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# to you under the Apache License, Version 2.0 (the
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from abc import ABC, abstractmethod
from typing import Union, Any, Generic, TypeVar, Iterable
from py4j.java_gateway import JavaObject
from pyflink.datastream.state import ValueState, ValueStateDescriptor, ListStateDescriptor, \
ListState, MapStateDescriptor, MapState, ReducingStateDescriptor, ReducingState, \
AggregatingStateDescriptor, AggregatingState
from pyflink.datastream.time_domain import TimeDomain
from pyflink.datastream.timerservice import TimerService
from pyflink.java_gateway import get_gateway
from pyflink.metrics import MetricGroup
__all__ = [
'RuntimeContext',
'MapFunction',
'CoMapFunction',
'FlatMapFunction',
'CoFlatMapFunction',
'ReduceFunction',
'AggregateFunction',
'KeySelector',
'FilterFunction',
'Partitioner',
'SourceFunction',
'SinkFunction',
'ProcessFunction',
'KeyedProcessFunction',
'KeyedCoProcessFunction',
'TimerService',
'WindowFunction',
'ProcessWindowFunction']
W = TypeVar('W')
W2 = TypeVar('W2')
IN = TypeVar('IN')
OUT = TypeVar('OUT')
KEY = TypeVar('KEY')
class KeyedStateStore(ABC):
@abstractmethod
def get_state(self, state_descriptor: ValueStateDescriptor) -> ValueState:
"""
Gets a handle to the system's key/value state. THe key/value state is only accessible if the
function is executed on a KeyedStream. On each access, the state exposes the value for the
key of the element currently processed by the function. Each function may have multiple
partitioned states, addressed with different names.
Because the scope of each value is the key of the currently processed element, and the
elements are distributed by the Flink runtime, the system can transparently scale out and
redistribute the state and KeyedStream.
"""
pass
@abstractmethod
def get_list_state(self, state_descriptor: ListStateDescriptor) -> ListState:
"""
Gets a handle to the system's key/value list state. This state is similar to the value state
access, but is optimized for state that holds lists. One can add elements to the list, or
retrieve the list as a whle.
This state is only accessible if the function is executed on a KeyedStream.
"""
pass
@abstractmethod
def get_map_state(self, state_descriptor: MapStateDescriptor) -> MapState:
"""
Gets a handle to the system's key/value map state. This state is similar to the value state
access, but is optimized for state that is composed of user-defined key-value pairs.
This state is only accessible if the function is executed on a KeyedStream.
"""
pass
@abstractmethod
def get_reducing_state(self, state_descriptor: ReducingStateDescriptor) -> ReducingState:
"""
Gets a handle to the system's key/value reducing state. This state is similar to the state
accessed via get_state(ValueStateDescriptor), but is optimized for state that aggregates
values.
This state is only accessible if the function is executed on a KeyedStream.
"""
pass
@abstractmethod
def get_aggregating_state(
self, state_descriptor: AggregatingStateDescriptor) -> AggregatingState:
"""
Gets a handle to the system's key/value aggregating state. This state is similar to the
state accessed via get_state(ValueStateDescriptor), but is optimized for state that
aggregates values with different types.
This state is only accessible if the function is executed on a KeyedStream.
"""
pass
[docs]class RuntimeContext(KeyedStateStore):
"""
A RuntimeContext contains information about the context in which functions are executed.
Each parallel instance of the function will have a context through which it can access
static contextual information (such as the current parallelism).
"""
[docs] @abstractmethod
def get_task_name(self) -> str:
"""
Returns the name of the task in which the UDF runs, as assigned during plan construction.
"""
pass
[docs] @abstractmethod
def get_number_of_parallel_subtasks(self) -> int:
"""
Gets the parallelism with which the parallel task runs.
"""
pass
[docs] @abstractmethod
def get_max_number_of_parallel_subtasks(self) -> int:
"""
Gets the number of max-parallelism with which the parallel task runs.
"""
pass
[docs] @abstractmethod
def get_index_of_this_subtask(self) -> int:
"""
Gets the number of this parallel subtask. The numbering starts from 0 and goes up to
parallelism-1 (parallelism as returned by
:func:`~RuntimeContext.get_number_of_parallel_subtasks`).
