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

-- Perform tests on the Memoize node.
-- The cache hits/misses/evictions from the Memoize node can vary between
-- machines.  Let's just replace the number with an 'N'.  In order to allow us
-- to perform validation when the measure was zero, we replace a zero value
-- with "Zero".  All other numbers are replaced with 'N'.
create function explain_memoize(query text, hide_hitmiss bool) returns setof text
language plpgsql as
$$
declare
    ln text;
begin
    for ln in
        execute format('explain (analyze, costs off, summary off, timing off) %s',
            query)
    loop
        if hide_hitmiss = true then
                ln := regexp_replace(ln, 'Hits: 0', 'Hits: Zero');
                ln := regexp_replace(ln, 'Hits: \d+', 'Hits: N');
                ln := regexp_replace(ln, 'Misses: 0', 'Misses: Zero');
                ln := regexp_replace(ln, 'Misses: \d+', 'Misses: N');
        end if;
        ln := regexp_replace(ln, 'Evictions: 0', 'Evictions: Zero');
        ln := regexp_replace(ln, 'Evictions: \d+', 'Evictions: N');
        ln := regexp_replace(ln, 'Memory Usage: \d+', 'Memory Usage: N');
	ln := regexp_replace(ln, 'Heap Fetches: \d+', 'Heap Fetches: N');
	ln := regexp_replace(ln, 'loops=\d+', 'loops=N');
        return next ln;
    end loop;
end;
$$;
-- Ensure we get a memoize node on the inner side of the nested loop
SET enable_hashjoin TO off;
SET enable_bitmapscan TO off;
SELECT explain_memoize('
SELECT COUNT(*),AVG(t1.unique1) FROM tenk1 t1
INNER JOIN tenk1 t2 ON t1.unique1 = t2.twenty
WHERE t2.unique1 < 1000;', false);
                                      explain_memoize                                      
-------------------------------------------------------------------------------------------
 Aggregate (actual rows=1 loops=N)
   ->  Nested Loop (actual rows=1000 loops=N)
         ->  Seq Scan on tenk1 t2 (actual rows=1000 loops=N)
               Filter: (unique1 < 1000)
               Rows Removed by Filter: 9000
         ->  Memoize (actual rows=1 loops=N)
               Cache Key: t2.twenty
               Cache Mode: logical
               Hits: 980  Misses: 20  Evictions: Zero  Overflows: 0  Memory Usage: NkB
               ->  Index Only Scan using tenk1_unique1 on tenk1 t1 (actual rows=1 loops=N)
                     Index Cond: (unique1 = t2.twenty)
                     Heap Fetches: N
(12 rows)

-- And check we get the expected results.
SELECT COUNT(*),AVG(t1.unique1) FROM tenk1 t1
INNER JOIN tenk1 t2 ON t1.unique1 = t2.twenty
WHERE t2.unique1 < 1000;
 count |        avg         
-------+--------------------
  1000 | 9.5000000000000000
(1 row)

-- Try with LATERAL joins
SELECT explain_memoize('
SELECT COUNT(*),AVG(t2.unique1) FROM tenk1 t1,
LATERAL (SELECT t2.unique1 FROM tenk1 t2
         WHERE t1.twenty = t2.unique1 OFFSET 0) t2
WHERE t1.unique1 < 1000;', false);
                                      explain_memoize                                      
-------------------------------------------------------------------------------------------
 Aggregate (actual rows=1 loops=N)
   ->  Nested Loop (actual rows=1000 loops=N)
         ->  Seq Scan on tenk1 t1 (actual rows=1000 loops=N)
               Filter: (unique1 < 1000)
               Rows Removed by Filter: 9000
         ->  Memoize (actual rows=1 loops=N)
               Cache Key: t1.twenty
               Cache Mode: binary
               Hits: 980  Misses: 20  Evictions: Zero  Overflows: 0  Memory Usage: NkB
               ->  Index Only Scan using tenk1_unique1 on tenk1 t2 (actual rows=1 loops=N)
                     Index Cond: (unique1 = t1.twenty)
                     Heap Fetches: N
(12 rows)

