Source code for apache_beam.runners.direct.transform_evaluator

#
# 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.
#

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

from __future__ import absolute_import

import collections
import logging
import random
import time
from builtins import object

from future.utils import iteritems

import apache_beam.io as io
from apache_beam import coders
from apache_beam import pvalue
from apache_beam import typehints
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 apache_beam.runners.dataflow.native_io.iobase import _NativeWrite  # pylint: disable=protected-access
from apache_beam.runners.direct.direct_runner import _DirectReadFromPubSub
from apache_beam.runners.direct.direct_runner import _StreamingGroupAlsoByWindow
from apache_beam.runners.direct.direct_runner import _StreamingGroupByKeyOnly
from apache_beam.runners.direct.direct_userstate import DirectUserStateContext
from apache_beam.runners.direct.sdf_direct_runner import ProcessElements
from apache_beam.runners.direct.sdf_direct_runner import ProcessFn
from apache_beam.runners.direct.sdf_direct_runner import SDFProcessElementInvoker
from apache_beam.runners.direct.util import KeyedWorkItem
from apache_beam.runners.direct.util import TransformResult
from apache_beam.runners.direct.watermark_manager import WatermarkManager
from apache_beam.testing.test_stream import ElementEvent
from apache_beam.testing.test_stream import ProcessingTimeEvent
from apache_beam.testing.test_stream import TestStream
from apache_beam.testing.test_stream import WatermarkEvent
from apache_beam.transforms import core
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 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


