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4.2. Value Expressions #

   4.2.1. Column References
   4.2.2. Positional Parameters
   4.2.3. Subscripts
   4.2.4. Field Selection
   4.2.5. Operator Invocations
   4.2.6. Function Calls
   4.2.7. Aggregate Expressions
   4.2.8. Window Function Calls
   4.2.9. Type Casts
   4.2.10. Collation Expressions
   4.2.11. Scalar Subqueries
   4.2.12. Array Constructors
   4.2.13. Row Constructors
   4.2.14. Expression Evaluation Rules

   Value expressions are used in a variety of contexts, such as in the
   target list of the SELECT command, as new column values in INSERT or
   UPDATE, or in search conditions in a number of commands. The result of
   a value expression is sometimes called a scalar, to distinguish it from
   the result of a table expression (which is a table). Value expressions
   are therefore also called scalar expressions (or even simply
   expressions). The expression syntax allows the calculation of values
   from primitive parts using arithmetic, logical, set, and other
   operations.

   A value expression is one of the following:
     * A constant or literal value
     * A column reference
     * A positional parameter reference, in the body of a function
       definition or prepared statement
     * A subscripted expression
     * A field selection expression
     * An operator invocation
     * A function call
     * An aggregate expression
     * A window function call
     * A type cast
     * A collation expression
     * A scalar subquery
     * An array constructor
     * A row constructor
     * Another value expression in parentheses (used to group
       subexpressions and override precedence)

   In addition to this list, there are a number of constructs that can be
   classified as an expression but do not follow any general syntax rules.
   These generally have the semantics of a function or operator and are
   explained in the appropriate location in Chapter 9. An example is the
   IS NULL clause.

   We have already discussed constants in Section 4.1.2. The following
   sections discuss the remaining options.

4.2.1. Column References #

   A column can be referenced in the form:
correlation.columnname

   correlation is the name of a table (possibly qualified with a schema
   name), or an alias for a table defined by means of a FROM clause. The
   correlation name and separating dot can be omitted if the column name
   is unique across all the tables being used in the current query. (See
   also Chapter 7.)

4.2.2. Positional Parameters #

   A positional parameter reference is used to indicate a value that is
   supplied externally to an SQL statement. Parameters are used in SQL
   function definitions and in prepared queries. Some client libraries
   also support specifying data values separately from the SQL command
   string, in which case parameters are used to refer to the out-of-line
   data values. The form of a parameter reference is:
$number

   For example, consider the definition of a function, dept, as:
CREATE FUNCTION dept(text) RETURNS dept
    AS $$ SELECT * FROM dept WHERE name = $1 $$
    LANGUAGE SQL;

   Here the $1 references the value of the first function argument
   whenever the function is invoked.

4.2.3. Subscripts #

   If an expression yields a value of an array type, then a specific
   element of the array value can be extracted by writing
expression[subscript]

   or multiple adjacent elements (an “array slice”) can be extracted by
   writing
expression[lower_subscript:upper_subscript]

   (Here, the brackets [ ] are meant to appear literally.) Each subscript
   is itself an expression, which will be rounded to the nearest integer
   value.

   In general the array expression must be parenthesized, but the
   parentheses can be omitted when the expression to be subscripted is
   just a column reference or positional parameter. Also, multiple
   subscripts can be concatenated when the original array is
   multidimensional. For example:
mytable.arraycolumn[4]
mytable.two_d_column[17][34]
$1[10:42]
(arrayfunction(a,b))[42]

   The parentheses in the last example are required. See Section 8.15 for
   more about arrays.

4.2.4. Field Selection #

   If an expression yields a value of a composite type (row type), then a
   specific field of the row can be extracted by writing
expression.fieldname

   In general the row expression must be parenthesized, but the
   parentheses can be omitted when the expression to be selected from is
   just a table reference or positional parameter. For example:
mytable.mycolumn
$1.somecolumn
(rowfunction(a,b)).col3

   (Thus, a qualified column reference is actually just a special case of
   the field selection syntax.) An important special case is extracting a
   field from a table column that is of a composite type:
(compositecol).somefield
(mytable.compositecol).somefield

   The parentheses are required here to show that compositecol is a column
   name not a table name, or that mytable is a table name not a schema
   name in the second case.

