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postgresql/src/test/regress/expected/partition_aggregate.out

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--
-- PARTITION_AGGREGATE
-- Test partitionwise aggregation on partitioned tables
--
-- Note: to ensure plan stability, it's a good idea to make the partitions of
-- any one partitioned table in this test all have different numbers of rows.
--
-- Enable partitionwise aggregate, which by default is disabled.
SET enable_partitionwise_aggregate TO true;
-- Enable partitionwise join, which by default is disabled.
SET enable_partitionwise_join TO true;
-- Disable parallel plans.
SET max_parallel_workers_per_gather TO 0;
-- Disable incremental sort, which can influence selected plans due to fuzz factor.
SET enable_incremental_sort TO off;
--
-- Tests for list partitioned tables.
--
CREATE TABLE pagg_tab (a int, b int, c text, d int) PARTITION BY LIST(c);
CREATE TABLE pagg_tab_p1 PARTITION OF pagg_tab FOR VALUES IN ('0000', '0001', '0002', '0003', '0004');
CREATE TABLE pagg_tab_p2 PARTITION OF pagg_tab FOR VALUES IN ('0005', '0006', '0007', '0008');
CREATE TABLE pagg_tab_p3 PARTITION OF pagg_tab FOR VALUES IN ('0009', '0010', '0011');
INSERT INTO pagg_tab SELECT i % 20, i % 30, to_char(i % 12, 'FM0000'), i % 30 FROM generate_series(0, 2999) i;
ANALYZE pagg_tab;
-- When GROUP BY clause matches; full aggregation is performed for each partition.
EXPLAIN (COSTS OFF)
SELECT c, sum(a), avg(b), count(*), min(a), max(b) FROM pagg_tab GROUP BY c HAVING avg(d) < 15 ORDER BY 1, 2, 3;
QUERY PLAN
--------------------------------------------------------------
Sort
Sort Key: pagg_tab.c, (sum(pagg_tab.a)), (avg(pagg_tab.b))
-> Append
-> HashAggregate
Group Key: pagg_tab.c
Filter: (avg(pagg_tab.d) < '15'::numeric)
-> Seq Scan on pagg_tab_p1 pagg_tab
-> HashAggregate
Group Key: pagg_tab_1.c
Filter: (avg(pagg_tab_1.d) < '15'::numeric)
-> Seq Scan on pagg_tab_p2 pagg_tab_1
-> HashAggregate
Group Key: pagg_tab_2.c
Filter: (avg(pagg_tab_2.d) < '15'::numeric)
-> Seq Scan on pagg_tab_p3 pagg_tab_2
(15 rows)
SELECT c, sum(a), avg(b), count(*), min(a), max(b) FROM pagg_tab GROUP BY c HAVING avg(d) < 15 ORDER BY 1, 2, 3;
c | sum | avg | count | min | max
------+------+---------------------+-------+-----+-----
0000 | 2000 | 12.0000000000000000 | 250 | 0 | 24
0001 | 2250 | 13.0000000000000000 | 250 | 1 | 25
0002 | 2500 | 14.0000000000000000 | 250 | 2 | 26
0006 | 2500 | 12.0000000000000000 | 250 | 2 | 24
0007 | 2750 | 13.0000000000000000 | 250 | 3 | 25
0008 | 2000 | 14.0000000000000000 | 250 | 0 | 26
(6 rows)
-- When GROUP BY clause does not match; partial aggregation is performed for each partition.
EXPLAIN (COSTS OFF)
SELECT a, sum(b), avg(b), count(*), min(a), max(b) FROM pagg_tab GROUP BY a HAVING avg(d) < 15 ORDER BY 1, 2, 3;
QUERY PLAN
--------------------------------------------------------------
Sort
Sort Key: pagg_tab.a, (sum(pagg_tab.b)), (avg(pagg_tab.b))
-> Finalize HashAggregate
Group Key: pagg_tab.a
Filter: (avg(pagg_tab.d) < '15'::numeric)
-> Append
-> Partial HashAggregate
Group Key: pagg_tab.a
-> Seq Scan on pagg_tab_p1 pagg_tab
-> Partial HashAggregate
Group Key: pagg_tab_1.a
-> Seq Scan on pagg_tab_p2 pagg_tab_1
-> Partial HashAggregate
Group Key: pagg_tab_2.a
-> Seq Scan on pagg_tab_p3 pagg_tab_2
(15 rows)
SELECT a, sum(b), avg(b), count(*), min(a), max(b) FROM pagg_tab GROUP BY a HAVING avg(d) < 15 ORDER BY 1, 2, 3;
a | sum | avg | count | min | max
----+------+---------------------+-------+-----+-----
0 | 1500 | 10.0000000000000000 | 150 | 0 | 20
1 | 1650 | 11.0000000000000000 | 150 | 1 | 21
2 | 1800 | 12.0000000000000000 | 150 | 2 | 22
3 | 1950 | 13.0000000000000000 | 150 | 3 | 23
4 | 2100 | 14.0000000000000000 | 150 | 4 | 24
10 | 1500 | 10.0000000000000000 | 150 | 10 | 20
11 | 1650 | 11.0000000000000000 | 150 | 11 | 21
12 | 1800 | 12.0000000000000000 | 150 | 12 | 22
13 | 1950 | 13.0000000000000000 | 150 | 13 | 23
14 | 2100 | 14.0000000000000000 | 150 | 14 | 24
(10 rows)
-- Check with multiple columns in GROUP BY
EXPLAIN (COSTS OFF)
SELECT a, c, count(*) FROM pagg_tab GROUP BY a, c;
QUERY PLAN
------------------------------------------------
Append
-> HashAggregate
Group Key: pagg_tab.a, pagg_tab.c
-> Seq Scan on pagg_tab_p1 pagg_tab
-> HashAggregate
Group Key: pagg_tab_1.a, pagg_tab_1.c
-> Seq Scan on pagg_tab_p2 pagg_tab_1
-> HashAggregate
Group Key: pagg_tab_2.a, pagg_tab_2.c
-> Seq Scan on pagg_tab_p3 pagg_tab_2
(10 rows)
-- Check with multiple columns in GROUP BY, order in GROUP BY is reversed
EXPLAIN (COSTS OFF)
SELECT a, c, count(*) FROM pagg_tab GROUP BY c, a;
QUERY PLAN
------------------------------------------------
Append
-> HashAggregate
Group Key: pagg_tab.c, pagg_tab.a
-> Seq Scan on pagg_tab_p1 pagg_tab
-> HashAggregate
Group Key: pagg_tab_1.c, pagg_tab_1.a
-> Seq Scan on pagg_tab_p2 pagg_tab_1
-> HashAggregate
Group Key: pagg_tab_2.c, pagg_tab_2.a
-> Seq Scan on pagg_tab_p3 pagg_tab_2
(10 rows)
-- Check with multiple columns in GROUP BY, order in target-list is reversed
EXPLAIN (COSTS OFF)
SELECT c, a, count(*) FROM pagg_tab GROUP BY a, c;
QUERY PLAN
------------------------------------------------
Append
-> HashAggregate
Group Key: pagg_tab.a, pagg_tab.c
-> Seq Scan on pagg_tab_p1 pagg_tab
-> HashAggregate
Group Key: pagg_tab_1.a, pagg_tab_1.c
-> Seq Scan on pagg_tab_p2 pagg_tab_1
-> HashAggregate
Group Key: pagg_tab_2.a, pagg_tab_2.c
-> Seq Scan on pagg_tab_p3 pagg_tab_2
(10 rows)
-- Test when input relation for grouping is dummy
EXPLAIN (COSTS OFF)
SELECT c, sum(a) FROM pagg_tab WHERE 1 = 2 GROUP BY c;
QUERY PLAN
--------------------------------
HashAggregate
Group Key: c
-> Result
One-Time Filter: false
(4 rows)
SELECT c, sum(a) FROM pagg_tab WHERE 1 = 2 GROUP BY c;
c | sum
---+-----
(0 rows)
EXPLAIN (COSTS OFF)
SELECT c, sum(a) FROM pagg_tab WHERE c = 'x' GROUP BY c;
QUERY PLAN
--------------------------------
GroupAggregate
-> Result
One-Time Filter: false
(3 rows)
SELECT c, sum(a) FROM pagg_tab WHERE c = 'x' GROUP BY c;
c | sum
---+-----
(0 rows)
-- Test GroupAggregate paths by disabling hash aggregates.
SET enable_hashagg TO false;
-- When GROUP BY clause matches full aggregation is performed for each partition.
