Source code for

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"""An evaluator of a specific application of a transform."""

# pytype: skip-file

import atexit
import collections
import logging
import random
import time
from collections import abc
from typing import TYPE_CHECKING
from typing import Any
from typing import Dict
from typing import List
from typing import Tuple
from typing import Type

from apache_beam import coders
from apache_beam import io
from apache_beam import pvalue
from apache_beam.internal import pickler
from apache_beam.runners import common
from apache_beam.runners.common import DoFnRunner
from apache_beam.runners.common import DoFnState
from import _DirectReadFromPubSub
from import _GroupByKeyOnly
from import _StreamingGroupAlsoByWindow
from import _StreamingGroupByKeyOnly
from import DirectUserStateContext
from import ProcessElements
from import ProcessFn
from import SDFProcessElementInvoker
from import _TestStream
from import _WatermarkController
from import KeyedWorkItem
from import TransformResult
from import WatermarkManager
from apache_beam.testing.test_stream import ElementEvent
from apache_beam.testing.test_stream import PairWithTiming
from apache_beam.testing.test_stream import ProcessingTimeEvent
from apache_beam.testing.test_stream import TimingInfo
from apache_beam.testing.test_stream import WatermarkEvent
from apache_beam.testing.test_stream import WindowedValueHolder
from apache_beam.transforms import core
from apache_beam.transforms.trigger import InMemoryUnmergedState
from apache_beam.transforms.trigger import TimeDomain
from apache_beam.transforms.trigger import _CombiningValueStateTag
from apache_beam.transforms.trigger import _ListStateTag
from apache_beam.transforms.trigger import _ReadModifyWriteStateTag
from apache_beam.transforms.trigger import create_trigger_driver
from apache_beam.transforms.userstate import get_dofn_specs
from apache_beam.transforms.userstate import is_stateful_dofn
from apache_beam.transforms.window import GlobalWindows
from apache_beam.transforms.window import WindowedValue
from apache_beam.typehints.typecheck import TypeCheckError
from apache_beam.utils import counters
from apache_beam.utils.timestamp import MIN_TIMESTAMP
from apache_beam.utils.timestamp import Timestamp

  from import _PubSubSource
  from import PubsubMessage
  from apache_beam.pipeline import AppliedPTransform
  from import EvaluationContext

_LOGGER = logging.getLogger(__name__)

[docs]class TransformEvaluatorRegistry(object): """For internal use only; no backwards-compatibility guarantees. Creates instances of TransformEvaluator for the application of a transform. """ _test_evaluators_overrides = { } # type: Dict[Type[core.PTransform], Type[_TransformEvaluator]] def __init__(self, evaluation_context): # type: (EvaluationContext) -> None assert evaluation_context self._evaluation_context = evaluation_context self._evaluators = { io.Read: _BoundedReadEvaluator, _DirectReadFromPubSub: _PubSubReadEvaluator, core.Flatten: _FlattenEvaluator, core.Impulse: _ImpulseEvaluator, core.ParDo: _ParDoEvaluator, _GroupByKeyOnly: _GroupByKeyOnlyEvaluator, _StreamingGroupByKeyOnly: _StreamingGroupByKeyOnlyEvaluator, _StreamingGroupAlsoByWindow: _StreamingGroupAlsoByWindowEvaluator, _TestStream: _TestStreamEvaluator, ProcessElements: _ProcessElementsEvaluator, _WatermarkController: _WatermarkControllerEvaluator, PairWithTiming: _PairWithTimingEvaluator, } # type: Dict[Type[core.PTransform], Type[_TransformEvaluator]] self._evaluators.update(self._test_evaluators_overrides) self._root_bundle_providers = { core.PTransform: DefaultRootBundleProvider, _TestStream: _TestStreamRootBundleProvider, }
[docs] def get_evaluator( self, applied_ptransform, input_committed_bundle, side_inputs): """Returns a TransformEvaluator suitable for processing given inputs.""" assert applied_ptransform assert bool(applied_ptransform.side_inputs) == bool(side_inputs) # Walk up the class hierarchy to find an evaluable type. This is necessary # for supporting sub-classes of core transforms. for cls in applied_ptransform.transform.__class__.mro(): evaluator = self._evaluators.get(cls) if evaluator: break if not evaluator: raise NotImplementedError( 'Execution of [%s] not implemented in runner %s.' % (type(applied_ptransform.transform), self)) return evaluator( self._evaluation_context, applied_ptransform, input_committed_bundle, side_inputs)
[docs] def get_root_bundle_provider(self, applied_ptransform): provider_cls = None for cls in applied_ptransform.transform.__class__.mro(): provider_cls = self._root_bundle_providers.get(cls) if provider_cls: break if not provider_cls: raise NotImplementedError( 'Root provider for [%s] not implemented in runner %s' % (type(applied_ptransform.transform), self)) return provider_cls(self._evaluation_context, applied_ptransform)