-- And check we get the expected results.
SELECT COUNT(*),AVG(t2.unique1) FROM tenk1 t1,
LATERAL (SELECT t2.unique1 FROM tenk1 t2
         WHERE t1.twenty = t2.unique1 OFFSET 0) t2
WHERE t1.unique1 < 1000;
 count |        avg         
-------+--------------------
  1000 | 9.5000000000000000
(1 row)

-- Try with LATERAL joins
SELECT explain_memoize('
SELECT COUNT(*),AVG(t2.t1two) FROM tenk1 t1 LEFT JOIN
LATERAL (
    SELECT t1.two as t1two, * FROM tenk1 t2 WHERE t2.unique1 < 4 OFFSET 0
) t2
ON t1.two = t2.two
WHERE t1.unique1 < 10;', false);
                                       explain_memoize                                        
----------------------------------------------------------------------------------------------
 Aggregate (actual rows=1 loops=N)
   ->  Nested Loop Left Join (actual rows=20 loops=N)
         ->  Index Scan using tenk1_unique1 on tenk1 t1 (actual rows=10 loops=N)
               Index Cond: (unique1 < 10)
         ->  Memoize (actual rows=2 loops=N)
               Cache Key: t1.two
               Cache Mode: binary
               Hits: 8  Misses: 2  Evictions: Zero  Overflows: 0  Memory Usage: NkB
               ->  Subquery Scan on t2 (actual rows=2 loops=N)
                     Filter: (t1.two = t2.two)
                     Rows Removed by Filter: 2
                     ->  Index Scan using tenk1_unique1 on tenk1 t2_1 (actual rows=4 loops=N)
                           Index Cond: (unique1 < 4)
(13 rows)

-- And check we get the expected results.
SELECT COUNT(*),AVG(t2.t1two) FROM tenk1 t1 LEFT JOIN
LATERAL (
    SELECT t1.two as t1two, * FROM tenk1 t2 WHERE t2.unique1 < 4 OFFSET 0
) t2
ON t1.two = t2.two
WHERE t1.unique1 < 10;
 count |          avg           
-------+------------------------
    20 | 0.50000000000000000000
(1 row)

SET enable_mergejoin TO off;
-- Test for varlena datatype with expr evaluation
CREATE TABLE expr_key (x numeric, t text);
INSERT INTO expr_key (x, t)
SELECT d1::numeric, d1::text FROM (
    SELECT round((d / pi())::numeric, 7) AS d1 FROM generate_series(1, 20) AS d
) t;
-- duplicate rows so we get some cache hits
INSERT INTO expr_key SELECT * FROM expr_key;
CREATE INDEX expr_key_idx_x_t ON expr_key (x, t);
VACUUM ANALYZE expr_key;
-- Ensure we get we get a cache miss and hit for each of the 20 distinct values
SELECT explain_memoize('
SELECT * FROM expr_key t1 INNER JOIN expr_key t2
ON t1.x = t2.t::numeric AND t1.t::numeric = t2.x;', false);
                                      explain_memoize                                      
-------------------------------------------------------------------------------------------
 Nested Loop (actual rows=80 loops=N)
   ->  Seq Scan on expr_key t1 (actual rows=40 loops=N)
   ->  Memoize (actual rows=2 loops=N)
         Cache Key: t1.x, (t1.t)::numeric
         Cache Mode: logical
         Hits: 20  Misses: 20  Evictions: Zero  Overflows: 0  Memory Usage: NkB
         ->  Index Only Scan using expr_key_idx_x_t on expr_key t2 (actual rows=2 loops=N)
               Index Cond: (x = (t1.t)::numeric)
               Filter: (t1.x = (t)::numeric)
               Heap Fetches: N
(10 rows)