EXPLAIN (COSTS OFF)
SELECT c, sum(a), avg(b), count(*) FROM pagg_tab GROUP BY 1 HAVING avg(d) < 15 ORDER BY 1, 2, 3;
QUERY PLAN
--------------------------------------------------------------
Sort
Sort Key: pagg_tab.c, (sum(pagg_tab.a)), (avg(pagg_tab.b))
-> Append
-> GroupAggregate
Group Key: pagg_tab.c
Filter: (avg(pagg_tab.d) < '15'::numeric)
-> Sort
Sort Key: pagg_tab.c
-> Seq Scan on pagg_tab_p1 pagg_tab
-> GroupAggregate
Group Key: pagg_tab_1.c
Filter: (avg(pagg_tab_1.d) < '15'::numeric)
-> Sort
Sort Key: pagg_tab_1.c
-> Seq Scan on pagg_tab_p2 pagg_tab_1
-> GroupAggregate
Group Key: pagg_tab_2.c
Filter: (avg(pagg_tab_2.d) < '15'::numeric)
-> Sort
Sort Key: pagg_tab_2.c
-> Seq Scan on pagg_tab_p3 pagg_tab_2
(21 rows)
SELECT c, sum(a), avg(b), count(*) FROM pagg_tab GROUP BY 1 HAVING avg(d) < 15 ORDER BY 1, 2, 3;
c | sum | avg | count
------+------+---------------------+-------
0000 | 2000 | 12.0000000000000000 | 250
0001 | 2250 | 13.0000000000000000 | 250
0002 | 2500 | 14.0000000000000000 | 250
0006 | 2500 | 12.0000000000000000 | 250
0007 | 2750 | 13.0000000000000000 | 250
0008 | 2000 | 14.0000000000000000 | 250
(6 rows)
-- When GROUP BY clause does not match; partial aggregation is performed for each partition.
EXPLAIN (COSTS OFF)
SELECT a, sum(b), avg(b), count(*) FROM pagg_tab GROUP BY 1 HAVING avg(d) < 15 ORDER BY 1, 2, 3;
QUERY PLAN
------------------------------------------------------------------
Sort
Sort Key: pagg_tab.a, (sum(pagg_tab.b)), (avg(pagg_tab.b))
-> Finalize GroupAggregate
Group Key: pagg_tab.a
Filter: (avg(pagg_tab.d) < '15'::numeric)
-> Merge Append
Sort Key: pagg_tab.a
-> Partial GroupAggregate
Group Key: pagg_tab.a
-> Sort
Sort Key: pagg_tab.a
-> Seq Scan on pagg_tab_p1 pagg_tab
-> Partial GroupAggregate
Group Key: pagg_tab_1.a
-> Sort
Sort Key: pagg_tab_1.a
-> Seq Scan on pagg_tab_p2 pagg_tab_1
-> Partial GroupAggregate
Group Key: pagg_tab_2.a
-> Sort
Sort Key: pagg_tab_2.a
-> Seq Scan on pagg_tab_p3 pagg_tab_2
(22 rows)
SELECT a, sum(b), avg(b), count(*) FROM pagg_tab GROUP BY 1 HAVING avg(d) < 15 ORDER BY 1, 2, 3;
a | sum | avg | count
----+------+---------------------+-------
0 | 1500 | 10.0000000000000000 | 150
1 | 1650 | 11.0000000000000000 | 150
2 | 1800 | 12.0000000000000000 | 150
3 | 1950 | 13.0000000000000000 | 150
4 | 2100 | 14.0000000000000000 | 150
10 | 1500 | 10.0000000000000000 | 150
11 | 1650 | 11.0000000000000000 | 150
12 | 1800 | 12.0000000000000000 | 150
13 | 1950 | 13.0000000000000000 | 150
14 | 2100 | 14.0000000000000000 | 150
(10 rows)
-- Test partitionwise grouping without any aggregates
EXPLAIN (COSTS OFF)
SELECT c FROM pagg_tab GROUP BY c ORDER BY 1;
QUERY PLAN
------------------------------------------------------
Merge Append
Sort Key: pagg_tab.c
-> Group
Group Key: pagg_tab.c
-> Sort
Sort Key: pagg_tab.c
-> Seq Scan on pagg_tab_p1 pagg_tab
-> Group
Group Key: pagg_tab_1.c
-> Sort
Sort Key: pagg_tab_1.c
-> Seq Scan on pagg_tab_p2 pagg_tab_1
-> Group
Group Key: pagg_tab_2.c
-> Sort
Sort Key: pagg_tab_2.c
-> Seq Scan on pagg_tab_p3 pagg_tab_2
(17 rows)
SELECT c FROM pagg_tab GROUP BY c ORDER BY 1;
c
------
0000
0001
0002
0003
0004
0005
0006
0007
0008
0009
0010
0011
(12 rows)
EXPLAIN (COSTS OFF)
SELECT a FROM pagg_tab WHERE a < 3 GROUP BY a ORDER BY 1;
QUERY PLAN
------------------------------------------------------------
Group
Group Key: pagg_tab.a
-> Merge Append
Sort Key: pagg_tab.a
-> Group
Group Key: pagg_tab.a
-> Sort
Sort Key: pagg_tab.a
-> Seq Scan on pagg_tab_p1 pagg_tab
Filter: (a < 3)
-> Group
Group Key: pagg_tab_1.a
-> Sort
Sort Key: pagg_tab_1.a
-> Seq Scan on pagg_tab_p2 pagg_tab_1
Filter: (a < 3)
-> Group
Group Key: pagg_tab_2.a
-> Sort
Sort Key: pagg_tab_2.a
-> Seq Scan on pagg_tab_p3 pagg_tab_2
Filter: (a < 3)
(22 rows)
SELECT a FROM pagg_tab WHERE a < 3 GROUP BY a ORDER BY 1;
a
---
0
1
2
(3 rows)
RESET enable_hashagg;
-- ROLLUP, partitionwise aggregation does not apply
EXPLAIN (COSTS OFF)
SELECT c, sum(a) FROM pagg_tab GROUP BY rollup(c) ORDER BY 1, 2;
QUERY PLAN
------------------------------------------------------
Sort
Sort Key: pagg_tab.c, (sum(pagg_tab.a))
-> MixedAggregate
Hash Key: pagg_tab.c
Group Key: ()
-> Append
-> Seq Scan on pagg_tab_p1 pagg_tab_1
-> Seq Scan on pagg_tab_p2 pagg_tab_2
-> Seq Scan on pagg_tab_p3 pagg_tab_3
(9 rows)
-- ORDERED SET within the aggregate.
-- Full aggregation; since all the rows that belong to the same group come
-- from the same partition, having an ORDER BY within the aggregate doesn't
-- make any difference.
EXPLAIN (COSTS OFF)
SELECT c, sum(b order by a) FROM pagg_tab GROUP BY c ORDER BY 1, 2;
QUERY PLAN
---------------------------------------------------------------
Sort
Sort Key: pagg_tab.c, (sum(pagg_tab.b ORDER BY pagg_tab.a))
-> Append
-> GroupAggregate
Group Key: pagg_tab.c
-> Sort
Sort Key: pagg_tab.c, pagg_tab.a
-> Seq Scan on pagg_tab_p1 pagg_tab
-> GroupAggregate
Group Key: pagg_tab_1.c
-> Sort
Sort Key: pagg_tab_1.c, pagg_tab_1.a
-> Seq Scan on pagg_tab_p2 pagg_tab_1
-> GroupAggregate
Group Key: pagg_tab_2.c
-> Sort
Sort Key: pagg_tab_2.c, pagg_tab_2.a
-> Seq Scan on pagg_tab_p3 pagg_tab_2
(18 rows)
-- Since GROUP BY clause does not match with PARTITION KEY; we need to do
-- partial aggregation. However, ORDERED SET are not partial safe and thus
-- partitionwise aggregation plan is not generated.
EXPLAIN (COSTS OFF)
SELECT a, sum(b order by a) FROM pagg_tab GROUP BY a ORDER BY 1, 2;
QUERY PLAN
---------------------------------------------------------------
Sort
Sort Key: pagg_tab.a, (sum(pagg_tab.b ORDER BY pagg_tab.a))
-> GroupAggregate
Group Key: pagg_tab.a
-> Sort
Sort Key: pagg_tab.a
-> Append
-> Seq Scan on pagg_tab_p1 pagg_tab_1
-> Seq Scan on pagg_tab_p2 pagg_tab_2
-> Seq Scan on pagg_tab_p3 pagg_tab_3
(10 rows)
-- JOIN query
CREATE TABLE pagg_tab1(x int, y int) PARTITION BY RANGE(x);
CREATE TABLE pagg_tab1_p1 PARTITION OF pagg_tab1 FOR VALUES FROM (0) TO (10);
CREATE TABLE pagg_tab1_p2 PARTITION OF pagg_tab1 FOR VALUES FROM (10) TO (20);
CREATE TABLE pagg_tab1_p3 PARTITION OF pagg_tab1 FOR VALUES FROM (20) TO (30);
CREATE TABLE pagg_tab2(x int, y int) PARTITION BY RANGE(y);
CREATE TABLE pagg_tab2_p1 PARTITION OF pagg_tab2 FOR VALUES FROM (0) TO (10);
CREATE TABLE pagg_tab2_p2 PARTITION OF pagg_tab2 FOR VALUES FROM (10) TO (20);
CREATE TABLE pagg_tab2_p3 PARTITION OF pagg_tab2 FOR VALUES FROM (20) TO (30);
INSERT INTO pagg_tab1 SELECT i % 30, i % 20 FROM generate_series(0, 299, 2) i;
INSERT INTO pagg_tab2 SELECT i % 20, i % 30 FROM generate_series(0, 299, 3) i;
ANALYZE pagg_tab1;
ANALYZE pagg_tab2;
-- When GROUP BY clause matches; full aggregation is performed for each partition.
