--
-- AGGREGATES
--
-- avoid bit-exact output here because operations may not be bit-exact.
SET extra_float_digits = 0;
SELECT avg(four) AS avg_1 FROM onek;
       avg_1        
--------------------
 1.5000000000000000
(1 row)

SELECT avg(a) AS avg_32 FROM aggtest WHERE a < 100;
       avg_32        
---------------------
 32.6666666666666667
(1 row)

-- In 7.1, avg(float4) is computed using float8 arithmetic.
-- Round the result to 3 digits to avoid platform-specific results.
SELECT avg(b)::numeric(10,3) AS avg_107_943 FROM aggtest;
 avg_107_943 
-------------
     107.943
(1 row)

SELECT avg(gpa) AS avg_3_4 FROM ONLY student;
 avg_3_4 
---------
     3.4
(1 row)

SELECT sum(four) AS sum_1500 FROM onek;
 sum_1500 
----------
     1500
(1 row)

SELECT sum(a) AS sum_198 FROM aggtest;
 sum_198 
---------
     198
(1 row)

SELECT sum(b) AS avg_431_773 FROM aggtest;
 avg_431_773 
-------------
     431.773
(1 row)

SELECT sum(gpa) AS avg_6_8 FROM ONLY student;
 avg_6_8 
---------
     6.8
(1 row)

SELECT max(four) AS max_3 FROM onek;
 max_3 
-------
     3
(1 row)

SELECT max(a) AS max_100 FROM aggtest;
 max_100 
---------
     100
(1 row)

SELECT max(aggtest.b) AS max_324_78 FROM aggtest;
 max_324_78 
------------
     324.78
(1 row)

SELECT max(student.gpa) AS max_3_7 FROM student;
 max_3_7 
---------
     3.7
(1 row)

SELECT stddev_pop(b) FROM aggtest;
   stddev_pop    
-----------------
 131.10703231895
(1 row)

SELECT stddev_samp(b) FROM aggtest;
   stddev_samp    
------------------
 151.389360803998
(1 row)

SELECT var_pop(b) FROM aggtest;
     var_pop      
------------------
 17189.0539234823
(1 row)

SELECT var_samp(b) FROM aggtest;
     var_samp     
------------------
 22918.7385646431
(1 row)

SELECT stddev_pop(b::numeric) FROM aggtest;
    stddev_pop    
------------------
 131.107032862199
(1 row)

SELECT stddev_samp(b::numeric) FROM aggtest;
   stddev_samp    
------------------
 151.389361431288
(1 row)

SELECT var_pop(b::numeric) FROM aggtest;
      var_pop       
--------------------
 17189.054065929769
(1 row)

SELECT var_samp(b::numeric) FROM aggtest;
      var_samp      
--------------------
 22918.738754573025
(1 row)

-- population variance is defined for a single tuple, sample variance
-- is not
SELECT var_pop(1.0::float8), var_samp(2.0::float8);
 var_pop | var_samp 
---------+----------
       0 |         
(1 row)

SELECT stddev_pop(3.0::float8), stddev_samp(4.0::float8);
 stddev_pop | stddev_samp 
------------+-------------
          0 |            
(1 row)

SELECT var_pop('inf'::float8), var_samp('inf'::float8);
 var_pop | var_samp 
---------+----------
     NaN |         
(1 row)

SELECT stddev_pop('inf'::float8), stddev_samp('inf'::float8);
 stddev_pop | stddev_samp 
------------+-------------
        NaN |            
(1 row)

SELECT var_pop('nan'::float8), var_samp('nan'::float8);
 var_pop | var_samp 
---------+----------
     NaN |         
(1 row)

SELECT stddev_pop('nan'::float8), stddev_samp('nan'::float8);
 stddev_pop | stddev_samp 
------------+-------------
        NaN |            
(1 row)

SELECT var_pop(1.0::float4), var_samp(2.0::float4);
 var_pop | var_samp 
---------+----------
       0 |         
(1 row)

SELECT stddev_pop(3.0::float4), stddev_samp(4.0::float4);
 stddev_pop | stddev_samp 
------------+-------------
          0 |            
(1 row)

SELECT var_pop('inf'::float4), var_samp('inf'::float4);
 var_pop | var_samp 
---------+----------
     NaN |         
(1 row)

SELECT stddev_pop('inf'::float4), stddev_samp('inf'::float4);
 stddev_pop | stddev_samp 
------------+-------------
        NaN |            
(1 row)

SELECT var_pop('nan'::float4), var_samp('nan'::float4);
 var_pop | var_samp 
---------+----------
     NaN |         
(1 row)

SELECT stddev_pop('nan'::float4), stddev_samp('nan'::float4);
 stddev_pop | stddev_samp 
------------+-------------
        NaN |            
(1 row)

SELECT var_pop(1.0::numeric), var_samp(2.0::numeric);
 var_pop | var_samp 
---------+----------
       0 |         
(1 row)

SELECT stddev_pop(3.0::numeric), stddev_samp(4.0::numeric);
 stddev_pop | stddev_samp 
------------+-------------
          0 |            
(1 row)

SELECT var_pop('nan'::numeric), var_samp('nan'::numeric);
 var_pop | var_samp 
---------+----------
     NaN |      NaN
(1 row)

SELECT stddev_pop('nan'::numeric), stddev_samp('nan'::numeric);
 stddev_pop | stddev_samp 
------------+-------------
        NaN |         NaN
(1 row)

-- verify correct results for null and NaN inputs
select sum(null::int4) from generate_series(1,3);
 sum 
-----
    
(1 row)

select sum(null::int8) from generate_series(1,3);
 sum 
-----
    
(1 row)

select sum(null::numeric) from generate_series(1,3);
 sum 
-----
    
(1 row)

select sum(null::float8) from generate_series(1,3);
 sum 
-----
    
(1 row)

select avg(null::int4) from generate_series(1,3);
 avg 
-----
    
(1 row)

select avg(null::int8) from generate_series(1,3);
 avg 
-----
    
(1 row)

select avg(null::numeric) from generate_series(1,3);
 avg 
-----
    
(1 row)

select avg(null::float8) from generate_series(1,3);
 avg 
-----
    
(1 row)

select sum('NaN'::numeric) from generate_series(1,3);
 sum 
-----
 NaN
(1 row)

select avg('NaN'::numeric) from generate_series(1,3);
 avg 
-----
 NaN
(1 row)

-- verify correct results for infinite inputs
SELECT avg(x::float8), var_pop(x::float8)
FROM (VALUES ('1'), ('infinity')) v(x);
   avg    | var_pop 
----------+---------
 Infinity |     NaN
(1 row)

SELECT avg(x::float8), var_pop(x::float8)
FROM (VALUES ('infinity'), ('1')) v(x);
   avg    | var_pop 
----------+---------
 Infinity |     NaN
(1 row)

SELECT avg(x::float8), var_pop(x::float8)
FROM (VALUES ('infinity'), ('infinity')) v(x);
   avg    | var_pop 
----------+---------
 Infinity |     NaN
(1 row)

SELECT avg(x::float8), var_pop(x::float8)
FROM (VALUES ('-infinity'), ('infinity')) v(x);
 avg | var_pop 
-----+---------
 NaN |     NaN
(1 row)

-- test accuracy with a large input offset
SELECT avg(x::float8), var_pop(x::float8)
FROM (VALUES (100000003), (100000004), (100000006), (100000007)) v(x);
    avg    | var_pop 
-----------+---------
 100000005 |     2.5
(1 row)

SELECT avg(x::float8), var_pop(x::float8)
FROM (VALUES (7000000000005), (7000000000007)) v(x);
      avg      | var_pop 
---------------+---------
 7000000000006 |       1
(1 row)

-- SQL2003 binary aggregates
SELECT regr_count(b, a) FROM aggtest;
 regr_count 
------------
          4
(1 row)

SELECT regr_sxx(b, a) FROM aggtest;
 regr_sxx 
----------
     5099
(1 row)

SELECT regr_syy(b, a) FROM aggtest;
     regr_syy     
------------------
 68756.2156939293
(1 row)

SELECT regr_sxy(b, a) FROM aggtest;
     regr_sxy     
------------------
 2614.51582155004
(1 row)

SELECT regr_avgx(b, a), regr_avgy(b, a) FROM aggtest;
 regr_avgx |    regr_avgy     
-----------+------------------
      49.5 | 107.943152273074
(1 row)

SELECT regr_r2(b, a) FROM aggtest;
      regr_r2       
--------------------
 0.0194977982031803
(1 row)

SELECT regr_slope(b, a), regr_intercept(b, a) FROM aggtest;
    regr_slope     |  regr_intercept  
-------------------+------------------
 0.512750700441271 | 82.5619926012309
(1 row)

SELECT covar_pop(b, a), covar_samp(b, a) FROM aggtest;
    covar_pop    |    covar_samp    
-----------------+------------------
 653.62895538751 | 871.505273850014
(1 row)

SELECT corr(b, a) FROM aggtest;
       corr        
-------------------
 0.139634516517873
(1 row)

-- check single-tuple behavior
SELECT covar_pop(1::float8,2::float8), covar_samp(3::float8,4::float8);
 covar_pop | covar_samp 
-----------+------------
         0 |           
(1 row)

SELECT covar_pop(1::float8,'inf'::float8), covar_samp(3::float8,'inf'::float8);
 covar_pop | covar_samp 
-----------+------------
       NaN |           
(1 row)

SELECT covar_pop(1::float8,'nan'::float8), covar_samp(3::float8,'nan'::float8);
 covar_pop | covar_samp 
-----------+------------
       NaN |           
(1 row)

-- test accum and combine functions directly
CREATE TABLE regr_test (x float8, y float8);
INSERT INTO regr_test VALUES (10,150),(20,250),(30,350),(80,540),(100,200);
SELECT count(*), sum(x), regr_sxx(y,x), sum(y),regr_syy(y,x), regr_sxy(y,x)
FROM regr_test WHERE x IN (10,20,30,80);
 count | sum | regr_sxx | sum  | regr_syy | regr_sxy 
-------+-----+----------+------+----------+----------
     4 | 140 |     2900 | 1290 |    83075 |    15050
(1 row)

SELECT count(*), sum(x), regr_sxx(y,x), sum(y),regr_syy(y,x), regr_sxy(y,x)
FROM regr_test;
 count | sum | regr_sxx | sum  | regr_syy | regr_sxy 
-------+-----+----------+------+----------+----------
     5 | 240 |     6280 | 1490 |    95080 |     8680
(1 row)

SELECT float8_accum('{4,140,2900}'::float8[], 100);
 float8_accum 
--------------
 {5,240,6280}
(1 row)

SELECT float8_regr_accum('{4,140,2900,1290,83075,15050}'::float8[], 200, 100);
      float8_regr_accum       
------------------------------
 {5,240,6280,1490,95080,8680}
(1 row)

SELECT count(*), sum(x), regr_sxx(y,x), sum(y),regr_syy(y,x), regr_sxy(y,x)
FROM regr_test WHERE x IN (10,20,30);
 count | sum | regr_sxx | sum | regr_syy | regr_sxy 
-------+-----+----------+-----+----------+----------
     3 |  60 |      200 | 750 |    20000 |     2000
(1 row)

