create schema bfv_statistic;
set search_path=bfv_statistic;
create table bfv_statistics_foo (a int, b int) distributed by (a);
insert into bfv_statistics_foo values (1,1);
insert into bfv_statistics_foo values (0,1);
insert into bfv_statistics_foo values (2,1);
insert into bfv_statistics_foo values (null,1);
analyze bfv_statistics_foo;
-- current statistics
select stanullfrac, stadistinct, stanumbers1 from pg_statistic where starelid='bfv_statistics_foo'::regclass and staattnum=1;
 stanullfrac | stadistinct | stanumbers1 
-------------+-------------+-------------
        0.25 |       -0.75 | 
(1 row)

-- exercise GPORCA translator
explain select * from bfv_statistics_foo where a is not null and b >= 1;
                                 QUERY PLAN                                 
----------------------------------------------------------------------------
 Gather Motion 3:1  (slice1; segments: 3)  (cost=0.00..2.05 rows=3 width=8)
   ->  Seq Scan on bfv_statistics_foo  (cost=0.00..2.05 rows=1 width=8)
         Filter: a IS NOT NULL AND b >= 1
 Optimizer: Postgres query optimizer
(4 rows)

create table bfv_statistics_foo2(a int) distributed by (a);
insert into bfv_statistics_foo2 select generate_series(1,5);
insert into bfv_statistics_foo2 select 1 from generate_series(1,5);
insert into bfv_statistics_foo2 select 2 from generate_series(1,4);
insert into bfv_statistics_foo2 select 3 from generate_series(1,3);
insert into bfv_statistics_foo2 select 4 from generate_series(1,2);
insert into bfv_statistics_foo2 select 5 from generate_series(1,1);
analyze bfv_statistics_foo2;
-- current stats
select stanumbers1, stavalues1 from pg_statistic where starelid='bfv_statistics_foo2'::regclass;
       stanumbers1       | stavalues1  
-------------------------+-------------
 {0.3,0.25,0.2,0.15,0.1} | {1,2,3,4,5}
(1 row)

explain select a from bfv_statistics_foo2 where a > 1 order by a;
                                 QUERY PLAN                                  
-----------------------------------------------------------------------------
 Gather Motion 3:1  (slice1; segments: 3)  (cost=2.52..2.55 rows=14 width=4)
   Merge Key: a
   ->  Sort  (cost=2.52..2.55 rows=5 width=4)
         Sort Key: a
         ->  Seq Scan on bfv_statistics_foo2  (cost=0.00..2.25 rows=5 width=4)
               Filter: a > 1
 Optimizer: Postgres query optimizer
(7 rows)

-- change stats manually so that MCV and MCF numbers do not match
set allow_system_table_mods=true;
update pg_statistic set stavalues1='{6,3,1,5,4,2}'::int[] where starelid='bfv_statistics_foo2'::regclass;
-- excercise the translator
explain select a from bfv_statistics_foo2 where a > 1 order by a;
                                 QUERY PLAN                                  
-----------------------------------------------------------------------------
 Gather Motion 3:1  (slice1; segments: 3)  (cost=2.57..2.61 rows=17 width=4)
   Merge Key: a
   ->  Sort  (cost=2.57..2.61 rows=6 width=4)
         Sort Key: a
         ->  Seq Scan on bfv_statistics_foo2  (cost=0.00..2.25 rows=6 width=4)
               Filter: a > 1
 Optimizer: Postgres query optimizer
(7 rows)

--
-- test missing statistics
--
set gp_create_table_random_default_distribution=off;
create table bfv_statistics_foo3(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.
select * from gp_toolkit.gp_stats_missing where smischema = 'bfv_statistic' AND  smitable = 'bfv_statistics_foo3';
   smischema   |      smitable       | smisize | smicols | smirecs 
---------------+---------------------+---------+---------+---------
 bfv_statistic | bfv_statistics_foo3 | f       |       1 |       0
(1 row)