DROP TABLE expr_key;
-- Reduce work_mem and hash_mem_multiplier so that we see some cache evictions
SET work_mem TO '64kB';
SET hash_mem_multiplier TO 1.0;
-- Ensure we get some evictions.  We're unable to validate the hits and misses
-- here as the number of entries that fit in the cache at once will vary
-- between different machines.
SELECT explain_memoize('
SELECT COUNT(*),AVG(t1.unique1) FROM tenk1 t1
INNER JOIN tenk1 t2 ON t1.unique1 = t2.thousand
WHERE t2.unique1 < 1200;', true);
                                      explain_memoize                                      
-------------------------------------------------------------------------------------------
 Aggregate (actual rows=1 loops=N)
   ->  Nested Loop (actual rows=1200 loops=N)
         ->  Seq Scan on tenk1 t2 (actual rows=1200 loops=N)
               Filter: (unique1 < 1200)
               Rows Removed by Filter: 8800
         ->  Memoize (actual rows=1 loops=N)
               Cache Key: t2.thousand
               Cache Mode: logical
               Hits: N  Misses: N  Evictions: N  Overflows: 0  Memory Usage: NkB
               ->  Index Only Scan using tenk1_unique1 on tenk1 t1 (actual rows=1 loops=N)
                     Index Cond: (unique1 = t2.thousand)
                     Heap Fetches: N
(12 rows)

CREATE TABLE flt (f float);
CREATE INDEX flt_f_idx ON flt (f);
INSERT INTO flt VALUES('-0.0'::float),('+0.0'::float);
ANALYZE flt;
SET enable_seqscan TO off;
-- Ensure memoize operates in logical mode
SELECT explain_memoize('
SELECT * FROM flt f1 INNER JOIN flt f2 ON f1.f = f2.f;', false);
                                explain_memoize                                
-------------------------------------------------------------------------------
 Nested Loop (actual rows=4 loops=N)
   ->  Index Only Scan using flt_f_idx on flt f1 (actual rows=2 loops=N)
         Heap Fetches: N
   ->  Memoize (actual rows=2 loops=N)
         Cache Key: f1.f
         Cache Mode: logical
         Hits: 1  Misses: 1  Evictions: Zero  Overflows: 0  Memory Usage: NkB
         ->  Index Only Scan using flt_f_idx on flt f2 (actual rows=2 loops=N)
               Index Cond: (f = f1.f)
               Heap Fetches: N
(10 rows)

-- Ensure memoize operates in binary mode
SELECT explain_memoize('
SELECT * FROM flt f1 INNER JOIN flt f2 ON f1.f >= f2.f;', false);
                                explain_memoize                                
-------------------------------------------------------------------------------
 Nested Loop (actual rows=4 loops=N)
   ->  Index Only Scan using flt_f_idx on flt f1 (actual rows=2 loops=N)
         Heap Fetches: N
   ->  Memoize (actual rows=2 loops=N)
         Cache Key: f1.f
         Cache Mode: binary
         Hits: 0  Misses: 2  Evictions: Zero  Overflows: 0  Memory Usage: NkB
         ->  Index Only Scan using flt_f_idx on flt f2 (actual rows=2 loops=N)
               Index Cond: (f <= f1.f)
               Heap Fetches: N
(10 rows)

DROP TABLE flt;
-- Exercise Memoize in binary mode with a large fixed width type and a
-- varlena type.
CREATE TABLE strtest (n name, t text);
CREATE INDEX strtest_n_idx ON strtest (n);
CREATE INDEX strtest_t_idx ON strtest (t);
INSERT INTO strtest VALUES('one','one'),('two','two'),('three',repeat(fipshash('three'),100));
-- duplicate rows so we get some cache hits
INSERT INTO strtest SELECT * FROM strtest;
ANALYZE strtest;
-- Ensure we get 3 hits and 3 misses
SELECT explain_memoize('
SELECT * FROM strtest s1 INNER JOIN strtest s2 ON s1.n >= s2.n;', false);
                                 explain_memoize                                  
----------------------------------------------------------------------------------
 Nested Loop (actual rows=24 loops=N)
   ->  Seq Scan on strtest s1 (actual rows=6 loops=N)
   ->  Memoize (actual rows=4 loops=N)
         Cache Key: s1.n
         Cache Mode: binary
         Hits: 3  Misses: 3  Evictions: Zero  Overflows: 0  Memory Usage: NkB
         ->  Index Scan using strtest_n_idx on strtest s2 (actual rows=4 loops=N)
               Index Cond: (n <= s1.n)
(8 rows)