EXPLAIN (COSTS OFF)
SELECT t1.x, sum(t1.y), count(*) FROM pagg_tab1 t1, pagg_tab2 t2 WHERE t1.x = t2.y GROUP BY t1.x ORDER BY 1, 2, 3;
QUERY PLAN
-------------------------------------------------------------
Sort
Sort Key: t1.x, (sum(t1.y)), (count(*))
-> Append
-> HashAggregate
Group Key: t1.x
-> Hash Join
Hash Cond: (t1.x = t2.y)
-> Seq Scan on pagg_tab1_p1 t1
-> Hash
-> Seq Scan on pagg_tab2_p1 t2
-> HashAggregate
Group Key: t1_1.x
-> Hash Join
Hash Cond: (t1_1.x = t2_1.y)
-> Seq Scan on pagg_tab1_p2 t1_1
-> Hash
-> Seq Scan on pagg_tab2_p2 t2_1
-> HashAggregate
Group Key: t1_2.x
-> Hash Join
Hash Cond: (t2_2.y = t1_2.x)
-> Seq Scan on pagg_tab2_p3 t2_2
-> Hash
-> Seq Scan on pagg_tab1_p3 t1_2
(24 rows)
SELECT t1.x, sum(t1.y), count(*) FROM pagg_tab1 t1, pagg_tab2 t2 WHERE t1.x = t2.y GROUP BY t1.x ORDER BY 1, 2, 3;
x | sum | count
----+------+-------
0 | 500 | 100
6 | 1100 | 100
12 | 700 | 100
18 | 1300 | 100
24 | 900 | 100
(5 rows)
-- Check with whole-row reference; partitionwise aggregation does not apply
EXPLAIN (COSTS OFF)
SELECT t1.x, sum(t1.y), count(t1) FROM pagg_tab1 t1, pagg_tab2 t2 WHERE t1.x = t2.y GROUP BY t1.x ORDER BY 1, 2, 3;
QUERY PLAN
-------------------------------------------------------------
Sort
Sort Key: t1.x, (sum(t1.y)), (count(((t1.*)::pagg_tab1)))
-> HashAggregate
Group Key: t1.x
-> Hash Join
Hash Cond: (t1.x = t2.y)
-> Append
-> Seq Scan on pagg_tab1_p1 t1_1
-> Seq Scan on pagg_tab1_p2 t1_2
-> Seq Scan on pagg_tab1_p3 t1_3
-> Hash
-> Append
-> Seq Scan on pagg_tab2_p1 t2_1
-> Seq Scan on pagg_tab2_p2 t2_2
-> Seq Scan on pagg_tab2_p3 t2_3
(15 rows)
SELECT t1.x, sum(t1.y), count(t1) FROM pagg_tab1 t1, pagg_tab2 t2 WHERE t1.x = t2.y GROUP BY t1.x ORDER BY 1, 2, 3;
x | sum | count
----+------+-------
0 | 500 | 100
6 | 1100 | 100
12 | 700 | 100
18 | 1300 | 100
24 | 900 | 100
(5 rows)
-- GROUP BY having other matching key
EXPLAIN (COSTS OFF)
SELECT t2.y, sum(t1.y), count(*) FROM pagg_tab1 t1, pagg_tab2 t2 WHERE t1.x = t2.y GROUP BY t2.y ORDER BY 1, 2, 3;
QUERY PLAN
-------------------------------------------------------------
Sort
Sort Key: t2.y, (sum(t1.y)), (count(*))
-> Append
-> HashAggregate
Group Key: t2.y
-> Hash Join
Hash Cond: (t1.x = t2.y)
-> Seq Scan on pagg_tab1_p1 t1
-> Hash
-> Seq Scan on pagg_tab2_p1 t2
-> HashAggregate
Group Key: t2_1.y
-> Hash Join
Hash Cond: (t1_1.x = t2_1.y)
-> Seq Scan on pagg_tab1_p2 t1_1
-> Hash
-> Seq Scan on pagg_tab2_p2 t2_1
-> HashAggregate
Group Key: t2_2.y
-> Hash Join
Hash Cond: (t2_2.y = t1_2.x)
-> Seq Scan on pagg_tab2_p3 t2_2
-> Hash
-> Seq Scan on pagg_tab1_p3 t1_2
(24 rows)
-- When GROUP BY clause does not match; partial aggregation is performed for each partition.
-- Also test GroupAggregate paths by disabling hash aggregates.
SET enable_hashagg TO false;
EXPLAIN (COSTS OFF)
SELECT t1.y, sum(t1.x), count(*) FROM pagg_tab1 t1, pagg_tab2 t2 WHERE t1.x = t2.y GROUP BY t1.y HAVING avg(t1.x) > 10 ORDER BY 1, 2, 3;
QUERY PLAN
-------------------------------------------------------------------------
Sort
Sort Key: t1.y, (sum(t1.x)), (count(*))
-> Finalize GroupAggregate
Group Key: t1.y
Filter: (avg(t1.x) > '10'::numeric)
-> Merge Append
Sort Key: t1.y
-> Partial GroupAggregate
Group Key: t1.y
-> Sort
Sort Key: t1.y
-> Hash Join
Hash Cond: (t1.x = t2.y)
-> Seq Scan on pagg_tab1_p1 t1
-> Hash
-> Seq Scan on pagg_tab2_p1 t2
-> Partial GroupAggregate
Group Key: t1_1.y
-> Sort
Sort Key: t1_1.y
-> Hash Join
Hash Cond: (t1_1.x = t2_1.y)
-> Seq Scan on pagg_tab1_p2 t1_1
-> Hash
-> Seq Scan on pagg_tab2_p2 t2_1
-> Partial GroupAggregate
Group Key: t1_2.y
-> Sort
Sort Key: t1_2.y
-> Hash Join
Hash Cond: (t2_2.y = t1_2.x)
-> Seq Scan on pagg_tab2_p3 t2_2
-> Hash
-> Seq Scan on pagg_tab1_p3 t1_2
(34 rows)
SELECT t1.y, sum(t1.x), count(*) FROM pagg_tab1 t1, pagg_tab2 t2 WHERE t1.x = t2.y GROUP BY t1.y HAVING avg(t1.x) > 10 ORDER BY 1, 2, 3;
y | sum | count
----+------+-------
2 | 600 | 50
4 | 1200 | 50
8 | 900 | 50
12 | 600 | 50
14 | 1200 | 50
18 | 900 | 50
(6 rows)
RESET enable_hashagg;
-- Check with LEFT/RIGHT/FULL OUTER JOINs which produces NULL values for
-- aggregation
-- LEFT JOIN, should produce partial partitionwise aggregation plan as
-- GROUP BY is on nullable column
EXPLAIN (COSTS OFF)
SELECT b.y, sum(a.y) FROM pagg_tab1 a LEFT JOIN pagg_tab2 b ON a.x = b.y GROUP BY b.y ORDER BY 1 NULLS LAST;
QUERY PLAN
------------------------------------------------------------------
Finalize GroupAggregate
Group Key: b.y
-> Sort
Sort Key: b.y
-> Append
-> Partial HashAggregate
Group Key: b.y
-> Hash Left Join
Hash Cond: (a.x = b.y)
-> Seq Scan on pagg_tab1_p1 a
-> Hash
-> Seq Scan on pagg_tab2_p1 b
-> Partial HashAggregate
Group Key: b_1.y
-> Hash Left Join
Hash Cond: (a_1.x = b_1.y)
-> Seq Scan on pagg_tab1_p2 a_1
-> Hash
-> Seq Scan on pagg_tab2_p2 b_1
-> Partial HashAggregate
Group Key: b_2.y
-> Hash Right Join
Hash Cond: (b_2.y = a_2.x)
-> Seq Scan on pagg_tab2_p3 b_2
-> Hash
-> Seq Scan on pagg_tab1_p3 a_2
(26 rows)
SELECT b.y, sum(a.y) FROM pagg_tab1 a LEFT JOIN pagg_tab2 b ON a.x = b.y GROUP BY b.y ORDER BY 1 NULLS LAST;
y | sum
----+------
0 | 500
6 | 1100
12 | 700
18 | 1300
24 | 900
| 900
(6 rows)
-- RIGHT JOIN, should produce full partitionwise aggregation plan as
-- GROUP BY is on non-nullable column
EXPLAIN (COSTS OFF)
SELECT b.y, sum(a.y) FROM pagg_tab1 a RIGHT JOIN pagg_tab2 b ON a.x = b.y GROUP BY b.y ORDER BY 1 NULLS LAST;
QUERY PLAN
