doc/src/sgml/queries.sgml - master - balmaster/postgres

doc/src/sgml/queries.sgml
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Tom Lane
Doc: reword discussion of asterisk after table names in FROM.
15 июн 2026, 20:07
15 июн 2026, 20:078ebbf79
<!-- doc/src/sgml/queries.sgml -->

<chapter id="queries">
 <title>Queries</title>

 <indexterm zone="queries">
  <primary>query</primary>
 </indexterm>

 <indexterm zone="queries">
  <primary>SELECT</primary>
 </indexterm>

 <para>
  The previous chapters explained how to create tables, how to fill
  them with data, and how to manipulate that data.  Now we finally
  discuss how to retrieve the data from the database.
 </para>


 <sect1 id="queries-overview">
  <title>Overview</title>

  <para>
   The process of retrieving or the command to retrieve data from a
   database is called a <firstterm>query</firstterm>.  In SQL the
   <link linkend="sql-select"><command>SELECT</command></link> command is
   used to specify queries.  The general syntax of the
   <command>SELECT</command> command is
<synopsis>
<optional>WITH <replaceable>with_queries</replaceable></optional> SELECT <replaceable>select_list</replaceable> FROM <replaceable>table_expression</replaceable> <optional><replaceable>sort_specification</replaceable></optional>
</synopsis>
   The following sections describe the details of the select list, the
   table expression, and the sort specification.  <literal>WITH</literal>
   queries are treated last since they are an advanced feature.
  </para>

  <para>
   A simple kind of query has the form:
<programlisting>
SELECT * FROM table1;
</programlisting>
  Assuming that there is a table called <literal>table1</literal>,
  this command would retrieve all rows and all user-defined columns from
  <literal>table1</literal>.  (The method of retrieval depends on the
  client application.  For example, the
  <application>psql</application> program will display an ASCII-art
  table on the screen, while client libraries will offer functions to
  extract individual values from the query result.)  The select list
  specification <literal>*</literal> means all columns that the table
  expression happens to provide.  A select list can also select a
  subset of the available columns or make calculations using the
  columns.  For example, if
  <literal>table1</literal> has columns named <literal>a</literal>,
  <literal>b</literal>, and <literal>c</literal> (and perhaps others) you can make
  the following query:
<programlisting>
SELECT a, b + c FROM table1;
</programlisting>
  (assuming that <literal>b</literal> and <literal>c</literal> are of a numerical
  data type).
  See <xref linkend="queries-select-lists"/> for more details.
 </para>

 <para>
  <literal>FROM table1</literal> is a simple kind of
  table expression: it reads just one table.  In general, table
  expressions can be complex constructs of base tables, joins, and
  subqueries.  But you can also omit the table expression entirely and
  use the <command>SELECT</command> command as a calculator:
<programlisting>
SELECT 3 * 4;
</programlisting>
  This is more useful if the expressions in the select list return
  varying results.  For example, you could call a function this way:
<programlisting>
SELECT random();
</programlisting>
  </para>
 </sect1>


 <sect1 id="queries-table-expressions">
  <title>Table Expressions</title>

  <indexterm zone="queries-table-expressions">
   <primary>table expression</primary>
  </indexterm>

  <para>
   A <firstterm>table expression</firstterm> computes a table.  The
   table expression contains a <literal>FROM</literal> clause that is
   optionally followed by <literal>WHERE</literal>, <literal>GROUP BY</literal>, and
   <literal>HAVING</literal> clauses.  Trivial table expressions simply refer
   to a table on disk, a so-called base table, but more complex
   expressions can be used to modify or combine base tables in various
   ways.
  </para>

  <para>
   The optional <literal>WHERE</literal>, <literal>GROUP BY</literal>, and
   <literal>HAVING</literal> clauses in the table expression specify a
   pipeline of successive transformations performed on the table
   derived in the <literal>FROM</literal> clause.  All these transformations
   produce a virtual table that provides the rows that are passed to
   the select list to compute the output rows of the query.
  </para>

  <sect2 id="queries-from">
   <title>The <literal>FROM</literal> Clause</title>

   <para>
    The <link linkend="sql-from"><literal>FROM</literal></link> clause derives a
    table from one or more other tables given in a comma-separated
    table reference list.
<synopsis>
FROM <replaceable>table_reference</replaceable> <optional>, <replaceable>table_reference</replaceable> <optional>, ...</optional></optional>
</synopsis>

    A table reference can be a table name (possibly schema-qualified),
    or a derived table such as a subquery, a <literal>JOIN</literal> construct, or
    complex combinations of these.  If more than one table reference is
    listed in the <literal>FROM</literal> clause, the tables are cross-joined
    (that is, the Cartesian product of their rows is formed; see below).
    The result of the <literal>FROM</literal> list is an intermediate virtual
    table that can then be subject to
    transformations by the <literal>WHERE</literal>, <literal>GROUP BY</literal>,
    and <literal>HAVING</literal> clauses and is finally the result of the
    overall table expression.
   </para>

   <indexterm>
    <primary>ONLY</primary>
   </indexterm>

   <para>
    When a table reference names a table that is the parent of a
    table inheritance hierarchy, the table reference produces rows of
    not only that table but all of its descendant tables, unless the
    key word <literal>ONLY</literal> precedes the table name.  However, the
    reference produces only the columns that appear in the named table
    &mdash; any columns added in subtables are ignored.
   </para>

   <para>
    You can write <literal>*</literal> after the table name to explicitly
    specify that descendant tables are included, which is the opposite of
    <literal>ONLY</literal>.  There is no real reason to write
    <literal>*</literal> any more, because searching descendant tables has
    been the default behavior since <productname>PostgreSQL</productname>
    version 10.  However, it remains supported for compatibility with older
    releases.
   </para>

   <sect3 id="queries-join">
    <title>Joined Tables</title>

    <indexterm zone="queries-join">
     <primary>join</primary>
    </indexterm>

    <para>
     A joined table is a table derived from two other (real or
     derived) tables according to the rules of the particular join
     type.  Inner, outer, and cross-joins are available.
     The general syntax of a joined table is
<synopsis>
<replaceable>T1</replaceable> <replaceable>join_type</replaceable> <replaceable>T2</replaceable> <optional> <replaceable>join_condition</replaceable> </optional>
</synopsis>
     Joins of all types can be chained together, or nested: either or
     both <replaceable>T1</replaceable> and
     <replaceable>T2</replaceable> can be joined tables.  Parentheses
     can be used around <literal>JOIN</literal> clauses to control the join
     order.  In the absence of parentheses, <literal>JOIN</literal> clauses
     nest left-to-right.
    </para>

    <variablelist>
     <title>Join Types</title>

     <varlistentry>
      <term>Cross join
      <indexterm>
       <primary>join</primary>
       <secondary>cross</secondary>
      </indexterm>

      <indexterm>
       <primary>cross join</primary>
      </indexterm>
      </term>

      <listitem>
<synopsis>
<replaceable>T1</replaceable> CROSS JOIN <replaceable>T2</replaceable>
</synopsis>

       <para>
        For every possible combination of rows from
        <replaceable>T1</replaceable> and
        <replaceable>T2</replaceable> (i.e., a Cartesian product),
        the joined table will contain a
        row consisting of all columns in <replaceable>T1</replaceable>
        followed by all columns in <replaceable>T2</replaceable>.  If
        the tables have N and M rows respectively, the joined
        table will have N * M rows.
       </para>

       <para>
        <literal>FROM <replaceable>T1</replaceable> CROSS JOIN
        <replaceable>T2</replaceable></literal> is equivalent to
        <literal>FROM <replaceable>T1</replaceable> INNER JOIN
        <replaceable>T2</replaceable> ON TRUE</literal> (see below).
        It is also equivalent to
        <literal>FROM <replaceable>T1</replaceable>,
        <replaceable>T2</replaceable></literal>.
        <note>
        <para>
         This latter equivalence does not hold exactly when more than two
         tables appear, because <literal>JOIN</literal> binds more tightly than
         comma.  For example
         <literal>FROM <replaceable>T1</replaceable> CROSS JOIN
         <replaceable>T2</replaceable> INNER JOIN <replaceable>T3</replaceable>
         ON <replaceable>condition</replaceable></literal>
         is not the same as
         <literal>FROM <replaceable>T1</replaceable>,
         <replaceable>T2</replaceable> INNER JOIN <replaceable>T3</replaceable>
         ON <replaceable>condition</replaceable></literal>
         because the <replaceable>condition</replaceable> can
         reference <replaceable>T1</replaceable> in the first case but not
         the second.
        </para>
        </note>
       </para>
      </listitem>
     </varlistentry>

     <varlistentry>
      <term>Qualified joins
      <indexterm>
       <primary>join</primary>
       <secondary>outer</secondary>
      </indexterm>

      <indexterm>
       <primary>outer join</primary>
      </indexterm>
      </term>

      <listitem>
<synopsis>
<replaceable>T1</replaceable> { <optional>INNER</optional> | { LEFT | RIGHT | FULL } <optional>OUTER</optional> } JOIN <replaceable>T2</replaceable> ON <replaceable>boolean_expression</replaceable>
<replaceable>T1</replaceable> { <optional>INNER</optional> | { LEFT | RIGHT | FULL } <optional>OUTER</optional> } JOIN <replaceable>T2</replaceable> USING ( <replaceable>join column list</replaceable> )
<replaceable>T1</replaceable> NATURAL { <optional>INNER</optional> | { LEFT | RIGHT | FULL } <optional>OUTER</optional> } JOIN <replaceable>T2</replaceable>
</synopsis>

       <para>
        The words <literal>INNER</literal> and
        <literal>OUTER</literal> are optional in all forms.
        <literal>INNER</literal> is the default;
        <literal>LEFT</literal>, <literal>RIGHT</literal>, and
        <literal>FULL</literal> imply an outer join.
       </para>

       <para>
        The <firstterm>join condition</firstterm> is specified in the
        <literal>ON</literal> or <literal>USING</literal> clause, or implicitly by
        the word <literal>NATURAL</literal>.  The join condition determines
        which rows from the two source tables are considered to
        <quote>match</quote>, as explained in detail below.
       </para>

       <para>
        The possible types of qualified join are:

       <variablelist>
        <varlistentry>
         <term><literal>INNER JOIN</literal></term>

         <listitem>
          <para>
           For each row R1 of T1, the joined table has a row for each
           row in T2 that satisfies the join condition with R1.
          </para>
         </listitem>
        </varlistentry>

        <varlistentry>
         <term><literal>LEFT OUTER JOIN</literal>
         <indexterm>
          <primary>join</primary>
          <secondary>left</secondary>
         </indexterm>

         <indexterm>
          <primary>left join</primary>
         </indexterm>
         </term>

         <listitem>
          <para>
           First, an inner join is performed.  Then, for each row in
           T1 that does not satisfy the join condition with any row in
           T2, a joined row is added with null values in columns of
           T2.  Thus, the joined table always has at least
           one row for each row in T1.
          </para>
         </listitem>
        </varlistentry>

        <varlistentry>
         <term><literal>RIGHT OUTER JOIN</literal>
         <indexterm>
          <primary>join</primary>
          <secondary>right</secondary>
         </indexterm>

         <indexterm>
          <primary>right join</primary>
         </indexterm>
         </term>

         <listitem>
          <para>
           First, an inner join is performed.  Then, for each row in
           T2 that does not satisfy the join condition with any row in
           T1, a joined row is added with null values in columns of
           T1.  This is the converse of a left join: the result table
           will always have a row for each row in T2.
          </para>
         </listitem>
        </varlistentry>

        <varlistentry>
         <term><literal>FULL OUTER JOIN</literal></term>

         <listitem>
          <para>
           First, an inner join is performed.  Then, for each row in
           T1 that does not satisfy the join condition with any row in
           T2, a joined row is added with null values in columns of
           T2.  Also, for each row of T2 that does not satisfy the
           join condition with any row in T1, a joined row with null
           values in the columns of T1 is added.
          </para>
         </listitem>
        </varlistentry>
       </variablelist>
       </para>

       <para>
        The <literal>ON</literal> clause is the most general kind of join
        condition: it takes a Boolean value expression of the same
        kind as is used in a <literal>WHERE</literal> clause.  A pair of rows
        from <replaceable>T1</replaceable> and <replaceable>T2</replaceable> match if the
        <literal>ON</literal> expression evaluates to true.
       </para>

       <para>
        The <literal>USING</literal> clause is a shorthand that allows you to take
        advantage of the specific situation where both sides of the join use
        the same name for the joining column(s).  It takes a
        comma-separated list of the shared column names
        and forms a join condition that includes an equality comparison
        for each one.  For example, joining <replaceable>T1</replaceable>
        and <replaceable>T2</replaceable> with <literal>USING (a, b)</literal> produces
        the join condition <literal>ON <replaceable>T1</replaceable>.a
        = <replaceable>T2</replaceable>.a AND <replaceable>T1</replaceable>.b
        = <replaceable>T2</replaceable>.b</literal>.
       </para>

