Result  (cost=6.51..6.77 rows=4 width=8)
                                   ->  Materialize  (cost=6.51..6.77 rows=4 width=8)
                                         ->  Broadcast Motion 3:3  (slice1; segments: 3)  (cost=6.51..6.72 rows=4 width=8)
                                               ->  Shared Scan (share slice:id 1:0)  (cost=6.51..6.72 rows=4 width=8)
                                                     ->  Materialize  (cost=3.23..6.51 rows=4 width=8)
                                                           ->  Hash Join  (cost=3.23..6.46 rows=4 width=8)
                                                                 Hash Cond: (onetimefilter1.a = onetimefilter2.a)
                                                                 ->  Seq Scan on onetimefilter1  (cost=0.00..3.10 rows=4 width=8)
                                                                 ->  Hash  (cost=3.10..3.10 rows=4 width=4)
                                                                       ->  Seq Scan on onetimefilter2  (cost=0.00..3.10 rows=4 width=4)
         SubPlan 2  (slice6; segments: 3)
           ->  Result  (cost=0.00..0.28 rows=1 width=0)
                 One-Time Filter: (f1.a = 2)
                 ->  Subquery Scan on abc  (cost=0.00..0.28 rows=1 width=0)
                       Filter: ((f1.a)::double precision = random())
                       ->  Result  (cost=6.51..6.77 rows=4 width=8)
                             ->  Materialize  (cost=6.51..6.77 rows=4 width=8)
                                   ->  Broadcast Motion 3:3  (slice2; segments: 3)  (cost=6.51..6.72 rows=4 width=8)
                                         ->  Shared Scan (share slice:id 2:0)  (cost=6.51..6.72 rows=4 width=8)
         SubPlan 3  (slice6; segments: 3)
           ->  Subquery Scan on abc_1  (cost=0.00..0.22 rows=1 width=4)
                 Filter: (abc_1.b = f1.b)
                 ->  Result  (cost=6.51..6.77 rows=4 width=8)
                       ->  Materialize  (cost=6.51..6.77 rows=4 width=8)
                             ->  Broadcast Motion 3:3  (slice3; segments: 3)  (cost=6.51..6.72 rows=4 width=8)
                                   ->  Shared Scan (share slice:id 3:0)  (cost=6.51..6.72 rows=4 width=8)
 Planning time: 0.276 ms
 Optimizer: Postgres query optimizer
(43 rows)

WITH abc AS (SELECT onetimefilter1.a, onetimefilter1.b FROM onetimefilter1, onetimefilter2 WHERE onetimefilter1.a=onetimefilter2.a) SELECT (SELECT 1 FROM abc WHERE f1.b = f2.b LIMIT 1), COALESCE((SELECT 2 FROM abc WHERE f1.a=random() AND f1.a=2), 0), (SELECT b FROM abc WHERE b=f1.b) FROM onetimefilter1 f1, onetimefilter2 f2 WHERE f1.b = f2.b;
 ?column? | coalesce | b  
----------+----------+----
        1 |        0 |  1
        1 |        0 |  3
        1 |        0 |  4
        1 |        0 |  7
        1 |        0 |  8
        1 |        0 |  2
        1 |        0 |  5
        1 |        0 |  6
        1 |        0 |  9
        1 |        0 | 10
(10 rows)