------------------------------------------------------------
Sort
Sort Key: b.y
-> Append
-> HashAggregate
Group Key: b.y
-> Hash Right Join
Hash Cond: (a.x = b.y)
-> Seq Scan on pagg_tab1_p1 a
-> Hash
-> Seq Scan on pagg_tab2_p1 b
-> HashAggregate
Group Key: b_1.y
-> Hash Right Join
Hash Cond: (a_1.x = b_1.y)
-> Seq Scan on pagg_tab1_p2 a_1
-> Hash
-> Seq Scan on pagg_tab2_p2 b_1
-> HashAggregate
Group Key: b_2.y
-> Hash Left Join
Hash Cond: (b_2.y = a_2.x)
-> Seq Scan on pagg_tab2_p3 b_2
-> Hash
-> Seq Scan on pagg_tab1_p3 a_2
(24 rows)
SELECT b.y, sum(a.y) FROM pagg_tab1 a RIGHT JOIN pagg_tab2 b ON a.x = b.y GROUP BY b.y ORDER BY 1 NULLS LAST;
y | sum
----+------
0 | 500
3 |
6 | 1100
9 |
12 | 700
15 |
18 | 1300
21 |
24 | 900
27 |
(10 rows)
-- FULL JOIN, should produce partial partitionwise aggregation plan as
-- GROUP BY is on nullable column
EXPLAIN (COSTS OFF)
SELECT a.x, sum(b.x) FROM pagg_tab1 a FULL OUTER JOIN pagg_tab2 b ON a.x = b.y GROUP BY a.x ORDER BY 1 NULLS LAST;
QUERY PLAN
------------------------------------------------------------------
Finalize GroupAggregate
Group Key: a.x
-> Sort
Sort Key: a.x
-> Append
-> Partial HashAggregate
Group Key: a.x
-> Hash Full Join
Hash Cond: (a.x = b.y)
-> Seq Scan on pagg_tab1_p1 a
-> Hash
-> Seq Scan on pagg_tab2_p1 b
-> Partial HashAggregate
Group Key: a_1.x
-> Hash Full Join
Hash Cond: (a_1.x = b_1.y)
-> Seq Scan on pagg_tab1_p2 a_1
-> Hash
-> Seq Scan on pagg_tab2_p2 b_1
-> Partial HashAggregate
Group Key: a_2.x
-> Hash Full Join
Hash Cond: (b_2.y = a_2.x)
-> Seq Scan on pagg_tab2_p3 b_2
-> Hash
-> Seq Scan on pagg_tab1_p3 a_2
(26 rows)
SELECT a.x, sum(b.x) FROM pagg_tab1 a FULL OUTER JOIN pagg_tab2 b ON a.x = b.y GROUP BY a.x ORDER BY 1 NULLS LAST;
x | sum
----+------
0 | 500
2 |
4 |
6 | 1100
8 |
10 |
12 | 700
14 |
16 |
18 | 1300
20 |
22 |
24 | 900
26 |
28 |
| 500
(16 rows)
-- LEFT JOIN, with dummy relation on right side, ideally
-- should produce full partitionwise aggregation plan as GROUP BY is on
-- non-nullable columns.
-- But right now we are unable to do partitionwise join in this case.
EXPLAIN (COSTS OFF)
SELECT a.x, b.y, count(*) FROM (SELECT * FROM pagg_tab1 WHERE x < 20) a LEFT JOIN (SELECT * FROM pagg_tab2 WHERE y > 10) b ON a.x = b.y WHERE a.x > 5 or b.y < 20 GROUP BY a.x, b.y ORDER BY 1, 2;
QUERY PLAN
--------------------------------------------------------------------
Sort
Sort Key: pagg_tab1.x, pagg_tab2.y
-> HashAggregate
Group Key: pagg_tab1.x, pagg_tab2.y
-> Hash Left Join
Hash Cond: (pagg_tab1.x = pagg_tab2.y)
Filter: ((pagg_tab1.x > 5) OR (pagg_tab2.y < 20))
-> Append
-> Seq Scan on pagg_tab1_p1 pagg_tab1_1
Filter: (x < 20)
-> Seq Scan on pagg_tab1_p2 pagg_tab1_2
Filter: (x < 20)
-> Hash
-> Append
-> Seq Scan on pagg_tab2_p2 pagg_tab2_1
Filter: (y > 10)
-> Seq Scan on pagg_tab2_p3 pagg_tab2_2
Filter: (y > 10)
(18 rows)
SELECT a.x, b.y, count(*) FROM (SELECT * FROM pagg_tab1 WHERE x < 20) a LEFT JOIN (SELECT * FROM pagg_tab2 WHERE y > 10) b ON a.x = b.y WHERE a.x > 5 or b.y < 20 GROUP BY a.x, b.y ORDER BY 1, 2;
x | y | count
----+----+-------
6 | | 10
8 | | 10
10 | | 10
12 | 12 | 100
14 | | 10
16 | | 10
18 | 18 | 100
(7 rows)
-- FULL JOIN, with dummy relations on both sides, ideally
-- should produce partial partitionwise aggregation plan as GROUP BY is on
-- nullable columns.
-- But right now we are unable to do partitionwise join in this case.
EXPLAIN (COSTS OFF)
SELECT a.x, b.y, count(*) FROM (SELECT * FROM pagg_tab1 WHERE x < 20) a FULL JOIN (SELECT * FROM pagg_tab2 WHERE y > 10) b ON a.x = b.y WHERE a.x > 5 or b.y < 20 GROUP BY a.x, b.y ORDER BY 1, 2;
QUERY PLAN
--------------------------------------------------------------------
Sort
Sort Key: pagg_tab1.x, pagg_tab2.y
-> HashAggregate
Group Key: pagg_tab1.x, pagg_tab2.y
-> Hash Full Join
Hash Cond: (pagg_tab1.x = pagg_tab2.y)
Filter: ((pagg_tab1.x > 5) OR (pagg_tab2.y < 20))
-> Append
-> Seq Scan on pagg_tab1_p1 pagg_tab1_1
Filter: (x < 20)
-> Seq Scan on pagg_tab1_p2 pagg_tab1_2
Filter: (x < 20)
-> Hash
-> Append
-> Seq Scan on pagg_tab2_p2 pagg_tab2_1
Filter: (y > 10)
-> Seq Scan on pagg_tab2_p3 pagg_tab2_2
Filter: (y > 10)
(18 rows)
SELECT a.x, b.y, count(*) FROM (SELECT * FROM pagg_tab1 WHERE x < 20) a FULL JOIN (SELECT * FROM pagg_tab2 WHERE y > 10) b ON a.x = b.y WHERE a.x > 5 or b.y < 20 GROUP BY a.x, b.y ORDER BY 1, 2;
x | y | count
----+----+-------
6 | | 10
8 | | 10
10 | | 10
12 | 12 | 100
14 | | 10
16 | | 10
18 | 18 | 100
| 15 | 10
(8 rows)
-- Empty join relation because of empty outer side, no partitionwise agg plan
EXPLAIN (COSTS OFF)
SELECT a.x, a.y, count(*) FROM (SELECT * FROM pagg_tab1 WHERE x = 1 AND x = 2) a LEFT JOIN pagg_tab2 b ON a.x = b.y GROUP BY a.x, a.y ORDER BY 1, 2;
QUERY PLAN
--------------------------------------
GroupAggregate
Group Key: pagg_tab1.y
-> Sort
Sort Key: pagg_tab1.y
-> Result
One-Time Filter: false
(6 rows)
SELECT a.x, a.y, count(*) FROM (SELECT * FROM pagg_tab1 WHERE x = 1 AND x = 2) a LEFT JOIN pagg_tab2 b ON a.x = b.y GROUP BY a.x, a.y ORDER BY 1, 2;
x | y | count
---+---+-------
(0 rows)
-- Partition by multiple columns
CREATE TABLE pagg_tab_m (a int, b int, c int) PARTITION BY RANGE(a, ((a+b)/2));
CREATE TABLE pagg_tab_m_p1 PARTITION OF pagg_tab_m FOR VALUES FROM (0, 0) TO (12, 12);
CREATE TABLE pagg_tab_m_p2 PARTITION OF pagg_tab_m FOR VALUES FROM (12, 12) TO (22, 22);
CREATE TABLE pagg_tab_m_p3 PARTITION OF pagg_tab_m FOR VALUES FROM (22, 22) TO (30, 30);