[docs] def should_execute_serially(self, applied_ptransform): """Returns True if this applied_ptransform should run one bundle at a time. Some TransformEvaluators use a global state object to keep track of their global execution state. For example evaluator for _GroupByKeyOnly uses this state as an in memory dictionary to buffer keys. Serially executed evaluators will act as syncing point in the graph and execution will not move forward until they receive all of their inputs. Once they receive all of their input, they will release the combined output. Their output may consist of multiple bundles as they may divide their output into pieces before releasing. Args: applied_ptransform: Transform to be used for execution. Returns: True if executor should execute applied_ptransform serially. """ if isinstance( applied_ptransform.transform, (_GroupByKeyOnly, _StreamingGroupByKeyOnly, _StreamingGroupAlsoByWindow)): return True elif (isinstance(applied_ptransform.transform, core.ParDo) and is_stateful_dofn(applied_ptransform.transform.dofn)): return True return False
[docs]class RootBundleProvider(object): """Provides bundles for the initial execution of a root transform.""" def __init__(self, evaluation_context, applied_ptransform): self._evaluation_context = evaluation_context self._applied_ptransform = applied_ptransform
[docs] def get_root_bundles(self): raise NotImplementedError
[docs]class DefaultRootBundleProvider(RootBundleProvider): """Provides an empty bundle by default for root transforms."""
[docs] def get_root_bundles(self): input_node = pvalue.PBegin(self._applied_ptransform.transform.pipeline) empty_bundle = ( self._evaluation_context.create_empty_committed_bundle(input_node)) return [empty_bundle]
class _TestStreamRootBundleProvider(RootBundleProvider): """Provides an initial bundle for the TestStream evaluator. This bundle is used as the initial state to the TestStream. Each unprocessed bundle emitted from the TestStream afterwards is its state: index into the stream, and the watermark. """ def get_root_bundles(self): test_stream = self._applied_ptransform.transform # If there was an endpoint defined then get the events from the # TestStreamService. if test_stream.endpoint: _TestStreamEvaluator.event_stream = _TestStream.events_from_rpc( test_stream.endpoint, test_stream.output_tags, test_stream.coder, self._evaluation_context) else: _TestStreamEvaluator.event_stream = ( _TestStream.events_from_script(test_stream._events)) bundle = self._evaluation_context.create_bundle( pvalue.PBegin(self._applied_ptransform.transform.pipeline)) bundle.add(GlobalWindows.windowed_value(b'', timestamp=MIN_TIMESTAMP)) bundle.commit(None) return [bundle] class _TransformEvaluator(object): """An evaluator of a specific application of a transform.""" def __init__(self, evaluation_context, # type: EvaluationContext applied_ptransform, # type: AppliedPTransform input_committed_bundle, side_inputs ): self._evaluation_context = evaluation_context self._applied_ptransform = applied_ptransform self._input_committed_bundle = input_committed_bundle self._side_inputs = side_inputs self._expand_outputs() self._execution_context = evaluation_context.get_execution_context( applied_ptransform) self._step_context = self._execution_context.get_step_context() def _expand_outputs(self): outputs = set() for pval in self._applied_ptransform.outputs.values(): if isinstance(pval, pvalue.DoOutputsTuple): pvals = (v for v in pval) else: pvals = (pval, ) for v in pvals: outputs.add(v) self._outputs = frozenset(outputs) def _split_list_into_bundles( self, output_pcollection, elements, max_element_per_bundle, element_size_fn): """Splits elements, an iterable, into multiple output bundles. Args: output_pcollection: PCollection that the elements belong to. elements: elements to be chunked into bundles. max_element_per_bundle: (approximately) the maximum element per bundle. If it is None, only a single bundle will be produced. element_size_fn: Function to return the size of a given element. Returns: List of output uncommitted bundles with at least one bundle. """ bundle = self._evaluation_context.create_bundle(output_pcollection) bundle_size = 0 bundles = [bundle] for element in elements: if max_element_per_bundle and bundle_size >= max_element_per_bundle: bundle = self._evaluation_context.create_bundle(output_pcollection) bundle_size = 0 bundles.append(bundle) bundle.output(element) bundle_size += element_size_fn(element) return bundles def