T- final result type of the aggregation
ACC- intermediate result type during the aggregation
@PublicEvolving public abstract class TableAggregateFunction<T,ACC> extends ImperativeAggregateFunction<T,ACC>
Similar to an
AggregateFunction, the behavior of a
TableAggregateFunction is centered
around the concept of an accumulator. The accumulator is an intermediate data structure that stores
the aggregated values until a final aggregation result is computed.
For each set of rows that needs to be aggregated, the runtime will create an empty accumulator
ImperativeAggregateFunction.createAccumulator(). Subsequently, the
accumulate() method of the
function is called for each input row to update the accumulator. Once all rows have been processed,
emitUpdateWithRetract() method of the function is called to compute
and return the final result.
The main behavior of an
TableAggregateFunction can be defined by implementing a custom accumulate
method. An accumulate method must be declared publicly, not static, and named
Accumulate methods can also be overloaded by implementing multiple methods named
By default, input, accumulator, and output data types are automatically extracted using reflection.
This includes the generic argument
ACC of the class for determining an accumulator data type and
the generic argument
T for determining an accumulator data type. Input arguments are derived
from one or more
accumulate() methods. If the reflective information is not sufficient, it
can be supported and enriched with
TableAggregateFunction needs at least three methods:
There are a few other methods that are optional:
All these methods must be declared publicly, not static, and named exactly as the names mentioned above to be called by generated code.
For storing a user-defined function in a catalog, the class must have a default constructor and must be instantiable during runtime.
Processes the input values and updates the provided accumulator instance. The method accumulate can be overloaded with different custom types and arguments. A table aggregate function requires at least one accumulate() method. param: accumulator the accumulator which contains the current aggregated results param: [user defined inputs] the input value (usually obtained from new arrived data). public void accumulate(ACC accumulator, [user defined inputs])
Retracts the input values from the accumulator instance. The current design assumes the inputs are the values that have been previously accumulated. The method retract can be overloaded with different custom types and arguments. This method must be implemented for bounded OVER aggregates over unbounded tables. param: accumulator the accumulator which contains the current aggregated results param: [user defined inputs] the input value (usually obtained from a new arrived data). public void retract(ACC accumulator, [user defined inputs])
Merges a group of accumulator instances into one accumulator instance. This method must be implemented for unbounded session window grouping aggregates and bounded grouping aggregates. param: accumulator the accumulator which will keep the merged aggregate results. It should be noted that the accumulator may contain the previous aggregated results. Therefore user should not replace or clean this instance in the custom merge method. param: iterable an java.lang.Iterable pointed to a group of accumulators that will be merged. public void merge(ACC accumulator, java.lang.Iterable<ACC> iterable)
Called every time when an aggregation result should be materialized. The returned value could be either an early and incomplete result (periodically emitted as data arrives) or the final result of the aggregation. param: accumulator the accumulator which contains the current aggregated results param: out the collector used to output data. public void emitValue(ACC accumulator, org.apache.flink.util.Collector<T> out)
Called every time when an aggregation result should be materialized. The returned value could be either an early and incomplete result (periodically emitted as data arrives) or the final result of the aggregation. Compared to emitValue(), emitUpdateWithRetract() is used to emit values that have been updated. This method outputs data incrementally in retraction mode (also known as "update before" and "update after"). Once there is an update, we have to retract old records before sending new updated ones. The emitUpdateWithRetract() method will be used in preference to the emitValue() method if both methods are defined in the table aggregate function, because the method is treated to be more efficient than emitValue as it can output values incrementally. param: accumulator the accumulator which contains the current aggregated results param: out the retractable collector used to output data. Use the collect() method to output(add) records and use retract method to retract(delete) records. public void emitUpdateWithRetract(ACC accumulator, RetractableCollector<T> out)
|Modifier and Type||Class and Description|
Collects a record and forwards it.
|Constructor and Description|
|Modifier and Type||Method and Description|
Returns the kind of function this definition describes.
Returns the logic for performing type inference of a call to this function definition.
createAccumulator, getAccumulatorType, getResultType
close, functionIdentifier, open, toString
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
public final FunctionKind getKind()
public TypeInference getTypeInference(DataTypeFactory typeFactory)
The type inference process is responsible for inferring unknown types of input arguments, validating input arguments, and producing result types. The type inference process happens independent of a function body. The output of the type inference is used to search for a corresponding runtime implementation.
Instances of type inference can be created by using
BuiltInFunctionDefinitions for concrete usage examples.
The type inference for user-defined functions is automatically extracted using reflection. It
does this by analyzing implementation methods such as
eval() or accumulate() and the generic
parameters of a function class if present. If the reflective information is not sufficient, it can
be supported and enriched with
Note: Overriding this method is only recommended for advanced users. If a custom type inference is specified, it is the responsibility of the implementer to make sure that the output of the type inference process matches with the implementation method:
The implementation method must comply with each
by the type inference. For example, if
is an expected argument type, the method must accept a call
Regular Java calling semantics (including type widening and autoboxing) are applied when calling
an implementation method which means that the signature can be
The runtime will take care of converting the data to the data format specified by the
DataType.getConversionClass() coming from the type inference logic.
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