Use extended stats for precise estimation of bucket size in hash join

Recognizing the real-life complexity where columns in the table often have
functional dependencies, PostgreSQL's estimation of the number of distinct
values over a set of columns can be underestimated (or much rarely,
overestimated) when dealing with multi-clause JOIN.  In the case of hash
join, it can end up with a small number of predicted hash  buckets and, as
a result, picking non-optimal merge join.

To improve the situation, we introduce one additional stage of bucket size
estimation - having two or more join clauses estimator lookup for extended
statistics and use it for multicolumn estimation.  Clauses are grouped into
lists, each containing expressions referencing the same relation.  The result
of the multicolumn estimation made over such a list is combined with others
according to the caller's logic.  Clauses that are not estimated are returned
to the caller for further estimation.

Discussion: https://postgr.es/m/52257607-57f6-850d-399a-ec33a654457b%40postgrespro.ru
Author: Andrei Lepikhov <[email protected]>
Reviewed-by: Andy Fan <[email protected]>
Reviewed-by: Tomas Vondra <[email protected]>
Reviewed-by: Alena Rybakina <[email protected]>
Reviewed-by: Alexander Korotkov <[email protected]>

Author: akorotkov

src/backend/optimizer/path/costsize.c

@@ -4339,9 +4339,19 @@ final_cost_hashjoin(PlannerInfo *root, HashPath *path,
 	}
 	else
 	{
+		List	   *otherclauses;
+
 		innerbucketsize = 1.0;
 		innermcvfreq = 1.0;
-		foreach(hcl, hashclauses)
+
+		/* At first, try to estimate bucket size using extended statistics. */
+		otherclauses = estimate_multivariate_bucketsize(root,
+														inner_path->parent,
+														hashclauses,
+														&innerbucketsize);
+
+		/* Pass through the remaining clauses */
+		foreach(hcl, otherclauses)
 		{
 			RestrictInfo *restrictinfo = lfirst_node(RestrictInfo, hcl);
 			Selectivity thisbucketsize;

src/backend/utils/adt/selfuncs.c

@@ -3765,6 +3765,183 @@ estimate_num_groups(PlannerInfo *root, List *groupExprs, double input_rows,
 	return numdistinct;
 }
 
