class EvaluationContext(object):
"""Evaluation context with the global state information of the pipeline.
The evaluation context for a specific pipeline being executed by the
DirectRunner. Contains state shared within the execution across all
transforms.
EvaluationContext contains shared state for an execution of the
DirectRunner that can be used while evaluating a PTransform. This
consists of views into underlying state and watermark implementations, access
to read and write side inputs, and constructing counter sets and
execution contexts. This includes executing callbacks asynchronously when
state changes to the appropriate point (e.g. when a side input is
requested and known to be empty).
EvaluationContext also handles results by committing finalizing
bundles based on the current global state and updating the global state
appropriately. This includes updating the per-(step,key) state, updating
global watermarks, and executing any callbacks that can be executed.
"""
def __init__(self, pipeline_options, bundle_factory, root_transforms,
value_to_consumers, step_names, views, clock):
self.pipeline_options = pipeline_options
self._bundle_factory = bundle_factory
self._root_transforms = root_transforms
self._value_to_consumers = value_to_consumers
self._step_names = step_names
self.views = views
self._pcollection_to_views = collections.defaultdict(list)
for view in views:
self._pcollection_to_views[view.pvalue].append(view)
self._transform_keyed_states = self._initialize_keyed_states(
root_transforms, value_to_consumers)
self._side_inputs_container = _SideInputsContainer(views)
self._watermark_manager = WatermarkManager(
clock, root_transforms, value_to_consumers,
self._transform_keyed_states)
self._pending_unblocked_tasks = []
self._counter_factory = counters.CounterFactory()
self._metrics = DirectMetrics()
self._lock = threading.Lock()
def _initialize_keyed_states(self, root_transforms, value_to_consumers):
"""Initialize user state dicts.
These dicts track user state per-key, per-transform and per-window.
"""
transform_keyed_states = {}
for transform in root_transforms:
transform_keyed_states[transform] = {}
for consumers in value_to_consumers.values():
for consumer in consumers:
transform_keyed_states[consumer] = {}
return transform_keyed_states
def metrics(self):
# TODO. Should this be made a @property?
return self._metrics
def is_root_transform(self, applied_ptransform):
return applied_ptransform in self._root_transforms
def handle_result(self, completed_bundle, completed_timers, result):
"""Handle the provided result produced after evaluating the input bundle.
Handle the provided TransformResult, produced after evaluating
the provided committed bundle (potentially None, if the result of a root
PTransform).
The result is the output of running the transform contained in the
TransformResult on the contents of the provided bundle.
Args:
completed_bundle: the bundle that was processed to produce the result.
completed_timers: the timers that were delivered to produce the
completed_bundle.
result: the ``TransformResult`` of evaluating the input bundle
Returns:
the committed bundles contained within the handled result.
"""
with self._lock:
committed_bundles, unprocessed_bundles = self._commit_bundles(
result.uncommitted_output_bundles, result.unprocessed_bundles)
self._metrics.commit_logical(completed_bundle,
result.logical_metric_updates)
# If the result is for a view, update side inputs container.
self._update_side_inputs_container(committed_bundles, result)
# Tasks generated from unblocked side inputs as the watermark progresses.
tasks = self._watermark_manager.update_watermarks(
completed_bundle, result.transform, completed_timers,
committed_bundles, unprocessed_bundles,
result.keyed_watermark_holds, self._side_inputs_container)
self._pending_unblocked_tasks.extend(tasks)
if result.counters:
for counter in result.counters:
merged_counter = self._counter_factory.get_counter(
counter.name, counter.combine_fn)
merged_counter.accumulator.merge([counter.accumulator])
# Commit partial GBK states
existing_keyed_state = self._transform_keyed_states[
result.transform]
for k, v in result.partial_keyed_state.items():
existing_keyed_state[k] = v
return committed_bundles
def _update_side_inputs_container(self, committed_bundles, result):
"""Update the side inputs container if we are outputting into a side input.
Look at the result, and if it's outputing into a PCollection that we have
registered as a PCollectionView, we add the result to the PCollectionView.
"""
if (result.uncommitted_output_bundles
and result.uncommitted_output_bundles[0].pcollection
in self._pcollection_to_views):
for view in self._pcollection_to_views[
result.uncommitted_output_bundles[0].pcollection]:
for committed_bundle in committed_bundles:
# side_input must be materialized.
self._side_inputs_container.add_values(
view,
committed_bundle.get_elements_iterable(make_copy=True))
def get_aggregator_values(self, aggregator_or_name):
return self._counter_factory.get_aggregator_values(aggregator_or_name)
def schedule_pending_unblocked_tasks(self, executor_service):
if self._pending_unblocked_tasks:
with self._lock:
for task in self._pending_unblocked_tasks:
executor_service.submit(task)
self._pending_unblocked_tasks = []
def _commit_bundles(self, uncommitted_bundles, unprocessed_bundles):
"""Commits bundles and returns a immutable set of committed bundles."""