       <para>
        Furthermore, the output of <literal>JOIN USING</literal> suppresses
        redundant columns: there is no need to print both of the matched
        columns, since they must have equal values.  While <literal>JOIN
        ON</literal> produces all columns from <replaceable>T1</replaceable> followed by all
        columns from <replaceable>T2</replaceable>, <literal>JOIN USING</literal> produces one
        output column for each of the listed column pairs (in the listed
        order), followed by any remaining columns from <replaceable>T1</replaceable>,
        followed by any remaining columns from <replaceable>T2</replaceable>.
       </para>

       <para>
        <indexterm>
         <primary>join</primary>
         <secondary>natural</secondary>
        </indexterm>
        <indexterm>
         <primary>natural join</primary>
        </indexterm>
        Finally, <literal>NATURAL</literal> is a shorthand form of
        <literal>USING</literal>: it forms a <literal>USING</literal> list
        consisting of all column names that appear in both
        input tables.  As with <literal>USING</literal>, these columns appear
        only once in the output table.  If there are no common
        column names, <literal>NATURAL JOIN</literal> behaves like
        <literal>CROSS JOIN</literal>.
       </para>

       <note>
        <para>
         <literal>USING</literal> is reasonably safe from column changes
         in the joined relations since only the listed columns
         are combined.  <literal>NATURAL</literal> is considerably more risky since
         any schema changes to either relation that cause a new matching
         column name to be present will cause the join to combine that new
         column as well.
        </para>
       </note>
      </listitem>
     </varlistentry>
    </variablelist>

    <para>
     To put this together, assume we have tables <structname>t1</structname>:
<programlisting>
 num | name
-----+------
   1 | a
   2 | b
   3 | c
</programlisting>
     and <structname>t2</structname>:
<programlisting>
 num | value
-----+-------
   1 | xxx
   3 | yyy
   5 | zzz
</programlisting>
     then we get the following results for the various joins:
<screen>
<prompt>=&gt;</prompt> <userinput>SELECT * FROM t1 CROSS JOIN t2;</userinput>
 num | name | num | value
-----+------+-----+-------
   1 | a    |   1 | xxx
   1 | a    |   3 | yyy
   1 | a    |   5 | zzz
   2 | b    |   1 | xxx
   2 | b    |   3 | yyy
   2 | b    |   5 | zzz
   3 | c    |   1 | xxx
   3 | c    |   3 | yyy
   3 | c    |   5 | zzz
(9 rows)

<prompt>=&gt;</prompt> <userinput>SELECT * FROM t1 INNER JOIN t2 ON t1.num = t2.num;</userinput>
 num | name | num | value
-----+------+-----+-------
   1 | a    |   1 | xxx
   3 | c    |   3 | yyy
(2 rows)

<prompt>=&gt;</prompt> <userinput>SELECT * FROM t1 INNER JOIN t2 USING (num);</userinput>
 num | name | value
-----+------+-------
   1 | a    | xxx
   3 | c    | yyy
(2 rows)

<prompt>=&gt;</prompt> <userinput>SELECT * FROM t1 NATURAL INNER JOIN t2;</userinput>
 num | name | value
-----+------+-------
   1 | a    | xxx
   3 | c    | yyy
(2 rows)

<prompt>=&gt;</prompt> <userinput>SELECT * FROM t1 LEFT JOIN t2 ON t1.num = t2.num;</userinput>
 num | name | num | value
-----+------+-----+-------
   1 | a    |   1 | xxx
   2 | b    |     |
   3 | c    |   3 | yyy
(3 rows)

<prompt>=&gt;</prompt> <userinput>SELECT * FROM t1 LEFT JOIN t2 USING (num);</userinput>
 num | name | value
-----+------+-------
   1 | a    | xxx
   2 | b    |
   3 | c    | yyy
(3 rows)

<prompt>=&gt;</prompt> <userinput>SELECT * FROM t1 RIGHT JOIN t2 ON t1.num = t2.num;</userinput>
 num | name | num | value
-----+------+-----+-------
   1 | a    |   1 | xxx
   3 | c    |   3 | yyy
     |      |   5 | zzz
(3 rows)

<prompt>=&gt;</prompt> <userinput>SELECT * FROM t1 FULL JOIN t2 ON t1.num = t2.num;</userinput>
 num | name | num | value
-----+------+-----+-------
   1 | a    |   1 | xxx
   2 | b    |     |
   3 | c    |   3 | yyy
     |      |   5 | zzz
(4 rows)
</screen>
    </para>

    <para>
     The join condition specified with <literal>ON</literal> can also contain
     conditions that do not relate directly to the join.  This can
     prove useful for some queries but needs to be thought out
     carefully.  For example:
<screen>
<prompt>=&gt;</prompt> <userinput>SELECT * FROM t1 LEFT JOIN t2 ON t1.num = t2.num AND t2.value = 'xxx';</userinput>
 num | name | num | value
-----+------+-----+-------
   1 | a    |   1 | xxx
   2 | b    |     |
   3 | c    |     |
(3 rows)
</screen>
     Notice that placing the restriction in the <literal>WHERE</literal> clause
     produces a different result:
<screen>
<prompt>=&gt;</prompt> <userinput>SELECT * FROM t1 LEFT JOIN t2 ON t1.num = t2.num WHERE t2.value = 'xxx';</userinput>
 num | name | num | value
-----+------+-----+-------
   1 | a    |   1 | xxx
(1 row)
</screen>
     This is because a restriction placed in the <literal>ON</literal>
     clause is processed <emphasis>before</emphasis> the join, while
     a restriction placed in the <literal>WHERE</literal> clause is processed
     <emphasis>after</emphasis> the join.
     That does not matter with inner joins, but it matters a lot with outer
     joins.
    </para>
   </sect3>

   <sect3 id="queries-table-aliases">
    <title>Table and Column Aliases</title>

    <indexterm zone="queries-table-aliases">
     <primary>alias</primary>
     <secondary>in the FROM clause</secondary>
    </indexterm>

    <indexterm>
     <primary>label</primary>
     <see>alias</see>
    </indexterm>

    <para>
     A temporary name can be given to tables and complex table
     references to be used for references to the derived table in
     the rest of the query.  This is called a <firstterm>table
     alias</firstterm>.
    </para>

    <para>
     To create a table alias, write
<synopsis>
FROM <replaceable>table_reference</replaceable> AS <replaceable>alias</replaceable>
</synopsis>
     or
<synopsis>
FROM <replaceable>table_reference</replaceable> <replaceable>alias</replaceable>
</synopsis>
     The <literal>AS</literal> key word is optional noise.
     <replaceable>alias</replaceable> can be any identifier.
    </para>

    <para>
     A typical application of table aliases is to assign short
     identifiers to long table names to keep the join clauses
     readable.  For example:
<programlisting>
SELECT * FROM some_very_long_table_name s JOIN another_fairly_long_name a ON s.id = a.num;
</programlisting>
    </para>

    <para>
     The alias becomes the new name of the table reference so far as the
     current query is concerned &mdash; it is not allowed to refer to the
     table by the original name elsewhere in the query.  Thus, this is not
     valid:
<programlisting>
SELECT * FROM my_table AS m WHERE my_table.a &gt; 5;    -- wrong
</programlisting>
    </para>

    <para>
     Table aliases are mainly for notational convenience, but it is
     necessary to use them when joining a table to itself, e.g.:
<programlisting>
SELECT * FROM people AS mother JOIN people AS child ON mother.id = child.mother_id;
</programlisting>
    </para>

    <para>
     Parentheses are used to resolve ambiguities.  In the following example,
     the first statement assigns the alias <literal>b</literal> to the second
     instance of <literal>my_table</literal>, but the second statement assigns the
     alias to the result of the join:
<programlisting>
SELECT * FROM my_table AS a CROSS JOIN my_table AS b ...
SELECT * FROM (my_table AS a CROSS JOIN my_table) AS b ...
</programlisting>
    </para>

    <para>
     Another form of table aliasing gives temporary names to the columns of
     the table, as well as the table itself:
<synopsis>
FROM <replaceable>table_reference</replaceable> <optional>AS</optional> <replaceable>alias</replaceable> ( <replaceable>column1</replaceable> <optional>, <replaceable>column2</replaceable> <optional>, ...</optional></optional> )
</synopsis>
     If fewer column aliases are specified than the actual table has
     columns, the remaining columns are not renamed.  This syntax is
     especially useful for self-joins or subqueries.
    </para>

    <para>
     When an alias is applied to the output of a <literal>JOIN</literal>
     clause, the alias hides the original
     name(s) within the <literal>JOIN</literal>.  For example:
<programlisting>
SELECT a.* FROM my_table AS a JOIN your_table AS b ON ...
</programlisting>
     is valid SQL, but:
<programlisting>
SELECT a.* FROM (my_table AS a JOIN your_table AS b ON ...) AS c
</programlisting>
     is not valid; the table alias <literal>a</literal> is not visible
     outside the alias <literal>c</literal>.
    </para>
   </sect3>

   <sect3 id="queries-subqueries">
    <title>Subqueries</title>

    <indexterm zone="queries-subqueries">
     <primary>subquery</primary>
    </indexterm>

    <para>
     Subqueries specifying a derived table must be enclosed in
     parentheses.  They may be assigned a table alias name, and optionally
     column alias names (as in <xref linkend="queries-table-aliases"/>).
     For example:
<programlisting>
FROM (SELECT * FROM table1) AS alias_name
</programlisting>
    </para>

    <para>
     This example is equivalent to <literal>FROM table1 AS
     alias_name</literal>.  More interesting cases, which cannot be
     reduced to a plain join, arise when the subquery involves
     grouping or aggregation.
    </para>

    <para>
     A subquery can also be a <command>VALUES</command> list:
<programlisting>
FROM (VALUES ('anne', 'smith'), ('bob', 'jones'), ('joe', 'blow'))
     AS names(first, last)
</programlisting>
     Again, a table alias is optional.  Assigning alias names to the columns
     of the <command>VALUES</command> list is optional, but is good practice.
     For more information see <xref linkend="queries-values"/>.
    </para>

    <para>
     According to the SQL standard, a table alias name must be supplied
     for a subquery.  <productname>PostgreSQL</productname>
     allows <literal>AS</literal> and the alias to be omitted, but
     writing one is good practice in SQL code that might be ported to
     another system.
    </para>
   </sect3>

   <sect3 id="queries-tablefunctions">
    <title>Table Functions</title>

    <indexterm zone="queries-tablefunctions"><primary>table function</primary></indexterm>

    <indexterm zone="queries-tablefunctions">
     <primary>function</primary>
     <secondary>in the FROM clause</secondary>
    </indexterm>

    <para>
     Table functions are functions that produce a set of rows, made up
     of either base data types (scalar types) or composite data types
     (table rows).  They are used like a table, view, or subquery in
     the <literal>FROM</literal> clause of a query. Columns returned by table
     functions can be included in <literal>SELECT</literal>,
     <literal>JOIN</literal>, or <literal>WHERE</literal> clauses in the same manner
     as columns of a table, view, or subquery.
    </para>

    <para>
     Table functions may also be combined using the <literal>ROWS FROM</literal>
     syntax, with the results returned in parallel columns; the number of
     result rows in this case is that of the largest function result, with
     smaller results padded with null values to match.
    </para>

<synopsis>
<replaceable>function_call</replaceable> <optional>WITH ORDINALITY</optional> <optional><optional>AS</optional> <replaceable>table_alias</replaceable> <optional>(<replaceable>column_alias</replaceable> <optional>, ... </optional>)</optional></optional>
ROWS FROM( <replaceable>function_call</replaceable> <optional>, ... </optional> ) <optional>WITH ORDINALITY</optional> <optional><optional>AS</optional> <replaceable>table_alias</replaceable> <optional>(<replaceable>column_alias</replaceable> <optional>, ... </optional>)</optional></optional>
</synopsis>

    <para>
     If the <literal>WITH ORDINALITY</literal> clause is specified, an
     additional column of type <type>bigint</type> will be added to the
     function result columns.  This column numbers the rows of the function
     result set, starting from 1. (This is a generalization of the
     SQL-standard syntax for <literal>UNNEST ... WITH ORDINALITY</literal>.)
     By default, the ordinal column is called <literal>ordinality</literal>, but
     a different column name can be assigned to it using
     an <literal>AS</literal> clause.
    </para>

    <para>
     The special table function <literal>UNNEST</literal> may be called with
     any number of array parameters, and it returns a corresponding number of
     columns, as if <literal>UNNEST</literal>
     (<xref linkend="functions-array"/>) had been called on each parameter
     separately and combined using the <literal>ROWS FROM</literal> construct.
    </para>

<synopsis>
UNNEST( <replaceable>array_expression</replaceable> <optional>, ... </optional> ) <optional>WITH ORDINALITY</optional> <optional><optional>AS</optional> <replaceable>table_alias</replaceable> <optional>(<replaceable>column_alias</replaceable> <optional>, ... </optional>)</optional></optional>
</synopsis>

    <para>
     If no <replaceable>table_alias</replaceable> is specified, the function
     name is used as the table name; in the case of a <literal>ROWS FROM()</literal>
     construct, the first function's name is used.
    </para>

    <para>
     If column aliases are not supplied, then for a function returning a base
     data type, the column name is also the same as the function name.  For a
     function returning a composite type, the result columns get the names
     of the individual attributes of the type.
    </para>

    <para>
     Some examples:
<programlisting>
CREATE TABLE foo (fooid int, foosubid int, fooname text);