   You can ask for all fields of a composite value by writing .*:
(compositecol).*

   This notation behaves differently depending on context; see
   Section 8.16.5 for details.

4.2.5. Operator Invocations #

   There are two possible syntaxes for an operator invocation:
   expression operator expression (binary infix operator)
   operator expression (unary prefix operator)

   where the operator token follows the syntax rules of Section 4.1.3, or
   is one of the key words AND, OR, and NOT, or is a qualified operator
   name in the form:
OPERATOR(schema.operatorname)

   Which particular operators exist and whether they are unary or binary
   depends on what operators have been defined by the system or the user.
   Chapter 9 describes the built-in operators.

4.2.6. Function Calls #

   The syntax for a function call is the name of a function (possibly
   qualified with a schema name), followed by its argument list enclosed
   in parentheses:
function_name ([expression [, expression ... ]] )

   For example, the following computes the square root of 2:
sqrt(2)

   The list of built-in functions is in Chapter 9. Other functions can be
   added by the user.

   When issuing queries in a database where some users mistrust other
   users, observe security precautions from Section 10.3 when writing
   function calls.

   The arguments can optionally have names attached. See Section 4.3 for
   details.

Note

   A function that takes a single argument of composite type can
   optionally be called using field-selection syntax, and conversely field
   selection can be written in functional style. That is, the notations
   col(table) and table.col are interchangeable. This behavior is not
   SQL-standard but is provided in PostgreSQL because it allows use of
   functions to emulate “computed fields”. For more information see
   Section 8.16.5.

4.2.7. Aggregate Expressions #

   An aggregate expression represents the application of an aggregate
   function across the rows selected by a query. An aggregate function
   reduces multiple inputs to a single output value, such as the sum or
   average of the inputs. The syntax of an aggregate expression is one of
   the following:
aggregate_name (expression [ , ... ] [ order_by_clause ] ) [ FILTER ( WHERE filt
er_clause ) ]
aggregate_name (ALL expression [ , ... ] [ order_by_clause ] ) [ FILTER ( WHERE
filter_clause ) ]
aggregate_name (DISTINCT expression [ , ... ] [ order_by_clause ] ) [ FILTER ( W
HERE filter_clause ) ]
aggregate_name ( * ) [ FILTER ( WHERE filter_clause ) ]
aggregate_name ( [ expression [ , ... ] ] ) WITHIN GROUP ( order_by_clause ) [ F
ILTER ( WHERE filter_clause ) ]

   where aggregate_name is a previously defined aggregate (possibly
   qualified with a schema name) and expression is any value expression
   that does not itself contain an aggregate expression or a window
   function call. The optional order_by_clause and filter_clause are
   described below.

   The first form of aggregate expression invokes the aggregate once for
   each input row. The second form is the same as the first, since ALL is
   the default. The third form invokes the aggregate once for each
   distinct value of the expression (or distinct set of values, for
   multiple expressions) found in the input rows. The fourth form invokes
   the aggregate once for each input row; since no particular input value
   is specified, it is generally only useful for the count(*) aggregate
   function. The last form is used with ordered-set aggregate functions,
   which are described below.

   Most aggregate functions ignore null inputs, so that rows in which one
   or more of the expression(s) yield null are discarded. This can be
   assumed to be true, unless otherwise specified, for all built-in
   aggregates.

   For example, count(*) yields the total number of input rows; count(f1)
   yields the number of input rows in which f1 is non-null, since count
   ignores nulls; and count(distinct f1) yields the number of distinct
   non-null values of f1.

   Ordinarily, the input rows are fed to the aggregate function in an
   unspecified order. In many cases this does not matter; for example, min
   produces the same result no matter what order it receives the inputs
   in. However, some aggregate functions (such as array_agg and
   string_agg) produce results that depend on the ordering of the input
   rows. When using such an aggregate, the optional order_by_clause can be
   used to specify the desired ordering. The order_by_clause has the same
   syntax as for a query-level ORDER BY clause, as described in
   Section 7.5, except that its expressions are always just expressions
   and cannot be output-column names or numbers. For example:
WITH vals (v) AS ( VALUES (1),(3),(4),(3),(2) )
SELECT array_agg(v ORDER BY v DESC) FROM vals;
  array_agg
-------------
 {4,3,3,2,1}

