CREATE STATISTICS(7) PostgreSQL 15.7 Documentation CREATE STATISTICS(7)
NAME
CREATE_STATISTICS - define extended statistics
SYNOPSIS
CREATE STATISTICS [ IF NOT EXISTS ] statistics_name
ON ( expression )
FROM table_name
CREATE STATISTICS [ IF NOT EXISTS ] statistics_name
[ ( statistics_kind [, ... ] ) ]
ON { column_name | ( expression ) }, { column_name | ( expression ) } [, ...]
FROM table_name
DESCRIPTION
CREATE STATISTICS will create a new extended statistics object tracking
data about the specified table, foreign table or materialized view. The
statistics object will be created in the current database and will be
owned by the user issuing the command.
The CREATE STATISTICS command has two basic forms. The first form
allows univariate statistics for a single expression to be collected,
providing benefits similar to an expression index without the overhead
of index maintenance. This form does not allow the statistics kind to
be specified, since the various statistics kinds refer only to
multivariate statistics. The second form of the command allows
multivariate statistics on multiple columns and/or expressions to be
collected, optionally specifying which statistics kinds to include.
This form will also automatically cause univariate statistics to be
collected on any expressions included in the list.
If a schema name is given (for example, CREATE STATISTICS
myschema.mystat ...) then the statistics object is created in the
specified schema. Otherwise it is created in the current schema. The
name of the statistics object must be distinct from the name of any
other statistics object in the same schema.
PARAMETERS
IF NOT EXISTS
Do not throw an error if a statistics object with the same name
already exists. A notice is issued in this case. Note that only the
name of the statistics object is considered here, not the details
of its definition.
statistics_name
The name (optionally schema-qualified) of the statistics object to
be created.
statistics_kind
A multivariate statistics kind to be computed in this statistics
object. Currently supported kinds are ndistinct, which enables
n-distinct statistics, dependencies, which enables functional
dependency statistics, and mcv which enables most-common values
lists. If this clause is omitted, all supported statistics kinds
are included in the statistics object. Univariate expression
statistics are built automatically if the statistics definition
includes any complex expressions rather than just simple column
references. For more information, see Section 14.2.2 and
Section 75.2.
column_name
The name of a table column to be covered by the computed
statistics. This is only allowed when building multivariate
statistics. At least two column names or expressions must be
specified, and their order is not significant.
expression
An expression to be covered by the computed statistics. This may be
used to build univariate statistics on a single expression, or as
part of a list of multiple column names and/or expressions to build
multivariate statistics. In the latter case, separate univariate
statistics are built automatically for each expression in the list.
table_name
The name (optionally schema-qualified) of the table containing the
column(s) the statistics are computed on; see ANALYZE(7) for an
explanation of the handling of inheritance and partitions.
NOTES
You must be the owner of a table to create a statistics object reading
it. Once created, however, the ownership of the statistics object is
independent of the underlying table(s).
Expression statistics are per-expression and are similar to creating an
index on the expression, except that they avoid the overhead of index
maintenance. Expression statistics are built automatically for each
expression in the statistics object definition.
Extended statistics are not currently used by the planner for
selectivity estimations made for table joins. This limitation will
likely be removed in a future version of PostgreSQL.
EXAMPLES
Create table t1 with two functionally dependent columns, i.e.,
knowledge of a value in the first column is sufficient for determining
the value in the other column. Then functional dependency statistics
are built on those columns:
CREATE TABLE t1 (
a int,
b int
);
INSERT INTO t1 SELECT i/100, i/500
FROM generate_series(1,1000000) s(i);
ANALYZE t1;
-- the number of matching rows will be drastically underestimated:
EXPLAIN ANALYZE SELECT * FROM t1 WHERE (a = 1) AND (b = 0);
CREATE STATISTICS s1 (dependencies) ON a, b FROM t1;
ANALYZE t1;
-- now the row count estimate is more accurate:
EXPLAIN ANALYZE SELECT * FROM t1 WHERE (a = 1) AND (b = 0);
Without functional-dependency statistics, the planner would assume that
the two WHERE conditions are independent, and would multiply their
selectivities together to arrive at a much-too-small row count
estimate. With such statistics, the planner recognizes that the WHERE
conditions are redundant and does not underestimate the row count.
