v2-0001-Teach-Append-to-consider-tuple_fraction-when-accu.patch

From 4de8b1f9d8f22472c8811aad0f5853c436e79e4f Mon Sep 17 00:00:00 2001
From: "Andrei V. Lepikhov" <lepihov@gmail.com>
Date: Thu, 5 Dec 2024 15:15:49 +0700
Subject: [PATCH v2] Teach Append to consider tuple_fraction when accumulating
 subpaths.

This change is dedicated to more active usage of IndexScan and parameterised
NestLoop paths in partitioned cases under an Append node, as it already works
with plain tables. As newly added regression tests demonstrate, it should
provide more smartness to the partitionwise technique.

Having an indication of how many tuples are needed, it may be more meaningful
to use the 'fractional branch' subpaths of the Append path list, which are more
optimal for this specific number of tuples. Planning on a higher level, if
the optimiser needs all the tuples, it will choose non-fractional paths.
In the case when, during execution, Append needs to return fewer tuples than
declared by tuple_fraction, it would not be harmful to use the 'intermediate'
variant of paths. However, it will earn a considerable profit if a sensible set
of tuples is selected.

The change of the existing regression test demonstrates the positive outcome of
this feature - instead of scanning the whole table, the optimiser prefers
to use a parameterised scan, being aware of the only single tuple the join has
to produce to perform the query.

Discussion: https://www.postgresql.org/message-id/flat/CAN-LCVPxnWB39CUBTgOQ9O7Dd8DrA_tpT1EY3LNVnUuvAX1NjA%40mail.gmail.com
---
 src/backend/optimizer/path/allpaths.c        |  18 ++-
 src/backend/optimizer/plan/planner.c         |   7 ++
 src/test/regress/expected/partition_join.out | 116 +++++++++++++++++++
 src/test/regress/expected/union.out          |  15 ++-
 src/test/regress/sql/partition_join.sql      |  21 ++++
 5 files changed, 167 insertions(+), 10 deletions(-)

diff --git a/src/backend/optimizer/path/allpaths.c b/src/backend/optimizer/path/allpaths.c
index b5bc9b602e2..d01acaf0b14 100644
--- a/src/backend/optimizer/path/allpaths.c
+++ b/src/backend/optimizer/path/allpaths.c
@@ -1371,9 +1371,23 @@ add_paths_to_append_rel(PlannerInfo *root, RelOptInfo *rel,
 		 */
 		if (rel->consider_startup && childrel->cheapest_startup_path != NULL)
 		{
+			Path	   *cheapest_path;
+
+			/*
+			 * Having an indication on how much tuples the query should provide
+			 * the optimiser tries to choose the path, optimal for that specific
+			 * number of tuples.
+			 */
+			if (root->tuple_fraction > 0.0)
+				cheapest_path =
+					get_cheapest_fractional_path(childrel,
+													 root->tuple_fraction);
+			else
+				cheapest_path = childrel->cheapest_startup_path;
+
 			/* cheapest_startup_path must not be a parameterized path. */
-			Assert(childrel->cheapest_startup_path->param_info == NULL);
-			accumulate_append_subpath(childrel->cheapest_startup_path,
+			Assert(cheapest_path->param_info == NULL);
+			accumulate_append_subpath(cheapest_path,
 									  &startup_subpaths,
 									  NULL);
 		}
diff --git a/src/backend/optimizer/plan/planner.c b/src/backend/optimizer/plan/planner.c
index 36ee6dd43de..1a3f2670208 100644
--- a/src/backend/optimizer/plan/planner.c
+++ b/src/backend/optimizer/plan/planner.c
@@ -6414,6 +6414,10 @@ make_sort_input_target(PlannerInfo *root,
  *	  Find the cheapest path for retrieving a specified fraction of all
  *	  the tuples expected to be returned by the given relation.
  *
+ * Do not consider parameterized paths; the caller might need a path as an
+ * append subpath and could become limited by the treatment of similar
+ * parameterization of all the subpaths. See 5b7b551 for details.
+ *
  * We interpret tuple_fraction the same way as grouping_planner.
  *
  * We assume set_cheapest() has been run on the given rel.
@@ -6436,6 +6440,9 @@ get_cheapest_fractional_path(RelOptInfo *rel, double tuple_fraction)
 	{
 		Path	   *path = (Path *) lfirst(l);
 