INSERT INTO pagg_tab_m SELECT i % 30, i % 40, i % 50 FROM generate_series(0, 2999) i;
ANALYZE pagg_tab_m;
-- Partial aggregation as GROUP BY clause does not match with PARTITION KEY
EXPLAIN (COSTS OFF)
SELECT a, sum(b), avg(c), count(*) FROM pagg_tab_m GROUP BY a HAVING avg(c) < 22 ORDER BY 1, 2, 3;
QUERY PLAN
--------------------------------------------------------------------
Sort
Sort Key: pagg_tab_m.a, (sum(pagg_tab_m.b)), (avg(pagg_tab_m.c))
-> Finalize HashAggregate
Group Key: pagg_tab_m.a
Filter: (avg(pagg_tab_m.c) < '22'::numeric)
-> Append
-> Partial HashAggregate
Group Key: pagg_tab_m.a
-> Seq Scan on pagg_tab_m_p1 pagg_tab_m
-> Partial HashAggregate
Group Key: pagg_tab_m_1.a
-> Seq Scan on pagg_tab_m_p2 pagg_tab_m_1
-> Partial HashAggregate
Group Key: pagg_tab_m_2.a
-> Seq Scan on pagg_tab_m_p3 pagg_tab_m_2
(15 rows)
SELECT a, sum(b), avg(c), count(*) FROM pagg_tab_m GROUP BY a HAVING avg(c) < 22 ORDER BY 1, 2, 3;
a | sum | avg | count
----+------+---------------------+-------
0 | 1500 | 20.0000000000000000 | 100
1 | 1600 | 21.0000000000000000 | 100
10 | 1500 | 20.0000000000000000 | 100
11 | 1600 | 21.0000000000000000 | 100
20 | 1500 | 20.0000000000000000 | 100
21 | 1600 | 21.0000000000000000 | 100
(6 rows)
-- Full aggregation as GROUP BY clause matches with PARTITION KEY
EXPLAIN (COSTS OFF)
SELECT a, sum(b), avg(c), count(*) FROM pagg_tab_m GROUP BY a, (a+b)/2 HAVING sum(b) < 50 ORDER BY 1, 2, 3;
QUERY PLAN
----------------------------------------------------------------------------------
Sort
Sort Key: pagg_tab_m.a, (sum(pagg_tab_m.b)), (avg(pagg_tab_m.c))
-> Append
-> HashAggregate
Group Key: pagg_tab_m.a, ((pagg_tab_m.a + pagg_tab_m.b) / 2)
Filter: (sum(pagg_tab_m.b) < 50)
-> Seq Scan on pagg_tab_m_p1 pagg_tab_m
-> HashAggregate
Group Key: pagg_tab_m_1.a, ((pagg_tab_m_1.a + pagg_tab_m_1.b) / 2)
Filter: (sum(pagg_tab_m_1.b) < 50)
-> Seq Scan on pagg_tab_m_p2 pagg_tab_m_1
-> HashAggregate
Group Key: pagg_tab_m_2.a, ((pagg_tab_m_2.a + pagg_tab_m_2.b) / 2)
Filter: (sum(pagg_tab_m_2.b) < 50)
-> Seq Scan on pagg_tab_m_p3 pagg_tab_m_2
(15 rows)
SELECT a, sum(b), avg(c), count(*) FROM pagg_tab_m GROUP BY a, (a+b)/2 HAVING sum(b) < 50 ORDER BY 1, 2, 3;
a | sum | avg | count
----+-----+---------------------+-------
0 | 0 | 20.0000000000000000 | 25
1 | 25 | 21.0000000000000000 | 25
10 | 0 | 20.0000000000000000 | 25
11 | 25 | 21.0000000000000000 | 25
20 | 0 | 20.0000000000000000 | 25
21 | 25 | 21.0000000000000000 | 25
(6 rows)
-- Full aggregation as PARTITION KEY is part of GROUP BY clause
EXPLAIN (COSTS OFF)
SELECT a, c, sum(b), avg(c), count(*) FROM pagg_tab_m GROUP BY (a+b)/2, 2, 1 HAVING sum(b) = 50 AND avg(c) > 25 ORDER BY 1, 2, 3;
QUERY PLAN
--------------------------------------------------------------------------------------------------
Sort
Sort Key: pagg_tab_m.a, pagg_tab_m.c, (sum(pagg_tab_m.b))
-> Append
-> HashAggregate
Group Key: ((pagg_tab_m.a + pagg_tab_m.b) / 2), pagg_tab_m.c, pagg_tab_m.a
Filter: ((sum(pagg_tab_m.b) = 50) AND (avg(pagg_tab_m.c) > '25'::numeric))
-> Seq Scan on pagg_tab_m_p1 pagg_tab_m
-> HashAggregate
Group Key: ((pagg_tab_m_1.a + pagg_tab_m_1.b) / 2), pagg_tab_m_1.c, pagg_tab_m_1.a
Filter: ((sum(pagg_tab_m_1.b) = 50) AND (avg(pagg_tab_m_1.c) > '25'::numeric))
-> Seq Scan on pagg_tab_m_p2 pagg_tab_m_1
-> HashAggregate
Group Key: ((pagg_tab_m_2.a + pagg_tab_m_2.b) / 2), pagg_tab_m_2.c, pagg_tab_m_2.a
Filter: ((sum(pagg_tab_m_2.b) = 50) AND (avg(pagg_tab_m_2.c) > '25'::numeric))
-> Seq Scan on pagg_tab_m_p3 pagg_tab_m_2
(15 rows)
SELECT a, c, sum(b), avg(c), count(*) FROM pagg_tab_m GROUP BY (a+b)/2, 2, 1 HAVING sum(b) = 50 AND avg(c) > 25 ORDER BY 1, 2, 3;
a | c | sum | avg | count
----+----+-----+---------------------+-------
0 | 30 | 50 | 30.0000000000000000 | 5
0 | 40 | 50 | 40.0000000000000000 | 5
10 | 30 | 50 | 30.0000000000000000 | 5
10 | 40 | 50 | 40.0000000000000000 | 5
20 | 30 | 50 | 30.0000000000000000 | 5
20 | 40 | 50 | 40.0000000000000000 | 5
(6 rows)
-- Test with multi-level partitioning scheme
CREATE TABLE pagg_tab_ml (a int, b int, c text) PARTITION BY RANGE(a);
CREATE TABLE pagg_tab_ml_p1 PARTITION OF pagg_tab_ml FOR VALUES FROM (0) TO (12);
CREATE TABLE pagg_tab_ml_p2 PARTITION OF pagg_tab_ml FOR VALUES FROM (12) TO (20) PARTITION BY LIST (c);
CREATE TABLE pagg_tab_ml_p2_s1 PARTITION OF pagg_tab_ml_p2 FOR VALUES IN ('0000', '0001', '0002');
CREATE TABLE pagg_tab_ml_p2_s2 PARTITION OF pagg_tab_ml_p2 FOR VALUES IN ('0003');
-- This level of partitioning has different column positions than the parent
CREATE TABLE pagg_tab_ml_p3(b int, c text, a int) PARTITION BY RANGE (b);
CREATE TABLE pagg_tab_ml_p3_s1(c text, a int, b int);
CREATE TABLE pagg_tab_ml_p3_s2 PARTITION OF pagg_tab_ml_p3 FOR VALUES FROM (7) TO (10);
ALTER TABLE pagg_tab_ml_p3 ATTACH PARTITION pagg_tab_ml_p3_s1 FOR VALUES FROM (0) TO (7);
ALTER TABLE pagg_tab_ml ATTACH PARTITION pagg_tab_ml_p3 FOR VALUES FROM (20) TO (30);
INSERT INTO pagg_tab_ml SELECT i % 30, i % 10, to_char(i % 4, 'FM0000') FROM generate_series(0, 29999) i;
ANALYZE pagg_tab_ml;
-- For Parallel Append
SET max_parallel_workers_per_gather TO 2;
SET parallel_setup_cost = 0;
-- Full aggregation at level 1 as GROUP BY clause matches with PARTITION KEY
-- for level 1 only. For subpartitions, GROUP BY clause does not match with
-- PARTITION KEY, but still we do not see a partial aggregation as array_agg()
-- is not partial agg safe.
