# # 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. # """Worker operations executor. For internal use only; no backwards-compatibility guarantees. """ # pytype: skip-file import logging import sys import threading import traceback from enum import Enum from typing import TYPE_CHECKING from typing import Any from typing import Dict from typing import Iterable from typing import List from typing import Mapping from typing import Optional from typing import Tuple from apache_beam.coders import TupleCoder from apache_beam.coders import coders from apache_beam.internal import util from apache_beam.options.value_provider import RuntimeValueProvider from apache_beam.pvalue import TaggedOutput from apache_beam.runners.sdf_utils import NoOpWatermarkEstimatorProvider from apache_beam.runners.sdf_utils import RestrictionTrackerView from apache_beam.runners.sdf_utils import SplitResultPrimary from apache_beam.runners.sdf_utils import SplitResultResidual from apache_beam.runners.sdf_utils import ThreadsafeRestrictionTracker from apache_beam.runners.sdf_utils import ThreadsafeWatermarkEstimator from apache_beam.transforms import DoFn from apache_beam.transforms import core from apache_beam.transforms import userstate from apache_beam.transforms.core import RestrictionProvider from apache_beam.transforms.core import WatermarkEstimatorProvider from apache_beam.transforms.window import GlobalWindow from apache_beam.transforms.window import GlobalWindows from apache_beam.transforms.window import TimestampedValue from apache_beam.transforms.window import WindowFn from apache_beam.typehints.batch import BatchConverter from apache_beam.utils.counters import Counter from apache_beam.utils.counters import CounterName from apache_beam.utils.timestamp import Timestamp from apache_beam.utils.windowed_value import HomogeneousWindowedBatch from apache_beam.utils.windowed_value import WindowedBatch from apache_beam.utils.windowed_value import WindowedValue if TYPE_CHECKING: from apache_beam.runners.worker.bundle_processor import ExecutionContext from apache_beam.transforms import sideinputs from apache_beam.transforms.core import TimerSpec from apache_beam.io.iobase import RestrictionProgress from apache_beam.iobase import RestrictionTracker from apache_beam.iobase import WatermarkEstimator IMPULSE_VALUE_CODER_IMPL = coders.WindowedValueCoder( coders.BytesCoder(), coders.GlobalWindowCoder()).get_impl() ENCODED_IMPULSE_VALUE = IMPULSE_VALUE_CODER_IMPL.encode_nested( GlobalWindows.windowed_value(b'')) _LOGGER = logging.getLogger(__name__) class NameContext(object): """Holds the name information for a step.""" def __init__(self, step_name, transform_id=None): # type: (str, Optional[str]) -> None """Creates a new step NameContext. Args: step_name: The name of the step. """ self.step_name = step_name self.transform_id = transform_id def __eq__(self, other): return self.step_name == other.step_name def __repr__(self): return 'NameContext(%s)' % self.__dict__ def __hash__(self): return hash(self.step_name) def metrics_name(self): """Returns the step name used for metrics reporting.""" return self.step_name def logging_name(self): """Returns the step name used for logging.""" return self.step_name class Receiver(object): """For internal use only; no backwards-compatibility guarantees. An object that consumes a WindowedValue. This class can be efficiently used to pass values between the sdk and worker harnesses. """ def receive(self, windowed_value): # type: (WindowedValue) -> None raise NotImplementedError def receive_batch(self, windowed_batch): # type: (WindowedBatch) -> None raise NotImplementedError def flush(self): raise NotImplementedError class MethodWrapper(object): """For internal use only; no backwards-compatibility guarantees. Represents a method that can be invoked by `DoFnInvoker`.""" def __init__(self, obj_to_invoke, method_name): """ Initiates a ``MethodWrapper``. Args: obj_to_invoke: the object that contains the method. Has to either be a `DoFn` object or a `RestrictionProvider` object. method_name: name of the method as a string. """ if not isinstance(obj_to_invoke, (DoFn, RestrictionProvider, WatermarkEstimatorProvider)): raise ValueError( '\'obj_to_invoke\' has to be either a \'DoFn\' or ' 'a \'RestrictionProvider\'. Received %r instead.' % obj_to_invoke) self.args, self.defaults = core.get_function_arguments(obj_to_invoke, method_name) # TODO(BEAM-5878) support kwonlyargs on Python 3. self.method_value = getattr(obj_to_invoke, method_name) self.method_name = method_name self.has_userstate_arguments = False self.state_args_to_replace = {} # type: Dict[str, core.StateSpec] self.timer_args_to_replace = {} # type: Dict[str, core.TimerSpec] self.timestamp_arg_name = None # type: Optional[str] self.window_arg_name = None # type: Optional[str] self.key_arg_name = None # type: Optional[str] self.restriction_provider = None self.restriction_provider_arg_name = None self.watermark_estimator_provider = None self.watermark_estimator_provider_arg_name = None self.dynamic_timer_tag_arg_name = None if hasattr(self.method_value, 'unbounded_per_element'): self.unbounded_per_element = True else: self.unbounded_per_element = False for kw, v in zip(self.args[-len(self.defaults):], self.defaults): if isinstance(v, core.DoFn.StateParam): self.state_args_to_replace[kw] = v.state_spec self.has_userstate_arguments = True elif isinstance(v, core.DoFn.TimerParam): self.timer_args_to_replace[kw] = v.timer_spec self.has_userstate_arguments = True elif core.DoFn.TimestampParam == v: self.timestamp_arg_name = kw elif core.DoFn.WindowParam == v: self.window_arg_name = kw elif core.DoFn.WindowedValueParam == v: self.window_arg_name = kw elif core.DoFn.KeyParam == v: self.key_arg_name = kw elif isinstance(v, core.DoFn.RestrictionParam): self.restriction_provider = v.restriction_provider or obj_to_invoke self.restriction_provider_arg_name = kw elif isinstance(v, core.DoFn.WatermarkEstimatorParam): self.watermark_estimator_provider = ( v.watermark_estimator_provider or obj_to_invoke) self.watermark_estimator_provider_arg_name = kw elif core.DoFn.DynamicTimerTagParam == v: self.dynamic_timer_tag_arg_name = kw # Create NoOpWatermarkEstimatorProvider if there is no # WatermarkEstimatorParam provided. if self.watermark_estimator_provider is None: self.watermark_estimator_provider = NoOpWatermarkEstimatorProvider() def invoke_timer_callback( self, user_state_context, key, window, timestamp, pane_info, dynamic_timer_tag): # TODO(ccy): support side inputs. kwargs = {} if self.has_userstate_arguments: for kw, state_spec in self.state_args_to_replace.items(): kwargs[kw] = user_state_context.get_state(state_spec, key, window) for kw, timer_spec in self.timer_args_to_replace.items(): kwargs[kw] = user_state_context.get_timer( timer_spec, key, window, timestamp, pane_info) if self.timestamp_arg_name: kwargs[self.timestamp_arg_name] = Timestamp.of(timestamp) if self.window_arg_name: kwargs[self.window_arg_name] = window if self.key_arg_name: kwargs[self.key_arg_name] = key if self.dynamic_timer_tag_arg_name: kwargs[self.dynamic_timer_tag_arg_name] = dynamic_timer_tag if kwargs: return self.method_value(**kwargs) else: return self.method_value() class BatchingPreference(Enum): DO_NOT_CARE = 1 # This operation can operate on batches or element-at-a-time # TODO: Should we also store batching parameters here? (time/size preferences) BATCH_REQUIRED = 2 # This operation can only operate on batches BATCH_FORBIDDEN = 3 # This operation can only work element-at-a-time # Other possibilities: BATCH_PREFERRED (with min batch size specified) @property def supports_batches(self) -> bool: return self in (self.BATCH_REQUIRED, self.DO_NOT_CARE) @property def supports_elements(self) -> bool: return self in (self.BATCH_FORBIDDEN, self.DO_NOT_CARE) @property def requires_batches(self) -> bool: return self == self.BATCH_REQUIRED class DoFnSignature(object): """Represents the signature of a given ``DoFn`` object. Signature of a ``DoFn`` provides a view of the properties of a given ``DoFn``. Among other things, this will give an extensible way for for (1) accessing the structure of the ``DoFn`` including methods and method parameters (2) identifying features that a given ``DoFn`` support, for example, whether a given ``DoFn`` is a Splittable ``DoFn`` ( https://s.apache.org/splittable-do-fn) (3) validating a ``DoFn`` based on the feature set offered by it. """ def __init__(self, do_fn): # type: (core.DoFn) -> None # We add a property here for all methods defined by Beam DoFn features. assert isinstance(do_fn, core.DoFn) self.do_fn = do_fn self.process_method = MethodWrapper(do_fn, 'process') self.process_batch_method = MethodWrapper(do_fn, 'process_batch') self.start_bundle_method = MethodWrapper(do_fn, 'start_bundle') self.finish_bundle_method = MethodWrapper(do_fn, 'finish_bundle') self.setup_lifecycle_method = MethodWrapper(do_fn, 'setup') self.teardown_lifecycle_method = MethodWrapper(do_fn, 'teardown') restriction_provider = self.get_restriction_provider() watermark_estimator_provider = self.get_watermark_estimator_provider() self.create_watermark_estimator_method = ( MethodWrapper( watermark_estimator_provider, 'create_watermark_estimator')) self.initial_restriction_method = ( MethodWrapper(restriction_provider, 'initial_restriction') if restriction_provider else None) self.create_tracker_method = ( MethodWrapper(restriction_provider, 'create_tracker') if restriction_provider else None) self.split_method = ( MethodWrapper(restriction_provider, 'split') if restriction_provider else None) self._validate() # Handle stateful DoFns. self._is_stateful_dofn = userstate.is_stateful_dofn(do_fn) self.timer_methods = {} # type: Dict[TimerSpec, MethodWrapper] if self._is_stateful_dofn: # Populate timer firing methods, keyed by TimerSpec. _, all_timer_specs = userstate.get_dofn_specs(do_fn) for timer_spec in all_timer_specs: method = timer_spec._attached_callback self.timer_methods[timer_spec] = MethodWrapper(do_fn, method.__name__) def get_restriction_provider(self): # type: () -> RestrictionProvider return self.process_method.restriction_provider def get_watermark_estimator_provider(self): # type: () -> WatermarkEstimatorProvider return self.process_method.watermark_estimator_provider def is_unbounded_per_element(self): return self.process_method.unbounded_per_element def _validate(self): # type: () -> None self._validate_process() self._validate_process_batch() self._validate_bundle_method(self.start_bundle_method) self._validate_bundle_method(self.finish_bundle_method) self._validate_stateful_dofn() def _check_duplicate_dofn_params(self, method: MethodWrapper): param_ids = [ d.param_id for d in method.defaults if isinstance(d, core._DoFnParam) ] if len(param_ids) != len(set(param_ids)): raise ValueError( 'DoFn %r has duplicate %s method parameters: %s.' % (self.do_fn, method.method_name, param_ids)) def _validate_process(self): # type: () -> None """Validate that none of the DoFnParameters are repeated in the function """ self._check_duplicate_dofn_params(self.process_method) def _validate_process_batch(self): # type: () -> None self._check_duplicate_dofn_params(self.process_batch_method) for d in self.process_batch_method.defaults: if not isinstance(d, core._DoFnParam): continue # Helpful errors for params which will be supported in the future if d == (core.DoFn.ElementParam): # We currently assume we can just get the typehint from the first # parameter. ElementParam breaks this assumption raise NotImplementedError( f"DoFn {self.do_fn!r} uses unsupported DoFn param ElementParam.") if d in (core.DoFn.KeyParam, core.DoFn.StateParam, core.DoFn.TimerParam): raise NotImplementedError( f"DoFn {self.do_fn!r} has unsupported per-key DoFn param {d}. " "Per-key DoFn params are not yet supported for process_batch " "(https://github.com/apache/beam/issues/21653).") # Fallback to catch anything not explicitly supported if not d in (core.DoFn.WindowParam, core.DoFn.TimestampParam, core.DoFn.PaneInfoParam): raise ValueError( f"DoFn {self.do_fn!r} has unsupported process_batch " f"method parameter {d}") def _validate_bundle_method(self, method_wrapper): """Validate that none of the DoFnParameters are used in the function """ for param in core.DoFn.DoFnProcessParams: if param in method_wrapper.defaults: raise ValueError( 'DoFn.process() method-only parameter %s cannot be used in %s.' % (param, method_wrapper)) def _validate_stateful_dofn(self): # type: () -> None userstate.validate_stateful_dofn(self.do_fn) def is_splittable_dofn(self): # type: () -> bool return self.get_restriction_provider() is not None def get_restriction_coder(self): # type: () -> Optional[TupleCoder] """Get coder for a restriction when processing an SDF. """ if self.is_splittable_dofn(): return TupleCoder([ (self.get_restriction_provider().restriction_coder()), (self.get_watermark_estimator_provider().estimator_state_coder()) ]) else: return None def is_stateful_dofn(self): # type: () -> bool return self._is_stateful_dofn def has_timers(self): # type: () -> bool _, all_timer_specs = userstate.get_dofn_specs(self.do_fn) return