Provided by: postgresql-client-17_17.2-1_amd64 
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. If given, 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 is required when
IF NOT EXISTS is specified.
statistics_name
The name (optionally schema-qualified) of the statistics object to be created. If the
name is omitted, PostgreSQL chooses a suitable name based on the parent table's name
and the defined column name(s) and/or expression(s).
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 68.2.
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))