Create table t2 with two perfectly correlated columns (containing
identical data), and an MCV list on those columns:
CREATE TABLE t2 (
a int,
b int
);
INSERT INTO t2 SELECT mod(i,100), mod(i,100)
FROM generate_series(1,1000000) s(i);
CREATE STATISTICS s2 (mcv) ON a, b FROM t2;
ANALYZE t2;
-- valid combination (found in MCV)
EXPLAIN ANALYZE SELECT * FROM t2 WHERE (a = 1) AND (b = 1);
-- invalid combination (not found in MCV)
EXPLAIN ANALYZE SELECT * FROM t2 WHERE (a = 1) AND (b = 2);
The MCV list gives the planner more detailed information about the
specific values that commonly appear in the table, as well as an upper
bound on the selectivities of combinations of values that do not appear
in the table, allowing it to generate better estimates in both cases.
Create table t3 with a single timestamp column, and run queries using
expressions on that column. Without extended statistics, the planner
has no information about the data distribution for the expressions, and
uses default estimates. The planner also does not realize that the
value of the date truncated to the month is fully determined by the
value of the date truncated to the day. Then expression and ndistinct
statistics are built on those two expressions:
CREATE TABLE t3 (
a timestamp
);
INSERT INTO t3 SELECT i FROM generate_series('2020-01-01'::timestamp,
'2020-12-31'::timestamp,
'1 minute'::interval) s(i);
ANALYZE t3;
-- the number of matching rows will be drastically underestimated:
EXPLAIN ANALYZE SELECT * FROM t3
WHERE date_trunc('month', a) = '2020-01-01'::timestamp;
EXPLAIN ANALYZE SELECT * FROM t3
WHERE date_trunc('day', a) BETWEEN '2020-01-01'::timestamp
AND '2020-06-30'::timestamp;
EXPLAIN ANALYZE SELECT date_trunc('month', a), date_trunc('day', a)
FROM t3 GROUP BY 1, 2;
-- build ndistinct statistics on the pair of expressions (per-expression
-- statistics are built automatically)
CREATE STATISTICS s3 (ndistinct) ON date_trunc('month', a), date_trunc('day', a) FROM t3;
ANALYZE t3;
-- now the row count estimates are more accurate:
EXPLAIN ANALYZE SELECT * FROM t3
WHERE date_trunc('month', a) = '2020-01-01'::timestamp;
EXPLAIN ANALYZE SELECT * FROM t3
WHERE date_trunc('day', a) BETWEEN '2020-01-01'::timestamp
AND '2020-06-30'::timestamp;
EXPLAIN ANALYZE SELECT date_trunc('month', a), date_trunc('day', a)
FROM t3 GROUP BY 1, 2;
Without expression and ndistinct statistics, the planner has no
information about the number of distinct values for the expressions,
and has to rely on default estimates. The equality and range conditions
are assumed to have 0.5% selectivity, and the number of distinct values
in the expression is assumed to be the same as for the column (i.e.
unique). This results in a significant underestimate of the row count
in the first two queries. Moreover, the planner has no information
about the relationship between the expressions, so it assumes the two
WHERE and GROUP BY conditions are independent, and multiplies their
selectivities together to arrive at a severe overestimate of the group
count in the aggregate query. This is further exacerbated by the lack
of accurate statistics for the expressions, forcing the planner to use
a default ndistinct estimate for the expression derived from ndistinct
for the column. With such statistics, the planner recognizes that the
conditions are correlated, and arrives at much more accurate estimates.
COMPATIBILITY
There is no CREATE STATISTICS command in the SQL standard.
SEE ALSO
ALTER STATISTICS (ALTER_STATISTICS(7)), DROP STATISTICS
(DROP_STATISTICS(7))
PostgreSQL 15.7 2024 CREATE STATISTICS(7)
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