@Internal public abstract class BuiltInAggregateFunction<T,ACC> extends AggregateFunction<T,ACC>
AggregateFunction
that is
constructed from SpecializedFunction.specialize(SpecializedContext)
.
Subclasses must offer a constructor that takes SpecializedFunction.SpecializedContext
if they are
constructed from a BuiltInFunctionDefinition
. Otherwise the BuiltInAggregateFunction()
constructor might be more appropriate.
By default, all built-in functions work on internal data structures. However, this can be
changed by overriding getArgumentDataTypes()
, getAccumulatorDataType()
, and
getOutputDataType()
. Or by overriding getTypeInference(DataTypeFactory)
directly.
Since the accumulator type is runtime specific, it must be declared explicitly; otherwise it is derived from the output type.
Modifier | Constructor and Description |
---|---|
protected |
BuiltInAggregateFunction() |
protected |
BuiltInAggregateFunction(BuiltInFunctionDefinition definition,
SpecializedFunction.SpecializedContext context) |
Modifier and Type | Method and Description |
---|---|
DataType |
getAccumulatorDataType() |
List<DataType> |
getArgumentDataTypes() |
DataType |
getOutputDataType() |
Set<FunctionRequirement> |
getRequirements()
Returns the set of requirements this definition demands.
|
TypeInference |
getTypeInference(DataTypeFactory typeFactory)
Returns the logic for performing type inference of a call to this function definition.
|
boolean |
isDeterministic()
Returns information about the determinism of the function's results.
|
getKind, getValue
createAccumulator, getAccumulatorType, getResultType
close, functionIdentifier, open, toString
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
supportsConstantFolding
protected BuiltInAggregateFunction(BuiltInFunctionDefinition definition, SpecializedFunction.SpecializedContext context)
protected BuiltInAggregateFunction()
public DataType getAccumulatorDataType()
public DataType getOutputDataType()
public TypeInference getTypeInference(DataTypeFactory typeFactory)
UserDefinedFunction
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 TypeInference.newBuilder()
.
See 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 DataTypeHint
and FunctionHint
annotations.
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 DataType.getConversionClass()
returned by the type inference. For example, if DataTypes.TIMESTAMP(3).bridgedTo(java.sql.Timestamp.class)
is an expected argument type, the
method must accept a call eval(java.sql.Timestamp)
.
Regular Java calling semantics (including type widening and autoboxing) are applied when
calling an implementation method which means that the signature can be eval(java.lang.Object)
.
The runtime will take care of converting the data to the data format specified by the
DataType.getConversionClass()
coming from the type inference logic.
getTypeInference
in interface FunctionDefinition
getTypeInference
in class AggregateFunction<T,ACC>
public Set<FunctionRequirement> getRequirements()
FunctionDefinition
public boolean isDeterministic()
FunctionDefinition
It returns true
if and only if a call to this function is guaranteed to
always return the same result given the same parameters. true
is assumed by
default. If the function is not purely functional like random(), date(), now(), ...
this method must return false
.
Furthermore, return false
if the planner should always execute this function
on the cluster side. In other words: the planner should not perform constant expression
reduction during planning for constant calls to this function.
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