SELECT count(*), sum(x), regr_sxx(y,x), sum(y),regr_syy(y,x), regr_sxy(y,x)
FROM regr_test WHERE x IN (80,100);
 count | sum | regr_sxx | sum | regr_syy | regr_sxy 
-------+-----+----------+-----+----------+----------
     2 | 180 |      200 | 740 |    57800 |    -3400
(1 row)

SELECT float8_combine('{3,60,200}'::float8[], '{0,0,0}'::float8[]);
 float8_combine 
----------------
 {3,60,200}
(1 row)

SELECT float8_combine('{0,0,0}'::float8[], '{2,180,200}'::float8[]);
 float8_combine 
----------------
 {2,180,200}
(1 row)

SELECT float8_combine('{3,60,200}'::float8[], '{2,180,200}'::float8[]);
 float8_combine 
----------------
 {5,240,6280}
(1 row)

SELECT float8_regr_combine('{3,60,200,750,20000,2000}'::float8[],
                           '{0,0,0,0,0,0}'::float8[]);
    float8_regr_combine    
---------------------------
 {3,60,200,750,20000,2000}
(1 row)

SELECT float8_regr_combine('{0,0,0,0,0,0}'::float8[],
                           '{2,180,200,740,57800,-3400}'::float8[]);
     float8_regr_combine     
-----------------------------
 {2,180,200,740,57800,-3400}
(1 row)

SELECT float8_regr_combine('{3,60,200,750,20000,2000}'::float8[],
                           '{2,180,200,740,57800,-3400}'::float8[]);
     float8_regr_combine      
------------------------------
 {5,240,6280,1490,95080,8680}
(1 row)

DROP TABLE regr_test;
-- test count, distinct
SELECT count(four) AS cnt_1000 FROM onek;
 cnt_1000 
----------
     1000
(1 row)

SELECT count(DISTINCT four) AS cnt_4 FROM onek;
 cnt_4 
-------
     4
(1 row)

select ten, count(*), sum(four) from onek
group by ten order by ten;
 ten | count | sum 
-----+-------+-----
   0 |   100 | 100
   1 |   100 | 200
   2 |   100 | 100
   3 |   100 | 200
   4 |   100 | 100
   5 |   100 | 200
   6 |   100 | 100
   7 |   100 | 200
   8 |   100 | 100
   9 |   100 | 200
(10 rows)

select ten, count(four), sum(DISTINCT four) from onek
group by ten order by ten;
 ten | count | sum 
-----+-------+-----
   0 |   100 |   2
   1 |   100 |   4
   2 |   100 |   2
   3 |   100 |   4
   4 |   100 |   2
   5 |   100 |   4
   6 |   100 |   2
   7 |   100 |   4
   8 |   100 |   2
   9 |   100 |   4
(10 rows)

-- user-defined aggregates
SELECT newavg(four) AS avg_1 FROM onek;
       avg_1        
--------------------
 1.5000000000000000
(1 row)

SELECT newsum(four) AS sum_1500 FROM onek;
 sum_1500 
----------
     1500
(1 row)

SELECT newcnt(four) AS cnt_1000 FROM onek;
 cnt_1000 
----------
     1000
(1 row)

SELECT newcnt(*) AS cnt_1000 FROM onek;
 cnt_1000 
----------
     1000
(1 row)

SELECT oldcnt(*) AS cnt_1000 FROM onek;
 cnt_1000 
----------
     1000
(1 row)

SELECT sum2(q1,q2) FROM int8_tbl;
       sum2        
-------------------
 18271560493827981
(1 row)

-- test for outer-level aggregates
-- this should work
select ten, sum(distinct four) from onek a
group by ten
having exists (select 1 from onek b where sum(distinct a.four) = b.four);
 ten | sum 
-----+-----
   0 |   2
   2 |   2
   4 |   2
   6 |   2
   8 |   2
(5 rows)

-- this should fail because subquery has an agg of its own in WHERE
select ten, sum(distinct four) from onek a
group by ten
having exists (select 1 from onek b
               where sum(distinct a.four + b.four) = b.four);
ERROR:  aggregate functions are not allowed in WHERE
LINE 4:                where sum(distinct a.four + b.four) = b.four)...
                             ^
-- Test handling of sublinks within outer-level aggregates.
-- Per bug report from Daniel Grace.
select
  (select max((select i.unique2 from tenk1 i where i.unique1 = o.unique1)))
from tenk1 o;
 max  
------
 9999
(1 row)

-- Test handling of Params within aggregate arguments in hashed aggregation.
-- Per bug report from Jeevan Chalke.
explain (verbose, costs off)
select s1, s2, sm
from generate_series(1, 3) s1,
     lateral (select s2, sum(s1 + s2) sm
              from generate_series(1, 3) s2 group by s2) ss
order by 1, 2;
                            QUERY PLAN                            
------------------------------------------------------------------
 Sort
   Output: s1.s1, s2.s2, (sum((s1.s1 + s2.s2)))
   Sort Key: s1.s1, s2.s2
   ->  Nested Loop
         Output: s1.s1, s2.s2, (sum((s1.s1 + s2.s2)))
         ->  Function Scan on pg_catalog.generate_series s1
               Output: s1.s1
               Function Call: generate_series(1, 3)
         ->  HashAggregate
               Output: s2.s2, sum((s1.s1 + s2.s2))
               Group Key: s2.s2
               ->  Function Scan on pg_catalog.generate_series s2
                     Output: s2.s2
                     Function Call: generate_series(1, 3)
(14 rows)

select s1, s2, sm
from generate_series(1, 3) s1,
     lateral (select s2, sum(s1 + s2) sm
              from generate_series(1, 3) s2 group by s2) ss
order by 1, 2;
 s1 | s2 | sm 
----+----+----
  1 |  1 |  2
  1 |  2 |  3
  1 |  3 |  4
  2 |  1 |  3
  2 |  2 |  4
  2 |  3 |  5
  3 |  1 |  4
  3 |  2 |  5
  3 |  3 |  6
(9 rows)

explain (verbose, costs off)
select array(select sum(x+y) s
            from generate_series(1,3) y group by y order by s)
  from generate_series(1,3) x;
                            QUERY PLAN                             
-------------------------------------------------------------------
 Function Scan on pg_catalog.generate_series x
   Output: (SubPlan 1)
   Function Call: generate_series(1, 3)
   SubPlan 1
     ->  Sort
           Output: (sum((x.x + y.y))), y.y
           Sort Key: (sum((x.x + y.y)))
           ->  HashAggregate
                 Output: sum((x.x + y.y)), y.y
                 Group Key: y.y
                 ->  Function Scan on pg_catalog.generate_series y
                       Output: y.y
                       Function Call: generate_series(1, 3)
(13 rows)

select array(select sum(x+y) s
            from generate_series(1,3) y group by y order by s)
  from generate_series(1,3) x;
  array  
---------
 {2,3,4}
 {3,4,5}
 {4,5,6}
(3 rows)

--
-- test for bitwise integer aggregates
--
CREATE TEMPORARY TABLE bitwise_test(
  i2 INT2,
  i4 INT4,
  i8 INT8,
  i INTEGER,
  x INT2,
  y BIT(4)
);
-- empty case
SELECT
  BIT_AND(i2) AS "?",
  BIT_OR(i4)  AS "?"
FROM bitwise_test;
 ? | ? 
---+---
   |  
(1 row)

COPY bitwise_test FROM STDIN NULL 'null';
SELECT
  BIT_AND(i2) AS "1",
  BIT_AND(i4) AS "1",
  BIT_AND(i8) AS "1",
  BIT_AND(i)  AS "?",
  BIT_AND(x)  AS "0",
  BIT_AND(y)  AS "0100",
  BIT_OR(i2)  AS "7",
  BIT_OR(i4)  AS "7",
  BIT_OR(i8)  AS "7",
  BIT_OR(i)   AS "?",
  BIT_OR(x)   AS "7",
  BIT_OR(y)   AS "1101"
FROM bitwise_test;
 1 | 1 | 1 | ? | 0 | 0100 | 7 | 7 | 7 | ? | 7 | 1101 
---+---+---+---+---+------+---+---+---+---+---+------
 1 | 1 | 1 | 1 | 0 | 0100 | 7 | 7 | 7 | 3 | 7 | 1101
(1 row)

--
-- test boolean aggregates
--
-- first test all possible transition and final states
SELECT
  -- boolean and transitions
  -- null because strict
  booland_statefunc(NULL, NULL)  IS NULL AS "t",
  booland_statefunc(TRUE, NULL)  IS NULL AS "t",
  booland_statefunc(FALSE, NULL) IS NULL AS "t",
  booland_statefunc(NULL, TRUE)  IS NULL AS "t",
  booland_statefunc(NULL, FALSE) IS NULL AS "t",
  -- and actual computations
  booland_statefunc(TRUE, TRUE) AS "t",
  NOT booland_statefunc(TRUE, FALSE) AS "t",
  NOT booland_statefunc(FALSE, TRUE) AS "t",
  NOT booland_statefunc(FALSE, FALSE) AS "t";
 t | t | t | t | t | t | t | t | t 
---+---+---+---+---+---+---+---+---
 t | t | t | t | t | t | t | t | t
(1 row)

SELECT
  -- boolean or transitions
  -- null because strict
  boolor_statefunc(NULL, NULL)  IS NULL AS "t",
  boolor_statefunc(TRUE, NULL)  IS NULL AS "t",
  boolor_statefunc(FALSE, NULL) IS NULL AS "t",
  boolor_statefunc(NULL, TRUE)  IS NULL AS "t",
  boolor_statefunc(NULL, FALSE) IS NULL AS "t",
  -- actual computations
  boolor_statefunc(TRUE, TRUE) AS "t",
  boolor_statefunc(TRUE, FALSE) AS "t",
  boolor_statefunc(FALSE, TRUE) AS "t",
  NOT boolor_statefunc(FALSE, FALSE) AS "t";
 t | t | t | t | t | t | t | t | t 
---+---+---+---+---+---+---+---+---
 t | t | t | t | t | t | t | t | t
(1 row)

CREATE TEMPORARY TABLE bool_test(
  b1 BOOL,
  b2 BOOL,
  b3 BOOL,
  b4 BOOL);
-- empty case
SELECT
  BOOL_AND(b1)   AS "n",
  BOOL_OR(b3)    AS "n"
FROM bool_test;
 n | n 
---+---
   | 
(1 row)

COPY bool_test FROM STDIN NULL 'null';
SELECT
  BOOL_AND(b1)     AS "f",
  BOOL_AND(b2)     AS "t",
  BOOL_AND(b3)     AS "f",
  BOOL_AND(b4)     AS "n",
  BOOL_AND(NOT b2) AS "f",
  BOOL_AND(NOT b3) AS "t"
FROM bool_test;
 f | t | f | n | f | t 
---+---+---+---+---+---
 f | t | f |   | f | t
(1 row)