for in_progress_bundle in uncommitted_bundles:
producing_applied_ptransform = in_progress_bundle.pcollection.producer
watermarks = self._watermark_manager.get_watermarks(
producing_applied_ptransform)
in_progress_bundle.commit(
watermarks.synchronized_processing_output_time)
for unprocessed_bundle in unprocessed_bundles:
unprocessed_bundle.commit(None)
return tuple(uncommitted_bundles), tuple(unprocessed_bundles)
def get_execution_context(self, applied_ptransform):
return _ExecutionContext(
self._watermark_manager.get_watermarks(applied_ptransform),
self._transform_keyed_states[applied_ptransform])
def create_bundle(self, output_pcollection):
"""Create an uncommitted bundle for the specified PCollection."""
return self._bundle_factory.create_bundle(output_pcollection)
def create_empty_committed_bundle(self, output_pcollection):
"""Create empty bundle useful for triggering evaluation."""
return self._bundle_factory.create_empty_committed_bundle(
output_pcollection)
def extract_all_timers(self):
return self._watermark_manager.extract_all_timers()
def is_done(self, transform=None):
"""Checks completion of a step or the pipeline.
Args:
transform: AppliedPTransform to check for completion.
Returns:
True if the step will not produce additional output. If transform is None
returns true if all steps are done.
"""
if transform:
return self._is_transform_done(transform)
for applied_ptransform in self._step_names:
if not self._is_transform_done(applied_ptransform):
return False
return True
def _is_transform_done(self, transform):
tw = self._watermark_manager.get_watermarks(transform)
return tw.output_watermark == WatermarkManager.WATERMARK_POS_INF
def get_value_or_block_until_ready(self, side_input, task, block_until):
assert isinstance(task, TransformExecutor)
return self._side_inputs_container.get_value_or_block_until_ready(
side_input, task, block_until)
class EvaluationContext(object):
"""Evaluation context with the global state information of the pipeline.
The evaluation context for a specific pipeline being executed by the
DirectRunner. Contains state shared within the execution across all
transforms.
EvaluationContext contains shared state for an execution of the
DirectRunner that can be used while evaluating a PTransform. This
consists of views into underlying state and watermark implementations, access
to read and write side inputs, and constructing counter sets and
execution contexts. This includes executing callbacks asynchronously when
state changes to the appropriate point (e.g. when a side input is
requested and known to be empty).
EvaluationContext also handles results by committing finalizing
bundles based on the current global state and updating the global state
appropriately. This includes updating the per-(step,key) state, updating
global watermarks, and executing any callbacks that can be executed.
"""
def __init__(self, pipeline_options, bundle_factory, root_transforms,
value_to_consumers, step_names, views, clock):
self.pipeline_options = pipeline_options
self._bundle_factory = bundle_factory
self._root_transforms = root_transforms
self._value_to_consumers = value_to_consumers
self._step_names = step_names
self.views = views
self._pcollection_to_views = collections.defaultdict(list)
for view in views:
self._pcollection_to_views[view.pvalue].append(view)
self._transform_keyed_states = self._initialize_keyed_states(
root_transforms, value_to_consumers)
self._watermark_manager = WatermarkManager(
clock, root_transforms, value_to_consumers,
self._transform_keyed_states)
self._side_inputs_container = _SideInputsContainer(views)
self._pending_unblocked_tasks = []
self._counter_factory = counters.CounterFactory()
self._cache = None
self._metrics = DirectMetrics()
self._lock = threading.Lock()
def _initialize_keyed_states(self, root_transforms, value_to_consumers):
transform_keyed_states = {}
for transform in root_transforms:
transform_keyed_states[transform] = {}
for consumers in value_to_consumers.values():
for consumer in consumers:
transform_keyed_states[consumer] = {}
return transform_keyed_states
def use_pvalue_cache(self, cache):
assert not self._cache
self._cache = cache
def metrics(self):
# TODO. Should this be made a @property?
return self._metrics
@property
def has_cache(self):
return self._cache is not None
def append_to_cache(self, applied_ptransform, tag, elements):
with self._lock:
assert self._cache
self._cache.append(applied_ptransform, tag, elements)
def is_root_transform(self, applied_ptransform):
return applied_ptransform in self._root_transforms
def handle_result(
self, completed_bundle, completed_timers, result):
"""Handle the provided result produced after evaluating the input bundle.
Handle the provided TransformResult, produced after evaluating
the provided committed bundle (potentially None, if the result of a root
PTransform).
The result is the output of running the transform contained in the
TransformResult on the contents of the provided bundle.
Args:
completed_bundle: the bundle that was processed to produce the result.
completed_timers: the timers that were delivered to produce the
completed_bundle.
result: the TransformResult of evaluating the input bundle
Returns:
the committed bundles contained within the handled result.