--
-- for Orca's Split Operator ensure that the columns needed for stats derivation is correct
--
set gp_create_table_random_default_distribution=off;
CREATE TABLE bar_dml (
    vtrg character varying(6) NOT NULL,
    tec_schuld_whg character varying(3) NOT NULL,
    inv character varying(11) NOT NULL,
    zed_id character varying(6) NOT NULL,
    mkl_id character varying(6) NOT NULL,
    zj integer NOT NULL,
    folio integer NOT NULL,
    zhlg_typ character varying(1) NOT NULL,
    zhlg character varying(8) NOT NULL,
    ant_zhlg double precision,
    zuordn_sys_dat character varying(11),
    zhlg_whg_bilkurs numeric(15,8),
    tec_whg_bilkurs numeric(15,8),
    zhlg_ziel_id character varying(1) NOT NULL,
    btg_tec_whg_gesh numeric(13,2),
    btg_tec_whg_makl numeric(13,2),
    btg_zhlg_whg numeric(13,2),
    zhlg_typ_org character varying(1),
    zhlg_org character varying(8),
    upd_dat date
)
WITH (appendonly=true) DISTRIBUTED RANDOMLY;
update bar_dml set (zhlg_org, zhlg_typ_org) = (zhlg, zhlg_typ);
--
-- Cardinality estimation when there is no histogram and MCV
--
create table bfv_statistics_foo4 (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 bfv_statistics_foo4 select i from generate_series(1,99) i;
insert into bfv_statistics_foo4 values (NULL);
analyze bfv_statistics_foo4;
select stanullfrac, stadistinct, stanumbers1 from pg_statistic where starelid='bfv_statistics_foo4'::regclass and staattnum=1;
 stanullfrac | stadistinct | stanumbers1 
-------------+-------------+-------------
        0.01 |       -0.99 | 
(1 row)

explain select a from bfv_statistics_foo4 where a > 888;
                                 QUERY PLAN                                 
----------------------------------------------------------------------------
 Gather Motion 3:1  (slice1; segments: 3)  (cost=0.00..4.25 rows=1 width=4)
   ->  Seq Scan on bfv_statistics_foo4  (cost=0.00..4.25 rows=1 width=4)
         Filter: a > 888
 Optimizer: Postgres query optimizer
(4 rows)

--
-- Testing that the merging of memo groups inside Orca does not crash cardinality estimation inside Orca
--
create table t1(c1 int);
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 t1 values(1);
select v from (select max(c1) as v, 1 as r from t1 union select 1 as v, 2 as r ) as bfv_statistics_foo group by v;
 v 
---
 1
(1 row)

select v from (select max(c1) as v, 1 as r from t1 union all select 1 as v, 2 as r ) as bfv_statistics_foo group by v;
 v 
---
 1
(1 row)

select v from (select max(c1) as v, 1 as r from t1 union select 1 as v, 2 as r ) as bfv_statistics_foo;
 v 
---
 1
 1
(2 rows)