   Since jsonb only keeps the last matching key, ordering of its keys can
   be significant:
WITH vals (k, v) AS ( VALUES ('key0','1'), ('key1','3'), ('key1','2') )
SELECT jsonb_object_agg(k, v ORDER BY v) FROM vals;
      jsonb_object_agg
----------------------------
 {"key0": "1", "key1": "3"}

   When dealing with multiple-argument aggregate functions, note that the
   ORDER BY clause goes after all the aggregate arguments. For example,
   write this:
SELECT string_agg(a, ',' ORDER BY a) FROM table;

   not this:
SELECT string_agg(a ORDER BY a, ',') FROM table;  -- incorrect

   The latter is syntactically valid, but it represents a call of a
   single-argument aggregate function with two ORDER BY keys (the second
   one being rather useless since it's a constant).

   If DISTINCT is specified with an order_by_clause, ORDER BY expressions
   can only reference columns in the DISTINCT list. For example:
WITH vals (v) AS ( VALUES (1),(3),(4),(3),(2) )
SELECT array_agg(DISTINCT v ORDER BY v DESC) FROM vals;
 array_agg
-----------
 {4,3,2,1}

   Placing ORDER BY within the aggregate's regular argument list, as
   described so far, is used when ordering the input rows for
   general-purpose and statistical aggregates, for which ordering is
   optional. There is a subclass of aggregate functions called ordered-set
   aggregates for which an order_by_clause is required, usually because
   the aggregate's computation is only sensible in terms of a specific
   ordering of its input rows. Typical examples of ordered-set aggregates
   include rank and percentile calculations. For an ordered-set aggregate,
   the order_by_clause is written inside WITHIN GROUP (...), as shown in
   the final syntax alternative above. The expressions in the
   order_by_clause are evaluated once per input row just like regular
   aggregate arguments, sorted as per the order_by_clause's requirements,
   and fed to the aggregate function as input arguments. (This is unlike
   the case for a non-WITHIN GROUP order_by_clause, which is not treated
   as argument(s) to the aggregate function.) The argument expressions
   preceding WITHIN GROUP, if any, are called direct arguments to
   distinguish them from the aggregated arguments listed in the
   order_by_clause. Unlike regular aggregate arguments, direct arguments
   are evaluated only once per aggregate call, not once per input row.
   This means that they can contain variables only if those variables are
   grouped by GROUP BY; this restriction is the same as if the direct
   arguments were not inside an aggregate expression at all. Direct
   arguments are typically used for things like percentile fractions,
   which only make sense as a single value per aggregation calculation.
   The direct argument list can be empty; in this case, write just () not
   (*). (PostgreSQL will actually accept either spelling, but only the
   first way conforms to the SQL standard.)

   An example of an ordered-set aggregate call is:
SELECT percentile_cont(0.5) WITHIN GROUP (ORDER BY income) FROM households;
 percentile_cont
-----------------
           50489

   which obtains the 50th percentile, or median, value of the income
   column from table households. Here, 0.5 is a direct argument; it would
   make no sense for the percentile fraction to be a value varying across
   rows.

   If FILTER is specified, then only the input rows for which the
   filter_clause evaluates to true are fed to the aggregate function;
   other rows are discarded. For example:
SELECT
    count(*) AS unfiltered,
    count(*) FILTER (WHERE i < 5) AS filtered
FROM generate_series(1,10) AS s(i);
 unfiltered | filtered
------------+----------
         10 |        4
(1 row)

   The predefined aggregate functions are described in Section 9.21. Other
   aggregate functions can be added by the user.

   An aggregate expression can only appear in the result list or HAVING
   clause of a SELECT command. It is forbidden in other clauses, such as
   WHERE, because those clauses are logically evaluated before the results
   of aggregates are formed.

   When an aggregate expression appears in a subquery (see Section 4.2.11
   and Section 9.24), the aggregate is normally evaluated over the rows of
   the subquery. But an exception occurs if the aggregate's arguments (and
   filter_clause if any) contain only outer-level variables: the aggregate
   then belongs to the nearest such outer level, and is evaluated over the
   rows of that query. The aggregate expression as a whole is then an
   outer reference for the subquery it appears in, and acts as a constant
   over any one evaluation of that subquery. The restriction about
   appearing only in the result list or HAVING clause applies with respect
   to the query level that the aggregate belongs to.