+		if (path->param_info)
+			continue;
+
 		if (path == rel->cheapest_total_path ||
 			compare_fractional_path_costs(best_path, path, tuple_fraction) <= 0)
 			continue;
diff --git a/src/test/regress/expected/partition_join.out b/src/test/regress/expected/partition_join.out
index 34b963ce6fe..fb17eb37b43 100644
--- a/src/test/regress/expected/partition_join.out
+++ b/src/test/regress/expected/partition_join.out
@@ -5260,6 +5260,122 @@ SELECT x.id, y.id FROM fract_t x LEFT JOIN fract_t y USING (id) ORDER BY x.id DE
                      Index Cond: (id = x_2.id)
 (11 rows)
 
+--
+-- Test Append's fractional paths
+--
+CREATE INDEX pht1_c_idx ON pht1(c);
+-- SeqScan might be the best choice if we need one single tuple
+EXPLAIN (COSTS OFF) SELECT * FROM pht1 p1 JOIN pht1 p2 USING (c) LIMIT 1;
+                    QUERY PLAN                    
+--------------------------------------------------
+ Limit
+   ->  Append
+         ->  Nested Loop
+               Join Filter: (p1_1.c = p2_1.c)
+               ->  Seq Scan on pht1_p1 p1_1
+               ->  Materialize
+                     ->  Seq Scan on pht1_p1 p2_1
+         ->  Nested Loop
+               Join Filter: (p1_2.c = p2_2.c)
+               ->  Seq Scan on pht1_p2 p1_2
+               ->  Materialize
+                     ->  Seq Scan on pht1_p2 p2_2
+         ->  Nested Loop
+               Join Filter: (p1_3.c = p2_3.c)
+               ->  Seq Scan on pht1_p3 p1_3
+               ->  Materialize
+                     ->  Seq Scan on pht1_p3 p2_3
+(17 rows)
+
+-- Increase number of tuples requested and an IndexScan will be chosen
+EXPLAIN (COSTS OFF) SELECT * FROM pht1 p1 JOIN pht1 p2 USING (c) LIMIT 100;
+                               QUERY PLAN                               
+------------------------------------------------------------------------
+ Limit
+   ->  Append
+         ->  Nested Loop
+               ->  Seq Scan on pht1_p1 p1_1
+               ->  Memoize
+                     Cache Key: p1_1.c
+                     Cache Mode: logical
+                     ->  Index Scan using pht1_p1_c_idx on pht1_p1 p2_1
+                           Index Cond: (c = p1_1.c)
+         ->  Nested Loop
+               ->  Seq Scan on pht1_p2 p1_2
+               ->  Memoize
+                     Cache Key: p1_2.c
+                     Cache Mode: logical
+                     ->  Index Scan using pht1_p2_c_idx on pht1_p2 p2_2
+                           Index Cond: (c = p1_2.c)
+         ->  Nested Loop
+               ->  Seq Scan on pht1_p3 p1_3
+               ->  Memoize
+                     Cache Key: p1_3.c
+                     Cache Mode: logical
+                     ->  Index Scan using pht1_p3_c_idx on pht1_p3 p2_3
+                           Index Cond: (c = p1_3.c)
+(23 rows)
+
+-- If the almost all the data should be fetched - prefer SeqScan
+EXPLAIN (COSTS OFF) SELECT * FROM pht1 p1 JOIN pht1 p2 USING (c) LIMIT 1000;
+                    QUERY PLAN                    
+--------------------------------------------------
+ Limit
+   ->  Append
+         ->  Hash Join
+               Hash Cond: (p1_1.c = p2_1.c)
+               ->  Seq Scan on pht1_p1 p1_1
+               ->  Hash
+                     ->  Seq Scan on pht1_p1 p2_1
+         ->  Hash Join
+               Hash Cond: (p1_2.c = p2_2.c)
+               ->  Seq Scan on pht1_p2 p1_2
+               ->  Hash
+                     ->  Seq Scan on pht1_p2 p2_2
+         ->  Hash Join
+               Hash Cond: (p1_3.c = p2_3.c)
+               ->  Seq Scan on pht1_p3 p1_3
+               ->  Hash
+                     ->  Seq Scan on pht1_p3 p2_3
+(17 rows)
+
+SET max_parallel_workers_per_gather = 1;
+SET debug_parallel_query = on;
+-- Partial paths also should be smart enough to employ limits
+EXPLAIN (COSTS OFF) SELECT * FROM pht1 p1 JOIN pht1 p2 USING (c) LIMIT 100;
+                                  QUERY PLAN                                  
+------------------------------------------------------------------------------
+ Gather
+   Workers Planned: 1
+   Single Copy: true
+   ->  Limit
+         ->  Append
+               ->  Nested Loop
+                     ->  Seq Scan on pht1_p1 p1_1
+                     ->  Memoize
+                           Cache Key: p1_1.c
+                           Cache Mode: logical
+                           ->  Index Scan using pht1_p1_c_idx on pht1_p1 p2_1
+                                 Index Cond: (c = p1_1.c)
+               ->  Nested Loop
+                     ->  Seq Scan on pht1_p2 p1_2
+                     ->  Memoize
+                           Cache Key: p1_2.c
+                           Cache Mode: logical
+                           ->  Index Scan using pht1_p2_c_idx on pht1_p2 p2_2
+                                 Index Cond: (c = p1_2.c)
+               ->  Nested Loop
+                     ->  Seq Scan on pht1_p3 p1_3
+                     ->  Memoize
+                           Cache Key: p1_3.c
+                           Cache Mode: logical
+                           ->  Index Scan using pht1_p3_c_idx on pht1_p3 p2_3
+                                 Index Cond: (c = p1_3.c)
+(26 rows)
+
+RESET debug_parallel_query;
+-- Remove indexes from the partitioned table and its partitions
+DROP INDEX pht1_c_idx CASCADE;
 -- cleanup
 DROP TABLE fract_t;
 RESET max_parallel_workers_per_gather;
diff --git a/src/test/regress/expected/union.out b/src/test/regress/expected/union.out
index caa8fe70a05..96962817ed4 100644
--- a/src/test/regress/expected/union.out
+++ b/src/test/regress/expected/union.out
@@ -1472,18 +1472,17 @@ select t1.unique1 from tenk1 t1
 inner join tenk2 t2 on t1.tenthous = t2.tenthous and t2.thousand = 0
    union all
 (values(1)) limit 1;
-                       QUERY PLAN                       
---------------------------------------------------------
+                             QUERY PLAN                              
+---------------------------------------------------------------------
  Limit
    ->  Append
          ->  Nested Loop
-               Join Filter: (t1.tenthous = t2.tenthous)
-               ->  Seq Scan on tenk1 t1
-               ->  Materialize
-                     ->  Seq Scan on tenk2 t2
-                           Filter: (thousand = 0)
+               ->  Seq Scan on tenk2 t2
+                     Filter: (thousand = 0)
+               ->  Index Scan using tenk1_thous_tenthous on tenk1 t1
+                     Index Cond: (tenthous = t2.tenthous)
          ->  Result
-(9 rows)
+(8 rows)
 
