postgres/doc/src/sgml/ref/create_statistics.sgml

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doc/src/sgml/ref/create_statistics.sgml
PostgreSQL documentation
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<refentry id="sql-createstatistics">
 <indexterm zone="sql-createstatistics">
  <primary>CREATE STATISTICS</primary>
 </indexterm>

 <refmeta>
  <refentrytitle>CREATE STATISTICS</refentrytitle>
  <manvolnum>7</manvolnum>
  <refmiscinfo>SQL - Language Statements</refmiscinfo>
 </refmeta>

 <refnamediv>
  <refname>CREATE STATISTICS</refname>
  <refpurpose>define extended statistics</refpurpose>
 </refnamediv>

 <refsynopsisdiv>
<synopsis>
CREATE STATISTICS [ IF NOT EXISTS ] <replaceable class="parameter">statistics_name</replaceable>
    ON ( <replaceable class="parameter">expression</replaceable> )
    FROM <replaceable class="parameter">table_name</replaceable>

CREATE STATISTICS [ IF NOT EXISTS ] <replaceable class="parameter">statistics_name</replaceable>
    [ ( <replaceable class="parameter">statistics_kind</replaceable> [, ... ] ) ]
    ON { <replaceable class="parameter">column_name</replaceable> | ( <replaceable class="parameter">expression</replaceable> ) }, { <replaceable class="parameter">column_name</replaceable> | ( <replaceable class="parameter">expression</replaceable> ) } [, ...]
    FROM <replaceable class="parameter">table_name</replaceable>
</synopsis>

 </refsynopsisdiv>

 <refsect1 id="sql-createstatistics-description">
  <title>Description</title>

  <para>
   <command>CREATE STATISTICS</command> 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.
  </para>

  <para>
   The <command>CREATE STATISTICS</command> 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.
  </para>

  <para>
   If a schema name is given (for example, <literal>CREATE STATISTICS
   myschema.mystat ...</literal>) 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.
  </para>
 </refsect1>

 <refsect1>
  <title>Parameters</title>

  <variablelist>

   <varlistentry>
    <term><literal>IF NOT EXISTS</literal></term>
    <listitem>
     <para>
      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.
     </para>
    </listitem>
   </varlistentry>

   <varlistentry>
    <term><replaceable class="parameter">statistics_name</replaceable></term>
    <listitem>
     <para>
      The name (optionally schema-qualified) of the statistics object to be
      created.
     </para>
    </listitem>
   </varlistentry>

   <varlistentry>
    <term><replaceable class="parameter">statistics_kind</replaceable></term>
    <listitem>
     <para>
      A multivariate statistics kind to be computed in this statistics object.
      Currently supported kinds are
      <literal>ndistinct</literal>, which enables n-distinct statistics,
      <literal>dependencies</literal>, which enables functional
      dependency statistics, and <literal>mcv</literal> 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 <xref linkend="planner-stats-extended"/>
      and <xref linkend="multivariate-statistics-examples"/>.
     </para>
    </listitem>
   </varlistentry>

   <varlistentry>
    <term><replaceable class="parameter">column_name</replaceable></term>
    <listitem>
     <para>
      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.
     </para>
    </listitem>
   </varlistentry>

   <varlistentry>
    <term><replaceable class="parameter">expression</replaceable></term>
    <listitem>
     <para>
      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.
     </para>
    </listitem>
   </varlistentry>

   <varlistentry>
    <term><replaceable class="parameter">table_name</replaceable></term>
    <listitem>
     <para>
      The name (optionally schema-qualified) of the table containing the
      column(s) the statistics are computed on;  see <xref
      linkend="sql-analyze"/> for an explanation of the handling of
      inheritance and partitions.
     </para>
    </listitem>
   </varlistentry>

  </variablelist>
 </refsect1>

 <refsect1>
  <title>Notes</title>

  <para>
   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).
  </para>

  <para>
   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.
  </para>

  <para>
   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 <productname>PostgreSQL</productname>.
  </para>
 </refsect1>

 <refsect1 id="sql-createstatistics-examples">
  <title>Examples</title>

  <para>
   Create table <structname>t1</structname> 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:

<programlisting>
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);
</programlisting>

   Without functional-dependency statistics, the planner would assume
   that the two <literal>WHERE</literal> 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 <literal>WHERE</literal>
   conditions are redundant and does not underestimate the row count.
  </para>

  <para>
   Create table <structname>t2</structname> with two perfectly correlated columns
   (containing identical data), and an MCV list on those columns:

<programlisting>
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);
</programlisting>

   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.
  </para>

  <para>
   Create table <structname>t3</structname> 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:

<programlisting>
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;
</programlisting>

   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 <literal>WHERE</literal> and <literal>GROUP BY</literal>
   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.
  </para>

 </refsect1>

 <refsect1>
  <title>Compatibility</title>

  <para>
   There is no <command>CREATE STATISTICS</command> command in the SQL standard.
  </para>
 </refsect1>

 <refsect1>
  <title>See Also</title>

  <simplelist type="inline">
   <member><xref linkend="sql-alterstatistics"/></member>
   <member><xref linkend="sql-dropstatistics"/></member>
  </simplelist>
 </refsect1>
</refentry>