--
-- test the generation of histogram boundaries for numeric and real data types
--
create table bfv_statistics_foo_real (a int4, b real) distributed randomly;
insert into bfv_statistics_foo_real values (0, 'Infinity');
insert into bfv_statistics_foo_real values (0, '-Infinity');
insert into bfv_statistics_foo_real values (0, 'NaN');
insert into bfv_statistics_foo_real values (0, 'Infinity');
insert into bfv_statistics_foo_real values (0, '-Infinity');
insert into bfv_statistics_foo_real values (0, 'NaN');
insert into bfv_statistics_foo_real values (0, 'Infinity');
insert into bfv_statistics_foo_real values (0, '-Infinity');
insert into bfv_statistics_foo_real values (0, 'NaN');
insert into bfv_statistics_foo_real values (0, 'Infinity');
insert into bfv_statistics_foo_real values (0, '-Infinity');
insert into bfv_statistics_foo_real values (0, 'NaN');
INSERT INTO bfv_statistics_foo_real VALUES (0, '0');
INSERT INTO bfv_statistics_foo_real VALUES (1, '0');
INSERT INTO bfv_statistics_foo_real VALUES (2, '-34338492.215397047');
INSERT INTO bfv_statistics_foo_real VALUES (3, '4.31');
INSERT INTO bfv_statistics_foo_real VALUES (4, '7799461.4119');
INSERT INTO bfv_statistics_foo_real VALUES (5, '16397.038491');
INSERT INTO bfv_statistics_foo_real VALUES (6, '93901.57763026');
INSERT INTO bfv_statistics_foo_real VALUES (7, '-83028485');
INSERT INTO bfv_statistics_foo_real VALUES (8, '74881');
INSERT INTO bfv_statistics_foo_real VALUES (9, '-24926804.045047420');
INSERT INTO bfv_statistics_foo_real VALUES (0, '0');
INSERT INTO bfv_statistics_foo_real VALUES (1, '0');
INSERT INTO bfv_statistics_foo_real VALUES (2, '-34338492.215397047');
INSERT INTO bfv_statistics_foo_real VALUES (3, '4.31');
INSERT INTO bfv_statistics_foo_real VALUES (4, '7799461.4119');
INSERT INTO bfv_statistics_foo_real VALUES (5, '16397.038491');
INSERT INTO bfv_statistics_foo_real VALUES (6, '93901.57763026');
INSERT INTO bfv_statistics_foo_real VALUES (7, '-83028485');
INSERT INTO bfv_statistics_foo_real VALUES (8, '74881');
INSERT INTO bfv_statistics_foo_real VALUES (9, '-24926804.045047420');
INSERT INTO bfv_statistics_foo_real VALUES (10, NULL);
INSERT INTO bfv_statistics_foo_real VALUES (10, NULL);
INSERT INTO bfv_statistics_foo_real VALUES (10, NULL);
INSERT INTO bfv_statistics_foo_real VALUES (10, NULL);
INSERT INTO bfv_statistics_foo_real VALUES (10, NULL);
INSERT INTO bfv_statistics_foo_real VALUES (10, NULL);
INSERT INTO bfv_statistics_foo_real VALUES (10, NULL);
INSERT INTO bfv_statistics_foo_real VALUES (10, NULL);
INSERT INTO bfv_statistics_foo_real VALUES (10, NULL);
INSERT INTO bfv_statistics_foo_real VALUES (9, '-24926804.045047420');
INSERT INTO bfv_statistics_foo_real VALUES (0, '0');
INSERT INTO bfv_statistics_foo_real VALUES (1, '0');
INSERT INTO bfv_statistics_foo_real VALUES (2, '-34338492.215397047');
INSERT INTO bfv_statistics_foo_real VALUES (3, '4.31');
INSERT INTO bfv_statistics_foo_real VALUES (4, '7799461.4119');
INSERT INTO bfv_statistics_foo_real VALUES (5, '16397.038491');
INSERT INTO bfv_statistics_foo_real VALUES (6, '93901.57763026');
INSERT INTO bfv_statistics_foo_real VALUES (7, '-83028485');
INSERT INTO bfv_statistics_foo_real VALUES (8, '74881');
INSERT INTO bfv_statistics_foo_real VALUES (9, '-24926804.045047420');
ANALYZE bfv_statistics_foo_real;
select histogram_bounds from pg_stats where tablename = 'bfv_statistics_foo_real' and attname = 'b';
 histogram_bounds 
------------------
 
(1 row)

select most_common_vals from pg_stats where tablename = 'bfv_statistics_foo_real' and attname = 'b';
                                            most_common_vals                                            
--------------------------------------------------------------------------------------------------------
 {0,-Infinity,-2.49268e+07,Infinity,NaN,-8.30285e+07,-3.43385e+07,4.31,16397,74881,93901.6,7.79946e+06}
(1 row)

