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Apply multiple multivariate MCV lists when possible

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Until now we've only used a single multivariate MCV list per relation,
covering the largest number of clauses. So for example given a query

    SELECT * FROM t WHERE a = 1 AND b =1 AND c = 1 AND d = 1

and extended statistics on (a,b) and (c,d), we'd only pick and use one
of them. This commit improves this by repeatedly picking and applying
the best statistics (matching the largest number of remaining clauses)
until no additional statistics is applicable.

This greedy algorithm is simple, but may not be optimal. A different
choice of statistics may leave fewer clauses unestimated and/or give
better estimates for some other reason.

This can however happen only when there are overlapping statistics, and
selecting one makes it impossible to use the other. E.g. with statistics
on (a,b), (c,d), (b,c,d), we may pick either (a,b) and (c,d) or (b,c,d).
But it's not clear which option is the best one.

We however assume cases like this are rare, and the easiest solution is
to define statistics covering the whole group of correlated columns. In
the future we might support overlapping stats, using some of the clauses
as conditions (in conditional probability sense).

Author: Tomas Vondra
Reviewed-by: Mark Dilger, Kyotaro Horiguchi
Discussion: https://postgr.es/m/20191028152048.jc6pqv5hb7j77ocp@development
This commit is contained in:
Tomas Vondra 2020-01-13 01:20:57 +01:00
parent aaa6761876
commit eae056c19e
3 changed files with 173 additions and 70 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

