CREATE AGGREGATE — define a new aggregate function
CREATE AGGREGATEname( [argmode] [argname]arg_data_type[ , ... ] ) ( SFUNC =sfunc, STYPE =state_data_type[ , SSPACE =state_data_size] [ , FINALFUNC =ffunc] [ , FINALFUNC_EXTRA ] [ , COMBINEFUNC =combinefunc] [ , SERIALFUNC =serialfunc] [ , DESERIALFUNC =deserialfunc] [ , INITCOND =initial_condition] [ , MSFUNC =msfunc] [ , MINVFUNC =minvfunc] [ , MSTYPE =mstate_data_type] [ , MSSPACE =mstate_data_size] [ , MFINALFUNC =mffunc] [ , MFINALFUNC_EXTRA ] [ , MINITCOND =minitial_condition] [ , SORTOP =sort_operator] [ , PARALLEL = { SAFE | RESTRICTED | UNSAFE } ] ) CREATE AGGREGATEname( [ [argmode] [argname]arg_data_type[ , ... ] ] ORDER BY [argmode] [argname]arg_data_type[ , ... ] ) ( SFUNC =sfunc, STYPE =state_data_type[ , SSPACE =state_data_size] [ , FINALFUNC =ffunc] [ , FINALFUNC_EXTRA ] [ , INITCOND =initial_condition] [ , PARALLEL = { SAFE | RESTRICTED | UNSAFE } ] [ , HYPOTHETICAL ] ) or the old syntax CREATE AGGREGATEname( BASETYPE =base_type, SFUNC =sfunc, STYPE =state_data_type[ , SSPACE =state_data_size] [ , FINALFUNC =ffunc] [ , FINALFUNC_EXTRA ] [ , COMBINEFUNC =combinefunc] [ , SERIALFUNC =serialfunc] [ , DESERIALFUNC =deserialfunc] [ , INITCOND =initial_condition] [ , MSFUNC =msfunc] [ , MINVFUNC =minvfunc] [ , MSTYPE =mstate_data_type] [ , MSSPACE =mstate_data_size] [ , MFINALFUNC =mffunc] [ , MFINALFUNC_EXTRA ] [ , MINITCOND =minitial_condition] [ , SORTOP =sort_operator] )
CREATE AGGREGATE defines a new aggregate
function. Some basic and commonly-used aggregate functions are
included with the distribution; they are documented in Section 9.20. If one defines new types or needs
an aggregate function not already provided, then CREATE
AGGREGATE can be used to provide the desired features.
If a schema name is given (for example, CREATE AGGREGATE
myschema.myagg ...) then the aggregate function is created in the
specified schema. Otherwise it is created in the current schema.
An aggregate function is identified by its name and input data type(s). Two aggregates in the same schema can have the same name if they operate on different input types. The name and input data type(s) of an aggregate must also be distinct from the name and input data type(s) of every ordinary function in the same schema. This behavior is identical to overloading of ordinary function names (see CREATE FUNCTION).
A simple aggregate function is made from one or two ordinary
functions:
a state transition function
sfunc,
and an optional final calculation function
ffunc.
These are used as follows:
sfunc( internal-state, next-data-values ) ---> next-internal-stateffunc( internal-state ) ---> aggregate-value
PostgreSQL creates a temporary variable
of data type stype
to hold the current internal state of the aggregate. At each input row,
the aggregate argument value(s) are calculated and
the state transition function is invoked with the current state value
and the new argument value(s) to calculate a new
internal state value. After all the rows have been processed,
the final function is invoked once to calculate the aggregate's return
value. If there is no final function then the ending state value
is returned as-is.
An aggregate function can provide an initial condition,
that is, an initial value for the internal state value.
This is specified and stored in the database as a value of type
text, but it must be a valid external representation
of a constant of the state value data type. If it is not supplied
then the state value starts out null.
If the state transition function is declared “strict”,
then it cannot be called with null inputs. With such a transition
function, aggregate execution behaves as follows. Rows with any null input
values are ignored (the function is not called and the previous state value
is retained). If the initial state value is null, then at the first row
with all-nonnull input values, the first argument value replaces the state
value, and the transition function is invoked at each subsequent row with
all-nonnull input values.
This is handy for implementing aggregates like max.