SELECT
  EVERY(b1)     AS "f",
  EVERY(b2)     AS "t",
  EVERY(b3)     AS "f",
  EVERY(b4)     AS "n",
  EVERY(NOT b2) AS "f",
  EVERY(NOT b3) AS "t"
FROM bool_test;
 f | t | f | n | f | t 
---+---+---+---+---+---
 f | t | f |   | f | t
(1 row)

SELECT
  BOOL_OR(b1)      AS "t",
  BOOL_OR(b2)      AS "t",
  BOOL_OR(b3)      AS "f",
  BOOL_OR(b4)      AS "n",
  BOOL_OR(NOT b2)  AS "f",
  BOOL_OR(NOT b3)  AS "t"
FROM bool_test;
 t | t | f | n | f | t 
---+---+---+---+---+---
 t | t | f |   | f | t
(1 row)

--
-- Test cases that should be optimized into indexscans instead of
-- the generic aggregate implementation.
--
-- Basic cases
explain (costs off)
  select min(unique1) from tenk1;
                         QUERY PLAN                         
------------------------------------------------------------
 Result
   InitPlan 1 (returns $0)
     ->  Limit
           ->  Index Only Scan using tenk1_unique1 on tenk1
                 Index Cond: (unique1 IS NOT NULL)
(5 rows)

select min(unique1) from tenk1;
 min 
-----
   0
(1 row)

explain (costs off)
  select max(unique1) from tenk1;
                             QUERY PLAN                              
---------------------------------------------------------------------
 Result
   InitPlan 1 (returns $0)
     ->  Limit
           ->  Index Only Scan Backward using tenk1_unique1 on tenk1
                 Index Cond: (unique1 IS NOT NULL)
(5 rows)

select max(unique1) from tenk1;
 max  
------
 9999
(1 row)

explain (costs off)
  select max(unique1) from tenk1 where unique1 < 42;
                               QUERY PLAN                               
------------------------------------------------------------------------
 Result
   InitPlan 1 (returns $0)
     ->  Limit
           ->  Index Only Scan Backward using tenk1_unique1 on tenk1
                 Index Cond: ((unique1 IS NOT NULL) AND (unique1 < 42))
(5 rows)

select max(unique1) from tenk1 where unique1 < 42;
 max 
-----
  41
(1 row)

explain (costs off)
  select max(unique1) from tenk1 where unique1 > 42;
                               QUERY PLAN                               
------------------------------------------------------------------------
 Result
   InitPlan 1 (returns $0)
     ->  Limit
           ->  Index Only Scan Backward using tenk1_unique1 on tenk1
                 Index Cond: ((unique1 IS NOT NULL) AND (unique1 > 42))
(5 rows)

select max(unique1) from tenk1 where unique1 > 42;
 max  
------
 9999
(1 row)

-- the planner may choose a generic aggregate here if parallel query is
-- enabled, since that plan will be parallel safe and the "optimized"
-- plan, which has almost identical cost, will not be.  we want to test
-- the optimized plan, so temporarily disable parallel query.
begin;
set local max_parallel_workers_per_gather = 0;
explain (costs off)
  select max(unique1) from tenk1 where unique1 > 42000;
                                QUERY PLAN                                 
---------------------------------------------------------------------------
 Result
   InitPlan 1 (returns $0)
     ->  Limit
           ->  Index Only Scan Backward using tenk1_unique1 on tenk1
                 Index Cond: ((unique1 IS NOT NULL) AND (unique1 > 42000))
(5 rows)

select max(unique1) from tenk1 where unique1 > 42000;
 max 
-----
    
(1 row)

rollback;
-- multi-column index (uses tenk1_thous_tenthous)
explain (costs off)
  select max(tenthous) from tenk1 where thousand = 33;
                                 QUERY PLAN                                 
----------------------------------------------------------------------------
 Result
   InitPlan 1 (returns $0)
     ->  Limit
           ->  Index Only Scan Backward using tenk1_thous_tenthous on tenk1
                 Index Cond: ((thousand = 33) AND (tenthous IS NOT NULL))
(5 rows)

select max(tenthous) from tenk1 where thousand = 33;
 max  
------
 9033
(1 row)

explain (costs off)
  select min(tenthous) from tenk1 where thousand = 33;
                                QUERY PLAN                                
--------------------------------------------------------------------------
 Result
   InitPlan 1 (returns $0)
     ->  Limit
           ->  Index Only Scan using tenk1_thous_tenthous on tenk1
                 Index Cond: ((thousand = 33) AND (tenthous IS NOT NULL))
(5 rows)

select min(tenthous) from tenk1 where thousand = 33;
 min 
-----
  33
(1 row)

-- check parameter propagation into an indexscan subquery
explain (costs off)
  select f1, (select min(unique1) from tenk1 where unique1 > f1) AS gt
    from int4_tbl;
                                       QUERY PLAN                                        
-----------------------------------------------------------------------------------------
 Seq Scan on int4_tbl
   SubPlan 2
     ->  Result
           InitPlan 1 (returns $1)
             ->  Limit
                   ->  Index Only Scan using tenk1_unique1 on tenk1
                         Index Cond: ((unique1 IS NOT NULL) AND (unique1 > int4_tbl.f1))
(7 rows)

select f1, (select min(unique1) from tenk1 where unique1 > f1) AS gt
  from int4_tbl;
     f1      | gt 
-------------+----
           0 |  1
      123456 |   
     -123456 |  0
  2147483647 |   
 -2147483647 |  0
(5 rows)

-- check some cases that were handled incorrectly in 8.3.0
explain (costs off)
  select distinct max(unique2) from tenk1;
                             QUERY PLAN                              
---------------------------------------------------------------------
 HashAggregate
   Group Key: $0
   InitPlan 1 (returns $0)
     ->  Limit
           ->  Index Only Scan Backward using tenk1_unique2 on tenk1
                 Index Cond: (unique2 IS NOT NULL)
   ->  Result
(7 rows)

select distinct max(unique2) from tenk1;
 max  
------
 9999
(1 row)

explain (costs off)
  select max(unique2) from tenk1 order by 1;
                             QUERY PLAN                              
---------------------------------------------------------------------
 Sort
   Sort Key: ($0)
   InitPlan 1 (returns $0)
     ->  Limit
           ->  Index Only Scan Backward using tenk1_unique2 on tenk1
                 Index Cond: (unique2 IS NOT NULL)
   ->  Result
(7 rows)

select max(unique2) from tenk1 order by 1;
 max  
------
 9999
(1 row)

explain (costs off)
  select max(unique2) from tenk1 order by max(unique2);
                             QUERY PLAN                              
---------------------------------------------------------------------
 Sort
   Sort Key: ($0)
   InitPlan 1 (returns $0)
     ->  Limit
           ->  Index Only Scan Backward using tenk1_unique2 on tenk1
                 Index Cond: (unique2 IS NOT NULL)
   ->  Result
(7 rows)

select max(unique2) from tenk1 order by max(unique2);
 max  
------
 9999
(1 row)

explain (costs off)
  select max(unique2) from tenk1 order by max(unique2)+1;
                             QUERY PLAN                              
---------------------------------------------------------------------
 Sort
   Sort Key: (($0 + 1))
   InitPlan 1 (returns $0)
     ->  Limit
           ->  Index Only Scan Backward using tenk1_unique2 on tenk1
                 Index Cond: (unique2 IS NOT NULL)
   ->  Result
(7 rows)

select max(unique2) from tenk1 order by max(unique2)+1;
 max  
------
 9999
(1 row)

explain (costs off)
  select max(unique2), generate_series(1,3) as g from tenk1 order by g desc;
                             QUERY PLAN                              
---------------------------------------------------------------------
 Sort
   Sort Key: (generate_series(1, 3)) DESC
   InitPlan 1 (returns $0)
     ->  Limit
           ->  Index Only Scan Backward using tenk1_unique2 on tenk1
                 Index Cond: (unique2 IS NOT NULL)
   ->  ProjectSet
         ->  Result
(8 rows)

select max(unique2), generate_series(1,3) as g from tenk1 order by g desc;
 max  | g 
------+---
 9999 | 3
 9999 | 2
 9999 | 1
(3 rows)

-- interesting corner case: constant gets optimized into a seqscan
explain (costs off)
  select max(100) from tenk1;
                     QUERY PLAN                     
----------------------------------------------------
 Result
   InitPlan 1 (returns $0)
     ->  Limit
           ->  Result
                 One-Time Filter: (100 IS NOT NULL)
                 ->  Seq Scan on tenk1
(6 rows)

select max(100) from tenk1;
 max 
-----
 100
(1 row)

-- try it on an inheritance tree
create table minmaxtest(f1 int);
create table minmaxtest1() inherits (minmaxtest);
create table minmaxtest2() inherits (minmaxtest);
create table minmaxtest3() inherits (minmaxtest);
create index minmaxtesti on minmaxtest(f1);
create index minmaxtest1i on minmaxtest1(f1);
create index minmaxtest2i on minmaxtest2(f1 desc);
create index minmaxtest3i on minmaxtest3(f1) where f1 is not null;
insert into minmaxtest values(11), (12);
insert into minmaxtest1 values(13), (14);
insert into minmaxtest2 values(15), (16);
insert into minmaxtest3 values(17), (18);
explain (costs off)
  select min(f1), max(f1) from minmaxtest;
                                         QUERY PLAN                                          
---------------------------------------------------------------------------------------------
 Result
   InitPlan 1 (returns $0)
     ->  Limit
           ->  Merge Append
                 Sort Key: minmaxtest.f1
                 ->  Index Only Scan using minmaxtesti on minmaxtest minmaxtest_1
                       Index Cond: (f1 IS NOT NULL)
                 ->  Index Only Scan using minmaxtest1i on minmaxtest1 minmaxtest_2
                       Index Cond: (f1 IS NOT NULL)
                 ->  Index Only Scan Backward using minmaxtest2i on minmaxtest2 minmaxtest_3
                       Index Cond: (f1 IS NOT NULL)
                 ->  Index Only Scan using minmaxtest3i on minmaxtest3 minmaxtest_4
   InitPlan 2 (returns $1)
     ->  Limit
           ->  Merge Append
                 Sort Key: minmaxtest_5.f1 DESC
                 ->  Index Only Scan Backward using minmaxtesti on minmaxtest minmaxtest_6
                       Index Cond: (f1 IS NOT NULL)
                 ->  Index Only Scan Backward using minmaxtest1i on minmaxtest1 minmaxtest_7
                       Index Cond: (f1 IS NOT NULL)
                 ->  Index Only Scan using minmaxtest2i on minmaxtest2 minmaxtest_8
                       Index Cond: (f1 IS NOT NULL)
                 ->  Index Only Scan Backward using minmaxtest3i on minmaxtest3 minmaxtest_9
(23 rows)

select min(f1), max(f1) from minmaxtest;
 min | max 
-----+-----
  11 |  18
(1 row)