EXPLAIN (COSTS OFF)
SELECT a, sum(b), array_agg(distinct c), count(*) FROM pagg_tab_ml GROUP BY a HAVING avg(b) < 3 ORDER BY 1, 2, 3;
QUERY PLAN
--------------------------------------------------------------------------------------
Sort
Sort Key: pagg_tab_ml.a, (sum(pagg_tab_ml.b)), (array_agg(DISTINCT pagg_tab_ml.c))
-> Gather
Workers Planned: 2
-> Parallel Append
-> GroupAggregate
Group Key: pagg_tab_ml.a
Filter: (avg(pagg_tab_ml.b) < '3'::numeric)
-> Sort
Sort Key: pagg_tab_ml.a, pagg_tab_ml.c
-> Seq Scan on pagg_tab_ml_p1 pagg_tab_ml
-> GroupAggregate
Group Key: pagg_tab_ml_5.a
Filter: (avg(pagg_tab_ml_5.b) < '3'::numeric)
-> Sort
Sort Key: pagg_tab_ml_5.a, pagg_tab_ml_5.c
-> Append
-> Seq Scan on pagg_tab_ml_p3_s1 pagg_tab_ml_5
-> Seq Scan on pagg_tab_ml_p3_s2 pagg_tab_ml_6
-> GroupAggregate
Group Key: pagg_tab_ml_2.a
Filter: (avg(pagg_tab_ml_2.b) < '3'::numeric)
-> Sort
Sort Key: pagg_tab_ml_2.a, pagg_tab_ml_2.c
-> Append
-> Seq Scan on pagg_tab_ml_p2_s1 pagg_tab_ml_2
-> Seq Scan on pagg_tab_ml_p2_s2 pagg_tab_ml_3
(27 rows)
SELECT a, sum(b), array_agg(distinct c), count(*) FROM pagg_tab_ml GROUP BY a HAVING avg(b) < 3 ORDER BY 1, 2, 3;
a | sum | array_agg | count
----+------+-------------+-------
0 | 0 | {0000,0002} | 1000
1 | 1000 | {0001,0003} | 1000
2 | 2000 | {0000,0002} | 1000
10 | 0 | {0000,0002} | 1000
11 | 1000 | {0001,0003} | 1000
12 | 2000 | {0000,0002} | 1000
20 | 0 | {0000,0002} | 1000
21 | 1000 | {0001,0003} | 1000
22 | 2000 | {0000,0002} | 1000
(9 rows)
-- Without ORDER BY clause, to test Gather at top-most path
EXPLAIN (COSTS OFF)
SELECT a, sum(b), array_agg(distinct c), count(*) FROM pagg_tab_ml GROUP BY a HAVING avg(b) < 3;
QUERY PLAN
---------------------------------------------------------------------------
Gather
Workers Planned: 2
-> Parallel Append
-> GroupAggregate
Group Key: pagg_tab_ml.a
Filter: (avg(pagg_tab_ml.b) < '3'::numeric)
-> Sort
Sort Key: pagg_tab_ml.a, pagg_tab_ml.c
-> Seq Scan on pagg_tab_ml_p1 pagg_tab_ml
-> GroupAggregate
Group Key: pagg_tab_ml_5.a
Filter: (avg(pagg_tab_ml_5.b) < '3'::numeric)
-> Sort
Sort Key: pagg_tab_ml_5.a, pagg_tab_ml_5.c
-> Append
-> Seq Scan on pagg_tab_ml_p3_s1 pagg_tab_ml_5
-> Seq Scan on pagg_tab_ml_p3_s2 pagg_tab_ml_6
-> GroupAggregate
Group Key: pagg_tab_ml_2.a
Filter: (avg(pagg_tab_ml_2.b) < '3'::numeric)
-> Sort
Sort Key: pagg_tab_ml_2.a, pagg_tab_ml_2.c
-> Append
-> Seq Scan on pagg_tab_ml_p2_s1 pagg_tab_ml_2
-> Seq Scan on pagg_tab_ml_p2_s2 pagg_tab_ml_3
(25 rows)
RESET parallel_setup_cost;
-- Full aggregation at level 1 as GROUP BY clause matches with PARTITION KEY
-- for level 1 only. For subpartitions, GROUP BY clause does not match with
-- PARTITION KEY, thus we will have a partial aggregation for them.
EXPLAIN (COSTS OFF)
SELECT a, sum(b), count(*) FROM pagg_tab_ml GROUP BY a HAVING avg(b) < 3 ORDER BY 1, 2, 3;
QUERY PLAN
---------------------------------------------------------------------------------
Sort
Sort Key: pagg_tab_ml.a, (sum(pagg_tab_ml.b)), (count(*))
-> Append
-> HashAggregate
Group Key: pagg_tab_ml.a
Filter: (avg(pagg_tab_ml.b) < '3'::numeric)
-> Seq Scan on pagg_tab_ml_p1 pagg_tab_ml
-> Finalize GroupAggregate
Group Key: pagg_tab_ml_2.a
Filter: (avg(pagg_tab_ml_2.b) < '3'::numeric)
-> Sort
Sort Key: pagg_tab_ml_2.a
-> Append
-> Partial HashAggregate
Group Key: pagg_tab_ml_2.a
-> Seq Scan on pagg_tab_ml_p2_s1 pagg_tab_ml_2
-> Partial HashAggregate
Group Key: pagg_tab_ml_3.a
-> Seq Scan on pagg_tab_ml_p2_s2 pagg_tab_ml_3
-> Finalize GroupAggregate
Group Key: pagg_tab_ml_5.a
Filter: (avg(pagg_tab_ml_5.b) < '3'::numeric)
-> Sort
Sort Key: pagg_tab_ml_5.a
-> Append
-> Partial HashAggregate
Group Key: pagg_tab_ml_5.a
-> Seq Scan on pagg_tab_ml_p3_s1 pagg_tab_ml_5
-> Partial HashAggregate
Group Key: pagg_tab_ml_6.a
-> Seq Scan on pagg_tab_ml_p3_s2 pagg_tab_ml_6
(31 rows)
SELECT a, sum(b), count(*) FROM pagg_tab_ml GROUP BY a HAVING avg(b) < 3 ORDER BY 1, 2, 3;
a | sum | count
----+------+-------
0 | 0 | 1000
1 | 1000 | 1000
2 | 2000 | 1000
10 | 0 | 1000
11 | 1000 | 1000
12 | 2000 | 1000
20 | 0 | 1000
21 | 1000 | 1000
22 | 2000 | 1000
(9 rows)
-- Partial aggregation at all levels as GROUP BY clause does not match with
-- PARTITION KEY
EXPLAIN (COSTS OFF)
SELECT b, sum(a), count(*) FROM pagg_tab_ml GROUP BY b ORDER BY 1, 2, 3;
QUERY PLAN
---------------------------------------------------------------------------
Sort
Sort Key: pagg_tab_ml.b, (sum(pagg_tab_ml.a)), (count(*))
-> Finalize GroupAggregate
Group Key: pagg_tab_ml.b
-> Sort
Sort Key: pagg_tab_ml.b
-> Append
-> Partial HashAggregate
Group Key: pagg_tab_ml.b
-> Seq Scan on pagg_tab_ml_p1 pagg_tab_ml
-> Partial HashAggregate
Group Key: pagg_tab_ml_1.b
-> Seq Scan on pagg_tab_ml_p2_s1 pagg_tab_ml_1
-> Partial HashAggregate
Group Key: pagg_tab_ml_2.b
-> Seq Scan on pagg_tab_ml_p2_s2 pagg_tab_ml_2
-> Partial HashAggregate
Group Key: pagg_tab_ml_3.b
-> Seq Scan on pagg_tab_ml_p3_s1 pagg_tab_ml_3
-> Partial HashAggregate
Group Key: pagg_tab_ml_4.b
-> Seq Scan on pagg_tab_ml_p3_s2 pagg_tab_ml_4
(22 rows)
SELECT b, sum(a), count(*) FROM pagg_tab_ml GROUP BY b HAVING avg(a) < 15 ORDER BY 1, 2, 3;
b | sum | count
---+-------+-------
0 | 30000 | 3000
1 | 33000 | 3000
2 | 36000 | 3000
3 | 39000 | 3000
4 | 42000 | 3000
(5 rows)
-- Full aggregation at all levels as GROUP BY clause matches with PARTITION KEY
EXPLAIN (COSTS OFF)
SELECT a, sum(b), count(*) FROM pagg_tab_ml GROUP BY a, b, c HAVING avg(b) > 7 ORDER BY 1, 2, 3;
QUERY PLAN
----------------------------------------------------------------------------
Sort
Sort Key: pagg_tab_ml.a, (sum(pagg_tab_ml.b)), (count(*))
-> Append
-> HashAggregate
Group Key: pagg_tab_ml.a, pagg_tab_ml.b, pagg_tab_ml.c
Filter: (avg(pagg_tab_ml.b) > '7'::numeric)
-> Seq Scan on pagg_tab_ml_p1 pagg_tab_ml
-> HashAggregate
Group Key: pagg_tab_ml_1.a, pagg_tab_ml_1.b, pagg_tab_ml_1.c
Filter: (avg(pagg_tab_ml_1.b) > '7'::numeric)
-> Seq Scan on pagg_tab_ml_p2_s1 pagg_tab_ml_1
-> HashAggregate
Group Key: pagg_tab_ml_2.a, pagg_tab_ml_2.b, pagg_tab_ml_2.c
Filter: (avg(pagg_tab_ml_2.b) > '7'::numeric)
-> Seq Scan on pagg_tab_ml_p2_s2 pagg_tab_ml_2
-> HashAggregate
Group Key: pagg_tab_ml_3.a, pagg_tab_ml_3.b, pagg_tab_ml_3.c
Filter: (avg(pagg_tab_ml_3.b) > '7'::numeric)
-> Seq Scan on pagg_tab_ml_p3_s1 pagg_tab_ml_3
-> HashAggregate
Group Key: pagg_tab_ml_4.a, pagg_tab_ml_4.b, pagg_tab_ml_4.c
Filter: (avg(pagg_tab_ml_4.b) > '7'::numeric)
-> Seq Scan on pagg_tab_ml_p3_s2 pagg_tab_ml_4
(23 rows)
SELECT a, sum(b), count(*) FROM pagg_tab_ml GROUP BY a, b, c HAVING avg(b) > 7 ORDER BY 1, 2, 3;
a | sum | count
----+------+-------
8 | 4000 | 500
8 | 4000 | 500
9 | 4500 | 500
9 | 4500 | 500
18 | 4000 | 500
18 | 4000 | 500
19 | 4500 | 500
19 | 4500 | 500
28 | 4000 | 500
28 | 4000 | 500
29 | 4500 | 500
29 | 4500 | 500
(12 rows)
-- Parallelism within partitionwise aggregates
SET min_parallel_table_scan_size TO '8kB';
SET parallel_setup_cost TO 0;
-- Full aggregation at level 1 as GROUP BY clause matches with PARTITION KEY
-- for level 1 only. For subpartitions, GROUP BY clause does not match with
-- PARTITION KEY, thus we will have a partial aggregation for them.