CREATE FUNCTION getfoo(int) RETURNS SETOF foo AS $$
    SELECT * FROM foo WHERE fooid = $1;
$$ LANGUAGE SQL;

SELECT * FROM getfoo(1) AS t1;

SELECT * FROM foo
    WHERE foosubid IN (
                        SELECT foosubid
                        FROM getfoo(foo.fooid) z
                        WHERE z.fooid = foo.fooid
                      );

CREATE VIEW vw_getfoo AS SELECT * FROM getfoo(1);

SELECT * FROM vw_getfoo;
</programlisting>
    </para>

    <para>
     In some cases it is useful to define table functions that can
     return different column sets depending on how they are invoked.
     To support this, the table function can be declared as returning
     the pseudo-type <type>record</type> with no <literal>OUT</literal>
     parameters.  When such a function is used in
     a query, the expected row structure must be specified in the
     query itself, so that the system can know how to parse and plan
     the query.  This syntax looks like:
    </para>

<synopsis>
<replaceable>function_call</replaceable> <optional>AS</optional> <replaceable>alias</replaceable> (<replaceable>column_definition</replaceable> <optional>, ... </optional>)
<replaceable>function_call</replaceable> AS <optional><replaceable>alias</replaceable></optional> (<replaceable>column_definition</replaceable> <optional>, ... </optional>)
ROWS FROM( ... <replaceable>function_call</replaceable> AS (<replaceable>column_definition</replaceable> <optional>, ... </optional>) <optional>, ... </optional> )
</synopsis>

    <para>
     When not using the <literal>ROWS FROM()</literal> syntax,
     the <replaceable>column_definition</replaceable> list replaces the column
     alias list that could otherwise be attached to the <literal>FROM</literal>
     item; the names in the column definitions serve as column aliases.
     When using the <literal>ROWS FROM()</literal> syntax,
     a <replaceable>column_definition</replaceable> list can be attached to
     each member function separately; or if there is only one member function
     and no <literal>WITH ORDINALITY</literal> clause,
     a <replaceable>column_definition</replaceable> list can be written in
     place of a column alias list following <literal>ROWS FROM()</literal>.
    </para>

    <para>
     Consider this example:
<programlisting>
SELECT *
    FROM dblink('dbname=mydb', 'SELECT proname, prosrc FROM pg_proc')
      AS t1(proname name, prosrc text)
    WHERE proname LIKE 'bytea%';
</programlisting>
     The <xref linkend="contrib-dblink-function"/> function
     (part of the <xref linkend="dblink"/> module) executes
     a remote query.  It is declared to return
     <type>record</type> since it might be used for any kind of query.
     The actual column set must be specified in the calling query so
     that the parser knows, for example, what <literal>*</literal> should
     expand to.
    </para>

    <para>
     This example uses <literal>ROWS FROM</literal>:
<programlisting>
SELECT *
FROM ROWS FROM
    (
        json_to_recordset('[{"a":40,"b":"foo"},{"a":"100","b":"bar"}]')
            AS (a INTEGER, b TEXT),
        generate_series(1, 3)
    ) AS x (p, q, s)
ORDER BY p;

  p  |  q  | s
-----+-----+---
  40 | foo | 1
 100 | bar | 2
     |     | 3
</programlisting>
     It joins two functions into a single <literal>FROM</literal>
     target.  <function>json_to_recordset()</function> is instructed
     to return two columns, the first <type>integer</type>
     and the second <type>text</type>.  The result of
     <function>generate_series()</function> is used directly.
     The <literal>ORDER BY</literal> clause sorts the column values
     as integers.
    </para>
   </sect3>

   <sect3 id="queries-lateral">
    <title><literal>LATERAL</literal> Subqueries</title>

    <indexterm zone="queries-lateral">
     <primary>LATERAL</primary>
     <secondary>in the FROM clause</secondary>
    </indexterm>

    <para>
     Subqueries appearing in <literal>FROM</literal> can be
     preceded by the key word <literal>LATERAL</literal>.  This allows them to
     reference columns provided by preceding <literal>FROM</literal> items.
     (Without <literal>LATERAL</literal>, each subquery is
     evaluated independently and so cannot cross-reference any other
     <literal>FROM</literal> item.)
    </para>

    <para>
     Table functions appearing in <literal>FROM</literal> can also be
     preceded by the key word <literal>LATERAL</literal>, but for functions the
     key word is optional; the function's arguments can contain references
     to columns provided by preceding <literal>FROM</literal> items in any case.
    </para>

    <para>
     A <literal>GRAPH_TABLE</literal> <literal>FROM</literal> item can also
     always contain lateral references.
    </para>

    <para>
     A <literal>LATERAL</literal> item can appear at the top level in the
     <literal>FROM</literal> list, or within a <literal>JOIN</literal> tree.  In the latter
     case it can also refer to any items that are on the left-hand side of a
     <literal>JOIN</literal> that it is on the right-hand side of.
    </para>

    <para>
     When a <literal>FROM</literal> item contains <literal>LATERAL</literal>
     cross-references, evaluation proceeds as follows: for each row of the
     <literal>FROM</literal> item providing the cross-referenced column(s), or
     set of rows of multiple <literal>FROM</literal> items providing the
     columns, the <literal>LATERAL</literal> item is evaluated using that
     row or row set's values of the columns.  The resulting row(s) are
     joined as usual with the rows they were computed from.  This is
     repeated for each row or set of rows from the column source table(s).
    </para>

    <para>
     A trivial example of <literal>LATERAL</literal> is
<programlisting>
SELECT * FROM foo, LATERAL (SELECT * FROM bar WHERE bar.id = foo.bar_id) ss;
</programlisting>
     This is not especially useful since it has exactly the same result as
     the more conventional
<programlisting>
SELECT * FROM foo, bar WHERE bar.id = foo.bar_id;
</programlisting>
     <literal>LATERAL</literal> is primarily useful when the cross-referenced
     column is necessary for computing the row(s) to be joined.  A common
     application is providing an argument value for a set-returning function.
     For example, supposing that <function>vertices(polygon)</function> returns the
     set of vertices of a polygon, we could identify close-together vertices
     of polygons stored in a table with:
<programlisting>
SELECT p1.id, p2.id, v1, v2
FROM polygons p1, polygons p2,
     LATERAL vertices(p1.poly) v1,
     LATERAL vertices(p2.poly) v2
WHERE (v1 &lt;-&gt; v2) &lt; 10 AND p1.id != p2.id;
</programlisting>
     This query could also be written
<programlisting>
SELECT p1.id, p2.id, v1, v2
FROM polygons p1 CROSS JOIN LATERAL vertices(p1.poly) v1,
     polygons p2 CROSS JOIN LATERAL vertices(p2.poly) v2
WHERE (v1 &lt;-&gt; v2) &lt; 10 AND p1.id != p2.id;
</programlisting>
     or in several other equivalent formulations.  (As already mentioned,
     the <literal>LATERAL</literal> key word is unnecessary in this example, but
     we use it for clarity.)
    </para>

    <para>
     It is often particularly handy to <literal>LEFT JOIN</literal> to a
     <literal>LATERAL</literal> subquery, so that source rows will appear in
     the result even if the <literal>LATERAL</literal> subquery produces no
     rows for them.  For example, if <function>get_product_names()</function> returns
     the names of products made by a manufacturer, but some manufacturers in
     our table currently produce no products, we could find out which ones
     those are like this:
<programlisting>
SELECT m.name
FROM manufacturers m LEFT JOIN LATERAL get_product_names(m.id) pname ON true
WHERE pname IS NULL;
</programlisting>
    </para>
   </sect3>
  </sect2>

  <sect2 id="queries-where">
   <title>The <literal>WHERE</literal> Clause</title>

   <indexterm zone="queries-where">
    <primary>WHERE</primary>
   </indexterm>

   <para>
    The syntax of the <link linkend="sql-where"><literal>WHERE</literal></link>
    clause is
<synopsis>
WHERE <replaceable>search_condition</replaceable>
</synopsis>
    where <replaceable>search_condition</replaceable> is any value
    expression (see <xref linkend="sql-expressions"/>) that
    returns a value of type <type>boolean</type>.
   </para>

   <para>
    After the processing of the <literal>FROM</literal> clause is done, each
    row of the derived virtual table is checked against the search
    condition.  If the result of the condition is true, the row is
    kept in the output table, otherwise (i.e., if the result is
    false or null) it is discarded.  The search condition typically
    references at least one column of the table generated in the
    <literal>FROM</literal> clause; this is not required, but otherwise the
    <literal>WHERE</literal> clause will be fairly useless.
   </para>

   <note>
    <para>
     The join condition of an inner join can be written either in
     the <literal>WHERE</literal> clause or in the <literal>JOIN</literal> clause.
     For example, these table expressions are equivalent:
<programlisting>
FROM a, b WHERE a.id = b.id AND b.val &gt; 5
</programlisting>
     and:
<programlisting>
FROM a INNER JOIN b ON (a.id = b.id) WHERE b.val &gt; 5
</programlisting>
     or perhaps even:
<programlisting>
FROM a NATURAL JOIN b WHERE b.val &gt; 5
</programlisting>
     Which one of these you use is mainly a matter of style.  The
     <literal>JOIN</literal> syntax in the <literal>FROM</literal> clause is
     probably not as portable to other SQL database management systems,
     even though it is in the SQL standard.  For
     outer joins there is no choice:  they must be done in
     the <literal>FROM</literal> clause.  The <literal>ON</literal> or <literal>USING</literal>
     clause of an outer join is <emphasis>not</emphasis> equivalent to a
     <literal>WHERE</literal> condition, because it results in the addition
     of rows (for unmatched input rows) as well as the removal of rows
     in the final result.
    </para>
   </note>

   <para>
    Here are some examples of <literal>WHERE</literal> clauses:
<programlisting>
SELECT ... FROM fdt WHERE c1 &gt; 5

SELECT ... FROM fdt WHERE c1 IN (1, 2, 3)

SELECT ... FROM fdt WHERE c1 IN (SELECT c1 FROM t2)

SELECT ... FROM fdt WHERE c1 IN (SELECT c3 FROM t2 WHERE c2 = fdt.c1 + 10)

SELECT ... FROM fdt WHERE c1 BETWEEN (SELECT c3 FROM t2 WHERE c2 = fdt.c1 + 10) AND 100

SELECT ... FROM fdt WHERE EXISTS (SELECT c1 FROM t2 WHERE c2 &gt; fdt.c1)
</programlisting>
    <literal>fdt</literal> is the table derived in the
    <literal>FROM</literal> clause. Rows that do not meet the search
    condition of the <literal>WHERE</literal> clause are eliminated from
    <literal>fdt</literal>. Notice the use of scalar subqueries as
    value expressions.  Just like any other query, the subqueries can
    employ complex table expressions.  Notice also how
    <literal>fdt</literal> is referenced in the subqueries.
    Qualifying <literal>c1</literal> as <literal>fdt.c1</literal> is only necessary
    if <literal>c1</literal> is also the name of a column in the derived
    input table of the subquery.  But qualifying the column name adds
    clarity even when it is not needed.  This example shows how the column
    naming scope of an outer query extends into its inner queries.
   </para>
  </sect2>


  <sect2 id="queries-group">
   <title>The <literal>GROUP BY</literal> and <literal>HAVING</literal> Clauses</title>

   <indexterm zone="queries-group">
    <primary>GROUP BY</primary>
   </indexterm>

   <indexterm zone="queries-group">
    <primary>grouping</primary>
   </indexterm>

   <para>
    After passing the <literal>WHERE</literal> filter, the derived input
    table might be subject to grouping, using the <literal>GROUP BY</literal>
    clause, and elimination of group rows using the <literal>HAVING</literal>
    clause.
   </para>

<synopsis>
SELECT <replaceable>select_list</replaceable>
    FROM ...
    <optional>WHERE ...</optional>
    GROUP BY <replaceable>grouping_column_reference</replaceable> <optional>, <replaceable>grouping_column_reference</replaceable></optional>...
</synopsis>

   <para>
    The <link linkend="sql-groupby"><literal>GROUP BY</literal></link> clause is
    used to group together those rows in a table that have the same
    values in all the columns listed. The order in which the columns
    are listed does not matter.  The effect is to combine each set
    of rows having common values into one group row that
    represents all rows in the group.  This is done to
    eliminate redundancy in the output and/or compute aggregates that
    apply to these groups.  For instance:
<screen>
<prompt>=&gt;</prompt> <userinput>SELECT * FROM test1;</userinput>
 x | y
---+---
 a | 3
 c | 2
 b | 5
 a | 1
(4 rows)

<prompt>=&gt;</prompt> <userinput>SELECT x FROM test1 GROUP BY x;</userinput>
 x
---
 a
 b
 c
(3 rows)
</screen>
   </para>

   <para>
    In the second query, we could not have written <literal>SELECT *
    FROM test1 GROUP BY x</literal>, because there is no single value
    for the column <structfield>y</structfield> that could be associated with each
    group.  The grouped-by columns can be referenced in the select list since
    they have a single value in each group.
   </para>

