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Posted to issues@flink.apache.org by GitBox <gi...@apache.org> on 2022/03/02 04:41:45 UTC
[GitHub] [flink] cun8cun8 opened a new pull request #18957: Window allocator supporting pyflink datastream API
cun8cun8 opened a new pull request #18957:
URL: https://github.com/apache/flink/pull/18957
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot commented on pull request #18957: Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot commented on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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## CI report:
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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## CI report:
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* 195bf4369bf1eb0cdc1727847e63ce0d6fd31f66 Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32952)
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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## CI report:
* ec1e0a435186082e5ac1481bc093f9bdd9d94d70 UNKNOWN
* a36182858952a61ef43ccbdc184440354bb86110 Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32721)
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[GitHub] [flink] dianfu commented on a change in pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
dianfu commented on a change in pull request #18957:
URL: https://github.com/apache/flink/pull/18957#discussion_r821588832
##########
File path: flink-python/pyflink/datastream/data_stream.py
##########
@@ -905,24 +887,6 @@ def set_parallelism(self, parallelism: int) -> 'DataStreamSink':
self._j_data_stream_sink.setParallelism(parallelism)
return self
- def set_description(self, description: str) -> 'DataStreamSink':
Review comment:
ditto
##########
File path: flink-python/pyflink/datastream/data_stream.py
##########
@@ -1216,6 +1180,18 @@ def window(self, window_assigner: WindowAssigner) -> 'WindowedStream':
"""
return WindowedStream(self, window_assigner)
+ def count_window(self, window_size: int, window_slide = 0):
+ """
+ Windows this KeyedStream into tumbling count windows.
+ Params:
Review comment:
```suggestion
```
##########
File path: flink-python/pyflink/datastream/data_stream.py
##########
@@ -231,24 +231,6 @@ def slot_sharing_group(self, slot_sharing_group: Union[str, SlotSharingGroup]) -
self._j_data_stream.slotSharingGroup(slot_sharing_group)
return self
- def set_description(self, description: str) -> 'DataStream':
Review comment:
What's the purpose of this change?
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -65,6 +66,23 @@ def max_timestamp(self) -> int:
pass
+class SessionWindowTimeGapExtractor(ABC):
+ """
+ Window is a grouping of elements into finite buckets. Windows have a maximum timestamp
+ which means that, at some point, all elements that go into one window will have arrived.
+ """
+
+ @abstractmethod
+ def extract(self, element: Any) -> int:
+ """
+ Extracts the session time gap.
+ Params:
Review comment:
```suggestion
```
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +538,721 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
Review comment:
```suggestion
window: TimeWindow,
```
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -36,8 +39,6 @@
'TimeWindowSerializer',
'CountWindowSerializer']
Review comment:
Declare the introduced classes which are planned to be exposed to users here.
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +538,721 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of the
Review comment:
The comment is misleading for users as it support both event time and processing time.
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +538,721 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of the
+ machine the operation is running on. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(10), False))
+
+ A WindowAssigner that windows elements into windows based on the timestamp of the elements.
+ Windows cannot overlap.
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(60000, 0, True))
+ """
+
+ def __init__(self, size: int, offset: int, is_event_time: bool):
+ if abs(offset) >= size:
+ raise Exception("TumblingWindowAssigner parameters must satisfy abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+ self._is_event_time = is_event_time
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if self._is_event_time is False:
+ current_processing_time = context.get_current_processing_time()
+ start = TimeWindow.get_window_start_with_offset(current_processing_time, self._offset,
+ self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ if timestamp > MIN_LONG_VALUE:
+ start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ raise Exception("Record has MIN_LONG_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ if self._is_event_time is True:
+ return EventTimeTrigger()
+ else:
+ return ProcessingTimeTrigger()
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
Review comment:
```suggestion
def get_window_serializer(self) -> TypeSerializer[TimeWindow]:
```
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +538,721 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of the
+ machine the operation is running on. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(10), False))
+
+ A WindowAssigner that windows elements into windows based on the timestamp of the elements.
+ Windows cannot overlap.
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(60000, 0, True))
+ """
+
+ def __init__(self, size: int, offset: int, is_event_time: bool):
+ if abs(offset) >= size:
+ raise Exception("TumblingWindowAssigner parameters must satisfy abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+ self._is_event_time = is_event_time
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if self._is_event_time is False:
+ current_processing_time = context.get_current_processing_time()
+ start = TimeWindow.get_window_start_with_offset(current_processing_time, self._offset,
+ self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ if timestamp > MIN_LONG_VALUE:
+ start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ raise Exception("Record has MIN_LONG_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
Review comment:
```suggestion
def get_default_trigger(self, env) -> Trigger[T, TimeWindow]:
```
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +538,721 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of the
+ machine the operation is running on. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(10), False))
+
+ A WindowAssigner that windows elements into windows based on the timestamp of the elements.
+ Windows cannot overlap.
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(60000, 0, True))
+ """
+
+ def __init__(self, size: int, offset: int, is_event_time: bool):
+ if abs(offset) >= size:
+ raise Exception("TumblingWindowAssigner parameters must satisfy abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+ self._is_event_time = is_event_time
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if self._is_event_time is False:
+ current_processing_time = context.get_current_processing_time()
+ start = TimeWindow.get_window_start_with_offset(current_processing_time, self._offset,
+ self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ if timestamp > MIN_LONG_VALUE:
+ start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ raise Exception("Record has MIN_LONG_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ if self._is_event_time is True:
+ return EventTimeTrigger()
+ else:
+ return ProcessingTimeTrigger()
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return self._is_event_time
+
+ def __repr__(self):
+ return "TumblingWindowAssigner(%s,%s,%s)" % (self._size, self._offset, self.is_event_time)
+
+
+class CountTumblingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into fixed-size windows based on the count number
+ of the elements. Windows cannot overlap.
+ """
+
+ def __init__(self, window_size: int):
+ """
+ Windows this KeyedStream into tumbling count windows.
+ :param window_size: The size of the windows in number of elements.
+ """
+ self._window_size = window_size
+ self._counter_state_descriptor = ReducingStateDescriptor(
+ "assigner_counter", lambda a, b: a + b, Types.LONG())
+
+ @staticmethod
+ def of(window_size: int):
+ return CountTumblingWindowAssigner(window_size)
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ counter = context.get_runtime_context().get_reducing_state(
+ self._counter_state_descriptor)
+ if counter.get() is None:
+ counter.add(0)
+ result = [CountWindow(counter.get() // self._window_size)]
+ counter.add(1)
+ return result
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self) -> str:
+ return "CountTumblingWindowAssigner(%s)" % (self._window_size)
+
+
+class SlidingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the current system time
+ of the machine the operation is running on. Windows can possibly overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingWindowAssigner(60000, 10000, 0, False))
+
+ A WindowAssigner that windows elements into sliding windows based on the timestamp of the
+ elements. Windows can possibly overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingWindowAssigner(60000, 10000, 0, True))
+ """
+
+ def __init__(self, size: int, slide: int, offset: int, is_event_time: bool):
+ if abs(offset) >= slide or size <= 0:
+ raise Exception("SlidingWindowAssigner parameters must satisfy "
+ + "abs(offset) < slide and size > 0")
+
+ self._size = size
+ self._slide = slide
+ self._offset = offset
+ self._is_event_time = is_event_time
+ self._pane_size = math.gcd(size, slide)
+ self._num_panes_per_window = size // self._pane_size
+
+ def assign_windows(
+ self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
Review comment:
```suggestion
context: 'WindowAssigner.WindowAssignerContext') -> Collection[TimeWindow]:
```
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +538,721 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of the
+ machine the operation is running on. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(10), False))
+
+ A WindowAssigner that windows elements into windows based on the timestamp of the elements.
+ Windows cannot overlap.
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(60000, 0, True))
+ """
+
+ def __init__(self, size: int, offset: int, is_event_time: bool):
+ if abs(offset) >= size:
+ raise Exception("TumblingWindowAssigner parameters must satisfy abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+ self._is_event_time = is_event_time
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if self._is_event_time is False:
+ current_processing_time = context.get_current_processing_time()
+ start = TimeWindow.get_window_start_with_offset(current_processing_time, self._offset,
+ self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ if timestamp > MIN_LONG_VALUE:
+ start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ raise Exception("Record has MIN_LONG_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ if self._is_event_time is True:
+ return EventTimeTrigger()
+ else:
+ return ProcessingTimeTrigger()
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return self._is_event_time
+
+ def __repr__(self):
+ return "TumblingWindowAssigner(%s,%s,%s)" % (self._size, self._offset, self.is_event_time)
+
+
+class CountTumblingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into fixed-size windows based on the count number
+ of the elements. Windows cannot overlap.
+ """
+
+ def __init__(self, window_size: int):
+ """
+ Windows this KeyedStream into tumbling count windows.
+ :param window_size: The size of the windows in number of elements.
+ """
+ self._window_size = window_size
+ self._counter_state_descriptor = ReducingStateDescriptor(
+ "assigner_counter", lambda a, b: a + b, Types.LONG())
+
+ @staticmethod
+ def of(window_size: int):
+ return CountTumblingWindowAssigner(window_size)
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ counter = context.get_runtime_context().get_reducing_state(
+ self._counter_state_descriptor)
+ if counter.get() is None:
+ counter.add(0)
+ result = [CountWindow(counter.get() // self._window_size)]
+ counter.add(1)
+ return result
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self) -> str:
+ return "CountTumblingWindowAssigner(%s)" % (self._window_size)
+
+
+class SlidingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the current system time
+ of the machine the operation is running on. Windows can possibly overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingWindowAssigner(60000, 10000, 0, False))
+
+ A WindowAssigner that windows elements into sliding windows based on the timestamp of the
+ elements. Windows can possibly overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingWindowAssigner(60000, 10000, 0, True))
+ """
+
+ def __init__(self, size: int, slide: int, offset: int, is_event_time: bool):
+ if abs(offset) >= slide or size <= 0:
+ raise Exception("SlidingWindowAssigner parameters must satisfy "
+ + "abs(offset) < slide and size > 0")
+
+ self._size = size
+ self._slide = slide
+ self._offset = offset
+ self._is_event_time = is_event_time
+ self._pane_size = math.gcd(size, slide)
+ self._num_panes_per_window = size // self._pane_size
+
+ def assign_windows(
+ self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ if self._is_event_time is False:
+ current_processing_time = context.get_current_processing_time()
+ last_start = TimeWindow.get_window_start_with_offset(
+ current_processing_time, self._offset, self._slide)
+ windows = [TimeWindow(start, start + self._size)
+ for start in range(last_start,
+ current_processing_time - self._size, -self._slide)]
+ return windows
+ else:
+ if timestamp > MIN_LONG_VALUE:
+ last_start = TimeWindow.get_window_start_with_offset(timestamp,
+ self._offset, self._slide)
+ windows = [TimeWindow(start, start + self._size)
+ for start in range(last_start, timestamp - self._size, -self._slide)]
+ return windows
+ else:
+ raise Exception("Record has MIN_LONG_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', "
+ "or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ if self._is_event_time is True:
+ return EventTimeTrigger()
+ else:
+ return ProcessingTimeTrigger()
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return self._is_event_time
+
+ def __repr__(self) -> str:
+ return "SlidingWindowAssigner(%s, %s, %s, %s)" % (
+ self._size, self._slide, self._offset, self._is_event_time)
+
+
+class CountSlidingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the count number of
+ the elements. Windows can possibly overlap.
+ """
+
+ def __init__(self, window_size: int, window_slide: int):
+ """
+ Windows this KeyedStream into sliding count windows.
+ :param window_size: The size of the windows in number of elements.
+ :param window_slide: The slide interval in number of elements.
+ """
+ self._window_size = window_size
+ self._window_slide = window_slide
+ self._count = None # type: ValueState
+ self._counter_state_descriptor = ReducingStateDescriptor(
+ "slide-count-assigner", lambda a, b: a + b, Types.LONG())
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ count_descriptor = ValueStateDescriptor('slide-count-assigner', Types.LONG())
+ self._count = context.get_runtime_context().get_state(count_descriptor)
+ count_value = self._count.value()
+ if count_value is None:
+ current_count = 0
+ else:
+ current_count = count_value
+ self._count.update(current_count + 1)
+ last_id = current_count // self._window_slide
+ last_start = last_id * self._window_slide
+ last_end = last_start + self._window_size - 1
+ windows = []
+ while last_id >= 0 and last_start <= current_count <= last_end:
+ if last_start <= current_count <= last_end:
+ windows.append(CountWindow(last_id))
+ last_id -= 1
+ last_start -= self._window_slide
+ last_end -= self._window_slide
+ return windows
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self):
+ return "CountSlidingWindowAssigner(%s, %s)" % (self._window_size, self._window_slide)
+
+
+class SessionWindowAssigner(MergingWindowAssigner[T, TimeWindow]):
+ """
+ WindowAssigner that windows elements into sessions based on the timestamp. Windows cannot
Review comment:
correct the indentation.
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +538,721 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of the
+ machine the operation is running on. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(10), False))
+
+ A WindowAssigner that windows elements into windows based on the timestamp of the elements.
+ Windows cannot overlap.
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(60000, 0, True))
+ """
+
+ def __init__(self, size: int, offset: int, is_event_time: bool):
+ if abs(offset) >= size:
+ raise Exception("TumblingWindowAssigner parameters must satisfy abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+ self._is_event_time = is_event_time
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if self._is_event_time is False:
+ current_processing_time = context.get_current_processing_time()
+ start = TimeWindow.get_window_start_with_offset(current_processing_time, self._offset,
+ self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ if timestamp > MIN_LONG_VALUE:
+ start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ raise Exception("Record has MIN_LONG_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ if self._is_event_time is True:
+ return EventTimeTrigger()
+ else:
+ return ProcessingTimeTrigger()
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return self._is_event_time
+
+ def __repr__(self):
+ return "TumblingWindowAssigner(%s,%s,%s)" % (self._size, self._offset, self.is_event_time)
+
+
+class CountTumblingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into fixed-size windows based on the count number
+ of the elements. Windows cannot overlap.
+ """
+
+ def __init__(self, window_size: int):
+ """
+ Windows this KeyedStream into tumbling count windows.
+ :param window_size: The size of the windows in number of elements.
+ """
+ self._window_size = window_size
+ self._counter_state_descriptor = ReducingStateDescriptor(
+ "assigner_counter", lambda a, b: a + b, Types.LONG())
+
+ @staticmethod
+ def of(window_size: int):
+ return CountTumblingWindowAssigner(window_size)
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ counter = context.get_runtime_context().get_reducing_state(
+ self._counter_state_descriptor)
+ if counter.get() is None:
+ counter.add(0)
+ result = [CountWindow(counter.get() // self._window_size)]
+ counter.add(1)
+ return result
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self) -> str:
+ return "CountTumblingWindowAssigner(%s)" % (self._window_size)
+
+
+class SlidingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the current system time
+ of the machine the operation is running on. Windows can possibly overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingWindowAssigner(60000, 10000, 0, False))
+
+ A WindowAssigner that windows elements into sliding windows based on the timestamp of the
+ elements. Windows can possibly overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingWindowAssigner(60000, 10000, 0, True))
+ """
+
+ def __init__(self, size: int, slide: int, offset: int, is_event_time: bool):
+ if abs(offset) >= slide or size <= 0:
+ raise Exception("SlidingWindowAssigner parameters must satisfy "
+ + "abs(offset) < slide and size > 0")
+
+ self._size = size
+ self._slide = slide
+ self._offset = offset
+ self._is_event_time = is_event_time
+ self._pane_size = math.gcd(size, slide)
+ self._num_panes_per_window = size // self._pane_size
+
+ def assign_windows(
+ self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ if self._is_event_time is False:
+ current_processing_time = context.get_current_processing_time()
+ last_start = TimeWindow.get_window_start_with_offset(
+ current_processing_time, self._offset, self._slide)
+ windows = [TimeWindow(start, start + self._size)
+ for start in range(last_start,
+ current_processing_time - self._size, -self._slide)]
+ return windows
+ else:
+ if timestamp > MIN_LONG_VALUE:
+ last_start = TimeWindow.get_window_start_with_offset(timestamp,
+ self._offset, self._slide)
+ windows = [TimeWindow(start, start + self._size)
+ for start in range(last_start, timestamp - self._size, -self._slide)]
+ return windows
+ else:
+ raise Exception("Record has MIN_LONG_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', "
+ "or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ if self._is_event_time is True:
+ return EventTimeTrigger()
+ else:
+ return ProcessingTimeTrigger()
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return self._is_event_time
+
+ def __repr__(self) -> str:
+ return "SlidingWindowAssigner(%s, %s, %s, %s)" % (
+ self._size, self._slide, self._offset, self._is_event_time)
+
+
+class CountSlidingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the count number of
+ the elements. Windows can possibly overlap.
+ """
+
+ def __init__(self, window_size: int, window_slide: int):
+ """
+ Windows this KeyedStream into sliding count windows.
+ :param window_size: The size of the windows in number of elements.
+ :param window_slide: The slide interval in number of elements.
+ """
+ self._window_size = window_size
+ self._window_slide = window_slide
+ self._count = None # type: ValueState
+ self._counter_state_descriptor = ReducingStateDescriptor(
+ "slide-count-assigner", lambda a, b: a + b, Types.LONG())
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ count_descriptor = ValueStateDescriptor('slide-count-assigner', Types.LONG())
+ self._count = context.get_runtime_context().get_state(count_descriptor)
+ count_value = self._count.value()
+ if count_value is None:
+ current_count = 0
+ else:
+ current_count = count_value
+ self._count.update(current_count + 1)
+ last_id = current_count // self._window_slide
+ last_start = last_id * self._window_slide
+ last_end = last_start + self._window_size - 1
+ windows = []
+ while last_id >= 0 and last_start <= current_count <= last_end:
+ if last_start <= current_count <= last_end:
+ windows.append(CountWindow(last_id))
+ last_id -= 1
+ last_start -= self._window_slide
+ last_end -= self._window_slide
+ return windows
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self):
+ return "CountSlidingWindowAssigner(%s, %s)" % (self._window_size, self._window_slide)
+
+
+class SessionWindowAssigner(MergingWindowAssigner[T, TimeWindow]):
+ """
+ WindowAssigner that windows elements into sessions based on the timestamp. Windows cannot
+ overlap.
+ """
+
+ def __init__(self, session_gap: int, is_event_time: bool):
+ if session_gap <= 0:
+ raise Exception("SessionWindowAssigner parameters must satisfy 0 < size")
+
+ self._session_gap = session_gap
+ self._is_event_time = is_event_time
+
+ def merge_windows(self,
+ windows: Iterable[W],
+ callback: 'MergingWindowAssigner.MergeCallback[W]') -> None:
+ window_list = [w for w in windows]
+ window_list.sort()
+ for i in range(1, len(window_list)):
+ if window_list[i - 1].end > window_list[i].start:
+ callback.merge([window_list[i - 1], window_list[i]],
+ TimeWindow(window_list[i - 1].start, window_list[i].end))
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ if self._is_event_time is False:
+ timestamp = context.get_current_processing_time()
+
+ return [TimeWindow(timestamp, timestamp + self._session_gap)]
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
Review comment:
```suggestion
def get_default_trigger(self, env) -> Trigger[T, TimeWindow]:
```
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +538,721 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of the
+ machine the operation is running on. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(10), False))
+
+ A WindowAssigner that windows elements into windows based on the timestamp of the elements.
+ Windows cannot overlap.
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(60000, 0, True))
+ """
+
+ def __init__(self, size: int, offset: int, is_event_time: bool):
+ if abs(offset) >= size:
+ raise Exception("TumblingWindowAssigner parameters must satisfy abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+ self._is_event_time = is_event_time
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if self._is_event_time is False:
+ current_processing_time = context.get_current_processing_time()
+ start = TimeWindow.get_window_start_with_offset(current_processing_time, self._offset,
+ self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ if timestamp > MIN_LONG_VALUE:
+ start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ raise Exception("Record has MIN_LONG_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ if self._is_event_time is True:
+ return EventTimeTrigger()
+ else:
+ return ProcessingTimeTrigger()
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return self._is_event_time
+
+ def __repr__(self):
+ return "TumblingWindowAssigner(%s,%s,%s)" % (self._size, self._offset, self.is_event_time)
+
+
+class CountTumblingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into fixed-size windows based on the count number
+ of the elements. Windows cannot overlap.
+ """
+
+ def __init__(self, window_size: int):
+ """
+ Windows this KeyedStream into tumbling count windows.
+ :param window_size: The size of the windows in number of elements.
+ """
+ self._window_size = window_size
+ self._counter_state_descriptor = ReducingStateDescriptor(
+ "assigner_counter", lambda a, b: a + b, Types.LONG())
+
+ @staticmethod
+ def of(window_size: int):
+ return CountTumblingWindowAssigner(window_size)
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ counter = context.get_runtime_context().get_reducing_state(
+ self._counter_state_descriptor)
+ if counter.get() is None:
+ counter.add(0)
+ result = [CountWindow(counter.get() // self._window_size)]
+ counter.add(1)
+ return result
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self) -> str:
+ return "CountTumblingWindowAssigner(%s)" % (self._window_size)
+
+
+class SlidingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the current system time
+ of the machine the operation is running on. Windows can possibly overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingWindowAssigner(60000, 10000, 0, False))
+
+ A WindowAssigner that windows elements into sliding windows based on the timestamp of the
+ elements. Windows can possibly overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingWindowAssigner(60000, 10000, 0, True))
+ """
+
+ def __init__(self, size: int, slide: int, offset: int, is_event_time: bool):
+ if abs(offset) >= slide or size <= 0:
+ raise Exception("SlidingWindowAssigner parameters must satisfy "
+ + "abs(offset) < slide and size > 0")
+
+ self._size = size
+ self._slide = slide
+ self._offset = offset
+ self._is_event_time = is_event_time
+ self._pane_size = math.gcd(size, slide)
+ self._num_panes_per_window = size // self._pane_size
+
+ def assign_windows(
+ self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ if self._is_event_time is False:
+ current_processing_time = context.get_current_processing_time()
+ last_start = TimeWindow.get_window_start_with_offset(
+ current_processing_time, self._offset, self._slide)
+ windows = [TimeWindow(start, start + self._size)
+ for start in range(last_start,
+ current_processing_time - self._size, -self._slide)]
+ return windows
+ else:
+ if timestamp > MIN_LONG_VALUE:
+ last_start = TimeWindow.get_window_start_with_offset(timestamp,
+ self._offset, self._slide)
+ windows = [TimeWindow(start, start + self._size)
+ for start in range(last_start, timestamp - self._size, -self._slide)]
+ return windows
+ else:
+ raise Exception("Record has MIN_LONG_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', "
+ "or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ if self._is_event_time is True:
+ return EventTimeTrigger()
+ else:
+ return ProcessingTimeTrigger()
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
Review comment:
```suggestion
def get_window_serializer(self) -> TypeSerializer[TimeWindow]:
```
##########
File path: flink-python/pyflink/datastream/data_stream.py
##########
@@ -1216,6 +1180,18 @@ def window(self, window_assigner: WindowAssigner) -> 'WindowedStream':
"""
return WindowedStream(self, window_assigner)
+ def count_window(self, window_size: int, window_slide = 0):
+ """
+ Windows this KeyedStream into tumbling count windows.
Review comment:
```suggestion
Windows this KeyedStream into tumbling or sliding count windows.
```
##########
File path: flink-python/pyflink/datastream/data_stream.py
##########
@@ -1216,6 +1180,18 @@ def window(self, window_assigner: WindowAssigner) -> 'WindowedStream':
"""
return WindowedStream(self, window_assigner)
+ def count_window(self, window_size: int, window_slide = 0):
+ """
+ Windows this KeyedStream into tumbling count windows.
+ Params:
+ :param window_size: The size of the windows in number of elements.
+ :param window_slide: The slide interval in number of elements.
+ """
+ if window_slide is 0:
Review comment:
```suggestion
if window_slide == 0:
```
##########
File path: flink-python/pyflink/datastream/data_stream.py
##########
@@ -1216,6 +1180,18 @@ def window(self, window_assigner: WindowAssigner) -> 'WindowedStream':
"""
return WindowedStream(self, window_assigner)
+ def count_window(self, window_size: int, window_slide = 0):
Review comment:
```suggestion
def count_window(self, size: int, slide=0):
```
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -65,6 +66,23 @@ def max_timestamp(self) -> int:
pass
+class SessionWindowTimeGapExtractor(ABC):
+ """
+ Window is a grouping of elements into finite buckets. Windows have a maximum timestamp
Review comment:
The doc doesn't apply here. Could you refer to the Java doc of the Java SessionWindowTimeGapExtractor?