-- DISTINCT doesn't do anything useful here, but it shouldn't fail
explain (costs off)
  select distinct min(f1), max(f1) from minmaxtest;
                                         QUERY PLAN                                          
---------------------------------------------------------------------------------------------
 Unique
   InitPlan 1 (returns $0)
     ->  Limit
           ->  Merge Append
                 Sort Key: minmaxtest.f1
                 ->  Index Only Scan using minmaxtesti on minmaxtest minmaxtest_1
                       Index Cond: (f1 IS NOT NULL)
                 ->  Index Only Scan using minmaxtest1i on minmaxtest1 minmaxtest_2
                       Index Cond: (f1 IS NOT NULL)
                 ->  Index Only Scan Backward using minmaxtest2i on minmaxtest2 minmaxtest_3
                       Index Cond: (f1 IS NOT NULL)
                 ->  Index Only Scan using minmaxtest3i on minmaxtest3 minmaxtest_4
   InitPlan 2 (returns $1)
     ->  Limit
           ->  Merge Append
                 Sort Key: minmaxtest_5.f1 DESC
                 ->  Index Only Scan Backward using minmaxtesti on minmaxtest minmaxtest_6
                       Index Cond: (f1 IS NOT NULL)
                 ->  Index Only Scan Backward using minmaxtest1i on minmaxtest1 minmaxtest_7
                       Index Cond: (f1 IS NOT NULL)
                 ->  Index Only Scan using minmaxtest2i on minmaxtest2 minmaxtest_8
                       Index Cond: (f1 IS NOT NULL)
                 ->  Index Only Scan Backward using minmaxtest3i on minmaxtest3 minmaxtest_9
   ->  Sort
         Sort Key: ($0), ($1)
         ->  Result
(26 rows)

select distinct min(f1), max(f1) from minmaxtest;
 min | max 
-----+-----
  11 |  18
(1 row)

drop table minmaxtest cascade;
NOTICE:  drop cascades to 3 other objects
DETAIL:  drop cascades to table minmaxtest1
drop cascades to table minmaxtest2
drop cascades to table minmaxtest3
-- check for correct detection of nested-aggregate errors
select max(min(unique1)) from tenk1;
ERROR:  aggregate function calls cannot be nested
LINE 1: select max(min(unique1)) from tenk1;
                   ^
select (select max(min(unique1)) from int8_tbl) from tenk1;
ERROR:  aggregate function calls cannot be nested
LINE 1: select (select max(min(unique1)) from int8_tbl) from tenk1;
                           ^
--
-- Test removal of redundant GROUP BY columns
--
create temp table t1 (a int, b int, c int, d int, primary key (a, b));
create temp table t2 (x int, y int, z int, primary key (x, y));
create temp table t3 (a int, b int, c int, primary key(a, b) deferrable);
-- Non-primary-key columns can be removed from GROUP BY
explain (costs off) select * from t1 group by a,b,c,d;
      QUERY PLAN      
----------------------
 HashAggregate
   Group Key: a, b
   ->  Seq Scan on t1
(3 rows)

-- No removal can happen if the complete PK is not present in GROUP BY
explain (costs off) select a,c from t1 group by a,c,d;
      QUERY PLAN      
----------------------
 HashAggregate
   Group Key: a, c, d
   ->  Seq Scan on t1
(3 rows)

-- Test removal across multiple relations
explain (costs off) select *
from t1 inner join t2 on t1.a = t2.x and t1.b = t2.y
group by t1.a,t1.b,t1.c,t1.d,t2.x,t2.y,t2.z;
                      QUERY PLAN                      
------------------------------------------------------
 HashAggregate
   Group Key: t1.a, t1.b, t2.x, t2.y
   ->  Hash Join
         Hash Cond: ((t2.x = t1.a) AND (t2.y = t1.b))
         ->  Seq Scan on t2
         ->  Hash
               ->  Seq Scan on t1
(7 rows)

-- Test case where t1 can be optimized but not t2
explain (costs off) select t1.*,t2.x,t2.z
from t1 inner join t2 on t1.a = t2.x and t1.b = t2.y
group by t1.a,t1.b,t1.c,t1.d,t2.x,t2.z;
                      QUERY PLAN                      
------------------------------------------------------
 HashAggregate
   Group Key: t1.a, t1.b, t2.x, t2.z
   ->  Hash Join
         Hash Cond: ((t2.x = t1.a) AND (t2.y = t1.b))
         ->  Seq Scan on t2
         ->  Hash
               ->  Seq Scan on t1
(7 rows)

-- Cannot optimize when PK is deferrable
explain (costs off) select * from t3 group by a,b,c;
      QUERY PLAN      
----------------------
 HashAggregate
   Group Key: a, b, c
   ->  Seq Scan on t3
(3 rows)

create temp table t1c () inherits (t1);
-- Ensure we don't remove any columns when t1 has a child table
explain (costs off) select * from t1 group by a,b,c,d;
             QUERY PLAN              
-------------------------------------
 HashAggregate
   Group Key: t1.a, t1.b, t1.c, t1.d
   ->  Append
         ->  Seq Scan on t1 t1_1
         ->  Seq Scan on t1c t1_2
(5 rows)

-- Okay to remove columns if we're only querying the parent.
explain (costs off) select * from only t1 group by a,b,c,d;
      QUERY PLAN      
----------------------
 HashAggregate
   Group Key: a, b
   ->  Seq Scan on t1
(3 rows)

create temp table p_t1 (
  a int,
  b int,
  c int,
  d int,
  primary key(a,b)
) partition by list(a);
create temp table p_t1_1 partition of p_t1 for values in(1);
create temp table p_t1_2 partition of p_t1 for values in(2);
-- Ensure we can remove non-PK columns for partitioned tables.
explain (costs off) select * from p_t1 group by a,b,c,d;
           QUERY PLAN           
--------------------------------
 HashAggregate
   Group Key: p_t1.a, p_t1.b
   ->  Append
         ->  Seq Scan on p_t1_1
         ->  Seq Scan on p_t1_2
(5 rows)

drop table t1 cascade;
NOTICE:  drop cascades to table t1c
drop table t2;
drop table t3;
drop table p_t1;
--
-- Test GROUP BY matching of join columns that are type-coerced due to USING
--
create temp table t1(f1 int, f2 bigint);
create temp table t2(f1 bigint, f22 bigint);
select f1 from t1 left join t2 using (f1) group by f1;
 f1 
----
(0 rows)

select f1 from t1 left join t2 using (f1) group by t1.f1;
 f1 
----
(0 rows)

select t1.f1 from t1 left join t2 using (f1) group by t1.f1;
 f1 
----
(0 rows)

-- only this one should fail:
select t1.f1 from t1 left join t2 using (f1) group by f1;
ERROR:  column "t1.f1" must appear in the GROUP BY clause or be used in an aggregate function
LINE 1: select t1.f1 from t1 left join t2 using (f1) group by f1;
               ^
drop table t1, t2;
--
-- Test combinations of DISTINCT and/or ORDER BY
--
select array_agg(a order by b)
  from (values (1,4),(2,3),(3,1),(4,2)) v(a,b);
 array_agg 
-----------
 {3,4,2,1}
(1 row)

select array_agg(a order by a)
  from (values (1,4),(2,3),(3,1),(4,2)) v(a,b);
 array_agg 
-----------
 {1,2,3,4}
(1 row)

select array_agg(a order by a desc)
  from (values (1,4),(2,3),(3,1),(4,2)) v(a,b);
 array_agg 
-----------
 {4,3,2,1}
(1 row)

select array_agg(b order by a desc)
  from (values (1,4),(2,3),(3,1),(4,2)) v(a,b);
 array_agg 
-----------
 {2,1,3,4}
(1 row)

select array_agg(distinct a)
  from (values (1),(2),(1),(3),(null),(2)) v(a);
  array_agg   
--------------
 {1,2,3,NULL}
(1 row)

select array_agg(distinct a order by a)
  from (values (1),(2),(1),(3),(null),(2)) v(a);
  array_agg   
--------------
 {1,2,3,NULL}
(1 row)

select array_agg(distinct a order by a desc)
  from (values (1),(2),(1),(3),(null),(2)) v(a);
  array_agg   
--------------
 {NULL,3,2,1}
(1 row)

select array_agg(distinct a order by a desc nulls last)
  from (values (1),(2),(1),(3),(null),(2)) v(a);
  array_agg   
--------------
 {3,2,1,NULL}
(1 row)

-- multi-arg aggs, strict/nonstrict, distinct/order by
select aggfstr(a,b,c)
  from (values (1,3,'foo'),(0,null,null),(2,2,'bar'),(3,1,'baz')) v(a,b,c);
                aggfstr                
---------------------------------------
 {"(1,3,foo)","(2,2,bar)","(3,1,baz)"}
(1 row)

select aggfns(a,b,c)
  from (values (1,3,'foo'),(0,null,null),(2,2,'bar'),(3,1,'baz')) v(a,b,c);
                    aggfns                     
-----------------------------------------------
 {"(1,3,foo)","(0,,)","(2,2,bar)","(3,1,baz)"}
(1 row)

select aggfstr(distinct a,b,c)
  from (values (1,3,'foo'),(0,null,null),(2,2,'bar'),(3,1,'baz')) v(a,b,c),
       generate_series(1,3) i;
                aggfstr                
---------------------------------------
 {"(1,3,foo)","(2,2,bar)","(3,1,baz)"}
(1 row)

select aggfns(distinct a,b,c)
  from (values (1,3,'foo'),(0,null,null),(2,2,'bar'),(3,1,'baz')) v(a,b,c),
       generate_series(1,3) i;
                    aggfns                     
-----------------------------------------------
 {"(0,,)","(1,3,foo)","(2,2,bar)","(3,1,baz)"}
(1 row)

select aggfstr(distinct a,b,c order by b)
  from (values (1,3,'foo'),(0,null,null),(2,2,'bar'),(3,1,'baz')) v(a,b,c),
       generate_series(1,3) i;
                aggfstr                
---------------------------------------
 {"(3,1,baz)","(2,2,bar)","(1,3,foo)"}
(1 row)

select aggfns(distinct a,b,c order by b)
  from (values (1,3,'foo'),(0,null,null),(2,2,'bar'),(3,1,'baz')) v(a,b,c),
       generate_series(1,3) i;
                    aggfns                     
-----------------------------------------------
 {"(3,1,baz)","(2,2,bar)","(1,3,foo)","(0,,)"}
(1 row)

-- test specific code paths
select aggfns(distinct a,a,c order by c using ~<~,a)
  from (values (1,3,'foo'),(0,null,null),(2,2,'bar'),(3,1,'baz')) v(a,b,c),
       generate_series(1,2) i;
                     aggfns                     
------------------------------------------------
 {"(2,2,bar)","(3,3,baz)","(1,1,foo)","(0,0,)"}
(1 row)

select aggfns(distinct a,a,c order by c using ~<~)
  from (values (1,3,'foo'),(0,null,null),(2,2,'bar'),(3,1,'baz')) v(a,b,c),
       generate_series(1,2) i;
                     aggfns                     
------------------------------------------------
 {"(2,2,bar)","(3,3,baz)","(1,1,foo)","(0,0,)"}
(1 row)

select aggfns(distinct a,a,c order by a)
  from (values (1,3,'foo'),(0,null,null),(2,2,'bar'),(3,1,'baz')) v(a,b,c),
       generate_series(1,2) i;
                     aggfns                     
------------------------------------------------
 {"(0,0,)","(1,1,foo)","(2,2,bar)","(3,3,baz)"}
(1 row)

select aggfns(distinct a,b,c order by a,c using ~<~,b)
  from (values (1,3,'foo'),(0,null,null),(2,2,'bar'),(3,1,'baz')) v(a,b,c),
       generate_series(1,2) i;
                    aggfns                     
-----------------------------------------------
 {"(0,,)","(1,3,foo)","(2,2,bar)","(3,1,baz)"}
(1 row)