"""
pass
[docs] @abstractmethod
def get_attempt_number(self) -> int:
"""
Gets the attempt number of this parallel subtask. First attempt is numbered 0.
"""
pass
[docs] @abstractmethod
def get_task_name_with_subtasks(self) -> str:
"""
Returns the name of the task, appended with the subtask indicator, such as "MyTask (3/6)",
where 3 would be (:func:`~RuntimeContext.get_index_of_this_subtask` + 1), and 6 would be
:func:`~RuntimeContext.get_number_of_parallel_subtasks`.
"""
pass
[docs] @abstractmethod
def get_job_parameter(self, key: str, default_value: str):
"""
Gets the global job parameter value associated with the given key as a string.
"""
pass
[docs] @abstractmethod
def get_metrics_group(self) -> MetricGroup:
"""
Gets the metric group.
"""
pass
class Function(ABC):
"""
The base class for all user-defined functions.
"""
def open(self, runtime_context: RuntimeContext):
pass
def close(self):
pass
[docs]class MapFunction(Function):
"""
Base class for Map functions. Map functions take elements and transform them, element wise. A
Map function always produces a single result element for each input element. Typical
applications are parsing elements, converting data types, or projecting out fields. Operations
that produce multiple result elements from a single input element can be implemented using the
FlatMapFunction.
The basic syntax for using a MapFunction is as follows:
::
>>> ds = ...
>>> new_ds = ds.map(MyMapFunction())
"""
[docs] @abstractmethod
def map(self, value):
"""
The mapping method. Takes an element from the input data and transforms it into exactly one
element.
:param value: The input value.
:return: The transformed value.
"""
pass
[docs]class CoMapFunction(Function):
"""
A CoMapFunction implements a map() transformation over two connected streams.
The same instance of the transformation function is used to transform both of
the connected streams. That way, the stream transformations can share state.
The basic syntax for using a CoMapFunction is as follows:
::
>>> ds1 = ...
>>> ds2 = ...
>>> new_ds = ds1.connect(ds2).map(MyCoMapFunction())
"""
[docs] @abstractmethod
def map1(self, value):
"""
This method is called for each element in the first of the connected streams.
:param value: The stream element
:return: The resulting element
"""
pass
[docs] @abstractmethod
def map2(self, value):
"""
This method is called for each element in the second of the connected streams.
:param value: The stream element
:return: The resulting element
"""
pass
[docs]class FlatMapFunction(Function):
"""
Base class for flatMap functions. FlatMap functions take elements and transform them, into zero,
one, or more elements. Typical applications can be splitting elements, or unnesting lists and
arrays. Operations that produce multiple strictly one result element per input element can also
use the MapFunction.
The basic syntax for using a MapFUnction is as follows:
::
>>> ds = ...
>>> new_ds = ds.flat_map(MyFlatMapFunction())
"""
[docs] @abstractmethod
def flat_map(self, value):
"""
The core mthod of the FlatMapFunction. Takes an element from the input data and transforms
it into zero, one, or more elements.
A basic implementation of flat map is as follows:
::
>>> class MyFlatMapFunction(FlatMapFunction):
>>> def flat_map(self, value):
>>> for i in range(value):
>>> yield i
:param value: The input value.
:return: A generator
"""
pass
[docs]class CoFlatMapFunction(Function):
"""
A CoFlatMapFunction implements a flat-map transformation over two connected streams.
The same instance of the transformation function is used to transform both of the
connected streams. That way, the stream transformations can share state.
An example for the use of connected streams would be to apply rules that change over time
onto elements of a stream. One of the connected streams has the rules, the other stream the
elements to apply the rules to. The operation on the connected stream maintains the
current set of rules in the state. It may receive either a rule update (from the first stream)
and update the state, or a data element (from the second stream) and apply the rules in the
state to the element. The result of applying the rules would be emitted.
The basic syntax for using a CoFlatMapFunction is as follows:
::
>>> ds1 = ...
>>> ds2 = ...
>>> class MyCoFlatMapFunction(CoFlatMapFunction):
>>> def flat_map1(self, value):
>>> for i in range(value):
>>> yield i
>>> def flat_map2(self, value):
>>> for i in range(value):
>>> yield i
>>> new_ds = ds1.connect(ds2).flat_map(MyCoFlatMapFunction())
"""
[docs] @abstractmethod
def flat_map1(self, value):
"""
This method is called for each element in the first of the connected streams.