   <para>
    In general, if a table is grouped, columns that are not
    listed in <literal>GROUP BY</literal> cannot be referenced except in aggregate
    expressions.  An example with aggregate expressions is:
<screen>
<prompt>=&gt;</prompt> <userinput>SELECT x, sum(y) FROM test1 GROUP BY x;</userinput>
 x | sum
---+-----
 a |   4
 b |   5
 c |   2
(3 rows)
</screen>
    Here <literal>sum</literal> is an aggregate function that
    computes a single value over the entire group.  More information
    about the available aggregate functions can be found in <xref
    linkend="functions-aggregate"/>.
   </para>

   <tip>
    <para>
     Grouping without aggregate expressions effectively calculates the
     set of distinct values in a column.  This can also be achieved
     using the <literal>DISTINCT</literal> clause (see <xref
     linkend="queries-distinct"/>).
    </para>
   </tip>

   <para>
    Here is another example:  it calculates the total sales for each
    product (rather than the total sales of all products):
<programlisting>
SELECT product_id, p.name, (sum(s.units) * p.price) AS sales
    FROM products p LEFT JOIN sales s USING (product_id)
    GROUP BY product_id, p.name, p.price;
</programlisting>
    In this example, the columns <literal>product_id</literal>,
    <literal>p.name</literal>, and <literal>p.price</literal> must be
    in the <literal>GROUP BY</literal> clause since they are referenced in
    the query select list (but see below).  The column
    <literal>s.units</literal> does not have to be in the <literal>GROUP
    BY</literal> list since it is only used in an aggregate expression
    (<literal>sum(...)</literal>), which represents the sales
    of a product.  For each product, the query returns a summary row about
    all sales of the product.
   </para>

   <indexterm><primary>functional dependency</primary></indexterm>

   <para>
    If the products table is set up so that, say,
    <literal>product_id</literal> is the primary key, then it would be
    enough to group by <literal>product_id</literal> in the above example,
    since name and price would be <firstterm>functionally
    dependent</firstterm> on the product ID, and so there would be no
    ambiguity about which name and price value to return for each product
    ID group.
   </para>

   <para>
    In strict SQL, <literal>GROUP BY</literal> can only group by columns of
    the source table, but <productname>PostgreSQL</productname> extends
    this to also allow <literal>GROUP BY</literal> to group by columns in the
    select list.  Grouping by value expressions instead of simple
    column names is also allowed (but <literal>GROUP BY</literal>
    expressions cannot contain aggregate functions or window functions).
   </para>

   <para>
    PostgreSQL also supports the syntax <literal>GROUP BY ALL</literal>,
    which is equivalent to explicitly writing all select-list entries that
    do not contain either an aggregate function or a window function.
    This can greatly simplify ad-hoc exploration of data.
    As an example, these queries are equivalent:
<screen>
<prompt>=&gt;</prompt> <userinput>SELECT a, b, a + b, sum(c) FROM test1 GROUP BY ALL;</userinput>
 a | b | ?column? | sum
---+---+----------+----
 1 | 4 |        5 |  9
 2 | 5 |        7 | 12
 3 | 6 |        9 | 15
(3 rows)

<prompt>=&gt;</prompt> <userinput>SELECT a, b, a + b, sum(c) FROM test1 GROUP BY a, b, a + b;</userinput>
 a | b | ?column? | sum
---+---+----------+----
 1 | 4 |        5 |  9
 2 | 5 |        7 | 12
 3 | 6 |        9 | 15
(3 rows)
</screen>
   </para>

   <indexterm>
    <primary>HAVING</primary>
   </indexterm>

   <para>
    If a table has been grouped using <literal>GROUP BY</literal>,
    but only certain groups are of interest, the
    <literal>HAVING</literal> clause can be used, much like a
    <literal>WHERE</literal> clause, to eliminate groups from the result.
    The syntax is:
<synopsis>
SELECT <replaceable>select_list</replaceable> FROM ... <optional>WHERE ...</optional> GROUP BY ... HAVING <replaceable>boolean_expression</replaceable>
</synopsis>
    Expressions in the <literal>HAVING</literal> clause can refer both to
    grouped expressions and to ungrouped expressions (which necessarily
    involve an aggregate function).
   </para>

   <para>
    Example:
<screen>
<prompt>=&gt;</prompt> <userinput>SELECT x, sum(y) FROM test1 GROUP BY x HAVING sum(y) &gt; 3;</userinput>
 x | sum
---+-----
 a |   4
 b |   5
(2 rows)

<prompt>=&gt;</prompt> <userinput>SELECT x, sum(y) FROM test1 GROUP BY x HAVING x &lt; 'c';</userinput>
 x | sum
---+-----
 a |   4
 b |   5
(2 rows)
</screen>
   </para>

   <para>
    Again, a more realistic example:
<programlisting>
SELECT product_id, p.name, (sum(s.units) * (p.price - p.cost)) AS profit
    FROM products p LEFT JOIN sales s USING (product_id)
    WHERE s.date &gt; CURRENT_DATE - INTERVAL '4 weeks'
    GROUP BY product_id, p.name, p.price, p.cost
    HAVING sum(p.price * s.units) &gt; 5000;
</programlisting>
    In the example above, the <literal>WHERE</literal> clause is selecting
    rows by a column that is not grouped (the expression is only true for
    sales during the last four weeks), while the <literal>HAVING</literal>
    clause restricts the output to groups with total gross sales over
    5000.  Note that the aggregate expressions do not necessarily need
    to be the same in all parts of the query.
   </para>

   <para>
    If a query contains aggregate function calls, but no <literal>GROUP BY</literal>
    clause, grouping still occurs: the result is a single group row (or
    perhaps no rows at all, if the single row is then eliminated by
    <literal>HAVING</literal>).
    The same is true if it contains a <literal>HAVING</literal> clause, even
    without any aggregate function calls or <literal>GROUP BY</literal> clause.
   </para>
  </sect2>

  <sect2 id="queries-grouping-sets">
   <title><literal>GROUPING SETS</literal>, <literal>CUBE</literal>, and <literal>ROLLUP</literal></title>

   <indexterm zone="queries-grouping-sets">
    <primary>GROUPING SETS</primary>
   </indexterm>
   <indexterm zone="queries-grouping-sets">
    <primary>CUBE</primary>
   </indexterm>
   <indexterm zone="queries-grouping-sets">
    <primary>ROLLUP</primary>
   </indexterm>

   <para>
    More complex grouping operations than those described above are possible
    using the concept of <firstterm>grouping sets</firstterm>.  The data selected by
    the <literal>FROM</literal> and <literal>WHERE</literal> clauses is grouped separately
    by each specified grouping set, aggregates computed for each group just as
    for simple <literal>GROUP BY</literal> clauses, and then the results returned.
    For example:
<screen>
<prompt>=&gt;</prompt> <userinput>SELECT * FROM items_sold;</userinput>
 brand | size | sales
-------+------+-------
 Foo   | L    |  10
 Foo   | M    |  20
 Bar   | M    |  15
 Bar   | L    |  5
(4 rows)

<prompt>=&gt;</prompt> <userinput>SELECT brand, size, sum(sales) FROM items_sold GROUP BY GROUPING SETS ((brand), (size), ());</userinput>
 brand | size | sum
-------+------+-----
 Foo   |      |  30
 Bar   |      |  20
       | L    |  15
       | M    |  35
       |      |  50
(5 rows)
</screen>
   </para>

   <para>
    Each sublist of <literal>GROUPING SETS</literal> may specify zero or more columns
    or expressions and is interpreted the same way as though it were directly
    in the <literal>GROUP BY</literal> clause.  An empty grouping set means that all
    rows are aggregated down to a single group (which is output even if no
    input rows were present), as described above for the case of aggregate
    functions with no <literal>GROUP BY</literal> clause.
   </para>

   <para>
    References to the grouping columns or expressions are replaced
    by null values in result rows for grouping sets in which those
    columns do not appear.  To distinguish which grouping a particular output
    row resulted from, see <xref linkend="functions-grouping-table"/>.
   </para>

   <para>
    A shorthand notation is provided for specifying two common types of grouping set.
    A clause of the form
<programlisting>
ROLLUP ( <replaceable>e1</replaceable>, <replaceable>e2</replaceable>, <replaceable>e3</replaceable>, ... )
</programlisting>
    represents the given list of expressions and all prefixes of the list including
    the empty list; thus it is equivalent to
<programlisting>
GROUPING SETS (
    ( <replaceable>e1</replaceable>, <replaceable>e2</replaceable>, <replaceable>e3</replaceable>, ... ),
    ...
    ( <replaceable>e1</replaceable>, <replaceable>e2</replaceable> ),
    ( <replaceable>e1</replaceable> ),
    ( )
)
</programlisting>
    This is commonly used for analysis over hierarchical data; e.g., total
    salary by department, division, and company-wide total.
   </para>

   <para>
    A clause of the form
<programlisting>
CUBE ( <replaceable>e1</replaceable>, <replaceable>e2</replaceable>, ... )
</programlisting>
    represents the given list and all of its possible subsets (i.e., the power
    set).  Thus
<programlisting>
CUBE ( a, b, c )
</programlisting>
    is equivalent to
<programlisting>
GROUPING SETS (
    ( a, b, c ),
    ( a, b    ),
    ( a,    c ),
    ( a       ),
    (    b, c ),
    (    b    ),
    (       c ),
    (         )
)
</programlisting>
   </para>

   <para>
    The individual elements of a <literal>CUBE</literal> or <literal>ROLLUP</literal>
    clause may be either individual expressions, or sublists of elements in
    parentheses.  In the latter case, the sublists are treated as single
    units for the purposes of generating the individual grouping sets.
    For example:
<programlisting>
CUBE ( (a, b), (c, d) )
</programlisting>
    is equivalent to
<programlisting>
GROUPING SETS (
    ( a, b, c, d ),
    ( a, b       ),
    (       c, d ),
    (            )
)
</programlisting>
    and
<programlisting>
ROLLUP ( a, (b, c), d )
</programlisting>
    is equivalent to
<programlisting>
GROUPING SETS (
    ( a, b, c, d ),
    ( a, b, c    ),
    ( a          ),
    (            )
)
</programlisting>
   </para>

   <para>
    The <literal>CUBE</literal> and <literal>ROLLUP</literal> constructs can be used either
    directly in the <literal>GROUP BY</literal> clause, or nested inside a
    <literal>GROUPING SETS</literal> clause.  If one <literal>GROUPING SETS</literal> clause
    is nested inside another, the effect is the same as if all the elements of
    the inner clause had been written directly in the outer clause.
   </para>

   <para>
    If multiple grouping items are specified in a single <literal>GROUP BY</literal>
    clause, then the final list of grouping sets is the Cartesian product of the
    individual items.  For example:
<programlisting>
GROUP BY a, CUBE (b, c), GROUPING SETS ((d), (e))
</programlisting>
    is equivalent to
<programlisting>
GROUP BY GROUPING SETS (
    (a, b, c, d), (a, b, c, e),
    (a, b, d),    (a, b, e),
    (a, c, d),    (a, c, e),
    (a, d),       (a, e)
)
</programlisting>
   </para>

   <para>
    <indexterm zone="queries-grouping-sets">
     <primary>ALL</primary>
     <secondary>GROUP BY ALL</secondary>
    </indexterm>
    <indexterm zone="queries-grouping-sets">
     <primary>DISTINCT</primary>
     <secondary>GROUP BY DISTINCT</secondary>
    </indexterm>
    When specifying multiple grouping items together, the final set of grouping
    sets might contain duplicates. For example:
<programlisting>
GROUP BY ROLLUP (a, b), ROLLUP (a, c)
</programlisting>
    is equivalent to
<programlisting>
GROUP BY GROUPING SETS (
    (a, b, c),
    (a, b),
    (a, b),
    (a, c),
    (a),
    (a),
    (a, c),
    (a),
    ()
)
</programlisting>
    If these duplicates are undesirable, they can be removed using the
    <literal>DISTINCT</literal> clause directly on the <literal>GROUP BY</literal>.
    Therefore:
<programlisting>
GROUP BY <emphasis>DISTINCT</emphasis> ROLLUP (a, b), ROLLUP (a, c)
</programlisting>
    is equivalent to
<programlisting>
GROUP BY GROUPING SETS (
    (a, b, c),
    (a, b),
    (a, c),
    (a),
    ()
)
</programlisting>
    This is not the same as using <literal>SELECT DISTINCT</literal> because the output
    rows may still contain duplicates.  If any of the ungrouped columns contains NULL,
    it will be indistinguishable from the NULL used when that same column is grouped.
   </para>

  <note>
   <para>
    The construct <literal>(a, b)</literal> is normally recognized in expressions as
    a <link linkend="sql-syntax-row-constructors">row constructor</link>.
    Within the <literal>GROUP BY</literal> clause, this does not apply at the top
    levels of expressions, and <literal>(a, b)</literal> is parsed as a list of
    expressions as described above.  If for some reason you <emphasis>need</emphasis>
    a row constructor in a grouping expression, use <literal>ROW(a, b)</literal>.
   </para>
  </note>
  </sect2>

  <sect2 id="queries-window">
   <title>Window Function Processing</title>

   <indexterm zone="queries-window">
    <primary>window function</primary>
    <secondary>order of execution</secondary>
   </indexterm>