-- check node I/O via view creation and usage, also deparsing logic
create view agg_view1 as
  select aggfns(a,b,c)
    from (values (1,3,'foo'),(0,null,null),(2,2,'bar'),(3,1,'baz')) v(a,b,c);
select * from agg_view1;
                    aggfns                     
-----------------------------------------------
 {"(1,3,foo)","(0,,)","(2,2,bar)","(3,1,baz)"}
(1 row)

select pg_get_viewdef('agg_view1'::regclass);
                                                   pg_get_viewdef                                                    
---------------------------------------------------------------------------------------------------------------------
  SELECT aggfns(v.a, v.b, v.c) AS aggfns                                                                            +
    FROM ( VALUES (1,3,'foo'::text), (0,NULL::integer,NULL::text), (2,2,'bar'::text), (3,1,'baz'::text)) v(a, b, c);
(1 row)

create or replace view agg_view1 as
  select aggfns(distinct a,b,c)
    from (values (1,3,'foo'),(0,null,null),(2,2,'bar'),(3,1,'baz')) v(a,b,c),
         generate_series(1,3) i;
select * from agg_view1;
                    aggfns                     
-----------------------------------------------
 {"(0,,)","(1,3,foo)","(2,2,bar)","(3,1,baz)"}
(1 row)

select pg_get_viewdef('agg_view1'::regclass);
                                                   pg_get_viewdef                                                    
---------------------------------------------------------------------------------------------------------------------
  SELECT aggfns(DISTINCT v.a, v.b, v.c) AS aggfns                                                                   +
    FROM ( VALUES (1,3,'foo'::text), (0,NULL::integer,NULL::text), (2,2,'bar'::text), (3,1,'baz'::text)) v(a, b, c),+
     generate_series(1, 3) i(i);
(1 row)

create or replace view agg_view1 as
  select aggfns(distinct a,b,c order by b)
    from (values (1,3,'foo'),(0,null,null),(2,2,'bar'),(3,1,'baz')) v(a,b,c),
         generate_series(1,3) i;
select * from agg_view1;
                    aggfns                     
-----------------------------------------------
 {"(3,1,baz)","(2,2,bar)","(1,3,foo)","(0,,)"}
(1 row)

select pg_get_viewdef('agg_view1'::regclass);
                                                   pg_get_viewdef                                                    
---------------------------------------------------------------------------------------------------------------------
  SELECT aggfns(DISTINCT v.a, v.b, v.c ORDER BY v.b) AS aggfns                                                      +
    FROM ( VALUES (1,3,'foo'::text), (0,NULL::integer,NULL::text), (2,2,'bar'::text), (3,1,'baz'::text)) v(a, b, c),+
     generate_series(1, 3) i(i);
(1 row)

create or replace view agg_view1 as
  select aggfns(a,b,c order by b+1)
    from (values (1,3,'foo'),(0,null,null),(2,2,'bar'),(3,1,'baz')) v(a,b,c);
select * from agg_view1;
                    aggfns                     
-----------------------------------------------
 {"(3,1,baz)","(2,2,bar)","(1,3,foo)","(0,,)"}
(1 row)

select pg_get_viewdef('agg_view1'::regclass);
                                                   pg_get_viewdef                                                    
---------------------------------------------------------------------------------------------------------------------
  SELECT aggfns(v.a, v.b, v.c ORDER BY (v.b + 1)) AS aggfns                                                         +
    FROM ( VALUES (1,3,'foo'::text), (0,NULL::integer,NULL::text), (2,2,'bar'::text), (3,1,'baz'::text)) v(a, b, c);
(1 row)

create or replace view agg_view1 as
  select aggfns(a,a,c order by b)
    from (values (1,3,'foo'),(0,null,null),(2,2,'bar'),(3,1,'baz')) v(a,b,c);
select * from agg_view1;
                     aggfns                     
------------------------------------------------
 {"(3,3,baz)","(2,2,bar)","(1,1,foo)","(0,0,)"}
(1 row)

select pg_get_viewdef('agg_view1'::regclass);
                                                   pg_get_viewdef                                                    
---------------------------------------------------------------------------------------------------------------------
  SELECT aggfns(v.a, v.a, v.c ORDER BY v.b) AS aggfns                                                               +
    FROM ( VALUES (1,3,'foo'::text), (0,NULL::integer,NULL::text), (2,2,'bar'::text), (3,1,'baz'::text)) v(a, b, c);
(1 row)

create or replace view agg_view1 as
  select aggfns(a,b,c order by c using ~<~)
    from (values (1,3,'foo'),(0,null,null),(2,2,'bar'),(3,1,'baz')) v(a,b,c);
select * from agg_view1;
                    aggfns                     
-----------------------------------------------
 {"(2,2,bar)","(3,1,baz)","(1,3,foo)","(0,,)"}
(1 row)

select pg_get_viewdef('agg_view1'::regclass);
                                                   pg_get_viewdef                                                    
---------------------------------------------------------------------------------------------------------------------
  SELECT aggfns(v.a, v.b, v.c ORDER BY v.c USING ~<~ NULLS LAST) AS aggfns                                          +
    FROM ( VALUES (1,3,'foo'::text), (0,NULL::integer,NULL::text), (2,2,'bar'::text), (3,1,'baz'::text)) v(a, b, c);
(1 row)

create or replace view agg_view1 as
  select aggfns(distinct a,b,c order by a,c using ~<~,b)
    from (values (1,3,'foo'),(0,null,null),(2,2,'bar'),(3,1,'baz')) v(a,b,c),
         generate_series(1,2) i;
select * from agg_view1;
                    aggfns                     
-----------------------------------------------
 {"(0,,)","(1,3,foo)","(2,2,bar)","(3,1,baz)"}
(1 row)

select pg_get_viewdef('agg_view1'::regclass);
                                                   pg_get_viewdef                                                    
---------------------------------------------------------------------------------------------------------------------
  SELECT aggfns(DISTINCT v.a, v.b, v.c ORDER BY v.a, v.c USING ~<~ NULLS LAST, v.b) AS aggfns                       +
    FROM ( VALUES (1,3,'foo'::text), (0,NULL::integer,NULL::text), (2,2,'bar'::text), (3,1,'baz'::text)) v(a, b, c),+
     generate_series(1, 2) i(i);
(1 row)

drop view agg_view1;
-- incorrect DISTINCT usage errors
select aggfns(distinct a,b,c order by i)
  from (values (1,1,'foo')) v(a,b,c), generate_series(1,2) i;
ERROR:  in an aggregate with DISTINCT, ORDER BY expressions must appear in argument list
LINE 1: select aggfns(distinct a,b,c order by i)
                                              ^
select aggfns(distinct a,b,c order by a,b+1)
  from (values (1,1,'foo')) v(a,b,c), generate_series(1,2) i;
ERROR:  in an aggregate with DISTINCT, ORDER BY expressions must appear in argument list
LINE 1: select aggfns(distinct a,b,c order by a,b+1)
                                                ^
select aggfns(distinct a,b,c order by a,b,i,c)
  from (values (1,1,'foo')) v(a,b,c), generate_series(1,2) i;
ERROR:  in an aggregate with DISTINCT, ORDER BY expressions must appear in argument list
LINE 1: select aggfns(distinct a,b,c order by a,b,i,c)
                                                  ^
select aggfns(distinct a,a,c order by a,b)
  from (values (1,1,'foo')) v(a,b,c), generate_series(1,2) i;
ERROR:  in an aggregate with DISTINCT, ORDER BY expressions must appear in argument list
LINE 1: select aggfns(distinct a,a,c order by a,b)
                                                ^
-- string_agg tests
select string_agg(a,',') from (values('aaaa'),('bbbb'),('cccc')) g(a);
   string_agg   
----------------
 aaaa,bbbb,cccc
(1 row)

select string_agg(a,',') from (values('aaaa'),(null),('bbbb'),('cccc')) g(a);
   string_agg   
----------------
 aaaa,bbbb,cccc
(1 row)

select string_agg(a,'AB') from (values(null),(null),('bbbb'),('cccc')) g(a);
 string_agg 
------------
 bbbbABcccc
(1 row)

select string_agg(a,',') from (values(null),(null)) g(a);
 string_agg 
------------
 
(1 row)

-- check some implicit casting cases, as per bug #5564
select string_agg(distinct f1, ',' order by f1) from varchar_tbl;  -- ok
 string_agg 
------------
 a,ab,abcd
(1 row)

select string_agg(distinct f1::text, ',' order by f1) from varchar_tbl;  -- not ok
ERROR:  in an aggregate with DISTINCT, ORDER BY expressions must appear in argument list
LINE 1: select string_agg(distinct f1::text, ',' order by f1) from v...
                                                          ^
select string_agg(distinct f1, ',' order by f1::text) from varchar_tbl;  -- not ok
ERROR:  in an aggregate with DISTINCT, ORDER BY expressions must appear in argument list
LINE 1: select string_agg(distinct f1, ',' order by f1::text) from v...
                                                    ^
select string_agg(distinct f1::text, ',' order by f1::text) from varchar_tbl;  -- ok
 string_agg 
------------
 a,ab,abcd
(1 row)

-- string_agg bytea tests
create table bytea_test_table(v bytea);
select string_agg(v, '') from bytea_test_table;
 string_agg 
------------
 
(1 row)

insert into bytea_test_table values(decode('ff','hex'));
select string_agg(v, '') from bytea_test_table;
 string_agg 
------------
 \xff
(1 row)

insert into bytea_test_table values(decode('aa','hex'));
select string_agg(v, '') from bytea_test_table;
 string_agg 
------------
 \xffaa
(1 row)

select string_agg(v, NULL) from bytea_test_table;
 string_agg 
------------
 \xffaa
(1 row)

select string_agg(v, decode('ee', 'hex')) from bytea_test_table;
 string_agg 
------------
 \xffeeaa
(1 row)

drop table bytea_test_table;
-- FILTER tests
select min(unique1) filter (where unique1 > 100) from tenk1;
 min 
-----
 101
(1 row)

select sum(1/ten) filter (where ten > 0) from tenk1;
 sum  
------
 1000
(1 row)

select ten, sum(distinct four) filter (where four::text ~ '123') from onek a
group by ten;
 ten | sum 
-----+-----
   0 |    
   1 |    
   2 |    
   3 |    
   4 |    
   5 |    
   6 |    
   7 |    
   8 |    
   9 |    
(10 rows)

select ten, sum(distinct four) filter (where four > 10) from onek a
group by ten
having exists (select 1 from onek b where sum(distinct a.four) = b.four);
 ten | sum 
-----+-----
   0 |    
   2 |    
   4 |    
   6 |    
   8 |    
(5 rows)

select max(foo COLLATE "C") filter (where (bar collate "POSIX") > '0')
from (values ('a', 'b')) AS v(foo,bar);
 max 
-----
 a
(1 row)