4.2.8. Window Function Calls #

   A window function call represents the application of an aggregate-like
   function over some portion of the rows selected by a query. Unlike
   non-window aggregate calls, this is not tied to grouping of the
   selected rows into a single output row — each row remains separate in
   the query output. However the window function has access to all the
   rows that would be part of the current row's group according to the
   grouping specification (PARTITION BY list) of the window function call.
   The syntax of a window function call is one of the following:
function_name ([expression [, expression ... ]]) [ FILTER ( WHERE filter_clause
) ] OVER window_name
function_name ([expression [, expression ... ]]) [ FILTER ( WHERE filter_clause
) ] OVER ( window_definition )
function_name ( * ) [ FILTER ( WHERE filter_clause ) ] OVER window_name
function_name ( * ) [ FILTER ( WHERE filter_clause ) ] OVER ( window_definition
)

   where window_definition has the syntax
[ existing_window_name ]
[ PARTITION BY expression [, ...] ]
[ ORDER BY expression [ ASC | DESC | USING operator ] [ NULLS { FIRST | LAST } ]
 [, ...] ]
[ frame_clause ]

   The optional frame_clause can be one of
{ RANGE | ROWS | GROUPS } frame_start [ frame_exclusion ]
{ RANGE | ROWS | GROUPS } BETWEEN frame_start AND frame_end [ frame_exclusion ]

   where frame_start and frame_end can be one of
UNBOUNDED PRECEDING
offset PRECEDING
CURRENT ROW
offset FOLLOWING
UNBOUNDED FOLLOWING

   and frame_exclusion can be one of
EXCLUDE CURRENT ROW
EXCLUDE GROUP
EXCLUDE TIES
EXCLUDE NO OTHERS

   Here, expression represents any value expression that does not itself
   contain window function calls.

   window_name is a reference to a named window specification defined in
   the query's WINDOW clause. Alternatively, a full window_definition can
   be given within parentheses, using the same syntax as for defining a
   named window in the WINDOW clause; see the SELECT reference page for
   details. It's worth pointing out that OVER wname is not exactly
   equivalent to OVER (wname ...); the latter implies copying and
   modifying the window definition, and will be rejected if the referenced
   window specification includes a frame clause.

   The PARTITION BY clause groups the rows of the query into partitions,
   which are processed separately by the window function. PARTITION BY
   works similarly to a query-level GROUP BY clause, except that its
   expressions are always just expressions and cannot be output-column
   names or numbers. Without PARTITION BY, all rows produced by the query
   are treated as a single partition. The ORDER BY clause determines the
   order in which the rows of a partition are processed by the window
   function. It works similarly to a query-level ORDER BY clause, but
   likewise cannot use output-column names or numbers. Without ORDER BY,
   rows are processed in an unspecified order.

   The frame_clause specifies the set of rows constituting the window
   frame, which is a subset of the current partition, for those window
   functions that act on the frame instead of the whole partition. The set
   of rows in the frame can vary depending on which row is the current
   row. The frame can be specified in RANGE, ROWS or GROUPS mode; in each
   case, it runs from the frame_start to the frame_end. If frame_end is
   omitted, the end defaults to CURRENT ROW.

   A frame_start of UNBOUNDED PRECEDING means that the frame starts with
   the first row of the partition, and similarly a frame_end of UNBOUNDED
   FOLLOWING means that the frame ends with the last row of the partition.

   In RANGE or GROUPS mode, a frame_start of CURRENT ROW means the frame
   starts with the current row's first peer row (a row that the window's
   ORDER BY clause sorts as equivalent to the current row), while a
   frame_end of CURRENT ROW means the frame ends with the current row's
   last peer row. In ROWS mode, CURRENT ROW simply means the current row.