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +538,721 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
Review comment:
state.clear()
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +538,721 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
Review comment:
```suggestion
window: TimeWindow,
```
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +538,721 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of the
+ machine the operation is running on. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(10), False))
Review comment:
```suggestion
>>> .window(TumblingWindowAssigner(60000, 10000, False))
```
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +538,721 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of the
+ machine the operation is running on. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(10), False))
+
+ A WindowAssigner that windows elements into windows based on the timestamp of the elements.
+ Windows cannot overlap.
+ For example, in order to window into windows of 1 minute:
+ ::
Review comment:
correct indentation
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +538,721 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of the
+ machine the operation is running on. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(10), False))
+
+ A WindowAssigner that windows elements into windows based on the timestamp of the elements.
+ Windows cannot overlap.
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(60000, 0, True))
+ """
+
+ def __init__(self, size: int, offset: int, is_event_time: bool):
+ if abs(offset) >= size:
Review comment:
check offset is positive?
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +538,721 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of the
+ machine the operation is running on. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(10), False))
+
+ A WindowAssigner that windows elements into windows based on the timestamp of the elements.
+ Windows cannot overlap.
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(60000, 0, True))
+ """
+
+ def __init__(self, size: int, offset: int, is_event_time: bool):
+ if abs(offset) >= size:
+ raise Exception("TumblingWindowAssigner parameters must satisfy abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+ self._is_event_time = is_event_time
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if self._is_event_time is False:
+ current_processing_time = context.get_current_processing_time()
+ start = TimeWindow.get_window_start_with_offset(current_processing_time, self._offset,
+ self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ if timestamp > MIN_LONG_VALUE:
+ start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ raise Exception("Record has MIN_LONG_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ if self._is_event_time is True:
+ return EventTimeTrigger()
+ else:
+ return ProcessingTimeTrigger()
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return self._is_event_time
+
+ def __repr__(self):
+ return "TumblingWindowAssigner(%s,%s,%s)" % (self._size, self._offset, self.is_event_time)
+
+
+class CountTumblingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into fixed-size windows based on the count number
+ of the elements. Windows cannot overlap.
+ """
+
+ def __init__(self, window_size: int):
+ """
+ Windows this KeyedStream into tumbling count windows.
+ :param window_size: The size of the windows in number of elements.
+ """
+ self._window_size = window_size
+ self._counter_state_descriptor = ReducingStateDescriptor(
+ "assigner_counter", lambda a, b: a + b, Types.LONG())
+
+ @staticmethod
+ def of(window_size: int):
+ return CountTumblingWindowAssigner(window_size)
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ counter = context.get_runtime_context().get_reducing_state(
+ self._counter_state_descriptor)
+ if counter.get() is None:
+ counter.add(0)
+ result = [CountWindow(counter.get() // self._window_size)]
+ counter.add(1)
+ return result
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
Review comment:
```suggestion
def get_default_trigger(self, env) -> Trigger[T, CountWindow]:
```
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -16,15 +16,18 @@
# limitations under the License.
################################################################################
import sys
+import math
from abc import ABC, abstractmethod
from enum import Enum
from io import BytesIO
-from typing import TypeVar, Generic, Iterable, Collection
-
+from typing import TypeVar, Generic, Iterable, Collection, Any
+from pyflink.common import TypeSerializer, Time
Review comment:
```suggestion
from pyflink.common import Time
```
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +538,721 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of the
+ machine the operation is running on. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(10), False))
+
+ A WindowAssigner that windows elements into windows based on the timestamp of the elements.
+ Windows cannot overlap.
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(60000, 0, True))
+ """
+
+ def __init__(self, size: int, offset: int, is_event_time: bool):
+ if abs(offset) >= size:
+ raise Exception("TumblingWindowAssigner parameters must satisfy abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+ self._is_event_time = is_event_time
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if self._is_event_time is False:
+ current_processing_time = context.get_current_processing_time()
+ start = TimeWindow.get_window_start_with_offset(current_processing_time, self._offset,
+ self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ if timestamp > MIN_LONG_VALUE:
+ start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ raise Exception("Record has MIN_LONG_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', or did you forget to call "
Review comment:
you can run ./dev/lint-python.sh locally to make sure that there is no checkstyle issues.
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +538,721 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of the
+ machine the operation is running on. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(10), False))
+
+ A WindowAssigner that windows elements into windows based on the timestamp of the elements.
+ Windows cannot overlap.
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(60000, 0, True))
+ """
+
+ def __init__(self, size: int, offset: int, is_event_time: bool):
+ if abs(offset) >= size:
+ raise Exception("TumblingWindowAssigner parameters must satisfy abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+ self._is_event_time = is_event_time
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if self._is_event_time is False:
+ current_processing_time = context.get_current_processing_time()
+ start = TimeWindow.get_window_start_with_offset(current_processing_time, self._offset,
+ self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ if timestamp > MIN_LONG_VALUE:
+ start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ raise Exception("Record has MIN_LONG_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ if self._is_event_time is True:
+ return EventTimeTrigger()
+ else:
+ return ProcessingTimeTrigger()
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return self._is_event_time
+
+ def __repr__(self):
+ return "TumblingWindowAssigner(%s,%s,%s)" % (self._size, self._offset, self.is_event_time)
+
+
+class CountTumblingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into fixed-size windows based on the count number
+ of the elements. Windows cannot overlap.
+ """
+
+ def __init__(self, window_size: int):
+ """
+ Windows this KeyedStream into tumbling count windows.
+ :param window_size: The size of the windows in number of elements.
+ """
+ self._window_size = window_size
+ self._counter_state_descriptor = ReducingStateDescriptor(
+ "assigner_counter", lambda a, b: a + b, Types.LONG())
+
+ @staticmethod
+ def of(window_size: int):
+ return CountTumblingWindowAssigner(window_size)
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ counter = context.get_runtime_context().get_reducing_state(
+ self._counter_state_descriptor)
+ if counter.get() is None:
+ counter.add(0)
+ result = [CountWindow(counter.get() // self._window_size)]
+ counter.add(1)
+ return result
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
Review comment:
```suggestion
def get_window_serializer(self) -> TypeSerializer[CountWindow]:
```
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +538,721 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of the
+ machine the operation is running on. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(10), False))
+
+ A WindowAssigner that windows elements into windows based on the timestamp of the elements.
+ Windows cannot overlap.
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(60000, 0, True))
+ """
+
+ def __init__(self, size: int, offset: int, is_event_time: bool):
+ if abs(offset) >= size:
+ raise Exception("TumblingWindowAssigner parameters must satisfy abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+ self._is_event_time = is_event_time
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if self._is_event_time is False:
+ current_processing_time = context.get_current_processing_time()
+ start = TimeWindow.get_window_start_with_offset(current_processing_time, self._offset,
+ self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ if timestamp > MIN_LONG_VALUE:
+ start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ raise Exception("Record has MIN_LONG_VALUE timestamp (= no timestamp marker). "
Review comment:
use the value of MIN_LONG_VALUE
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +538,721 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of the
+ machine the operation is running on. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(10), False))
+
+ A WindowAssigner that windows elements into windows based on the timestamp of the elements.
+ Windows cannot overlap.
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(60000, 0, True))
+ """
+
+ def __init__(self, size: int, offset: int, is_event_time: bool):
+ if abs(offset) >= size:
+ raise Exception("TumblingWindowAssigner parameters must satisfy abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+ self._is_event_time = is_event_time
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if self._is_event_time is False:
+ current_processing_time = context.get_current_processing_time()
+ start = TimeWindow.get_window_start_with_offset(current_processing_time, self._offset,
+ self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ if timestamp > MIN_LONG_VALUE:
+ start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ raise Exception("Record has MIN_LONG_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ if self._is_event_time is True:
+ return EventTimeTrigger()
+ else:
+ return ProcessingTimeTrigger()
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return self._is_event_time
+
+ def __repr__(self):
+ return "TumblingWindowAssigner(%s,%s,%s)" % (self._size, self._offset, self.is_event_time)
+
+
+class CountTumblingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into fixed-size windows based on the count number
+ of the elements. Windows cannot overlap.
+ """
+
+ def __init__(self, window_size: int):
+ """
+ Windows this KeyedStream into tumbling count windows.
+ :param window_size: The size of the windows in number of elements.
+ """
+ self._window_size = window_size
+ self._counter_state_descriptor = ReducingStateDescriptor(
+ "assigner_counter", lambda a, b: a + b, Types.LONG())
+
+ @staticmethod
+ def of(window_size: int):
+ return CountTumblingWindowAssigner(window_size)
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ counter = context.get_runtime_context().get_reducing_state(
+ self._counter_state_descriptor)
+ if counter.get() is None:
+ counter.add(0)
+ result = [CountWindow(counter.get() // self._window_size)]
+ counter.add(1)
+ return result
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self) -> str:
+ return "CountTumblingWindowAssigner(%s)" % (self._window_size)
Review comment:
```suggestion
return "CountTumblingWindowAssigner(%s)" % self._window_size
```
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +538,721 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of the
+ machine the operation is running on. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(10), False))
+
+ A WindowAssigner that windows elements into windows based on the timestamp of the elements.
+ Windows cannot overlap.
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(60000, 0, True))
+ """
+
+ def __init__(self, size: int, offset: int, is_event_time: bool):
+ if abs(offset) >= size:
+ raise Exception("TumblingWindowAssigner parameters must satisfy abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+ self._is_event_time = is_event_time
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if self._is_event_time is False:
+ current_processing_time = context.get_current_processing_time()
+ start = TimeWindow.get_window_start_with_offset(current_processing_time, self._offset,
+ self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ if timestamp > MIN_LONG_VALUE:
+ start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ raise Exception("Record has MIN_LONG_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ if self._is_event_time is True:
+ return EventTimeTrigger()
+ else:
+ return ProcessingTimeTrigger()
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return self._is_event_time
+
+ def __repr__(self):
+ return "TumblingWindowAssigner(%s,%s,%s)" % (self._size, self._offset, self.is_event_time)
+
+
+class CountTumblingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into fixed-size windows based on the count number
+ of the elements. Windows cannot overlap.
+ """
+
+ def __init__(self, window_size: int):
+ """
+ Windows this KeyedStream into tumbling count windows.
+ :param window_size: The size of the windows in number of elements.
+ """
+ self._window_size = window_size
+ self._counter_state_descriptor = ReducingStateDescriptor(
+ "assigner_counter", lambda a, b: a + b, Types.LONG())
+
+ @staticmethod
+ def of(window_size: int):
+ return CountTumblingWindowAssigner(window_size)
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ counter = context.get_runtime_context().get_reducing_state(
+ self._counter_state_descriptor)
+ if counter.get() is None:
+ counter.add(0)
+ result = [CountWindow(counter.get() // self._window_size)]
+ counter.add(1)
+ return result
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self) -> str:
+ return "CountTumblingWindowAssigner(%s)" % (self._window_size)
+
+
+class SlidingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the current system time
+ of the machine the operation is running on. Windows can possibly overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingWindowAssigner(60000, 10000, 0, False))
+
+ A WindowAssigner that windows elements into sliding windows based on the timestamp of the
+ elements. Windows can possibly overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingWindowAssigner(60000, 10000, 0, True))
+ """
+
+ def __init__(self, size: int, slide: int, offset: int, is_event_time: bool):
+ if abs(offset) >= slide or size <= 0:
+ raise Exception("SlidingWindowAssigner parameters must satisfy "
+ + "abs(offset) < slide and size > 0")
+
+ self._size = size
+ self._slide = slide
+ self._offset = offset
+ self._is_event_time = is_event_time
+ self._pane_size = math.gcd(size, slide)
+ self._num_panes_per_window = size // self._pane_size
+
+ def assign_windows(
+ self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ if self._is_event_time is False:
+ current_processing_time = context.get_current_processing_time()
+ last_start = TimeWindow.get_window_start_with_offset(
+ current_processing_time, self._offset, self._slide)
+ windows = [TimeWindow(start, start + self._size)
+ for start in range(last_start,
+ current_processing_time - self._size, -self._slide)]
+ return windows
+ else:
+ if timestamp > MIN_LONG_VALUE:
+ last_start = TimeWindow.get_window_start_with_offset(timestamp,
+ self._offset, self._slide)
+ windows = [TimeWindow(start, start + self._size)
+ for start in range(last_start, timestamp - self._size, -self._slide)]
+ return windows
+ else:
+ raise Exception("Record has MIN_LONG_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', "
+ "or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
Review comment:
```suggestion
def get_default_trigger(self, env) -> Trigger[T, TimeWindow]:
```
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +538,721 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of the
+ machine the operation is running on. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(10), False))
+
+ A WindowAssigner that windows elements into windows based on the timestamp of the elements.
+ Windows cannot overlap.
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(60000, 0, True))
+ """
+
+ def __init__(self, size: int, offset: int, is_event_time: bool):
+ if abs(offset) >= size:
+ raise Exception("TumblingWindowAssigner parameters must satisfy abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+ self._is_event_time = is_event_time
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if self._is_event_time is False:
+ current_processing_time = context.get_current_processing_time()
+ start = TimeWindow.get_window_start_with_offset(current_processing_time, self._offset,
+ self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ if timestamp > MIN_LONG_VALUE:
+ start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ raise Exception("Record has MIN_LONG_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ if self._is_event_time is True:
+ return EventTimeTrigger()
+ else:
+ return ProcessingTimeTrigger()
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return self._is_event_time
+
+ def __repr__(self):
+ return "TumblingWindowAssigner(%s,%s,%s)" % (self._size, self._offset, self.is_event_time)
+
+
+class CountTumblingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into fixed-size windows based on the count number
+ of the elements. Windows cannot overlap.
+ """
+
+ def __init__(self, window_size: int):
+ """
+ Windows this KeyedStream into tumbling count windows.
+ :param window_size: The size of the windows in number of elements.
+ """
+ self._window_size = window_size
+ self._counter_state_descriptor = ReducingStateDescriptor(
Review comment:
Why not use ValueState? I think it has better performance over ReducingState.
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +538,721 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of the
+ machine the operation is running on. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(10), False))
+
+ A WindowAssigner that windows elements into windows based on the timestamp of the elements.
+ Windows cannot overlap.
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(60000, 0, True))
+ """
+
+ def __init__(self, size: int, offset: int, is_event_time: bool):
+ if abs(offset) >= size:
+ raise Exception("TumblingWindowAssigner parameters must satisfy abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+ self._is_event_time = is_event_time
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if self._is_event_time is False:
+ current_processing_time = context.get_current_processing_time()
+ start = TimeWindow.get_window_start_with_offset(current_processing_time, self._offset,
+ self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ if timestamp > MIN_LONG_VALUE:
+ start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ raise Exception("Record has MIN_LONG_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ if self._is_event_time is True:
+ return EventTimeTrigger()
+ else:
+ return ProcessingTimeTrigger()
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return self._is_event_time
+
+ def __repr__(self):
+ return "TumblingWindowAssigner(%s,%s,%s)" % (self._size, self._offset, self.is_event_time)
+
+
+class CountTumblingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into fixed-size windows based on the count number
+ of the elements. Windows cannot overlap.
+ """
+
+ def __init__(self, window_size: int):
+ """
+ Windows this KeyedStream into tumbling count windows.
+ :param window_size: The size of the windows in number of elements.
+ """
+ self._window_size = window_size
+ self._counter_state_descriptor = ReducingStateDescriptor(
+ "assigner_counter", lambda a, b: a + b, Types.LONG())
+
+ @staticmethod
+ def of(window_size: int):
+ return CountTumblingWindowAssigner(window_size)
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ counter = context.get_runtime_context().get_reducing_state(
+ self._counter_state_descriptor)
+ if counter.get() is None:
+ counter.add(0)
+ result = [CountWindow(counter.get() // self._window_size)]
+ counter.add(1)
+ return result
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self) -> str:
+ return "CountTumblingWindowAssigner(%s)" % (self._window_size)
+
+
+class SlidingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the current system time
+ of the machine the operation is running on. Windows can possibly overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingWindowAssigner(60000, 10000, 0, False))
+
+ A WindowAssigner that windows elements into sliding windows based on the timestamp of the
+ elements. Windows can possibly overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingWindowAssigner(60000, 10000, 0, True))
+ """
+
+ def __init__(self, size: int, slide: int, offset: int, is_event_time: bool):
+ if abs(offset) >= slide or size <= 0:
+ raise Exception("SlidingWindowAssigner parameters must satisfy "
+ + "abs(offset) < slide and size > 0")
+
+ self._size = size
+ self._slide = slide
+ self._offset = offset
+ self._is_event_time = is_event_time
+ self._pane_size = math.gcd(size, slide)
+ self._num_panes_per_window = size // self._pane_size
+
+ def assign_windows(
+ self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ if self._is_event_time is False:
+ current_processing_time = context.get_current_processing_time()
+ last_start = TimeWindow.get_window_start_with_offset(
+ current_processing_time, self._offset, self._slide)
+ windows = [TimeWindow(start, start + self._size)
+ for start in range(last_start,
+ current_processing_time - self._size, -self._slide)]
+ return windows
+ else:
+ if timestamp > MIN_LONG_VALUE:
+ last_start = TimeWindow.get_window_start_with_offset(timestamp,
+ self._offset, self._slide)
+ windows = [TimeWindow(start, start + self._size)
+ for start in range(last_start, timestamp - self._size, -self._slide)]
+ return windows
+ else:
+ raise Exception("Record has MIN_LONG_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', "
+ "or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ if self._is_event_time is True:
+ return EventTimeTrigger()
+ else:
+ return ProcessingTimeTrigger()
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return self._is_event_time
+
+ def __repr__(self) -> str:
+ return "SlidingWindowAssigner(%s, %s, %s, %s)" % (
+ self._size, self._slide, self._offset, self._is_event_time)
+
+
+class CountSlidingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the count number of
+ the elements. Windows can possibly overlap.
+ """
+
+ def __init__(self, window_size: int, window_slide: int):
+ """
+ Windows this KeyedStream into sliding count windows.
+ :param window_size: The size of the windows in number of elements.
+ :param window_slide: The slide interval in number of elements.
+ """
+ self._window_size = window_size
+ self._window_slide = window_slide
+ self._count = None # type: ValueState
+ self._counter_state_descriptor = ReducingStateDescriptor(
+ "slide-count-assigner", lambda a, b: a + b, Types.LONG())
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ count_descriptor = ValueStateDescriptor('slide-count-assigner', Types.LONG())
+ self._count = context.get_runtime_context().get_state(count_descriptor)
+ count_value = self._count.value()
+ if count_value is None:
+ current_count = 0
+ else:
+ current_count = count_value
+ self._count.update(current_count + 1)
+ last_id = current_count // self._window_slide
+ last_start = last_id * self._window_slide
+ last_end = last_start + self._window_size - 1
+ windows = []
+ while last_id >= 0 and last_start <= current_count <= last_end:
+ if last_start <= current_count <= last_end:
+ windows.append(CountWindow(last_id))
+ last_id -= 1
+ last_start -= self._window_slide
+ last_end -= self._window_slide
+ return windows
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self):
+ return "CountSlidingWindowAssigner(%s, %s)" % (self._window_size, self._window_slide)
+
+
+class SessionWindowAssigner(MergingWindowAssigner[T, TimeWindow]):
+ """
+ WindowAssigner that windows elements into sessions based on the timestamp. Windows cannot
+ overlap.
+ """
+
+ def __init__(self, session_gap: int, is_event_time: bool):
+ if session_gap <= 0:
+ raise Exception("SessionWindowAssigner parameters must satisfy 0 < size")
+
+ self._session_gap = session_gap
+ self._is_event_time = is_event_time
+
+ def merge_windows(self,
+ windows: Iterable[W],
Review comment:
```suggestion
windows: Iterable[TimeWindow],
```
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +538,721 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of the
+ machine the operation is running on. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(10), False))
+
+ A WindowAssigner that windows elements into windows based on the timestamp of the elements.
+ Windows cannot overlap.
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(60000, 0, True))
+ """
+
+ def __init__(self, size: int, offset: int, is_event_time: bool):
+ if abs(offset) >= size:
+ raise Exception("TumblingWindowAssigner parameters must satisfy abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+ self._is_event_time = is_event_time
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if self._is_event_time is False:
+ current_processing_time = context.get_current_processing_time()
+ start = TimeWindow.get_window_start_with_offset(current_processing_time, self._offset,
+ self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ if timestamp > MIN_LONG_VALUE:
+ start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ raise Exception("Record has MIN_LONG_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ if self._is_event_time is True:
+ return EventTimeTrigger()
+ else:
+ return ProcessingTimeTrigger()
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return self._is_event_time
+
+ def __repr__(self):
+ return "TumblingWindowAssigner(%s,%s,%s)" % (self._size, self._offset, self.is_event_time)
+
+
+class CountTumblingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into fixed-size windows based on the count number
+ of the elements. Windows cannot overlap.
+ """
+
+ def __init__(self, window_size: int):
+ """
+ Windows this KeyedStream into tumbling count windows.
+ :param window_size: The size of the windows in number of elements.
+ """
+ self._window_size = window_size
+ self._counter_state_descriptor = ReducingStateDescriptor(
+ "assigner_counter", lambda a, b: a + b, Types.LONG())
+
+ @staticmethod
+ def of(window_size: int):
+ return CountTumblingWindowAssigner(window_size)
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ counter = context.get_runtime_context().get_reducing_state(
+ self._counter_state_descriptor)
+ if counter.get() is None:
+ counter.add(0)
+ result = [CountWindow(counter.get() // self._window_size)]
+ counter.add(1)
+ return result
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self) -> str:
+ return "CountTumblingWindowAssigner(%s)" % (self._window_size)
+
+
+class SlidingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the current system time
+ of the machine the operation is running on. Windows can possibly overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingWindowAssigner(60000, 10000, 0, False))
+
+ A WindowAssigner that windows elements into sliding windows based on the timestamp of the
+ elements. Windows can possibly overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingWindowAssigner(60000, 10000, 0, True))
+ """
+
+ def __init__(self, size: int, slide: int, offset: int, is_event_time: bool):
+ if abs(offset) >= slide or size <= 0:
+ raise Exception("SlidingWindowAssigner parameters must satisfy "
+ + "abs(offset) < slide and size > 0")
+
+ self._size = size
+ self._slide = slide
+ self._offset = offset
+ self._is_event_time = is_event_time
+ self._pane_size = math.gcd(size, slide)
+ self._num_panes_per_window = size // self._pane_size
+
+ def assign_windows(
+ self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ if self._is_event_time is False:
+ current_processing_time = context.get_current_processing_time()
+ last_start = TimeWindow.get_window_start_with_offset(
+ current_processing_time, self._offset, self._slide)
+ windows = [TimeWindow(start, start + self._size)
+ for start in range(last_start,
+ current_processing_time - self._size, -self._slide)]
+ return windows
+ else:
+ if timestamp > MIN_LONG_VALUE:
+ last_start = TimeWindow.get_window_start_with_offset(timestamp,
+ self._offset, self._slide)
+ windows = [TimeWindow(start, start + self._size)
+ for start in range(last_start, timestamp - self._size, -self._slide)]
+ return windows
+ else:
+ raise Exception("Record has MIN_LONG_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', "
+ "or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ if self._is_event_time is True:
+ return EventTimeTrigger()
+ else:
+ return ProcessingTimeTrigger()
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return self._is_event_time
+
+ def __repr__(self) -> str:
+ return "SlidingWindowAssigner(%s, %s, %s, %s)" % (
+ self._size, self._slide, self._offset, self._is_event_time)
+
+
+class CountSlidingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the count number of
+ the elements. Windows can possibly overlap.
+ """
+
+ def __init__(self, window_size: int, window_slide: int):
+ """
+ Windows this KeyedStream into sliding count windows.
+ :param window_size: The size of the windows in number of elements.
+ :param window_slide: The slide interval in number of elements.