start_bundle(self): """Starts a new bundle.""" pass def process_timer_wrapper(self, timer_firing): """Process timer by clearing and then calling process_timer(). This method is called with any timer firing and clears the delivered timer from the keyed state and then calls process_timer(). The default process_timer() implementation emits a KeyedWorkItem for the particular timer and passes it to process_element(). Evaluator subclasses which desire different timer delivery semantics can override process_timer(). """ state = self._step_context.get_keyed_state(timer_firing.encoded_key) state.clear_timer( timer_firing.window,, timer_firing.time_domain, dynamic_timer_tag=timer_firing.dynamic_timer_tag) self.process_timer(timer_firing) def process_timer(self, timer_firing): """Default process_timer() impl. generating KeyedWorkItem element.""" self.process_element( GlobalWindows.windowed_value( KeyedWorkItem( timer_firing.encoded_key, timer_firings=[timer_firing]))) def process_element(self, element): """Processes a new element as part of the current bundle.""" raise NotImplementedError('%s do not process elements.' % type(self)) def finish_bundle(self): # type: () -> TransformResult """Finishes the bundle and produces output.""" pass class _BoundedReadEvaluator(_TransformEvaluator): """TransformEvaluator for bounded Read transform.""" # After some benchmarks, 1000 was optimal among {100,1000,10000} MAX_ELEMENT_PER_BUNDLE = 1000 def __init__( self, evaluation_context, applied_ptransform, input_committed_bundle, side_inputs): assert not side_inputs self._source = applied_ptransform.transform.source self._source.pipeline_options = evaluation_context.pipeline_options super().__init__( evaluation_context, applied_ptransform, input_committed_bundle, side_inputs) def finish_bundle(self): assert len(self._outputs) == 1 output_pcollection = list(self._outputs)[0] def _read_values_to_bundles(reader): read_result = [GlobalWindows.windowed_value(e) for e in reader] return self._split_list_into_bundles( output_pcollection, read_result, _BoundedReadEvaluator.MAX_ELEMENT_PER_BUNDLE, lambda _: 1) if isinstance(self._source, io.iobase.BoundedSource): # Getting a RangeTracker for the default range of the source and reading # the full source using that. range_tracker = self._source.get_range_tracker(None, None) reader = bundles = _read_values_to_bundles(reader) else: with self._source.reader() as reader: bundles = _read_values_to_bundles(reader) return TransformResult(self, bundles, [], None, None) class _WatermarkControllerEvaluator(_TransformEvaluator): """TransformEvaluator for the _WatermarkController transform. This is used to enable multiple output watermarks for the TestStream. """ # The state tag used to store the watermark. WATERMARK_TAG = _ReadModifyWriteStateTag( '_WatermarkControllerEvaluator_Watermark_Tag') def __init__( self, evaluation_context, applied_ptransform, input_committed_bundle, side_inputs): assert not side_inputs self.transform = applied_ptransform.transform super().__init__( evaluation_context, applied_ptransform, input_committed_bundle, side_inputs) self._init_state() def _init_state(self): """Gets and sets the initial state. This is used to keep track of the watermark hold between calls. """ transform_states = self._evaluation_context._transform_keyed_states state = transform_states[self._applied_ptransform] if self.WATERMARK_TAG not in state: watermark_state = InMemoryUnmergedState() watermark_state.set_global_state(self.WATERMARK_TAG, MIN_TIMESTAMP) state[self.WATERMARK_TAG] = watermark_state self._state = state[self.WATERMARK_TAG] @property def _watermark(self): return self._state.get_global_state(self.WATERMARK_TAG) @_watermark.setter def _watermark(self, watermark): self._state.set_global_state(self.WATERMARK_TAG, watermark) def start_bundle(self): self.bundles = [] def process_element(self, element): # In order to keep the order of the elements between the script and what # flows through the pipeline the same, emit the elements here. event = element.value if isinstance(event, WatermarkEvent): self._watermark = event.new_watermark elif isinstance(event, ElementEvent): main_output = list(self._outputs)[0] bundle = self._evaluation_context.create_bundle(main_output) for tv in event.timestamped_values: # Unreify the value into the correct window. if isinstance(tv.value, WindowedValueHolder): bundle.output(tv.value.windowed_value) else: bundle.output( GlobalWindows.windowed_value(tv.value, timestamp=tv.timestamp)) self.bundles.append(bundle) def finish_bundle(self): # The watermark hold we set here is the way we allow the TestStream events # to control the output watermark. return TransformResult( self, self.bundles, [], None, {None: self._watermark}) class _PairWithTimingEvaluator(_TransformEvaluator): """TransformEvaluator for the PairWithTiming transform. This transform takes an element as an input and outputs KV(element, `TimingInfo`). Where the `TimingInfo` contains both the processing time timestamp and watermark. """ def __init__( self, evaluation_context, applied_ptransform, input_committed_bundle, side_inputs): assert not side_inputs super().