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# 36.10. User-defined Aggregates

Aggregate functions in PostgreSQL
are defined in terms of *state values*
and *state transition functions*.
That is, an aggregate operates using a state value that is updated
as each successive input row is processed.
To define a new aggregate
function, one selects a data type for the state value,
an initial value for the state, and a state transition
function. The state transition function takes the previous state
value and the aggregate's input value(s) for the current row, and
returns a new state value.
A *final function*
can also be specified, in case the desired result of the aggregate
is different from the data that needs to be kept in the running
state value. The final function takes the ending state value
and returns whatever is wanted as the aggregate result.
In principle, the transition and final functions are just ordinary
functions that could also be used outside the context of the
aggregate. (In practice, it's often helpful for performance reasons
to create specialized transition functions that can only work when
called as part of an aggregate.)

Thus, in addition to the argument and result data types seen by a user of the aggregate, there is an internal state-value data type that might be different from both the argument and result types.

If we define an aggregate that does not use a final function,
we have an aggregate that computes a running function of
the column values from each row. `sum`

is an
example of this kind of aggregate. `sum`

starts at
zero and always adds the current row's value to
its running total. For example, if we want to make a `sum`

aggregate to work on a data type for complex numbers,
we only need the addition function for that data type.
The aggregate definition would be:

CREATE AGGREGATE sum (complex) ( sfunc = complex_add, stype = complex, initcond = '(0,0)' );

which we might use like this:

SELECT sum(a) FROM test_complex; sum ----------- (34,53.9)

(Notice that we are relying on function overloading: there is more than
one aggregate named `sum`

, but
PostgreSQL can figure out which kind
of sum applies to a column of type `complex`.)

The above definition of `sum`

will return zero
(the initial state value) if there are no nonnull input values.
Perhaps we want to return null in that case instead — the SQL standard
expects `sum`

to behave that way. We can do this simply by
omitting the `initcond` phrase, so that the initial state
value is null. Ordinarily this would mean that the `sfunc`
would need to check for a null state-value input. But for
`sum`

and some other simple aggregates like
`max`

and `min`

,
it is sufficient to insert the first nonnull input value into
the state variable and then start applying the transition function
at the second nonnull input value. PostgreSQL
will do that automatically if the initial state value is null and
the transition function is marked "strict" (i.e., not to be called
for null inputs).

Another bit of default behavior for a "strict" transition function is that the previous state value is retained unchanged whenever a null input value is encountered. Thus, null values are ignored. If you need some other behavior for null inputs, do not declare your transition function as strict; instead code it to test for null inputs and do whatever is needed.

`avg`

(average) is a more complex example of an aggregate.
It requires
two pieces of running state: the sum of the inputs and the count
of the number of inputs. The final result is obtained by dividing
these quantities. Average is typically implemented by using an
array as the state value. For example,
the built-in implementation of `avg(float8)`

looks like:

CREATE AGGREGATE avg (float8) ( sfunc = float8_accum, stype = float8[], finalfunc = float8_avg, initcond = '{0,0,0}' );

Note:`float8_accum`

requires a three-element array, not just two elements, because it accumulates the sum of squares as well as the sum and count of the inputs. This is so that it can be used for some other aggregates as well as`avg`

.

Aggregate function calls in SQL allow `DISTINCT`
and `ORDER BY` options that control which rows are fed
to the aggregate's transition function and in what order. These
options are implemented behind the scenes and are not the concern
of the aggregate's support functions.

For further details see the CREATE AGGREGATE command.

## 36.10.1. Moving-Aggregate Mode

Aggregate functions can optionally support *moving-aggregate
mode*, which allows substantially faster execution of aggregate
functions within windows with moving frame starting points.
(See Section 3.5
and Section 4.2.8 for information about use of
aggregate functions as window functions.)
The basic idea is that in addition to a normal "forward"
transition function, the aggregate provides an *inverse
transition function*, which allows rows to be removed from the
aggregate's running state value when they exit the window frame.
For example a `sum`

aggregate, which uses addition as the
forward transition function, would use subtraction as the inverse
transition function. Without an inverse transition function, the window
function mechanism must recalculate the aggregate from scratch each time
the frame starting point moves, resulting in run time proportional to the
number of input rows times the average frame length. With an inverse
transition function, the run time is only proportional to the number of
input rows.