+ """
+ self._window_size = window_size
+ self._window_slide = window_slide
+ self._count = None # type: ValueState
+ self._counter_state_descriptor = ReducingStateDescriptor(
+ "slide-count-assigner", lambda a, b: a + b, Types.LONG())
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ count_descriptor = ValueStateDescriptor('slide-count-assigner', Types.LONG())
+ self._count = context.get_runtime_context().get_state(count_descriptor)
+ count_value = self._count.value()
+ if count_value is None:
+ current_count = 0
+ else:
+ current_count = count_value
+ self._count.update(current_count + 1)
+ last_id = current_count // self._window_slide
+ last_start = last_id * self._window_slide
+ last_end = last_start + self._window_size - 1
+ windows = []
+ while last_id >= 0 and last_start <= current_count <= last_end:
+ if last_start <= current_count <= last_end:
+ windows.append(CountWindow(last_id))
+ last_id -= 1
+ last_start -= self._window_slide
+ last_end -= self._window_slide
+ return windows
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self):
+ return "CountSlidingWindowAssigner(%s, %s)" % (self._window_size, self._window_slide)
+
+
+class SessionWindowAssigner(MergingWindowAssigner[T, TimeWindow]):
+ """
+ WindowAssigner that windows elements into sessions based on the timestamp. Windows cannot
+ overlap.
+ """
+
+ def __init__(self, session_gap: int, is_event_time: bool):
+ if session_gap <= 0:
+ raise Exception("SessionWindowAssigner parameters must satisfy 0 < size")
+
+ self._session_gap = session_gap
+ self._is_event_time = is_event_time
+
+ def merge_windows(self,
+ windows: Iterable[W],
+ callback: 'MergingWindowAssigner.MergeCallback[W]') -> None:
+ window_list = [w for w in windows]
+ window_list.sort()
+ for i in range(1, len(window_list)):
+ if window_list[i - 1].end > window_list[i].start:
+ callback.merge([window_list[i - 1], window_list[i]],
+ TimeWindow(window_list[i - 1].start, window_list[i].end))
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ if self._is_event_time is False:
+ timestamp = context.get_current_processing_time()
+
+ return [TimeWindow(timestamp, timestamp + self._session_gap)]
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ if self._is_event_time is True:
+ return EventTimeTrigger()
+ else:
+ return ProcessingTimeTrigger()
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return self._is_event_time
+
+ def __repr__(self):
+ return "SessionWindowAssigner(%s, %s)" % (self._session_gap, self._is_event_time)
+
+
+class DynamicSessionWindowAssigner(MergingWindowAssigner[T, TimeWindow]):
+ """
+ WindowAssigner that windows elements into sessions based on the timestamp. Windows cannot
+ overlap.
+ """
+
+ def __init__(self,
+ session_window_time_gap_extractor: SessionWindowTimeGapExtractor,
+ is_event_time: bool):
+ self._session_gap = None
+ self._session_window_time_gap_extractor = session_window_time_gap_extractor
+ self._is_event_time = is_event_time
+
+ def merge_windows(self,
+ windows: Iterable[W],
+ callback: 'MergingWindowAssigner.MergeCallback[W]') -> None:
+ window_list = [w for w in windows]
+ window_list.sort()
+ for i in range(1, len(window_list)):
+ if window_list[i - 1].end > window_list[i].start:
+ callback.merge([window_list[i - 1], window_list[i]],
+ TimeWindow(window_list[i - 1].start, window_list[i].end))
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ if self._is_event_time is False:
+ timestamp = context.get_current_processing_time()
+ self._session_gap = self._session_window_time_gap_extractor.extract(element)
+ if self._session_gap <= 0:
+ raise Exception("Dynamic session time gap must satisfy 0 < gap")
+
+ return [TimeWindow(timestamp, timestamp + self._session_gap)]
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ if self._is_event_time is True:
+ return EventTimeTrigger()
+ else:
+ return ProcessingTimeTrigger()
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return self._is_event_time
+
+ def __repr__(self):
+ return "DynamicSessionWindowAssigner(%s, %s)" % (self._session_gap, self._is_event_time)
+
+
+class TumblingProcessingTimeWindows:
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of
+ the machine the operation is running on. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+
+ in = ...
+ keyed = in.keyBy(...)
+ windowed =
+ keyed.window(TumblingProcessingTimeWindows.of(Time.of(1, MINUTES), Time.of(10, SECONDS))
+ """
+
+ @staticmethod
+ def of(size: Time, offset=None):
+ """
+ Creates a new TumblingProcessingTimeWindows WindowAssigner that assigns elements to time
+ windows based on the element timestamp and offset.
+ For example, if you want window a stream by hour,but window begins at the 15th minutes of
+ each hour, you can use of(Time.hours(1),Time.minutes(15)),then you will get time windows
+ start at 0:15:00,1:15:00,2:15:00,etc.
+ Rather than that,if you are living in somewhere which is not using UTC±00:00 time,
+ such as China which is using UTC+08:00,and you want a time window with size of one day,
+ and window begins at every 00:00:00 of local time,you may use of(Time.days(1),
+ Time.hours(-8)). The parameter of offset is Time.hours(-8))
+ since UTC+08:00 is 8 hours earlier than UTC time.
+ Params:
+ size – The size of the generated windows.
+ offset – The offset which window start would be shifted by.
+ Returns: The time policy.
+ """
+ if offset is None:
+ return TumblingWindowAssigner(size.to_milliseconds(), 0, False)
+ else:
+ return TumblingWindowAssigner(size.to_milliseconds(), offset.to_milliseconds(), False)
+
+
+class TumblingEventTimeWindows:
+ """
+ A WindowAssigner that windows elements into windows based on the timestamp of the elements.
+ Windows cannot overlap.
+ For example, in order to window into windows of 1 minute:
+
+ in = ...
+ keyed = in.keyBy(...)
+ windowed = keyed.window(TumblingEventTimeWindows.of(Time.minutes(1)))
+ """
+
+ @staticmethod
+ def of(size: Time, offset=None):
Review comment:
Move this method into TumblingWindowAssigner?
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +538,721 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of the
+ machine the operation is running on. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(10), False))
+
+ A WindowAssigner that windows elements into windows based on the timestamp of the elements.
+ Windows cannot overlap.
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(60000, 0, True))
+ """
+
+ def __init__(self, size: int, offset: int, is_event_time: bool):
+ if abs(offset) >= size:
+ raise Exception("TumblingWindowAssigner parameters must satisfy abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+ self._is_event_time = is_event_time
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if self._is_event_time is False:
+ current_processing_time = context.get_current_processing_time()
+ start = TimeWindow.get_window_start_with_offset(current_processing_time, self._offset,
+ self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ if timestamp > MIN_LONG_VALUE:
+ start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ raise Exception("Record has MIN_LONG_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ if self._is_event_time is True:
+ return EventTimeTrigger()
+ else:
+ return ProcessingTimeTrigger()
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return self._is_event_time
+
+ def __repr__(self):
+ return "TumblingWindowAssigner(%s,%s,%s)" % (self._size, self._offset, self.is_event_time)
+
+
+class CountTumblingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into fixed-size windows based on the count number
+ of the elements. Windows cannot overlap.
+ """
+
+ def __init__(self, window_size: int):
+ """
+ Windows this KeyedStream into tumbling count windows.
+ :param window_size: The size of the windows in number of elements.
+ """
+ self._window_size = window_size
+ self._counter_state_descriptor = ReducingStateDescriptor(
+ "assigner_counter", lambda a, b: a + b, Types.LONG())
+
+ @staticmethod
+ def of(window_size: int):
+ return CountTumblingWindowAssigner(window_size)
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ counter = context.get_runtime_context().get_reducing_state(
+ self._counter_state_descriptor)
+ if counter.get() is None:
+ counter.add(0)
+ result = [CountWindow(counter.get() // self._window_size)]
+ counter.add(1)
+ return result
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self) -> str:
+ return "CountTumblingWindowAssigner(%s)" % (self._window_size)
+
+
+class SlidingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the current system time
+ of the machine the operation is running on. Windows can possibly overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingWindowAssigner(60000, 10000, 0, False))
+
+ A WindowAssigner that windows elements into sliding windows based on the timestamp of the
+ elements. Windows can possibly overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingWindowAssigner(60000, 10000, 0, True))
+ """
+
+ def __init__(self, size: int, slide: int, offset: int, is_event_time: bool):
+ if abs(offset) >= slide or size <= 0:
+ raise Exception("SlidingWindowAssigner parameters must satisfy "
+ + "abs(offset) < slide and size > 0")
+
+ self._size = size
+ self._slide = slide
+ self._offset = offset
+ self._is_event_time = is_event_time
+ self._pane_size = math.gcd(size, slide)
+ self._num_panes_per_window = size // self._pane_size
+
+ def assign_windows(
+ self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ if self._is_event_time is False:
+ current_processing_time = context.get_current_processing_time()
+ last_start = TimeWindow.get_window_start_with_offset(
+ current_processing_time, self._offset, self._slide)
+ windows = [TimeWindow(start, start + self._size)
+ for start in range(last_start,
+ current_processing_time - self._size, -self._slide)]
+ return windows
+ else:
+ if timestamp > MIN_LONG_VALUE:
+ last_start = TimeWindow.get_window_start_with_offset(timestamp,
+ self._offset, self._slide)
+ windows = [TimeWindow(start, start + self._size)
+ for start in range(last_start, timestamp - self._size, -self._slide)]
+ return windows
+ else:
+ raise Exception("Record has MIN_LONG_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', "
+ "or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ if self._is_event_time is True:
+ return EventTimeTrigger()
+ else:
+ return ProcessingTimeTrigger()
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return self._is_event_time
+
+ def __repr__(self) -> str:
+ return "SlidingWindowAssigner(%s, %s, %s, %s)" % (
+ self._size, self._slide, self._offset, self._is_event_time)
+
+
+class CountSlidingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the count number of
+ the elements. Windows can possibly overlap.
+ """
+
+ def __init__(self, window_size: int, window_slide: int):
+ """
+ Windows this KeyedStream into sliding count windows.
+ :param window_size: The size of the windows in number of elements.
+ :param window_slide: The slide interval in number of elements.
+ """
+ self._window_size = window_size
+ self._window_slide = window_slide
+ self._count = None # type: ValueState
+ self._counter_state_descriptor = ReducingStateDescriptor(
+ "slide-count-assigner", lambda a, b: a + b, Types.LONG())
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ count_descriptor = ValueStateDescriptor('slide-count-assigner', Types.LONG())
+ self._count = context.get_runtime_context().get_state(count_descriptor)
+ count_value = self._count.value()
+ if count_value is None:
+ current_count = 0
+ else:
+ current_count = count_value
+ self._count.update(current_count + 1)
+ last_id = current_count // self._window_slide
+ last_start = last_id * self._window_slide
+ last_end = last_start + self._window_size - 1
+ windows = []
+ while last_id >= 0 and last_start <= current_count <= last_end:
+ if last_start <= current_count <= last_end:
+ windows.append(CountWindow(last_id))
+ last_id -= 1
+ last_start -= self._window_slide
+ last_end -= self._window_slide
+ return windows
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self):
+ return "CountSlidingWindowAssigner(%s, %s)" % (self._window_size, self._window_slide)
+
+
+class SessionWindowAssigner(MergingWindowAssigner[T, TimeWindow]):
+ """
+ WindowAssigner that windows elements into sessions based on the timestamp. Windows cannot
+ overlap.
+ """
+
+ def __init__(self, session_gap: int, is_event_time: bool):
+ if session_gap <= 0:
+ raise Exception("SessionWindowAssigner parameters must satisfy 0 < size")
+
+ self._session_gap = session_gap
+ self._is_event_time = is_event_time
+
+ def merge_windows(self,
+ windows: Iterable[W],
+ callback: 'MergingWindowAssigner.MergeCallback[W]') -> None:
+ window_list = [w for w in windows]
+ window_list.sort()
+ for i in range(1, len(window_list)):
+ if window_list[i - 1].end > window_list[i].start:
+ callback.merge([window_list[i - 1], window_list[i]],
+ TimeWindow(window_list[i - 1].start, window_list[i].end))
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ if self._is_event_time is False:
+ timestamp = context.get_current_processing_time()
+
+ return [TimeWindow(timestamp, timestamp + self._session_gap)]
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ if self._is_event_time is True:
+ return EventTimeTrigger()
+ else:
+ return ProcessingTimeTrigger()
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
Review comment:
```suggestion
def get_window_serializer(self) -> TypeSerializer[TimeWindow]:
```
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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## CI report:
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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## CI report:
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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* 14ae6d15c25f454fe7675bf7b0e129834b99ae37 Azure: [CANCELED](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32708)
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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* 1240d4313817b4ae0bfca21090eb6f1cbd40f78e Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32684)
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
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## CI report:
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* 1240d4313817b4ae0bfca21090eb6f1cbd40f78e Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32684)
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## CI report:
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## CI report:
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* 8e46affb05226eeed6b8eb20df971445139654a8 Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32535)
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* 1240d4313817b4ae0bfca21090eb6f1cbd40f78e Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32684)
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Posted by GitBox <gi...@apache.org>.
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## CI report:
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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## CI report:
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] dianfu commented on a change in pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
dianfu commented on a change in pull request #18957:
URL: https://github.com/apache/flink/pull/18957#discussion_r824431485
##########
File path: flink-python/pyflink/datastream/data_stream.py
##########
@@ -38,7 +38,7 @@
from pyflink.datastream.state import ValueStateDescriptor, ValueState, ListStateDescriptor
from pyflink.datastream.utils import convert_to_python_obj
from pyflink.java_gateway import get_gateway
-
+from pyflink.datastream.window import CountTumblingWindowAssigner, CountSlidingWindowAssigner
Review comment:
Merge it with the import statement in line 24
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +543,795 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class SessionWindowTimeGapExtractor(ABC):
+ """
+ A {@code SessionWindowTimeGapExtractor} extracts session time gaps for Dynamic Session Window
+ Assigners.
+ :param <ABC> The type of elements that this {@code SessionWindowTimeGapExtractor} can extract
+ session time gaps from.
+ """
+
+ @abstractmethod
+ def extract(self, element: Any) -> int:
+ """
+ Extracts the session time gap.
+ :param element The input element.
+ :return The session time gap in milliseconds.
+ """
+ pass
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ reduce_state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ reduce_state.add(1)
+ if reduce_state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
Review comment:
correct the indentation
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +543,795 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class SessionWindowTimeGapExtractor(ABC):
+ """
+ A {@code SessionWindowTimeGapExtractor} extracts session time gaps for Dynamic Session Window
+ Assigners.
+ :param <ABC> The type of elements that this {@code SessionWindowTimeGapExtractor} can extract
+ session time gaps from.
Review comment:
correct the indentation
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +543,795 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class SessionWindowTimeGapExtractor(ABC):
+ """
+ A {@code SessionWindowTimeGapExtractor} extracts session time gaps for Dynamic Session Window
+ Assigners.
+ :param <ABC> The type of elements that this {@code SessionWindowTimeGapExtractor} can extract
+ session time gaps from.
+ """
+
+ @abstractmethod
+ def extract(self, element: Any) -> int:
+ """
+ Extracts the session time gap.
+ :param element The input element.
+ :return The session time gap in milliseconds.
+ """
+ pass
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ reduce_state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ reduce_state.add(1)
+ if reduce_state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ reduce_state.clear()
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class CountTumblingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into fixed-size windows based on the count number
+ of the elements. Windows cannot overlap.
+ """
+
+ def __init__(self, window_size: int):
+ """
+ Windows this KeyedStream into tumbling count windows.
+ :param window_size: The size of the windows in number of elements.
+ """
+ self._window_size = window_size
+ self._count_descriptor = ValueStateDescriptor('count-assigner', Types.LONG())
+
+ @staticmethod
+ def of(window_size: int):
+ return CountTumblingWindowAssigner(window_size)
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[CountWindow]:
+ count = context.get_runtime_context().get_state(self._count_descriptor)
+ count_value = count.value()
+ if count_value is None:
+ current_count = 0
+ else:
+ current_count = count_value
+ count.update(current_count + 1)
+ result = [CountWindow(current_count // self._window_size)]
+ return result
+
+ def get_default_trigger(self, env) -> Trigger[T, CountWindow]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[CountWindow]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self) -> str:
+ return "CountTumblingWindowAssigner(%s)" % self._window_size
+
+
+class CountSlidingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the count number of
+ the elements. Windows can possibly overlap.
+ """
+
+ def __init__(self, window_size: int, window_slide: int):
+ """
+ Windows this KeyedStream into sliding count windows.
+ :param window_size: The size of the windows in number of elements.
+ :param window_slide: The slide interval in number of elements.
+ """
+ self._window_size = window_size
+ self._window_slide = window_slide
+ self._count_descriptor = ValueStateDescriptor('slide-count-assigner', Types.LONG())
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[CountWindow]:
+ count = context.get_runtime_context().get_state(self._count_descriptor) # type: ValueState
+ count_value = count.value()
+ if count_value is None:
+ current_count = 0
+ else:
+ current_count = count_value
+ count.update(current_count + 1)
+ last_id = current_count // self._window_slide
+ last_start = last_id * self._window_slide
+ last_end = last_start + self._window_size - 1
+ windows = []
+ while last_id >= 0 and last_start <= current_count <= last_end:
+ if last_start <= current_count <= last_end:
+ windows.append(CountWindow(last_id))
+ last_id -= 1
+ last_start -= self._window_slide
+ last_end -= self._window_slide
+ return windows
+
+ def get_default_trigger(self, env) -> Trigger[T, CountWindow]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[CountWindow]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self):
+ return "CountSlidingWindowAssigner(%s, %s)" % (self._window_size, self._window_slide)
+
+
+class TumblingProcessingTimeWindows(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of
+ the machine the operation is running on. Windows cannot overlap.
+
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING())
+ >>> .window(TumblingProcessingTimeWindows.of(Time.minutes(1), Time.seconds(10)))
+ """
+ def __init__(self, size: int, offset: int):
+ if abs(offset) >= size:
+ raise Exception("TumblingProcessingTimeWindows parameters must satisfy "
+ "abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ current_processing_time = context.get_current_processing_time()
+ start = TimeWindow.get_window_start_with_offset(current_processing_time, self._offset,
+ self._size)
+ return [TimeWindow(start, start + self._size)]
+
+ def get_default_trigger(self, env) -> Trigger[T, TimeWindow]:
+ return ProcessingTimeTrigger()
+
+ @staticmethod
+ def of(size: Time, offset=None):
Review comment:
```suggestion
def of(size: Time, offset: Time = None):
```
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +543,795 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class SessionWindowTimeGapExtractor(ABC):
+ """
+ A {@code SessionWindowTimeGapExtractor} extracts session time gaps for Dynamic Session Window
+ Assigners.
+ :param <ABC> The type of elements that this {@code SessionWindowTimeGapExtractor} can extract
+ session time gaps from.
+ """
+
+ @abstractmethod
+ def extract(self, element: Any) -> int:
+ """
+ Extracts the session time gap.
+ :param element The input element.
+ :return The session time gap in milliseconds.
+ """
+ pass
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ reduce_state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ reduce_state.add(1)
+ if reduce_state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ reduce_state.clear()
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class CountTumblingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into fixed-size windows based on the count number
+ of the elements. Windows cannot overlap.
+ """
+
+ def __init__(self, window_size: int):
+ """
+ Windows this KeyedStream into tumbling count windows.
+ :param window_size: The size of the windows in number of elements.
+ """
+ self._window_size = window_size
+ self._count_descriptor = ValueStateDescriptor('count-assigner', Types.LONG())
Review comment:
```suggestion
self._count_descriptor = ValueStateDescriptor('tumble-count-assigner', Types.LONG())
```
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +543,795 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class SessionWindowTimeGapExtractor(ABC):
+ """
+ A {@code SessionWindowTimeGapExtractor} extracts session time gaps for Dynamic Session Window
+ Assigners.
+ :param <ABC> The type of elements that this {@code SessionWindowTimeGapExtractor} can extract
+ session time gaps from.
+ """
+
+ @abstractmethod
+ def extract(self, element: Any) -> int:
+ """
+ Extracts the session time gap.
+ :param element The input element.
+ :return The session time gap in milliseconds.
+ """
+ pass
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ reduce_state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ reduce_state.add(1)
+ if reduce_state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ reduce_state.clear()
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class CountTumblingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into fixed-size windows based on the count number
+ of the elements. Windows cannot overlap.
+ """
+
+ def __init__(self, window_size: int):
+ """
+ Windows this KeyedStream into tumbling count windows.
+ :param window_size: The size of the windows in number of elements.
+ """
+ self._window_size = window_size
+ self._count_descriptor = ValueStateDescriptor('count-assigner', Types.LONG())
+
+ @staticmethod
+ def of(window_size: int):
+ return CountTumblingWindowAssigner(window_size)
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[CountWindow]:
+ count = context.get_runtime_context().get_state(self._count_descriptor)
+ count_value = count.value()
+ if count_value is None:
+ current_count = 0
+ else:
+ current_count = count_value
+ count.update(current_count + 1)
+ result = [CountWindow(current_count // self._window_size)]
+ return result
+
+ def get_default_trigger(self, env) -> Trigger[T, CountWindow]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[CountWindow]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self) -> str:
+ return "CountTumblingWindowAssigner(%s)" % self._window_size
+
+
+class CountSlidingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the count number of
+ the elements. Windows can possibly overlap.
+ """
+
+ def __init__(self, window_size: int, window_slide: int):
+ """
+ Windows this KeyedStream into sliding count windows.
+ :param window_size: The size of the windows in number of elements.
+ :param window_slide: The slide interval in number of elements.
+ """
+ self._window_size = window_size
+ self._window_slide = window_slide
+ self._count_descriptor = ValueStateDescriptor('slide-count-assigner', Types.LONG())
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[CountWindow]:
+ count = context.get_runtime_context().get_state(self._count_descriptor) # type: ValueState
+ count_value = count.value()
+ if count_value is None:
+ current_count = 0
+ else:
+ current_count = count_value
+ count.update(current_count + 1)
+ last_id = current_count // self._window_slide
+ last_start = last_id * self._window_slide
+ last_end = last_start + self._window_size - 1
+ windows = []
+ while last_id >= 0 and last_start <= current_count <= last_end:
+ if last_start <= current_count <= last_end:
+ windows.append(CountWindow(last_id))
+ last_id -= 1
+ last_start -= self._window_slide
+ last_end -= self._window_slide
+ return windows
+
+ def get_default_trigger(self, env) -> Trigger[T, CountWindow]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[CountWindow]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self):
+ return "CountSlidingWindowAssigner(%s, %s)" % (self._window_size, self._window_slide)
+
+
+class TumblingProcessingTimeWindows(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of
+ the machine the operation is running on. Windows cannot overlap.
+
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING())
+ >>> .window(TumblingProcessingTimeWindows.of(Time.minutes(1), Time.seconds(10)))
+ """
+ def __init__(self, size: int, offset: int):
+ if abs(offset) >= size:
+ raise Exception("TumblingProcessingTimeWindows parameters must satisfy "
+ "abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ current_processing_time = context.get_current_processing_time()
+ start = TimeWindow.get_window_start_with_offset(current_processing_time, self._offset,
+ self._size)
+ return [TimeWindow(start, start + self._size)]
+
+ def get_default_trigger(self, env) -> Trigger[T, TimeWindow]:
+ return ProcessingTimeTrigger()
+
+ @staticmethod
+ def of(size: Time, offset=None):
+ """
+ Creates a new {@code TumblingProcessingTimeWindows} {@link WindowAssigner} that assigns
+ elements to time windows based on the element timestamp and offset.
+
+ For example, if you want window a stream by hour,but window begins at the 15th minutes of
+ each hour, you can use {@code of(Time.hours(1),Time.minutes(15))},then you will get time
+ windows start at 0:15:00,1:15:00,2:15:00,etc.
+
+ Rather than that,if you are living in somewhere which is not using UTC±00:00 time, such as
+ China which is using UTC+08:00,and you want a time window with size of one day, and window
+ begins at every 00:00:00 of local time,you may use {@code of(Time.days(1),Time.hours(-8))}.
+ The parameter of offset is {@code Time.hours(-8))} since UTC+08:00 is 8 hours earlier than
+ UTC time.
+
+ :param size The size of the generated windows.
+ :param offset The offset which window start would be shifted by.
+ :return The time policy.