+/*
+ * Try to estimate the bucket size of the hash join inner side when the join
+ * condition contains two or more clauses by employing extended statistics.
+ *
+ * The main idea of this approach is that the distinct value generated by
+ * multivariate estimation on two or more columns would provide less bucket size
+ * than estimation on one separate column.
+ *
+ * IMPORTANT: It is crucial to synchronize the approach of combining different
+ * estimations with the caller's method.
+ *
+ * Return a list of clauses that didn't fetch any extended statistics.
+ */
+List *
+estimate_multivariate_bucketsize(PlannerInfo *root, RelOptInfo *inner,
+								 List *hashclauses,
+								 Selectivity *innerbucketsize)
+{
+	List	   *clauses = list_copy(hashclauses);
+	List	   *otherclauses = NIL;
+	double		ndistinct = 1.0;
+
+	if (list_length(hashclauses) <= 1)
+
+		/*
+		 * Nothing to do for a single clause. Could we employ univariate
+		 * extended stat here?
+		 */
+		return hashclauses;
+
+	while (clauses != NIL)
+	{
+		ListCell   *lc;
+		int			relid = -1;
+		List	   *varinfos = NIL;
+		List	   *origin_rinfos = NIL;
+		double		mvndistinct;
+		List	   *origin_varinfos;
+		int			group_relid = -1;
+		RelOptInfo *group_rel = NULL;
+		ListCell   *lc1,
+				   *lc2;
+
+		/*
+		 * Find clauses, referencing the same single base relation and try to
+		 * estimate such a group with extended statistics.  Create varinfo for
+		 * an approved clause, push it to otherclauses, if it can't be
+		 * estimated here or ignore to process at the next iteration.
+		 */
+		foreach(lc, clauses)
+		{
+			RestrictInfo *rinfo = lfirst_node(RestrictInfo, lc);
+			Node	   *expr;
+			Relids		relids;
+			GroupVarInfo *varinfo;
+
+			/*
+			 * Find the inner side of the join, which we need to estimate the
+			 * number of buckets.  Use outer_is_left because the
+			 * clause_sides_match_join routine has called on hash clauses.
+			 */
+			relids = rinfo->outer_is_left ?
+				rinfo->right_relids : rinfo->left_relids;
+			expr = rinfo->outer_is_left ?
+				get_rightop(rinfo->clause) : get_leftop(rinfo->clause);
+
+			if (bms_get_singleton_member(relids, &relid) &&
+				root->simple_rel_array[relid]->statlist != NIL)
+			{
+				/*
+				 * This inner-side expression references only one relation.
+				 * Extended statistics on this clause can exist.
+				 */
+				if (group_relid < 0)
+				{
+					RangeTblEntry *rte = root->simple_rte_array[relid];
+
+					if (!rte || (rte->relkind != RELKIND_RELATION &&
+								 rte->relkind != RELKIND_MATVIEW &&
+								 rte->relkind != RELKIND_FOREIGN_TABLE &&
+								 rte->relkind != RELKIND_PARTITIONED_TABLE))
+					{
+						/* Extended statistics can't exist in principle */
+						otherclauses = lappend(otherclauses, rinfo);
+						clauses = foreach_delete_current(clauses, lc);
+						continue;
+					}
+
+					group_relid = relid;
+					group_rel = root->simple_rel_array[relid];
+				}
+				else if (group_relid != relid)
+
+					/*
+					 * Being in the group forming state we don't need other
+					 * clauses.
+					 */
+					continue;
+
+				varinfo = (GroupVarInfo *) palloc(sizeof(GroupVarInfo));
+				varinfo->var = expr;
+				varinfo->rel = root->simple_rel_array[relid];
+				varinfo->ndistinct = 0.0;
+				varinfo->isdefault = false;
+				varinfos = lappend(varinfos, varinfo);
+
+				/*
+				 * Remember the link to RestrictInfo for the case the clause
+				 * is failed to be estimated.
+				 */
+				origin_rinfos = lappend(origin_rinfos, rinfo);
+			}
+			else
+				/* This clause can't be estimated with extended statistics */
+				otherclauses = lappend(otherclauses, rinfo);
+
+			clauses = foreach_delete_current(clauses, lc);
+		}
+
+		if (list_length(varinfos) < 2)
+		{
+			/*
+			 * Multivariate statistics doesn't apply to single columns except
+			 * for expressions, but it has not been implemented yet.
+			 */
+			otherclauses = list_concat(otherclauses, origin_rinfos);
+			list_free_deep(varinfos);
+			list_free(origin_rinfos);
+			continue;
+		}
+
+		Assert(group_rel != NULL);
+
+		/* Let's employ extended statistics. */
+		origin_varinfos = varinfos;
+		for (;;)
+		{
+			bool		estimated = estimate_multivariate_ndistinct(root,
+																	group_rel,
+																	&varinfos,
+																	&mvndistinct);
+
+			if (!estimated)
+				break;
+
+			/*
+			 * We've got an estimation. Use ndistinct value in a consistent
+			 * way - according to the caller's logic (see
+			 * final_cost_hashjoin).
+			 */
+			if (ndistinct < mvndistinct)
+				ndistinct = mvndistinct;
+			Assert(ndistinct >= 1.0);
+		}
+
+		Assert(list_length(origin_varinfos) == list_length(origin_rinfos));
+
+		/*
+		 * Remove already estimated clauses from the origin_rinfos
+		 */
+		forboth(lc1, origin_varinfos, lc2, origin_rinfos)
+		{
+			GroupVarInfo *vinfo = lfirst(lc1);
+
+			if (!list_member_ptr(varinfos, vinfo))
+				/* Already estimated */
+				continue;
+
+			/* Can't be estimated here - push to the returning list */
+			otherclauses = lappend(otherclauses, lfirst(lc2));
+		}
+	}
+
+	*innerbucketsize = 1.0 / ndistinct;
+	return otherclauses;
+}
+
 /*
  * Estimate hash bucket statistics when the specified expression is used
  * as a hash key for the given number of buckets.

src/include/utils/selfuncs.h

@@ -218,6 +218,10 @@ extern double estimate_num_groups(PlannerInfo *root, List *groupExprs,
 								  double input_rows, List **pgset,
 								  EstimationInfo *estinfo);
 
+extern List *estimate_multivariate_bucketsize(PlannerInfo *root,
+											  RelOptInfo *inner,
+											  List *hashclauses,
+											  Selectivity *innerbucketsize);
 extern void estimate_hash_bucket_stats(PlannerInfo *root,
 									   Node *hashkey, double nbuckets,
 									   Selectivity *mcv_freq,

src/test/regress/expected/stats_ext.out

@@ -3383,3 +3383,48 @@ SELECT * FROM check_estimated_rows('
 (1 row)
 