EXPLAIN (COSTS OFF)
SELECT a, sum(b), count(*) FROM pagg_tab_ml GROUP BY a HAVING avg(b) < 3 ORDER BY 1, 2, 3;
QUERY PLAN
------------------------------------------------------------------------------------------------
Sort
Sort Key: pagg_tab_ml.a, (sum(pagg_tab_ml.b)), (count(*))
-> Append
-> Finalize GroupAggregate
Group Key: pagg_tab_ml.a
Filter: (avg(pagg_tab_ml.b) < '3'::numeric)
-> Gather Merge
Workers Planned: 2
-> Sort
Sort Key: pagg_tab_ml.a
-> Partial HashAggregate
Group Key: pagg_tab_ml.a
-> Parallel Seq Scan on pagg_tab_ml_p1 pagg_tab_ml
-> Finalize GroupAggregate
Group Key: pagg_tab_ml_2.a
Filter: (avg(pagg_tab_ml_2.b) < '3'::numeric)
-> Gather Merge
Workers Planned: 2
-> Sort
Sort Key: pagg_tab_ml_2.a
-> Parallel Append
-> Partial HashAggregate
Group Key: pagg_tab_ml_2.a
-> Parallel Seq Scan on pagg_tab_ml_p2_s1 pagg_tab_ml_2
-> Partial HashAggregate
Group Key: pagg_tab_ml_3.a
-> Parallel Seq Scan on pagg_tab_ml_p2_s2 pagg_tab_ml_3
-> Finalize GroupAggregate
Group Key: pagg_tab_ml_5.a
Filter: (avg(pagg_tab_ml_5.b) < '3'::numeric)
-> Gather Merge
Workers Planned: 2
-> Sort
Sort Key: pagg_tab_ml_5.a
-> Parallel Append
-> Partial HashAggregate
Group Key: pagg_tab_ml_5.a
-> Parallel Seq Scan on pagg_tab_ml_p3_s1 pagg_tab_ml_5
-> Partial HashAggregate
Group Key: pagg_tab_ml_6.a
-> Parallel Seq Scan on pagg_tab_ml_p3_s2 pagg_tab_ml_6
(41 rows)
SELECT a, sum(b), count(*) FROM pagg_tab_ml GROUP BY a HAVING avg(b) < 3 ORDER BY 1, 2, 3;
a | sum | count
----+------+-------
0 | 0 | 1000
1 | 1000 | 1000
2 | 2000 | 1000
10 | 0 | 1000
11 | 1000 | 1000
12 | 2000 | 1000
20 | 0 | 1000
21 | 1000 | 1000
22 | 2000 | 1000
(9 rows)
-- Partial aggregation at all levels as GROUP BY clause does not match with
-- PARTITION KEY
EXPLAIN (COSTS OFF)
SELECT b, sum(a), count(*) FROM pagg_tab_ml GROUP BY b ORDER BY 1, 2, 3;
QUERY PLAN
------------------------------------------------------------------------------------------
Sort
Sort Key: pagg_tab_ml.b, (sum(pagg_tab_ml.a)), (count(*))
-> Finalize GroupAggregate
Group Key: pagg_tab_ml.b
-> Gather Merge
Workers Planned: 2
-> Sort
Sort Key: pagg_tab_ml.b
-> Parallel Append
-> Partial HashAggregate
Group Key: pagg_tab_ml.b
-> Parallel Seq Scan on pagg_tab_ml_p1 pagg_tab_ml
-> Partial HashAggregate
Group Key: pagg_tab_ml_3.b
-> Parallel Seq Scan on pagg_tab_ml_p3_s1 pagg_tab_ml_3
-> Partial HashAggregate
Group Key: pagg_tab_ml_1.b
-> Parallel Seq Scan on pagg_tab_ml_p2_s1 pagg_tab_ml_1
-> Partial HashAggregate
Group Key: pagg_tab_ml_4.b
-> Parallel Seq Scan on pagg_tab_ml_p3_s2 pagg_tab_ml_4
-> Partial HashAggregate
Group Key: pagg_tab_ml_2.b
-> Parallel Seq Scan on pagg_tab_ml_p2_s2 pagg_tab_ml_2
(24 rows)
SELECT b, sum(a), count(*) FROM pagg_tab_ml GROUP BY b HAVING avg(a) < 15 ORDER BY 1, 2, 3;
b | sum | count
---+-------+-------
0 | 30000 | 3000
1 | 33000 | 3000
2 | 36000 | 3000
3 | 39000 | 3000
4 | 42000 | 3000
(5 rows)
-- Full aggregation at all levels as GROUP BY clause matches with PARTITION KEY
EXPLAIN (COSTS OFF)
SELECT a, sum(b), count(*) FROM pagg_tab_ml GROUP BY a, b, c HAVING avg(b) > 7 ORDER BY 1, 2, 3;
QUERY PLAN
----------------------------------------------------------------------------------
Gather Merge
Workers Planned: 2
-> Sort
Sort Key: pagg_tab_ml.a, (sum(pagg_tab_ml.b)), (count(*))
-> Parallel Append
-> HashAggregate
Group Key: pagg_tab_ml.a, pagg_tab_ml.b, pagg_tab_ml.c
Filter: (avg(pagg_tab_ml.b) > '7'::numeric)
-> Seq Scan on pagg_tab_ml_p1 pagg_tab_ml
-> HashAggregate
Group Key: pagg_tab_ml_3.a, pagg_tab_ml_3.b, pagg_tab_ml_3.c
Filter: (avg(pagg_tab_ml_3.b) > '7'::numeric)
-> Seq Scan on pagg_tab_ml_p3_s1 pagg_tab_ml_3
-> HashAggregate
Group Key: pagg_tab_ml_1.a, pagg_tab_ml_1.b, pagg_tab_ml_1.c
Filter: (avg(pagg_tab_ml_1.b) > '7'::numeric)
-> Seq Scan on pagg_tab_ml_p2_s1 pagg_tab_ml_1
-> HashAggregate
Group Key: pagg_tab_ml_4.a, pagg_tab_ml_4.b, pagg_tab_ml_4.c
Filter: (avg(pagg_tab_ml_4.b) > '7'::numeric)
-> Seq Scan on pagg_tab_ml_p3_s2 pagg_tab_ml_4
-> HashAggregate
Group Key: pagg_tab_ml_2.a, pagg_tab_ml_2.b, pagg_tab_ml_2.c
Filter: (avg(pagg_tab_ml_2.b) > '7'::numeric)
-> Seq Scan on pagg_tab_ml_p2_s2 pagg_tab_ml_2
(25 rows)
SELECT a, sum(b), count(*) FROM pagg_tab_ml GROUP BY a, b, c HAVING avg(b) > 7 ORDER BY 1, 2, 3;
a | sum | count
----+------+-------
8 | 4000 | 500
8 | 4000 | 500
9 | 4500 | 500
9 | 4500 | 500
18 | 4000 | 500
18 | 4000 | 500
19 | 4500 | 500
19 | 4500 | 500
28 | 4000 | 500
28 | 4000 | 500
29 | 4500 | 500
29 | 4500 | 500
(12 rows)
-- Parallelism within partitionwise aggregates (single level)
-- Add few parallel setup cost, so that we will see a plan which gathers
-- partially created paths even for full aggregation and sticks a single Gather
-- followed by finalization step.
-- Without this, the cost of doing partial aggregation + Gather + finalization
-- for each partition and then Append over it turns out to be same and this
-- wins as we add it first. This parallel_setup_cost plays a vital role in
-- costing such plans.
SET parallel_setup_cost TO 10;
CREATE TABLE pagg_tab_para(x int, y int) PARTITION BY RANGE(x);
CREATE TABLE pagg_tab_para_p1 PARTITION OF pagg_tab_para FOR VALUES FROM (0) TO (12);
CREATE TABLE pagg_tab_para_p2 PARTITION OF pagg_tab_para FOR VALUES FROM (12) TO (22);
CREATE TABLE pagg_tab_para_p3 PARTITION OF pagg_tab_para FOR VALUES FROM (22) TO (30);
INSERT INTO pagg_tab_para SELECT i % 30, i % 20 FROM generate_series(0, 29999) i;
ANALYZE pagg_tab_para;
-- When GROUP BY clause matches; full aggregation is performed for each partition.
