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.
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.)
   
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.
   
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 must yield an 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.
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.
There are three possible syntaxes for an operator invocation:
| expressionoperatorexpression(binary infix operator) | 
| operatorexpression(unary prefix operator) | 
| expressionoperator(unary postfix 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.
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.
     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.
    
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 ( WHEREfilter_clause) ]aggregate_name(ALLexpression[ , ... ] [order_by_clause] ) [ FILTER ( WHEREfilter_clause) ]aggregate_name(DISTINCTexpression[ , ... ] [order_by_clause] ) [ FILTER ( WHEREfilter_clause) ]aggregate_name( * ) [ FILTER ( WHEREfilter_clause) ]aggregate_name( [expression[ , ... ] ] ) WITHIN GROUP (order_by_clause) [ FILTER ( WHEREfilter_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:
SELECT array_agg(a ORDER BY b DESC) FROM table;
    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 in addition to an
    order_by_clause, then all the ORDER BY
    expressions must match regular arguments of the aggregate; that is,
    you cannot sort on an expression that is not included in the
    DISTINCT list.
   
     The ability to specify both DISTINCT and ORDER BY
     in an aggregate function is a PostgreSQL extension.
    
    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 9.20. 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 9.22), 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.
   
    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 ( WHEREfilter_clause) ] OVERwindow_namefunction_name([expression[,expression... ]]) [ FILTER ( WHEREfilter_clause) ] OVER (window_definition)function_name( * ) [ FILTER ( WHEREfilter_clause) ] OVERwindow_namefunction_name( * ) [ FILTER ( WHEREfilter_clause) ] OVER (window_definition)
    where window_definition
    has the syntax
[existing_window_name] [ PARTITION BYexpression[, ...] ] [ ORDER BYexpression[ ASC | DESC | USINGoperator] [ 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 PRECEDINGoffsetPRECEDING CURRENT ROWoffsetFOLLOWING 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  is not allowed.
    But, for example, offset
    PRECEDINGROWS 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 9.57. 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 9.21, and Section 7.2.5.
A type cast specifies a conversion from one data type to another. PostgreSQL accepts two equivalent syntaxes for type casts:
CAST (expressionAStype)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.
   
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.
    The COLLATE clause overrides the collation of
    an expression.  It is appended to the expression it applies to:
exprCOLLATEcollation
    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.
   
    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 9.22 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;
    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
-----------------------------------------------------------------------
 {2011,1954,1948,1952,1951,1244,1950,2005,1949,1953,2006,31,2412,2413}
(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.
  
    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;
     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 compare two row values or test a row with
   IS NULL or IS NOT NULL, for example:
SELECT ROW(1,2.5,'this is a test') = ROW(1, 3, 'not the same'); SELECT ROW(table.*) IS NULL FROM table; -- detect all-null rows
For more detail see Section 9.23. Row constructors can also be used in connection with subqueries, as discussed in Section 9.22.
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 9.17) 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 38.7, 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.