   In the offset PRECEDING and offset FOLLOWING frame options, the offset
   must be an expression not containing any variables, aggregate
   functions, or window functions. The meaning of the offset depends on
   the frame mode:
     * In ROWS mode, the offset must yield a non-null, non-negative
       integer, and the option means that the frame starts or ends the
       specified number of rows before or after the current row.
     * In GROUPS mode, the offset again must yield a non-null,
       non-negative integer, and the option means that the frame starts or
       ends the specified number of peer groups before or after the
       current row's peer group, where a peer group is a set of rows that
       are equivalent in the ORDER BY ordering. (There must be an ORDER BY
       clause in the window definition to use GROUPS mode.)
     * In RANGE mode, these options require that the ORDER BY clause
       specify exactly one column. The offset specifies the maximum
       difference between the value of that column in the current row and
       its value in preceding or following rows of the frame. The data
       type of the offset expression varies depending on the data type of
       the ordering column. For numeric ordering columns it is typically
       of the same type as the ordering column, but for datetime ordering
       columns it is an interval. For example, if the ordering column is
       of type date or timestamp, one could write RANGE BETWEEN '1 day'
       PRECEDING AND '10 days' FOLLOWING. The offset is still required to
       be non-null and non-negative, though the meaning of “non-negative”
       depends on its data type.

   In any case, the distance to the end of the frame is limited by the
   distance to the end of the partition, so that for rows near the
   partition ends the frame might contain fewer rows than elsewhere.

   Notice that in both ROWS and GROUPS mode, 0 PRECEDING and 0 FOLLOWING
   are equivalent to CURRENT ROW. This normally holds in RANGE mode as
   well, for an appropriate data-type-specific meaning of “zero”.

   The frame_exclusion option allows rows around the current row to be
   excluded from the frame, even if they would be included according to
   the frame start and frame end options. EXCLUDE CURRENT ROW excludes the
   current row from the frame. EXCLUDE GROUP excludes the current row and
   its ordering peers from the frame. EXCLUDE TIES excludes any peers of
   the current row from the frame, but not the current row itself. EXCLUDE
   NO OTHERS simply specifies explicitly the default behavior of not
   excluding the current row or its peers.

   The default framing option is RANGE UNBOUNDED PRECEDING, which is the
   same as RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW. With ORDER
   BY, this sets the frame to be all rows from the partition start up
   through the current row's last ORDER BY peer. Without ORDER BY, this
   means all rows of the partition are included in the window frame, since
   all rows become peers of the current row.

   Restrictions are that frame_start cannot be UNBOUNDED FOLLOWING,
   frame_end cannot be UNBOUNDED PRECEDING, and the frame_end choice
   cannot appear earlier in the above list of frame_start and frame_end
   options than the frame_start choice does — for example RANGE BETWEEN
   CURRENT ROW AND offset PRECEDING is not allowed. But, for example, ROWS
   BETWEEN 7 PRECEDING AND 8 PRECEDING is allowed, even though it would
   never select any rows.

   If FILTER is specified, then only the input rows for which the
   filter_clause evaluates to true are fed to the window function; other
   rows are discarded. Only window functions that are aggregates accept a
   FILTER clause.

   The built-in window functions are described in Table 9.67. Other window
   functions can be added by the user. Also, any built-in or user-defined
   general-purpose or statistical aggregate can be used as a window
   function. (Ordered-set and hypothetical-set aggregates cannot presently
   be used as window functions.)

   The syntaxes using * are used for calling parameter-less aggregate
   functions as window functions, for example count(*) OVER (PARTITION BY
   x ORDER BY y). The asterisk (*) is customarily not used for
   window-specific functions. Window-specific functions do not allow
   DISTINCT or ORDER BY to be used within the function argument list.

   Window function calls are permitted only in the SELECT list and the
   ORDER BY clause of the query.

   More information about window functions can be found in Section 3.5,
   Section 9.22, and Section 7.2.5.

4.2.9. Type Casts #

   A type cast specifies a conversion from one data type to another.
   PostgreSQL accepts two equivalent syntaxes for type casts:
CAST ( expression AS type )
expression::type

   The CAST syntax conforms to SQL; the syntax with :: is historical
   PostgreSQL usage.

   When a cast is applied to a value expression of a known type, it
   represents a run-time type conversion. The cast will succeed only if a
   suitable type conversion operation has been defined. Notice that this
   is subtly different from the use of casts with constants, as shown in
   Section 4.1.2.7. A cast applied to an unadorned string literal
   represents the initial assignment of a type to a literal constant
   value, and so it will succeed for any type (if the contents of the
   string literal are acceptable input syntax for the data type).

