@Internal public class PythonTableFunction extends TableFunction<Row> implements PythonFunction
Constructor and Description |
---|
PythonTableFunction(String name,
byte[] serializedScalarFunction,
DataType[] inputTypes,
DataType resultType,
PythonFunctionKind pythonFunctionKind,
boolean deterministic,
boolean takesRowAsInput,
PythonEnv pythonEnv) |
PythonTableFunction(String name,
byte[] serializedScalarFunction,
PythonFunctionKind pythonFunctionKind,
boolean deterministic,
boolean takesRowAsInput,
PythonEnv pythonEnv) |
PythonTableFunction(String name,
byte[] serializedScalarFunction,
String[] inputTypesString,
String resultTypeString,
PythonFunctionKind pythonFunctionKind,
boolean deterministic,
boolean takesRowAsInput,
PythonEnv pythonEnv) |
Modifier and Type | Method and Description |
---|---|
void |
eval(Object... args) |
TypeInformation[] |
getParameterTypes(Class[] signature)
Returns
TypeInformation about the operands of the evaluation method with a given
signature. |
PythonEnv |
getPythonEnv()
Returns the Python execution environment.
|
PythonFunctionKind |
getPythonFunctionKind()
Returns the kind of the user-defined python function.
|
TypeInformation<Row> |
getResultType()
Returns the result type of the evaluation method.
|
byte[] |
getSerializedPythonFunction()
Returns the serialized representation of the user-defined python function.
|
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.
|
boolean |
takesRowAsInput()
Returns Whether the Python function takes row as input instead of each columns of a row.
|
String |
toString()
Returns the name of the UDF that is used for plan explanation and logging.
|
collect, finish, getKind, setCollector
close, functionIdentifier, open
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
getRequirements, supportsConstantFolding
public PythonTableFunction(String name, byte[] serializedScalarFunction, DataType[] inputTypes, DataType resultType, PythonFunctionKind pythonFunctionKind, boolean deterministic, boolean takesRowAsInput, PythonEnv pythonEnv)
public PythonTableFunction(String name, byte[] serializedScalarFunction, String[] inputTypesString, String resultTypeString, PythonFunctionKind pythonFunctionKind, boolean deterministic, boolean takesRowAsInput, PythonEnv pythonEnv)
public PythonTableFunction(String name, byte[] serializedScalarFunction, PythonFunctionKind pythonFunctionKind, boolean deterministic, boolean takesRowAsInput, PythonEnv pythonEnv)
public void eval(Object... args)
public byte[] getSerializedPythonFunction()
PythonFunction
getSerializedPythonFunction
in interface PythonFunction
public PythonEnv getPythonEnv()
PythonFunction
getPythonEnv
in interface PythonFunction
public PythonFunctionKind getPythonFunctionKind()
PythonFunction
getPythonFunctionKind
in interface PythonFunction
public boolean takesRowAsInput()
PythonFunction
takesRowAsInput
in interface PythonFunction
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.
isDeterministic
in interface FunctionDefinition
public TypeInformation[] getParameterTypes(Class[] signature)
TableFunction
TypeInformation
about the operands of the evaluation method with a given
signature.getParameterTypes
in class TableFunction<Row>
public TypeInformation<Row> getResultType()
TableFunction
getResultType
in class TableFunction<Row>
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 TableFunction<Row>
public String toString()
UserDefinedFunction
toString
in class UserDefinedFunction
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