bool(all_timer_specs) def has_bundle_finalization(self): for sig in (self.start_bundle_method, self.process_method, self.finish_bundle_method): for d in sig.defaults: try: if d == DoFn.BundleFinalizerParam: return True except Exception: # pylint: disable=broad-except # Default value might be incomparable. pass return False def get_bundle_contexts(self): seen = set() for sig in (self.setup_lifecycle_method, self.start_bundle_method, self.process_method, self.process_batch_method, self.finish_bundle_method, self.teardown_lifecycle_method): for d in sig.defaults: try: if isinstance(d, DoFn.BundleContextParam): if d not in seen: seen.add(d) yield d except Exception: # pylint: disable=broad-except # Default value might be incomparable. pass def get_setup_contexts(self): seen = set() for sig in (self.setup_lifecycle_method, self.start_bundle_method, self.process_method, self.process_batch_method, self.finish_bundle_method, self.teardown_lifecycle_method): for d in sig.defaults: try: if isinstance(d, DoFn.SetupContextParam): if d not in seen: seen.add(d) yield d except Exception: # pylint: disable=broad-except # Default value might be incomparable. pass class DoFnInvoker(object): """An abstraction that can be used to execute DoFn methods. A DoFnInvoker describes a particular way for invoking methods of a DoFn represented by a given DoFnSignature.""" def __init__(self, output_handler, # type: _OutputHandler signature # type: DoFnSignature ): # type: (...) -> None """ Initializes `DoFnInvoker` :param output_handler: an OutputHandler for receiving elements produced by invoking functions of the DoFn. :param signature: a DoFnSignature for the DoFn being invoked """ self.output_handler = output_handler self.signature = signature self.user_state_context = None # type: Optional[userstate.UserStateContext] self.bundle_finalizer_param = None # type: Optional[core._BundleFinalizerParam] @staticmethod def create_invoker( signature, # type: DoFnSignature output_handler, # type: OutputHandler context=None, # type: Optional[DoFnContext] side_inputs=None, # type: Optional[List[sideinputs.SideInputMap]] input_args=None, input_kwargs=None, process_invocation=True, user_state_context=None, # type: Optional[userstate.UserStateContext] bundle_finalizer_param=None # type: Optional[core._BundleFinalizerParam] ): # type: (...) -> DoFnInvoker """ Creates a new DoFnInvoker based on given arguments. Args: output_handler: an OutputHandler for receiving elements produced by invoking functions of the DoFn. signature: a DoFnSignature for the DoFn being invoked. context: Context to be used when invoking the DoFn (deprecated). side_inputs: side inputs to be used when invoking th process method. input_args: arguments to be used when invoking the process method. Some of the arguments given here might be placeholders (for example for side inputs) that get filled before invoking the process method. input_kwargs: keyword arguments to be used when invoking the process method. Some of the keyword arguments given here might be placeholders (for example for side inputs) that get filled before invoking the process method. process_invocation: If True, this function may return an invoker that performs extra optimizations for invoking process() method efficiently. user_state_context: The UserStateContext instance for the current Stateful DoFn. bundle_finalizer_param: The param that passed to a process method, which allows a callback to be registered. """ side_inputs = side_inputs or [] use_per_window_invoker = process_invocation and ( side_inputs or input_args or input_kwargs or signature.process_method.defaults or signature.process_batch_method.defaults or signature.is_stateful_dofn()) if not use_per_window_invoker: return SimpleInvoker(output_handler, signature) else: if context is None: raise TypeError("Must provide context when not using SimpleInvoker") return PerWindowInvoker( output_handler, signature, context, side_inputs, input_args, input_kwargs, user_state_context, bundle_finalizer_param) def invoke_process(self, windowed_value, # type: WindowedValue restriction=None, watermark_estimator_state=None, additional_args=None, additional_kwargs=None ): # type: (...) -> Iterable[SplitResultResidual] """Invokes the DoFn.process() function. Args: windowed_value: a WindowedValue object that gives the element for which process() method should be invoked along with the