   <para>
    If the query contains any window functions (see
    <xref linkend="tutorial-window"/>,
    <xref linkend="functions-window"/> and
    <xref linkend="syntax-window-functions"/>), these functions are evaluated
    after any grouping, aggregation, and <literal>HAVING</literal> filtering is
    performed.  That is, if the query uses any aggregates, <literal>GROUP
    BY</literal>, or <literal>HAVING</literal>, then the rows seen by the window functions
    are the group rows instead of the original table rows from
    <literal>FROM</literal>/<literal>WHERE</literal>.
   </para>

   <para>
    When multiple window functions are used, all the window functions having
    equivalent <literal>PARTITION BY</literal> and <literal>ORDER BY</literal>
    clauses in their window definitions are guaranteed to see the same
    ordering of the input rows, even if the <literal>ORDER BY</literal> does
    not uniquely determine the ordering.
    However, no guarantees are made about the evaluation of functions having
    different <literal>PARTITION BY</literal> or <literal>ORDER BY</literal> specifications.
    (In such cases a sort step is typically required between the passes of
    window function evaluations, and the sort is not guaranteed to preserve
    ordering of rows that its <literal>ORDER BY</literal> sees as equivalent.)
   </para>

   <para>
    Currently, window functions always require presorted data, and so the
    query output will be ordered according to one or another of the window
    functions' <literal>PARTITION BY</literal>/<literal>ORDER BY</literal> clauses.
    It is not recommended to rely on this, however.  Use an explicit
    top-level <literal>ORDER BY</literal> clause if you want to be sure the
    results are sorted in a particular way.
   </para>
  </sect2>
 </sect1>


 <sect1 id="queries-select-lists">
  <title>Select Lists</title>

  <indexterm>
   <primary>SELECT</primary>
   <secondary>select list</secondary>
  </indexterm>

  <para>
   As shown in the previous section,
   the table expression in the <command>SELECT</command> command
   constructs an intermediate virtual table by possibly combining
   tables, views, eliminating rows, grouping, etc.  This table is
   finally passed on to processing by the <firstterm>select list</firstterm>.  The select
   list determines which <emphasis>columns</emphasis> of the
   intermediate table are actually output.
  </para>

  <sect2 id="queries-select-list-items">
   <title>Select-List Items</title>

   <indexterm>
    <primary>*</primary>
   </indexterm>

   <para>
    The simplest kind of select list is <literal>*</literal> which
    emits all columns that the table expression produces.  Otherwise,
    a select list is a comma-separated list of value expressions (as
    defined in <xref linkend="sql-expressions"/>).  For instance, it
    could be a list of column names:
<programlisting>
SELECT a, b, c FROM ...
</programlisting>
     The columns names <literal>a</literal>, <literal>b</literal>, and <literal>c</literal>
     are either the actual names of the columns of tables referenced
     in the <literal>FROM</literal> clause, or the aliases given to them as
     explained in <xref linkend="queries-table-aliases"/>.  The name
     space available in the select list is the same as in the
     <literal>WHERE</literal> clause, unless grouping is used, in which case
     it is the same as in the <literal>HAVING</literal> clause.
   </para>

   <para>
    If more than one table has a column of the same name, the table
    name must also be given, as in:
<programlisting>
SELECT tbl1.a, tbl2.a, tbl1.b FROM ...
</programlisting>
    When working with multiple tables, it can also be useful to ask for
    all the columns of a particular table:
<programlisting>
SELECT tbl1.*, tbl2.a FROM ...
</programlisting>
    See <xref linkend="rowtypes-usage"/> for more about
    the <replaceable>table_name</replaceable><literal>.*</literal> notation.
   </para>

   <para>
    If an arbitrary value expression is used in the select list, it
    conceptually adds a new virtual column to the returned table.  The
    value expression is evaluated once for each result row, with
    the row's values substituted for any column references.  But the
    expressions in the select list do not have to reference any
    columns in the table expression of the <literal>FROM</literal> clause;
    they can be constant arithmetic expressions, for instance.
   </para>
  </sect2>

  <sect2 id="queries-column-labels">
   <title>Column Labels</title>

   <indexterm zone="queries-column-labels">
    <primary>alias</primary>
    <secondary>in the select list</secondary>
   </indexterm>

   <para>
    The entries in the select list can be assigned names for subsequent
    processing, such as for use in an <literal>ORDER BY</literal> clause
    or for display by the client application.  For example:
<programlisting>
SELECT a AS value, b + c AS sum FROM ...
</programlisting>
   </para>

   <para>
    If no output column name is specified using <literal>AS</literal>,
    the system assigns a default column name.  For simple column references,
    this is the name of the referenced column.  For function
    calls, this is the name of the function.  For complex expressions,
    the system will generate a generic name.
   </para>

   <para>
    The <literal>AS</literal> key word is usually optional, but in some
    cases where the desired column name matches a
    <productname>PostgreSQL</productname> key word, you must write
    <literal>AS</literal> or double-quote the column name in order to
    avoid ambiguity.
    (<xref linkend="sql-keywords-appendix"/> shows which key words
    require <literal>AS</literal> to be used as a column label.)
    For example, <literal>FROM</literal> is one such key word, so this
    does not work:
<programlisting>
SELECT a from, b + c AS sum FROM ...
</programlisting>
    but either of these do:
<programlisting>
SELECT a AS from, b + c AS sum FROM ...
SELECT a "from", b + c AS sum FROM ...
</programlisting>
    For greatest safety against possible
    future key word additions, it is recommended that you always either
    write <literal>AS</literal> or double-quote the output column name.
   </para>

   <note>
    <para>
     The naming of output columns here is different from that done in
     the <literal>FROM</literal> clause (see <xref
     linkend="queries-table-aliases"/>).  It is possible
     to rename the same column twice, but the name assigned in
     the select list is the one that will be passed on.
    </para>
   </note>
  </sect2>

  <sect2 id="queries-distinct">
   <title><literal>DISTINCT</literal></title>

   <indexterm zone="queries-distinct">
    <primary>ALL</primary>
    <secondary>SELECT ALL</secondary>
   </indexterm>
   <indexterm zone="queries-distinct">
    <primary>DISTINCT</primary>
    <secondary>SELECT DISTINCT</secondary>
   </indexterm>

   <indexterm zone="queries-distinct">
    <primary>duplicates</primary>
   </indexterm>

   <para>
    After the select list has been processed, the result table can
    optionally be subject to the elimination of duplicate rows.  The
    <literal>DISTINCT</literal> key word is written directly after
    <literal>SELECT</literal> to specify this:
<synopsis>
SELECT DISTINCT <replaceable>select_list</replaceable> ...
</synopsis>
    (Instead of <literal>DISTINCT</literal> the key word <literal>ALL</literal>
    can be used to specify the default behavior of retaining all rows.)
   </para>

   <indexterm>
    <primary>null value</primary>
    <secondary sortas="DISTINCT">in DISTINCT</secondary>
   </indexterm>

   <para>
    Obviously, two rows are considered distinct if they differ in at
    least one column value.  Null values are considered equal in this
    comparison.
   </para>

   <para>
    Alternatively, an arbitrary expression can determine what rows are
    to be considered distinct:
<synopsis>
SELECT DISTINCT ON (<replaceable>expression</replaceable> <optional>, <replaceable>expression</replaceable> ...</optional>) <replaceable>select_list</replaceable> ...
</synopsis>
    Here <replaceable>expression</replaceable> is an arbitrary value
    expression that is evaluated for all rows.  A set of rows for
    which all the expressions are equal are considered duplicates, and
    only the first row of the set is kept in the output.  Note that
    the <quote>first row</quote> of a set is unpredictable unless the
    query is sorted on enough columns to guarantee a unique ordering
    of the rows arriving at the <literal>DISTINCT</literal> filter.
    (<literal>DISTINCT ON</literal> processing occurs after <literal>ORDER
    BY</literal> sorting.)
   </para>

   <para>
    The <literal>DISTINCT ON</literal> clause is not part of the SQL standard
    and is sometimes considered bad style because of the potentially
    indeterminate nature of its results.  With judicious use of
    <literal>GROUP BY</literal> and subqueries in <literal>FROM</literal>, this
    construct can be avoided, but it is often the most convenient
    alternative.
   </para>
  </sect2>
 </sect1>


 <sect1 id="queries-union">
  <title>Combining Queries (<literal>UNION</literal>, <literal>INTERSECT</literal>, <literal>EXCEPT</literal>)</title>

  <indexterm zone="queries-union">
   <primary>UNION</primary>
  </indexterm>
  <indexterm zone="queries-union">
   <primary>INTERSECT</primary>
  </indexterm>
  <indexterm zone="queries-union">
   <primary>EXCEPT</primary>
  </indexterm>
  <indexterm zone="queries-union">
   <primary>set union</primary>
  </indexterm>
  <indexterm zone="queries-union">
   <primary>set intersection</primary>
  </indexterm>
  <indexterm zone="queries-union">
   <primary>set difference</primary>
  </indexterm>
  <indexterm zone="queries-union">
   <primary>set operation</primary>
  </indexterm>

  <para>
   The results of two queries can be combined using the set operations
   union, intersection, and difference.  The syntax is
<synopsis>
<replaceable>query1</replaceable> UNION <optional>ALL</optional> <replaceable>query2</replaceable>
<replaceable>query1</replaceable> INTERSECT <optional>ALL</optional> <replaceable>query2</replaceable>
<replaceable>query1</replaceable> EXCEPT <optional>ALL</optional> <replaceable>query2</replaceable>
</synopsis>
   where <replaceable>query1</replaceable> and
   <replaceable>query2</replaceable> are queries that can use any of
   the features discussed up to this point.
  </para>

  <para>
   <literal>UNION</literal> effectively appends the result of
   <replaceable>query2</replaceable> to the result of
   <replaceable>query1</replaceable> (although there is no guarantee
   that this is the order in which the rows are actually returned).
   Furthermore, it eliminates duplicate rows from its result, in the same
   way as <literal>DISTINCT</literal>, unless <literal>UNION ALL</literal> is used.
  </para>

  <para>
   <literal>INTERSECT</literal> returns all rows that are both in the result
   of <replaceable>query1</replaceable> and in the result of
   <replaceable>query2</replaceable>.  Duplicate rows are eliminated
   unless <literal>INTERSECT ALL</literal> is used.
  </para>

  <para>
   <literal>EXCEPT</literal> returns all rows that are in the result of
   <replaceable>query1</replaceable> but not in the result of
   <replaceable>query2</replaceable>.  (This is sometimes called the
   <firstterm>difference</firstterm> between two queries.)  Again, duplicates
   are eliminated unless <literal>EXCEPT ALL</literal> is used.
  </para>

  <para>
   In order to calculate the union, intersection, or difference of two
   queries, the two queries must be <quote>union compatible</quote>,
   which means that they return the same number of columns and
   the corresponding columns have compatible data types, as
   described in <xref linkend="typeconv-union-case"/>.
  </para>

  <para>
   Set operations can be combined, for example
<synopsis>
<replaceable>query1</replaceable> UNION <replaceable>query2</replaceable> EXCEPT <replaceable>query3</replaceable>
</synopsis>
   which is equivalent to
<synopsis>
(<replaceable>query1</replaceable> UNION <replaceable>query2</replaceable>) EXCEPT <replaceable>query3</replaceable>
</synopsis>
   As shown here, you can use parentheses to control the order of
   evaluation.  Without parentheses, <literal>UNION</literal>
   and <literal>EXCEPT</literal> associate left-to-right,
   but <literal>INTERSECT</literal> binds more tightly than those two
   operators.  Thus
<synopsis>
<replaceable>query1</replaceable> UNION <replaceable>query2</replaceable> INTERSECT <replaceable>query3</replaceable>
</synopsis>
   means
<synopsis>
<replaceable>query1</replaceable> UNION (<replaceable>query2</replaceable> INTERSECT <replaceable>query3</replaceable>)
</synopsis>
   You can also surround an individual <replaceable>query</replaceable>
   with parentheses.  This is important if
   the <replaceable>query</replaceable> needs to use any of the clauses
   discussed in following sections, such as <literal>LIMIT</literal>.
   Without parentheses, you'll get a syntax error, or else the clause will
   be understood as applying to the output of the set operation rather
   than one of its inputs.  For example,
<synopsis>
SELECT a FROM b UNION SELECT x FROM y LIMIT 10
</synopsis>
   is accepted, but it means
<synopsis>
(SELECT a FROM b UNION SELECT x FROM y) LIMIT 10
</synopsis>
   not
<synopsis>
SELECT a FROM b UNION (SELECT x FROM y LIMIT 10)
</synopsis>
  </para>
 </sect1>


 <sect1 id="queries-order">
  <title>Sorting Rows (<literal>ORDER BY</literal>)</title>

  <indexterm zone="queries-order">
   <primary>sorting</primary>
  </indexterm>

  <indexterm zone="queries-order">
   <primary>ORDER BY</primary>
  </indexterm>

  <para>
   After a query has produced an output table (after the select list
   has been processed) it can optionally be sorted.  If sorting is not
   chosen, the rows will be returned in an unspecified order.  The actual
   order in that case will depend on the scan and join plan types and
   the order on disk, but it must not be relied on.  A particular
   output ordering can only be guaranteed if the sort step is explicitly
   chosen.
  </para>