   An explicit type cast can usually be omitted if there is no ambiguity
   as to the type that a value expression must produce (for example, when
   it is assigned to a table column); the system will automatically apply
   a type cast in such cases. However, automatic casting is only done for
   casts that are marked “OK to apply implicitly” in the system catalogs.
   Other casts must be invoked with explicit casting syntax. This
   restriction is intended to prevent surprising conversions from being
   applied silently.

   It is also possible to specify a type cast using a function-like
   syntax:
typename ( expression )

   However, this only works for types whose names are also valid as
   function names. For example, double precision cannot be used this way,
   but the equivalent float8 can. Also, the names interval, time, and
   timestamp can only be used in this fashion if they are double-quoted,
   because of syntactic conflicts. Therefore, the use of the function-like
   cast syntax leads to inconsistencies and should probably be avoided.

Note

   The function-like syntax is in fact just a function call. When one of
   the two standard cast syntaxes is used to do a run-time conversion, it
   will internally invoke a registered function to perform the conversion.
   By convention, these conversion functions have the same name as their
   output type, and thus the “function-like syntax” is nothing more than a
   direct invocation of the underlying conversion function. Obviously,
   this is not something that a portable application should rely on. For
   further details see CREATE CAST.

4.2.10. Collation Expressions #

   The COLLATE clause overrides the collation of an expression. It is
   appended to the expression it applies to:
expr COLLATE collation

   where collation is a possibly schema-qualified identifier. The COLLATE
   clause binds tighter than operators; parentheses can be used when
   necessary.

   If no collation is explicitly specified, the database system either
   derives a collation from the columns involved in the expression, or it
   defaults to the default collation of the database if no column is
   involved in the expression.

   The two common uses of the COLLATE clause are overriding the sort order
   in an ORDER BY clause, for example:
SELECT a, b, c FROM tbl WHERE ... ORDER BY a COLLATE "C";

   and overriding the collation of a function or operator call that has
   locale-sensitive results, for example:
SELECT * FROM tbl WHERE a > 'foo' COLLATE "C";

   Note that in the latter case the COLLATE clause is attached to an input
   argument of the operator we wish to affect. It doesn't matter which
   argument of the operator or function call the COLLATE clause is
   attached to, because the collation that is applied by the operator or
   function is derived by considering all arguments, and an explicit
   COLLATE clause will override the collations of all other arguments.
   (Attaching non-matching COLLATE clauses to more than one argument,
   however, is an error. For more details see Section 23.2.) Thus, this
   gives the same result as the previous example:
SELECT * FROM tbl WHERE a COLLATE "C" > 'foo';

   But this is an error:
SELECT * FROM tbl WHERE (a > 'foo') COLLATE "C";

   because it attempts to apply a collation to the result of the >
   operator, which is of the non-collatable data type boolean.

4.2.11. Scalar Subqueries #

   A scalar subquery is an ordinary SELECT query in parentheses that
   returns exactly one row with one column. (See Chapter 7 for information
   about writing queries.) The SELECT query is executed and the single
   returned value is used in the surrounding value expression. It is an
   error to use a query that returns more than one row or more than one
   column as a scalar subquery. (But if, during a particular execution,
   the subquery returns no rows, there is no error; the scalar result is
   taken to be null.) The subquery can refer to variables from the
   surrounding query, which will act as constants during any one
   evaluation of the subquery. See also Section 9.24 for other expressions
   involving subqueries.

   For example, the following finds the largest city population in each
   state:
SELECT name, (SELECT max(pop) FROM cities WHERE cities.state = states.name)
    FROM states;

4.2.12. Array Constructors #

   An array constructor is an expression that builds an array value using
   values for its member elements. A simple array constructor consists of
   the key word ARRAY, a left square bracket [, a list of expressions
   (separated by commas) for the array element values, and finally a right
   square bracket ]. For example:
SELECT ARRAY[1,2,3+4];
  array
---------
 {1,2,7}
(1 row)

   By default, the array element type is the common type of the member
   expressions, determined using the same rules as for UNION or CASE
   constructs (see Section 10.5). You can override this by explicitly
   casting the array constructor to the desired type, for example:
SELECT ARRAY[1,2,22.7]::integer[];
  array
----------
 {1,2,23}
(1 row)

   This has the same effect as casting each expression to the array
   element type individually. For more on casting, see Section 4.2.9.