-- outer reference in FILTER (PostgreSQL extension)
select (select count(*)
        from (values (1)) t0(inner_c))
from (values (2),(3)) t1(outer_c); -- inner query is aggregation query
 count 
-------
     1
     1
(2 rows)

select (select count(*) filter (where outer_c <> 0)
        from (values (1)) t0(inner_c))
from (values (2),(3)) t1(outer_c); -- outer query is aggregation query
 count 
-------
     2
(1 row)

select (select count(inner_c) filter (where outer_c <> 0)
        from (values (1)) t0(inner_c))
from (values (2),(3)) t1(outer_c); -- inner query is aggregation query
 count 
-------
     1
     1
(2 rows)

select
  (select max((select i.unique2 from tenk1 i where i.unique1 = o.unique1))
     filter (where o.unique1 < 10))
from tenk1 o;					-- outer query is aggregation query
 max  
------
 9998
(1 row)

-- subquery in FILTER clause (PostgreSQL extension)
select sum(unique1) FILTER (WHERE
  unique1 IN (SELECT unique1 FROM onek where unique1 < 100)) FROM tenk1;
 sum  
------
 4950
(1 row)

-- exercise lots of aggregate parts with FILTER
select aggfns(distinct a,b,c order by a,c using ~<~,b) filter (where a > 1)
    from (values (1,3,'foo'),(0,null,null),(2,2,'bar'),(3,1,'baz')) v(a,b,c),
    generate_series(1,2) i;
          aggfns           
---------------------------
 {"(2,2,bar)","(3,1,baz)"}
(1 row)

-- ordered-set aggregates
select p, percentile_cont(p) within group (order by x::float8)
from generate_series(1,5) x,
     (values (0::float8),(0.1),(0.25),(0.4),(0.5),(0.6),(0.75),(0.9),(1)) v(p)
group by p order by p;
  p   | percentile_cont 
------+-----------------
    0 |               1
  0.1 |             1.4
 0.25 |               2
  0.4 |             2.6
  0.5 |               3
  0.6 |             3.4
 0.75 |               4
  0.9 |             4.6
    1 |               5
(9 rows)

select p, percentile_cont(p order by p) within group (order by x)  -- error
from generate_series(1,5) x,
     (values (0::float8),(0.1),(0.25),(0.4),(0.5),(0.6),(0.75),(0.9),(1)) v(p)
group by p order by p;
ERROR:  cannot use multiple ORDER BY clauses with WITHIN GROUP
LINE 1: select p, percentile_cont(p order by p) within group (order ...
                                                ^
select p, sum() within group (order by x::float8)  -- error
from generate_series(1,5) x,
     (values (0::float8),(0.1),(0.25),(0.4),(0.5),(0.6),(0.75),(0.9),(1)) v(p)
group by p order by p;
ERROR:  sum is not an ordered-set aggregate, so it cannot have WITHIN GROUP
LINE 1: select p, sum() within group (order by x::float8)  
                  ^
select p, percentile_cont(p,p)  -- error
from generate_series(1,5) x,
     (values (0::float8),(0.1),(0.25),(0.4),(0.5),(0.6),(0.75),(0.9),(1)) v(p)
group by p order by p;
ERROR:  WITHIN GROUP is required for ordered-set aggregate percentile_cont
LINE 1: select p, percentile_cont(p,p)  
                  ^
select percentile_cont(0.5) within group (order by b) from aggtest;
 percentile_cont  
------------------
 53.4485001564026
(1 row)

select percentile_cont(0.5) within group (order by b), sum(b) from aggtest;
 percentile_cont  |   sum   
------------------+---------
 53.4485001564026 | 431.773
(1 row)

select percentile_cont(0.5) within group (order by thousand) from tenk1;
 percentile_cont 
-----------------
           499.5
(1 row)

select percentile_disc(0.5) within group (order by thousand) from tenk1;
 percentile_disc 
-----------------
             499
(1 row)

select rank(3) within group (order by x)
from (values (1),(1),(2),(2),(3),(3),(4)) v(x);
 rank 
------
    5
(1 row)

select cume_dist(3) within group (order by x)
from (values (1),(1),(2),(2),(3),(3),(4)) v(x);
 cume_dist 
-----------
     0.875
(1 row)

select percent_rank(3) within group (order by x)
from (values (1),(1),(2),(2),(3),(3),(4),(5)) v(x);
 percent_rank 
--------------
          0.5
(1 row)

select dense_rank(3) within group (order by x)
from (values (1),(1),(2),(2),(3),(3),(4)) v(x);
 dense_rank 
------------
          3
(1 row)

select percentile_disc(array[0,0.1,0.25,0.5,0.75,0.9,1]) within group (order by thousand)
from tenk1;
      percentile_disc       
----------------------------
 {0,99,249,499,749,899,999}
(1 row)

select percentile_cont(array[0,0.25,0.5,0.75,1]) within group (order by thousand)
from tenk1;
       percentile_cont       
-----------------------------
 {0,249.75,499.5,749.25,999}
(1 row)

select percentile_disc(array[[null,1,0.5],[0.75,0.25,null]]) within group (order by thousand)
from tenk1;
         percentile_disc         
---------------------------------
 {{NULL,999,499},{749,249,NULL}}
(1 row)

select percentile_cont(array[0,1,0.25,0.75,0.5,1,0.3,0.32,0.35,0.38,0.4]) within group (order by x)
from generate_series(1,6) x;
             percentile_cont              
------------------------------------------
 {1,6,2.25,4.75,3.5,6,2.5,2.6,2.75,2.9,3}
(1 row)

select ten, mode() within group (order by string4) from tenk1 group by ten;
 ten |  mode  
-----+--------
   0 | HHHHxx
   1 | OOOOxx
   2 | VVVVxx
   3 | OOOOxx
   4 | HHHHxx
   5 | HHHHxx
   6 | OOOOxx
   7 | AAAAxx
   8 | VVVVxx
   9 | VVVVxx
(10 rows)

select percentile_disc(array[0.25,0.5,0.75]) within group (order by x)
from unnest('{fred,jim,fred,jack,jill,fred,jill,jim,jim,sheila,jim,sheila}'::text[]) u(x);
 percentile_disc 
-----------------
 {fred,jill,jim}
(1 row)

-- check collation propagates up in suitable cases:
select pg_collation_for(percentile_disc(1) within group (order by x collate "POSIX"))
  from (values ('fred'),('jim')) v(x);
 pg_collation_for 
------------------
 "POSIX"
(1 row)

-- ordered-set aggs created with CREATE AGGREGATE
select test_rank(3) within group (order by x)
from (values (1),(1),(2),(2),(3),(3),(4)) v(x);
 test_rank 
-----------
         5
(1 row)

select test_percentile_disc(0.5) within group (order by thousand) from tenk1;
 test_percentile_disc 
----------------------
                  499
(1 row)

-- ordered-set aggs can't use ungrouped vars in direct args:
select rank(x) within group (order by x) from generate_series(1,5) x;
ERROR:  column "x.x" must appear in the GROUP BY clause or be used in an aggregate function
LINE 1: select rank(x) within group (order by x) from generate_serie...
                    ^
DETAIL:  Direct arguments of an ordered-set aggregate must use only grouped columns.
-- outer-level agg can't use a grouped arg of a lower level, either:
select array(select percentile_disc(a) within group (order by x)
               from (values (0.3),(0.7)) v(a) group by a)
  from generate_series(1,5) g(x);
ERROR:  outer-level aggregate cannot contain a lower-level variable in its direct arguments
LINE 1: select array(select percentile_disc(a) within group (order b...
                                            ^
-- agg in the direct args is a grouping violation, too:
select rank(sum(x)) within group (order by x) from generate_series(1,5) x;
ERROR:  aggregate function calls cannot be nested
LINE 1: select rank(sum(x)) within group (order by x) from generate_...
                    ^
-- hypothetical-set type unification and argument-count failures:
select rank(3) within group (order by x) from (values ('fred'),('jim')) v(x);
ERROR:  WITHIN GROUP types text and integer cannot be matched
LINE 1: select rank(3) within group (order by x) from (values ('fred...
                    ^
select rank(3) within group (order by stringu1,stringu2) from tenk1;
ERROR:  function rank(integer, name, name) does not exist
LINE 1: select rank(3) within group (order by stringu1,stringu2) fro...
               ^
HINT:  To use the hypothetical-set aggregate rank, the number of hypothetical direct arguments (here 1) must match the number of ordering columns (here 2).
select rank('fred') within group (order by x) from generate_series(1,5) x;
ERROR:  invalid input syntax for type integer: "fred"
LINE 1: select rank('fred') within group (order by x) from generate_...
                    ^
select rank('adam'::text collate "C") within group (order by x collate "POSIX")
  from (values ('fred'),('jim')) v(x);
ERROR:  collation mismatch between explicit collations "C" and "POSIX"
LINE 1: ...adam'::text collate "C") within group (order by x collate "P...
                                                             ^
-- hypothetical-set type unification successes:
select rank('adam'::varchar) within group (order by x) from (values ('fred'),('jim')) v(x);
 rank 
------
    1
(1 row)

select rank('3') within group (order by x) from generate_series(1,5) x;
 rank 
------
    3
(1 row)

-- divide by zero check
select percent_rank(0) within group (order by x) from generate_series(1,0) x;
 percent_rank 
--------------
            0
(1 row)

-- deparse and multiple features:
create view aggordview1 as
select ten,
       percentile_disc(0.5) within group (order by thousand) as p50,
       percentile_disc(0.5) within group (order by thousand) filter (where hundred=1) as px,
       rank(5,'AZZZZ',50) within group (order by hundred, string4 desc, hundred)
  from tenk1
 group by ten order by ten;
select pg_get_viewdef('aggordview1');
                                                        pg_get_viewdef                                                         
-------------------------------------------------------------------------------------------------------------------------------
  SELECT tenk1.ten,                                                                                                           +
     percentile_disc((0.5)::double precision) WITHIN GROUP (ORDER BY tenk1.thousand) AS p50,                                  +
     percentile_disc((0.5)::double precision) WITHIN GROUP (ORDER BY tenk1.thousand) FILTER (WHERE (tenk1.hundred = 1)) AS px,+
     rank(5, 'AZZZZ'::name, 50) WITHIN GROUP (ORDER BY tenk1.hundred, tenk1.string4 DESC, tenk1.hundred) AS rank              +
    FROM tenk1                                                                                                                +
   GROUP BY tenk1.ten                                                                                                         +
   ORDER BY tenk1.ten;
(1 row)

select * from aggordview1 order by ten;
 ten | p50 | px  | rank 
-----+-----+-----+------
   0 | 490 |     |  101
   1 | 491 | 401 |  101
   2 | 492 |     |  101
   3 | 493 |     |  101
   4 | 494 |     |  101
   5 | 495 |     |   67
   6 | 496 |     |    1
   7 | 497 |     |    1
   8 | 498 |     |    1
   9 | 499 |     |    1
(10 rows)

drop view aggordview1;
-- variadic aggregates
select least_agg(q1,q2) from int8_tbl;
     least_agg     
-------------------
 -4567890123456789
(1 row)

select least_agg(variadic array[q1,q2]) from int8_tbl;
     least_agg     
-------------------
 -4567890123456789
(1 row)

select cleast_agg(q1,q2) from int8_tbl;
    cleast_agg     
-------------------
 -4567890123456789
(1 row)

select cleast_agg(4.5,f1) from int4_tbl;
 cleast_agg  
-------------
 -2147483647
(1 row)

select cleast_agg(variadic array[4.5,f1]) from int4_tbl;
 cleast_agg  
-------------
 -2147483647
(1 row)

select pg_typeof(cleast_agg(variadic array[4.5,f1])) from int4_tbl;
 pg_typeof 
-----------
 numeric
(1 row)