"""
with self._lock:
committed_bundles, unprocessed_bundles = self._commit_bundles(
result.uncommitted_output_bundles,
result.unprocessed_bundles)
self._watermark_manager.update_watermarks(
completed_bundle, result.transform, completed_timers,
committed_bundles, unprocessed_bundles, result.keyed_watermark_holds)
self._metrics.commit_logical(completed_bundle,
result.logical_metric_updates)
# If the result is for a view, update side inputs container.
if (result.uncommitted_output_bundles
and result.uncommitted_output_bundles[0].pcollection
in self._pcollection_to_views):
for view in self._pcollection_to_views[
result.uncommitted_output_bundles[0].pcollection]:
for committed_bundle in committed_bundles:
# side_input must be materialized.
self._side_inputs_container.add_values(
view,
committed_bundle.get_elements_iterable(make_copy=True))
if (self.get_execution_context(result.transform)
.watermarks.input_watermark
== WatermarkManager.WATERMARK_POS_INF):
self._pending_unblocked_tasks.extend(
self._side_inputs_container.finalize_value_and_get_tasks(view))
if result.counters:
for counter in result.counters:
merged_counter = self._counter_factory.get_counter(
counter.name, counter.combine_fn)
merged_counter.accumulator.merge([counter.accumulator])
# Commit partial GBK states
existing_keyed_state = self._transform_keyed_states[result.transform]
for k, v in result.partial_keyed_state.iteritems():
existing_keyed_state[k] = v
return committed_bundles
def get_aggregator_values(self, aggregator_or_name):
return self._counter_factory.get_aggregator_values(aggregator_or_name)
def schedule_pending_unblocked_tasks(self, executor_service):
if self._pending_unblocked_tasks:
with self._lock:
for task in self._pending_unblocked_tasks:
executor_service.submit(task)
self._pending_unblocked_tasks = []
def _commit_bundles(self, uncommitted_bundles, unprocessed_bundles):
"""Commits bundles and returns a immutable set of committed bundles."""
for in_progress_bundle in uncommitted_bundles:
producing_applied_ptransform = in_progress_bundle.pcollection.producer
watermarks = self._watermark_manager.get_watermarks(
producing_applied_ptransform)
in_progress_bundle.commit(watermarks.synchronized_processing_output_time)
for unprocessed_bundle in unprocessed_bundles:
unprocessed_bundle.commit(None)
return tuple(uncommitted_bundles), tuple(unprocessed_bundles)
def get_execution_context(self, applied_ptransform):
return _ExecutionContext(
self._watermark_manager.get_watermarks(applied_ptransform),
self._transform_keyed_states[applied_ptransform])
def create_bundle(self, output_pcollection):
"""Create an uncommitted bundle for the specified PCollection."""
return self._bundle_factory.create_bundle(output_pcollection)
def create_empty_committed_bundle(self, output_pcollection):
"""Create empty bundle useful for triggering evaluation."""
return self._bundle_factory.create_empty_committed_bundle(
output_pcollection)
def extract_all_timers(self):
return self._watermark_manager.extract_all_timers()
def is_done(self, transform=None):
"""Checks completion of a step or the pipeline.
Args:
transform: AppliedPTransform to check for completion.
Returns:
True if the step will not produce additional output. If transform is None
returns true if all steps are done.
"""
if transform:
return self._is_transform_done(transform)
for applied_ptransform in self._step_names:
if not self._is_transform_done(applied_ptransform):
return False
return True
def _is_transform_done(self, transform):
tw = self._watermark_manager.get_watermarks(transform)
return tw.output_watermark == WatermarkManager.WATERMARK_POS_INF
def get_value_or_schedule_after_output(self, side_input, task):
assert isinstance(task, TransformExecutor)
return self._side_inputs_container.get_value_or_schedule_after_output(
side_input, task)
class EvaluationContext(object):
"""Evaluation context with the global state information of the pipeline.
The evaluation context for a specific pipeline being executed by the
DirectRunner. Contains state shared within the execution across all
transforms.
EvaluationContext contains shared state for an execution of the
DirectRunner that can be used while evaluating a PTransform. This
consists of views into underlying state and watermark implementations, access
to read and write PCollectionViews, and constructing counter sets and
execution contexts. This includes executing callbacks asynchronously when
state changes to the appropriate point (e.g. when a PCollectionView is
requested and known to be empty).
EvaluationContext also handles results by committing finalizing
bundles based on the current global state and updating the global state
appropriately. This includes updating the per-(step,key) state, updating
global watermarks, and executing any callbacks that can be executed.