[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 = {} def __init__(self, evaluation_context): assert evaluation_context self._evaluation_context = evaluation_context self._evaluators = { io.Read: _BoundedReadEvaluator, _DirectReadFromPubSub: _PubSubReadEvaluator, core.Flatten: _FlattenEvaluator, core.ParDo: _ParDoEvaluator, core._GroupByKeyOnly: _GroupByKeyOnlyEvaluator, _StreamingGroupByKeyOnly: _StreamingGroupByKeyOnlyEvaluator, _StreamingGroupAlsoByWindow: _StreamingGroupAlsoByWindowEvaluator, _NativeWrite: _NativeWriteEvaluator, TestStream: _TestStreamEvaluator, ProcessElements: _ProcessElementsEvaluator } 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, (core._GroupByKeyOnly, _StreamingGroupByKeyOnly, _StreamingGroupAlsoByWindow, _NativeWrite)): 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.""" def get_root_bundles(self): test_stream = self._applied_ptransform.transform bundles = [] if len(test_stream.events) > 0: bundle = self._evaluation_context.create_bundle( pvalue.PBegin(self._applied_ptransform.transform.pipeline)) # Explicitly set timestamp to MIN_TIMESTAMP to ensure that we hold the # watermark. bundle.add(GlobalWindows.windowed_value(0, timestamp=MIN_TIMESTAMP)) bundle.commit(None) bundles.append(bundle) return bundles class _TransformEvaluator(object): """An evaluator of a specific application of a transform.""" def __init__(self, evaluation_context, applied_ptransform, 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.name, timer_firing.time_domain) 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): """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(_BoundedReadEvaluator, self).__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 = self._source.read(range_tracker) 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 _TestStreamEvaluator(_TransformEvaluator): """TransformEvaluator for the TestStream transform.""" def __init__(self, evaluation_context, applied_ptransform, input_committed_bundle, side_inputs): assert not side_inputs self.test_stream = applied_ptransform.transform super(_TestStreamEvaluator, self).__init__( evaluation_context, applied_ptransform, input_committed_bundle, side_inputs) def start_bundle(self): self.current_index = -1 self.watermark = MIN_TIMESTAMP self.bundles = [] def process_element(self, element): index = element.value self.watermark = element.timestamp assert isinstance(index, int) assert 0 <= index <= len(self.test_stream.events) self.current_index = index event = self.test_stream.events[self.current_index] if isinstance(event, ElementEvent): assert len(self._outputs) == 1 output_pcollection = list(self._outputs)[0] bundle = self._evaluation_context.create_bundle(output_pcollection) for tv in event.timestamped_values: bundle.output( GlobalWindows.windowed_value(tv.value, timestamp=tv.timestamp)) self.bundles.append(bundle) elif isinstance(event, WatermarkEvent): assert event.new_watermark >= self.watermark self.watermark = event.new_watermark 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 = [] hold = None if self.current_index < len(self.test_stream.events) - 1: unprocessed_bundle = self._evaluation_context.create_bundle( pvalue.PBegin(self._applied_ptransform.transform.pipeline)) unprocessed_bundle.add(GlobalWindows.windowed_value( self.current_index + 1, timestamp=self.watermark)) unprocessed_bundles.append(unprocessed_bundle) hold = self.watermark return TransformResult( self, self.bundles, unprocessed_bundles, None, {None: hold}) class _PubSubSubscriptionWrapper(object): """Wrapper for garbage-collecting temporary PubSub subscriptions.""" def __init__(self, subscription, should_cleanup): self.subscription = subscription self.should_cleanup = should_cleanup def __del__(self): if self.should_cleanup: self.subscription.delete() class _PubSubReadEvaluator(_TransformEvaluator): """TransformEvaluator for PubSub read.""" _subscription_cache = {} def __init__(self, evaluation_context, applied_ptransform, input_committed_bundle, side_inputs): assert not side_inputs super(_PubSubReadEvaluator, self).__init__( evaluation_context, applied_ptransform, input_committed_bundle, side_inputs) self.source = self._applied_ptransform.transform._source if self.source.id_label: raise NotImplementedError( 'DirectRunner: id_label is not supported for PubSub reads') self._subscription = _PubSubReadEvaluator.get_subscription( self._applied_ptransform, self.source.project, self.source.topic_name, self.source.subscription_name) @classmethod def get_subscription(cls, transform, project, topic, subscription_name): if transform not in cls._subscription_cache: from google.cloud import pubsub should_create = not subscription_name if should_create: subscription_name = 'beam_%d_%x' % ( int(time.time()), random.randrange(1 << 32)) wrapper = _PubSubSubscriptionWrapper( pubsub.Client(project=project).topic(topic).subscription( subscription_name), should_create) if should_create: wrapper.subscription.create() cls._subscription_cache[transform] = wrapper return cls._subscription_cache[transform].subscription def start_bundle(self): pass def process_element(self, element): pass def _read_from_pubsub(self, timestamp_attribute): from apache_beam.io.gcp.pubsub import PubsubMessage from google.cloud import pubsub # 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. with pubsub.subscription.AutoAck( self._subscription, return_immediately=True, max_messages=10) as results: 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.from_rfc3339(rfc3339_or_milli) except ValueError: try: timestamp = Timestamp(micros=int(rfc3339_or_milli) * 1000) except ValueError as e: raise ValueError('Bad timestamp value: %s' % e) else: timestamp = Timestamp.from_rfc3339(message.service_timestamp) return timestamp, parsed_message return [_get_element(message) for unused_ack_id, message in iteritems(results)] def finish_bundle(self): 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 = message.data bundle.output( GlobalWindows.windowed_value(element, timestamp=timestamp)) bundles = [bundle] else: bundles = [] 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(_FlattenEvaluator, self).