Note that this behavior is only available when
state_data_type
is the same as the first
arg_data_type.
When these types are different, you must supply a nonnull initial
condition or use a nonstrict transition function.
If the state transition function is not strict, then it will be called unconditionally at each input row, and must deal with null inputs and null state values for itself. This allows the aggregate author to have full control over the aggregate's handling of null values.
If the final function is declared “strict”, then it will not
be called when the ending state value is null; instead a null result
will be returned automatically. (Of course this is just the normal
behavior of strict functions.) In any case the final function has
the option of returning a null value. For example, the final function for
avg returns null when it sees there were zero
input rows.
Sometimes it is useful to declare the final function as taking not just
the state value, but extra parameters corresponding to the aggregate's
input values. The main reason for doing this is if the final function
is polymorphic and the state value's data type would be inadequate to
pin down the result type. These extra parameters are always passed as
NULL (and so the final function must not be strict when
the FINALFUNC_EXTRA option is used), but nonetheless they
are valid parameters. The final function could for example make use
of get_fn_expr_argtype to identify the actual argument type
in the current call.
An aggregate can optionally support moving-aggregate mode,
as described in Section 37.10.1. This requires
specifying the MSFUNC, MINVFUNC,
and MSTYPE parameters, and optionally
the MSPACE, MFINALFUNC, MFINALFUNC_EXTRA,
and MINITCOND parameters. Except for MINVFUNC,
these parameters work like the corresponding simple-aggregate parameters
without M; they define a separate implementation of the
aggregate that includes an inverse transition function.
The syntax with ORDER BY in the parameter list creates
a special type of aggregate called an ordered-set
aggregate; or if HYPOTHETICAL is specified, then
a hypothetical-set aggregate is created. These
aggregates operate over groups of sorted values in order-dependent ways,
so that specification of an input sort order is an essential part of a
call. Also, they can have direct arguments, which are
arguments that are evaluated only once per aggregation rather than once
per input row. Hypothetical-set aggregates are a subclass of ordered-set
aggregates in which some of the direct arguments are required to match,
in number and data types, the aggregated argument columns. This allows
the values of those direct arguments to be added to the collection of
aggregate-input rows as an additional “hypothetical” row.
An aggregate can optionally support partial aggregation,
as described in Section 37.10.4.
This requires specifying the COMBINEFUNC parameter.
If the state_data_type
is internal, it's usually also appropriate to provide the
SERIALFUNC and DESERIALFUNC parameters so that
parallel aggregation is possible. Note that the aggregate must also be
marked PARALLEL SAFE to enable parallel aggregation.
Aggregates that behave like MIN or MAX can
sometimes be optimized by looking into an index instead of scanning every
input row. If this aggregate can be so optimized, indicate it by
specifying a sort operator. The basic requirement is that
the aggregate must yield the first element in the sort ordering induced by
the operator; in other words:
SELECT agg(col) FROM tab;
must be equivalent to:
SELECT col FROM tab ORDER BY col USING sortop LIMIT 1;
Further assumptions are that the aggregate ignores null inputs, and that
it delivers a null result if and only if there were no non-null inputs.
Ordinarily, a data type's < operator is the proper sort
operator for MIN, and > is the proper sort
operator for MAX. Note that the optimization will never
actually take effect unless the specified operator is the “less
than” or “greater than” strategy member of a B-tree
index operator class.
To be able to create an aggregate function, you must
have USAGE privilege on the argument types, the state
type(s), and the return type, as well as EXECUTE
privilege on the supporting functions.
nameThe name (optionally schema-qualified) of the aggregate function to create.
argmode The mode of an argument: IN or VARIADIC.
(Aggregate functions do not support OUT arguments.)
If omitted, the default is IN. Only the last argument
can be marked VARIADIC.
argnameThe name of an argument. This is currently only useful for documentation purposes. If omitted, the argument has no name.
arg_data_type An input data type on which this aggregate function operates.
To create a zero-argument aggregate function, write *
in place of the list of argument specifications. (An example of such an
aggregate is count(*).)
base_type In the old syntax for CREATE AGGREGATE, the input data type
is specified by a basetype parameter rather than being
written next to the aggregate name. Note that this syntax allows
only one input parameter. To define a zero-argument aggregate function
with this syntax, specify the basetype as
"ANY" (not *).