   Multidimensional array values can be built by nesting array
   constructors. In the inner constructors, the key word ARRAY can be
   omitted. For example, these produce the same result:
SELECT ARRAY[ARRAY[1,2], ARRAY[3,4]];
     array
---------------
 {{1,2},{3,4}}
(1 row)

SELECT ARRAY[[1,2],[3,4]];
     array
---------------
 {{1,2},{3,4}}
(1 row)

   Since multidimensional arrays must be rectangular, inner constructors
   at the same level must produce sub-arrays of identical dimensions. Any
   cast applied to the outer ARRAY constructor propagates automatically to
   all the inner constructors.

   Multidimensional array constructor elements can be anything yielding an
   array of the proper kind, not only a sub-ARRAY construct. For example:
CREATE TABLE arr(f1 int[], f2 int[]);

INSERT INTO arr VALUES (ARRAY[[1,2],[3,4]], ARRAY[[5,6],[7,8]]);

SELECT ARRAY[f1, f2, '{{9,10},{11,12}}'::int[]] FROM arr;
                     array
------------------------------------------------
 {{{1,2},{3,4}},{{5,6},{7,8}},{{9,10},{11,12}}}
(1 row)

   You can construct an empty array, but since it's impossible to have an
   array with no type, you must explicitly cast your empty array to the
   desired type. For example:
SELECT ARRAY[]::integer[];
 array
-------
 {}
(1 row)

   It is also possible to construct an array from the results of a
   subquery. In this form, the array constructor is written with the key
   word ARRAY followed by a parenthesized (not bracketed) subquery. For
   example:
SELECT ARRAY(SELECT oid FROM pg_proc WHERE proname LIKE 'bytea%');
                              array
------------------------------------------------------------------
 {2011,1954,1948,1952,1951,1244,1950,2005,1949,1953,2006,31,2412}
(1 row)

SELECT ARRAY(SELECT ARRAY[i, i*2] FROM generate_series(1,5) AS a(i));
              array
----------------------------------
 {{1,2},{2,4},{3,6},{4,8},{5,10}}
(1 row)

   The subquery must return a single column. If the subquery's output
   column is of a non-array type, the resulting one-dimensional array will
   have an element for each row in the subquery result, with an element
   type matching that of the subquery's output column. If the subquery's
   output column is of an array type, the result will be an array of the
   same type but one higher dimension; in this case all the subquery rows
   must yield arrays of identical dimensionality, else the result would
   not be rectangular.

   The subscripts of an array value built with ARRAY always begin with
   one. For more information about arrays, see Section 8.15.

4.2.13. Row Constructors #

   A row constructor is an expression that builds a row value (also called
   a composite value) using values for its member fields. A row
   constructor consists of the key word ROW, a left parenthesis, zero or
   more expressions (separated by commas) for the row field values, and
   finally a right parenthesis. For example:
SELECT ROW(1,2.5,'this is a test');

   The key word ROW is optional when there is more than one expression in
   the list.

   A row constructor can include the syntax rowvalue.*, which will be
   expanded to a list of the elements of the row value, just as occurs
   when the .* syntax is used at the top level of a SELECT list (see
   Section 8.16.5). For example, if table t has columns f1 and f2, these
   are the same:
SELECT ROW(t.*, 42) FROM t;
SELECT ROW(t.f1, t.f2, 42) FROM t;

Note

   Before PostgreSQL 8.2, the .* syntax was not expanded in row
   constructors, so that writing ROW(t.*, 42) created a two-field row
   whose first field was another row value. The new behavior is usually
   more useful. If you need the old behavior of nested row values, write
   the inner row value without .*, for instance ROW(t, 42).