__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 _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(_TaggedReceivers, self).__init__() class NullReceiver(common.Receiver): """Ignores undeclared outputs, default execution mode.""" def receive(self, element): 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): 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, applied_ptransform, input_committed_bundle, side_inputs, perform_dofn_pickle_test=True): super(_ParDoEvaluator, self).__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 != typehints.Any: coder = coders.registry.get_coder(kv_type_hint) self.key_coder = coder.key_coder() else: self.key_coder = coders.registry.get_coder(typehints.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.name] = 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.start() def process_timer(self, timer_firing): if timer_firing.name not in self.user_timer_map: logging.warning('Unknown timer fired: %s', timer_firing) timer_spec = self.user_timer_map[timer_firing.name] self.runner.process_user_timer( timer_spec, self.key_coder.decode(timer_firing.encoded_key), timer_firing.window, timer_firing.timestamp) def process_element(self, element): self.runner.process(element) def finish_bundle(self): self.runner.finish() bundles = list(self._tagged_receivers.values()) result_counters = self._counter_factory.get_counters() if self.user_state_context: self.user_state_context.commit() 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(_GroupByKeyOnlyEvaluator, self).__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 KV[Any, Any] or more specific. # TODO(BEAM-2717): 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, collections.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(_StreamingGroupByKeyOnlyEvaluator, self).__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 KV[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 typehints.Any) self.key_coder = coders.registry.get_coder(key_type_hint) def process_element(self, element): if (isinstance(element, WindowedValue) and isinstance(element.value, collections.Iterable) 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 iteritems(self.gbk_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/core.py. """ def __init__(self, evaluation_context, applied_ptransform, input_committed_bundle, side_inputs): assert not side_inputs super(_StreamingGroupAlsoByWindowEvaluator, self).__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 KV[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 typehints.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) for timer_firing in timer_firings: for wvalue in self.driver.process_timer( timer_firing.window, timer_firing.name, timer_firing.time_domain, timer_firing.timestamp, state): self.gabw_items.append(wvalue.with_value((k, wvalue.value))) if vs: for wvalue in self.driver.process_elements(state, vs, MIN_TIMESTAMP): 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 _NativeWriteEvaluator(_TransformEvaluator): """TransformEvaluator for _NativeWrite transform.""" ELEMENTS_TAG = _ListStateTag('elements') def __init__(self, evaluation_context, applied_ptransform, input_committed_bundle, side_inputs): assert not side_inputs super(_NativeWriteEvaluator, self).__init__( evaluation_context, applied_ptransform, input_committed_bundle, side_inputs) assert applied_ptransform.transform.sink self._sink = applied_ptransform.transform.sink @property def _is_final_bundle(self): return (self._execution_context.watermarks.input_watermark == WatermarkManager.WATERMARK_POS_INF) @property def _has_already_produced_output(self): return (self._execution_context.watermarks.output_watermark == WatermarkManager.WATERMARK_POS_INF) def start_bundle(self): self.global_state = self._step_context.get_keyed_state(None) def process_timer(self, timer_firing): # We do not need to emit a KeyedWorkItem to process_element(). pass def process_element(self, element): self.global_state.add_state( None, _NativeWriteEvaluator.ELEMENTS_TAG, element) def finish_bundle(self): # finish_bundle will append incoming bundles in memory until all the bundles # carrying data is processed. This is done to produce only a single output # shard (some tests depends on this behavior). It is possible to have # incoming empty bundles after the output is produced, these bundles will be # ignored and would not generate additional output files. # TODO(altay): Do not wait until the last bundle to write in a single shard. if self._is_final_bundle: elements = self.global_state.get_state( None, _NativeWriteEvaluator.ELEMENTS_TAG) if self._has_already_produced_output: # Ignore empty bundles that arrive after the output is produced. assert elements == [] else: self._sink.pipeline_options = self._evaluation_context.pipeline_options with self._sink.writer() as writer: for v in elements: writer.Write(v.value) hold = WatermarkManager.WATERMARK_POS_INF else: hold = WatermarkManager.WATERMARK_NEG_INF self.global_state.set_timer( None, '', TimeDomain.WATERMARK, WatermarkManager.WATERMARK_POS_INF) return TransformResult(self, [], [], None, {None: hold}) 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 = 100 # 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(_ProcessElementsEvaluator, self).__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(element.windows) == 1 window = element.windows[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 in self.keyed_holds: transform_result.keyed_watermark_holds[key] = self.keyed_holds[key] return transform_result