window the element belongs to. restriction: The restriction to use when executing this splittable DoFn. Should only be specified for splittable DoFns. watermark_estimator_state: The watermark estimator state to use when executing this splittable DoFn. Should only be specified for splittable DoFns. additional_args: additional arguments to be passed to the current `DoFn.process()` invocation, usually as side inputs. additional_kwargs: additional keyword arguments to be passed to the current `DoFn.process()` invocation. """ raise NotImplementedError def invoke_process_batch(self, windowed_batch, # type: WindowedBatch additional_args=None, additional_kwargs=None ): # type: (...) -> None """Invokes the DoFn.process() function. Args: windowed_batch: a WindowedBatch object that gives a batch of elements for which process_batch() method should be invoked, along with the window each element belongs to. additional_args: additional arguments to be passed to the current `DoFn.process()` invocation, usually as side inputs. additional_kwargs: additional keyword arguments to be passed to the current `DoFn.process()` invocation. """ raise NotImplementedError def invoke_setup(self): # type: () -> None """Invokes the DoFn.setup() method """ self._setup_context_values = { c: c.create_and_enter() for c in self.signature.get_setup_contexts() } self.signature.setup_lifecycle_method.method_value() def invoke_start_bundle(self): # type: () -> None """Invokes the DoFn.start_bundle() method. """ self._bundle_context_values = { c: c.create_and_enter() for c in self.signature.get_bundle_contexts() } self.output_handler.start_bundle_outputs( self.signature.start_bundle_method.method_value()) def invoke_finish_bundle(self): # type: () -> None """Invokes the DoFn.finish_bundle() method. """ self.output_handler.finish_bundle_outputs( self.signature.finish_bundle_method.method_value()) for c in self._bundle_context_values.values(): c[0].__exit__(None, None, None) self._bundle_context_values = None def invoke_teardown(self): # type: () -> None """Invokes the DoFn.teardown() method """ self.signature.teardown_lifecycle_method.method_value() for c in self._setup_context_values.values(): c[0].__exit__(None, None, None) self._setup_context_values = None def invoke_user_timer( self, timer_spec, key, window, timestamp, pane_info, dynamic_timer_tag): # self.output_handler is Optional, but in practice it won't be None here self.output_handler.handle_process_outputs( WindowedValue(None, timestamp, (window, )), self.signature.timer_methods[timer_spec].invoke_timer_callback( self.user_state_context, key, window, timestamp, pane_info, dynamic_timer_tag)) def invoke_create_watermark_estimator(self, estimator_state): return self.signature.create_watermark_estimator_method.method_value( estimator_state) def invoke_split(self, element, restriction): return self.signature.split_method.method_value(element, restriction) def invoke_initial_restriction(self, element): return self.signature.initial_restriction_method.method_value(element) def invoke_create_tracker(self, restriction): return self.signature.create_tracker_method.method_value(restriction) class SimpleInvoker(DoFnInvoker): """An invoker that processes elements ignoring windowing information.""" def __init__(self, output_handler, # type: OutputHandler signature # type: DoFnSignature ): # type: (...) -> None super().__init__(output_handler, signature) self.process_method = signature.process_method.method_value self.process_batch_method = signature.process_batch_method.method_value def invoke_process(self, windowed_value, # type: WindowedValue restriction=None, watermark_estimator_state=None, additional_args=None, additional_kwargs=None ): # type: (...) -> Iterable[SplitResultResidual] self.output_handler.handle_process_outputs( windowed_value, self.process_method(windowed_value.value)) return [] def invoke_process_batch(self, windowed_batch, # type: WindowedBatch restriction=None, watermark_estimator_state=None, additional_args=None, additional_kwargs=None ): # type: (...) -> None self.output_handler.handle_process_batch_outputs( windowed_batch, self.process_batch_method(windowed_batch.values)) def _get_arg_placeholders( method: MethodWrapper, input_args: Optional[List[Any]], input_kwargs: Optional[Dict[str, any]]): input_args = input_args if input_args else [] input_kwargs = input_kwargs if input_kwargs