y1chi commented on a change in pull request #11296:
URL: https://github.com/apache/beam/pull/11296#discussion_r613605219
##########
File path:
sdks/python/apache_beam/runners/portability/fn_api_runner/execution.py
##########
@@ -588,6 +589,13 @@ def __init__(self,
self.safe_coders = safe_coders
self.data_channel_coders = data_channel_coders
+ self.input_transform_to_buffer_id = {
+ t.unique_name: t.spec.payload
+ for s in stages for t in s.transforms
+ if t.spec.urn == bundle_processor.DATA_INPUT_URN
+ }
+ self.watermark_manager = WatermarkManager(stages)
+ # self.watermark_manager.show()
Review comment:
clean up?
##########
File path:
sdks/python/apache_beam/runners/portability/fn_api_runner/watermark_manager.py
##########
@@ -0,0 +1,171 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Utilities for managing watermarks for a pipeline execution by
FnApiRunner."""
+
+from __future__ import absolute_import
+
+from typing import Dict
+from typing import List
+from typing import Set
+from typing import Union
+
+from apache_beam.portability.api import beam_runner_api_pb2
+from apache_beam.runners.portability.fn_api_runner import translations
+from apache_beam.runners.portability.fn_api_runner.translations import
split_buffer_id
+from apache_beam.runners.worker import bundle_processor
+from apache_beam.utils import proto_utils
+from apache_beam.utils import timestamp
+
+
+class WatermarkManager(object):
+ """Manages the watermarks of a pipeline's stages.
+ It works by constructing an internal graph representation of the pipeline,
+ and keeping track of dependencies."""
+ class PCollectionNode(object):
+ def __init__(self, name):
+ self.name = name
+ self._watermark = timestamp.MIN_TIMESTAMP
+ self.producers: Set[WatermarkManager.StageNode] = set()
+
+ def __str__(self):
+ return 'PCollectionNode<producers=%s>' % list(self.producers)
+
+ def set_watermark(self, wm: timestamp.Timestamp):
+ self._watermark = min(self.upstream_watermark(), wm)
+
+ def upstream_watermark(self):
+ if self.producers:
+ return min(p.input_watermark() for p in self.producers)
+ else:
+ return timestamp.MAX_TIMESTAMP
+
+ def watermark(self):
+ return self._watermark
+
+ class StageNode(object):
+ def __init__(self, name):
+ # We keep separate inputs and side inputs because side inputs
+ # should hold back a stage's input watermark, to hold back execution
+ # for that stage; but they should not be considered when calculating
+ # the output watermark of the stage, because only the main input
+ # can actually advance that watermark.
+ self.name = name
+ self.inputs: Set[WatermarkManager.PCollectionNode] = set()
+ self.side_inputs: Set[WatermarkManager.PCollectionNode] = set()
+ self.outputs: Set[WatermarkManager.PCollectionNode] = set()
+
+ def __str__(self):
+ return 'StageNode<inputs=%s,side_inputs=%s' % (
+ [i.name for i in self.inputs], [i.name for i in self.side_inputs])
+
+ def output_watermark(self):
+ if not self.outputs:
+ return self.input_watermark()
+ else:
+ return min(o.watermark() for o in self.outputs)
+
+ def input_watermark(self):
+ if not self.inputs:
+ return timestamp.MAX_TIMESTAMP
+ w = min(i.upstream_watermark() for i in self.inputs)
+
+ if self.side_inputs:
+ w = min(w, min(i.upstream_watermark() for i in self.side_inputs))
+ return w
+
+ def __init__(self, stages):
+ # type: (List[translations.Stage]) -> None
+ self._pcollections_by_name: Dict[Union[str, translations.TimerFamilyId],
+ WatermarkManager.PCollectionNode] = {}
+ self._stages_by_name: Dict[str, WatermarkManager.StageNode] = {}
+
+ for s in stages:
+ stage_name = s.name
+ stage_node = WatermarkManager.StageNode(stage_name)
+ self._stages_by_name[stage_name] = stage_node
+
+ def add_pcollection(
Review comment:
Should this function declared outside of the loop?
##########
File path:
sdks/python/apache_beam/runners/portability/fn_api_runner/fn_runner.py
##########
@@ -364,10 +368,36 @@ def run_stages(self,
bundle_context_manager = execution.BundleContextManager(
runner_execution_context, stage, self._num_workers)
+ assert (
+ runner_execution_context.watermark_manager.get_stage_node(
+ bundle_context_manager.stage.name
+ ).input_watermark() == timestamp.MAX_TIMESTAMP), (
Review comment:
are the assertions only for batch?