__init__( evaluation_context, applied_ptransform, input_committed_bundle, side_inputs) def start_bundle(self): main_output = list(self._outputs)[0] self.bundle = self._evaluation_context.create_bundle(main_output) watermark_manager = self._evaluation_context._watermark_manager watermarks = watermark_manager.get_watermarks(self._applied_ptransform) output_watermark = watermarks.output_watermark now = Timestamp(seconds=watermark_manager._clock.time()) self.timing_info = TimingInfo(now, output_watermark) def process_element(self, element): result = WindowedValue((element.value, self.timing_info), element.timestamp,, element.pane_info) self.bundle.output(result) def finish_bundle(self): return TransformResult(self, [self.bundle], [], None, {}) class _TestStreamEvaluator(_TransformEvaluator): """TransformEvaluator for the TestStream transform. This evaluator's responsibility is to retrieve the next event from the _TestStream and either: advance the clock, advance the _TestStream watermark, or pass the event to the _WatermarkController. The _WatermarkController is in charge of emitting the elements to the downstream consumers and setting its own output watermark. """ event_stream = None def __init__( self, evaluation_context, applied_ptransform, input_committed_bundle, side_inputs): assert not side_inputs super().__init__( evaluation_context, applied_ptransform, input_committed_bundle, side_inputs) self.test_stream = applied_ptransform.transform self.is_done = False def start_bundle(self): self.bundles = [] self.watermark = MIN_TIMESTAMP def process_element(self, element): # The watermark of the _TestStream transform itself. self.watermark = element.timestamp # Set up the correct watermark holds in the Watermark controllers and the # TestStream so that the watermarks will not automatically advance to +inf # when elements start streaming. This can happen multiple times in the first # bundle, but the operations are idempotent and adding state to keep track # of this would add unnecessary code complexity. events = [] if self.watermark == MIN_TIMESTAMP: for event in self.test_stream._set_up(self.test_stream.output_tags): events.append(event) # Retrieve the TestStream's event stream and read from it. try: events.append(next(self.event_stream)) except StopIteration: # Advance the watermarks to +inf to cleanly stop the pipeline. self.is_done = True events += ([ e for e in self.test_stream._tear_down(self.test_stream.output_tags) ]) for event in events: # We can either have the _TestStream or the _WatermarkController to emit # the elements. We chose to emit in the _WatermarkController so that the # element is emitted at the correct watermark value. if isinstance(event, (ElementEvent, WatermarkEvent)): # The WATERMARK_CONTROL_TAG is used to hold the _TestStream's # watermark to -inf, then +inf-1, then +inf. This watermark progression # is ultimately used to set up the proper holds to allow the # _WatermarkControllers to control their own output watermarks. if event.tag == _TestStream.WATERMARK_CONTROL_TAG: self.watermark = event.new_watermark else: main_output = list(self._outputs)[0] bundle = self._evaluation_context.create_bundle(main_output) bundle.output(GlobalWindows.windowed_value(event)) self.bundles.append(bundle) elif isinstance(event, ProcessingTimeEvent): self._evaluation_context._watermark_manager._clock.advance_time( event.advance_by) else: raise ValueError('Invalid TestStream event: %s.' % event) def finish_bundle(self): unprocessed_bundles = [] # Continue to send its own state to itself via an unprocessed bundle. This # acts as a heartbeat, where each element will read the next event from the # event stream. if not self.is_done: unprocessed_bundle = self._evaluation_context.create_bundle( pvalue.PBegin(self._applied_ptransform.transform.pipeline)) unprocessed_bundle.add( GlobalWindows.windowed_value(b'', timestamp=self.watermark)) unprocessed_bundles.append(unprocessed_bundle) # Returning the watermark in the dict here is used as a watermark hold. return TransformResult( self, self.bundles, unprocessed_bundles, None, {None: self.watermark}) class _PubSubReadEvaluator(_TransformEvaluator): """TransformEvaluator for PubSub read.""" # A mapping of transform to _PubSubSubscriptionWrapper. # TODO( Prevents garbage # collection of pipeline instances. _subscription_cache = {} # type: Dict[AppliedPTransform, str] def __init__( self, evaluation_context, applied_ptransform, input_committed_bundle, side_inputs): assert not side_inputs super().