EXPLAIN (COSTS OFF)
SELECT x, sum(y), avg(y), count(*) FROM pagg_tab_para GROUP BY x HAVING avg(y) < 7 ORDER BY 1, 2, 3;
QUERY PLAN
-------------------------------------------------------------------------------------------
Sort
Sort Key: pagg_tab_para.x, (sum(pagg_tab_para.y)), (avg(pagg_tab_para.y))
-> Finalize GroupAggregate
Group Key: pagg_tab_para.x
Filter: (avg(pagg_tab_para.y) < '7'::numeric)
-> Gather Merge
Workers Planned: 2
-> Sort
Sort Key: pagg_tab_para.x
-> Parallel Append
-> Partial HashAggregate
Group Key: pagg_tab_para.x
-> Parallel Seq Scan on pagg_tab_para_p1 pagg_tab_para
-> Partial HashAggregate
Group Key: pagg_tab_para_1.x
-> Parallel Seq Scan on pagg_tab_para_p2 pagg_tab_para_1
-> Partial HashAggregate
Group Key: pagg_tab_para_2.x
-> Parallel Seq Scan on pagg_tab_para_p3 pagg_tab_para_2
(19 rows)
SELECT x, sum(y), avg(y), count(*) FROM pagg_tab_para GROUP BY x HAVING avg(y) < 7 ORDER BY 1, 2, 3;
x | sum | avg | count
----+------+--------------------+-------
0 | 5000 | 5.0000000000000000 | 1000
1 | 6000 | 6.0000000000000000 | 1000
10 | 5000 | 5.0000000000000000 | 1000
11 | 6000 | 6.0000000000000000 | 1000
20 | 5000 | 5.0000000000000000 | 1000
21 | 6000 | 6.0000000000000000 | 1000
(6 rows)
-- When GROUP BY clause does not match; partial aggregation is performed for each partition.
EXPLAIN (COSTS OFF)
SELECT y, sum(x), avg(x), count(*) FROM pagg_tab_para GROUP BY y HAVING avg(x) < 12 ORDER BY 1, 2, 3;
QUERY PLAN
-------------------------------------------------------------------------------------------
Sort
Sort Key: pagg_tab_para.y, (sum(pagg_tab_para.x)), (avg(pagg_tab_para.x))
-> Finalize GroupAggregate
Group Key: pagg_tab_para.y
Filter: (avg(pagg_tab_para.x) < '12'::numeric)
-> Gather Merge
Workers Planned: 2
-> Sort
Sort Key: pagg_tab_para.y
-> Parallel Append
-> Partial HashAggregate
Group Key: pagg_tab_para.y
-> Parallel Seq Scan on pagg_tab_para_p1 pagg_tab_para
-> Partial HashAggregate
Group Key: pagg_tab_para_1.y
-> Parallel Seq Scan on pagg_tab_para_p2 pagg_tab_para_1
-> Partial HashAggregate
Group Key: pagg_tab_para_2.y
-> Parallel Seq Scan on pagg_tab_para_p3 pagg_tab_para_2
(19 rows)
SELECT y, sum(x), avg(x), count(*) FROM pagg_tab_para GROUP BY y HAVING avg(x) < 12 ORDER BY 1, 2, 3;
y | sum | avg | count
----+-------+---------------------+-------
0 | 15000 | 10.0000000000000000 | 1500
1 | 16500 | 11.0000000000000000 | 1500
10 | 15000 | 10.0000000000000000 | 1500
11 | 16500 | 11.0000000000000000 | 1500
(4 rows)
-- Test when parent can produce parallel paths but not any (or some) of its children
-- (Use one more aggregate to tilt the cost estimates for the plan we want)
ALTER TABLE pagg_tab_para_p1 SET (parallel_workers = 0);
ALTER TABLE pagg_tab_para_p3 SET (parallel_workers = 0);
ANALYZE pagg_tab_para;
EXPLAIN (COSTS OFF)
SELECT x, sum(y), avg(y), sum(x+y), count(*) FROM pagg_tab_para GROUP BY x HAVING avg(y) < 7 ORDER BY 1, 2, 3;
QUERY PLAN
-------------------------------------------------------------------------------------------
Sort
Sort Key: pagg_tab_para.x, (sum(pagg_tab_para.y)), (avg(pagg_tab_para.y))
-> Finalize GroupAggregate
Group Key: pagg_tab_para.x
Filter: (avg(pagg_tab_para.y) < '7'::numeric)
-> Gather Merge
Workers Planned: 2
-> Sort
Sort Key: pagg_tab_para.x
-> Partial HashAggregate
Group Key: pagg_tab_para.x
-> Parallel Append
-> Seq Scan on pagg_tab_para_p1 pagg_tab_para_1
-> Seq Scan on pagg_tab_para_p3 pagg_tab_para_3
-> Parallel Seq Scan on pagg_tab_para_p2 pagg_tab_para_2
(15 rows)
SELECT x, sum(y), avg(y), sum(x+y), count(*) FROM pagg_tab_para GROUP BY x HAVING avg(y) < 7 ORDER BY 1, 2, 3;
x | sum | avg | sum | count
----+------+--------------------+-------+-------
0 | 5000 | 5.0000000000000000 | 5000 | 1000
1 | 6000 | 6.0000000000000000 | 7000 | 1000
10 | 5000 | 5.0000000000000000 | 15000 | 1000
11 | 6000 | 6.0000000000000000 | 17000 | 1000
20 | 5000 | 5.0000000000000000 | 25000 | 1000
21 | 6000 | 6.0000000000000000 | 27000 | 1000
(6 rows)
ALTER TABLE pagg_tab_para_p2 SET (parallel_workers = 0);
ANALYZE pagg_tab_para;
EXPLAIN (COSTS OFF)
SELECT x, sum(y), avg(y), sum(x+y), count(*) FROM pagg_tab_para GROUP BY x HAVING avg(y) < 7 ORDER BY 1, 2, 3;
QUERY PLAN
----------------------------------------------------------------------------------
Sort
Sort Key: pagg_tab_para.x, (sum(pagg_tab_para.y)), (avg(pagg_tab_para.y))
-> Finalize GroupAggregate
Group Key: pagg_tab_para.x
Filter: (avg(pagg_tab_para.y) < '7'::numeric)
-> Gather Merge
Workers Planned: 2
-> Sort
Sort Key: pagg_tab_para.x
-> Partial HashAggregate
Group Key: pagg_tab_para.x
-> Parallel Append
-> Seq Scan on pagg_tab_para_p1 pagg_tab_para_1
-> Seq Scan on pagg_tab_para_p2 pagg_tab_para_2
-> Seq Scan on pagg_tab_para_p3 pagg_tab_para_3
(15 rows)
SELECT x, sum(y), avg(y), sum(x+y), count(*) FROM pagg_tab_para GROUP BY x HAVING avg(y) < 7 ORDER BY 1, 2, 3;
x | sum | avg | sum | count
----+------+--------------------+-------+-------
0 | 5000 | 5.0000000000000000 | 5000 | 1000
1 | 6000 | 6.0000000000000000 | 7000 | 1000
10 | 5000 | 5.0000000000000000 | 15000 | 1000
11 | 6000 | 6.0000000000000000 | 17000 | 1000
20 | 5000 | 5.0000000000000000 | 25000 | 1000
21 | 6000 | 6.0000000000000000 | 27000 | 1000
(6 rows)
-- Reset parallelism parameters to get partitionwise aggregation plan.
RESET min_parallel_table_scan_size;
RESET parallel_setup_cost;
EXPLAIN (COSTS OFF)
SELECT x, sum(y), avg(y), count(*) FROM pagg_tab_para GROUP BY x HAVING avg(y) < 7 ORDER BY 1, 2, 3;
QUERY PLAN
-----------------------------------------------------------------------------
Sort
Sort Key: pagg_tab_para.x, (sum(pagg_tab_para.y)), (avg(pagg_tab_para.y))
-> Append
-> HashAggregate
Group Key: pagg_tab_para.x
Filter: (avg(pagg_tab_para.y) < '7'::numeric)
-> Seq Scan on pagg_tab_para_p1 pagg_tab_para
-> HashAggregate
Group Key: pagg_tab_para_1.x
Filter: (avg(pagg_tab_para_1.y) < '7'::numeric)
-> Seq Scan on pagg_tab_para_p2 pagg_tab_para_1
-> HashAggregate
Group Key: pagg_tab_para_2.x
Filter: (avg(pagg_tab_para_2.y) < '7'::numeric)
-> Seq Scan on pagg_tab_para_p3 pagg_tab_para_2
(15 rows)
SELECT x, sum(y), avg(y), count(*) FROM pagg_tab_para GROUP BY x HAVING avg(y) < 7 ORDER BY 1, 2, 3;
x | sum | avg | count
----+------+--------------------+-------
0 | 5000 | 5.0000000000000000 | 1000
1 | 6000 | 6.0000000000000000 | 1000
10 | 5000 | 5.0000000000000000 | 1000
11 | 6000 | 6.0000000000000000 | 1000
20 | 5000 | 5.0000000000000000 | 1000
21 | 6000 | 6.0000000000000000 | 1000
(6 rows)
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