151
src/backend/statistics/extended_stats.c
View File

@ -1148,9 +1148,13 @@ statext_is_compatible_clause(PlannerInfo *root, Node *clause, Index relid,
* statext_mcv_clauselist_selectivity
* Estimate clauses using the best multi-column statistics.
*
* Selects the best extended (multi-column) statistic on a table (measured by
* the number of attributes extracted from the clauses and covered by it), and
* computes the selectivity for the supplied clauses.
* Applies available extended (multi-column) statistics on a table. There may
* be multiple applicable statistics (with respect to the clauses), in which
* case we use greedy approach. In each round we select the best statistic on
* a table (measured by the number of attributes extracted from the clauses
* and covered by it), and compute the selectivity for the supplied clauses.
* We repeat this process with the remaining clauses (if any), until none of
* the available statistics can be used.
*
* One of the main challenges with using MCV lists is how to extrapolate the
* estimate to the data not covered by the MCV list. To do that, we compute
@ -1194,11 +1198,6 @@ statext_is_compatible_clause(PlannerInfo *root, Node *clause, Index relid,
* 'estimatedclauses' is an input/output parameter. We set bits for the
* 0-based 'clauses' indexes we estimate for and also skip clause items that
* already have a bit set.
*
* XXX If we were to use multiple statistics, this is where it would happen.
* We would simply repeat this on a loop on the "remaining" clauses, possibly
* using the already estimated clauses as conditions (and combining the values
* using conditional probability formula).
*/
static Selectivity
statext_mcv_clauselist_selectivity(PlannerInfo *root, List *clauses, int varRelid,
@ -1208,14 +1207,7 @@ statext_mcv_clauselist_selectivity(PlannerInfo *root, List *clauses, int varReli
ListCell *l;
Bitmapset **list_attnums;
int listidx;
StatisticExtInfo *stat;
List *stat_clauses;
Selectivity simple_sel,
mcv_sel,
mcv_basesel,
mcv_totalsel,
other_sel,
sel;
Selectivity sel = 1.0;
/* check if there's any stats that might be useful for us. */
if (!has_stats_of_kind(rel->statlist, STATS_EXT_MCV))
@ -1250,66 +1242,85 @@ statext_mcv_clauselist_selectivity(PlannerInfo *root, List *clauses, int varReli
listidx++;
}
/* find the best suited statistics object for these attnums */
stat = choose_best_statistics(rel->statlist, STATS_EXT_MCV,
list_attnums, list_length(clauses));
/* if no matching stats could be found then we've nothing to do */
if (!stat)
return 1.0;
/* Ensure choose_best_statistics produced an expected stats type. */
Assert(stat->kind == STATS_EXT_MCV);
/* now filter the clauses to be estimated using the selected MCV */
stat_clauses = NIL;
listidx = 0;
foreach(l, clauses)
/* apply as many extended statistics as possible */
while (true)
{
/*
* If the clause is compatible with the selected statistics, mark it
* as estimated and add it to the list to estimate.
*/
if (list_attnums[listidx] != NULL &&
bms_is_subset(list_attnums[listidx], stat->keys))
StatisticExtInfo *stat;
List *stat_clauses;
Selectivity simple_sel,
mcv_sel,
mcv_basesel,
mcv_totalsel,
other_sel,
stat_sel;
/* find the best suited statistics object for these attnums */
stat = choose_best_statistics(rel->statlist, STATS_EXT_MCV,
list_attnums, list_length(clauses));
/* if no (additional) matching stats could be found then we've nothing to do */
if (!stat)
break;
/* Ensure choose_best_statistics produced an expected stats type. */
Assert(stat->kind == STATS_EXT_MCV);
/* now filter the clauses to be estimated using the selected MCV */
stat_clauses = NIL;
listidx = 0;
foreach(l, clauses)
{
stat_clauses = lappend(stat_clauses, (Node *) lfirst(l));
*estimatedclauses = bms_add_member(*estimatedclauses, listidx);
/*
* If the clause is compatible with the selected statistics, mark it
* as estimated and add it to the list to estimate.
*/
if (list_attnums[listidx] != NULL &&
bms_is_subset(list_attnums[listidx], stat->keys))
{
stat_clauses = lappend(stat_clauses, (Node *) lfirst(l));
*estimatedclauses = bms_add_member(*estimatedclauses, listidx);
bms_free(list_attnums[listidx]);
list_attnums[listidx] = NULL;
}
listidx++;
}
listidx++;
/*
* First compute "simple" selectivity, i.e. without the extended
* statistics, and essentially assuming independence of the
* columns/clauses. We'll then use the various selectivities computed from
* MCV list to improve it.
*/
simple_sel = clauselist_selectivity_simple(root, stat_clauses, varRelid,
jointype, sjinfo, NULL);
/*
* Now compute the multi-column estimate from the MCV list, along with the
* other selectivities (base & total selectivity).
*/
mcv_sel = mcv_clauselist_selectivity(root, stat, stat_clauses, varRelid,
jointype, sjinfo, rel,
&mcv_basesel, &mcv_totalsel);
/* Estimated selectivity of values not covered by MCV matches */
other_sel = simple_sel - mcv_basesel;
CLAMP_PROBABILITY(other_sel);
/* The non-MCV selectivity can't exceed the 1 - mcv_totalsel. */
if (other_sel > 1.0 - mcv_totalsel)
other_sel = 1.0 - mcv_totalsel;
/* Overall selectivity is the combination of MCV and non-MCV estimates. */
stat_sel = mcv_sel + other_sel;
CLAMP_PROBABILITY(stat_sel);
/* Factor the estimate from this MCV to the oveall estimate. */
sel *= stat_sel;
}
/*
* First compute "simple" selectivity, i.e. without the extended
* statistics, and essentially assuming independence of the
* columns/clauses. We'll then use the various selectivities computed from
* MCV list to improve it.
*/
simple_sel = clauselist_selectivity_simple(root, stat_clauses, varRelid,
jointype, sjinfo, NULL);
/*
* Now compute the multi-column estimate from the MCV list, along with the
* other selectivities (base & total selectivity).
*/
mcv_sel = mcv_clauselist_selectivity(root, stat, stat_clauses, varRelid,
jointype, sjinfo, rel,
&mcv_basesel, &mcv_totalsel);
/* Estimated selectivity of values not covered by MCV matches */
other_sel = simple_sel - mcv_basesel;
CLAMP_PROBABILITY(other_sel);
/* The non-MCV selectivity can't exceed the 1 - mcv_totalsel. */
if (other_sel > 1.0 - mcv_totalsel)
other_sel = 1.0 - mcv_totalsel;
/* Overall selectivity is the combination of MCV and non-MCV estimates. */
sel = mcv_sel + other_sel;
CLAMP_PROBABILITY(sel);
return sel;
}