else {} arg_names = method.args default_arg_values = method.defaults # Create placeholder for element parameter of DoFn.process() method. # Not to be confused with ArgumentPlaceHolder, which may be passed in # input_args and is a placeholder for side-inputs. class ArgPlaceholder(object): def __init__(self, placeholder): self.placeholder = placeholder if all(core.DoFn.ElementParam != arg for arg in default_arg_values): # TODO(https://github.com/apache/beam/issues/19631): Handle cases in which # len(arg_names) == len(default_arg_values). args_to_pick = len(arg_names) - len(default_arg_values) - 1 # Positional argument values for process(), with placeholders for special # values such as the element, timestamp, etc. args_with_placeholders = ([ArgPlaceholder(core.DoFn.ElementParam)] + input_args[:args_to_pick]) else: args_to_pick = len(arg_names) - len(default_arg_values) args_with_placeholders = input_args[:args_to_pick] # Fill the OtherPlaceholders for context, key, window or timestamp remaining_args_iter = iter(input_args[args_to_pick:]) for a, d in zip(arg_names[-len(default_arg_values):], default_arg_values): if core.DoFn.ElementParam == d: args_with_placeholders.append(ArgPlaceholder(d)) elif core.DoFn.KeyParam == d: args_with_placeholders.append(ArgPlaceholder(d)) elif core.DoFn.WindowParam == d: args_with_placeholders.append(ArgPlaceholder(d)) elif core.DoFn.WindowedValueParam == d: args_with_placeholders.append(ArgPlaceholder(d)) elif core.DoFn.TimestampParam == d: args_with_placeholders.append(ArgPlaceholder(d)) elif core.DoFn.PaneInfoParam == d: args_with_placeholders.append(ArgPlaceholder(d)) elif core.DoFn.SideInputParam == d: # If no more args are present then the value must be passed via kwarg try: args_with_placeholders.append(next(remaining_args_iter)) except StopIteration: if a not in input_kwargs: raise ValueError("Value for sideinput %s not provided" % a) elif isinstance(d, core.DoFn.StateParam): args_with_placeholders.append(ArgPlaceholder(d)) elif isinstance(d, core.DoFn.TimerParam): args_with_placeholders.append(ArgPlaceholder(d)) elif isinstance(d, type) and core.DoFn.BundleFinalizerParam == d: args_with_placeholders.append(ArgPlaceholder(d)) elif isinstance(d, core.DoFn.BundleContextParam): args_with_placeholders.append(ArgPlaceholder(d)) elif isinstance(d, core.DoFn.SetupContextParam): args_with_placeholders.append(ArgPlaceholder(d)) else: # If no more args are present then the value must be passed via kwarg try: args_with_placeholders.append(next(remaining_args_iter)) except StopIteration: pass args_with_placeholders.extend(list(remaining_args_iter)) # Stash the list of placeholder positions for performance placeholders = [(i, x.placeholder) for (i, x) in enumerate(args_with_placeholders) if isinstance(x, ArgPlaceholder)] return placeholders, args_with_placeholders, input_kwargs class PerWindowInvoker(DoFnInvoker): """An invoker that processes elements considering windowing information.""" def __init__(self, output_handler, # type: OutputHandler signature, # type: DoFnSignature context, # type: DoFnContext side_inputs, # type: Iterable[sideinputs.SideInputMap] input_args, input_kwargs, user_state_context, # type: Optional[userstate.UserStateContext] bundle_finalizer_param # type: Optional[core._BundleFinalizerParam] ): super().__init__(output_handler, signature) self.side_inputs = side_inputs self.context = context self.process_method = signature.process_method.method_value default_arg_values = signature.process_method.defaults self.has_windowed_inputs = ( not all(si.is_globally_windowed() for si in side_inputs) or any( core.DoFn.WindowParam == arg for arg in signature.process_method.defaults) or any( core.DoFn.WindowParam == arg for arg in signature.process_batch_method.defaults) or signature.is_stateful_dofn()) self.user_state_context = user_state_context self.is_splittable = signature.is_splittable_dofn() self.is_key_param_required = any( core.DoFn.KeyParam == arg for arg in default_arg_values) self.threadsafe_restriction_tracker = None # type: Optional[ThreadsafeRestrictionTracker] self.threadsafe_watermark_estimator = None # type: Optional[ThreadsafeWatermarkEstimator] self.current_windowed_value = None # type: Optional[WindowedValue] self.bundle_finalizer_param = bundle_finalizer_param if self.is_splittable: self.splitting_lock = threading.Lock() self.current_window_index = None