##########
File path:
sdks/python/apache_beam/runners/portability/fn_api_runner/fn_runner.py
##########
@@ -364,10 +368,36 @@ def run_stages(self,
bundle_context_manager = execution.BundleContextManager(
runner_execution_context, stage, self._num_workers)
+ assert (
+ runner_execution_context.watermark_manager.get_stage_node(
+ bundle_context_manager.stage.name
+ ).input_watermark() == timestamp.MAX_TIMESTAMP), (
+ 'wrong watermark for %s. Expected %s, but got %s.' % (
+ runner_execution_context.watermark_manager.get_stage_node(
+ bundle_context_manager.stage.name),
+ timestamp.MAX_TIMESTAMP,
+ runner_execution_context.watermark_manager.get_stage_node(
+ bundle_context_manager.stage.name
+ ).input_watermark()
+ )
+ )
+
stage_results = self._run_stage(
- runner_execution_context,
- bundle_context_manager,
+ runner_execution_context, bundle_context_manager)
+
+ assert (
+ runner_execution_context.watermark_manager.get_stage_node(
+ bundle_context_manager.stage.name
+ ).input_watermark() == timestamp.MAX_TIMESTAMP), (
Review comment:
output_watermark()?
##########
File path:
sdks/python/apache_beam/runners/portability/fn_api_runner/fn_runner.py
##########
@@ -424,26 +466,50 @@ def _collect_written_timers_and_add_to_fired_timers(
# Only add not cleared timer to fired timers.
if not decoded_timer.clear_bit:
timer_coder_impl.encode_to_stream(decoded_timer, out, True)
- fired_timers[(transform_id, timer_family_id)] = ListBuffer(
+ if (transform_id, timer_family_id) not in timer_watermark_data:
+ timer_watermark_data[(transform_id,
+ timer_family_id)] = timestamp.MAX_TIMESTAMP
+ timer_watermark_data[(transform_id, timer_family_id)] = min(
+ timer_watermark_data[(transform_id, timer_family_id)],
+ decoded_timer.fire_timestamp)
Review comment:
I think this should be decoded_timer.hold_timestamp, currently timers
will set hold_timestamp to fire_timestamp but I think for watermark we should
still use decoded_timer.hold_timestamp which prevents potential breakage from
BEAM-11507.
##########
File path:
sdks/python/apache_beam/runners/portability/fn_api_runner/watermark_manager.py
##########
@@ -0,0 +1,171 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Utilities for managing watermarks for a pipeline execution by
FnApiRunner."""
+
+from __future__ import absolute_import
+
+from typing import Dict
+from typing import List
+from typing import Set
+from typing import Union
+
+from apache_beam.portability.api import beam_runner_api_pb2
+from apache_beam.runners.portability.fn_api_runner import translations
+from apache_beam.runners.portability.fn_api_runner.translations import
split_buffer_id
+from apache_beam.runners.worker import bundle_processor
+from apache_beam.utils import proto_utils
+from apache_beam.utils import timestamp
+
+
+class WatermarkManager(object):
+ """Manages the watermarks of a pipeline's stages.
+ It works by constructing an internal graph representation of the pipeline,
+ and keeping track of dependencies."""
+ class PCollectionNode(object):
+ def __init__(self, name):
+ self.name = name
+ self._watermark = timestamp.MIN_TIMESTAMP
+ self.producers: Set[WatermarkManager.StageNode] = set()
+
+ def __str__(self):
+ return 'PCollectionNode<producers=%s>' % list(self.producers)
+
+ def set_watermark(self, wm: timestamp.Timestamp):
+ self._watermark = min(self.upstream_watermark(), wm)
+
+ def upstream_watermark(self):
+ if self.producers:
+ return min(p.input_watermark() for p in self.producers)
+ else:
+ return timestamp.MAX_TIMESTAMP
+
+ def watermark(self):
+ return self._watermark
+
+ class StageNode(object):
+ def __init__(self, name):
+ # We keep separate inputs and side inputs because side inputs
+ # should hold back a stage's input watermark, to hold back execution
+ # for that stage; but they should not be considered when calculating
+ # the output watermark of the stage, because only the main input
+ # can actually advance that watermark.