57
src/test/regress/expected/stats_ext.out
View File

@ -836,6 +836,63 @@ SELECT * FROM check_estimated_rows('SELECT * FROM mcv_lists_bool WHERE NOT a AND
1 | 0
(1 row)
-- check the ability to use multiple MCV lists
CREATE TABLE mcv_lists_multi (
a INTEGER,
b INTEGER,
c INTEGER,
d INTEGER
);
INSERT INTO mcv_lists_multi (a, b, c, d)
SELECT
mod(i,5),
mod(i,5),
mod(i,7),
mod(i,7)
FROM generate_series(1,5000) s(i);
ANALYZE mcv_lists_multi;
-- estimates without any mcv statistics
SELECT * FROM check_estimated_rows('SELECT * FROM mcv_lists_multi WHERE a = 0 AND b = 0');
estimated | actual
-----------+--------
200 | 1000
(1 row)
SELECT * FROM check_estimated_rows('SELECT * FROM mcv_lists_multi WHERE c = 0 AND d = 0');
estimated | actual
-----------+--------
102 | 714
(1 row)
SELECT * FROM check_estimated_rows('SELECT * FROM mcv_lists_multi WHERE a = 0 AND b = 0 AND c = 0 AND d = 0');
estimated | actual
-----------+--------
4 | 142
(1 row)
-- create separate MCV statistics
CREATE STATISTICS mcv_lists_multi_1 (mcv) ON a, b FROM mcv_lists_multi;
CREATE STATISTICS mcv_lists_multi_2 (mcv) ON c, d FROM mcv_lists_multi;
ANALYZE mcv_lists_multi;
SELECT * FROM check_estimated_rows('SELECT * FROM mcv_lists_multi WHERE a = 0 AND b = 0');
estimated | actual
-----------+--------
1000 | 1000
(1 row)
SELECT * FROM check_estimated_rows('SELECT * FROM mcv_lists_multi WHERE c = 0 AND d = 0');
estimated | actual
-----------+--------
714 | 714
(1 row)
SELECT * FROM check_estimated_rows('SELECT * FROM mcv_lists_multi WHERE a = 0 AND b = 0 AND c = 0 AND d = 0');
estimated | actual
-----------+--------
143 | 142
(1 row)
DROP TABLE mcv_lists_multi;
-- Permission tests. Users should not be able to see specific data values in
-- the extended statistics, if they lack permission to see those values in
-- the underlying table.

35
src/test/regress/sql/stats_ext.sql
View File

@ -535,6 +535,41 @@ SELECT * FROM check_estimated_rows('SELECT * FROM mcv_lists_bool WHERE NOT a AND
SELECT * FROM check_estimated_rows('SELECT * FROM mcv_lists_bool WHERE NOT a AND b AND NOT c');
-- check the ability to use multiple MCV lists
CREATE TABLE mcv_lists_multi (
a INTEGER,
b INTEGER,
c INTEGER,
d INTEGER
);
INSERT INTO mcv_lists_multi (a, b, c, d)
SELECT
mod(i,5),
mod(i,5),
mod(i,7),
mod(i,7)
FROM generate_series(1,5000) s(i);
ANALYZE mcv_lists_multi;
-- estimates without any mcv statistics
SELECT * FROM check_estimated_rows('SELECT * FROM mcv_lists_multi WHERE a = 0 AND b = 0');
SELECT * FROM check_estimated_rows('SELECT * FROM mcv_lists_multi WHERE c = 0 AND d = 0');
SELECT * FROM check_estimated_rows('SELECT * FROM mcv_lists_multi WHERE a = 0 AND b = 0 AND c = 0 AND d = 0');
-- create separate MCV statistics
CREATE STATISTICS mcv_lists_multi_1 (mcv) ON a, b FROM mcv_lists_multi;
CREATE STATISTICS mcv_lists_multi_2 (mcv) ON c, d FROM mcv_lists_multi;
ANALYZE mcv_lists_multi;
SELECT * FROM check_estimated_rows('SELECT * FROM mcv_lists_multi WHERE a = 0 AND b = 0');
SELECT * FROM check_estimated_rows('SELECT * FROM mcv_lists_multi WHERE c = 0 AND d = 0');
SELECT * FROM check_estimated_rows('SELECT * FROM mcv_lists_multi WHERE a = 0 AND b = 0 AND c = 0 AND d = 0');
DROP TABLE mcv_lists_multi;
-- Permission tests. Users should not be able to see specific data values in
-- the extended statistics, if they lack permission to see those values in
-- the underlying table.