-- test aggregates with common transition functions share the same states
begin work;
create type avg_state as (total bigint, count bigint);
create or replace function avg_transfn(state avg_state, n int) returns avg_state as
$$
declare new_state avg_state;
begin
	raise notice 'avg_transfn called with %', n;
	if state is null then
		if n is not null then
			new_state.total := n;
			new_state.count := 1;
			return new_state;
		end if;
		return null;
	elsif n is not null then
		state.total := state.total + n;
		state.count := state.count + 1;
		return state;
	end if;

	return null;
end
$$ language plpgsql;
create function avg_finalfn(state avg_state) returns int4 as
$$
begin
	if state is null then
		return NULL;
	else
		return state.total / state.count;
	end if;
end
$$ language plpgsql;
create function sum_finalfn(state avg_state) returns int4 as
$$
begin
	if state is null then
		return NULL;
	else
		return state.total;
	end if;
end
$$ language plpgsql;
create aggregate my_avg(int4)
(
   stype = avg_state,
   sfunc = avg_transfn,
   finalfunc = avg_finalfn
);
create aggregate my_sum(int4)
(
   stype = avg_state,
   sfunc = avg_transfn,
   finalfunc = sum_finalfn
);
-- aggregate state should be shared as aggs are the same.
select my_avg(one),my_avg(one) from (values(1),(3)) t(one);
NOTICE:  avg_transfn called with 1
NOTICE:  avg_transfn called with 3
 my_avg | my_avg 
--------+--------
      2 |      2
(1 row)

-- aggregate state should be shared as transfn is the same for both aggs.
select my_avg(one),my_sum(one) from (values(1),(3)) t(one);
NOTICE:  avg_transfn called with 1
NOTICE:  avg_transfn called with 3
 my_avg | my_sum 
--------+--------
      2 |      4
(1 row)

-- same as previous one, but with DISTINCT, which requires sorting the input.
select my_avg(distinct one),my_sum(distinct one) from (values(1),(3),(1)) t(one);
NOTICE:  avg_transfn called with 1
NOTICE:  avg_transfn called with 3
 my_avg | my_sum 
--------+--------
      2 |      4
(1 row)

-- shouldn't share states due to the distinctness not matching.
select my_avg(distinct one),my_sum(one) from (values(1),(3)) t(one);
NOTICE:  avg_transfn called with 1
NOTICE:  avg_transfn called with 3
NOTICE:  avg_transfn called with 1
NOTICE:  avg_transfn called with 3
 my_avg | my_sum 
--------+--------
      2 |      4
(1 row)

-- shouldn't share states due to the filter clause not matching.
select my_avg(one) filter (where one > 1),my_sum(one) from (values(1),(3)) t(one);
NOTICE:  avg_transfn called with 1
NOTICE:  avg_transfn called with 3
NOTICE:  avg_transfn called with 3
 my_avg | my_sum 
--------+--------
      3 |      4
(1 row)

-- this should not share the state due to different input columns.
select my_avg(one),my_sum(two) from (values(1,2),(3,4)) t(one,two);
NOTICE:  avg_transfn called with 1
NOTICE:  avg_transfn called with 2
NOTICE:  avg_transfn called with 3
NOTICE:  avg_transfn called with 4
 my_avg | my_sum 
--------+--------
      2 |      6
(1 row)

-- exercise cases where OSAs share state
select
  percentile_cont(0.5) within group (order by a),
  percentile_disc(0.5) within group (order by a)
from (values(1::float8),(3),(5),(7)) t(a);
 percentile_cont | percentile_disc 
-----------------+-----------------
               4 |               3
(1 row)

select
  percentile_cont(0.25) within group (order by a),
  percentile_disc(0.5) within group (order by a)
from (values(1::float8),(3),(5),(7)) t(a);
 percentile_cont | percentile_disc 
-----------------+-----------------
             2.5 |               3
(1 row)

-- these can't share state currently
select
  rank(4) within group (order by a),
  dense_rank(4) within group (order by a)
from (values(1),(3),(5),(7)) t(a);
 rank | dense_rank 
------+------------
    3 |          3
(1 row)

-- test that aggs with the same sfunc and initcond share the same agg state
create aggregate my_sum_init(int4)
(
   stype = avg_state,
   sfunc = avg_transfn,
   finalfunc = sum_finalfn,
   initcond = '(10,0)'
);
create aggregate my_avg_init(int4)
(
   stype = avg_state,
   sfunc = avg_transfn,
   finalfunc = avg_finalfn,
   initcond = '(10,0)'
);
create aggregate my_avg_init2(int4)
(
   stype = avg_state,
   sfunc = avg_transfn,
   finalfunc = avg_finalfn,
   initcond = '(4,0)'
);
-- state should be shared if INITCONDs are matching
select my_sum_init(one),my_avg_init(one) from (values(1),(3)) t(one);
NOTICE:  avg_transfn called with 1
NOTICE:  avg_transfn called with 3
 my_sum_init | my_avg_init 
-------------+-------------
          14 |           7
(1 row)

-- Varying INITCONDs should cause the states not to be shared.
select my_sum_init(one),my_avg_init2(one) from (values(1),(3)) t(one);
NOTICE:  avg_transfn called with 1
NOTICE:  avg_transfn called with 1
NOTICE:  avg_transfn called with 3
NOTICE:  avg_transfn called with 3
 my_sum_init | my_avg_init2 
-------------+--------------
          14 |            4
(1 row)

rollback;
-- test aggregate state sharing to ensure it works if one aggregate has a
-- finalfn and the other one has none.
begin work;
create or replace function sum_transfn(state int4, n int4) returns int4 as
$$
declare new_state int4;
begin
	raise notice 'sum_transfn called with %', n;
	if state is null then
		if n is not null then
			new_state := n;
			return new_state;
		end if;
		return null;
	elsif n is not null then
		state := state + n;
		return state;
	end if;

	return null;
end
$$ language plpgsql;
create function halfsum_finalfn(state int4) returns int4 as
$$
begin
	if state is null then
		return NULL;
	else
		return state / 2;
	end if;
end
$$ language plpgsql;
create aggregate my_sum(int4)
(
   stype = int4,
   sfunc = sum_transfn
);
create aggregate my_half_sum(int4)
(
   stype = int4,
   sfunc = sum_transfn,
   finalfunc = halfsum_finalfn
);
-- Agg state should be shared even though my_sum has no finalfn
select my_sum(one),my_half_sum(one) from (values(1),(2),(3),(4)) t(one);
NOTICE:  sum_transfn called with 1
NOTICE:  sum_transfn called with 2
NOTICE:  sum_transfn called with 3
NOTICE:  sum_transfn called with 4
 my_sum | my_half_sum 
--------+-------------
     10 |           5
(1 row)

rollback;
-- test that the aggregate transition logic correctly handles
-- transition / combine functions returning NULL
-- First test the case of a normal transition function returning NULL
BEGIN;
CREATE FUNCTION balkifnull(int8, int4)
RETURNS int8
STRICT
LANGUAGE plpgsql AS $$
BEGIN
    IF $1 IS NULL THEN
       RAISE 'erroneously called with NULL argument';
    END IF;
    RETURN NULL;
END$$;
CREATE AGGREGATE balk(int4)
(
    SFUNC = balkifnull(int8, int4),
    STYPE = int8,
    PARALLEL = SAFE,
    INITCOND = '0'
);
SELECT balk(hundred) FROM tenk1;
 balk 
------
     
(1 row)

ROLLBACK;
-- Secondly test the case of a parallel aggregate combiner function
-- returning NULL. For that use normal transition function, but a
-- combiner function returning NULL.
BEGIN ISOLATION LEVEL REPEATABLE READ;
CREATE FUNCTION balkifnull(int8, int8)
RETURNS int8
PARALLEL SAFE
STRICT
LANGUAGE plpgsql AS $$
BEGIN
    IF $1 IS NULL THEN
       RAISE 'erroneously called with NULL argument';
    END IF;
    RETURN NULL;
END$$;
CREATE AGGREGATE balk(int4)
(
    SFUNC = int4_sum(int8, int4),
    STYPE = int8,
    COMBINEFUNC = balkifnull(int8, int8),
    PARALLEL = SAFE,
    INITCOND = '0'
);
-- force use of parallelism
ALTER TABLE tenk1 set (parallel_workers = 4);
SET LOCAL parallel_setup_cost=0;
SET LOCAL max_parallel_workers_per_gather=4;
EXPLAIN (COSTS OFF) SELECT balk(hundred) FROM tenk1;
                               QUERY PLAN                                
-------------------------------------------------------------------------
 Finalize Aggregate
   ->  Gather
         Workers Planned: 4
         ->  Partial Aggregate
               ->  Parallel Index Only Scan using tenk1_hundred on tenk1
(5 rows)

SELECT balk(hundred) FROM tenk1;
 balk 
------
     
(1 row)

ROLLBACK;
-- test coverage for aggregate combine/serial/deserial functions
BEGIN ISOLATION LEVEL REPEATABLE READ;
SET parallel_setup_cost = 0;
SET parallel_tuple_cost = 0;
SET min_parallel_table_scan_size = 0;
SET max_parallel_workers_per_gather = 4;
SET parallel_leader_participation = off;
SET enable_indexonlyscan = off;
-- variance(int4) covers numeric_poly_combine
-- sum(int8) covers int8_avg_combine
-- regr_count(float8, float8) covers int8inc_float8_float8 and aggregates with > 1 arg
EXPLAIN (COSTS OFF, VERBOSE)
SELECT variance(unique1::int4), sum(unique1::int8), regr_count(unique1::float8, unique1::float8)
FROM (SELECT * FROM tenk1
      UNION ALL SELECT * FROM tenk1
      UNION ALL SELECT * FROM tenk1
      UNION ALL SELECT * FROM tenk1) u;
                                                                                      QUERY PLAN                                                                                       
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 Finalize Aggregate
   Output: variance(tenk1.unique1), sum((tenk1.unique1)::bigint), regr_count((tenk1.unique1)::double precision, (tenk1.unique1)::double precision)
   ->  Gather
         Output: (PARTIAL variance(tenk1.unique1)), (PARTIAL sum((tenk1.unique1)::bigint)), (PARTIAL regr_count((tenk1.unique1)::double precision, (tenk1.unique1)::double precision))
         Workers Planned: 4
         ->  Partial Aggregate
               Output: PARTIAL variance(tenk1.unique1), PARTIAL sum((tenk1.unique1)::bigint), PARTIAL regr_count((tenk1.unique1)::double precision, (tenk1.unique1)::double precision)
               ->  Parallel Append
                     ->  Parallel Seq Scan on public.tenk1
                           Output: tenk1.unique1
                     ->  Parallel Seq Scan on public.tenk1 tenk1_1
                           Output: tenk1_1.unique1
                     ->  Parallel Seq Scan on public.tenk1 tenk1_2
                           Output: tenk1_2.unique1
                     ->  Parallel Seq Scan on public.tenk1 tenk1_3
                           Output: tenk1_3.unique1
(16 rows)