+ """
+ if offset is None:
+ return TumblingProcessingTimeWindows(size.to_milliseconds(), 0)
+ else:
+ return TumblingProcessingTimeWindows(size.to_milliseconds(), offset.to_milliseconds())
+
+ @property
+ def get_window_serializer(self) -> TypeSerializer[TimeWindow]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self):
+ return "TumblingProcessingTimeWindows(%s, %s)" % (self._size, self._offset)
+
+
+class TumblingEventTimeWindows(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the timestamp of the
+ elements. Windows cannot overlap.
+
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingEventTimeWindows.of(Time.minutes(1)))
+ """
+
+ def __init__(self, size: int, offset: int):
+ if abs(offset) >= size:
+ raise Exception("TumblingEventTimeWindows parameters must satisfy abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if timestamp > MIN_LONG_VALUE:
+ start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ raise Exception("Record has jvm.java.lang.Long.MIN_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', "
+ + "or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, TimeWindow]:
+ return EventTimeTrigger()
+
+ @staticmethod
+ def of(size: Time, offset=None):
+ """
+ Creates a new TumblingEventTimeWindows WindowAssigner that assigns elements
+ to time windows based on the element timestamp, offset and a staggering offset, depending on
+ the staggering policy.
+
+ :param size The size of the generated windows.
+ :param offset The globalOffset which window start would be shifted by.
+ """
+ if offset is None:
+ return TumblingEventTimeWindows(size.to_milliseconds(), 0)
+ else:
+ return TumblingEventTimeWindows(size.to_milliseconds(), offset.to_milliseconds())
+
+ def get_window_serializer(self) -> TypeSerializer[TimeWindow]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return True
+
+ def __repr__(self):
+ return "TumblingEventTimeWindows(%s, %s)" % (self._size, self._offset)
+
+
+class SlidingProcessingTimeWindows(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the current system
+ time of the machine the operation is running on. Windows can possibly overlap.
+
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingProcessingTimeWindows.of(Time.minutes(1), Time.seconds(10)))
+ """
+
+ def __init__(self, size: int, slide: int, offset: int):
+ if abs(offset) >= slide or size <= 0:
+ raise Exception("SlidingProcessingTimeWindows parameters must satisfy "
+ + "abs(offset) < slide and size > 0")
+
+ self._size = size
+ self._slide = slide
+ self._offset = offset
+ self._pane_size = math.gcd(size, slide)
+ self._num_panes_per_window = size // self._pane_size
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[TimeWindow]:
+ current_processing_time = context.get_current_processing_time()
+ last_start = TimeWindow.get_window_start_with_offset(
+ current_processing_time, self._offset, self._slide)
+ windows = [TimeWindow(start, start + self._size)
+ for start in range(last_start,
+ current_processing_time - self._size, -self._slide)]
+ return windows
+
+ def get_default_trigger(self, env) -> Trigger[T, TimeWindow]:
+ return ProcessingTimeTrigger()
+
+ @staticmethod
+ def of(size: Time, slide: Time, offset=None):
+ """
+ Creates a new {@code SlidingProcessingTimeWindows} {@link WindowAssigner} that assigns
+ elements to time windows based on the element timestamp and offset.
+
+ For example, if you want window a stream by hour,but window begins at the 15th minutes of
+ each hour, you can use {@code of(Time.hours(1),Time.minutes(15))},then you will get time
+ windows start at 0:15:00,1:15:00,2:15:00,etc.
+
+ Rather than that,if you are living in somewhere which is not using UTC±00:00 time, such as
+ China which is using UTC+08:00,and you want a time window with size of one day, and window
+ begins at every 00:00:00 of local time,you may use {@code of(Time.days(1),Time.hours(-8))}.
+ The parameter of offset is {@code Time.hours(-8))} since UTC+08:00 is 8 hours earlier than
+ UTC time.
+
+ :param size The size of the generated windows.
+ :param slide The slide interval of the generated windows.
+ :param offset The offset which window start would be shifted by.
+ :return The time policy.
+ """
+ if offset is None:
+ return SlidingProcessingTimeWindows(size.to_milliseconds(), slide.to_milliseconds(), 0)
+ else:
+ return SlidingProcessingTimeWindows(size.to_milliseconds(), slide.to_milliseconds(),
+ offset.to_milliseconds())
+
+ def get_window_serializer(self) -> TypeSerializer[TimeWindow]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self) -> str:
+ return "SlidingProcessingTimeWindows(%s, %s, %s)" % (self._size, self._slide, self._offset)
+
+
+class SlidingEventTimeWindows(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the timestamp of the
+ elements. Windows can possibly overlap.
+
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingEventTimeWindows.of(Time.minutes(1), Time.seconds(10)))
+ """
+
+ def __init__(self, size: int, slide: int, offset: int):
+ if abs(offset) >= slide or size <= 0:
+ raise Exception("SlidingEventTimeWindows parameters must satisfy "
+ + "abs(offset) < slide and size > 0")
+
+ self._size = size
+ self._slide = slide
+ self._offset = offset
+ self._pane_size = math.gcd(size, slide)
+ self._num_panes_per_window = size // self._pane_size
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[TimeWindow]:
+ if timestamp > MIN_LONG_VALUE:
+ last_start = TimeWindow.get_window_start_with_offset(timestamp,
+ self._offset, self._slide)
+ windows = [TimeWindow(start, start + self._size)
+ for start in range(last_start, timestamp - self._size, -self._slide)]
+ return windows
+ else:
+ raise Exception("Record has jvm.java.lang.Long.MIN_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', "
+ "or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, TimeWindow]:
+ return EventTimeTrigger()
+
+ @staticmethod
+ def of(size: Time, slide: Time, offset=None):
Review comment:
ditto
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +543,795 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class SessionWindowTimeGapExtractor(ABC):
+ """
+ A {@code SessionWindowTimeGapExtractor} extracts session time gaps for Dynamic Session Window
+ Assigners.
+ :param <ABC> The type of elements that this {@code SessionWindowTimeGapExtractor} can extract
+ session time gaps from.
+ """
+
+ @abstractmethod
+ def extract(self, element: Any) -> int:
+ """
+ Extracts the session time gap.
+ :param element The input element.
+ :return The session time gap in milliseconds.
+ """
+ pass
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ reduce_state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ reduce_state.add(1)
+ if reduce_state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ reduce_state.clear()
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class CountTumblingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into fixed-size windows based on the count number
+ of the elements. Windows cannot overlap.
+ """
+
+ def __init__(self, window_size: int):
+ """
+ Windows this KeyedStream into tumbling count windows.
+ :param window_size: The size of the windows in number of elements.
+ """
+ self._window_size = window_size
+ self._count_descriptor = ValueStateDescriptor('count-assigner', Types.LONG())
+
+ @staticmethod
+ def of(window_size: int):
+ return CountTumblingWindowAssigner(window_size)
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[CountWindow]:
+ count = context.get_runtime_context().get_state(self._count_descriptor)
+ count_value = count.value()
+ if count_value is None:
+ current_count = 0
+ else:
+ current_count = count_value
+ count.update(current_count + 1)
+ result = [CountWindow(current_count // self._window_size)]
+ return result
+
+ def get_default_trigger(self, env) -> Trigger[T, CountWindow]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[CountWindow]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self) -> str:
+ return "CountTumblingWindowAssigner(%s)" % self._window_size
+
+
+class CountSlidingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the count number of
+ the elements. Windows can possibly overlap.
+ """
+
+ def __init__(self, window_size: int, window_slide: int):
+ """
+ Windows this KeyedStream into sliding count windows.
+ :param window_size: The size of the windows in number of elements.
+ :param window_slide: The slide interval in number of elements.
+ """
+ self._window_size = window_size
+ self._window_slide = window_slide
+ self._count_descriptor = ValueStateDescriptor('slide-count-assigner', Types.LONG())
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[CountWindow]:
+ count = context.get_runtime_context().get_state(self._count_descriptor) # type: ValueState
+ count_value = count.value()
+ if count_value is None:
+ current_count = 0
+ else:
+ current_count = count_value
+ count.update(current_count + 1)
+ last_id = current_count // self._window_slide
+ last_start = last_id * self._window_slide
+ last_end = last_start + self._window_size - 1
+ windows = []
+ while last_id >= 0 and last_start <= current_count <= last_end:
+ if last_start <= current_count <= last_end:
+ windows.append(CountWindow(last_id))
+ last_id -= 1
+ last_start -= self._window_slide
+ last_end -= self._window_slide
+ return windows
+
+ def get_default_trigger(self, env) -> Trigger[T, CountWindow]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[CountWindow]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self):
+ return "CountSlidingWindowAssigner(%s, %s)" % (self._window_size, self._window_slide)
+
+
+class TumblingProcessingTimeWindows(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of
+ the machine the operation is running on. Windows cannot overlap.
+
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING())
+ >>> .window(TumblingProcessingTimeWindows.of(Time.minutes(1), Time.seconds(10)))
+ """
+ def __init__(self, size: int, offset: int):
+ if abs(offset) >= size:
+ raise Exception("TumblingProcessingTimeWindows parameters must satisfy "
+ "abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ current_processing_time = context.get_current_processing_time()
+ start = TimeWindow.get_window_start_with_offset(current_processing_time, self._offset,
+ self._size)
+ return [TimeWindow(start, start + self._size)]
+
+ def get_default_trigger(self, env) -> Trigger[T, TimeWindow]:
+ return ProcessingTimeTrigger()
+
+ @staticmethod
+ def of(size: Time, offset=None):
+ """
+ Creates a new {@code TumblingProcessingTimeWindows} {@link WindowAssigner} that assigns
+ elements to time windows based on the element timestamp and offset.
+
+ For example, if you want window a stream by hour,but window begins at the 15th minutes of
+ each hour, you can use {@code of(Time.hours(1),Time.minutes(15))},then you will get time
+ windows start at 0:15:00,1:15:00,2:15:00,etc.
+
+ Rather than that,if you are living in somewhere which is not using UTC±00:00 time, such as
+ China which is using UTC+08:00,and you want a time window with size of one day, and window
+ begins at every 00:00:00 of local time,you may use {@code of(Time.days(1),Time.hours(-8))}.
+ The parameter of offset is {@code Time.hours(-8))} since UTC+08:00 is 8 hours earlier than
+ UTC time.
+
+ :param size The size of the generated windows.
+ :param offset The offset which window start would be shifted by.
+ :return The time policy.
+ """
+ if offset is None:
+ return TumblingProcessingTimeWindows(size.to_milliseconds(), 0)
+ else:
+ return TumblingProcessingTimeWindows(size.to_milliseconds(), offset.to_milliseconds())
+
+ @property
+ def get_window_serializer(self) -> TypeSerializer[TimeWindow]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self):
+ return "TumblingProcessingTimeWindows(%s, %s)" % (self._size, self._offset)
+
+
+class TumblingEventTimeWindows(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the timestamp of the
+ elements. Windows cannot overlap.
+
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingEventTimeWindows.of(Time.minutes(1)))
+ """
+
+ def __init__(self, size: int, offset: int):
+ if abs(offset) >= size:
+ raise Exception("TumblingEventTimeWindows parameters must satisfy abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if timestamp > MIN_LONG_VALUE:
+ start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ raise Exception("Record has jvm.java.lang.Long.MIN_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', "
+ + "or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, TimeWindow]:
+ return EventTimeTrigger()
+
+ @staticmethod
+ def of(size: Time, offset=None):
+ """
+ Creates a new TumblingEventTimeWindows WindowAssigner that assigns elements
+ to time windows based on the element timestamp, offset and a staggering offset, depending on
+ the staggering policy.
+
+ :param size The size of the generated windows.
+ :param offset The globalOffset which window start would be shifted by.
+ """
+ if offset is None:
+ return TumblingEventTimeWindows(size.to_milliseconds(), 0)
+ else:
+ return TumblingEventTimeWindows(size.to_milliseconds(), offset.to_milliseconds())
+
+ def get_window_serializer(self) -> TypeSerializer[TimeWindow]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return True
+
+ def __repr__(self):
+ return "TumblingEventTimeWindows(%s, %s)" % (self._size, self._offset)
+
+
+class SlidingProcessingTimeWindows(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the current system
+ time of the machine the operation is running on. Windows can possibly overlap.
+
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingProcessingTimeWindows.of(Time.minutes(1), Time.seconds(10)))
+ """
+
+ def __init__(self, size: int, slide: int, offset: int):
+ if abs(offset) >= slide or size <= 0:
+ raise Exception("SlidingProcessingTimeWindows parameters must satisfy "
+ + "abs(offset) < slide and size > 0")
+
+ self._size = size
+ self._slide = slide
+ self._offset = offset
+ self._pane_size = math.gcd(size, slide)
+ self._num_panes_per_window = size // self._pane_size
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[TimeWindow]:
+ current_processing_time = context.get_current_processing_time()
+ last_start = TimeWindow.get_window_start_with_offset(
+ current_processing_time, self._offset, self._slide)
+ windows = [TimeWindow(start, start + self._size)
+ for start in range(last_start,
+ current_processing_time - self._size, -self._slide)]
+ return windows
+
+ def get_default_trigger(self, env) -> Trigger[T, TimeWindow]:
+ return ProcessingTimeTrigger()
+
+ @staticmethod
+ def of(size: Time, slide: Time, offset=None):
+ """
+ Creates a new {@code SlidingProcessingTimeWindows} {@link WindowAssigner} that assigns
+ elements to time windows based on the element timestamp and offset.
+
+ For example, if you want window a stream by hour,but window begins at the 15th minutes of
+ each hour, you can use {@code of(Time.hours(1),Time.minutes(15))},then you will get time
+ windows start at 0:15:00,1:15:00,2:15:00,etc.
+
+ Rather than that,if you are living in somewhere which is not using UTC±00:00 time, such as
+ China which is using UTC+08:00,and you want a time window with size of one day, and window
+ begins at every 00:00:00 of local time,you may use {@code of(Time.days(1),Time.hours(-8))}.
+ The parameter of offset is {@code Time.hours(-8))} since UTC+08:00 is 8 hours earlier than
+ UTC time.
+
+ :param size The size of the generated windows.
+ :param slide The slide interval of the generated windows.
+ :param offset The offset which window start would be shifted by.
+ :return The time policy.
+ """
+ if offset is None:
+ return SlidingProcessingTimeWindows(size.to_milliseconds(), slide.to_milliseconds(), 0)
+ else:
+ return SlidingProcessingTimeWindows(size.to_milliseconds(), slide.to_milliseconds(),
+ offset.to_milliseconds())
+
+ def get_window_serializer(self) -> TypeSerializer[TimeWindow]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self) -> str:
+ return "SlidingProcessingTimeWindows(%s, %s, %s)" % (self._size, self._slide, self._offset)
+
+
+class SlidingEventTimeWindows(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the timestamp of the
+ elements. Windows can possibly overlap.
+
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingEventTimeWindows.of(Time.minutes(1), Time.seconds(10)))
+ """
+
+ def __init__(self, size: int, slide: int, offset: int):
+ if abs(offset) >= slide or size <= 0:
+ raise Exception("SlidingEventTimeWindows parameters must satisfy "
+ + "abs(offset) < slide and size > 0")
+
+ self._size = size
+ self._slide = slide
+ self._offset = offset
+ self._pane_size = math.gcd(size, slide)
+ self._num_panes_per_window = size // self._pane_size
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[TimeWindow]:
+ if timestamp > MIN_LONG_VALUE:
+ last_start = TimeWindow.get_window_start_with_offset(timestamp,
+ self._offset, self._slide)
+ windows = [TimeWindow(start, start + self._size)
+ for start in range(last_start, timestamp - self._size, -self._slide)]
+ return windows
+ else:
+ raise Exception("Record has jvm.java.lang.Long.MIN_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', "
+ "or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, TimeWindow]:
+ return EventTimeTrigger()
+
+ @staticmethod
+ def of(size: Time, slide: Time, offset=None):
+ """
+ Creates a new {@code SlidingEventTimeWindows} {@link WindowAssigner} that assigns elements
+ to time windows based on the element timestamp and offset.
+
+ For example, if you want window a stream by hour,but window begins at the 15th minutes of
+ each hour, you can use {@code of(Time.hours(1),Time.minutes(15))},then you will get time
+ windows start at 0:15:00,1:15:00,2:15:00,etc.
+
+ Rather than that,if you are living in somewhere which is not using UTC±00:00 time, such as
+ China which is using UTC+08:00,and you want a time window with size of one day, and window
+ begins at every 00:00:00 of local time,you may use {@code of(Time.days(1),Time.hours(-8))}.
+ The parameter of offset is {@code Time.hours(-8))} since UTC+08:00 is 8 hours earlier than
+ UTC time.
+
+ :param size The size of the generated windows.
+ :param slide The slide interval of the generated windows.
+ :param offset The offset which window start would be shifted by.
+ :return The time policy.
+ """
+ if offset is None:
+ return SlidingEventTimeWindows(size.to_milliseconds(), slide.to_milliseconds(), 0)
+ else:
+ return SlidingEventTimeWindows(size.to_milliseconds(), slide.to_milliseconds(),
+ offset.to_milliseconds())
+
+ def get_window_serializer(self) -> TypeSerializer[TimeWindow]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return True
+
+ def __repr__(self) -> str:
+ return "SlidingEventTimeWindows(%s, %s, %s)" % (self._size, self._slide, self._offset)
+
+
+class ProcessingTimeSessionWindows(MergingWindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into sessions based on the current processing
+ time. Windows cannot overlap.
+
+ For example, the processing interval is set to 1 minutes:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(ProcessingTimeSessionWindows.with_gap(Time.minutes(1)))
+ """
+
+ def __init__(self, session_gap: int):
+ if session_gap <= 0:
+ raise Exception("SessionWindowAssigner parameters must satisfy 0 < size")
+
+ self._session_gap = session_gap
+
+ def merge_windows(self,
+ windows: Iterable[TimeWindow],
+ callback: 'MergingWindowAssigner.MergeCallback[TimeWindow]') -> None:
+ window_list = [w for w in windows]
+ window_list.sort()
+ for i in range(1, len(window_list)):
+ if window_list[i - 1].end > window_list[i].start:
+ callback.merge([window_list[i - 1], window_list[i]],
+ TimeWindow(window_list[i - 1].start, window_list[i].end))
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[TimeWindow]:
+ timestamp = context.get_current_processing_time()
+
+ return [TimeWindow(timestamp, timestamp + self._session_gap)]
+
+ def get_default_trigger(self, env) -> Trigger[T, TimeWindow]:
+ return ProcessingTimeTrigger()
+
+ @staticmethod
+ def with_gap(size: Time):
+ """
+ Creates a new SessionWindows WindowAssigner that assigns elements to sessions based on
+ the element timestamp.
+ :param size: The session timeout, i.e. the time gap between sessions
+ :return: The policy.
+ """
+ return ProcessingTimeSessionWindows(size.to_milliseconds())
+
+ @staticmethod
+ def with_dynamic_gap(session_window_time_gap_extractor: SessionWindowTimeGapExtractor):
+ """
+ Creates a new SessionWindows WindowAssigner that assigns elements to sessions based on the
+ element timestamp.
+ :param session_window_time_gap_extractor: The extractor to use to extract the time gap
+ from the input elements
+ :return: The policy.
+ """
+ return DynamicProcessingTimeSessionWindows(session_window_time_gap_extractor)
+
+ def get_window_serializer(self) -> TypeSerializer[TimeWindow]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self):
+ return "ProcessingTimeSessionWindows(%s, %s)" % self._session_gap
+
+
+class EventTimeSessionWindows(MergingWindowAssigner[T, TimeWindow]):
+ """
+ A {@link WindowAssigner} that windows elements into sessions based on the timestamp of the
+ elements. Windows cannot overlap.
+
+ For example, Set the timestamp of the element to 1 minutes:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(EventTimeSessionWindows.with_gap(Time.minutes(1)))
+ """
+
+ def __init__(self, session_gap: int):
+ if session_gap <= 0:
+ raise Exception("SessionWindowAssigner parameters must satisfy 0 < size")
Review comment:
```suggestion
raise Exception("EventTimeSessionWindows parameters must satisfy 0 < size")
```
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +543,795 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class SessionWindowTimeGapExtractor(ABC):
+ """
+ A {@code SessionWindowTimeGapExtractor} extracts session time gaps for Dynamic Session Window
+ Assigners.
+ :param <ABC> The type of elements that this {@code SessionWindowTimeGapExtractor} can extract
+ session time gaps from.
+ """
+
+ @abstractmethod
+ def extract(self, element: Any) -> int:
+ """
+ Extracts the session time gap.
+ :param element The input element.
+ :return The session time gap in milliseconds.
+ """
+ pass
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ reduce_state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ reduce_state.add(1)
+ if reduce_state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ reduce_state.clear()
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class CountTumblingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into fixed-size windows based on the count number
+ of the elements. Windows cannot overlap.
+ """
+
+ def __init__(self, window_size: int):
+ """
+ Windows this KeyedStream into tumbling count windows.
+ :param window_size: The size of the windows in number of elements.
+ """
+ self._window_size = window_size
+ self._count_descriptor = ValueStateDescriptor('count-assigner', Types.LONG())
+
+ @staticmethod
+ def of(window_size: int):
+ return CountTumblingWindowAssigner(window_size)
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[CountWindow]:
+ count = context.get_runtime_context().get_state(self._count_descriptor)
+ count_value = count.value()
+ if count_value is None:
+ current_count = 0
+ else:
+ current_count = count_value
+ count.update(current_count + 1)
+ result = [CountWindow(current_count // self._window_size)]
+ return result
+
+ def get_default_trigger(self, env) -> Trigger[T, CountWindow]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[CountWindow]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self) -> str:
+ return "CountTumblingWindowAssigner(%s)" % self._window_size
+
+
+class CountSlidingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the count number of
+ the elements. Windows can possibly overlap.
+ """
+
+ def __init__(self, window_size: int, window_slide: int):
+ """
+ Windows this KeyedStream into sliding count windows.
+ :param window_size: The size of the windows in number of elements.
+ :param window_slide: The slide interval in number of elements.
+ """
+ self._window_size = window_size
+ self._window_slide = window_slide
+ self._count_descriptor = ValueStateDescriptor('slide-count-assigner', Types.LONG())
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[CountWindow]:
+ count = context.get_runtime_context().get_state(self._count_descriptor) # type: ValueState
+ count_value = count.value()
+ if count_value is None:
+ current_count = 0
+ else:
+ current_count = count_value
+ count.update(current_count + 1)
+ last_id = current_count // self._window_slide
+ last_start = last_id * self._window_slide
+ last_end = last_start + self._window_size - 1
+ windows = []
+ while last_id >= 0 and last_start <= current_count <= last_end:
+ if last_start <= current_count <= last_end:
+ windows.append(CountWindow(last_id))
+ last_id -= 1
+ last_start -= self._window_slide
+ last_end -= self._window_slide
+ return windows
+
+ def get_default_trigger(self, env) -> Trigger[T, CountWindow]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[CountWindow]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self):
+ return "CountSlidingWindowAssigner(%s, %s)" % (self._window_size, self._window_slide)
+
+
+class TumblingProcessingTimeWindows(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of
+ the machine the operation is running on. Windows cannot overlap.
+
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING())
+ >>> .window(TumblingProcessingTimeWindows.of(Time.minutes(1), Time.seconds(10)))
+ """
+ def __init__(self, size: int, offset: int):
+ if abs(offset) >= size:
+ raise Exception("TumblingProcessingTimeWindows parameters must satisfy "
+ "abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ current_processing_time = context.get_current_processing_time()
+ start = TimeWindow.get_window_start_with_offset(current_processing_time, self._offset,
+ self._size)
+ return [TimeWindow(start, start + self._size)]
+
+ def get_default_trigger(self, env) -> Trigger[T, TimeWindow]:
+ return ProcessingTimeTrigger()
+
+ @staticmethod
+ def of(size: Time, offset=None):
+ """
+ Creates a new {@code TumblingProcessingTimeWindows} {@link WindowAssigner} that assigns
+ elements to time windows based on the element timestamp and offset.
+
+ For example, if you want window a stream by hour,but window begins at the 15th minutes of
+ each hour, you can use {@code of(Time.hours(1),Time.minutes(15))},then you will get time
+ windows start at 0:15:00,1:15:00,2:15:00,etc.
+
+ Rather than that,if you are living in somewhere which is not using UTC±00:00 time, such as
+ China which is using UTC+08:00,and you want a time window with size of one day, and window
+ begins at every 00:00:00 of local time,you may use {@code of(Time.days(1),Time.hours(-8))}.