  <para>
   The <literal>ORDER BY</literal> clause specifies the sort order:
<synopsis>
SELECT <replaceable>select_list</replaceable>
    FROM <replaceable>table_expression</replaceable>
    ORDER BY <replaceable>sort_expression1</replaceable> <optional>ASC | DESC</optional> <optional>NULLS { FIRST | LAST }</optional>
             <optional>, <replaceable>sort_expression2</replaceable> <optional>ASC | DESC</optional> <optional>NULLS { FIRST | LAST }</optional> ...</optional>
</synopsis>
   The sort expression(s) can be any expression that would be valid in the
   query's select list.  An example is:
<programlisting>
SELECT a, b FROM table1 ORDER BY a + b, c;
</programlisting>
   When more than one expression is specified,
   the later values are used to sort rows that are equal according to the
   earlier values.  Each expression can be followed by an optional
   <literal>ASC</literal> or <literal>DESC</literal> keyword to set the sort direction to
   ascending or descending.  <literal>ASC</literal> order is the default.
   Ascending order puts smaller values first, where
   <quote>smaller</quote> is defined in terms of the
   <literal>&lt;</literal> operator.  Similarly, descending order is
   determined with the <literal>&gt;</literal> operator.
    <footnote>
     <para>
      Actually, <productname>PostgreSQL</productname> uses the <firstterm>default B-tree
      operator class</firstterm> for the expression's data type to determine the sort
      ordering for <literal>ASC</literal> and <literal>DESC</literal>.  Conventionally,
      data types will be set up so that the <literal>&lt;</literal> and
      <literal>&gt;</literal> operators correspond to this sort ordering,
      but a user-defined data type's designer could choose to do something
      different.
     </para>
    </footnote>
  </para>

  <para>
   The <literal>NULLS FIRST</literal> and <literal>NULLS LAST</literal> options can be
   used to determine whether nulls appear before or after non-null values
   in the sort ordering.  By default, null values sort as if larger than any
   non-null value; that is, <literal>NULLS FIRST</literal> is the default for
   <literal>DESC</literal> order, and <literal>NULLS LAST</literal> otherwise.
  </para>

  <para>
   Note that the ordering options are considered independently for each
   sort column.  For example <literal>ORDER BY x, y DESC</literal> means
   <literal>ORDER BY x ASC, y DESC</literal>, which is not the same as
   <literal>ORDER BY x DESC, y DESC</literal>.
  </para>

  <para>
   A <replaceable>sort_expression</replaceable> can also be the column label or number
   of an output column, as in:
<programlisting>
SELECT a + b AS sum, c FROM table1 ORDER BY sum;
SELECT a, max(b) FROM table1 GROUP BY a ORDER BY 1;
</programlisting>
   both of which sort by the first output column.  Note that an output
   column name has to stand alone, that is, it cannot be used in an expression
   &mdash; for example, this is <emphasis>not</emphasis> correct:
<programlisting>
SELECT a + b AS sum, c FROM table1 ORDER BY sum + c;          -- wrong
</programlisting>
   This restriction is made to reduce ambiguity.  There is still
   ambiguity if an <literal>ORDER BY</literal> item is a simple name that
   could match either an output column name or a column from the table
   expression.  The output column is used in such cases.  This would
   only cause confusion if you use <literal>AS</literal> to rename an output
   column to match some other table column's name.
  </para>

  <para>
   <literal>ORDER BY</literal> can be applied to the result of a
   <literal>UNION</literal>, <literal>INTERSECT</literal>, or <literal>EXCEPT</literal>
   combination, but in this case it is only permitted to sort by
   output column names or numbers, not by expressions.
  </para>
 </sect1>


 <sect1 id="queries-limit">
  <title><literal>LIMIT</literal> and <literal>OFFSET</literal></title>

  <indexterm zone="queries-limit">
   <primary>LIMIT</primary>
  </indexterm>

  <indexterm zone="queries-limit">
   <primary>OFFSET</primary>
  </indexterm>

  <para>
   <literal>LIMIT</literal> and <literal>OFFSET</literal> allow you to retrieve just
   a portion of the rows that are generated by the rest of the query:
<synopsis>
SELECT <replaceable>select_list</replaceable>
    FROM <replaceable>table_expression</replaceable>
    <optional> ORDER BY ... </optional>
    <optional> LIMIT { <replaceable class="parameter">count</replaceable> | ALL } </optional>
    <optional> OFFSET <replaceable class="parameter">start</replaceable> </optional>
</synopsis>
  </para>

  <para>
   If a limit count is given, no more than that many rows will be
   returned (but possibly fewer, if the query itself yields fewer rows).
   <literal>LIMIT ALL</literal> is the same as omitting the <literal>LIMIT</literal>
   clause, as is <literal>LIMIT</literal> with a NULL argument.
  </para>

  <para>
   <literal>OFFSET</literal> says to skip that many rows before beginning to
   return rows.  <literal>OFFSET 0</literal> is the same as omitting the
   <literal>OFFSET</literal> clause, as is <literal>OFFSET</literal> with a NULL argument.
  </para>

  <para>
   If both <literal>OFFSET</literal>
   and <literal>LIMIT</literal> appear, then <literal>OFFSET</literal> rows are
   skipped before starting to count the <literal>LIMIT</literal> rows that
   are returned.
  </para>

  <para>
   When using <literal>LIMIT</literal>, it is important to use an
   <literal>ORDER BY</literal> clause that constrains the result rows into a
   unique order.  Otherwise you will get an unpredictable subset of
   the query's rows. You might be asking for the tenth through
   twentieth rows, but tenth through twentieth in what ordering? The
   ordering is unknown, unless you specified <literal>ORDER BY</literal>.
  </para>

  <para>
   The query optimizer takes <literal>LIMIT</literal> into account when
   generating query plans, so you are very likely to get different
   plans (yielding different row orders) depending on what you give
   for <literal>LIMIT</literal> and <literal>OFFSET</literal>.  Thus, using
   different <literal>LIMIT</literal>/<literal>OFFSET</literal> values to select
   different subsets of a query result <emphasis>will give
   inconsistent results</emphasis> unless you enforce a predictable
   result ordering with <literal>ORDER BY</literal>.  This is not a bug; it
   is an inherent consequence of the fact that SQL does not promise to
   deliver the results of a query in any particular order unless
   <literal>ORDER BY</literal> is used to constrain the order.
  </para>

  <para>
   The rows skipped by an <literal>OFFSET</literal> clause still have to be
   computed inside the server; therefore a large <literal>OFFSET</literal>
   might be inefficient.
  </para>
 </sect1>


 <sect1 id="queries-values">
  <title><literal>VALUES</literal> Lists</title>

  <indexterm zone="queries-values">
   <primary>VALUES</primary>
  </indexterm>

  <para>
   <literal>VALUES</literal> provides a way to generate a <quote>constant table</quote>
   that can be used in a query without having to actually create and populate
   a table on-disk.  The syntax is
<synopsis>
VALUES ( <replaceable class="parameter">expression</replaceable> [, ...] ) [, ...]
</synopsis>
   Each parenthesized list of expressions generates a row in the table.
   The lists must all have the same number of elements (i.e., the number
   of columns in the table), and corresponding entries in each list must
   have compatible data types.  The actual data type assigned to each column
   of the result is determined using the same rules as for <literal>UNION</literal>
   (see <xref linkend="typeconv-union-case"/>).
  </para>

  <para>
   As an example:
<programlisting>
VALUES (1, 'one'), (2, 'two'), (3, 'three');
</programlisting>

   will return a table of two columns and three rows.  It's effectively
   equivalent to:
<programlisting>
SELECT 1 AS column1, 'one' AS column2
UNION ALL
SELECT 2, 'two'
UNION ALL
SELECT 3, 'three';
</programlisting>

   By default, <productname>PostgreSQL</productname> assigns the names
   <literal>column1</literal>, <literal>column2</literal>, etc. to the columns of a
   <literal>VALUES</literal> table.  The column names are not specified by the
   SQL standard and different database systems do it differently, so
   it's usually better to override the default names with a table alias
   list, like this:
<programlisting>
=&gt; SELECT * FROM (VALUES (1, 'one'), (2, 'two'), (3, 'three')) AS t (num,letter);
 num | letter
-----+--------
   1 | one
   2 | two
   3 | three
(3 rows)
</programlisting>
  </para>

  <para>
   Syntactically, <literal>VALUES</literal> followed by expression lists is
   treated as equivalent to:
<synopsis>
SELECT <replaceable>select_list</replaceable> FROM <replaceable>table_expression</replaceable>
</synopsis>
   and can appear anywhere a <literal>SELECT</literal> can.  For example, you can
   use it as part of a <literal>UNION</literal>, or attach a
   <replaceable>sort_specification</replaceable> (<literal>ORDER BY</literal>,
   <literal>LIMIT</literal>, and/or <literal>OFFSET</literal>) to it.  <literal>VALUES</literal>
   is most commonly used as the data source in an <command>INSERT</command> command,
   and next most commonly as a subquery.
  </para>

  <para>
   For more information see <xref linkend="sql-values"/>.
  </para>

 </sect1>


 <sect1 id="queries-with">
  <title><literal>WITH</literal> Queries (Common Table Expressions)</title>

  <indexterm zone="queries-with">
   <primary>WITH</primary>
   <secondary>in SELECT</secondary>
  </indexterm>

  <indexterm>
   <primary>common table expression</primary>
   <see>WITH</see>
  </indexterm>

  <para>
   <literal>WITH</literal> provides a way to write auxiliary statements for use in a
   larger query.  These statements, which are often referred to as Common
   Table Expressions or <acronym>CTE</acronym>s, can be thought of as defining
   temporary tables that exist just for one query.  Each auxiliary statement
   in a <literal>WITH</literal> clause can be a <command>SELECT</command>,
   <command>INSERT</command>, <command>UPDATE</command>, <command>DELETE</command>,
   or <command>MERGE</command>; and the
   <literal>WITH</literal> clause itself is attached to a primary statement that can
   also be a <command>SELECT</command>, <command>INSERT</command>, <command>UPDATE</command>,
   <command>DELETE</command>, or <command>MERGE</command>.
  </para>

 <sect2 id="queries-with-select">
   <title><command>SELECT</command> in <literal>WITH</literal></title>

  <para>
   The basic value of <command>SELECT</command> in <literal>WITH</literal> is to
   break down complicated queries into simpler parts.  An example is:

<programlisting>
WITH regional_sales AS (
    SELECT region, SUM(amount) AS total_sales
    FROM orders
    GROUP BY region
), top_regions AS (
    SELECT region
    FROM regional_sales
    WHERE total_sales &gt; (SELECT SUM(total_sales)/10 FROM regional_sales)
)
SELECT region,
       product,
       SUM(quantity) AS product_units,
       SUM(amount) AS product_sales
FROM orders
WHERE region IN (SELECT region FROM top_regions)
GROUP BY region, product;
</programlisting>

   which displays per-product sales totals in only the top sales regions.
   The <literal>WITH</literal> clause defines two auxiliary statements named
   <structname>regional_sales</structname> and <structname>top_regions</structname>,
   where the output of <structname>regional_sales</structname> is used in
   <structname>top_regions</structname> and the output of <structname>top_regions</structname>
   is used in the primary <command>SELECT</command> query.
   This example could have been written without <literal>WITH</literal>,
   but we'd have needed two levels of nested sub-<command>SELECT</command>s.  It's a bit
   easier to follow this way.
  </para>
 </sect2>

 <sect2 id="queries-with-recursive">
  <title>Recursive Queries</title>

  <para>
   <indexterm>
    <primary>RECURSIVE</primary>
    <secondary>in common table expressions</secondary>
   </indexterm>
   The optional <literal>RECURSIVE</literal> modifier changes <literal>WITH</literal>
   from a mere syntactic convenience into a feature that accomplishes
   things not otherwise possible in standard SQL.  Using
   <literal>RECURSIVE</literal>, a <literal>WITH</literal> query can refer to its own
   output.  A very simple example is this query to sum the integers from 1
   through 100:

<programlisting>
WITH RECURSIVE t(n) AS (
    VALUES (1)
  UNION ALL
    SELECT n+1 FROM t WHERE n &lt; 100
)
SELECT sum(n) FROM t;
</programlisting>

   The general form of a recursive <literal>WITH</literal> query is always a
   <firstterm>non-recursive term</firstterm>, then <literal>UNION</literal> (or
   <literal>UNION ALL</literal>), then a
   <firstterm>recursive term</firstterm>, where only the recursive term can contain
   a reference to the query's own output.  Such a query is executed as
   follows:
  </para>

  <procedure>
   <title>Recursive Query Evaluation</title>

   <step performance="required">
    <para>
     Evaluate the non-recursive term.  For <literal>UNION</literal> (but not
     <literal>UNION ALL</literal>), discard duplicate rows.  Include all remaining
     rows in the result of the recursive query, and also place them in a
     temporary <firstterm>working table</firstterm>.
    </para>
   </step>

   <step performance="required">
    <para>
     So long as the working table is not empty, repeat these steps:
    </para>
    <substeps>
     <step performance="required">
      <para>
       Evaluate the recursive term, substituting the current contents of
       the working table for the recursive self-reference.
       For <literal>UNION</literal> (but not <literal>UNION ALL</literal>), discard
       duplicate rows and rows that duplicate any previous result row.
       Include all remaining rows in the result of the recursive query, and
       also place them in a temporary <firstterm>intermediate table</firstterm>.
      </para>
     </step>