SELECT variance(unique1::int4), sum(unique1::int8), regr_count(unique1::float8, unique1::float8)
FROM (SELECT * FROM tenk1
      UNION ALL SELECT * FROM tenk1
      UNION ALL SELECT * FROM tenk1
      UNION ALL SELECT * FROM tenk1) u;
       variance       |    sum    | regr_count 
----------------------+-----------+------------
 8333541.588539713493 | 199980000 |      40000
(1 row)

-- variance(int8) covers numeric_combine
-- avg(numeric) covers numeric_avg_combine
EXPLAIN (COSTS OFF, VERBOSE)
SELECT variance(unique1::int8), avg(unique1::numeric)
FROM (SELECT * FROM tenk1
      UNION ALL SELECT * FROM tenk1
      UNION ALL SELECT * FROM tenk1
      UNION ALL SELECT * FROM tenk1) u;
                                               QUERY PLAN                                               
--------------------------------------------------------------------------------------------------------
 Finalize Aggregate
   Output: variance((tenk1.unique1)::bigint), avg((tenk1.unique1)::numeric)
   ->  Gather
         Output: (PARTIAL variance((tenk1.unique1)::bigint)), (PARTIAL avg((tenk1.unique1)::numeric))
         Workers Planned: 4
         ->  Partial Aggregate
               Output: PARTIAL variance((tenk1.unique1)::bigint), PARTIAL avg((tenk1.unique1)::numeric)
               ->  Parallel Append
                     ->  Parallel Seq Scan on public.tenk1
                           Output: tenk1.unique1
                     ->  Parallel Seq Scan on public.tenk1 tenk1_1
                           Output: tenk1_1.unique1
                     ->  Parallel Seq Scan on public.tenk1 tenk1_2
                           Output: tenk1_2.unique1
                     ->  Parallel Seq Scan on public.tenk1 tenk1_3
                           Output: tenk1_3.unique1
(16 rows)

SELECT variance(unique1::int8), avg(unique1::numeric)
FROM (SELECT * FROM tenk1
      UNION ALL SELECT * FROM tenk1
      UNION ALL SELECT * FROM tenk1
      UNION ALL SELECT * FROM tenk1) u;
       variance       |          avg          
----------------------+-----------------------
 8333541.588539713493 | 4999.5000000000000000
(1 row)

ROLLBACK;
-- test coverage for dense_rank
SELECT dense_rank(x) WITHIN GROUP (ORDER BY x) FROM (VALUES (1),(1),(2),(2),(3),(3)) v(x) GROUP BY (x) ORDER BY 1;
 dense_rank 
------------
          1
          1
          1
(3 rows)

-- Ensure that the STRICT checks for aggregates does not take NULLness
-- of ORDER BY columns into account. See bug report around
-- 2a505161-2727-2473-7c46-591ed108ac52@email.cz
SELECT min(x ORDER BY y) FROM (VALUES(1, NULL)) AS d(x,y);
 min 
-----
   1
(1 row)

SELECT min(x ORDER BY y) FROM (VALUES(1, 2)) AS d(x,y);
 min 
-----
   1
(1 row)

-- check collation-sensitive matching between grouping expressions
select v||'a', case v||'a' when 'aa' then 1 else 0 end, count(*)
  from unnest(array['a','b']) u(v)
 group by v||'a' order by 1;
 ?column? | case | count 
----------+------+-------
 aa       |    1 |     1
 ba       |    0 |     1
(2 rows)

select v||'a', case when v||'a' = 'aa' then 1 else 0 end, count(*)
  from unnest(array['a','b']) u(v)
 group by v||'a' order by 1;
 ?column? | case | count 
----------+------+-------
 aa       |    1 |     1
 ba       |    0 |     1
(2 rows)

-- Make sure that generation of HashAggregate for uniqification purposes
-- does not lead to array overflow due to unexpected duplicate hash keys
-- see CAFeeJoKKu0u+A_A9R9316djW-YW3-+Gtgvy3ju655qRHR3jtdA@mail.gmail.com
explain (costs off)
  select 1 from tenk1
   where (hundred, thousand) in (select twothousand, twothousand from onek);
                         QUERY PLAN                          
-------------------------------------------------------------
 Hash Join
   Hash Cond: (tenk1.hundred = onek.twothousand)
   ->  Seq Scan on tenk1
         Filter: (hundred = thousand)
   ->  Hash
         ->  HashAggregate
               Group Key: onek.twothousand, onek.twothousand
               ->  Seq Scan on onek
(8 rows)

--
-- Hash Aggregation Spill tests
--
set enable_sort=false;
set work_mem='64kB';
select unique1, count(*), sum(twothousand) from tenk1
group by unique1
having sum(fivethous) > 4975
order by sum(twothousand);
 unique1 | count | sum  
---------+-------+------
    4976 |     1 |  976
    4977 |     1 |  977
    4978 |     1 |  978
    4979 |     1 |  979
    4980 |     1 |  980
    4981 |     1 |  981
    4982 |     1 |  982
    4983 |     1 |  983
    4984 |     1 |  984
    4985 |     1 |  985
    4986 |     1 |  986
    4987 |     1 |  987
    4988 |     1 |  988
    4989 |     1 |  989
    4990 |     1 |  990
    4991 |     1 |  991
    4992 |     1 |  992
    4993 |     1 |  993
    4994 |     1 |  994
    4995 |     1 |  995
    4996 |     1 |  996
    4997 |     1 |  997
    4998 |     1 |  998
    4999 |     1 |  999
    9976 |     1 | 1976
    9977 |     1 | 1977
    9978 |     1 | 1978
    9979 |     1 | 1979
    9980 |     1 | 1980
    9981 |     1 | 1981
    9982 |     1 | 1982
    9983 |     1 | 1983
    9984 |     1 | 1984
    9985 |     1 | 1985
    9986 |     1 | 1986
    9987 |     1 | 1987
    9988 |     1 | 1988
    9989 |     1 | 1989
    9990 |     1 | 1990
    9991 |     1 | 1991
    9992 |     1 | 1992
    9993 |     1 | 1993
    9994 |     1 | 1994
    9995 |     1 | 1995
    9996 |     1 | 1996
    9997 |     1 | 1997
    9998 |     1 | 1998
    9999 |     1 | 1999
(48 rows)

set work_mem to default;
set enable_sort to default;
--
-- Compare results between plans using sorting and plans using hash
-- aggregation. Force spilling in both cases by setting work_mem low.
--
set work_mem='64kB';
create table agg_data_2k as
select g from generate_series(0, 1999) g;
analyze agg_data_2k;
create table agg_data_20k as
select g from generate_series(0, 19999) g;
analyze agg_data_20k;
-- Produce results with sorting.
set enable_hashagg = false;
set jit_above_cost = 0;
explain (costs off)
select g%10000 as c1, sum(g::numeric) as c2, count(*) as c3
  from agg_data_20k group by g%10000;
              QUERY PLAN              
--------------------------------------
 GroupAggregate
   Group Key: ((g % 10000))
   ->  Sort
         Sort Key: ((g % 10000))
         ->  Seq Scan on agg_data_20k
(5 rows)

create table agg_group_1 as
select g%10000 as c1, sum(g::numeric) as c2, count(*) as c3
  from agg_data_20k group by g%10000;
create table agg_group_2 as
select * from
  (values (100), (300), (500)) as r(a),
  lateral (
    select (g/2)::numeric as c1,
           array_agg(g::numeric) as c2,
	   count(*) as c3
    from agg_data_2k
    where g < r.a
    group by g/2) as s;
set jit_above_cost to default;
create table agg_group_3 as
select (g/2)::numeric as c1, sum(7::int4) as c2, count(*) as c3
  from agg_data_2k group by g/2;
create table agg_group_4 as
select (g/2)::numeric as c1, array_agg(g::numeric) as c2, count(*) as c3
  from agg_data_2k group by g/2;
-- Produce results with hash aggregation
set enable_hashagg = true;
set enable_sort = false;
set jit_above_cost = 0;
explain (costs off)
select g%10000 as c1, sum(g::numeric) as c2, count(*) as c3
  from agg_data_20k group by g%10000;
           QUERY PLAN           
--------------------------------
 HashAggregate
   Group Key: (g % 10000)
   ->  Seq Scan on agg_data_20k
(3 rows)

create table agg_hash_1 as
select g%10000 as c1, sum(g::numeric) as c2, count(*) as c3
  from agg_data_20k group by g%10000;
create table agg_hash_2 as
select * from
  (values (100), (300), (500)) as r(a),
  lateral (
    select (g/2)::numeric as c1,
           array_agg(g::numeric) as c2,
	   count(*) as c3
    from agg_data_2k
    where g < r.a
    group by g/2) as s;
set jit_above_cost to default;
create table agg_hash_3 as
select (g/2)::numeric as c1, sum(7::int4) as c2, count(*) as c3
  from agg_data_2k group by g/2;
create table agg_hash_4 as
select (g/2)::numeric as c1, array_agg(g::numeric) as c2, count(*) as c3
  from agg_data_2k group by g/2;
set enable_sort = true;
set work_mem to default;
-- Compare group aggregation results to hash aggregation results
(select * from agg_hash_1 except select * from agg_group_1)
  union all
(select * from agg_group_1 except select * from agg_hash_1);
 c1 | c2 | c3 
----+----+----
(0 rows)

(select * from agg_hash_2 except select * from agg_group_2)
  union all
(select * from agg_group_2 except select * from agg_hash_2);
 a | c1 | c2 | c3 
---+----+----+----
(0 rows)

(select * from agg_hash_3 except select * from agg_group_3)
  union all
(select * from agg_group_3 except select * from agg_hash_3);
 c1 | c2 | c3 
----+----+----
(0 rows)

(select * from agg_hash_4 except select * from agg_group_4)
  union all
(select * from agg_group_4 except select * from agg_hash_4);
 c1 | c2 | c3 
----+----+----
(0 rows)

drop table agg_group_1;
drop table agg_group_2;
drop table agg_group_3;
drop table agg_group_4;
drop table agg_hash_1;
drop table agg_hash_2;
drop table agg_hash_3;
drop table agg_hash_4;