 -- Ensure there is no problem if cheapest_startup_path is NULL
 explain (costs off)
diff --git a/src/test/regress/sql/partition_join.sql b/src/test/regress/sql/partition_join.sql
index 26b8e3d063f..515d94878ab 100644
--- a/src/test/regress/sql/partition_join.sql
+++ b/src/test/regress/sql/partition_join.sql
@@ -1225,6 +1225,27 @@ SELECT x.id, y.id FROM fract_t x LEFT JOIN fract_t y USING (id) ORDER BY x.id AS
 EXPLAIN (COSTS OFF)
 SELECT x.id, y.id FROM fract_t x LEFT JOIN fract_t y USING (id) ORDER BY x.id DESC LIMIT 10;
 
+--
+-- Test Append's fractional paths
+--
+
+CREATE INDEX pht1_c_idx ON pht1(c);
+-- SeqScan might be the best choice if we need one single tuple
+EXPLAIN (COSTS OFF) SELECT * FROM pht1 p1 JOIN pht1 p2 USING (c) LIMIT 1;
+-- Increase number of tuples requested and an IndexScan will be chosen
+EXPLAIN (COSTS OFF) SELECT * FROM pht1 p1 JOIN pht1 p2 USING (c) LIMIT 100;
+-- If the almost all the data should be fetched - prefer SeqScan
+EXPLAIN (COSTS OFF) SELECT * FROM pht1 p1 JOIN pht1 p2 USING (c) LIMIT 1000;
+
+SET max_parallel_workers_per_gather = 1;
+SET debug_parallel_query = on;
+-- Partial paths also should be smart enough to employ limits
+EXPLAIN (COSTS OFF) SELECT * FROM pht1 p1 JOIN pht1 p2 USING (c) LIMIT 100;
+RESET debug_parallel_query;
+
+-- Remove indexes from the partitioned table and its partitions
+DROP INDEX pht1_c_idx CASCADE;
+
 -- cleanup
 DROP TABLE fract_t;
 
-- 
2.48.1