     <step performance="required">
      <para>
       Replace the contents of the working table with the contents of the
       intermediate table, then empty the intermediate table.
      </para>
     </step>
    </substeps>
   </step>
  </procedure>

  <note>
   <para>
    While <literal>RECURSIVE</literal> allows queries to be specified
    recursively, internally such queries are evaluated iteratively.
   </para>
  </note>

  <para>
   In the example above, the working table has just a single row in each step,
   and it takes on the values from 1 through 100 in successive steps.  In
   the 100th step, there is no output because of the <literal>WHERE</literal>
   clause, and so the query terminates.
  </para>

  <para>
   Recursive queries are typically used to deal with hierarchical or
   tree-structured data.  A useful example is this query to find all the
   direct and indirect sub-parts of a product, given only a table that
   shows immediate inclusions:

<programlisting>
WITH RECURSIVE included_parts(sub_part, part, quantity) AS (
    SELECT sub_part, part, quantity FROM parts WHERE part = 'our_product'
  UNION ALL
    SELECT p.sub_part, p.part, p.quantity * pr.quantity
    FROM included_parts pr, parts p
    WHERE p.part = pr.sub_part
)
SELECT sub_part, SUM(quantity) AS total_quantity
FROM included_parts
GROUP BY sub_part
</programlisting>
  </para>

  <sect3 id="queries-with-search">
   <title>Search Order</title>

   <para>
    When computing a tree traversal using a recursive query, you might want to
    order the results in either depth-first or breadth-first order.  This can
    be done by computing an ordering column alongside the other data columns
    and using that to sort the results at the end.  Note that this does not
    actually control in which order the query evaluation visits the rows; that
    is as always in SQL implementation-dependent.  This approach merely
    provides a convenient way to order the results afterwards.
   </para>

   <para>
    To create a depth-first order, we compute for each result row an array of
    rows that we have visited so far.  For example, consider the following
    query that searches a table <structname>tree</structname> using a
    <structfield>link</structfield> field:

<programlisting>
WITH RECURSIVE search_tree(id, link, data) AS (
    SELECT t.id, t.link, t.data
    FROM tree t
  UNION ALL
    SELECT t.id, t.link, t.data
    FROM tree t, search_tree st
    WHERE t.id = st.link
)
SELECT * FROM search_tree;
</programlisting>

    To add depth-first ordering information, you can write this:

<programlisting>
WITH RECURSIVE search_tree(id, link, data, <emphasis>path</emphasis>) AS (
    SELECT t.id, t.link, t.data, <emphasis>ARRAY[t.id]</emphasis>
    FROM tree t
  UNION ALL
    SELECT t.id, t.link, t.data, <emphasis>path || t.id</emphasis>
    FROM tree t, search_tree st
    WHERE t.id = st.link
)
SELECT * FROM search_tree <emphasis>ORDER BY path</emphasis>;
</programlisting>
   </para>

   <para>
    In the general case where more than one field needs to be used to identify
    a row, use an array of rows.  For example, if we needed to track fields
    <structfield>f1</structfield> and <structfield>f2</structfield>:

<programlisting>
WITH RECURSIVE search_tree(id, link, data, <emphasis>path</emphasis>) AS (
    SELECT t.id, t.link, t.data, <emphasis>ARRAY[ROW(t.f1, t.f2)]</emphasis>
    FROM tree t
  UNION ALL
    SELECT t.id, t.link, t.data, <emphasis>path || ROW(t.f1, t.f2)</emphasis>
    FROM tree t, search_tree st
    WHERE t.id = st.link
)
SELECT * FROM search_tree <emphasis>ORDER BY path</emphasis>;
</programlisting>
   </para>

   <tip>
    <para>
     Omit the <literal>ROW()</literal> syntax in the common case where only one
     field needs to be tracked.  This allows a simple array rather than a
     composite-type array to be used, gaining efficiency.
    </para>
   </tip>

   <para>
    To create a breadth-first order, you can add a column that tracks the depth
    of the search, for example:

<programlisting>
WITH RECURSIVE search_tree(id, link, data, <emphasis>depth</emphasis>) AS (
    SELECT t.id, t.link, t.data, <emphasis>0</emphasis>
    FROM tree t
  UNION ALL
    SELECT t.id, t.link, t.data, <emphasis>depth + 1</emphasis>
    FROM tree t, search_tree st
    WHERE t.id = st.link
)
SELECT * FROM search_tree <emphasis>ORDER BY depth</emphasis>;
</programlisting>

    To get a stable sort, add data columns as secondary sorting columns.
   </para>

   <tip>
    <para>
     The recursive query evaluation algorithm produces its output in
     breadth-first search order.  However, this is an implementation detail and
     it is perhaps unsound to rely on it.  The order of the rows within each
     level is certainly undefined, so some explicit ordering might be desired
     in any case.
    </para>
   </tip>

   <para>
    There is built-in syntax to compute a depth- or breadth-first sort column.
    For example:

<programlisting>
WITH RECURSIVE search_tree(id, link, data) AS (
    SELECT t.id, t.link, t.data
    FROM tree t
  UNION ALL
    SELECT t.id, t.link, t.data
    FROM tree t, search_tree st
    WHERE t.id = st.link
) <emphasis>SEARCH DEPTH FIRST BY id SET ordercol</emphasis>
SELECT * FROM search_tree ORDER BY ordercol;

WITH RECURSIVE search_tree(id, link, data) AS (
    SELECT t.id, t.link, t.data
    FROM tree t
  UNION ALL
    SELECT t.id, t.link, t.data
    FROM tree t, search_tree st
    WHERE t.id = st.link
) <emphasis>SEARCH BREADTH FIRST BY id SET ordercol</emphasis>
SELECT * FROM search_tree ORDER BY ordercol;
</programlisting>
    This syntax is internally expanded to something similar to the above
    hand-written forms.  The <literal>SEARCH</literal> clause specifies whether
    depth- or breadth first search is wanted, the list of columns to track for
    sorting, and a column name that will contain the result data that can be
    used for sorting.  That column will implicitly be added to the output rows
    of the CTE.
   </para>
  </sect3>

  <sect3 id="queries-with-cycle">
   <title>Cycle Detection</title>

  <para>
   When working with recursive queries it is important to be sure that
   the recursive part of the query will eventually return no tuples,
   or else the query will loop indefinitely.  Sometimes, using
   <literal>UNION</literal> instead of <literal>UNION ALL</literal> can accomplish this
   by discarding rows that duplicate previous output rows.  However, often a
   cycle does not involve output rows that are completely duplicate: it may be
   necessary to check just one or a few fields to see if the same point has
   been reached before.  The standard method for handling such situations is
   to compute an array of the already-visited values.  For example, consider again
   the following query that searches a table <structname>graph</structname> using a
   <structfield>link</structfield> field:

<programlisting>
WITH RECURSIVE search_graph(id, link, data, depth) AS (
    SELECT g.id, g.link, g.data, 0
    FROM graph g
  UNION ALL
    SELECT g.id, g.link, g.data, sg.depth + 1
    FROM graph g, search_graph sg
    WHERE g.id = sg.link
)
SELECT * FROM search_graph;
</programlisting>

   This query will loop if the <structfield>link</structfield> relationships contain
   cycles.  Because we require a <quote>depth</quote> output, just changing
   <literal>UNION ALL</literal> to <literal>UNION</literal> would not eliminate the looping.
   Instead we need to recognize whether we have reached the same row again
   while following a particular path of links.  We add two columns
   <structfield>is_cycle</structfield> and <structfield>path</structfield> to the loop-prone query:

<programlisting>
WITH RECURSIVE search_graph(id, link, data, depth, <emphasis>is_cycle, path</emphasis>) AS (
    SELECT g.id, g.link, g.data, 0,
      <emphasis>false,
      ARRAY[g.id]</emphasis>
    FROM graph g
  UNION ALL
    SELECT g.id, g.link, g.data, sg.depth + 1,
      <emphasis>g.id = ANY(path),
      path || g.id</emphasis>
    FROM graph g, search_graph sg
    WHERE g.id = sg.link <emphasis>AND NOT is_cycle</emphasis>
)
SELECT * FROM search_graph;
</programlisting>

   Aside from preventing cycles, the array value is often useful in its own
   right as representing the <quote>path</quote> taken to reach any particular row.
  </para>

  <para>
   In the general case where more than one field needs to be checked to
   recognize a cycle, use an array of rows.  For example, if we needed to
   compare fields <structfield>f1</structfield> and <structfield>f2</structfield>:

<programlisting>
WITH RECURSIVE search_graph(id, link, data, depth, <emphasis>is_cycle, path</emphasis>) AS (
    SELECT g.id, g.link, g.data, 0,
      <emphasis>false,
      ARRAY[ROW(g.f1, g.f2)]</emphasis>
    FROM graph g
  UNION ALL
    SELECT g.id, g.link, g.data, sg.depth + 1,
      <emphasis>ROW(g.f1, g.f2) = ANY(path),
      path || ROW(g.f1, g.f2)</emphasis>
    FROM graph g, search_graph sg
    WHERE g.id = sg.link <emphasis>AND NOT is_cycle</emphasis>
)
SELECT * FROM search_graph;
</programlisting>
  </para>

  <tip>
   <para>
    Omit the <literal>ROW()</literal> syntax in the common case where only one field
    needs to be checked to recognize a cycle.  This allows a simple array
    rather than a composite-type array to be used, gaining efficiency.
   </para>
  </tip>

  <para>
   There is built-in syntax to simplify cycle detection.  The above query can
   also be written like this:
<programlisting>
WITH RECURSIVE search_graph(id, link, data, depth) AS (
    SELECT g.id, g.link, g.data, 1
    FROM graph g
  UNION ALL
    SELECT g.id, g.link, g.data, sg.depth + 1
    FROM graph g, search_graph sg
    WHERE g.id = sg.link
) <emphasis>CYCLE id SET is_cycle USING path</emphasis>
SELECT * FROM search_graph;
</programlisting>
   and it will be internally rewritten to the above form.  The
   <literal>CYCLE</literal> clause specifies first the list of columns to
   track for cycle detection, then a column name that will show whether a
   cycle has been detected, and finally the name of another column that will track the
   path.  The cycle and path columns will implicitly be added to the output
   rows of the CTE.
  </para>

  <tip>
   <para>
    The cycle path column is computed in the same way as the depth-first
    ordering column show in the previous section.  A query can have both a
    <literal>SEARCH</literal> and a <literal>CYCLE</literal> clause, but a
    depth-first search specification and a cycle detection specification would
    create redundant computations, so it's more efficient to just use the
    <literal>CYCLE</literal> clause and order by the path column.  If
    breadth-first ordering is wanted, then specifying both
    <literal>SEARCH</literal> and <literal>CYCLE</literal> can be useful.
   </para>
  </tip>

  <para>
   A helpful trick for testing queries
   when you are not certain if they might loop is to place a <literal>LIMIT</literal>
   in the parent query.  For example, this query would loop forever without
   the <literal>LIMIT</literal>:

<programlisting>
WITH RECURSIVE t(n) AS (
    SELECT 1
  UNION ALL
    SELECT n+1 FROM t
)
SELECT n FROM t <emphasis>LIMIT 100</emphasis>;
</programlisting>

   This works because <productname>PostgreSQL</productname>'s implementation
   evaluates only as many rows of a <literal>WITH</literal> query as are actually
   fetched by the parent query.  Using this trick in production is not
   recommended, because other systems might work differently.  Also, it
   usually won't work if you make the outer query sort the recursive query's
   results or join them to some other table, because in such cases the
   outer query will usually try to fetch all of the <literal>WITH</literal> query's
   output anyway.
  </para>
  </sect3>
 </sect2>

 <sect2 id="queries-with-cte-materialization">
  <title>Common Table Expression Materialization</title>

  <para>
   A useful property of <literal>WITH</literal> queries is that they are
   normally evaluated only once per execution of the parent query, even if
   they are referred to more than once by the parent query or
   sibling <literal>WITH</literal> queries.
   Thus, expensive calculations that are needed in multiple places can be
   placed within a <literal>WITH</literal> query to avoid redundant work.  Another
   possible application is to prevent unwanted multiple evaluations of
   functions with side-effects.
   However, the other side of this coin is that the optimizer is not able to
   push restrictions from the parent query down into a multiply-referenced
   <literal>WITH</literal> query, since that might affect all uses of the
   <literal>WITH</literal> query's output when it should affect only one.
   The multiply-referenced <literal>WITH</literal> query will be
   evaluated as written, without suppression of rows that the parent query
   might discard afterwards.  (But, as mentioned above, evaluation might stop
   early if the reference(s) to the query demand only a limited number of
   rows.)
  </para>

  <para>
   However, if a <literal>WITH</literal> query is non-recursive and
   side-effect-free (that is, it is a <literal>SELECT</literal> containing
   no volatile functions) then it can be folded into the parent query,
   allowing joint optimization of the two query levels.  By default, this
   happens if the parent query references the <literal>WITH</literal> query
   just once, but not if it references the <literal>WITH</literal> query
   more than once.  You can override that decision by
   specifying <literal>MATERIALIZED</literal> to force separate calculation
   of the <literal>WITH</literal> query, or by specifying <literal>NOT
   MATERIALIZED</literal> to force it to be merged into the parent query.
   The latter choice risks duplicate computation of
   the <literal>WITH</literal> query, but it can still give a net savings if
   each usage of the <literal>WITH</literal> query needs only a small part
   of the <literal>WITH</literal> query's full output.
  </para>