__init__( evaluation_context, applied_ptransform, input_committed_bundle, side_inputs) self.source = self._applied_ptransform.transform._source # type: _PubSubSource if self.source.id_label: raise NotImplementedError( 'DirectRunner: id_label is not supported for PubSub reads') sub_project = None if hasattr(self._evaluation_context, 'pipeline_options'): from apache_beam.options.pipeline_options import GoogleCloudOptions sub_project = ( self._evaluation_context.pipeline_options.view_as( GoogleCloudOptions).project) if not sub_project: sub_project = self.source.project self._sub_name = self.get_subscription( self._applied_ptransform, self.source.project, self.source.topic_name, sub_project, self.source.subscription_name) @classmethod def get_subscription( cls, transform, project, short_topic_name, sub_project, short_sub_name): from import pubsub if short_sub_name: return pubsub.SubscriberClient.subscription_path(project, short_sub_name) if transform in cls._subscription_cache: return cls._subscription_cache[transform] sub_client = pubsub.SubscriberClient() sub_name = sub_client.subscription_path( sub_project, 'beam_%d_%x' % (int(time.time()), random.randrange(1 << 32))) topic_name = sub_client.topic_path(project, short_topic_name) sub_client.create_subscription(name=sub_name, topic=topic_name) atexit.register(sub_client.delete_subscription, subscription=sub_name) cls._subscription_cache[transform] = sub_name return cls._subscription_cache[transform] def start_bundle(self): pass def process_element(self, element): pass def _read_from_pubsub(self, timestamp_attribute): # type: (...) -> List[Tuple[Timestamp, PubsubMessage]] from import PubsubMessage from import pubsub def _get_element(message): parsed_message = PubsubMessage._from_message(message) if (timestamp_attribute and timestamp_attribute in parsed_message.attributes): rfc3339_or_milli = parsed_message.attributes[timestamp_attribute] try: timestamp = Timestamp(micros=int(rfc3339_or_milli) * 1000) except ValueError: try: timestamp = Timestamp.from_rfc3339(rfc3339_or_milli) except ValueError as e: raise ValueError('Bad timestamp value: %s' % e) else: if message.publish_time is None: raise ValueError('No publish time present in message: %s' % message) try: timestamp = Timestamp.from_utc_datetime(message.publish_time) except ValueError as e: raise ValueError('Bad timestamp value for message %s: %s', message, e) return timestamp, parsed_message # Because of the AutoAck, we are not able to reread messages if this # evaluator fails with an exception before emitting a bundle. However, # the DirectRunner currently doesn't retry work items anyway, so the # pipeline would enter an inconsistent state on any error. sub_client = pubsub.SubscriberClient() try: response = sub_client.pull( subscription=self._sub_name, max_messages=10, timeout=30) results = [_get_element(rm.message) for rm in response.received_messages] ack_ids = [rm.ack_id for rm in response.received_messages] if ack_ids: sub_client.acknowledge(subscription=self._sub_name, ack_ids=ack_ids) finally: sub_client.close() return results def finish_bundle(self): # type: () -> TransformResult data = self._read_from_pubsub(self.source.timestamp_attribute) if data: output_pcollection = list(self._outputs)[0] bundle = self._evaluation_context.create_bundle(output_pcollection) # TODO(ccy): Respect the PubSub source's id_label field. for timestamp, message in data: if self.source.with_attributes: element = message else: element = bundle.output( GlobalWindows.windowed_value(element, timestamp=timestamp)) bundles = [bundle] else: bundles = [] assert self._applied_ptransform.transform is not None if self._applied_ptransform.inputs: input_pvalue = self._applied_ptransform.inputs[0] else: input_pvalue = pvalue.PBegin(self._applied_ptransform.transform.pipeline) unprocessed_bundle = self._evaluation_context.create_bundle(input_pvalue) # TODO(udim): Correct value for watermark hold. return TransformResult( self, bundles, [unprocessed_bundle], None, {None: Timestamp.of(time.time())}) class _FlattenEvaluator(_TransformEvaluator): """TransformEvaluator for Flatten transform.""" def __init__( self, evaluation_context, applied_ptransform, input_committed_bundle, side_inputs): assert not side_inputs super().