create table bfv_statistics_foo_numeric (a int4, b numeric) distributed randomly;
insert into bfv_statistics_foo_numeric values (0, 'NaN');
insert into bfv_statistics_foo_numeric values (0, 'NaN');
insert into bfv_statistics_foo_numeric values (0, 'NaN');
insert into bfv_statistics_foo_numeric values (0, 'NaN');
insert into bfv_statistics_foo_numeric values (0, 'NaN');
insert into bfv_statistics_foo_numeric values (0, 'NaN');
insert into bfv_statistics_foo_numeric values (0, 'NaN');
insert into bfv_statistics_foo_numeric values (0, 'NaN');
INSERT INTO bfv_statistics_foo_numeric VALUES (0, '0');
INSERT INTO bfv_statistics_foo_numeric VALUES (1, '0');
INSERT INTO bfv_statistics_foo_numeric VALUES (2, '-34338492.215397047');
INSERT INTO bfv_statistics_foo_numeric VALUES (3, '4.31');
INSERT INTO bfv_statistics_foo_numeric VALUES (4, '7799461.4119');
INSERT INTO bfv_statistics_foo_numeric VALUES (5, '16397.038491');
INSERT INTO bfv_statistics_foo_numeric VALUES (6, '93901.57763026');
INSERT INTO bfv_statistics_foo_numeric VALUES (7, '-83028485');
INSERT INTO bfv_statistics_foo_numeric VALUES (8, '74881');
INSERT INTO bfv_statistics_foo_numeric VALUES (9, '-24926804.045047420');
INSERT INTO bfv_statistics_foo_numeric VALUES (0, '0');
INSERT INTO bfv_statistics_foo_numeric VALUES (1, '0');
INSERT INTO bfv_statistics_foo_numeric VALUES (2, '-34338492.215397047');
INSERT INTO bfv_statistics_foo_numeric VALUES (3, '4.31');
INSERT INTO bfv_statistics_foo_numeric VALUES (4, '7799461.4119');
INSERT INTO bfv_statistics_foo_numeric VALUES (5, '16397.038491');
INSERT INTO bfv_statistics_foo_numeric VALUES (6, '93901.57763026');
INSERT INTO bfv_statistics_foo_numeric VALUES (7, '-83028485');
INSERT INTO bfv_statistics_foo_numeric VALUES (8, '74881');
INSERT INTO bfv_statistics_foo_numeric VALUES (9, '-24926804.045047420');
INSERT INTO bfv_statistics_foo_numeric VALUES (10, NULL);
INSERT INTO bfv_statistics_foo_numeric VALUES (10, NULL);
INSERT INTO bfv_statistics_foo_numeric VALUES (10, NULL);
INSERT INTO bfv_statistics_foo_numeric VALUES (10, NULL);
INSERT INTO bfv_statistics_foo_numeric VALUES (10, NULL);
INSERT INTO bfv_statistics_foo_numeric VALUES (10, NULL);
INSERT INTO bfv_statistics_foo_numeric VALUES (10, NULL);
INSERT INTO bfv_statistics_foo_numeric VALUES (10, NULL);
INSERT INTO bfv_statistics_foo_numeric VALUES (10, NULL);
INSERT INTO bfv_statistics_foo_numeric VALUES (9, '-24926804.045047420');
INSERT INTO bfv_statistics_foo_numeric VALUES (0, '0');
INSERT INTO bfv_statistics_foo_numeric VALUES (1, '0');
INSERT INTO bfv_statistics_foo_numeric VALUES (2, '-34338492.215397047');
INSERT INTO bfv_statistics_foo_numeric VALUES (3, '4.31');
INSERT INTO bfv_statistics_foo_numeric VALUES (4, '7799461.4119');
INSERT INTO bfv_statistics_foo_numeric VALUES (5, '16397.038491');
INSERT INTO bfv_statistics_foo_numeric VALUES (6, '93901.57763026');
INSERT INTO bfv_statistics_foo_numeric VALUES (7, '-83028485');
INSERT INTO bfv_statistics_foo_numeric VALUES (8, '74881');
INSERT INTO bfv_statistics_foo_numeric VALUES (9, '-24926804.045047420');
INSERT INTO bfv_statistics_foo_numeric SELECT i,i FROM generate_series(1,30) i;
ANALYZE bfv_statistics_foo_numeric;
select histogram_bounds from pg_stats where tablename = 'bfv_statistics_foo_numeric' and attname = 'b';
                                  histogram_bounds                                  
------------------------------------------------------------------------------------
 {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}
(1 row)

select most_common_vals from pg_stats where tablename = 'bfv_statistics_foo_numeric' and attname = 'b';
                                               most_common_vals                                                
---------------------------------------------------------------------------------------------------------------
 {NaN,0,-24926804.045047420,-83028485,-34338492.215397047,4.31,16397.038491,74881,93901.57763026,7799461.4119}
(1 row)