  <para>
   A simple example of these rules is
<programlisting>
WITH w AS (
    SELECT * FROM big_table
)
SELECT * FROM w WHERE key = 123;
</programlisting>
   This <literal>WITH</literal> query will be folded, producing the same
   execution plan as
<programlisting>
SELECT * FROM big_table WHERE key = 123;
</programlisting>
   In particular, if there's an index on <structfield>key</structfield>,
   it will probably be used to fetch just the rows having <literal>key =
   123</literal>.  On the other hand, in
<programlisting>
WITH w AS (
    SELECT * FROM big_table
)
SELECT * FROM w AS w1 JOIN w AS w2 ON w1.key = w2.ref
WHERE w2.key = 123;
</programlisting>
   the <literal>WITH</literal> query will be materialized, producing a
   temporary copy of <structname>big_table</structname> that is then
   joined with itself &mdash; without benefit of any index.  This query
   will be executed much more efficiently if written as
<programlisting>
WITH w AS NOT MATERIALIZED (
    SELECT * FROM big_table
)
SELECT * FROM w AS w1 JOIN w AS w2 ON w1.key = w2.ref
WHERE w2.key = 123;
</programlisting>
   so that the parent query's restrictions can be applied directly
   to scans of <structname>big_table</structname>.
  </para>

  <para>
   An example where <literal>NOT MATERIALIZED</literal> could be
   undesirable is
<programlisting>
WITH w AS (
    SELECT key, very_expensive_function(val) AS f FROM some_table
)
SELECT * FROM w AS w1 JOIN w AS w2 ON w1.f = w2.f;
</programlisting>
   Here, materialization of the <literal>WITH</literal> query ensures
   that <function>very_expensive_function</function> is evaluated only
   once per table row, not twice.
  </para>

  <para>
   The examples above only show <literal>WITH</literal> being used with
   <command>SELECT</command>, but it can be attached in the same way to
   <command>INSERT</command>, <command>UPDATE</command>,
   <command>DELETE</command>, or <command>MERGE</command>.
   In each case it effectively provides temporary table(s) that can
   be referred to in the main command.
  </para>
 </sect2>

 <sect2 id="queries-with-modifying">
   <title>Data-Modifying Statements in <literal>WITH</literal></title>

   <para>
    You can use data-modifying statements (<command>INSERT</command>,
    <command>UPDATE</command>, <command>DELETE</command>, or
    <command>MERGE</command>) in <literal>WITH</literal>.  This
    allows you to perform several different operations in the same query.
    An example is:

<programlisting>
WITH moved_rows AS (
    DELETE FROM products
    WHERE
        "date" &gt;= '2010-10-01' AND
        "date" &lt; '2010-11-01'
    RETURNING *
)
INSERT INTO products_log
SELECT * FROM moved_rows;
</programlisting>

    This query effectively moves rows from <structname>products</structname> to
    <structname>products_log</structname>.  The <command>DELETE</command> in <literal>WITH</literal>
    deletes the specified rows from <structname>products</structname>, returning their
    contents by means of its <literal>RETURNING</literal> clause; and then the
    primary query reads that output and inserts it into
    <structname>products_log</structname>.
   </para>

   <para>
    A fine point of the above example is that the <literal>WITH</literal> clause is
    attached to the <command>INSERT</command>, not the sub-<command>SELECT</command> within
    the <command>INSERT</command>.  This is necessary because data-modifying
    statements are only allowed in <literal>WITH</literal> clauses that are attached
    to the top-level statement.  However, normal <literal>WITH</literal> visibility
    rules apply, so it is possible to refer to the <literal>WITH</literal>
    statement's output from the sub-<command>SELECT</command>.
   </para>

   <para>
    Data-modifying statements in <literal>WITH</literal> usually have
    <literal>RETURNING</literal> clauses (see <xref linkend="dml-returning"/>),
    as shown in the example above.
    It is the output of the <literal>RETURNING</literal> clause, <emphasis>not</emphasis> the
    target table of the data-modifying statement, that forms the temporary
    table that can be referred to by the rest of the query.  If a
    data-modifying statement in <literal>WITH</literal> lacks a <literal>RETURNING</literal>
    clause, then it forms no temporary table and cannot be referred to in
    the rest of the query.  Such a statement will be executed nonetheless.
    A not-particularly-useful example is:

<programlisting>
WITH t AS (
    DELETE FROM foo
)
DELETE FROM bar;
</programlisting>

    This example would remove all rows from tables <structname>foo</structname> and
    <structname>bar</structname>.  The number of affected rows reported to the client
    would only include rows removed from <structname>bar</structname>.
   </para>

   <para>
    Recursive self-references in data-modifying statements are not
    allowed.  In some cases it is possible to work around this limitation by
    referring to the output of a recursive <literal>WITH</literal>, for example:

<programlisting>
WITH RECURSIVE included_parts(sub_part, part) AS (
    SELECT sub_part, part FROM parts WHERE part = 'our_product'
  UNION ALL
    SELECT p.sub_part, p.part
    FROM included_parts pr, parts p
    WHERE p.part = pr.sub_part
)
DELETE FROM parts
  WHERE part IN (SELECT part FROM included_parts);
</programlisting>

    This query would remove all direct and indirect subparts of a product.
   </para>

   <para>
    Data-modifying statements in <literal>WITH</literal> are executed exactly once,
    and always to completion, independently of whether the primary query
    reads all (or indeed any) of their output.  Notice that this is different
    from the rule for <command>SELECT</command> in <literal>WITH</literal>: as stated in the
    previous section, execution of a <command>SELECT</command> is carried only as far
    as the primary query demands its output.
   </para>

   <para>
    The sub-statements in <literal>WITH</literal> are executed concurrently with
    each other and with the main query.  Therefore, when using data-modifying
    statements in <literal>WITH</literal>, the order in which the specified updates
    actually happen is unpredictable.  All the statements are executed with
    the same <firstterm>snapshot</firstterm> (see <xref linkend="mvcc"/>), so they
    cannot <quote>see</quote> one another's effects on the target tables.  This
    alleviates the effects of the unpredictability of the actual order of row
    updates, and means that <literal>RETURNING</literal> data is the only way to
    communicate changes between different <literal>WITH</literal> sub-statements and
    the main query.  An example of this is that in

<programlisting>
WITH t AS (
    UPDATE products SET price = price * 1.05
    RETURNING *
)
SELECT * FROM products;
</programlisting>

    the outer <command>SELECT</command> would return the original prices before the
    action of the <command>UPDATE</command>, while in

<programlisting>
WITH t AS (
    UPDATE products SET price = price * 1.05
    RETURNING *
)
SELECT * FROM t;
</programlisting>

    the outer <command>SELECT</command> would return the updated data.
   </para>

   <para>
    Trying to update the same row twice in a single statement is not
    supported.  Only one of the modifications takes place, but it is not easy
    (and sometimes not possible) to reliably predict which one.  This also
    applies to deleting a row that was already updated in the same statement:
    only the update is performed.  Therefore you should generally avoid trying
    to modify a single row twice in a single statement.  In particular avoid
    writing <literal>WITH</literal> sub-statements that could affect the same rows
    changed by the main statement or a sibling sub-statement.  The effects
    of such a statement will not be predictable.
   </para>

   <para>
    At present, any table used as the target of a data-modifying statement in
    <literal>WITH</literal> must not have a conditional rule, nor an <literal>ALSO</literal>
    rule, nor an <literal>INSTEAD</literal> rule that expands to multiple statements.
   </para>

  </sect2>

 </sect1>

 <sect1 id="queries-graph">
  <title>Graph Queries</title>

  <para>
   This section describes the sublanguage for querying property graphs,
   defined as described in <xref linkend="ddl-property-graphs"/>.
  </para>

  <sect2 id="queries-graph-overview">
   <title>Overview</title>

   <para>
    Consider this example from <xref linkend="ddl-property-graphs"/>:
<programlisting>
-- get list of customers active today
SELECT customer_name FROM GRAPH_TABLE (myshop MATCH (c IS customers)-[IS customer_orders]->(o IS orders WHERE o.ordered_when = current_date) COLUMNS (c.name AS customer_name));
</programlisting>
    The graph query part happens inside the <literal>GRAPH_TABLE</literal>
    construct.  As far as the rest of the query is concerned, this acts like a
    table function in that it produces a computed table as output.  Like other
    <literal>FROM</literal> clause elements, table alias and column alias
    names can be assigned to the result, and the result can be joined with
    other tables, subsequently filtered, and so on, for example:
<programlisting>
SELECT ... FROM GRAPH_TABLE (mygraph MATCH ... COLUMNS (...)) AS myresult (a, b, c) JOIN othertable USING (a) WHERE b > 0 ORDER BY c;
</programlisting>
   </para>

   <para>
    The <literal>GRAPH_TABLE</literal> clause consists of the graph name,
    followed by the keyword <literal>MATCH</literal>, followed by a graph
    pattern expression (see below), followed by the keyword
    <literal>COLUMNS</literal> and a column list.
   </para>
  </sect2>

  <sect2 id="queries-graph-patterns">
   <title>Graph Patterns</title>

   <para>
    The core of the graph querying functionality is the graph pattern, which
    appears after the keyword <literal>MATCH</literal>.  Formally, a graph
    pattern consists of one or more path patterns.  A path is a sequence of
    graph elements, starting and ending with a vertex and alternating between
    vertices and edges.  A path pattern is a syntactic expressions that
    matches paths.
   </para>

   <para>
    A path pattern thus matches a sequence of vertices and edges.  The
    simplest possible path pattern is
<programlisting>
()
</programlisting>
    which matches a single vertex.  The next simplest pattern would be
<programlisting>
()-[]->()
</programlisting>
    which matches a vertex followed by an edge followed by a vertex.  The
    characters <literal>()</literal> are a vertex pattern and the characters
    <literal>-[]-></literal> are an edge pattern.
   </para>

   <para>
    These characters can also be separated by whitespace, for example:
<programlisting>
( ) - [ ] - > ( )
</programlisting>
   </para>

   <tip>
    <para>
     A way to remember these symbols is that in visual representations of
     property graphs, vertices are usually circles (like
     <literal>()</literal>) and edges have rectangular labels (like
     <literal>[]</literal>).
    </para>
   </tip>

   <para>
    The above patterns would match any vertex, or any two vertices connected
    by any edge, which isn't very interesting.  Normally, we want to search
    for elements (vertices and edges) that have certain characteristics.
    These characteristics are written in between the parentheses or brackets.
    (This is also called an element pattern filler.)  Typically, we would
    search for elements with a certain label.  This is written by <literal>IS
    <replaceable>labelname</replaceable></literal>.  For example, this would
    match all vertices with the label <literal>person</literal>:
<programlisting>
(IS person)
</programlisting>
    The next
    example would match a vertex with the label <literal>person</literal>
    connected to a vertex with the label <literal>account</literal> connected
    by an edge with the label <literal>has</literal>.
<programlisting>
(IS person)-[IS has]->(IS account)
</programlisting>
    Multiple labels can also be matched, using <quote>or</quote> semantics:
<programlisting>
(IS person)-[IS has]->(IS account|creditcard)
</programlisting>
   </para>

   <para>
    Recall that edges are directed.  The other direction is also possible in a
    path pattern, for example:
<programlisting>
(IS account)&lt;-[IS has]-(IS person)
</programlisting>
    It is also possible to match both directions:
<programlisting>
(IS person)-[IS is_friend_of]-(IS person)
</programlisting>
    This has a meaning of <quote>or</quote>: An edge in either direction would
    match.
   </para>

   <para>
    In many cases, the edge patterns don't need a filler.  (All the filtering
    then happens on the vertices.)  For these cases, an abbreviated edge
    pattern syntax is available that omits the brackets, for example:
<programlisting>
(IS person)->(IS account)
(IS account)&lt;-(IS person)
(IS person)-(IS person)
</programlisting>
    As is often the case, abbreviated syntax can make expressions more compact
    but also sometimes harder to understand.
   </para>

   <para>
    Furthermore, it is possible to define graph pattern variables in the path
    pattern expressions.  These are bound to the matched elements and can be
    used to refer to the property values from those elements.  The most
    important use is to use them in the <literal>COLUMNS</literal> clause to
    define the tabular result of the <literal>GRAPH_TABLE</literal> clause.
    For example (assuming appropriate definitions of the property graph as
    well as the underlying tables):
<programlisting>
GRAPH_TABLE (mygraph MATCH (p IS person)-[h IS has]->(a IS account)
             COLUMNS (p.name AS person_name, h.since AS has_account_since, a.num AS account_number)
</programlisting>
    <literal>WHERE</literal> clauses can be used inside element patterns to
    filter matches:
<programlisting>
(IS person)-[IS has]->(a IS account WHERE a.type = 'savings')
</programlisting>
   </para>

   <!-- TODO: multiple path patterns in a graph pattern (comma-separated) -->

   <!-- TODO: quantifiers -->

  </sect2>
 </sect1>

</chapter>