__init__( evaluation_context, applied_ptransform, input_committed_bundle, side_inputs) def start_bundle(self): assert len(self._outputs) == 1 output_pcollection = list(self._outputs)[0] self.bundle = self._evaluation_context.create_bundle(output_pcollection) def process_element(self, element): self.bundle.output(element) def finish_bundle(self): bundles = [self.bundle] return TransformResult(self, bundles, [], None, None) class _ImpulseEvaluator(_TransformEvaluator): """TransformEvaluator for Impulse transform.""" def finish_bundle(self): assert len(self._outputs) == 1 output_pcollection = list(self._outputs)[0] bundle = self._evaluation_context.create_bundle(output_pcollection) bundle.output(GlobalWindows.windowed_value(b'')) return TransformResult(self, [bundle], [], None, None) class _TaggedReceivers(dict): """Received ParDo output and redirect to the associated output bundle.""" def __init__(self, evaluation_context): self._evaluation_context = evaluation_context self._null_receiver = None super().__init__() class NullReceiver(common.Receiver): """Ignores undeclared outputs, default execution mode.""" def receive(self, element): # type: (WindowedValue) -> None pass class _InMemoryReceiver(common.Receiver): """Buffers undeclared outputs to the given dictionary.""" def __init__(self, target, tag): self._target = target self._tag = tag def receive(self, element): # type: (WindowedValue) -> None self._target[self._tag].append(element) def __missing__(self, key): if not self._null_receiver: self._null_receiver = _TaggedReceivers.NullReceiver() return self._null_receiver class _ParDoEvaluator(_TransformEvaluator): """TransformEvaluator for ParDo transform.""" def __init__(self, evaluation_context, # type: EvaluationContext applied_ptransform, # type: AppliedPTransform input_committed_bundle, side_inputs, perform_dofn_pickle_test=True ): super().__init__( evaluation_context, applied_ptransform, input_committed_bundle, side_inputs) # This is a workaround for SDF implementation. SDF implementation adds state # to the SDF that is not picklable. self._perform_dofn_pickle_test = perform_dofn_pickle_test def start_bundle(self): transform = self._applied_ptransform.transform self._tagged_receivers = _TaggedReceivers(self._evaluation_context) for output_tag in self._applied_ptransform.outputs: output_pcollection = pvalue.PCollection(None, tag=output_tag) output_pcollection.producer = self._applied_ptransform self._tagged_receivers[output_tag] = ( self._evaluation_context.create_bundle(output_pcollection)) self._tagged_receivers[output_tag].tag = output_tag self._counter_factory = counters.CounterFactory() # TODO(aaltay): Consider storing the serialized form as an optimization. dofn = ( pickler.loads(pickler.dumps(transform.dofn)) if self._perform_dofn_pickle_test else transform.dofn) args = transform.args if hasattr(transform, 'args') else [] kwargs = transform.kwargs if hasattr(transform, 'kwargs') else {} self.user_state_context = None self.user_timer_map = {} if is_stateful_dofn(dofn): kv_type_hint = self._applied_ptransform.inputs[0].element_type if kv_type_hint and kv_type_hint != Any: coder = coders.registry.get_coder(kv_type_hint) self.key_coder = coder.key_coder() else: self.key_coder = coders.registry.get_coder(Any) self.user_state_context = DirectUserStateContext( self._step_context, dofn, self.key_coder) _, all_timer_specs = get_dofn_specs(dofn) for timer_spec in all_timer_specs: self.user_timer_map['user/%s' %] = timer_spec self.runner = DoFnRunner( dofn, args, kwargs, self._side_inputs, self._applied_ptransform.inputs[0].windowing, tagged_receivers=self._tagged_receivers, step_name=self._applied_ptransform.full_label, state=DoFnState(self._counter_factory), user_state_context=self.user_state_context) self.runner.setup() self.runner.start() def process_timer(self, timer_firing): if not in self.user_timer_map: _LOGGER.warning('Unknown timer fired: %s', timer_firing) timer_spec = self.user_timer_map[] self.runner.process_user_timer( timer_spec, self.key_coder.decode(timer_firing.encoded_key), timer_firing.window, timer_firing.timestamp, # TODO Add paneinfo to timer_firing in DirectRunner None, timer_firing.dynamic_timer_tag) def process_element(self, element): self.runner.process(element) def finish_bundle(self): self.runner.finish() self.runner.teardown() bundles = list(self._tagged_receivers.values()) result_counters = self._counter_factory.get_counters() if self.user_state_context: self.user_state_context.commit() self.user_state_context.reset() return TransformResult(self, bundles, [], result_counters, None) class _GroupByKeyOnlyEvaluator(_TransformEvaluator): """TransformEvaluator for _GroupByKeyOnly transform.""" MAX_ELEMENT_PER_BUNDLE = None ELEMENTS_TAG = _ListStateTag('elements') COMPLETION_TAG = _CombiningValueStateTag('completed', any) def __init__( self, evaluation_context, applied_ptransform, input_committed_bundle, side_inputs): assert not side_inputs super().