:param value: The input value.
:return: A generator
"""
pass
[docs] @abstractmethod
def flat_map2(self, value):
"""
This method is called for each element in the second of the connected streams.
:param value: The input value.
:return: A generator
"""
pass
[docs]class ReduceFunction(Function):
"""
Base interface for Reduce functions. Reduce functions combine groups of elements to a single
value, by taking always two elements and combining them into one. Reduce functions may be
used on entire data sets, or on grouped data sets. In the latter case, each group is reduced
individually.
The basic syntax for using a ReduceFunction is as follows:
::
>>> ds = ...
>>> new_ds = ds.key_by(lambda x: x[1]).reduce(MyReduceFunction())
"""
[docs] @abstractmethod
def reduce(self, value1, value2):
"""
The core method of ReduceFunction, combining two values into one value of the same type.
The reduce function is consecutively applied to all values of a group until only a single
value remains.
:param value1: The first value to combine.
:param value2: The second value to combine.
:return: The combined value of both input values.
"""
pass
[docs]class AggregateFunction(Function):
"""
The AggregateFunction is a flexible aggregation function, characterized by the following
features:
- The aggregates may use different types for input values, intermediate aggregates, and
result type, to support a wide range of aggregation types.
- Support for distributive aggregations: Different intermediate aggregates can be merged
together, to allow for pre-aggregation/final-aggregation optimizations.
The AggregateFunction's intermediate aggregate (in-progress aggregation state) is called the
`accumulator`. Values are added to the accumulator, and final aggregates are obtained by
finalizing the accumulator state. This supports aggregation functions where the intermediate
state needs to be different than the aggregated values and the final result type, such as for
example average (which typically keeps a count and sum). Merging intermediate aggregates
(partial aggregates) means merging the accumulators.
The AggregationFunction itself is stateless. To allow a single AggregationFunction instance to
maintain multiple aggregates (such as one aggregate per key), the AggregationFunction creates a
new accumulator whenever a new aggregation is started.
"""
[docs] @abstractmethod
def create_accumulator(self):
"""
Creates a new accumulator, starting a new aggregate.
The new accumulator is typically meaningless unless a value is added via
:func:`~AggregateFunction.add`.
The accumulator is the state of a running aggregation. When a program has multiple
aggregates in progress (such as per key and window), the state (per key and window) is the
size of the accumulator.
:return: A new accumulator, corresponding to an empty aggregate.
"""
pass
[docs] @abstractmethod
def add(self, value, accumulator):
"""
Adds the given input value to the given accumulator, returning the new accumulator value.
For efficiency, the input accumulator may be modified and returned.
:param value: The value to add.
:param accumulator: The accumulator to add the value to.
:return: The accumulator with the updated state.
"""
pass
[docs] @abstractmethod
def get_result(self, accumulator):
"""
Gets the result of the aggregation from the accumulator.
:param accumulator: The accumulator of the aggregation.
:return: The final aggregation result.
"""
pass
[docs] @abstractmethod
def merge(self, acc_a, acc_b):
"""
Merges two accumulators, returning an accumulator with the merged state.
This function may reuse any of the given accumulators as the target for the merge and
return that. The assumption is that the given accumulators will not be used any more after
having been passed to this function.
:param acc_a: An accumulator to merge.
:param acc_b: Another accumulator to merge.
:return: The accumulator with the merged state.
"""
pass
[docs]class KeySelector(Function):
"""
The KeySelector allows to use deterministic objects for operations such as reduce, reduceGroup,
join coGroup, etc. If invoked multiple times on the same object, the returned key must be the
same. The extractor takes an object an returns the deterministic key for that object.
"""
[docs] @abstractmethod
def get_key(self, value):
"""
User-defined function that deterministically extracts the key from an object.
:param value: The object to get the key from.
:return: The extracted key.
"""
pass
[docs]class FilterFunction(Function):
"""
A filter function is a predicate applied individually to each record. The predicate decides
whether to keep the element, or to discard it.
The basic syntax for using a FilterFunction is as follows:
::
>>> ds = ...
