This operator is used to aggregate and group the input stream.

#### Parameter

`group_by:`

An optional list of attributes over which the grouping should occur.`aggregations:`

A list if elements where each element contains up to four string:- The name of the aggregate function, e.g. MAX
- The input attribute over which the aggregation should be done
- The name of the output attribute for this aggregation
- The optional type of the output

`dumpAtValueCount:`

This parameter is used in the interval approach. Here a result is generated, each time an aggregation cannot be changed anymore. This leads to fewer results with a larger validity. With this parameter the production rate can be raised. A value of 1 means, that for every new element the aggregation operator receives new output elements are generated. This leads to more results with a shorter validity.`outputPA`

: This parameter allow to dump partial aggregates instead of evaluted values. The partial aggregates can be send to other aggregation operators and do a final aggregation (e.g. in case of distribution). The input schema of an aggregate operator that read partial aggregates must state a datatype that is a partial aggregated (see example below). Remark: Aggregate has one input and requires ordered input. To combine different parital aggregations e.g. a union operator is needed to reorder the input elements.`drainAtDone`

: Boolean, default true: If done is called, all not already written elements will be written.`drainAtClose`

: Boolean, default false: If close is called, all not already written elements will be written.`FastGrouping`

: Use hash code instead of compare to create group. Potentially unsafe!

#### Aggregation Functions

The set of aggregate functions is extensible. The following list is in the core Odysseus:

`MAX`

: The maximum element`MIN`

: The minimum element`AVG`

: The average element`SUM`

: The sum of all elements`COUNT`

: The number of elements`MEDIAN`

: The median element`STDDEV`

: The standard deviation- VAR: The variance
- CORR: The correlation between two attributes
- COV: The covariance between two attributes

Some nonstandard aggregations: These should only be used, if you a familiar with them:

`FIRST`

: The first element`LAST`

: The last element`NTH`

: The nth element`RATE`

: The number of elements per time unit`NEST`

: Nest the attribute values in a list`COMPLETENESS`

: Ratio of NULL-value elements to number of elements

#### Example:

##### PQL

**Aggregate Operator**

output = AGGREGATE({ group_by = ['bidder'], aggregations=[ ['MAX', 'price', 'max_price', 'double'] ] }, input) // Parital Aggregate example pa = AGGREGATE({ name='PRE_AGG', aggregations=[ ['count', 'id', 'count', 'PartialAggregate'], ['sum', 'id', 'avgsum', 'PartialAggregate'], ['min', 'id', 'min', 'PartialAggregate'], ['max', 'id', 'max', 'PartialAggregate'] ], outputpa='true' }, nexmark:person ) out = AGGREGATE({ name='AGG', aggregations=[ ['count', 'count', 'count', 'Integer'], ['sum', 'avgsum', 'sum', 'Double'], ['avg', 'avgsum', 'avg', 'Double'], ['min', 'min', 'min', 'Integer'], ['max', 'max', 'max', 'Integer'] ] }, pa ) /// Example for aggregations on multiple attributes out = AGGREGATE({ aggregations=[ ['corr', ['x', 'y'], 'correlation', 'Double'], ['cov', ['x', 'y'], 'covariance', 'Double'] ] }, input )

##### CQL

**Aggregate Operator**

SELECT MAX(price) AS max_price FROM input GROUP BY bidder