The inverse transition function is passed the current state value and the aggregate input value(s) for the earliest row included in the current state. It must reconstruct what the state value would have been if the given input row had never been aggregated, but only the rows following it. This sometimes requires that the forward transition function keep more state than is needed for plain aggregation mode. Therefore, the moving-aggregate mode uses a completely separate implementation from the plain mode: it has its own state data type, its own forward transition function, and its own final function if needed. These can be the same as the plain mode's data type and functions, if there is no need for extra state.

As an example, we could extend the `sum`

aggregate given above
to support moving-aggregate mode like this:

CREATE AGGREGATE sum (complex) ( sfunc = complex_add, stype = complex, initcond = '(0,0)', msfunc = complex_add, minvfunc = complex_sub, mstype = complex, minitcond = '(0,0)' );

The parameters whose names begin with `m` define the
moving-aggregate implementation. Except for the inverse transition
function `minvfunc`, they correspond to the plain-aggregate
parameters without `m`.

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. The inverse
transition function can "punt" on these cases, and yet still come
out ahead so long as it can work for most cases. As an example, an
aggregate working with floating-point numbers might choose to punt when
a `NaN` (not a number) input has to be removed from the running
state value.

When writing moving-aggregate support functions, it is important to be
sure that the inverse transition function can reconstruct the correct
state value exactly. Otherwise there might be user-visible differences
in results depending on whether the moving-aggregate mode is used.
An example of an aggregate for which adding an inverse transition
function seems easy at first, yet where this requirement cannot be met
is `sum`

over `float4` or `float8` inputs. A
naive declaration of `sum(`

could be
`float8`)

CREATE AGGREGATE unsafe_sum (float8) ( stype = float8, sfunc = float8pl, mstype = float8, msfunc = float8pl, minvfunc = float8mi );

This aggregate, however, can give wildly different results than it would have without the inverse transition function. For example, consider

SELECT unsafe_sum(x) OVER (ORDER BY n ROWS BETWEEN CURRENT ROW AND 1 FOLLOWING) FROM (VALUES (1, 1.0e20::float8), (2, 1.0::float8)) AS v (n,x);

This query returns `0` as its second result, rather than the
expected answer of `1`. The cause is the limited precision of
floating-point values: adding `1` to `1e20` results
in `1e20` again, and so subtracting `1e20` from that
yields `0`, not `1`. Note that this is a limitation
of floating-point arithmetic in general, not a limitation
of PostgreSQL.

## 36.10.2. Polymorphic and Variadic Aggregates

Aggregate functions can use polymorphic state transition functions or final functions, so that the same functions can be used to implement multiple aggregates. See Section 36.2.5 for an explanation of polymorphic functions. Going a step further, the aggregate function itself can be specified with polymorphic input type(s) and state type, allowing a single aggregate definition to serve for multiple input data types. Here is an example of a polymorphic aggregate:

CREATE AGGREGATE array_accum (anyelement) ( sfunc = array_append, stype = anyarray, initcond = '{}' );

Here, the actual state type for any given aggregate call is the array type
having the actual input type as elements. The behavior of the aggregate
is to concatenate all the inputs into an array of that type.
(Note: the built-in aggregate `array_agg`

provides similar
functionality, with better performance than this definition would have.)

Here's the output using two different actual data types as arguments:

SELECT attrelid::regclass, array_accum(attname) FROM pg_attribute WHERE attnum > 0 AND attrelid = 'pg_tablespace'::regclass GROUP BY attrelid; attrelid | array_accum ---------------+--------------------------------------- pg_tablespace | {spcname,spcowner,spcacl,spcoptions} (1 row) SELECT attrelid::regclass, array_accum(atttypid::regtype) FROM pg_attribute WHERE attnum > 0 AND attrelid = 'pg_tablespace'::regclass GROUP BY attrelid; attrelid | array_accum ---------------+--------------------------- pg_tablespace | {name,oid,aclitem[],text[]} (1 row)

Ordinarily, an aggregate function with a polymorphic result type has a
polymorphic state type, as in the above example. This is necessary
because otherwise the final function cannot be declared sensibly: it
would need to have a polymorphic result type but no polymorphic argument
type, which `CREATE FUNCTION` will reject on the grounds that
the result type cannot be deduced from a call. But sometimes it is
inconvenient to use a polymorphic state type. The most common case is
where the aggregate support functions are to be written in C and the
state type should be declared as `internal` because there is
no SQL-level equivalent for it. To address this case, it is possible to
declare the final function as taking extra "dummy" arguments
that match the input arguments of the aggregate. Such dummy arguments
are always passed as null values since no specific value is available when the
final function is called. Their only use is to allow a polymorphic
final function's result type to be connected to the aggregate's input
type(s). For example, the definition of the built-in
aggregate `array_agg`

is equivalent to

CREATE FUNCTION array_agg_transfn(internal, anynonarray) RETURNS internal ...; CREATE FUNCTION array_agg_finalfn(internal, anynonarray) RETURNS anyarray ...; CREATE AGGREGATE array_agg (anynonarray) ( sfunc = array_agg_transfn, stype = internal, finalfunc = array_agg_finalfn, finalfunc_extra );