>>> result = ds.filter(MyFilterFunction())
Note that the system assumes that the function does not modify the elements on which the
predicate is applied. Violating this assumption can lead to incorrect results.
"""
[docs] @abstractmethod
def filter(self, value):
"""
The filter function that evaluates the predicate.
:param value: The value to be filtered.
:return: True for values that should be retained, false for values to be filtered out.
"""
pass
[docs]class Partitioner(Function):
"""
Function to implement a custom partition assignment for keys.
"""
[docs] @abstractmethod
def partition(self, key: Any, num_partitions: int) -> int:
"""
Computes the partition for the given key.
:param key: The key.
:param num_partitions: The number of partitions to partition into.
:return: The partition index.
"""
pass
class FunctionWrapper(Function):
"""
A basic wrapper class for user defined function.
"""
def __init__(self, func):
self._func = func
class MapFunctionWrapper(FunctionWrapper):
"""
A wrapper class for MapFunction. It's used for wrapping up user defined function in a
MapFunction when user does not implement a MapFunction but directly pass a function object or
a lambda function to map() function.
"""
def __init__(self, func):
"""
The constructor of MapFunctionWrapper.
:param func: user defined function object.
"""
super(MapFunctionWrapper, self).__init__(func)
def map(self, value):
"""
A delegated map function to invoke user defined function.
:param value: The input value.
:return: the return value of user defined map function.
"""
return self._func(value)
class FlatMapFunctionWrapper(FunctionWrapper):
"""
A wrapper class for FlatMapFunction. It's used for wrapping up user defined function in a
FlatMapFunction when user does not implement a FlatMapFunction but directly pass a function
object or a lambda function to flat_map() function.
"""
def __init__(self, func):
"""
The constructor of MapFunctionWrapper.
:param func: user defined function object.
"""
super(FlatMapFunctionWrapper, self).__init__(func)
def flat_map(self, value):
"""
A delegated flat_map function to invoke user defined function.
:param value: The input value.
:return: the return value of user defined flat_map function.
"""
return self._func(value)
class FilterFunctionWrapper(FunctionWrapper):
"""
A wrapper class for FilterFunction. It's used for wrapping up user defined function in a
FilterFunction when user does not implement a FilterFunction but directly pass a function
object or a lambda function to filter() function.
"""
def __init__(self, func):
super(FilterFunctionWrapper, self).__init__(func)
def filter(self, value):
return self._func(value)
class ReduceFunctionWrapper(FunctionWrapper):
"""
A wrapper class for ReduceFunction. It's used for wrapping up user defined function in a
ReduceFunction when user does not implement a ReduceFunction but directly pass a function
object or a lambda function to reduce() function.
"""
def __init__(self, func):
"""
The constructor of ReduceFunctionWrapper.
:param func: user defined function object.
"""
super(ReduceFunctionWrapper, self).__init__(func)
def reduce(self, value1, value2):
"""
A delegated reduce function to invoke user defined function.
:param value1: The first value to combine.
:param value2: The second value to combine.
:return: The combined value of both input values.
"""
return self._func(value1, value2)
class KeySelectorFunctionWrapper(FunctionWrapper):
"""
A wrapper class for KeySelector. It's used for wrapping up user defined function in a
KeySelector when user does not implement a KeySelector but directly pass a function
object or a lambda function to key_by() function.
"""
def __init__(self, func):
"""
The constructor of MapFunctionWrapper.
:param func: user defined function object.
"""
super(KeySelectorFunctionWrapper, self).__init__(func)
def get_key(self, value):
"""
A delegated get_key function to invoke user defined function.
:param value: The input value.
:return: the return value of user defined get_key function.
"""
return self._func(value)
class PartitionerFunctionWrapper(FunctionWrapper):
"""
A wrapper class for Partitioner. It's used for wrapping up user defined function in a
Partitioner when user does not implement a Partitioner but directly pass a function
object or a lambda function to partition_custom() function.
"""
def __init__(self, func):
"""
The constructor of PartitionerFunctionWrapper.
:param func: user defined function object.
"""
super(PartitionerFunctionWrapper, self).__init__(func)
def partition(self, key: Any, num_partitions: int) -> int:
"""
A delegated partition function to invoke user defined function.
:param key: The key.
:param num_partitions: The number of partitions to partition into.