+ The parameter of offset is {@code Time.hours(-8))} since UTC+08:00 is 8 hours earlier than
+ UTC time.
+
+ :param size The size of the generated windows.
+ :param offset The offset which window start would be shifted by.
+ :return The time policy.
+ """
+ if offset is None:
+ return TumblingProcessingTimeWindows(size.to_milliseconds(), 0)
+ else:
+ return TumblingProcessingTimeWindows(size.to_milliseconds(), offset.to_milliseconds())
+
+ @property
+ def get_window_serializer(self) -> TypeSerializer[TimeWindow]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self):
+ return "TumblingProcessingTimeWindows(%s, %s)" % (self._size, self._offset)
+
+
+class TumblingEventTimeWindows(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the timestamp of the
+ elements. Windows cannot overlap.
+
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingEventTimeWindows.of(Time.minutes(1)))
+ """
+
+ def __init__(self, size: int, offset: int):
+ if abs(offset) >= size:
+ raise Exception("TumblingEventTimeWindows parameters must satisfy abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if timestamp > MIN_LONG_VALUE:
+ start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ raise Exception("Record has jvm.java.lang.Long.MIN_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', "
+ + "or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, TimeWindow]:
+ return EventTimeTrigger()
+
+ @staticmethod
+ def of(size: Time, offset=None):
+ """
+ Creates a new TumblingEventTimeWindows WindowAssigner that assigns elements
+ to time windows based on the element timestamp, offset and a staggering offset, depending on
+ the staggering policy.
+
+ :param size The size of the generated windows.
+ :param offset The globalOffset which window start would be shifted by.
+ """
+ if offset is None:
+ return TumblingEventTimeWindows(size.to_milliseconds(), 0)
+ else:
+ return TumblingEventTimeWindows(size.to_milliseconds(), offset.to_milliseconds())
+
+ def get_window_serializer(self) -> TypeSerializer[TimeWindow]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return True
+
+ def __repr__(self):
+ return "TumblingEventTimeWindows(%s, %s)" % (self._size, self._offset)
+
+
+class SlidingProcessingTimeWindows(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the current system
+ time of the machine the operation is running on. Windows can possibly overlap.
+
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingProcessingTimeWindows.of(Time.minutes(1), Time.seconds(10)))
+ """
+
+ def __init__(self, size: int, slide: int, offset: int):
+ if abs(offset) >= slide or size <= 0:
+ raise Exception("SlidingProcessingTimeWindows parameters must satisfy "
+ + "abs(offset) < slide and size > 0")
+
+ self._size = size
+ self._slide = slide
+ self._offset = offset
+ self._pane_size = math.gcd(size, slide)
+ self._num_panes_per_window = size // self._pane_size
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[TimeWindow]:
+ current_processing_time = context.get_current_processing_time()
+ last_start = TimeWindow.get_window_start_with_offset(
+ current_processing_time, self._offset, self._slide)
+ windows = [TimeWindow(start, start + self._size)
+ for start in range(last_start,
+ current_processing_time - self._size, -self._slide)]
+ return windows
+
+ def get_default_trigger(self, env) -> Trigger[T, TimeWindow]:
+ return ProcessingTimeTrigger()
+
+ @staticmethod
+ def of(size: Time, slide: Time, offset=None):
+ """
+ Creates a new {@code SlidingProcessingTimeWindows} {@link WindowAssigner} that assigns
+ elements to time windows based on the element timestamp and offset.
+
+ For example, if you want window a stream by hour,but window begins at the 15th minutes of
+ each hour, you can use {@code of(Time.hours(1),Time.minutes(15))},then you will get time
+ windows start at 0:15:00,1:15:00,2:15:00,etc.
+
+ Rather than that,if you are living in somewhere which is not using UTC±00:00 time, such as
+ China which is using UTC+08:00,and you want a time window with size of one day, and window
+ begins at every 00:00:00 of local time,you may use {@code of(Time.days(1),Time.hours(-8))}.
+ The parameter of offset is {@code Time.hours(-8))} since UTC+08:00 is 8 hours earlier than
+ UTC time.
+
+ :param size The size of the generated windows.
+ :param slide The slide interval of the generated windows.
+ :param offset The offset which window start would be shifted by.
+ :return The time policy.
+ """
+ if offset is None:
+ return SlidingProcessingTimeWindows(size.to_milliseconds(), slide.to_milliseconds(), 0)
+ else:
+ return SlidingProcessingTimeWindows(size.to_milliseconds(), slide.to_milliseconds(),
+ offset.to_milliseconds())
+
+ def get_window_serializer(self) -> TypeSerializer[TimeWindow]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self) -> str:
+ return "SlidingProcessingTimeWindows(%s, %s, %s)" % (self._size, self._slide, self._offset)
+
+
+class SlidingEventTimeWindows(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the timestamp of the
+ elements. Windows can possibly overlap.
+
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingEventTimeWindows.of(Time.minutes(1), Time.seconds(10)))
+ """
+
+ def __init__(self, size: int, slide: int, offset: int):
+ if abs(offset) >= slide or size <= 0:
+ raise Exception("SlidingEventTimeWindows parameters must satisfy "
+ + "abs(offset) < slide and size > 0")
+
+ self._size = size
+ self._slide = slide
+ self._offset = offset
+ self._pane_size = math.gcd(size, slide)
+ self._num_panes_per_window = size // self._pane_size
Review comment:
where it's used?
##########
File path: flink-python/pyflink/datastream/tests/test_data_stream.py
##########
@@ -1144,6 +1110,114 @@ def __init__(self, name, num):
expected = ['c', 'c', 'b']
self.assert_equals_sorted(expected, results)
+ def test_event_time_tumbling_window(self):
+ self.env.set_parallelism(1)
+ data_stream = self.env.from_collection([
+ ('hi', 1), ('hi', 2), ('hi', 3), ('hi', 4), ('hi', 5), ('hi', 8), ('hi', 9),
+ ('hi', 15)],
+ type_info=Types.TUPLE([Types.STRING(), Types.INT()])) # type: DataStream
+ watermark_strategy = WatermarkStrategy.for_monotonous_timestamps() \
+ .with_timestamp_assigner(SecondColumnTimestampAssigner())
+ data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ .window(TumblingEventTimeWindows.of(Time.milliseconds(5))) \
+ .process(CountWindowProcessFunction(), Types.TUPLE([Types.STRING(), Types.INT()])) \
+ .add_sink(self.test_sink)
+
+ self.env.execute('test_event_time_tumbling_window')
+ results = self.test_sink.get_results()
+ expected = ['(hi,4)', '(hi,3)', '(hi,1)']
+ self.assert_equals_sorted(expected, results)
+
+ def test_count_tumbling_window(self):
+ self.env.set_parallelism(1)
+ data_stream = self.env.from_collection([
+ (1, 'hi'), (2, 'hello'), (3, 'hi'), (4, 'hello'), (5, 'hi'), (6, 'hello'),
+ (6, 'hello')],
+ type_info=Types.TUPLE([Types.INT(), Types.STRING()])) # type: DataStream
+ data_stream.key_by(lambda x: x[1], key_type=Types.STRING()) \
+ .count_window(3, 2) \
+ .apply(SumWindowFunction(), Types.TUPLE([Types.STRING(), Types.INT()])) \
+ .add_sink(self.test_sink)
+
+ self.env.execute('test_count_tumbling_window')
+ results = self.test_sink.get_results()
+ expected = ['(hi,9)', '(hello,12)']
+ self.assert_equals_sorted(expected, results)
+
+ def test_event_time_sliding_window(self):
+ self.env.set_parallelism(1)
+ data_stream = self.env.from_collection([
+ ('hi', 1), ('hi', 2), ('hi', 3), ('hi', 4), ('hi', 5), ('hi', 8), ('hi', 9),
+ ('hi', 15)],
+ type_info=Types.TUPLE([Types.STRING(), Types.INT()])) # type: DataStream
+ watermark_strategy = WatermarkStrategy.for_monotonous_timestamps() \
+ .with_timestamp_assigner(SecondColumnTimestampAssigner())
+
+ data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ .window(SlidingEventTimeWindows.of(Time.milliseconds(5), Time.milliseconds(2))) \
+ .process(CountWindowProcessFunction(), Types.TUPLE([Types.STRING(), Types.INT()])) \
+ .add_sink(self.test_sink)
+
+ self.env.execute('test_event_time_sliding_window')
+ results = self.test_sink.get_results()
+ expected = ['(hi,2)', '(hi,4)', '(hi,4)', '(hi,3)', '(hi,2)', '(hi,2)', '(hi,1)', '(hi,1)']
+ self.assert_equals_sorted(expected, results)
+
+ def test_count_sliding_window(self):
+ self.env.set_parallelism(1)
+ data_stream = self.env.from_collection([
+ (1, 'hi'), (2, 'hello'), (3, 'hi'), (4, 'hello'), (5, 'hi'), (6, 'hello')],
+ type_info=Types.TUPLE([Types.INT(), Types.STRING()])) # type: DataStream
+ data_stream.key_by(lambda x: x[1], key_type=Types.STRING()) \
+ .window(CountSlidingWindowAssigner(2, 1)) \
+ .apply(SumWindowFunction(), Types.TUPLE([Types.STRING(), Types.INT()])) \
+ .add_sink(self.test_sink)
+
+ self.env.execute('test_count_sliding_window')
+ results = self.test_sink.get_results()
+ expected = ['(hello,6)', '(hi,8)', '(hi,4)', '(hello,10)']
+ self.assert_equals_sorted(expected, results)
+
+ def test_event_time_session_window(self):
+ self.env.set_parallelism(1)
+ data_stream = self.env.from_collection([
+ ('hi', 1), ('hi', 2), ('hi', 3), ('hi', 4), ('hi', 8), ('hi', 9), ('hi', 15)],
+ type_info=Types.TUPLE([Types.STRING(), Types.INT()])) # type: DataStream
+ watermark_strategy = WatermarkStrategy.for_monotonous_timestamps() \
+ .with_timestamp_assigner(SecondColumnTimestampAssigner())
+
+ data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ .window(EventTimeSessionWindows.with_gap(Time.seconds(5))) \
+ .process(CountWindowProcessFunction(), Types.TUPLE([Types.STRING(), Types.INT()])) \
+ .add_sink(self.test_sink)
+
+ self.env.execute('test_event_time_session_window')
+ results = self.test_sink.get_results()
+ expected = ['(hi,7)']
Review comment:
shouldn't the expected result is: (hi,6), (hi,1)?
##########
File path: flink-python/pyflink/datastream/tests/test_data_stream.py
##########
@@ -1144,6 +1110,114 @@ def __init__(self, name, num):
expected = ['c', 'c', 'b']
self.assert_equals_sorted(expected, results)
+ def test_event_time_tumbling_window(self):
+ self.env.set_parallelism(1)
+ data_stream = self.env.from_collection([
+ ('hi', 1), ('hi', 2), ('hi', 3), ('hi', 4), ('hi', 5), ('hi', 8), ('hi', 9),
+ ('hi', 15)],
+ type_info=Types.TUPLE([Types.STRING(), Types.INT()])) # type: DataStream
+ watermark_strategy = WatermarkStrategy.for_monotonous_timestamps() \
+ .with_timestamp_assigner(SecondColumnTimestampAssigner())
+ data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ .window(TumblingEventTimeWindows.of(Time.milliseconds(5))) \
+ .process(CountWindowProcessFunction(), Types.TUPLE([Types.STRING(), Types.INT()])) \
+ .add_sink(self.test_sink)
+
+ self.env.execute('test_event_time_tumbling_window')
+ results = self.test_sink.get_results()
+ expected = ['(hi,4)', '(hi,3)', '(hi,1)']
+ self.assert_equals_sorted(expected, results)
+
+ def test_count_tumbling_window(self):
+ self.env.set_parallelism(1)
+ data_stream = self.env.from_collection([
+ (1, 'hi'), (2, 'hello'), (3, 'hi'), (4, 'hello'), (5, 'hi'), (6, 'hello'),
+ (6, 'hello')],
+ type_info=Types.TUPLE([Types.INT(), Types.STRING()])) # type: DataStream
+ data_stream.key_by(lambda x: x[1], key_type=Types.STRING()) \
+ .count_window(3, 2) \
Review comment:
actually this is a sliding window, however the test case is named `test_count_tumbling_window`
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +543,795 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class SessionWindowTimeGapExtractor(ABC):
+ """
+ A {@code SessionWindowTimeGapExtractor} extracts session time gaps for Dynamic Session Window
+ Assigners.
+ :param <ABC> The type of elements that this {@code SessionWindowTimeGapExtractor} can extract
+ session time gaps from.
+ """
+
+ @abstractmethod
+ def extract(self, element: Any) -> int:
+ """
+ Extracts the session time gap.
+ :param element The input element.
+ :return The session time gap in milliseconds.
+ """
+ pass
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ reduce_state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ reduce_state.add(1)
+ if reduce_state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ reduce_state.clear()
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class CountTumblingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into fixed-size windows based on the count number
+ of the elements. Windows cannot overlap.
+ """
+
+ def __init__(self, window_size: int):
+ """
+ Windows this KeyedStream into tumbling count windows.
+ :param window_size: The size of the windows in number of elements.
+ """
+ self._window_size = window_size
+ self._count_descriptor = ValueStateDescriptor('count-assigner', Types.LONG())
+
+ @staticmethod
+ def of(window_size: int):
+ return CountTumblingWindowAssigner(window_size)
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[CountWindow]:
+ count = context.get_runtime_context().get_state(self._count_descriptor)
+ count_value = count.value()
+ if count_value is None:
+ current_count = 0
+ else:
+ current_count = count_value
+ count.update(current_count + 1)
+ result = [CountWindow(current_count // self._window_size)]
+ return result
+
+ def get_default_trigger(self, env) -> Trigger[T, CountWindow]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[CountWindow]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self) -> str:
+ return "CountTumblingWindowAssigner(%s)" % self._window_size
+
+
+class CountSlidingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the count number of
+ the elements. Windows can possibly overlap.
+ """
+
+ def __init__(self, window_size: int, window_slide: int):
+ """
+ Windows this KeyedStream into sliding count windows.
+ :param window_size: The size of the windows in number of elements.
+ :param window_slide: The slide interval in number of elements.
+ """
+ self._window_size = window_size
+ self._window_slide = window_slide
+ self._count_descriptor = ValueStateDescriptor('slide-count-assigner', Types.LONG())
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[CountWindow]:
+ count = context.get_runtime_context().get_state(self._count_descriptor) # type: ValueState
+ count_value = count.value()
+ if count_value is None:
+ current_count = 0
+ else:
+ current_count = count_value
+ count.update(current_count + 1)
+ last_id = current_count // self._window_slide
+ last_start = last_id * self._window_slide
+ last_end = last_start + self._window_size - 1
+ windows = []
+ while last_id >= 0 and last_start <= current_count <= last_end:
+ if last_start <= current_count <= last_end:
+ windows.append(CountWindow(last_id))
+ last_id -= 1
+ last_start -= self._window_slide
+ last_end -= self._window_slide
+ return windows
+
+ def get_default_trigger(self, env) -> Trigger[T, CountWindow]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[CountWindow]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self):
+ return "CountSlidingWindowAssigner(%s, %s)" % (self._window_size, self._window_slide)
+
+
+class TumblingProcessingTimeWindows(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of
+ the machine the operation is running on. Windows cannot overlap.
+
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING())
+ >>> .window(TumblingProcessingTimeWindows.of(Time.minutes(1), Time.seconds(10)))
+ """
+ def __init__(self, size: int, offset: int):
+ if abs(offset) >= size:
+ raise Exception("TumblingProcessingTimeWindows parameters must satisfy "
+ "abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ current_processing_time = context.get_current_processing_time()
+ start = TimeWindow.get_window_start_with_offset(current_processing_time, self._offset,
+ self._size)
+ return [TimeWindow(start, start + self._size)]
+
+ def get_default_trigger(self, env) -> Trigger[T, TimeWindow]:
+ return ProcessingTimeTrigger()
+
+ @staticmethod
+ def of(size: Time, offset=None):
+ """
+ Creates a new {@code TumblingProcessingTimeWindows} {@link WindowAssigner} that assigns
+ elements to time windows based on the element timestamp and offset.
+
+ For example, if you want window a stream by hour,but window begins at the 15th minutes of
+ each hour, you can use {@code of(Time.hours(1),Time.minutes(15))},then you will get time
+ windows start at 0:15:00,1:15:00,2:15:00,etc.
+
+ Rather than that,if you are living in somewhere which is not using UTC±00:00 time, such as
+ China which is using UTC+08:00,and you want a time window with size of one day, and window
+ begins at every 00:00:00 of local time,you may use {@code of(Time.days(1),Time.hours(-8))}.
+ The parameter of offset is {@code Time.hours(-8))} since UTC+08:00 is 8 hours earlier than
+ UTC time.
+
+ :param size The size of the generated windows.
+ :param offset The offset which window start would be shifted by.
+ :return The time policy.
+ """
+ if offset is None:
+ return TumblingProcessingTimeWindows(size.to_milliseconds(), 0)
+ else:
+ return TumblingProcessingTimeWindows(size.to_milliseconds(), offset.to_milliseconds())
+
+ @property
+ def get_window_serializer(self) -> TypeSerializer[TimeWindow]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self):
+ return "TumblingProcessingTimeWindows(%s, %s)" % (self._size, self._offset)
+
+
+class TumblingEventTimeWindows(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the timestamp of the
+ elements. Windows cannot overlap.
+
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingEventTimeWindows.of(Time.minutes(1)))
+ """
+
+ def __init__(self, size: int, offset: int):
+ if abs(offset) >= size:
+ raise Exception("TumblingEventTimeWindows parameters must satisfy abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if timestamp > MIN_LONG_VALUE:
+ start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ raise Exception("Record has jvm.java.lang.Long.MIN_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', "
+ + "or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, TimeWindow]:
+ return EventTimeTrigger()
+
+ @staticmethod
+ def of(size: Time, offset=None):
Review comment:
ditto
##########
File path: flink-python/pyflink/datastream/tests/test_data_stream.py
##########
@@ -1090,8 +1056,8 @@ def test_reduce(self):
ds = self.env.from_collection([(1, 'a'), (2, 'a'), (3, 'a'), (4, 'b')],
type_info=Types.ROW([Types.INT(), Types.STRING()]))
ds.key_by(lambda a: a[1]) \
- .reduce(lambda a, b: Row(a[0] + b[0], b[1])) \
Review comment:
revert
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +543,795 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class SessionWindowTimeGapExtractor(ABC):
+ """
+ A {@code SessionWindowTimeGapExtractor} extracts session time gaps for Dynamic Session Window
+ Assigners.
+ :param <ABC> The type of elements that this {@code SessionWindowTimeGapExtractor} can extract
+ session time gaps from.
+ """
+
+ @abstractmethod
+ def extract(self, element: Any) -> int:
+ """
+ Extracts the session time gap.
+ :param element The input element.
+ :return The session time gap in milliseconds.
+ """
+ pass
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ reduce_state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ reduce_state.add(1)
+ if reduce_state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ reduce_state.clear()
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class CountTumblingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into fixed-size windows based on the count number
+ of the elements. Windows cannot overlap.
+ """
+
+ def __init__(self, window_size: int):
+ """
+ Windows this KeyedStream into tumbling count windows.
+ :param window_size: The size of the windows in number of elements.
+ """
+ self._window_size = window_size
+ self._count_descriptor = ValueStateDescriptor('count-assigner', Types.LONG())
+
+ @staticmethod
+ def of(window_size: int):
+ return CountTumblingWindowAssigner(window_size)
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[CountWindow]:
+ count = context.get_runtime_context().get_state(self._count_descriptor)
+ count_value = count.value()
+ if count_value is None:
+ current_count = 0
+ else:
+ current_count = count_value
+ count.update(current_count + 1)
+ result = [CountWindow(current_count // self._window_size)]
+ return result
+
+ def get_default_trigger(self, env) -> Trigger[T, CountWindow]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[CountWindow]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self) -> str:
+ return "CountTumblingWindowAssigner(%s)" % self._window_size
+
+
+class CountSlidingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the count number of
+ the elements. Windows can possibly overlap.
+ """
+
+ def __init__(self, window_size: int, window_slide: int):
+ """
+ Windows this KeyedStream into sliding count windows.
+ :param window_size: The size of the windows in number of elements.
+ :param window_slide: The slide interval in number of elements.
+ """
+ self._window_size = window_size
+ self._window_slide = window_slide
+ self._count_descriptor = ValueStateDescriptor('slide-count-assigner', Types.LONG())
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[CountWindow]:
+ count = context.get_runtime_context().get_state(self._count_descriptor) # type: ValueState
+ count_value = count.value()
+ if count_value is None:
+ current_count = 0
+ else:
+ current_count = count_value
+ count.update(current_count + 1)
+ last_id = current_count // self._window_slide
+ last_start = last_id * self._window_slide
+ last_end = last_start + self._window_size - 1
+ windows = []
+ while last_id >= 0 and last_start <= current_count <= last_end:
+ if last_start <= current_count <= last_end:
+ windows.append(CountWindow(last_id))
+ last_id -= 1
+ last_start -= self._window_slide
+ last_end -= self._window_slide
+ return windows
+
+ def get_default_trigger(self, env) -> Trigger[T, CountWindow]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[CountWindow]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self):
+ return "CountSlidingWindowAssigner(%s, %s)" % (self._window_size, self._window_slide)
+
+
+class TumblingProcessingTimeWindows(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of
+ the machine the operation is running on. Windows cannot overlap.
+
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING())
+ >>> .window(TumblingProcessingTimeWindows.of(Time.minutes(1), Time.seconds(10)))
+ """
+ def __init__(self, size: int, offset: int):
+ if abs(offset) >= size:
+ raise Exception("TumblingProcessingTimeWindows parameters must satisfy "
+ "abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ current_processing_time = context.get_current_processing_time()
+ start = TimeWindow.get_window_start_with_offset(current_processing_time, self._offset,
+ self._size)
+ return [TimeWindow(start, start + self._size)]
+
+ def get_default_trigger(self, env) -> Trigger[T, TimeWindow]:
+ return ProcessingTimeTrigger()
+
+ @staticmethod
+ def of(size: Time, offset=None):
+ """
+ Creates a new {@code TumblingProcessingTimeWindows} {@link WindowAssigner} that assigns
+ elements to time windows based on the element timestamp and offset.
+
+ For example, if you want window a stream by hour,but window begins at the 15th minutes of
+ each hour, you can use {@code of(Time.hours(1),Time.minutes(15))},then you will get time
+ windows start at 0:15:00,1:15:00,2:15:00,etc.
+
+ Rather than that,if you are living in somewhere which is not using UTC±00:00 time, such as
+ China which is using UTC+08:00,and you want a time window with size of one day, and window
+ begins at every 00:00:00 of local time,you may use {@code of(Time.days(1),Time.hours(-8))}.
+ The parameter of offset is {@code Time.hours(-8))} since UTC+08:00 is 8 hours earlier than
+ UTC time.
+
+ :param size The size of the generated windows.
+ :param offset The offset which window start would be shifted by.
+ :return The time policy.
+ """
+ if offset is None:
+ return TumblingProcessingTimeWindows(size.to_milliseconds(), 0)
+ else:
+ return TumblingProcessingTimeWindows(size.to_milliseconds(), offset.to_milliseconds())
+
+ @property
+ def get_window_serializer(self) -> TypeSerializer[TimeWindow]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self):
+ return "TumblingProcessingTimeWindows(%s, %s)" % (self._size, self._offset)
+
+
+class TumblingEventTimeWindows(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the timestamp of the
+ elements. Windows cannot overlap.
+
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingEventTimeWindows.of(Time.minutes(1)))
+ """
+
+ def __init__(self, size: int, offset: int):
+ if abs(offset) >= size:
+ raise Exception("TumblingEventTimeWindows parameters must satisfy abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if timestamp > MIN_LONG_VALUE:
+ start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ raise Exception("Record has jvm.java.lang.Long.MIN_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', "
+ + "or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, TimeWindow]:
+ return EventTimeTrigger()
+
+ @staticmethod
+ def of(size: Time, offset=None):
+ """
+ Creates a new TumblingEventTimeWindows WindowAssigner that assigns elements
+ to time windows based on the element timestamp, offset and a staggering offset, depending on
+ the staggering policy.
+
+ :param size The size of the generated windows.
+ :param offset The globalOffset which window start would be shifted by.
