Improve selectivity estimation for assorted match-style operators.

Quite a few matching operators such as JSONB's @> used "contsel" and
"contjoinsel" as their selectivity estimators.  That was a bad idea,
because (a) contsel is only a stub, yielding a fixed default estimate,
and (b) that default is 0.001, meaning we estimate these operators as
five times more selective than equality, which is surely pretty silly.

There's a good model for improving this in ltree's ltreeparentsel():
for any "var OP constant" query, we can try applying the operator
to all of the column's MCV and histogram values, taking the latter
as being a random sample of the non-MCV values.  That code is
actually 100% generic, except for the question of exactly what
default selectivity ought to be plugged in when we don't have stats.

Hence, migrate the guts of ltreeparentsel() into the core code, provide
wrappers "matchingsel" and "matchingjoinsel" with a more-appropriate
default estimate, and use those for the non-geometric operators that
formerly used contsel (mostly JSONB containment operators and tsquery
matching).

Also apply this code to some match-like operators in hstore, ltree, and
pg_trgm, including the former users of ltreeparentsel as well as ones
that improperly used contsel.  Since commit 911e70207 just created new
versions of those extensions that we haven't released yet, we can sneak
this change into those new versions instead of having to create an
additional generation of update scripts.

Patch by me, reviewed by Alexey Bashtanov

Discussion: https://postgr.es/m/12237.1582833074@sss.pgh.pa.us

1 files changed, 160 insertions, 0 deletions

diff --git a/src/backend/utils/adt/selfuncs.c b/src/backend/utils/adt/selfuncs.c
index e62b69d6f2..4fdcb07d97 100644
--- a/src/backend/utils/adt/selfuncs.c
+++ b/src/backend/utils/adt/selfuncs.c
@@ -830,6 +830,132 @@ histogram_selectivity(VariableStatData *vardata, FmgrInfo *opproc,
 }
 
 /*
+ *	generic_restriction_selectivity		- Selectivity for almost anything
+ *
+ * This function estimates selectivity for operators that we don't have any
+ * special knowledge about, but are on data types that we collect standard
+ * MCV and/or histogram statistics for.  (Additional assumptions are that
+ * the operator is strict and immutable, or at least stable.)
+ *
+ * If we have "VAR OP CONST" or "CONST OP VAR", selectivity is estimated by
+ * applying the operator to each element of the column's MCV and/or histogram
+ * stats, and merging the results using the assumption that the histogram is
+ * a reasonable random sample of the column's non-MCV population.  Note that
+ * if the operator's semantics are related to the histogram ordering, this
+ * might not be such a great assumption; other functions such as
+ * scalarineqsel() are probably a better match in such cases.
+ *
+ * Otherwise, fall back to the default selectivity provided by the caller.
+ */
+double
+generic_restriction_selectivity(PlannerInfo *root, Oid operator,
+								List *args, int varRelid,
+								double default_selectivity)
+{
+	double		selec;
+	VariableStatData vardata;
+	Node	   *other;
+	bool		varonleft;
+
+	/*
+	 * If expression is not variable OP something or something OP variable,
+	 * then punt and return the default estimate.
+	 */
+	if (!get_restriction_variable(root, args, varRelid,
+								  &vardata, &other, &varonleft))
+		return default_selectivity;
+
+	/*
+	 * If the something is a NULL constant, assume operator is strict and
+	 * return zero, ie, operator will never return TRUE.
+	 */
+	if (IsA(other, Const) &&
+		((Const *) other)->constisnull)
+	{
+		ReleaseVariableStats(vardata);
+		return 0.0;
+	}
+
+	if (IsA(other, Const))
+	{
+		/* Variable is being compared to a known non-null constant */
+		Datum		constval = ((Const *) other)->constvalue;
+		FmgrInfo	opproc;
+		double		mcvsum;
+		double		mcvsel;
+		double		nullfrac;
+		int			hist_size;
+
+		fmgr_info(get_opcode(operator), &opproc);
+
+		/*
+		 * Calculate the selectivity for the column's most common values.
+		 */
+		mcvsel = mcv_selectivity(&vardata, &opproc, constval, varonleft,
+								 &mcvsum);
+
+		/*
+		 * If the histogram is large enough, see what fraction of it matches
+		 * the query, and assume that's representative of the non-MCV
+		 * population.  Otherwise use the default selectivity for the non-MCV
+		 * population.
+		 */
+		selec = histogram_selectivity(&vardata, &opproc,
+									  constval, varonleft,
+									  10, 1, &hist_size);
+		if (selec < 0)
+		{
+			/* Nope, fall back on default */
+			selec = default_selectivity;
+		}
+		else if (hist_size < 100)
+		{
+			/*
+			 * For histogram sizes from 10 to 100, we combine the histogram
+			 * and default selectivities, putting increasingly more trust in
+			 * the histogram for larger sizes.
+			 */
+			double		hist_weight = hist_size / 100.0;
+
+			selec = selec * hist_weight +
+				default_selectivity * (1.0 - hist_weight);
+		}
+
+		/* In any case, don't believe extremely small or large estimates. */
+		if (selec < 0.0001)
+			selec = 0.0001;
+		else if (selec > 0.9999)
+			selec = 0.9999;
+
+		/* Don't forget to account for nulls. */
+		if (HeapTupleIsValid(vardata.statsTuple))
+			nullfrac = ((Form_pg_statistic) GETSTRUCT(vardata.statsTuple))->stanullfrac;
+		else
+			nullfrac = 0.0;
+
+		/*
+		 * Now merge the results from the MCV and histogram calculations,
+		 * realizing that the histogram covers only the non-null values that
+		 * are not listed in MCV.
+		 */
+		selec *= 1.0 - nullfrac - mcvsum;
+		selec += mcvsel;
+	}
+	else
+	{
+		/* Comparison value is not constant, so we can't do anything */
+		selec = default_selectivity;
+	}
+
+	ReleaseVariableStats(vardata);
+
+	/* result should be in range, but make sure... */
+	CLAMP_PROBABILITY(selec);
+
+	return selec;
+}
+
+/*
  *	ineq_histogram_selectivity	- Examine the histogram for scalarineqsel
  *
  * Determine the fraction of the variable's histogram population that
@@ -2917,6 +3043,40 @@ fail:
 
 
 /*
+ *	matchingsel -- generic matching-operator selectivity support
+ *
+ * Use these for any operators that (a) are on data types for which we collect
+ * standard statistics, and (b) have behavior for which the default estimate
+ * (twice DEFAULT_EQ_SEL) is sane.  Typically that is good for match-like
+ * operators.
+ */
+
+Datum
+matchingsel(PG_FUNCTION_ARGS)
+{
+	PlannerInfo *root = (PlannerInfo *) PG_GETARG_POINTER(0);
+	Oid			operator = PG_GETARG_OID(1);
+	List	   *args = (List *) PG_GETARG_POINTER(2);
+	int			varRelid = PG_GETARG_INT32(3);
+	double		selec;
+
+	/* Use generic restriction selectivity logic. */
+	selec = generic_restriction_selectivity(root, operator,
+											args, varRelid,
+											DEFAULT_MATCHING_SEL);
+
+	PG_RETURN_FLOAT8((float8) selec);
+}
+
+Datum
+matchingjoinsel(PG_FUNCTION_ARGS)
+{
+	/* Just punt, for the moment. */
+	PG_RETURN_FLOAT8(DEFAULT_MATCHING_SEL);
+}
+
+
+/*
  * Helper routine for estimate_num_groups: add an item to a list of
  * GroupVarInfos, but only if it's not known equal to any of the existing
  * entries.