:return: The partition index.
"""
return self._func(key, num_partitions)
def _get_python_env():
"""
An util function to get a python user defined function execution environment.
"""
gateway = get_gateway()
exec_type = gateway.jvm.org.apache.flink.table.functions.python.PythonEnv.ExecType.PROCESS
return gateway.jvm.org.apache.flink.table.functions.python.PythonEnv(exec_type)
class JavaFunctionWrapper(object):
"""
A wrapper class that maintains a Function implemented in Java.
"""
def __init__(self, j_function: Union[str, JavaObject]):
if isinstance(j_function, str):
j_func_class = get_gateway().jvm.__getattr__(j_function)
j_function = j_func_class()
self._j_function = j_function
def get_java_function(self):
return self._j_function
[docs]class SourceFunction(JavaFunctionWrapper):
"""
Base class for all stream data source in Flink.
"""
def __init__(self, source_func: Union[str, JavaObject]):
"""
Constructor of SinkFunction.
:param source_func: The java SourceFunction object.
"""
super(SourceFunction, self).__init__(source_func)
[docs]class SinkFunction(JavaFunctionWrapper):
"""
The base class for SinkFunctions.
"""
def __init__(self, sink_func: Union[str, JavaObject]):
"""
Constructor of SinkFunction.
:param sink_func: The java SinkFunction object or the full name of the SinkFunction class.
"""
super(SinkFunction, self).__init__(sink_func)
[docs]class ProcessFunction(Function):
"""
A function that process elements of a stream.
For every element in the input stream process_element(value, ctx, out) is invoked. This can
produce zero or more elements as output. Implementations can also query the time and set timers
through the provided Context. For firing timers on_timer(long, ctx, out) will be invoked. This
can again produce zero or more elements as output and register further timers.
Note that access to keyed state and timers (which are also scoped to a key) is only available if
the ProcessFunction is applied on a KeyedStream.
"""
[docs] class Context(ABC):
"""
Information available in an invocation of process_element(value, ctx, out) or
on_timer(value, ctx, out).
"""
[docs] @abstractmethod
def timer_service(self) -> TimerService:
"""
A Timer service for querying time and registering timers.
"""
pass
[docs] @abstractmethod
def timestamp(self) -> int:
"""
Timestamp of the element currently being processed or timestamp of a firing timer.
This might be None, for example if the time characteristic of your program is set to
TimeCharacteristic.ProcessTime.
"""
pass
[docs] @abstractmethod
def process_element(self, value, ctx: 'ProcessFunction.Context'):
"""
Process one element from the input stream.
This function can output zero or more elements using the Collector parameter and also update
internal state or set timers using the Context parameter.
:param value: The input value.
:param ctx: A Context that allows querying the timestamp of the element and getting a
TimerService for registering timers and querying the time. The context is only
valid during the invocation of this method, do not store it.
"""
pass
[docs]class KeyedProcessFunction(Function):
"""
A keyed function processes elements of a stream.
For every element in the input stream, process_element() is invoked. This can produce zero or
more elements as output. Implementations can also query the time and set timers through the
provided Context. For firing timers on_timer() will be invoked. This can again produce zero or
more elements as output and register further timers.
Note that access to keyed state and timers (which are also scoped to a key) is only available if
the KeyedProcessFunction is applied on a KeyedStream.
"""
[docs] class Context(ABC):
[docs] @abstractmethod
def get_current_key(self):
pass
[docs] @abstractmethod
def timer_service(self) -> TimerService:
"""
A Timer service for querying time and registering timers.
"""
pass
[docs] @abstractmethod
def timestamp(self) -> int:
"""
Timestamp of the element currently being processed or timestamp of a firing timer.
This might be None, for example if the time characteristic of your program is set to
TimeCharacteristic.ProcessTime.
"""
pass
[docs] class OnTimerContext(Context):
[docs] @abstractmethod
def time_domain(self) -> TimeDomain:
"""
The TimeDomain of the firing timer.
:return: The TimeDomain of current fired timer.
"""
pass
[docs] @abstractmethod
def process_element(self, value, ctx: 'KeyedProcessFunction.Context'):
"""
Process one element from the input stream.
This function can output zero or more elements and also update
internal state or set timers using the Context parameter.
