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 36.10.1. 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 36.10.4. 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.

`name`-
The 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`. `argname`-
The 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_type`-
The 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_type`-
The 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.

If you see anything in the documentation that is not correct, does not match your experience with the particular feature or requires further clarification, please use this form to report a documentation issue.