"""
def __init__(self, pipeline_options, bundle_factory, root_transforms,
value_to_consumers, step_names, views):
self.pipeline_options = pipeline_options
self._bundle_factory = bundle_factory
self._root_transforms = root_transforms
self._value_to_consumers = value_to_consumers
self._step_names = step_names
self.views = views
# AppliedPTransform -> Evaluator specific state objects
self._application_state_interals = {}
self._watermark_manager = WatermarkManager(Clock(), root_transforms,
value_to_consumers)
self._side_inputs_container = _SideInputsContainer(views)
self._pending_unblocked_tasks = []
self._counter_factory = counters.CounterFactory()
self._cache = None
self._metrics = DirectMetrics()
self._lock = threading.Lock()
def use_pvalue_cache(self, cache):
assert not self._cache
self._cache = cache
def metrics(self):
# TODO. Should this be made a @property?
return self._metrics
@property
def has_cache(self):
return self._cache is not None
def append_to_cache(self, applied_ptransform, tag, elements):
with self._lock:
assert self._cache
self._cache.append(applied_ptransform, tag, elements)
def is_root_transform(self, applied_ptransform):
return applied_ptransform in self._root_transforms
def handle_result(self, completed_bundle, completed_timers, result):
"""Handle the provided result produced after evaluating the input bundle.
Handle the provided TransformResult, produced after evaluating
the provided committed bundle (potentially None, if the result of a root
PTransform).
The result is the output of running the transform contained in the
TransformResult on the contents of the provided bundle.
Args:
completed_bundle: the bundle that was processed to produce the result.
completed_timers: the timers that were delivered to produce the
completed_bundle.
result: the TransformResult of evaluating the input bundle
Returns:
the committed bundles contained within the handled result.
"""
with self._lock:
committed_bundles = self._commit_bundles(result.output_bundles)
self._watermark_manager.update_watermarks(completed_bundle,
result.transform,
completed_timers,
committed_bundles,
result.watermark_hold)
self._metrics.commit_logical(completed_bundle,
result.logical_metric_updates())
# If the result is for a view, update side inputs container.
if (result.output_bundles
and result.output_bundles[0].pcollection in self.views):
if committed_bundles:
assert len(committed_bundles) == 1
# side_input must be materialized.
side_input_result = committed_bundles[
0].get_elements_iterable(make_copy=True)
else:
side_input_result = []
tasks = self._side_inputs_container.set_value_and_get_callables(
result.output_bundles[0].pcollection, side_input_result)
self._pending_unblocked_tasks.extend(tasks)
if result.counters:
for counter in result.counters:
merged_counter = self._counter_factory.get_counter(
counter.name, counter.combine_fn)
merged_counter.accumulator.merge([counter.accumulator])
self._application_state_interals[result.transform] = result.state
return committed_bundles
def get_aggregator_values(self, aggregator_or_name):
return self._counter_factory.get_aggregator_values(aggregator_or_name)
def schedule_pending_unblocked_tasks(self, executor_service):
if self._pending_unblocked_tasks:
with self._lock:
for task in self._pending_unblocked_tasks:
executor_service.submit(task)
self._pending_unblocked_tasks = []
def _commit_bundles(self, uncommitted_bundles):
"""Commits bundles and returns a immutable set of committed bundles."""
for in_progress_bundle in uncommitted_bundles:
producing_applied_ptransform = in_progress_bundle.pcollection.producer
watermarks = self._watermark_manager.get_watermarks(
producing_applied_ptransform)
in_progress_bundle.commit(
watermarks.synchronized_processing_output_time)
return tuple(uncommitted_bundles)
def get_execution_context(self, applied_ptransform):
return _ExecutionContext(
self._watermark_manager.get_watermarks(applied_ptransform),
self._application_state_interals.get(applied_ptransform))
def create_bundle(self, output_pcollection):
"""Create an uncommitted bundle for the specified PCollection."""
return self._bundle_factory.create_bundle(output_pcollection)
def create_empty_committed_bundle(self, output_pcollection):
"""Create empty bundle useful for triggering evaluation."""
return self._bundle_factory.create_empty_committed_bundle(
output_pcollection)
def extract_fired_timers(self):
return self._watermark_manager.extract_fired_timers()
def is_done(self, transform=None):
"""Checks completion of a step or the pipeline.
Args:
transform: AppliedPTransform to check for completion.
Returns:
True if the step will not produce additional output. If transform is None
returns true if all steps are done.
"""
if transform:
return self._is_transform_done(transform)
else:
for applied_ptransform in self._step_names:
if not self._is_transform_done(applied_ptransform):
return False
return True
def _is_transform_done(self, transform):
tw = self._watermark_manager.get_watermarks(transform)
return tw.output_watermark == WatermarkManager.WATERMARK_POS_INF
def get_value_or_schedule_after_output(self, pcollection_view, task):
assert isinstance(task, TransformExecutor)
return self._side_inputs_container.get_value_or_schedule_after_output(
pcollection_view, task)