-- Ensure we get 3 hits and 3 misses
SELECT explain_memoize('
SELECT * FROM strtest s1 INNER JOIN strtest s2 ON s1.t >= s2.t;', false);
                                 explain_memoize                                  
----------------------------------------------------------------------------------
 Nested Loop (actual rows=24 loops=N)
   ->  Seq Scan on strtest s1 (actual rows=6 loops=N)
   ->  Memoize (actual rows=4 loops=N)
         Cache Key: s1.t
         Cache Mode: binary
         Hits: 3  Misses: 3  Evictions: Zero  Overflows: 0  Memory Usage: NkB
         ->  Index Scan using strtest_t_idx on strtest s2 (actual rows=4 loops=N)
               Index Cond: (t <= s1.t)
(8 rows)

DROP TABLE strtest;
-- Ensure memoize works with partitionwise join
SET enable_partitionwise_join TO on;
CREATE TABLE prt (a int) PARTITION BY RANGE(a);
CREATE TABLE prt_p1 PARTITION OF prt FOR VALUES FROM (0) TO (10);
CREATE TABLE prt_p2 PARTITION OF prt FOR VALUES FROM (10) TO (20);
INSERT INTO prt VALUES (0), (0), (0), (0);
INSERT INTO prt VALUES (10), (10), (10), (10);
CREATE INDEX iprt_p1_a ON prt_p1 (a);
CREATE INDEX iprt_p2_a ON prt_p2 (a);
ANALYZE prt;
SELECT explain_memoize('
SELECT * FROM prt t1 INNER JOIN prt t2 ON t1.a = t2.a;', false);
                                     explain_memoize                                      
------------------------------------------------------------------------------------------
 Append (actual rows=32 loops=N)
   ->  Nested Loop (actual rows=16 loops=N)
         ->  Index Only Scan using iprt_p1_a on prt_p1 t1_1 (actual rows=4 loops=N)
               Heap Fetches: N
         ->  Memoize (actual rows=4 loops=N)
               Cache Key: t1_1.a
               Cache Mode: logical
               Hits: 3  Misses: 1  Evictions: Zero  Overflows: 0  Memory Usage: NkB
               ->  Index Only Scan using iprt_p1_a on prt_p1 t2_1 (actual rows=4 loops=N)
                     Index Cond: (a = t1_1.a)
                     Heap Fetches: N
   ->  Nested Loop (actual rows=16 loops=N)
         ->  Index Only Scan using iprt_p2_a on prt_p2 t1_2 (actual rows=4 loops=N)
               Heap Fetches: N
         ->  Memoize (actual rows=4 loops=N)
               Cache Key: t1_2.a
               Cache Mode: logical
               Hits: 3  Misses: 1  Evictions: Zero  Overflows: 0  Memory Usage: NkB
               ->  Index Only Scan using iprt_p2_a on prt_p2 t2_2 (actual rows=4 loops=N)
                     Index Cond: (a = t1_2.a)
                     Heap Fetches: N
(21 rows)

-- Ensure memoize works with parameterized union-all Append path
SET enable_partitionwise_join TO off;
SELECT explain_memoize('
SELECT * FROM prt_p1 t1 INNER JOIN
(SELECT * FROM prt_p1 UNION ALL SELECT * FROM prt_p2) t2
ON t1.a = t2.a;', false);
                                   explain_memoize                                   
-------------------------------------------------------------------------------------
 Nested Loop (actual rows=16 loops=N)
   ->  Index Only Scan using iprt_p1_a on prt_p1 t1 (actual rows=4 loops=N)
         Heap Fetches: N
   ->  Memoize (actual rows=4 loops=N)
         Cache Key: t1.a
         Cache Mode: logical
         Hits: 3  Misses: 1  Evictions: Zero  Overflows: 0  Memory Usage: NkB
         ->  Append (actual rows=4 loops=N)
               ->  Index Only Scan using iprt_p1_a on prt_p1 (actual rows=4 loops=N)
                     Index Cond: (a = t1.a)
                     Heap Fetches: N
               ->  Index Only Scan using iprt_p2_a on prt_p2 (actual rows=0 loops=N)
                     Index Cond: (a = t1.a)
                     Heap Fetches: N
(14 rows)