:param value: The input value.
:param ctx: A Context that allows querying the timestamp of the element and getting a
TimerService for registering timers and querying the time. The context is only
valid during the invocation of this method, do not store it.
"""
pass
[docs] def on_timer(self, timestamp: int, ctx: 'KeyedProcessFunction.OnTimerContext'):
"""
Called when a timer set using TimerService fires.
:param timestamp: The timestamp of the firing timer.
:param ctx: An OnTimerContext that allows querying the timestamp of the firing timer,
querying the TimeDomain of the firing timer and getting a TimerService for
registering timers and querying the time. The context is only valid during the
invocation of this method, do not store it.
"""
pass
[docs]class KeyedCoProcessFunction(Function):
"""
A function that processes elements of two keyed streams and produces a single output one.
The function will be called for every element in the input streams and can produce zero or
more output elements. Contrary to the :class:`CoFlatMapFunction`, this function can also query the
time (both event and processing) and set timers, through the provided {@link Context}. When
reacting to the firing of set timers the function can emit yet more elements.
An example use-case for connected streams would be the application of a set of rules that
change over time ({@code stream A}) to the elements contained in another stream (stream {@code
B}). The rules contained in {@code stream A} can be stored in the state and wait for new elements
to arrive on {@code stream B}. Upon reception of a new element on {@code stream B}, the function
can now apply the previously stored rules to the element and directly emit a result, and/or
register a timer that will trigger an action in the future.
"""
[docs] class Context(ABC):
[docs] @abstractmethod
def get_current_key(self):
pass
[docs] @abstractmethod
def timer_service(self) -> TimerService:
"""
A Timer service for querying time and registering timers.
"""
pass
[docs] @abstractmethod
def timestamp(self) -> int:
"""
Timestamp of the element currently being processed or timestamp of a firing timer.
This might be None, for example if the time characteristic of your program is set to
TimeCharacteristic.ProcessTime.
"""
pass
[docs] class OnTimerContext(Context):
[docs] @abstractmethod
def time_domain(self) -> TimeDomain:
"""
The TimeDomain of the firing timer.
:return: The TimeDomain of current fired timer.
"""
pass
[docs] @abstractmethod
def process_element1(self, value, ctx: 'KeyedCoProcessFunction.Context'):
"""
Process one element from the input stream.
This function can output zero or more elements using the Collector parameter and also update
internal state or set timers using the Context parameter.
:param value: The input value.
:param ctx: A Context that allows querying the timestamp of the element and getting a
TimerService for registering timers and querying the time. The context is only
valid during the invocation of this method, do not store it.
"""
pass
[docs] @abstractmethod
def process_element2(self, value, ctx: 'KeyedCoProcessFunction.Context'):
"""
Process one element from the input stream.
This function can output zero or more elements using the Collector parameter and also update
internal state or set timers using the Context parameter.
:param value: The input value.
:param ctx: A Context that allows querying the timestamp of the element and getting a
TimerService for registering timers and querying the time. The context is only
valid during the invocation of this method, do not store it.
"""
pass
[docs] def on_timer(self, timestamp: int, ctx: 'KeyedCoProcessFunction.OnTimerContext'):
"""
Called when a timer set using TimerService fires.
:param timestamp: The timestamp of the firing timer.
:param ctx: An OnTimerContext that allows querying the timestamp of the firing timer,
querying the TimeDomain of the firing timer and getting a TimerService for
registering timers and querying the time. The context is only valid during the
invocation of this method, do not store it.
"""
pass
[docs]class WindowFunction(Function, Generic[IN, OUT, KEY, W]):
"""
Base interface for functions that are evaluated over keyed (grouped) windows.
"""
[docs] @abstractmethod
def apply(self, key: KEY, window: W, inputs: Iterable[IN]) -> Iterable[OUT]:
"""
Evaluates the window and outputs none or several elements.
:param key: The key for which this window is evaluated.
:param window: The window that is being evaluated.
:param inputs: The elements in the window being evaluated.
"""
pass
[docs]class ProcessWindowFunction(Function, Generic[IN, OUT, KEY, W]):
"""
Base interface for functions that are evaluated over keyed (grouped) windows using a context
for retrieving extra information.
"""
[docs] class Context(ABC, Generic[W2]):
"""
The context holding window metadata.