+ """
+ if offset is None:
+ return TumblingEventTimeWindows(size.to_milliseconds(), 0)
+ else:
+ return TumblingEventTimeWindows(size.to_milliseconds(), offset.to_milliseconds())
+
+ def get_window_serializer(self) -> TypeSerializer[TimeWindow]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return True
+
+ def __repr__(self):
+ return "TumblingEventTimeWindows(%s, %s)" % (self._size, self._offset)
+
+
+class SlidingProcessingTimeWindows(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the current system
+ time of the machine the operation is running on. Windows can possibly overlap.
+
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingProcessingTimeWindows.of(Time.minutes(1), Time.seconds(10)))
+ """
+
+ def __init__(self, size: int, slide: int, offset: int):
+ if abs(offset) >= slide or size <= 0:
+ raise Exception("SlidingProcessingTimeWindows parameters must satisfy "
+ + "abs(offset) < slide and size > 0")
+
+ self._size = size
+ self._slide = slide
+ self._offset = offset
+ self._pane_size = math.gcd(size, slide)
+ self._num_panes_per_window = size // self._pane_size
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[TimeWindow]:
+ current_processing_time = context.get_current_processing_time()
+ last_start = TimeWindow.get_window_start_with_offset(
+ current_processing_time, self._offset, self._slide)
+ windows = [TimeWindow(start, start + self._size)
+ for start in range(last_start,
+ current_processing_time - self._size, -self._slide)]
+ return windows
+
+ def get_default_trigger(self, env) -> Trigger[T, TimeWindow]:
+ return ProcessingTimeTrigger()
+
+ @staticmethod
+ def of(size: Time, slide: Time, offset=None):
+ """
+ Creates a new {@code SlidingProcessingTimeWindows} {@link WindowAssigner} that assigns
+ elements to time windows based on the element timestamp and offset.
+
+ For example, if you want window a stream by hour,but window begins at the 15th minutes of
+ each hour, you can use {@code of(Time.hours(1),Time.minutes(15))},then you will get time
+ windows start at 0:15:00,1:15:00,2:15:00,etc.
+
+ Rather than that,if you are living in somewhere which is not using UTC±00:00 time, such as
+ China which is using UTC+08:00,and you want a time window with size of one day, and window
+ begins at every 00:00:00 of local time,you may use {@code of(Time.days(1),Time.hours(-8))}.
+ The parameter of offset is {@code Time.hours(-8))} since UTC+08:00 is 8 hours earlier than
+ UTC time.
+
+ :param size The size of the generated windows.
+ :param slide The slide interval of the generated windows.
+ :param offset The offset which window start would be shifted by.
+ :return The time policy.
+ """
+ if offset is None:
+ return SlidingProcessingTimeWindows(size.to_milliseconds(), slide.to_milliseconds(), 0)
+ else:
+ return SlidingProcessingTimeWindows(size.to_milliseconds(), slide.to_milliseconds(),
+ offset.to_milliseconds())
+
+ def get_window_serializer(self) -> TypeSerializer[TimeWindow]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self) -> str:
+ return "SlidingProcessingTimeWindows(%s, %s, %s)" % (self._size, self._slide, self._offset)
+
+
+class SlidingEventTimeWindows(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the timestamp of the
+ elements. Windows can possibly overlap.
+
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingEventTimeWindows.of(Time.minutes(1), Time.seconds(10)))
+ """
+
+ def __init__(self, size: int, slide: int, offset: int):
+ if abs(offset) >= slide or size <= 0:
+ raise Exception("SlidingEventTimeWindows parameters must satisfy "
+ + "abs(offset) < slide and size > 0")
+
+ self._size = size
+ self._slide = slide
+ self._offset = offset
+ self._pane_size = math.gcd(size, slide)
+ self._num_panes_per_window = size // self._pane_size
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[TimeWindow]:
+ if timestamp > MIN_LONG_VALUE:
+ last_start = TimeWindow.get_window_start_with_offset(timestamp,
+ self._offset, self._slide)
+ windows = [TimeWindow(start, start + self._size)
+ for start in range(last_start, timestamp - self._size, -self._slide)]
+ return windows
+ else:
+ raise Exception("Record has jvm.java.lang.Long.MIN_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', "
+ "or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, TimeWindow]:
+ return EventTimeTrigger()
+
+ @staticmethod
+ def of(size: Time, slide: Time, offset=None):
+ """
+ Creates a new {@code SlidingEventTimeWindows} {@link WindowAssigner} that assigns elements
+ to time windows based on the element timestamp and offset.
+
+ For example, if you want window a stream by hour,but window begins at the 15th minutes of
+ each hour, you can use {@code of(Time.hours(1),Time.minutes(15))},then you will get time
+ windows start at 0:15:00,1:15:00,2:15:00,etc.
+
+ Rather than that,if you are living in somewhere which is not using UTC±00:00 time, such as
+ China which is using UTC+08:00,and you want a time window with size of one day, and window
+ begins at every 00:00:00 of local time,you may use {@code of(Time.days(1),Time.hours(-8))}.
+ The parameter of offset is {@code Time.hours(-8))} since UTC+08:00 is 8 hours earlier than
+ UTC time.
+
+ :param size The size of the generated windows.
+ :param slide The slide interval of the generated windows.
+ :param offset The offset which window start would be shifted by.
+ :return The time policy.
+ """
+ if offset is None:
+ return SlidingEventTimeWindows(size.to_milliseconds(), slide.to_milliseconds(), 0)
+ else:
+ return SlidingEventTimeWindows(size.to_milliseconds(), slide.to_milliseconds(),
+ offset.to_milliseconds())
+
+ def get_window_serializer(self) -> TypeSerializer[TimeWindow]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return True
+
+ def __repr__(self) -> str:
+ return "SlidingEventTimeWindows(%s, %s, %s)" % (self._size, self._slide, self._offset)
+
+
+class ProcessingTimeSessionWindows(MergingWindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into sessions based on the current processing
+ time. Windows cannot overlap.
+
+ For example, the processing interval is set to 1 minutes:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(ProcessingTimeSessionWindows.with_gap(Time.minutes(1)))
+ """
+
+ def __init__(self, session_gap: int):
+ if session_gap <= 0:
+ raise Exception("SessionWindowAssigner parameters must satisfy 0 < size")
Review comment:
```suggestion
raise Exception("ProcessingTimeSessionWindows parameters must satisfy 0 < size")
```
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +543,795 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class SessionWindowTimeGapExtractor(ABC):
+ """
+ A {@code SessionWindowTimeGapExtractor} extracts session time gaps for Dynamic Session Window
+ Assigners.
+ :param <ABC> The type of elements that this {@code SessionWindowTimeGapExtractor} can extract
+ session time gaps from.
+ """
+
+ @abstractmethod
+ def extract(self, element: Any) -> int:
+ """
+ Extracts the session time gap.
+ :param element The input element.
+ :return The session time gap in milliseconds.
+ """
+ pass
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ reduce_state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ reduce_state.add(1)
+ if reduce_state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ reduce_state.clear()
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class CountTumblingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into fixed-size windows based on the count number
+ of the elements. Windows cannot overlap.
+ """
+
+ def __init__(self, window_size: int):
+ """
+ Windows this KeyedStream into tumbling count windows.
+ :param window_size: The size of the windows in number of elements.
+ """
+ self._window_size = window_size
+ self._count_descriptor = ValueStateDescriptor('count-assigner', Types.LONG())
+
+ @staticmethod
+ def of(window_size: int):
+ return CountTumblingWindowAssigner(window_size)
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[CountWindow]:
+ count = context.get_runtime_context().get_state(self._count_descriptor)
+ count_value = count.value()
+ if count_value is None:
+ current_count = 0
+ else:
+ current_count = count_value
+ count.update(current_count + 1)
+ result = [CountWindow(current_count // self._window_size)]
+ return result
+
+ def get_default_trigger(self, env) -> Trigger[T, CountWindow]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[CountWindow]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self) -> str:
+ return "CountTumblingWindowAssigner(%s)" % self._window_size
+
+
+class CountSlidingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the count number of
+ the elements. Windows can possibly overlap.
+ """
+
+ def __init__(self, window_size: int, window_slide: int):
+ """
+ Windows this KeyedStream into sliding count windows.
+ :param window_size: The size of the windows in number of elements.
+ :param window_slide: The slide interval in number of elements.
+ """
+ self._window_size = window_size
+ self._window_slide = window_slide
+ self._count_descriptor = ValueStateDescriptor('slide-count-assigner', Types.LONG())
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[CountWindow]:
+ count = context.get_runtime_context().get_state(self._count_descriptor) # type: ValueState
+ count_value = count.value()
+ if count_value is None:
+ current_count = 0
+ else:
+ current_count = count_value
+ count.update(current_count + 1)
+ last_id = current_count // self._window_slide
+ last_start = last_id * self._window_slide
+ last_end = last_start + self._window_size - 1
+ windows = []
+ while last_id >= 0 and last_start <= current_count <= last_end:
+ if last_start <= current_count <= last_end:
+ windows.append(CountWindow(last_id))
+ last_id -= 1
+ last_start -= self._window_slide
+ last_end -= self._window_slide
+ return windows
+
+ def get_default_trigger(self, env) -> Trigger[T, CountWindow]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[CountWindow]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self):
+ return "CountSlidingWindowAssigner(%s, %s)" % (self._window_size, self._window_slide)
+
+
+class TumblingProcessingTimeWindows(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of
+ the machine the operation is running on. Windows cannot overlap.
+
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING())
+ >>> .window(TumblingProcessingTimeWindows.of(Time.minutes(1), Time.seconds(10)))
+ """
+ def __init__(self, size: int, offset: int):
+ if abs(offset) >= size:
+ raise Exception("TumblingProcessingTimeWindows parameters must satisfy "
+ "abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ current_processing_time = context.get_current_processing_time()
+ start = TimeWindow.get_window_start_with_offset(current_processing_time, self._offset,
+ self._size)
+ return [TimeWindow(start, start + self._size)]
+
+ def get_default_trigger(self, env) -> Trigger[T, TimeWindow]:
+ return ProcessingTimeTrigger()
+
+ @staticmethod
+ def of(size: Time, offset=None):
+ """
+ Creates a new {@code TumblingProcessingTimeWindows} {@link WindowAssigner} that assigns
+ elements to time windows based on the element timestamp and offset.
+
+ For example, if you want window a stream by hour,but window begins at the 15th minutes of
+ each hour, you can use {@code of(Time.hours(1),Time.minutes(15))},then you will get time
+ windows start at 0:15:00,1:15:00,2:15:00,etc.
+
+ Rather than that,if you are living in somewhere which is not using UTC±00:00 time, such as
+ China which is using UTC+08:00,and you want a time window with size of one day, and window
+ begins at every 00:00:00 of local time,you may use {@code of(Time.days(1),Time.hours(-8))}.
+ The parameter of offset is {@code Time.hours(-8))} since UTC+08:00 is 8 hours earlier than
+ UTC time.
+
+ :param size The size of the generated windows.
+ :param offset The offset which window start would be shifted by.
+ :return The time policy.
+ """
+ if offset is None:
+ return TumblingProcessingTimeWindows(size.to_milliseconds(), 0)
+ else:
+ return TumblingProcessingTimeWindows(size.to_milliseconds(), offset.to_milliseconds())
+
+ @property
+ def get_window_serializer(self) -> TypeSerializer[TimeWindow]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self):
+ return "TumblingProcessingTimeWindows(%s, %s)" % (self._size, self._offset)
+
+
+class TumblingEventTimeWindows(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the timestamp of the
+ elements. Windows cannot overlap.
+
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingEventTimeWindows.of(Time.minutes(1)))
+ """
+
+ def __init__(self, size: int, offset: int):
+ if abs(offset) >= size:
+ raise Exception("TumblingEventTimeWindows parameters must satisfy abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if timestamp > MIN_LONG_VALUE:
+ start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ raise Exception("Record has jvm.java.lang.Long.MIN_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', "
+ + "or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, TimeWindow]:
+ return EventTimeTrigger()
+
+ @staticmethod
+ def of(size: Time, offset=None):
+ """
+ Creates a new TumblingEventTimeWindows WindowAssigner that assigns elements
+ to time windows based on the element timestamp, offset and a staggering offset, depending on
+ the staggering policy.
+
+ :param size The size of the generated windows.
+ :param offset The globalOffset which window start would be shifted by.
+ """
+ if offset is None:
+ return TumblingEventTimeWindows(size.to_milliseconds(), 0)
+ else:
+ return TumblingEventTimeWindows(size.to_milliseconds(), offset.to_milliseconds())
+
+ def get_window_serializer(self) -> TypeSerializer[TimeWindow]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return True
+
+ def __repr__(self):
+ return "TumblingEventTimeWindows(%s, %s)" % (self._size, self._offset)
+
+
+class SlidingProcessingTimeWindows(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the current system
+ time of the machine the operation is running on. Windows can possibly overlap.
+
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingProcessingTimeWindows.of(Time.minutes(1), Time.seconds(10)))
+ """
+
+ def __init__(self, size: int, slide: int, offset: int):
+ if abs(offset) >= slide or size <= 0:
+ raise Exception("SlidingProcessingTimeWindows parameters must satisfy "
+ + "abs(offset) < slide and size > 0")
+
+ self._size = size
+ self._slide = slide
+ self._offset = offset
+ self._pane_size = math.gcd(size, slide)
+ self._num_panes_per_window = size // self._pane_size
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[TimeWindow]:
+ current_processing_time = context.get_current_processing_time()
+ last_start = TimeWindow.get_window_start_with_offset(
+ current_processing_time, self._offset, self._slide)
+ windows = [TimeWindow(start, start + self._size)
+ for start in range(last_start,
+ current_processing_time - self._size, -self._slide)]
+ return windows
+
+ def get_default_trigger(self, env) -> Trigger[T, TimeWindow]:
+ return ProcessingTimeTrigger()
+
+ @staticmethod
+ def of(size: Time, slide: Time, offset=None):
Review comment:
ditto
##########
File path: flink-python/pyflink/datastream/tests/test_data_stream.py
##########
@@ -178,8 +177,8 @@ def flat_map2(self, value):
ds_2 = ds_1.map(lambda x: x * 2)
(ds_1.connect(ds_2).flat_map(MyCoFlatMapFunction(), output_type=Types.INT())
- .connect(ds_2).map(MyCoMapFunction(), output_type=Types.INT())
Review comment:
revert
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +543,795 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class SessionWindowTimeGapExtractor(ABC):
+ """
+ A {@code SessionWindowTimeGapExtractor} extracts session time gaps for Dynamic Session Window
+ Assigners.
+ :param <ABC> The type of elements that this {@code SessionWindowTimeGapExtractor} can extract
+ session time gaps from.
+ """
+
+ @abstractmethod
+ def extract(self, element: Any) -> int:
+ """
+ Extracts the session time gap.
+ :param element The input element.
+ :return The session time gap in milliseconds.
+ """
+ pass
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ reduce_state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ reduce_state.add(1)
+ if reduce_state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ reduce_state.clear()
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class CountTumblingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into fixed-size windows based on the count number
+ of the elements. Windows cannot overlap.
+ """
+
+ def __init__(self, window_size: int):
+ """
+ Windows this KeyedStream into tumbling count windows.
+ :param window_size: The size of the windows in number of elements.
+ """
+ self._window_size = window_size
+ self._count_descriptor = ValueStateDescriptor('count-assigner', Types.LONG())
+
+ @staticmethod
+ def of(window_size: int):
+ return CountTumblingWindowAssigner(window_size)
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[CountWindow]:
+ count = context.get_runtime_context().get_state(self._count_descriptor)
+ count_value = count.value()
+ if count_value is None:
+ current_count = 0
+ else:
+ current_count = count_value
+ count.update(current_count + 1)
+ result = [CountWindow(current_count // self._window_size)]
+ return result
+
+ def get_default_trigger(self, env) -> Trigger[T, CountWindow]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[CountWindow]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self) -> str:
+ return "CountTumblingWindowAssigner(%s)" % self._window_size
+
+
+class CountSlidingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the count number of
+ the elements. Windows can possibly overlap.
+ """
+
+ def __init__(self, window_size: int, window_slide: int):
+ """
+ Windows this KeyedStream into sliding count windows.
+ :param window_size: The size of the windows in number of elements.
+ :param window_slide: The slide interval in number of elements.
+ """
+ self._window_size = window_size
+ self._window_slide = window_slide
+ self._count_descriptor = ValueStateDescriptor('slide-count-assigner', Types.LONG())
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[CountWindow]:
+ count = context.get_runtime_context().get_state(self._count_descriptor) # type: ValueState
+ count_value = count.value()
+ if count_value is None:
+ current_count = 0
+ else:
+ current_count = count_value
+ count.update(current_count + 1)
+ last_id = current_count // self._window_slide
+ last_start = last_id * self._window_slide
+ last_end = last_start + self._window_size - 1
+ windows = []
+ while last_id >= 0 and last_start <= current_count <= last_end:
+ if last_start <= current_count <= last_end:
+ windows.append(CountWindow(last_id))
+ last_id -= 1
+ last_start -= self._window_slide
+ last_end -= self._window_slide
+ return windows
+
+ def get_default_trigger(self, env) -> Trigger[T, CountWindow]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[CountWindow]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self):
+ return "CountSlidingWindowAssigner(%s, %s)" % (self._window_size, self._window_slide)
+
+
+class TumblingProcessingTimeWindows(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of
+ the machine the operation is running on. Windows cannot overlap.
+
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING())
+ >>> .window(TumblingProcessingTimeWindows.of(Time.minutes(1), Time.seconds(10)))
+ """
+ def __init__(self, size: int, offset: int):
+ if abs(offset) >= size:
+ raise Exception("TumblingProcessingTimeWindows parameters must satisfy "
+ "abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ current_processing_time = context.get_current_processing_time()
+ start = TimeWindow.get_window_start_with_offset(current_processing_time, self._offset,
+ self._size)
+ return [TimeWindow(start, start + self._size)]
+
+ def get_default_trigger(self, env) -> Trigger[T, TimeWindow]:
+ return ProcessingTimeTrigger()
+
+ @staticmethod
+ def of(size: Time, offset=None):
+ """
+ Creates a new {@code TumblingProcessingTimeWindows} {@link WindowAssigner} that assigns
+ elements to time windows based on the element timestamp and offset.
+
+ For example, if you want window a stream by hour,but window begins at the 15th minutes of
+ each hour, you can use {@code of(Time.hours(1),Time.minutes(15))},then you will get time
+ windows start at 0:15:00,1:15:00,2:15:00,etc.
+
+ Rather than that,if you are living in somewhere which is not using UTC±00:00 time, such as
+ China which is using UTC+08:00,and you want a time window with size of one day, and window
+ begins at every 00:00:00 of local time,you may use {@code of(Time.days(1),Time.hours(-8))}.
+ The parameter of offset is {@code Time.hours(-8))} since UTC+08:00 is 8 hours earlier than
+ UTC time.
+
+ :param size The size of the generated windows.
+ :param offset The offset which window start would be shifted by.
+ :return The time policy.
+ """
+ if offset is None:
+ return TumblingProcessingTimeWindows(size.to_milliseconds(), 0)
+ else:
+ return TumblingProcessingTimeWindows(size.to_milliseconds(), offset.to_milliseconds())
+
+ @property
+ def get_window_serializer(self) -> TypeSerializer[TimeWindow]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self):
+ return "TumblingProcessingTimeWindows(%s, %s)" % (self._size, self._offset)
+
+
+class TumblingEventTimeWindows(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the timestamp of the
+ elements. Windows cannot overlap.
+
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingEventTimeWindows.of(Time.minutes(1)))
+ """
+
+ def __init__(self, size: int, offset: int):
+ if abs(offset) >= size:
+ raise Exception("TumblingEventTimeWindows parameters must satisfy abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if timestamp > MIN_LONG_VALUE:
+ start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ raise Exception("Record has jvm.java.lang.Long.MIN_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', "
+ + "or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, TimeWindow]:
+ return EventTimeTrigger()
+
+ @staticmethod
+ def of(size: Time, offset=None):
+ """
+ Creates a new TumblingEventTimeWindows WindowAssigner that assigns elements
+ to time windows based on the element timestamp, offset and a staggering offset, depending on
+ the staggering policy.
+
+ :param size The size of the generated windows.
+ :param offset The globalOffset which window start would be shifted by.
+ """
+ if offset is None:
+ return TumblingEventTimeWindows(size.to_milliseconds(), 0)
+ else:
+ return TumblingEventTimeWindows(size.to_milliseconds(), offset.to_milliseconds())
+
+ def get_window_serializer(self) -> TypeSerializer[TimeWindow]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return True
+
+ def __repr__(self):
+ return "TumblingEventTimeWindows(%s, %s)" % (self._size, self._offset)
+
+
+class SlidingProcessingTimeWindows(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the current system
+ time of the machine the operation is running on. Windows can possibly overlap.
+
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingProcessingTimeWindows.of(Time.minutes(1), Time.seconds(10)))
+ """
+
+ def __init__(self, size: int, slide: int, offset: int):
+ if abs(offset) >= slide or size <= 0:
+ raise Exception("SlidingProcessingTimeWindows parameters must satisfy "
+ + "abs(offset) < slide and size > 0")
+
+ self._size = size
+ self._slide = slide
+ self._offset = offset
+ self._pane_size = math.gcd(size, slide)
+ self._num_panes_per_window = size // self._pane_size
Review comment:
where it's used?
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -15,28 +15,51 @@
# See the License for the specific language governing permissions and
# limitations under the License.
################################################################################
-import sys
+import math
from abc import ABC, abstractmethod
from enum import Enum
from io import BytesIO
-from typing import TypeVar, Generic, Iterable, Collection
+from typing import TypeVar, Generic, Iterable, Collection, Any
+from pyflink.common import Time, Types
from pyflink.common.serializer import TypeSerializer
from pyflink.datastream.functions import RuntimeContext, InternalWindowFunction
-from pyflink.datastream.state import StateDescriptor, State
+from pyflink.datastream.state import StateDescriptor, ReducingStateDescriptor, \
+ ValueStateDescriptor, ValueState, State, ReducingState
from pyflink.metrics import MetricGroup
__all__ = ['Window',
'TimeWindow',
'CountWindow',
+ 'TumblingProcessingTimeWindows',
+ 'TumblingEventTimeWindows',
+ 'SlidingProcessingTimeWindows',
+ 'SlidingEventTimeWindows',
+ 'ProcessingTimeSessionWindows',
+ 'EventTimeSessionWindows',
+ 'DynamicProcessingTimeSessionWindows',
+ 'DynamicEventTimeSessionWindows',
'WindowAssigner',
'MergingWindowAssigner',
+ 'CountTumblingWindowAssigner',
+ 'CountSlidingWindowAssigner',
'TriggerResult',
'Trigger',
+ 'EventTimeTrigger',
+ 'ProcessingTimeTrigger',
+ 'CountTrigger',
'TimeWindowSerializer',
- 'CountWindowSerializer']
+ 'CountWindowSerializer',
+ 'SessionWindowTimeGapExtractor']
-MAX_LONG_VALUE = sys.maxsize
+"""
+A constant holding the maximum value a long can have, 2^63 – 1.
+"""
+MAX_LONG_VALUE = 0x7fffffffffffffff
Review comment:
Should we also fix the MAX_LONG_VALUE/MIN_LONG_VALUE in other places?
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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* 894eb800e4045264cec298908892659466e78b7b Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32964)
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* 1240d4313817b4ae0bfca21090eb6f1cbd40f78e Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32684)
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
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## CI report:
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Posted by GitBox <gi...@apache.org>.
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## CI report:
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* 8e46affb05226eeed6b8eb20df971445139654a8 Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32535)
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## CI report:
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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## CI report:
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] Vancior commented on a change in pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
Vancior commented on a change in pull request #18957:
URL: https://github.com/apache/flink/pull/18957#discussion_r823294596
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +538,721 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of the
+ machine the operation is running on. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(10), False))
+
+ A WindowAssigner that windows elements into windows based on the timestamp of the elements.
+ Windows cannot overlap.