+ self.name = name
+ self.inputs: Set[WatermarkManager.PCollectionNode] = set()
+ self.side_inputs: Set[WatermarkManager.PCollectionNode] = set()
+ self.outputs: Set[WatermarkManager.PCollectionNode] = set()
+
+ def __str__(self):
+ return 'StageNode<inputs=%s,side_inputs=%s' % (
+ [i.name for i in self.inputs], [i.name for i in self.side_inputs])
+
+ def output_watermark(self):
+ if not self.outputs:
+ return self.input_watermark()
+ else:
+ return min(o.watermark() for o in self.outputs)
+
+ def input_watermark(self):
+ if not self.inputs:
+ return timestamp.MAX_TIMESTAMP
+ w = min(i.upstream_watermark() for i in self.inputs)
+
+ if self.side_inputs:
+ w = min(w, min(i.upstream_watermark() for i in self.side_inputs))
+ return w
+
+ def __init__(self, stages):
+ # type: (List[translations.Stage]) -> None
+ self._pcollections_by_name: Dict[Union[str, translations.TimerFamilyId],
+ WatermarkManager.PCollectionNode] = {}
+ self._stages_by_name: Dict[str, WatermarkManager.StageNode] = {}
+
+ for s in stages:
+ stage_name = s.name
+ stage_node = WatermarkManager.StageNode(stage_name)
+ self._stages_by_name[stage_name] = stage_node
+
+ def add_pcollection(
+ pcname: str, snode: WatermarkManager.StageNode
+ ) -> WatermarkManager.PCollectionNode:
+ if pcname not in self._pcollections_by_name:
+ self._pcollections_by_name[pcname] =
WatermarkManager.PCollectionNode(
+ pcname)
+ pcnode = self._pcollections_by_name[pcname]
+ assert isinstance(pcnode, WatermarkManager.PCollectionNode)
+ snode.inputs.add(pcnode)
+ return pcnode
+
+ # 1. Get stage inputs, create nodes for them, add to _stages_by_name,
+ # and add as inputs to stage node.
+ for transform in s.transforms:
+ if transform.spec.urn == bundle_processor.DATA_INPUT_URN:
+ buffer_id = transform.spec.payload
+ if buffer_id == translations.IMPULSE_BUFFER:
+ pcoll_name = transform.unique_name
+ add_pcollection(pcoll_name, stage_node)
+ continue
+ else:
+ _, pcoll_name = split_buffer_id(buffer_id)
+ add_pcollection(pcoll_name, stage_node)
+
+ # 2. Get stage timers, and add them as inputs to the stage.
+ for transform in s.transforms:
+ if transform.spec.urn in translations.PAR_DO_URNS:
+ payload = proto_utils.parse_Bytes(
+ transform.spec.payload, beam_runner_api_pb2.ParDoPayload)
+ for timer_family_id in payload.timer_family_specs.keys():
+ timer_pcoll_name = (transform.unique_name, timer_family_id)
+ self._pcollections_by_name[
+ timer_pcoll_name] = WatermarkManager.PCollectionNode(
+ timer_pcoll_name)
+ timer_pcoll_node = self._pcollections_by_name[timer_pcoll_name]
Review comment:
why do we need these assertions immediately after updating the map?
##########
File path:
sdks/python/apache_beam/runners/portability/fn_api_runner/watermark_manager.py
##########
@@ -0,0 +1,171 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Utilities for managing watermarks for a pipeline execution by
FnApiRunner."""
+
+from __future__ import absolute_import
+
+from typing import Dict
+from typing import List
+from typing import Set
+from typing import Union
+
+from apache_beam.portability.api import beam_runner_api_pb2
+from apache_beam.runners.portability.fn_api_runner import translations
+from apache_beam.runners.portability.fn_api_runner.translations import
split_buffer_id
+from apache_beam.runners.worker import bundle_processor
+from apache_beam.utils import proto_utils
+from apache_beam.utils import timestamp
+
+
+class WatermarkManager(object):
+ """Manages the watermarks of a pipeline's stages.
+ It works by constructing an internal graph representation of the pipeline,
+ and keeping track of dependencies."""