Here, the `finalfunc_extra` option specifies that the final
function receives, in addition to the state value, extra dummy
argument(s) corresponding to the aggregate's input argument(s).
The extra `anynonarray` argument allows the declaration
of `array_agg_finalfn`

to be valid.

An aggregate function can be made to accept a varying number of arguments
by declaring its last argument as a `VARIADIC` array, in much
the same fashion as for regular functions; see
Section 36.4.5. The aggregate's transition
function(s) must have the same array type as their last argument. The
transition function(s) typically would also be marked `VARIADIC`,
but this is not strictly required.

Note:Variadic aggregates are easily misused in connection with theORDER BYoption (see Section 4.2.7), since the parser cannot tell whether the wrong number of actual arguments have been given in such a combination. Keep in mind that everything to the right ofORDER BYis a sort key, not an argument to the aggregate. For example, inSELECT myaggregate(a ORDER BY a, b, c) FROM ...the parser will see this as a single aggregate function argument and three sort keys. However, the user might have intended

SELECT myaggregate(a, b, c ORDER BY a) FROM ...If

myaggregateis variadic, both these calls could be perfectly valid.For the same reason, it's wise to think twice before creating aggregate functions with the same names and different numbers of regular arguments.

## 36.10.3. Ordered-Set Aggregates

The aggregates we have been describing so far are "normal"
aggregates. PostgreSQL also
supports *ordered-set aggregates*, which differ from
normal aggregates in two key ways. First, in addition to ordinary
aggregated arguments that are evaluated once per input row, an
ordered-set aggregate can have "direct" arguments that are
evaluated only once per aggregation operation. Second, the syntax
for the ordinary aggregated arguments specifies a sort ordering
for them explicitly. An ordered-set aggregate is usually
used to implement a computation that depends on a specific row
ordering, for instance rank or percentile, so that the sort ordering
is a required aspect of any call. For example, the built-in
definition of `percentile_disc`

is equivalent to:

CREATE FUNCTION ordered_set_transition(internal, anyelement) RETURNS internal ...; CREATE FUNCTION percentile_disc_final(internal, float8, anyelement) RETURNS anyelement ...; CREATE AGGREGATE percentile_disc (float8 ORDER BY anyelement) ( sfunc = ordered_set_transition, stype = internal, finalfunc = percentile_disc_final, finalfunc_extra );

This aggregate takes a `float8` direct argument (the percentile
fraction) and an aggregated input that can be of any sortable data type.
It could be used to obtain a median household income like this:

SELECT percentile_disc(0.5) WITHIN GROUP (ORDER BY income) FROM households; percentile_disc ----------------- 50489

Here, `0.5` is a direct argument; it would make no sense
for the percentile fraction to be a value varying across rows.

Unlike the case for normal aggregates, the sorting of input rows for
an ordered-set aggregate is *not* done behind the scenes,
but is the responsibility of the aggregate's support functions.
The typical implementation approach is to keep a reference to
a "tuplesort" object in the aggregate's state value, feed the
incoming rows into that object, and then complete the sorting and
read out the data in the final function. This design allows the
final function to perform special operations such as injecting
additional "hypothetical" rows into the data to be sorted.
While normal aggregates can often be implemented with support
functions written in PL/pgSQL or another
PL language, ordered-set aggregates generally have to be written in
C, since their state values aren't definable as any SQL data type.
(In the above example, notice that the state value is declared as
type `internal` — this is typical.)

The state transition function for an ordered-set aggregate receives
the current state value plus the aggregated input values for
each row, and returns the updated state value. This is the
same definition as for normal aggregates, but note that the direct
arguments (if any) are not provided. The final function receives
the last state value, the values of the direct arguments if any,
and (if `finalfunc_extra` is specified) null values
corresponding to the aggregated input(s). As with normal
aggregates, `finalfunc_extra` is only really useful if the
aggregate is polymorphic; then the extra dummy argument(s) are needed
to connect the final function's result type to the aggregate's input
type(s).