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(60000, 0, True))
+ """
+
+ def __init__(self, size: int, offset: int, is_event_time: bool):
+ if abs(offset) >= size:
+ raise Exception("TumblingWindowAssigner parameters must satisfy abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+ self._is_event_time = is_event_time
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if self._is_event_time is False:
+ current_processing_time = context.get_current_processing_time()
+ start = TimeWindow.get_window_start_with_offset(current_processing_time, self._offset,
+ self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ if timestamp > MIN_LONG_VALUE:
+ start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ raise Exception("Record has MIN_LONG_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ if self._is_event_time is True:
+ return EventTimeTrigger()
+ else:
+ return ProcessingTimeTrigger()
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return self._is_event_time
+
+ def __repr__(self):
+ return "TumblingWindowAssigner(%s,%s,%s)" % (self._size, self._offset, self.is_event_time)
+
+
+class CountTumblingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into fixed-size windows based on the count number
+ of the elements. Windows cannot overlap.
+ """
+
+ def __init__(self, window_size: int):
+ """
+ Windows this KeyedStream into tumbling count windows.
+ :param window_size: The size of the windows in number of elements.
+ """
+ self._window_size = window_size
+ self._counter_state_descriptor = ReducingStateDescriptor(
+ "assigner_counter", lambda a, b: a + b, Types.LONG())
+
+ @staticmethod
+ def of(window_size: int):
+ return CountTumblingWindowAssigner(window_size)
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ counter = context.get_runtime_context().get_reducing_state(
+ self._counter_state_descriptor)
+ if counter.get() is None:
+ counter.add(0)
+ result = [CountWindow(counter.get() // self._window_size)]
+ counter.add(1)
+ return result
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self) -> str:
+ return "CountTumblingWindowAssigner(%s)" % (self._window_size)
+
+
+class SlidingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the current system time
+ of the machine the operation is running on. Windows can possibly overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingWindowAssigner(60000, 10000, 0, False))
+
+ A WindowAssigner that windows elements into sliding windows based on the timestamp of the
+ elements. Windows can possibly overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingWindowAssigner(60000, 10000, 0, True))
+ """
+
+ def __init__(self, size: int, slide: int, offset: int, is_event_time: bool):
+ if abs(offset) >= slide or size <= 0:
+ raise Exception("SlidingWindowAssigner parameters must satisfy "
+ + "abs(offset) < slide and size > 0")
+
+ self._size = size
+ self._slide = slide
+ self._offset = offset
+ self._is_event_time = is_event_time
+ self._pane_size = math.gcd(size, slide)
+ self._num_panes_per_window = size // self._pane_size
+
+ def assign_windows(
+ self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ if self._is_event_time is False:
+ current_processing_time = context.get_current_processing_time()
+ last_start = TimeWindow.get_window_start_with_offset(
+ current_processing_time, self._offset, self._slide)
+ windows = [TimeWindow(start, start + self._size)
+ for start in range(last_start,
+ current_processing_time - self._size, -self._slide)]
+ return windows
+ else:
+ if timestamp > MIN_LONG_VALUE:
+ last_start = TimeWindow.get_window_start_with_offset(timestamp,
+ self._offset, self._slide)
+ windows = [TimeWindow(start, start + self._size)
+ for start in range(last_start, timestamp - self._size, -self._slide)]
+ return windows
+ else:
+ raise Exception("Record has MIN_LONG_VALUE timestamp (= no timestamp marker). "
+ + "Is the time characteristic set to 'ProcessingTime', "
+ "or did you forget to call "
+ + "'data_stream.assign_timestamps_and_watermarks(...)'?")
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ if self._is_event_time is True:
+ return EventTimeTrigger()
+ else:
+ return ProcessingTimeTrigger()
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return self._is_event_time
+
+ def __repr__(self) -> str:
+ return "SlidingWindowAssigner(%s, %s, %s, %s)" % (
+ self._size, self._slide, self._offset, self._is_event_time)
+
+
+class CountSlidingWindowAssigner(WindowAssigner[T, CountWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the count number of
+ the elements. Windows can possibly overlap.
+ """
+
+ def __init__(self, window_size: int, window_slide: int):
+ """
+ Windows this KeyedStream into sliding count windows.
+ :param window_size: The size of the windows in number of elements.
+ :param window_slide: The slide interval in number of elements.
+ """
+ self._window_size = window_size
+ self._window_slide = window_slide
+ self._count = None # type: ValueState
+ self._counter_state_descriptor = ReducingStateDescriptor(
+ "slide-count-assigner", lambda a, b: a + b, Types.LONG())
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ count_descriptor = ValueStateDescriptor('slide-count-assigner', Types.LONG())
+ self._count = context.get_runtime_context().get_state(count_descriptor)
+ count_value = self._count.value()
+ if count_value is None:
+ current_count = 0
+ else:
+ current_count = count_value
+ self._count.update(current_count + 1)
+ last_id = current_count // self._window_slide
+ last_start = last_id * self._window_slide
+ last_end = last_start + self._window_size - 1
+ windows = []
+ while last_id >= 0 and last_start <= current_count <= last_end:
+ if last_start <= current_count <= last_end:
+ windows.append(CountWindow(last_id))
+ last_id -= 1
+ last_start -= self._window_slide
+ last_end -= self._window_slide
+ return windows
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self):
+ return "CountSlidingWindowAssigner(%s, %s)" % (self._window_size, self._window_slide)
+
+
+class SessionWindowAssigner(MergingWindowAssigner[T, TimeWindow]):
+ """
+ WindowAssigner that windows elements into sessions based on the timestamp. Windows cannot
+ overlap.
+ """
+
+ def __init__(self, session_gap: int, is_event_time: bool):
+ if session_gap <= 0:
+ raise Exception("SessionWindowAssigner parameters must satisfy 0 < size")
+
+ self._session_gap = session_gap
+ self._is_event_time = is_event_time
+
+ def merge_windows(self,
+ windows: Iterable[W],
+ callback: 'MergingWindowAssigner.MergeCallback[W]') -> None:
+ window_list = [w for w in windows]
+ window_list.sort()
+ for i in range(1, len(window_list)):
Review comment:
This logic is incorrect when receiving late records, checkout java implementation
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
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* ec1e0a435186082e5ac1481bc093f9bdd9d94d70 UNKNOWN
* 0ee22778504c1bf11c25b5e9df0a8b5fc88a83cc Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32810)
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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## CI report:
* ec1e0a435186082e5ac1481bc093f9bdd9d94d70 UNKNOWN
* 8e46affb05226eeed6b8eb20df971445139654a8 Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32535)
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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## CI report:
* ec1e0a435186082e5ac1481bc093f9bdd9d94d70 UNKNOWN
* bb918383e68f2c6539ceab99853972a73e9fec0c Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32423)
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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## CI report:
* 8fea20f61b27f7b803c12798ad85dbe97e05dcdf Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32405)
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## CI report:
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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## CI report:
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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## CI report:
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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## CI report:
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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## CI report:
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
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URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] dianfu closed pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/flink/pull/18957
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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## CI report:
* ec1e0a435186082e5ac1481bc093f9bdd9d94d70 UNKNOWN
* 84846361d6db21a5ac721494ebb5badf23b274a1 Azure: [CANCELED](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32947)
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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## CI report:
* ec1e0a435186082e5ac1481bc093f9bdd9d94d70 UNKNOWN
* 84846361d6db21a5ac721494ebb5badf23b274a1 Azure: [CANCELED](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32947)
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[GitHub] [flink] dianfu commented on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
dianfu commented on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1066221067
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## CI report:
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* 1240d4313817b4ae0bfca21090eb6f1cbd40f78e Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32684)
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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## CI report:
* ec1e0a435186082e5ac1481bc093f9bdd9d94d70 UNKNOWN
* 4a7b1aabd62ed4165f39ce499a2d9696fb4bef0d Azure: [CANCELED](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32707)
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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## CI report:
* ec1e0a435186082e5ac1481bc093f9bdd9d94d70 UNKNOWN
* 8e46affb05226eeed6b8eb20df971445139654a8 Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32535)
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Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] Vancior commented on a change in pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
Vancior commented on a change in pull request #18957:
URL: https://github.com/apache/flink/pull/18957#discussion_r823283875
##########
File path: flink-python/pyflink/datastream/window.py
##########
@@ -517,3 +538,721 @@ def __init__(self,
self.window_state_descriptor = window_state_descriptor
self.internal_window_function = internal_window_function
self.window_serializer = window_serializer
+
+
+class EventTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_watermark():
+ ctx.register_event_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to
+ which a pane belongs.
+ """
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp())
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ window_max_timestamp = window.max_timestamp()
+ if window_max_timestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(window_max_timestamp)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp())
+
+
+class CountTrigger(Trigger[T, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+
+ def __init__(self, window_size: int):
+ self._window_size = window_size
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.LONG())
+
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def can_merge(self) -> bool:
+ return True
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[T, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of the
+ machine the operation is running on. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(10), False))
+
+ A WindowAssigner that windows elements into windows based on the timestamp of the elements.
+ Windows cannot overlap.
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(60000, 0, True))
+ """
+
+ def __init__(self, size: int, offset: int, is_event_time: bool):
+ if abs(offset) >= size:
+ raise Exception("TumblingWindowAssigner parameters must satisfy abs(offset) < size")
+
+ self._size = size
+ self._offset = offset
+ self._is_event_time = is_event_time
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if self._is_event_time is False:
+ current_processing_time = context.get_current_processing_time()
+ start = TimeWindow.get_window_start_with_offset(current_processing_time, self._offset,
+ self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ if timestamp > MIN_LONG_VALUE:
+ start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._size)
+ return [TimeWindow(start, start + self._size)]
+ else:
+ raise Exception("Record has MIN_LONG_VALUE timestamp (= no timestamp marker). "
Review comment:
maybe use `jvm.java.lang.Long.MIN_VALUE` instead?
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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## CI report:
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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* d178dd422b702f51ee85b41e43ec4b306c60aa8c Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32914)
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
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* ec1e0a435186082e5ac1481bc093f9bdd9d94d70 UNKNOWN
* 0ee22778504c1bf11c25b5e9df0a8b5fc88a83cc Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32810)
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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* 1240d4313817b4ae0bfca21090eb6f1cbd40f78e Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32684)
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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* d178dd422b702f51ee85b41e43ec4b306c60aa8c Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32914)
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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* 195bf4369bf1eb0cdc1727847e63ce0d6fd31f66 Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32952)
* 894eb800e4045264cec298908892659466e78b7b UNKNOWN
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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## CI report:
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* 195bf4369bf1eb0cdc1727847e63ce0d6fd31f66 Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32952)
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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## CI report:
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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## CI report:
* ec1e0a435186082e5ac1481bc093f9bdd9d94d70 UNKNOWN
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* a4a5fc419dbf82c079a24327846d4a7baa500959 UNKNOWN
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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## CI report:
* ec1e0a435186082e5ac1481bc093f9bdd9d94d70 UNKNOWN
* bb918383e68f2c6539ceab99853972a73e9fec0c Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32423)
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[GitHub] [flink] dianfu commented on a change in pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
dianfu commented on a change in pull request #18957:
URL: https://github.com/apache/flink/pull/18957#discussion_r818274208
##########
File path: flink-python/pyflink/fn_execution/datastream/window/window_assigner.py
##########
@@ -0,0 +1,376 @@
+import math
+from typing import Iterable, Collection
+
+from pyflink.common import TypeSerializer, Time
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import Trigger
+from pyflink.datastream.state import ValueStateDescriptor, ValueState, ReducingStateDescriptor
+from pyflink.datastream.window import TimeWindow, CountWindow, WindowAssigner, T, TimeWindowSerializer, TriggerResult, \
+ CountWindowSerializer, MergingWindowAssigner
+from pyflink.fn_execution.table.window_context import W
+
+
+class EventTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
Review comment:
need to override the can_merge method
##########
File path: flink-python/pyflink/fn_execution/datastream/window/window_assigner.py
##########
@@ -0,0 +1,376 @@
+import math
Review comment:
missing license header
##########
File path: flink-python/pyflink/fn_execution/datastream/window/window_assigner.py
##########
@@ -0,0 +1,376 @@
+import math
+from typing import Iterable, Collection
+
+from pyflink.common import TypeSerializer, Time
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import Trigger
+from pyflink.datastream.state import ValueStateDescriptor, ValueState, ReducingStateDescriptor
+from pyflink.datastream.window import TimeWindow, CountWindow, WindowAssigner, T, TimeWindowSerializer, TriggerResult, \
+ CountWindowSerializer, MergingWindowAssigner
+from pyflink.fn_execution.table.window_context import W
+
+
+class EventTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp()
+ if windowMaxTimestamp >= ctx.get_current_watermark():
Review comment:
```suggestion
if windowMaxTimestamp > ctx.get_current_watermark():
```
##########
File path: flink-python/pyflink/fn_execution/datastream/window/window_assigner.py
##########
@@ -0,0 +1,376 @@
+import math
+from typing import Iterable, Collection
+
+from pyflink.common import TypeSerializer, Time
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import Trigger
+from pyflink.datastream.state import ValueStateDescriptor, ValueState, ReducingStateDescriptor
+from pyflink.datastream.window import TimeWindow, CountWindow, WindowAssigner, T, TimeWindowSerializer, TriggerResult, \
+ CountWindowSerializer, MergingWindowAssigner
+from pyflink.fn_execution.table.window_context import W
+
+
+class EventTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp()
+ if windowMaxTimestamp >= ctx.get_current_watermark():
+ ctx.register_event_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp());
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
Review comment:
need to override the can_merge method
##########
File path: flink-python/pyflink/fn_execution/datastream/window/window_assigner.py
##########
@@ -0,0 +1,376 @@
+import math
+from typing import Iterable, Collection
+
+from pyflink.common import TypeSerializer, Time
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import Trigger
+from pyflink.datastream.state import ValueStateDescriptor, ValueState, ReducingStateDescriptor
+from pyflink.datastream.window import TimeWindow, CountWindow, WindowAssigner, T, TimeWindowSerializer, TriggerResult, \
+ CountWindowSerializer, MergingWindowAssigner
+from pyflink.fn_execution.table.window_context import W
+
+
+class EventTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp()
+ if windowMaxTimestamp >= ctx.get_current_watermark():
+ ctx.register_event_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to which a pane belongs.
Review comment:
check style issue, it exceeds 100 characters in one line.
##########
File path: flink-python/pyflink/fn_execution/datastream/window/window_assigner.py
##########
@@ -0,0 +1,376 @@
+import math
+from typing import Iterable, Collection
+
+from pyflink.common import TypeSerializer, Time
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import Trigger
+from pyflink.datastream.state import ValueStateDescriptor, ValueState, ReducingStateDescriptor
+from pyflink.datastream.window import TimeWindow, CountWindow, WindowAssigner, T, TimeWindowSerializer, TriggerResult, \
+ CountWindowSerializer, MergingWindowAssigner
+from pyflink.fn_execution.table.window_context import W
+
+
+class EventTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp()
+ if windowMaxTimestamp >= ctx.get_current_watermark():
+ ctx.register_event_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp());
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp();
+ if windowMaxTimestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp());
+
+
+class CountTrigger(Trigger[object, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+ def __init__(self, window_size: int):
+ self._window_size = int(window_size)
Review comment:
```suggestion
self._window_size = window_size
```
##########
File path: flink-python/pyflink/fn_execution/datastream/window/window_assigner.py
##########
@@ -0,0 +1,376 @@
+import math
+from typing import Iterable, Collection
+
+from pyflink.common import TypeSerializer, Time
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import Trigger
+from pyflink.datastream.state import ValueStateDescriptor, ValueState, ReducingStateDescriptor
+from pyflink.datastream.window import TimeWindow, CountWindow, WindowAssigner, T, TimeWindowSerializer, TriggerResult, \
+ CountWindowSerializer, MergingWindowAssigner
+from pyflink.fn_execution.table.window_context import W
+
+
+class EventTimeTrigger(Trigger[object, TimeWindow]):
Review comment:
```suggestion
class EventTimeTrigger(Trigger[T, TimeWindow]):
```
##########
File path: flink-python/pyflink/fn_execution/datastream/window/window_assigner.py
##########
@@ -0,0 +1,376 @@
+import math
+from typing import Iterable, Collection
+
+from pyflink.common import TypeSerializer, Time
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import Trigger
+from pyflink.datastream.state import ValueStateDescriptor, ValueState, ReducingStateDescriptor
+from pyflink.datastream.window import TimeWindow, CountWindow, WindowAssigner, T, TimeWindowSerializer, TriggerResult, \
+ CountWindowSerializer, MergingWindowAssigner
+from pyflink.fn_execution.table.window_context import W
+
+
+class EventTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp()
+ if windowMaxTimestamp >= ctx.get_current_watermark():
+ ctx.register_event_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp());
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp();
+ if windowMaxTimestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp());
+
+
+class CountTrigger(Trigger[object, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+ def __init__(self, window_size: int):
+ self._window_size = int(window_size)
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.BIG_INT())
+
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[object, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of the machine the operation is running on. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(10), False))
+
+ A WindowAssigner that windows elements into windows based on the timestamp of the elements. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(0), True))
+ """
+ def __init__(self, size: Time, offset: Time, is_event_time: bool):
Review comment:
```suggestion
def __init__(self, size: int, offset: int, is_event_time: bool):
```
Use int to keep consistent with the Java API. Besides, it would be great to add a few static utility methods just as did in the Java API.
##########
File path: flink-python/pyflink/fn_execution/datastream/window/window_assigner.py
##########
@@ -0,0 +1,376 @@
+import math
+from typing import Iterable, Collection
+
+from pyflink.common import TypeSerializer, Time
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import Trigger
+from pyflink.datastream.state import ValueStateDescriptor, ValueState, ReducingStateDescriptor
+from pyflink.datastream.window import TimeWindow, CountWindow, WindowAssigner, T, TimeWindowSerializer, TriggerResult, \
+ CountWindowSerializer, MergingWindowAssigner
+from pyflink.fn_execution.table.window_context import W
+
+
+class EventTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp()
+ if windowMaxTimestamp >= ctx.get_current_watermark():
+ ctx.register_event_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp());
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp();
+ if windowMaxTimestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp());
+
+
+class CountTrigger(Trigger[object, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+ def __init__(self, window_size: int):
+ self._window_size = int(window_size)
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.BIG_INT())
+
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[object, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of the machine the operation is running on. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(10), False))
+
+ A WindowAssigner that windows elements into windows based on the timestamp of the elements. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(0), True))
+ """
+ def __init__(self, size: Time, offset: Time, is_event_time: bool):
+ self._size = size.to_milliseconds()
+ self._offset = offset.to_milliseconds()
+ self._is_event_time = is_event_time
+
+ def assign_windows(self,
+ element: object,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if self._is_event_time is False:
+ timestamp = context.get_current_processing_time()
+
+ start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._size)
+ return [TimeWindow(start, start + self._size)]
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ if self._is_event_time is True:
+ return EventTimeTrigger()
+ else:
+ return ProcessingTimeTrigger()
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return self._is_event_time
+
+ def __repr__(self):
+ return "TumblingWindowAssigner(%s,%s,%s)" % (self._size, self._offset, self.is_event_time)
+
+'''
+A WindowAssigner that windows elements into windows based on the number of the elements. Windows cannot overlap.
Review comment:
could be removed
##########
File path: flink-python/pyflink/fn_execution/datastream/window/window_assigner.py
##########
@@ -0,0 +1,376 @@
+import math
+from typing import Iterable, Collection
+
+from pyflink.common import TypeSerializer, Time
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import Trigger
+from pyflink.datastream.state import ValueStateDescriptor, ValueState, ReducingStateDescriptor
+from pyflink.datastream.window import TimeWindow, CountWindow, WindowAssigner, T, TimeWindowSerializer, TriggerResult, \
+ CountWindowSerializer, MergingWindowAssigner
+from pyflink.fn_execution.table.window_context import W
+
+
+class EventTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp()
+ if windowMaxTimestamp >= ctx.get_current_watermark():
+ ctx.register_event_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp());
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp();
+ if windowMaxTimestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp());
+
+
+class CountTrigger(Trigger[object, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+ def __init__(self, window_size: int):
+ self._window_size = int(window_size)
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.BIG_INT())
+
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[object, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of the machine the operation is running on. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(10), False))
+
+ A WindowAssigner that windows elements into windows based on the timestamp of the elements. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(0), True))
+ """
+ def __init__(self, size: Time, offset: Time, is_event_time: bool):
+ self._size = size.to_milliseconds()
+ self._offset = offset.to_milliseconds()
+ self._is_event_time = is_event_time
+
+ def assign_windows(self,
+ element: object,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if self._is_event_time is False:
+ timestamp = context.get_current_processing_time()
+
+ start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._size)
+ return [TimeWindow(start, start + self._size)]
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ if self._is_event_time is True:
+ return EventTimeTrigger()
+ else:
+ return ProcessingTimeTrigger()
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return self._is_event_time
+
+ def __repr__(self):
+ return "TumblingWindowAssigner(%s,%s,%s)" % (self._size, self._offset, self.is_event_time)
+
+'''
+A WindowAssigner that windows elements into windows based on the number of the elements. Windows cannot overlap.
+'''
+class CountTumblingWindowAssigner(WindowAssigner[object, CountWindow]):
+ """
+ A WindowAssigner that windows elements into fixed-size windows based on the count number
+ of the elements. Windows cannot overlap.
+ """
+ def __init__(self, window_size: int):
+ """
+ Windows this KeyedStream into tumbling count windows.
+ :param window_size: The size of the windows in number of elements.
+ """
+ self._window_size = window_size
+ self._counter_state_descriptor = ReducingStateDescriptor(
+ "assigner_counter", lambda a, b: a + b, Types.BIG_INT())
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ counter = context.get_runtime_context().get_reducing_state(
+ self._counter_state_descriptor)
+ if counter.get() is None:
+ counter.add(0)
+ result = [CountWindow(counter.get() // self._window_size)]
+ counter.add(1)
+ return result
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self) -> str:
+ return "CountTumblingWindowAssigner(%s)" % (self._window_size)
+
+
+class SlidingWindowAssigner(WindowAssigner[object, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the current system time of the machine the operation is running on. Windows can possibly overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingWindowAssigner(Time.minutes(1), Time.seconds(10), Time.seconds(0), False))
+
+ A WindowAssigner that windows elements into sliding windows based on the timestamp of the elements. Windows can possibly overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingWindowAssigner(Time.minutes(1), Time.seconds(10), Time.seconds(0), True))
+ """
+ def __init__(self, size: Time, slide: Time, offset: Time, is_event_time: bool):
+ self._size = size.to_milliseconds()
+ self._slide = slide.to_milliseconds()
+ self._offset = offset.to_milliseconds()
+ self._is_event_time = is_event_time
+ self._pane_size = math.gcd(size, slide)
+ self._num_panes_per_window = size // self._pane_size
+
+ def assign_windows(
+ self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ if self._is_event_time is False:
+ timestamp = context.get_current_processing_time()
+
+ last_start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._slide)
+ windows = [TimeWindow(start, start + self._size)
+ for start in range(last_start, timestamp - self._size, -self._slide)]
+ return windows
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ if self._is_event_time is True:
+ return EventTimeTrigger()
+ else:
+ return ProcessingTimeTrigger()
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return self._is_event_time
+
+ def __repr__(self) -> str:
+ return "SlidingWindowAssigner(%s, %s, %s, %s)" % (self._size, self._slide, self._offset, self._is_event_time)
+
+
+class CountSlidingWindowAssigner(WindowAssigner[object, CountWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the count number of
+ the elements. Windows can possibly overlap.
+ """
+ def __init__(self, window_size: int, window_slide: int):
+ """
+ Windows this KeyedStream into sliding count windows.
+ :param window_size: The size of the windows in number of elements.
+ :param window_slide: The slide interval in number of elements.
+ """
+ self._window_size = window_size
+ self._window_slide = window_slide
+ self._count = None # type: ValueState
+ self._counter_state_descriptor = ReducingStateDescriptor(
+ "slide-count-assigner", lambda a, b: a + b, Types.BIG_INT())
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ count_descriptor = ValueStateDescriptor('slide-count-assigner', Types.LONG())
+ self._count = context.get_runtime_context().get_state(count_descriptor)
+ count_value = self._count.value()
+ if count_value is None:
+ current_count = 0
+ else:
+ current_count = count_value
+ self._count.update(current_count + 1)
+ last_id = current_count // self._window_slide
+ last_start = last_id * self._window_slide
+ last_end = last_start + self._window_size - 1
+ windows = []
+ while last_id >= 0 and last_start <= current_count <= last_end:
+ if last_start <= current_count <= last_end:
+ windows.append(CountWindow(last_id))
+ last_id -= 1
+ last_start -= self._window_slide
+ last_end -= self._window_slide
+ return windows
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self):
+ return "CountSlidingWindowAssigner(%s, %s)" % (self._window_size, self._window_slide)
+
+
+class SessionWindowAssigner(MergingWindowAssigner[object, TimeWindow]):
Review comment:
It has already support dynamic session windows in the Java API and it would be great to align with it. See DynamicEventTimeSessionWindows for more details.