Ordered-set aggregates cannot be defined with the old syntax.
sfunc The name of the state transition function to be called for each
input row. For a normal N-argument
aggregate function, the sfunc
must take N+1 arguments,
the first being of type state_data_type and the rest
matching the declared input data type(s) of the aggregate.
The function must return a value of type state_data_type. This function
takes the current state value and the current input data value(s),
and returns the next state value.
For ordered-set (including hypothetical-set) aggregates, the state transition function receives only the current state value and the aggregated arguments, not the direct arguments. Otherwise it is the same.
state_data_typeThe data type for the aggregate's state value.
state_data_size The approximate average size (in bytes) of the aggregate's state value.
If this parameter is omitted or is zero, a default estimate is used
based on the state_data_type.
The planner uses this value to estimate the memory required for a
grouped aggregate query. The planner will consider using hash
aggregation for such a query only if the hash table is estimated to fit
in work_mem; therefore, large values of this
parameter discourage use of hash aggregation.
ffunc The name of the final function called to compute the aggregate's
result after all input rows have been traversed.
For a normal aggregate, this function
must take a single argument of type state_data_type. The return
data type of the aggregate is defined as the return type of this
function. If ffunc
is not specified, then the ending state value is used as the
aggregate's result, and the return type is state_data_type.
For ordered-set (including hypothetical-set) aggregates, the final function receives not only the final state value, but also the values of all the direct arguments.
If FINALFUNC_EXTRA is specified, then in addition to the
final state value and any direct arguments, the final function
receives extra NULL values corresponding to the aggregate's regular
(aggregated) arguments. This is mainly useful to allow correct
resolution of the aggregate result type when a polymorphic aggregate
is being defined.
combinefunc The combinefunc function
may optionally be specified to allow the aggregate function to support
partial aggregation. If provided,
the combinefunc must
combine two state_data_type
values, each containing the result of aggregation over some subset of
the input values, to produce a
new state_data_type that
represents the result of aggregating over both sets of inputs. This
function can be thought of as
an sfunc, where instead of
acting upon an individual input row and adding it to the running
aggregate state, it adds another aggregate state to the running state.
The combinefunc must be
declared as taking two arguments of
the state_data_type and
returning a value of
the state_data_type.
Optionally this function may be “strict”. In this case the
function will not be called when either of the input states are null;
the other state will be taken as the correct result.
For aggregate functions
whose state_data_type
is internal,
the combinefunc must not
be strict. In this case
the combinefunc must
ensure that null states are handled correctly and that the state being
returned is properly stored in the aggregate memory context.
serialfunc An aggregate function
whose state_data_type
is internal can participate in parallel aggregation only if it
has a serialfunc function,
which must serialize the aggregate state into a bytea value for
transmission to another process. This function must take a single
argument of type internal and return type bytea. A
corresponding deserialfunc
is also required.
deserialfunc Deserialize a previously serialized aggregate state back into
state_data_type. This
function must take two arguments of types bytea
and internal, and produce a result of type internal.
(Note: the second, internal argument is unused, but is required
for type safety reasons.)
initial_condition The initial setting for the state value. This must be a string
constant in the form accepted for the data type state_data_type. If not
specified, the state value starts out null.
msfunc The name of the forward state transition function to be called for each
input row in moving-aggregate mode. This is exactly like the regular
transition function, except that its first argument and result are of
type mstate_data_type, which might be different
from state_data_type.
minvfunc The name of the inverse state transition function to be used in
moving-aggregate mode. This function has the same argument and
result types as msfunc, but it is used to remove
a value from the current aggregate state, rather than add a value to
it. The inverse transition function must have the same strictness
attribute as the forward state transition function.
mstate_data_typeThe data type for the aggregate's state value, when using moving-aggregate mode.
mstate_data_size The approximate average size (in bytes) of the aggregate's state
value, when using moving-aggregate mode. This works the same as
state_data_size.
mffunc The name of the final function called to compute the aggregate's
result after all input rows have been traversed, when using
moving-aggregate mode. This works the same as ffunc,
except that its first argument's type
is mstate_data_type and extra dummy arguments are
specified by writing MFINALFUNC_EXTRA.