"""
[docs] @abstractmethod
def window(self) -> W2:
"""
:return: The window that is being evaluated.
"""
pass
[docs] @abstractmethod
def current_processing_time(self) -> int:
"""
:return: The current processing time.
"""
pass
[docs] @abstractmethod
def current_watermark(self) -> int:
"""
:return: The current event-time watermark.
"""
pass
[docs] @abstractmethod
def window_state(self) -> KeyedStateStore:
"""
State accessor for per-key and per-window state.
.. note::
If you use per-window state you have to ensure that you clean it up by implementing
:func:`~ProcessWindowFunction.clear`.
:return: The :class:`KeyedStateStore` used to access per-key and per-window states.
"""
pass
[docs] @abstractmethod
def global_state(self) -> KeyedStateStore:
"""
State accessor for per-key global state.
"""
pass
[docs] @abstractmethod
def process(self,
key: KEY,
content: 'ProcessWindowFunction.Context',
elements: Iterable[IN]) -> Iterable[OUT]:
"""
Evaluates the window and outputs none or several elements.
:param key: The key for which this window is evaluated.
:param content: The context in which the window is being evaluated.
:param elements: The elements in the window being evaluated.
:return: The iterable object which produces the elements to emit.
"""
pass
[docs] @abstractmethod
def clear(self, context: 'ProcessWindowFunction.Context') -> None:
"""
Deletes any state in the :class:`Context` when the Window expires (the watermark passes its
max_timestamp + allowed_lateness).
:param context: The context to which the window is being evaluated.
"""
pass
class InternalWindowFunction(Function, Generic[IN, KEY, W]):
class InternalWindowContext(ABC):
@abstractmethod
def current_processing_time(self) -> int:
pass
@abstractmethod
def current_watermark(self) -> int:
pass
@abstractmethod
def window_state(self) -> KeyedStateStore:
pass
@abstractmethod
def global_state(self) -> KeyedStateStore:
pass
@abstractmethod
def process(self,
key: KEY,
window: W,
context: InternalWindowContext,
input_data: Iterable[IN]) -> Iterable[OUT]:
pass
@abstractmethod
def clear(self, window: W, context: InternalWindowContext):
pass
class InternalIterableWindowFunction(InternalWindowFunction[IN, KEY, W]):
def __init__(self, wrapped_function: WindowFunction):
self._wrapped_function = wrapped_function
def open(self, runtime_context: RuntimeContext):
self._wrapped_function.open(runtime_context)
def close(self):
self._wrapped_function.close()
def process(self,
key: KEY,
window: W,
context: InternalWindowFunction.InternalWindowContext,
input_data: Iterable[IN]) -> Iterable[OUT]:
return self._wrapped_function.apply(key, window, input_data)
def clear(self,
window: W,
context: InternalWindowFunction.InternalWindowContext):
pass
class InternalProcessWindowContext(ProcessWindowFunction.Context[W]):
def __init__(self):
self._underlying = None
self._window = None
def window(self) -> W:
return self._window
def current_processing_time(self) -> int:
return self._underlying.current_processing_time()
def current_watermark(self) -> int:
return self._underlying.current_watermark()
def window_state(self) -> KeyedStateStore:
return self._underlying.window_state()
def global_state(self) -> KeyedStateStore:
return self._underlying.global_state()
class InternalIterableProcessWindowFunction(InternalWindowFunction[IN, KEY, W]):
def __init__(self, wrapped_function: ProcessWindowFunction):
self._wrapped_function = wrapped_function
self._internal_context = \
InternalProcessWindowContext() # type: InternalProcessWindowContext
def open(self, runtime_context: RuntimeContext):
self._wrapped_function.open(runtime_context)
def close(self):
self._wrapped_function.close()
def process(self,
key: KEY,
window: W,
context: InternalWindowFunction.InternalWindowContext,
input_data: Iterable[IN]) -> Iterable[OUT]:
self._internal_context._window = window
self._internal_context._underlying = context
return self._wrapped_function.process(key, self._internal_context, input_data)
def clear(self, window: W, context: InternalWindowFunction.InternalWindowContext):
self._internal_context._window = window
self._internal_context._underlying = context
self._wrapped_function.clear(self._internal_context)