##########
File path: flink-python/pyflink/fn_execution/datastream/window/window_assigner.py
##########
@@ -0,0 +1,376 @@
+import math
+from typing import Iterable, Collection
+
+from pyflink.common import TypeSerializer, Time
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import Trigger
+from pyflink.datastream.state import ValueStateDescriptor, ValueState, ReducingStateDescriptor
+from pyflink.datastream.window import TimeWindow, CountWindow, WindowAssigner, T, TimeWindowSerializer, TriggerResult, \
+ CountWindowSerializer, MergingWindowAssigner
+from pyflink.fn_execution.table.window_context import W
+
+
+class EventTimeTrigger(Trigger[object, TimeWindow]):
Review comment:
Move all these classes to module pyflink/datastream/window.py
##########
File path: flink-python/pyflink/fn_execution/datastream/window/window_assigner.py
##########
@@ -0,0 +1,376 @@
+import math
+from typing import Iterable, Collection
+
+from pyflink.common import TypeSerializer, Time
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import Trigger
+from pyflink.datastream.state import ValueStateDescriptor, ValueState, ReducingStateDescriptor
+from pyflink.datastream.window import TimeWindow, CountWindow, WindowAssigner, T, TimeWindowSerializer, TriggerResult, \
+ CountWindowSerializer, MergingWindowAssigner
+from pyflink.fn_execution.table.window_context import W
+
+
+class EventTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp()
+ if windowMaxTimestamp >= ctx.get_current_watermark():
+ ctx.register_event_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[object, TimeWindow]):
Review comment:
```suggestion
class ProcessingTimeTrigger(Trigger[T, TimeWindow]):
```
##########
File path: flink-python/pyflink/fn_execution/datastream/window/window_assigner.py
##########
@@ -0,0 +1,376 @@
+import math
+from typing import Iterable, Collection
+
+from pyflink.common import TypeSerializer, Time
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import Trigger
+from pyflink.datastream.state import ValueStateDescriptor, ValueState, ReducingStateDescriptor
+from pyflink.datastream.window import TimeWindow, CountWindow, WindowAssigner, T, TimeWindowSerializer, TriggerResult, \
+ CountWindowSerializer, MergingWindowAssigner
+from pyflink.fn_execution.table.window_context import W
+
+
+class EventTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp()
+ if windowMaxTimestamp >= ctx.get_current_watermark():
+ ctx.register_event_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp());
Review comment:
```suggestion
ctx.register_processing_time_timer(window.max_timestamp())
```
##########
File path: flink-python/pyflink/fn_execution/datastream/window/window_assigner.py
##########
@@ -0,0 +1,376 @@
+import math
+from typing import Iterable, Collection
+
+from pyflink.common import TypeSerializer, Time
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import Trigger
+from pyflink.datastream.state import ValueStateDescriptor, ValueState, ReducingStateDescriptor
+from pyflink.datastream.window import TimeWindow, CountWindow, WindowAssigner, T, TimeWindowSerializer, TriggerResult, \
+ CountWindowSerializer, MergingWindowAssigner
+from pyflink.fn_execution.table.window_context import W
+
+
+class EventTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp()
Review comment:
```suggestion
window_max_timestamp = window.max_timestamp()
```
##########
File path: flink-python/pyflink/fn_execution/datastream/window/window_assigner.py
##########
@@ -0,0 +1,376 @@
+import math
+from typing import Iterable, Collection
+
+from pyflink.common import TypeSerializer, Time
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import Trigger
+from pyflink.datastream.state import ValueStateDescriptor, ValueState, ReducingStateDescriptor
+from pyflink.datastream.window import TimeWindow, CountWindow, WindowAssigner, T, TimeWindowSerializer, TriggerResult, \
+ CountWindowSerializer, MergingWindowAssigner
+from pyflink.fn_execution.table.window_context import W
+
+
+class EventTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp()
+ if windowMaxTimestamp >= ctx.get_current_watermark():
+ ctx.register_event_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp());
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp();
+ if windowMaxTimestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp());
Review comment:
```suggestion
ctx.delete_processing_time_timer(window.max_timestamp())
```
##########
File path: flink-python/pyflink/fn_execution/datastream/window/window_assigner.py
##########
@@ -0,0 +1,376 @@
+import math
+from typing import Iterable, Collection
+
+from pyflink.common import TypeSerializer, Time
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import Trigger
+from pyflink.datastream.state import ValueStateDescriptor, ValueState, ReducingStateDescriptor
+from pyflink.datastream.window import TimeWindow, CountWindow, WindowAssigner, T, TimeWindowSerializer, TriggerResult, \
+ CountWindowSerializer, MergingWindowAssigner
+from pyflink.fn_execution.table.window_context import W
+
+
+class EventTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp()
+ if windowMaxTimestamp >= ctx.get_current_watermark():
+ ctx.register_event_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp());
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp();
+ if windowMaxTimestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp());
+
+
+class CountTrigger(Trigger[object, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+ def __init__(self, window_size: int):
+ self._window_size = int(window_size)
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.BIG_INT())
+
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
Review comment:
```suggestion
# though, all elements are retained.
state.clear()
```
##########
File path: flink-python/pyflink/fn_execution/datastream/window/window_assigner.py
##########
@@ -0,0 +1,376 @@
+import math
+from typing import Iterable, Collection
+
+from pyflink.common import TypeSerializer, Time
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import Trigger
+from pyflink.datastream.state import ValueStateDescriptor, ValueState, ReducingStateDescriptor
+from pyflink.datastream.window import TimeWindow, CountWindow, WindowAssigner, T, TimeWindowSerializer, TriggerResult, \
+ CountWindowSerializer, MergingWindowAssigner
+from pyflink.fn_execution.table.window_context import W
+
+
+class EventTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp()
+ if windowMaxTimestamp >= ctx.get_current_watermark():
+ ctx.register_event_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp());
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp();
+ if windowMaxTimestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp());
+
+
+class CountTrigger(Trigger[object, CountWindow]):
Review comment:
```suggestion
class CountTrigger(Trigger[T, CountWindow]):
```
##########
File path: flink-python/pyflink/fn_execution/datastream/window/window_assigner.py
##########
@@ -0,0 +1,376 @@
+import math
+from typing import Iterable, Collection
+
+from pyflink.common import TypeSerializer, Time
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import Trigger
+from pyflink.datastream.state import ValueStateDescriptor, ValueState, ReducingStateDescriptor
+from pyflink.datastream.window import TimeWindow, CountWindow, WindowAssigner, T, TimeWindowSerializer, TriggerResult, \
+ CountWindowSerializer, MergingWindowAssigner
+from pyflink.fn_execution.table.window_context import W
+
+
+class EventTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp()
+ if windowMaxTimestamp >= ctx.get_current_watermark():
+ ctx.register_event_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp());
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp();
+ if windowMaxTimestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp());
+
+
+class CountTrigger(Trigger[object, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+ def __init__(self, window_size: int):
+ self._window_size = int(window_size)
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.BIG_INT())
+
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[object, TimeWindow]):
Review comment:
```suggestion
class TumblingWindowAssigner(WindowAssigner[T, TimeWindow]):
```
##########
File path: flink-python/pyflink/fn_execution/datastream/window/window_assigner.py
##########
@@ -0,0 +1,376 @@
+import math
+from typing import Iterable, Collection
+
+from pyflink.common import TypeSerializer, Time
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import Trigger
+from pyflink.datastream.state import ValueStateDescriptor, ValueState, ReducingStateDescriptor
+from pyflink.datastream.window import TimeWindow, CountWindow, WindowAssigner, T, TimeWindowSerializer, TriggerResult, \
+ CountWindowSerializer, MergingWindowAssigner
+from pyflink.fn_execution.table.window_context import W
+
+
+class EventTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp()
+ if windowMaxTimestamp >= ctx.get_current_watermark():
+ ctx.register_event_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp());
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp();
+ if windowMaxTimestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp());
+
+
+class CountTrigger(Trigger[object, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+ def __init__(self, window_size: int):
+ self._window_size = int(window_size)
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.BIG_INT())
+
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
Review comment:
need to override the can_merge method
##########
File path: flink-python/pyflink/fn_execution/datastream/window/window_assigner.py
##########
@@ -0,0 +1,376 @@
+import math
+from typing import Iterable, Collection
+
+from pyflink.common import TypeSerializer, Time
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import Trigger
+from pyflink.datastream.state import ValueStateDescriptor, ValueState, ReducingStateDescriptor
+from pyflink.datastream.window import TimeWindow, CountWindow, WindowAssigner, T, TimeWindowSerializer, TriggerResult, \
+ CountWindowSerializer, MergingWindowAssigner
+from pyflink.fn_execution.table.window_context import W
+
+
+class EventTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp()
+ if windowMaxTimestamp >= ctx.get_current_watermark():
+ ctx.register_event_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp());
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp();
Review comment:
```suggestion
window_max_timestamp = window.max_timestamp();
```
##########
File path: flink-python/pyflink/fn_execution/datastream/window/window_assigner.py
##########
@@ -0,0 +1,376 @@
+import math
+from typing import Iterable, Collection
+
+from pyflink.common import TypeSerializer, Time
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import Trigger
+from pyflink.datastream.state import ValueStateDescriptor, ValueState, ReducingStateDescriptor
+from pyflink.datastream.window import TimeWindow, CountWindow, WindowAssigner, T, TimeWindowSerializer, TriggerResult, \
+ CountWindowSerializer, MergingWindowAssigner
+from pyflink.fn_execution.table.window_context import W
+
+
+class EventTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp()
+ if windowMaxTimestamp >= ctx.get_current_watermark():
+ ctx.register_event_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp());
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp();
+ if windowMaxTimestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp());
+
+
+class CountTrigger(Trigger[object, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+ def __init__(self, window_size: int):
+ self._window_size = int(window_size)
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.BIG_INT())
Review comment:
```suggestion
"count", lambda a, b: a + b, Types.LONG())
```
##########
File path: flink-python/pyflink/fn_execution/datastream/window/window_assigner.py
##########
@@ -0,0 +1,376 @@
+import math
+from typing import Iterable, Collection
+
+from pyflink.common import TypeSerializer, Time
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import Trigger
+from pyflink.datastream.state import ValueStateDescriptor, ValueState, ReducingStateDescriptor
+from pyflink.datastream.window import TimeWindow, CountWindow, WindowAssigner, T, TimeWindowSerializer, TriggerResult, \
+ CountWindowSerializer, MergingWindowAssigner
+from pyflink.fn_execution.table.window_context import W
+
+
+class EventTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp()
+ if windowMaxTimestamp >= ctx.get_current_watermark():
+ ctx.register_event_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp());
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp();
+ if windowMaxTimestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp());
+
+
+class CountTrigger(Trigger[object, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+ def __init__(self, window_size: int):
+ self._window_size = int(window_size)
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.BIG_INT())
+
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[object, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of the machine the operation is running on. Windows cannot overlap.
Review comment:
need fix the check style issue. Please also fix other places.
##########
File path: flink-python/pyflink/fn_execution/datastream/window/window_assigner.py
##########
@@ -0,0 +1,376 @@
+import math
+from typing import Iterable, Collection
+
+from pyflink.common import TypeSerializer, Time
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import Trigger
+from pyflink.datastream.state import ValueStateDescriptor, ValueState, ReducingStateDescriptor
+from pyflink.datastream.window import TimeWindow, CountWindow, WindowAssigner, T, TimeWindowSerializer, TriggerResult, \
+ CountWindowSerializer, MergingWindowAssigner
+from pyflink.fn_execution.table.window_context import W
+
+
+class EventTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp()
+ if windowMaxTimestamp >= ctx.get_current_watermark():
+ ctx.register_event_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp());
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp();
+ if windowMaxTimestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp());
+
+
+class CountTrigger(Trigger[object, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+ def __init__(self, window_size: int):
+ self._window_size = int(window_size)
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.BIG_INT())
+
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[object, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of the machine the operation is running on. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(10), False))
+
+ A WindowAssigner that windows elements into windows based on the timestamp of the elements. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(0), True))
+ """
+ def __init__(self, size: Time, offset: Time, is_event_time: bool):
+ self._size = size.to_milliseconds()
+ self._offset = offset.to_milliseconds()
+ self._is_event_time = is_event_time
+
+ def assign_windows(self,
+ element: object,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if self._is_event_time is False:
+ timestamp = context.get_current_processing_time()
+
+ start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._size)
+ return [TimeWindow(start, start + self._size)]
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ if self._is_event_time is True:
+ return EventTimeTrigger()
+ else:
+ return ProcessingTimeTrigger()
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return self._is_event_time
+
+ def __repr__(self):
+ return "TumblingWindowAssigner(%s,%s,%s)" % (self._size, self._offset, self.is_event_time)
+
+'''
+A WindowAssigner that windows elements into windows based on the number of the elements. Windows cannot overlap.
+'''
+class CountTumblingWindowAssigner(WindowAssigner[object, CountWindow]):
+ """
+ A WindowAssigner that windows elements into fixed-size windows based on the count number
+ of the elements. Windows cannot overlap.
+ """
+ def __init__(self, window_size: int):
+ """
+ Windows this KeyedStream into tumbling count windows.
+ :param window_size: The size of the windows in number of elements.
+ """
+ self._window_size = window_size
+ self._counter_state_descriptor = ReducingStateDescriptor(
+ "assigner_counter", lambda a, b: a + b, Types.BIG_INT())
+
+ def assign_windows(self,
+ element: T,
+ timestamp: int,
+ context: 'WindowAssigner.WindowAssignerContext') -> Collection[W]:
+ counter = context.get_runtime_context().get_reducing_state(
+ self._counter_state_descriptor)
+ if counter.get() is None:
+ counter.add(0)
+ result = [CountWindow(counter.get() // self._window_size)]
+ counter.add(1)
+ return result
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ return CountTrigger(self._window_size)
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return CountWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return False
+
+ def __repr__(self) -> str:
+ return "CountTumblingWindowAssigner(%s)" % (self._window_size)
+
+
+class SlidingWindowAssigner(WindowAssigner[object, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into sliding windows based on the current system time of the machine the operation is running on. Windows can possibly overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingWindowAssigner(Time.minutes(1), Time.seconds(10), Time.seconds(0), False))
+
+ A WindowAssigner that windows elements into sliding windows based on the timestamp of the elements. Windows can possibly overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(SlidingWindowAssigner(Time.minutes(1), Time.seconds(10), Time.seconds(0), True))
+ """
+ def __init__(self, size: Time, slide: Time, offset: Time, is_event_time: bool):
Review comment:
```suggestion
def __init__(self, size: int, slide: int, offset: int, is_event_time: bool):
```
##########
File path: flink-python/pyflink/fn_execution/datastream/window/window_assigner.py
##########
@@ -0,0 +1,376 @@
+import math
+from typing import Iterable, Collection
+
+from pyflink.common import TypeSerializer, Time
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import Trigger
+from pyflink.datastream.state import ValueStateDescriptor, ValueState, ReducingStateDescriptor
+from pyflink.datastream.window import TimeWindow, CountWindow, WindowAssigner, T, TimeWindowSerializer, TriggerResult, \
+ CountWindowSerializer, MergingWindowAssigner
+from pyflink.fn_execution.table.window_context import W
+
+
+class EventTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp()
+ if windowMaxTimestamp >= ctx.get_current_watermark():
+ ctx.register_event_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp());
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp();
+ if windowMaxTimestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp());
+
+
+class CountTrigger(Trigger[object, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+ def __init__(self, window_size: int):
+ self._window_size = int(window_size)
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.BIG_INT())
+
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[object, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of the machine the operation is running on. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(10), False))
+
+ A WindowAssigner that windows elements into windows based on the timestamp of the elements. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(0), True))
+ """
+ def __init__(self, size: Time, offset: Time, is_event_time: bool):
+ self._size = size.to_milliseconds()
+ self._offset = offset.to_milliseconds()
+ self._is_event_time = is_event_time
+
+ def assign_windows(self,
+ element: object,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if self._is_event_time is False:
+ timestamp = context.get_current_processing_time()
+
+ start = TimeWindow.get_window_start_with_offset(timestamp, self._offset, self._size)
+ return [TimeWindow(start, start + self._size)]
+
+ def get_default_trigger(self, env) -> Trigger[T, W]:
+ if self._is_event_time is True:
+ return EventTimeTrigger()
+ else:
+ return ProcessingTimeTrigger()
+
+ def get_window_serializer(self) -> TypeSerializer[W]:
+ return TimeWindowSerializer()
+
+ def is_event_time(self) -> bool:
+ return self._is_event_time
+
+ def __repr__(self):
+ return "TumblingWindowAssigner(%s,%s,%s)" % (self._size, self._offset, self.is_event_time)
+
+'''
+A WindowAssigner that windows elements into windows based on the number of the elements. Windows cannot overlap.
+'''
+class CountTumblingWindowAssigner(WindowAssigner[object, CountWindow]):
Review comment:
It uses GlobalWindows for count window in the Java API. Is it possible to do so in Python?
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[GitHub] [flink] Vancior commented on a change in pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
Vancior commented on a change in pull request #18957:
URL: https://github.com/apache/flink/pull/18957#discussion_r819330802
##########
File path: flink-python/pyflink/fn_execution/datastream/window/window_assigner.py
##########
@@ -0,0 +1,376 @@
+import math
+from typing import Iterable, Collection
+
+from pyflink.common import TypeSerializer, Time
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import Trigger
+from pyflink.datastream.state import ValueStateDescriptor, ValueState, ReducingStateDescriptor
+from pyflink.datastream.window import TimeWindow, CountWindow, WindowAssigner, T, TimeWindowSerializer, TriggerResult, \
+ CountWindowSerializer, MergingWindowAssigner
+from pyflink.fn_execution.table.window_context import W
+
+
+class EventTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the watermark passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if window.max_timestamp() <= ctx.get_current_watermark():
+ return TriggerResult.FIRE
+ else:
+ ctx.register_event_time_timer(window.max_timestamp())
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ if time == window.max_timestamp():
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: TimeWindow,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp()
+ if windowMaxTimestamp >= ctx.get_current_watermark():
+ ctx.register_event_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: TimeWindow,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_event_time_timer(window.max_timestamp())
+
+
+class ProcessingTimeTrigger(Trigger[object, TimeWindow]):
+ """
+ A Trigger that fires once the current system time passes the end of the window to which a pane belongs.
+ """
+ def on_element(self,
+ element: T,
+ timestamp: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ ctx.register_processing_time_timer(window.max_timestamp());
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.FIRE
+
+ def on_event_time(self,
+ time: int,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> TriggerResult:
+ return TriggerResult.CONTINUE
+
+ def on_merge(self,
+ window: W,
+ ctx: 'Trigger.OnMergeContext') -> None:
+ windowMaxTimestamp = window.max_timestamp();
+ if windowMaxTimestamp > ctx.get_current_processing_time():
+ ctx.register_processing_time_timer(windowMaxTimestamp)
+
+ def clear(self,
+ window: W,
+ ctx: 'Trigger.TriggerContext') -> None:
+ ctx.delete_processing_time_timer(window.max_timestamp());
+
+
+class CountTrigger(Trigger[object, CountWindow]):
+ """
+ A Trigger that fires once the count of elements in a pane reaches the given count.
+ """
+ def __init__(self, window_size: int):
+ self._window_size = int(window_size)
+ self._count_state_descriptor = ReducingStateDescriptor(
+ "trigger_counter", lambda a, b: a + b, Types.BIG_INT())
+
+ def on_element(self,
+ element: object,
+ timestamp: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ state = ctx.get_partitioned_state(self._count_state_descriptor) # type: ReducingState
+ state.add(1)
+ if state.get() >= self._window_size:
+ # On FIRE, the window is evaluated and results are emitted. The window is not purged
+ # though, all elements are retained.
+ return TriggerResult.FIRE
+ else:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_processing_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_event_time(self,
+ time: int,
+ window: CountWindow,
+ ctx: Trigger.TriggerContext) -> TriggerResult:
+ # No action is taken on the window.
+ return TriggerResult.CONTINUE
+
+ def on_merge(self, window: CountWindow, ctx: Trigger.OnMergeContext) -> None:
+ ctx.merge_partitioned_state(self._count_state_descriptor)
+
+ def clear(self, window: CountWindow, ctx: Trigger.TriggerContext) -> None:
+ state = ctx.get_partitioned_state(self._count_state_descriptor)
+ state.clear()
+
+
+class TumblingWindowAssigner(WindowAssigner[object, TimeWindow]):
+ """
+ A WindowAssigner that windows elements into windows based on the current system time of the machine the operation is running on. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute, every 10 seconds:
+ ::
+ >>> data_stream.key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(10), False))
+
+ A WindowAssigner that windows elements into windows based on the timestamp of the elements. Windows cannot overlap.
+ For example, in order to window into windows of 1 minute:
+ ::
+ >>> data_stream.assign_timestamps_and_watermarks(watermark_strategy) \
+ >>> .key_by(lambda x: x[0], key_type=Types.STRING()) \
+ >>> .window(TumblingWindowAssigner(Time.minutes(1), Time.seconds(0), True))
+ """
+ def __init__(self, size: Time, offset: Time, is_event_time: bool):
+ self._size = size.to_milliseconds()
+ self._offset = offset.to_milliseconds()
+ self._is_event_time = is_event_time
+
+ def assign_windows(self,
+ element: object,
+ timestamp: int,
+ context: WindowAssigner.WindowAssignerContext) -> Collection[TimeWindow]:
+ if self._is_event_time is False:
+ timestamp = context.get_current_processing_time()
+
Review comment:
check `timestamp > Long.MIN_VALUE` under event-time mode as java does, or there'll be struct packing error when watermark strategy is not set for data stream.
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
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Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
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## CI report:
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
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## CI report:
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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## CI report:
* ec1e0a435186082e5ac1481bc093f9bdd9d94d70 UNKNOWN
* 84846361d6db21a5ac721494ebb5badf23b274a1 Azure: [CANCELED](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32947)
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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## CI report:
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
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## CI report:
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Posted by GitBox <gi...@apache.org>.
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## CI report:
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## CI report:
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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* d178dd422b702f51ee85b41e43ec4b306c60aa8c Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32914)
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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## CI report:
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* 195bf4369bf1eb0cdc1727847e63ce0d6fd31f66 Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32952)
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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## CI report:
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
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URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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* ec1e0a435186082e5ac1481bc093f9bdd9d94d70 UNKNOWN
* d178dd422b702f51ee85b41e43ec4b306c60aa8c Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32914)
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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* d178dd422b702f51ee85b41e43ec4b306c60aa8c Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32914)
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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## CI report:
* ec1e0a435186082e5ac1481bc093f9bdd9d94d70 UNKNOWN
* 894eb800e4045264cec298908892659466e78b7b Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32964)
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[GitHub] [flink] javacaoyu commented on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
javacaoyu commented on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056802273
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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## CI report:
* ec1e0a435186082e5ac1481bc093f9bdd9d94d70 UNKNOWN
* bb918383e68f2c6539ceab99853972a73e9fec0c Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32423)
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
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## CI report:
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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## CI report:
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
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## CI report:
* ec1e0a435186082e5ac1481bc093f9bdd9d94d70 UNKNOWN
* a36182858952a61ef43ccbdc184440354bb86110 Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32721)
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[GitHub] [flink] flinkbot edited a comment on pull request #18957: [FLINK-26444][python]Window allocator supporting pyflink datastream API
Posted by GitBox <gi...@apache.org>.
flinkbot edited a comment on pull request #18957:
URL: https://github.com/apache/flink/pull/18957#issuecomment-1056202149
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## CI report:
* ec1e0a435186082e5ac1481bc093f9bdd9d94d70 UNKNOWN
* a36182858952a61ef43ccbdc184440354bb86110 Azure: [FAILURE](https://dev.azure.com/apache-flink/98463496-1af2-4620-8eab-a2ecc1a2e6fe/_build/results?buildId=32721)
* d18a0ac3e74011eddb3b544b49f48b1cd5bf621f UNKNOWN
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