#
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# 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.
#
"""Manages watermarks of PCollections and AppliedPTransforms."""
from __future__ import absolute_import
import threading
from builtins import object
from apache_beam import pipeline
from apache_beam import pvalue
from apache_beam.runners.direct.util import TimerFiring
from apache_beam.utils.timestamp import MAX_TIMESTAMP
from apache_beam.utils.timestamp import MIN_TIMESTAMP
from apache_beam.utils.timestamp import TIME_GRANULARITY
[docs]class WatermarkManager(object):
"""For internal use only; no backwards-compatibility guarantees.
Tracks and updates watermarks for all AppliedPTransforms."""
WATERMARK_POS_INF = MAX_TIMESTAMP
WATERMARK_NEG_INF = MIN_TIMESTAMP
def __init__(self, clock, root_transforms, value_to_consumers,
transform_keyed_states):
self._clock = clock
self._root_transforms = root_transforms
self._value_to_consumers = value_to_consumers
self._transform_keyed_states = transform_keyed_states
# AppliedPTransform -> TransformWatermarks
self._transform_to_watermarks = {}
for root_transform in root_transforms:
self._transform_to_watermarks[root_transform] = _TransformWatermarks(
self._clock, transform_keyed_states[root_transform], root_transform)
for consumers in value_to_consumers.values():
for consumer in consumers:
self._transform_to_watermarks[consumer] = _TransformWatermarks(
self._clock, transform_keyed_states[consumer], consumer)
for consumers in value_to_consumers.values():
for consumer in consumers:
self._update_input_transform_watermarks(consumer)
def _update_input_transform_watermarks(self, applied_ptransform):
assert isinstance(applied_ptransform, pipeline.AppliedPTransform)
input_transform_watermarks = []
for input_pvalue in applied_ptransform.inputs:
assert input_pvalue.producer or isinstance(input_pvalue, pvalue.PBegin)
if input_pvalue.producer:
input_transform_watermarks.append(
self.get_watermarks(input_pvalue.producer))
self._transform_to_watermarks[
applied_ptransform].update_input_transform_watermarks(
input_transform_watermarks)
[docs] def get_watermarks(self, applied_ptransform):
"""Gets the input and output watermarks for an AppliedPTransform.
If the applied_ptransform has not processed any elements, return a
watermark with minimum value.
Args:
applied_ptransform: AppliedPTransform to get the watermarks for.
Returns:
A snapshot (TransformWatermarks) of the input watermark and output
watermark for the provided transform.
"""
# TODO(altay): Composite transforms should have a composite watermark. Until
# then they are represented by their last transform.
while applied_ptransform.parts:
applied_ptransform = applied_ptransform.parts[-1]
return self._transform_to_watermarks[applied_ptransform]
[docs] def update_watermarks(self, completed_committed_bundle, applied_ptransform,
completed_timers, outputs, unprocessed_bundles,
keyed_earliest_holds, side_inputs_container):
assert isinstance(applied_ptransform, pipeline.AppliedPTransform)
self._update_pending(
completed_committed_bundle, applied_ptransform, completed_timers,
outputs, unprocessed_bundles)
tw = self.get_watermarks(applied_ptransform)
tw.hold(keyed_earliest_holds)
return self._refresh_watermarks(applied_ptransform, side_inputs_container)
def _update_pending(self, input_committed_bundle, applied_ptransform,
completed_timers, output_committed_bundles,
unprocessed_bundles):
"""Updated list of pending bundles for the given AppliedPTransform."""
# Update pending elements. Filter out empty bundles. They do not impact
# watermarks and should not trigger downstream execution.
for output in output_committed_bundles:
if output.has_elements():
if output.pcollection in self._value_to_consumers:
consumers = self._value_to_consumers[output.pcollection]
for consumer in consumers:
consumer_tw = self._transform_to_watermarks[consumer]
consumer_tw.add_pending(output)
completed_tw = self._transform_to_watermarks[applied_ptransform]
completed_tw.update_timers(completed_timers)
for unprocessed_bundle in unprocessed_bundles:
completed_tw.add_pending(unprocessed_bundle)
assert input_committed_bundle or applied_ptransform in self._root_transforms
if input_committed_bundle and input_committed_bundle.has_elements():
completed_tw.remove_pending(input_committed_bundle)
def _refresh_watermarks(self, applied_ptransform, side_inputs_container):
assert isinstance(applied_ptransform, pipeline.AppliedPTransform)
unblocked_tasks = []
tw = self.get_watermarks(applied_ptransform)
if tw.refresh():
for pval in applied_ptransform.outputs.values():
if isinstance(pval, pvalue.DoOutputsTuple):
pvals = (v for v in pval)
else:
pvals = (pval,)
for v in pvals:
if v in self._value_to_consumers: # If there are downstream consumers
consumers = self._value_to_consumers[v]
for consumer in consumers:
unblocked_tasks.extend(
self._refresh_watermarks(consumer, side_inputs_container))