reset gp_create_table_random_default_distribution;
--
-- Ensure that VACUUM ANALYZE does not result in incorrect statistics
--
CREATE TABLE T25289_T1 (c 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 T25289_T1 VALUES (1);
DELETE FROM T25289_T1;
ANALYZE T25289_T1;
--
-- expect NO more notice after customer run VACUUM FULL
-- 
CREATE TABLE T25289_T2 (c 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 T25289_T2 VALUES (1);
DELETE FROM T25289_T2;
VACUUM FULL T25289_T2;
ANALYZE T25289_T2;
--
-- expect NO notice during analyze if table doesn't have empty pages
--
CREATE TABLE T25289_T3 (c 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 T25289_T3 VALUES (1);
ANALYZE T25289_T3;
--
-- expect NO notice when analyzing append only tables
-- 
CREATE TABLE T25289_T4 (c int, d int)
WITH (APPENDONLY=ON) DISTRIBUTED BY (c)
PARTITION BY RANGE(d) (START(1) END (5) EVERY(1));
NOTICE:  CREATE TABLE will create partition "t25289_t4_1_prt_1" for table "t25289_t4"
NOTICE:  CREATE TABLE will create partition "t25289_t4_1_prt_2" for table "t25289_t4"
NOTICE:  CREATE TABLE will create partition "t25289_t4_1_prt_3" for table "t25289_t4"
NOTICE:  CREATE TABLE will create partition "t25289_t4_1_prt_4" for table "t25289_t4"
ANALYZE T25289_T4;
--
-- expect NO crash when the statistic slot for an attribute is broken
--
CREATE TABLE good_tab(a int, b text);
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 test_broken_stats(a int, b text);
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 test_broken_stats VALUES(1, 'abc'), (2, 'cde'), (3, 'efg'), (3, 'efg'), (3, 'efg'), (1, 'abc'), (2, 'cde'); 
ANALYZE test_broken_stats;
SET allow_system_table_mods=true;
-- Simulate broken stats by changing the data type of MCV slot to a different type than in pg_attribute 
-- start_matchsubs
-- m/ERROR:  invalid .* of type .*, for attribute of type .* \(selfuncs\.c\:\d+\)/
-- s/\(selfuncs\.c:\d+\)//
-- end_matchsubs
-- Broken MCVs
UPDATE pg_statistic SET stavalues1='{1,2,3}'::int[] WHERE starelid ='test_broken_stats'::regclass AND staattnum=2;
SELECT * FROM test_broken_stats t1, good_tab t2 WHERE t1.b = t2.b;
ERROR:  invalid MCV array of type integer, for attribute of type text (selfuncs.c:4700)
-- Broken histogram
UPDATE pg_statistic SET stakind2=2 WHERE starelid ='test_broken_stats'::regclass AND staattnum=2;
UPDATE pg_statistic SET stavalues2='{1,2,3}'::int[] WHERE starelid ='test_broken_stats'::regclass AND staattnum=2 and stakind2=2;
SELECT * FROM test_broken_stats t1, good_tab t2 WHERE t1.b = t2.b;
ERROR:  invalid histogram of type integer, for attribute of type text (selfuncs.c:4661)
RESET allow_system_table_mods;
-- cardinality estimation for join on varchar, text, char and bpchar columns must account for FreqRemaining and NDVRemaining
-- resulting in better cardinality numbers for the joins in orca
-- start_ignore
DROP TABLE IF EXISTS test_join_card1;
NOTICE:  table "test_join_card1" does not exist, skipping
DROP TABLE IF EXISTS test_join_card2;
NOTICE:  table "test_join_card2" does not exist, skipping
-- end_ignore
CREATE TABLE test_join_card1 (a varchar, b varchar);
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 test_join_card2 (a varchar, b varchar);
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 test_join_card3 (a varchar, b varchar);
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 test_join_card1 SELECT i::text, i::text FROM generate_series(1, 20000)i;
INSERT INTO test_join_card2 SELECT i::text, NULL FROM generate_series(1, 179)i;
INSERT INTO test_join_card2 SELECT 1::text, 'a' FROM generate_series(1, 5820)i;
INSERT INTO test_join_card3 SELECT i::text, i::text FROM generate_series(1,10000)i;
ANALYZE test_join_card1;
ANALYZE test_join_card2;
ANALYZE test_join_card3;
EXPLAIN SELECT * FROM test_join_card1 t1, test_join_card2 t2, test_join_card3 t3 WHERE t1.b = t2.b and t3.b = t2.b;
                                                 QUERY PLAN                                                 
------------------------------------------------------------------------------------------------------------
 Gather Motion 3:1  (slice3; segments: 3)  (cost=717.00..1479.01 rows=2910 width=22)
   ->  Hash Join  (cost=717.00..1479.01 rows=970 width=22)
         Hash Cond: t2.b::text = t1.b::text
         ->  Broadcast Motion 3:3  (slice2; segments: 3)  (cost=240.00..907.44 rows=5820 width=12)
               ->  Hash Join  (cost=240.00..674.64 rows=1940 width=12)
                     Hash Cond: t2.b::text = t3.b::text
                     ->  Broadcast Motion 3:3  (slice1; segments: 3)  (cost=0.00..308.95 rows=5999 width=4)
                           ->  Seq Scan on test_join_card2 t2  (cost=0.00..68.99 rows=2000 width=4)
                     ->  Hash  (cost=115.00..115.00 rows=3334 width=8)
                           ->  Seq Scan on test_join_card3 t3  (cost=0.00..115.00 rows=3334 width=8)
         ->  Hash  (cost=227.00..227.00 rows=6667 width=10)
               ->  Seq Scan on test_join_card1 t1  (cost=0.00..227.00 rows=6667 width=10)
 Optimizer: Postgres query optimizer
(13 rows)

-- start_ignore
DROP TABLE IF EXISTS test_join_card1;
DROP TABLE IF EXISTS test_join_card2;
-- end_ignore