__init__( evaluation_context, applied_ptransform, input_committed_bundle, side_inputs) def _is_final_bundle(self): return ( self._execution_context.watermarks.input_watermark == WatermarkManager.WATERMARK_POS_INF) def start_bundle(self): self.global_state = self._step_context.get_keyed_state(None) assert len(self._outputs) == 1 self.output_pcollection = list(self._outputs)[0] # The output type of a GroupByKey will be Tuple[Any, Any] or more specific. # TODO( Infer coders earlier. kv_type_hint = ( self._applied_ptransform.outputs[None].element_type or self._applied_ptransform.transform.get_type_hints().input_types[0][0]) self.key_coder = coders.registry.get_coder(kv_type_hint.tuple_types[0]) def process_timer(self, timer_firing): # We do not need to emit a KeyedWorkItem to process_element(). pass def process_element(self, element): assert not self.global_state.get_state( None, _GroupByKeyOnlyEvaluator.COMPLETION_TAG) if (isinstance(element, WindowedValue) and isinstance(element.value, abc.Iterable) and len(element.value) == 2): k, v = element.value encoded_k = self.key_coder.encode(k) state = self._step_context.get_keyed_state(encoded_k) state.add_state(None, _GroupByKeyOnlyEvaluator.ELEMENTS_TAG, v) else: raise TypeCheckError( 'Input to _GroupByKeyOnly must be a PCollection of ' 'windowed key-value pairs. Instead received: %r.' % element) def finish_bundle(self): if self._is_final_bundle(): if self.global_state.get_state(None, _GroupByKeyOnlyEvaluator.COMPLETION_TAG): # Ignore empty bundles after emitting output. (This may happen because # empty bundles do not affect input watermarks.) bundles = [] else: gbk_result = [] # TODO(ccy): perhaps we can clean this up to not use this # internal attribute of the DirectStepContext. for encoded_k in self._step_context.existing_keyed_state: # Ignore global state. if encoded_k is None: continue k = self.key_coder.decode(encoded_k) state = self._step_context.get_keyed_state(encoded_k) vs = state.get_state(None, _GroupByKeyOnlyEvaluator.ELEMENTS_TAG) gbk_result.append(GlobalWindows.windowed_value((k, vs))) def len_element_fn(element): _, v = element.value return len(v) bundles = self._split_list_into_bundles( self.output_pcollection, gbk_result, _GroupByKeyOnlyEvaluator.MAX_ELEMENT_PER_BUNDLE, len_element_fn) self.global_state.add_state( None, _GroupByKeyOnlyEvaluator.COMPLETION_TAG, True) hold = WatermarkManager.WATERMARK_POS_INF else: bundles = [] hold = WatermarkManager.WATERMARK_NEG_INF self.global_state.set_timer( None, '', TimeDomain.WATERMARK, WatermarkManager.WATERMARK_POS_INF) return TransformResult(self, bundles, [], None, {None: hold}) class _StreamingGroupByKeyOnlyEvaluator(_TransformEvaluator): """TransformEvaluator for _StreamingGroupByKeyOnly transform. The _GroupByKeyOnlyEvaluator buffers elements until its input watermark goes to infinity, which is suitable for batch mode execution. During streaming mode execution, we emit each bundle as it comes to the next transform. """ MAX_ELEMENT_PER_BUNDLE = None def __init__( self, evaluation_context, applied_ptransform, input_committed_bundle, side_inputs): assert not side_inputs super().__init__( evaluation_context, applied_ptransform, input_committed_bundle, side_inputs) def start_bundle(self): self.gbk_items = collections.defaultdict(list) assert len(self._outputs) == 1 self.output_pcollection = list(self._outputs)[0] # The input type of a GroupByKey will be Tuple[Any, Any] or more specific. kv_type_hint = self._applied_ptransform.inputs[0].element_type key_type_hint = (kv_type_hint.tuple_types[0] if kv_type_hint else Any) self.key_coder = coders.registry.get_coder(key_type_hint) def process_element(self, element): if (isinstance(element, WindowedValue) and isinstance(element.value, and len(element.value) == 2): k, v = element.value self.gbk_items[self.key_coder.encode(k)].append(v) else: raise TypeCheckError( 'Input to _GroupByKeyOnly must be a PCollection of ' 'windowed key-value pairs. Instead received: %r.' % element) def finish_bundle(self): bundles = [] bundle = None for encoded_k, vs in self.gbk_items.items(): if not bundle: bundle = self._evaluation_context.create_bundle(self.output_pcollection) bundles.append(bundle) kwi = KeyedWorkItem(encoded_k, elements=vs) bundle.add(GlobalWindows.windowed_value(kwi)) return TransformResult(self, bundles, [], None, None) class _StreamingGroupAlsoByWindowEvaluator(_TransformEvaluator): """TransformEvaluator for the _StreamingGroupAlsoByWindow transform. This evaluator is only used in streaming mode. In batch mode, the GroupAlsoByWindow operation is evaluated as a normal DoFn, as defined in transforms/ """ def __init__( self, evaluation_context, applied_ptransform, input_committed_bundle, side_inputs): assert not side_inputs super().