The aggregate result type determined by mffunc
or mstate_data_type must match that determined by the
aggregate's regular implementation.
minitial_condition The initial setting for the state value, when using moving-aggregate
mode. This works the same as initial_condition.
sort_operator The associated sort operator for a MIN- or
MAX-like aggregate.
This is just an operator name (possibly schema-qualified).
The operator is assumed to have the same input data types as
the aggregate (which must be a single-argument normal aggregate).
PARALLEL The meanings of PARALLEL SAFE, PARALLEL
RESTRICTED, and PARALLEL UNSAFE are the same as
for CREATE FUNCTION. An aggregate will not be
considered for parallelization if it is marked PARALLEL
UNSAFE (which is the default!) or PARALLEL RESTRICTED.
Note that the parallel-safety markings of the aggregate's support
functions are not consulted by the planner, only the marking of the
aggregate itself.
HYPOTHETICAL For ordered-set aggregates only, this flag specifies that the aggregate
arguments are to be processed according to the requirements for
hypothetical-set aggregates: that is, the last few direct arguments must
match the data types of the aggregated (WITHIN GROUP)
arguments. The HYPOTHETICAL flag has no effect on
run-time behavior, only on parse-time resolution of the data types and
collations of the aggregate's arguments.
The parameters of CREATE AGGREGATE can be
written in any order, not just the order illustrated above.
In parameters that specify support function names, you can write
a schema name if needed, for example SFUNC = public.sum.
Do not write argument types there, however — the argument types
of the support functions are determined from other parameters.
If an aggregate supports moving-aggregate mode, it will improve
calculation efficiency when the aggregate is used as a window function
for a window with moving frame start (that is, a frame start mode other
than UNBOUNDED PRECEDING). Conceptually, the forward
transition function adds input values to the aggregate's state when
they enter the window frame from the bottom, and the inverse transition
function removes them again when they leave the frame at the top. So,
when values are removed, they are always removed in the same order they
were added. Whenever the inverse transition function is invoked, it will
thus receive the earliest added but not yet removed argument value(s).
The inverse transition function can assume that at least one row will
remain in the current state after it removes the oldest row. (When this
would not be the case, the window function mechanism simply starts a
fresh aggregation, rather than using the inverse transition function.)
The forward transition function for moving-aggregate mode is not allowed to return NULL as the new state value. If the inverse transition function returns NULL, this is taken as an indication that the inverse function cannot reverse the state calculation for this particular input, and so the aggregate calculation will be redone from scratch for the current frame starting position. This convention allows moving-aggregate mode to be used in situations where there are some infrequent cases that are impractical to reverse out of the running state value.
If no moving-aggregate implementation is supplied, the aggregate can still be used with moving frames, but PostgreSQL will recompute the whole aggregation whenever the start of the frame moves. Note that whether or not the aggregate supports moving-aggregate mode, PostgreSQL can handle a moving frame end without recalculation; this is done by continuing to add new values to the aggregate's state. It is assumed that the final function does not damage the aggregate's state value, so that the aggregation can be continued even after an aggregate result value has been obtained for one set of frame boundaries.
The syntax for ordered-set aggregates allows VARIADIC
to be specified for both the last direct parameter and the last
aggregated (WITHIN GROUP) parameter. However, the
current implementation restricts use of VARIADIC
in two ways. First, ordered-set aggregates can only use
VARIADIC "any", not other variadic array types.
Second, if the last direct parameter is VARIADIC "any",
then there can be only one aggregated parameter and it must also
be VARIADIC "any". (In the representation used in the
system catalogs, these two parameters are merged into a single
VARIADIC "any" item, since pg_proc cannot
represent functions with more than one VARIADIC parameter.)
If the aggregate is a hypothetical-set aggregate, the direct arguments
that match the VARIADIC "any" parameter are the hypothetical
ones; any preceding parameters represent additional direct arguments
that are not constrained to match the aggregated arguments.
Currently, ordered-set aggregates do not need to support moving-aggregate mode, since they cannot be used as window functions.
Partial (including parallel) aggregation is currently not supported for
ordered-set aggregates. Also, it will never be used for aggregate calls
that include DISTINCT or ORDER BY clauses, since
those semantics cannot be supported during partial aggregation.
See Section 37.10.
CREATE AGGREGATE is a
PostgreSQL language extension. The SQL
standard does not provide for user-defined aggregate functions.