DROP TABLE prt;
RESET enable_partitionwise_join;
-- Exercise Memoize code that flushes the cache when a parameter changes which
-- is not part of the cache key.
-- Ensure we get a Memoize plan
EXPLAIN (COSTS OFF)
SELECT unique1 FROM tenk1 t0
WHERE unique1 < 3
  AND EXISTS (
	SELECT 1 FROM tenk1 t1
	INNER JOIN tenk1 t2 ON t1.unique1 = t2.hundred
	WHERE t0.ten = t1.twenty AND t0.two <> t2.four OFFSET 0);
                           QUERY PLAN                           
----------------------------------------------------------------
 Index Scan using tenk1_unique1 on tenk1 t0
   Index Cond: (unique1 < 3)
   Filter: EXISTS(SubPlan 1)
   SubPlan 1
     ->  Nested Loop
           ->  Index Scan using tenk1_hundred on tenk1 t2
                 Filter: (t0.two <> four)
           ->  Memoize
                 Cache Key: t2.hundred
                 Cache Mode: logical
                 ->  Index Scan using tenk1_unique1 on tenk1 t1
                       Index Cond: (unique1 = t2.hundred)
                       Filter: (t0.ten = twenty)
(13 rows)

-- Ensure the above query returns the correct result
SELECT unique1 FROM tenk1 t0
WHERE unique1 < 3
  AND EXISTS (
	SELECT 1 FROM tenk1 t1
	INNER JOIN tenk1 t2 ON t1.unique1 = t2.hundred
	WHERE t0.ten = t1.twenty AND t0.two <> t2.four OFFSET 0);
 unique1 
---------
       2
(1 row)

RESET enable_seqscan;
RESET enable_mergejoin;
RESET work_mem;
RESET hash_mem_multiplier;
RESET enable_bitmapscan;
RESET enable_hashjoin;
-- Test parallel plans with Memoize
SET min_parallel_table_scan_size TO 0;
SET parallel_setup_cost TO 0;
SET parallel_tuple_cost TO 0;
SET max_parallel_workers_per_gather TO 2;
-- Ensure we get a parallel plan.
EXPLAIN (COSTS OFF)
SELECT COUNT(*),AVG(t2.unique1) FROM tenk1 t1,
LATERAL (SELECT t2.unique1 FROM tenk1 t2 WHERE t1.twenty = t2.unique1) t2
WHERE t1.unique1 < 1000;
                                  QUERY PLAN                                   
-------------------------------------------------------------------------------
 Finalize Aggregate
   ->  Gather
         Workers Planned: 2
         ->  Partial Aggregate
               ->  Nested Loop
                     ->  Parallel Bitmap Heap Scan on tenk1 t1
                           Recheck Cond: (unique1 < 1000)
                           ->  Bitmap Index Scan on tenk1_unique1
                                 Index Cond: (unique1 < 1000)
                     ->  Memoize
                           Cache Key: t1.twenty
                           Cache Mode: logical
                           ->  Index Only Scan using tenk1_unique1 on tenk1 t2
                                 Index Cond: (unique1 = t1.twenty)
(14 rows)

-- And ensure the parallel plan gives us the correct results.
SELECT COUNT(*),AVG(t2.unique1) FROM tenk1 t1,
LATERAL (SELECT t2.unique1 FROM tenk1 t2 WHERE t1.twenty = t2.unique1) t2
WHERE t1.unique1 < 1000;
 count |        avg         
-------+--------------------
  1000 | 9.5000000000000000
(1 row)

RESET max_parallel_workers_per_gather;
RESET parallel_tuple_cost;
RESET parallel_setup_cost;
RESET min_parallel_table_scan_size;