+ class PCollectionNode(object):
+ def __init__(self, name):
+ self.name = name
+ self._watermark = timestamp.MIN_TIMESTAMP
+ self.producers: Set[WatermarkManager.StageNode] = set()
+
+ def __str__(self):
+ return 'PCollectionNode<producers=%s>' % list(self.producers)
+
+ def set_watermark(self, wm: timestamp.Timestamp):
+ self._watermark = min(self.upstream_watermark(), wm)
+
+ def upstream_watermark(self):
+ if self.producers:
+ return min(p.input_watermark() for p in self.producers)
+ else:
+ return timestamp.MAX_TIMESTAMP
+
+ def watermark(self):
+ return self._watermark
+
+ class StageNode(object):
+ def __init__(self, name):
+ # We keep separate inputs and side inputs because side inputs
+ # should hold back a stage's input watermark, to hold back execution
+ # for that stage; but they should not be considered when calculating
+ # the output watermark of the stage, because only the main input
+ # can actually advance that watermark.
+ self.name = name
+ self.inputs: Set[WatermarkManager.PCollectionNode] = set()
+ self.side_inputs: Set[WatermarkManager.PCollectionNode] = set()
+ self.outputs: Set[WatermarkManager.PCollectionNode] = set()
+
+ def __str__(self):
+ return 'StageNode<inputs=%s,side_inputs=%s' % (
+ [i.name for i in self.inputs], [i.name for i in self.side_inputs])
+
+ def output_watermark(self):
+ if not self.outputs:
+ return self.input_watermark()
+ else:
+ return min(o.watermark() for o in self.outputs)
+
+ def input_watermark(self):
+ if not self.inputs:
+ return timestamp.MAX_TIMESTAMP
+ w = min(i.upstream_watermark() for i in self.inputs)
+
+ if self.side_inputs:
+ w = min(w, min(i.upstream_watermark() for i in self.side_inputs))
+ return w
+
+ def __init__(self, stages):
+ # type: (List[translations.Stage]) -> None
+ self._pcollections_by_name: Dict[Union[str, translations.TimerFamilyId],
+ WatermarkManager.PCollectionNode] = {}
+ self._stages_by_name: Dict[str, WatermarkManager.StageNode] = {}
+
+ for s in stages:
+ stage_name = s.name
+ stage_node = WatermarkManager.StageNode(stage_name)
+ self._stages_by_name[stage_name] = stage_node
+
+ def add_pcollection(
+ pcname: str, snode: WatermarkManager.StageNode
+ ) -> WatermarkManager.PCollectionNode:
+ if pcname not in self._pcollections_by_name:
+ self._pcollections_by_name[pcname] =
WatermarkManager.PCollectionNode(
+ pcname)
+ pcnode = self._pcollections_by_name[pcname]
+ assert isinstance(pcnode, WatermarkManager.PCollectionNode)
+ snode.inputs.add(pcnode)
+ return pcnode
+
+ # 1. Get stage inputs, create nodes for them, add to _stages_by_name,
+ # and add as inputs to stage node.
+ for transform in s.transforms:
+ if transform.spec.urn == bundle_processor.DATA_INPUT_URN:
+ buffer_id = transform.spec.payload
+ if buffer_id == translations.IMPULSE_BUFFER:
+ pcoll_name = transform.unique_name
+ add_pcollection(pcoll_name, stage_node)
+ continue
+ else:
+ _, pcoll_name = split_buffer_id(buffer_id)
+ add_pcollection(pcoll_name, stage_node)
+
+ # 2. Get stage timers, and add them as inputs to the stage.
+ for transform in s.transforms:
+ if transform.spec.urn in translations.PAR_DO_URNS:
+ payload = proto_utils.parse_Bytes(
+ transform.spec.payload, beam_runner_api_pb2.ParDoPayload)
+ for timer_family_id in payload.timer_family_specs.keys():
+ timer_pcoll_name = (transform.unique_name, timer_family_id)
+ self._pcollections_by_name[
+ timer_pcoll_name] = WatermarkManager.PCollectionNode(
+ timer_pcoll_name)
+ timer_pcoll_node = self._pcollections_by_name[timer_pcoll_name]
+ assert isinstance(
+ timer_pcoll_node, WatermarkManager.PCollectionNode)
+ stage_node.inputs.add(timer_pcoll_node)
+
+ # 3. Get stage outputs, create nodes for them, add to
+ # _pcollections_by_name, and add stage as their producer
+ for transform in s.transforms:
+ if transform.spec.urn == bundle_processor.DATA_OUTPUT_URN:
+ buffer_id = transform.spec.payload
+ _, pcoll_name = split_buffer_id(buffer_id)
+ if pcoll_name not in self._pcollections_by_name:
+ self._pcollections_by_name[
+ pcoll_name] = WatermarkManager.PCollectionNode(pcoll_name)
+ pcoll_node = self._pcollections_by_name[pcoll_name]
Review comment:
ditto
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