__init__( evaluation_context, applied_ptransform, input_committed_bundle, side_inputs) def start_bundle(self): assert len(self._outputs) == 1 self.output_pcollection = list(self._outputs)[0] self.driver = create_trigger_driver( self._applied_ptransform.transform.windowing, clock=self._evaluation_context._watermark_manager._clock) self.gabw_items = [] self.keyed_holds = {} # The input type (which is the same as the output type) of a # GroupAlsoByWindow will be Tuple[Any, Iter[Any]] or more specific. kv_type_hint = self._applied_ptransform.outputs[None].element_type key_type_hint = (kv_type_hint.tuple_types[0] if kv_type_hint else Any) self.key_coder = coders.registry.get_coder(key_type_hint) def process_element(self, element): kwi = element.value assert isinstance(kwi, KeyedWorkItem), kwi encoded_k, timer_firings, vs = ( kwi.encoded_key, kwi.timer_firings, kwi.elements) k = self.key_coder.decode(encoded_k) state = self._step_context.get_keyed_state(encoded_k) watermarks = self._evaluation_context._watermark_manager.get_watermarks( self._applied_ptransform) for timer_firing in timer_firings: for wvalue in self.driver.process_timer(timer_firing.window,, timer_firing.time_domain, timer_firing.timestamp, state, watermarks.input_watermark): self.gabw_items.append(wvalue.with_value((k, wvalue.value))) if vs: for wvalue in self.driver.process_elements(state, vs, watermarks.output_watermark, watermarks.input_watermark): self.gabw_items.append(wvalue.with_value((k, wvalue.value))) self.keyed_holds[encoded_k] = state.get_earliest_hold() def finish_bundle(self): bundles = [] if self.gabw_items: bundle = self._evaluation_context.create_bundle(self.output_pcollection) for item in self.gabw_items: bundle.add(item) bundles.append(bundle) return TransformResult(self, bundles, [], None, self.keyed_holds) class _ProcessElementsEvaluator(_TransformEvaluator): """An evaluator for sdf_direct_runner.ProcessElements transform.""" # Maximum number of elements that will be produced by a Splittable DoFn before # a checkpoint is requested by the runner. DEFAULT_MAX_NUM_OUTPUTS = None # Maximum duration a Splittable DoFn will process an element before a # checkpoint is requested by the runner. DEFAULT_MAX_DURATION = 1 def __init__( self, evaluation_context, applied_ptransform, input_committed_bundle, side_inputs): super().__init__( evaluation_context, applied_ptransform, input_committed_bundle, side_inputs) process_elements_transform = applied_ptransform.transform assert isinstance(process_elements_transform, ProcessElements) # Replacing the do_fn of the transform with a wrapper do_fn that performs # SDF magic. transform = applied_ptransform.transform sdf = transform.sdf self._process_fn = transform.new_process_fn(sdf) transform.dofn = self._process_fn assert isinstance(self._process_fn, ProcessFn) self._process_fn.step_context = self._step_context process_element_invoker = ( SDFProcessElementInvoker( max_num_outputs=self.DEFAULT_MAX_NUM_OUTPUTS, max_duration=self.DEFAULT_MAX_DURATION)) self._process_fn.set_process_element_invoker(process_element_invoker) self._par_do_evaluator = _ParDoEvaluator( evaluation_context, applied_ptransform, input_committed_bundle, side_inputs, perform_dofn_pickle_test=False) self.keyed_holds = {} def start_bundle(self): self._par_do_evaluator.start_bundle() def process_element(self, element): assert isinstance(element, WindowedValue) assert len( == 1 window =[0] if isinstance(element.value, KeyedWorkItem): key = element.value.encoded_key else: # If not a `KeyedWorkItem`, this must be a tuple where key is a randomly # generated key and the value is a `WindowedValue` that contains an # `ElementAndRestriction` object. assert isinstance(element.value, tuple) key = element.value[0] self._par_do_evaluator.process_element(element) state = self._step_context.get_keyed_state(key) self.keyed_holds[key] = state.get_state( window, self._process_fn.watermark_hold_tag) def finish_bundle(self): par_do_result = self._par_do_evaluator.finish_bundle() transform_result = TransformResult( self, par_do_result.uncommitted_output_bundles, par_do_result.unprocessed_bundles, par_do_result.counters, par_do_result.keyed_watermark_holds, par_do_result.undeclared_tag_values) for key, keyed_hold in self.keyed_holds.items(): transform_result.keyed_watermark_holds[key] = keyed_hold return transform_result