   By default, the value created by a ROW expression is of an anonymous
   record type. If necessary, it can be cast to a named composite type —
   either the row type of a table, or a composite type created with CREATE
   TYPE AS. An explicit cast might be needed to avoid ambiguity. For
   example:
CREATE TABLE mytable(f1 int, f2 float, f3 text);

CREATE FUNCTION getf1(mytable) RETURNS int AS 'SELECT $1.f1' LANGUAGE SQL;

-- No cast needed since only one getf1() exists
SELECT getf1(ROW(1,2.5,'this is a test'));
 getf1
-------
     1
(1 row)

CREATE TYPE myrowtype AS (f1 int, f2 text, f3 numeric);

CREATE FUNCTION getf1(myrowtype) RETURNS int AS 'SELECT $1.f1' LANGUAGE SQL;

-- Now we need a cast to indicate which function to call:
SELECT getf1(ROW(1,2.5,'this is a test'));
ERROR:  function getf1(record) is not unique

SELECT getf1(ROW(1,2.5,'this is a test')::mytable);
 getf1
-------
     1
(1 row)

SELECT getf1(CAST(ROW(11,'this is a test',2.5) AS myrowtype));
 getf1
-------
    11
(1 row)

   Row constructors can be used to build composite values to be stored in
   a composite-type table column, or to be passed to a function that
   accepts a composite parameter. Also, it is possible to test rows using
   the standard comparison operators as described in Section 9.2, to
   compare one row against another as described in Section 9.25, and to
   use them in connection with subqueries, as discussed in Section 9.24.

4.2.14. Expression Evaluation Rules #

   The order of evaluation of subexpressions is not defined. In
   particular, the inputs of an operator or function are not necessarily
   evaluated left-to-right or in any other fixed order.

   Furthermore, if the result of an expression can be determined by
   evaluating only some parts of it, then other subexpressions might not
   be evaluated at all. For instance, if one wrote:
SELECT true OR somefunc();

   then somefunc() would (probably) not be called at all. The same would
   be the case if one wrote:
SELECT somefunc() OR true;

   Note that this is not the same as the left-to-right “short-circuiting”
   of Boolean operators that is found in some programming languages.

   As a consequence, it is unwise to use functions with side effects as
   part of complex expressions. It is particularly dangerous to rely on
   side effects or evaluation order in WHERE and HAVING clauses, since
   those clauses are extensively reprocessed as part of developing an
   execution plan. Boolean expressions (AND/OR/NOT combinations) in those
   clauses can be reorganized in any manner allowed by the laws of Boolean
   algebra.

   When it is essential to force evaluation order, a CASE construct (see
   Section 9.18) can be used. For example, this is an untrustworthy way of
   trying to avoid division by zero in a WHERE clause:
SELECT ... WHERE x > 0 AND y/x > 1.5;

   But this is safe:
SELECT ... WHERE CASE WHEN x > 0 THEN y/x > 1.5 ELSE false END;

   A CASE construct used in this fashion will defeat optimization
   attempts, so it should only be done when necessary. (In this particular
   example, it would be better to sidestep the problem by writing y >
   1.5*x instead.)

   CASE is not a cure-all for such issues, however. One limitation of the
   technique illustrated above is that it does not prevent early
   evaluation of constant subexpressions. As described in Section 36.7,
   functions and operators marked IMMUTABLE can be evaluated when the
   query is planned rather than when it is executed. Thus for example
SELECT CASE WHEN x > 0 THEN x ELSE 1/0 END FROM tab;

   is likely to result in a division-by-zero failure due to the planner
   trying to simplify the constant subexpression, even if every row in the
   table has x > 0 so that the ELSE arm would never be entered at run
   time.

   While that particular example might seem silly, related cases that
   don't obviously involve constants can occur in queries executed within
   functions, since the values of function arguments and local variables
   can be inserted into queries as constants for planning purposes. Within
   PL/pgSQL functions, for example, using an IF-THEN-ELSE statement to
   protect a risky computation is much safer than just nesting it in a
   CASE expression.

   Another limitation of the same kind is that a CASE cannot prevent
   evaluation of an aggregate expression contained within it, because
   aggregate expressions are computed before other expressions in a SELECT
   list or HAVING clause are considered. For example, the following query
   can cause a division-by-zero error despite seemingly having protected
   against it:
SELECT CASE WHEN min(employees) > 0
            THEN avg(expenses / employees)
       END
    FROM departments;

   The min() and avg() aggregates are computed concurrently over all the
   input rows, so if any row has employees equal to zero, the
   division-by-zero error will occur before there is any opportunity to
   test the result of min(). Instead, use a WHERE or FILTER clause to
   prevent problematic input rows from reaching an aggregate function in
   the first place.