 DROP TABLE grouping_unique;
+--
+-- Extended statistics on sb_2 (x, y, z) improve a bucket size estimation,
+-- and the optimizer may choose hash join.
+--
+CREATE TABLE sb_1 AS
+  SELECT gs % 10 AS x, gs % 10 AS y, gs % 10 AS z
+  FROM generate_series(1, 1e4) AS gs;
+CREATE TABLE sb_2 AS
+  SELECT gs % 49 AS x, gs % 51 AS y, gs % 73 AS z, 'abc' || gs AS payload
+  FROM generate_series(1, 1e4) AS gs;
+ANALYZE sb_1, sb_2;
+-- During hash join estimation, the number of distinct values on each column
+-- is calculated. The optimizer selects the smallest number of distinct values
+-- and the largest hash bucket size. The optimizer decides that the hash
+-- bucket size is quite big because there are possibly many correlations.
+EXPLAIN (COSTS OFF) -- Choose merge join
+SELECT * FROM sb_1 a, sb_2 b WHERE a.x = b.x AND a.y = b.y AND a.z = b.z;
+                         QUERY PLAN                          
+-------------------------------------------------------------
+ Merge Join
+   Merge Cond: ((a.z = b.z) AND (a.x = b.x) AND (a.y = b.y))
+   ->  Sort
+         Sort Key: a.z, a.x, a.y
+         ->  Seq Scan on sb_1 a
+   ->  Sort
+         Sort Key: b.z, b.x, b.y
+         ->  Seq Scan on sb_2 b
+(8 rows)
+
+-- The ndistinct extended statistics on (x, y, z) provides more reliable value
+-- of bucket size.
+CREATE STATISTICS extstat_sb_2 (ndistinct) ON x, y, z FROM sb_2;
+ANALYZE sb_2;
+EXPLAIN (COSTS OFF) -- Choose hash join
+SELECT * FROM sb_1 a, sb_2 b WHERE a.x = b.x AND a.y = b.y AND a.z = b.z;
+                         QUERY PLAN                         
+------------------------------------------------------------
+ Hash Join
+   Hash Cond: ((a.x = b.x) AND (a.y = b.y) AND (a.z = b.z))
+   ->  Seq Scan on sb_1 a
+   ->  Hash
+         ->  Seq Scan on sb_2 b
+(5 rows)
+
+DROP TABLE sb_1, sb_2 CASCADE;

src/test/regress/sql/stats_ext.sql

@@ -1719,3 +1719,32 @@ SELECT * FROM check_estimated_rows('
   ON t1.t1 = q1.x;
 ');
 DROP TABLE grouping_unique;
+
+--
+-- Extended statistics on sb_2 (x, y, z) improve a bucket size estimation,
+-- and the optimizer may choose hash join.
+--
+CREATE TABLE sb_1 AS
+  SELECT gs % 10 AS x, gs % 10 AS y, gs % 10 AS z
+  FROM generate_series(1, 1e4) AS gs;
+CREATE TABLE sb_2 AS
+  SELECT gs % 49 AS x, gs % 51 AS y, gs % 73 AS z, 'abc' || gs AS payload
+  FROM generate_series(1, 1e4) AS gs;
+ANALYZE sb_1, sb_2;
+
+-- During hash join estimation, the number of distinct values on each column
+-- is calculated. The optimizer selects the smallest number of distinct values
+-- and the largest hash bucket size. The optimizer decides that the hash
+-- bucket size is quite big because there are possibly many correlations.
+EXPLAIN (COSTS OFF) -- Choose merge join
+SELECT * FROM sb_1 a, sb_2 b WHERE a.x = b.x AND a.y = b.y AND a.z = b.z;
+
+-- The ndistinct extended statistics on (x, y, z) provides more reliable value
+-- of bucket size.
+CREATE STATISTICS extstat_sb_2 (ndistinct) ON x, y, z FROM sb_2;
+ANALYZE sb_2;
+
+EXPLAIN (COSTS OFF) -- Choose hash join
+SELECT * FROM sb_1 a, sb_2 b WHERE a.x = b.x AND a.y = b.y AND a.z = b.z;
+
+DROP TABLE sb_1, sb_2 CASCADE;