# Notify the side_inputs_container.
unblocked_tasks.extend(
side_inputs_container
.update_watermarks_for_transform_and_unblock_tasks(
applied_ptransform, tw))
return unblocked_tasks
class _TransformWatermarks(object):
"""Tracks input and output watermarks for an AppliedPTransform."""
def __init__(self, clock, keyed_states, transform):
self._clock = clock
self._keyed_states = keyed_states
self._input_transform_watermarks = []
self._input_watermark = WatermarkManager.WATERMARK_NEG_INF
self._output_watermark = WatermarkManager.WATERMARK_NEG_INF
self._keyed_earliest_holds = {}
self._pending = set() # Scheduled bundles targeted for this transform.
self._fired_timers = set()
self._lock = threading.Lock()
self._label = str(transform)
def update_input_transform_watermarks(self, input_transform_watermarks):
with self._lock:
self._input_transform_watermarks = input_transform_watermarks
def update_timers(self, completed_timers):
with self._lock:
for timer_firing in completed_timers:
self._fired_timers.remove(timer_firing)
@property
def input_watermark(self):
with self._lock:
return self._input_watermark
@property
def output_watermark(self):
with self._lock:
return self._output_watermark
def hold(self, keyed_earliest_holds):
with self._lock:
for key, hold_value in keyed_earliest_holds.items():
self._keyed_earliest_holds[key] = hold_value
if (hold_value is None or
hold_value == WatermarkManager.WATERMARK_POS_INF):
del self._keyed_earliest_holds[key]
def add_pending(self, pending):
with self._lock:
self._pending.add(pending)
def remove_pending(self, completed):
with self._lock:
# Ignore repeated removes. This will happen if a transform has a repeated
# input.
if completed in self._pending:
self._pending.remove(completed)
def refresh(self):
"""Refresh the watermark for a given transform.
This method looks at the watermark coming from all input PTransforms, and
the timestamp of the minimum element, as well as any watermark holds.
Returns:
True if the watermark has advanced, and False if it has not.
"""
with self._lock:
min_pending_timestamp = WatermarkManager.WATERMARK_POS_INF
has_pending_elements = False
for input_bundle in self._pending:
# TODO(ccy): we can have the Bundle class keep track of the minimum
# timestamp so we don't have to do an iteration here.
for wv in input_bundle.get_elements_iterable():
has_pending_elements = True
if wv.timestamp < min_pending_timestamp:
min_pending_timestamp = wv.timestamp
# If there is a pending element with a certain timestamp, we can at most
# advance our watermark to the maximum timestamp less than that
# timestamp.
pending_holder = WatermarkManager.WATERMARK_POS_INF
if has_pending_elements:
pending_holder = min_pending_timestamp - TIME_GRANULARITY
input_watermarks = [
tw.output_watermark for tw in self._input_transform_watermarks]
input_watermarks.append(WatermarkManager.WATERMARK_POS_INF)
producer_watermark = min(input_watermarks)
self._input_watermark = max(self._input_watermark,
min(pending_holder, producer_watermark))
earliest_hold = WatermarkManager.WATERMARK_POS_INF
for hold in self._keyed_earliest_holds.values():
if hold < earliest_hold:
earliest_hold = hold
new_output_watermark = min(self._input_watermark, earliest_hold)
advanced = new_output_watermark > self._output_watermark
self._output_watermark = new_output_watermark
return advanced
@property
def synchronized_processing_output_time(self):
return self._clock.time()
def extract_transform_timers(self):
"""Extracts fired timers and reports of any timers set per transform."""
with self._lock:
fired_timers = []
has_realtime_timer = False
for encoded_key, state in self._keyed_states.items():
timers, had_realtime_timer = state.get_timers(
watermark=self._input_watermark,
processing_time=self._clock.time())
if had_realtime_timer:
has_realtime_timer = True
for expired in timers:
window, (name, time_domain, timestamp) = expired
fired_timers.append(
TimerFiring(encoded_key, window, name, time_domain, timestamp))
self._fired_timers.update(fired_timers)
return fired_timers, has_realtime_timer