Currently, ordered-set aggregates cannot be used as window functions, and therefore there is no need for them to support moving-aggregate mode.

## 36.10.4. Partial Aggregation

Optionally, an aggregate function can support *partial
aggregation*. The idea of partial aggregation is to run the aggregate's
state transition function over different subsets of the input data
independently, and then to combine the state values resulting from those
subsets to produce the same state value that would have resulted from
scanning all the input in a single operation. This mode can be used for
parallel aggregation by having different worker processes scan different
portions of a table. Each worker produces a partial state value, and at
the end those state values are combined to produce a final state value.
(In the future this mode might also be used for purposes such as combining
aggregations over local and remote tables; but that is not implemented
yet.)

To support partial aggregation, the aggregate definition must provide
a *combine function*, which takes two values of the
aggregate's state type (representing the results of aggregating over two
subsets of the input rows) and produces a new value of the state type,
representing what the state would have been after aggregating over the
combination of those sets of rows. It is unspecified what the relative
order of the input rows from the two sets would have been. This means
that it's usually impossible to define a useful combine function for
aggregates that are sensitive to input row order.

As simple examples, `MAX` and `MIN` aggregates can be
made to support partial aggregation by specifying the combine function as
the same greater-of-two or lesser-of-two comparison function that is used
as their transition function. `SUM` aggregates just need an
addition function as combine function. (Again, this is the same as their
transition function, unless the state value is wider than the input data
type.)

The combine function is treated much like a transition function that
happens to take a value of the state type, not of the underlying input
type, as its second argument. In particular, the rules for dealing
with null values and strict functions are similar. Also, if the aggregate
definition specifies a non-null `initcond`, keep in mind that
that will be used not only as the initial state for each partial
aggregation run, but also as the initial state for the combine function,
which will be called to combine each partial result into that state.

If the aggregate's state type is declared as `internal`, it is
the combine function's responsibility that its result is allocated in
the correct memory context for aggregate state values. This means in
particular that when the first input is `NULL` it's invalid
to simply return the second input, as that value will be in the wrong
context and will not have sufficient lifespan.

When the aggregate's state type is declared as `internal`, it is
usually also appropriate for the aggregate definition to provide a
*serialization function* and a *deserialization
function*, which allow such a state value to be copied from one process
to another. Without these functions, parallel aggregation cannot be
performed, and future applications such as local/remote aggregation will
probably not work either.

A serialization function must take a single argument of
type `internal` and return a result of type `bytea`, which
represents the state value packaged up into a flat blob of bytes.
Conversely, a deserialization function reverses that conversion. It must
take two arguments of types `bytea` and `internal`, and
return a result of type `internal`. (The second argument is unused
and is always zero, but it is required for type-safety reasons.) The
result of the deserialization function should simply be allocated in the
current memory context, as unlike the combine function's result, it is not
long-lived.

Worth noting also is that for an aggregate to be executed in parallel,
the aggregate itself must be marked `PARALLEL SAFE`. The
parallel-safety markings on its support functions are not consulted.

## 36.10.5. Support Functions for Aggregates

A function written in C can detect that it is being called as an
aggregate support function by calling
`AggCheckCallContext`

, for example:

if (AggCheckCallContext(fcinfo, NULL))

One reason for checking this is that when it is true for a transition
function, the first input
must be a temporary state value and can therefore safely be modified
in-place rather than allocating a new copy.
See `int8inc()`

for an example.
(This is the *only*
case where it is safe for a function to modify a pass-by-reference input.
In particular, final functions for normal aggregates must not
modify their inputs in any case, because in some cases they will be
re-executed on the same final state value.)

The second argument of `AggCheckCallContext`

can be used to
retrieve the memory context in which aggregate state values are being kept.
This is useful for transition functions that wish to use "expanded"
objects (see Section 36.11.1) as their state values.
On first call, the transition function should return an expanded object
whose memory context is a child of the aggregate state context, and then
keep returning the same expanded object on subsequent calls. See
`array_append()`

for an example. (`array_append()`

is not the transition function of any built-in aggregate, but it is written
to behave efficiently when used as transition function of a custom
aggregate.)

Another support routine available to aggregate functions written in C
is `AggGetAggref`

, which returns the `Aggref`
parse node that defines the aggregate call. This is mainly useful
for ordered-set aggregates, which can inspect the substructure of
the `Aggref` node to find out what sort ordering they are
supposed to implement. Examples can be found
in `orderedsetaggs.c` in the PostgreSQL
source code.