self.stop_window_index = None # TODO(https://github.com/apache/beam/issues/28776): Remove caching after # fully rolling out. # If true, always recalculate window args. If false, has_cached_window_args # and has_cached_window_batch_args will be set to true if the corresponding # self.args_for_process,have been updated and should be reused directly. self.recalculate_window_args = ( self.has_windowed_inputs or 'disable_global_windowed_args_caching' in RuntimeValueProvider.experiments) self.has_cached_window_args = False self.has_cached_window_batch_args = False # Try to prepare all the arguments that can just be filled in # without any additional work. in the process function. # Also cache all the placeholders needed in the process function. input_args = list(input_args) ( self.placeholders_for_process, self.args_for_process, self.kwargs_for_process) = _get_arg_placeholders( signature.process_method, input_args, input_kwargs) self.process_batch_method = signature.process_batch_method.method_value ( self.placeholders_for_process_batch, self.args_for_process_batch, self.kwargs_for_process_batch) = _get_arg_placeholders( signature.process_batch_method, input_args, input_kwargs) def invoke_process(self, windowed_value, # type: WindowedValue restriction=None, watermark_estimator_state=None, additional_args=None, additional_kwargs=None ): # type: (...) -> Iterable[SplitResultResidual] if not additional_args: additional_args = [] if not additional_kwargs: additional_kwargs = {} self.context.set_element(windowed_value) # Call for the process function for each window if has windowed side inputs # or if the process accesses the window parameter. We can just call it once # otherwise as none of the arguments are changing residuals = [] if self.is_splittable: if restriction is None: # This may be a SDF invoked as an ordinary DoFn on runners that don't # understand SDF. See, e.g. BEAM-11472. # In this case, processing the element is simply processing it against # the entire initial restriction. restriction = self.signature.initial_restriction_method.method_value( windowed_value.value) with self.splitting_lock: self.current_windowed_value = windowed_value self.restriction = restriction self.watermark_estimator_state = watermark_estimator_state try: if self.has_windowed_inputs and len(windowed_value.windows) > 1: for i, w in enumerate(windowed_value.windows): if not self._should_process_window_for_sdf( windowed_value, additional_kwargs, i): break residual = self._invoke_process_per_window( WindowedValue( windowed_value.value, windowed_value.timestamp, (w, )), additional_args, additional_kwargs) if residual: residuals.append(residual) else: if self._should_process_window_for_sdf(windowed_value, additional_kwargs): residual = self._invoke_process_per_window( windowed_value, additional_args, additional_kwargs) if residual: residuals.append(residual) finally: with self.splitting_lock: self.current_windowed_value = None self.restriction = None self.watermark_estimator_state = None self.current_window_index = None self.threadsafe_restriction_tracker = None self.threadsafe_watermark_estimator = None elif self.has_windowed_inputs and len(windowed_value.windows) != 1: for w in windowed_value.windows: self._invoke_process_per_window( WindowedValue( windowed_value.value, windowed_value.timestamp, (w, )), additional_args, additional_kwargs) else: self._invoke_process_per_window( windowed_value, additional_args, additional_kwargs) return residuals def invoke_process_batch(self, windowed_batch, # type: WindowedBatch additional_args=None, additional_kwargs=None ): # type: (...) -> None if not additional_args: additional_args = [] if not additional_kwargs: additional_kwargs = {} assert isinstance(windowed_batch, HomogeneousWindowedBatch) if self.has_windowed_inputs and len(windowed_batch.windows) != 1: for w in windowed_batch.windows: self._invoke_process_batch_per_window( HomogeneousWindowedBatch.of( windowed_batch.values, windowed_batch.timestamp, (w, ), windowed_batch.pane_info), additional_args, additional_kwargs) else: self._invoke_process_batch_per_window( windowed_batch, additional_args, additional_kwargs) def _should_process_window_for_sdf( self, windowed_value, # type: WindowedValue additional_kwargs, window_index=None, # type: Optional[int] ): restriction_tracker = self.invoke_create_tracker(self.restriction) watermark_estimator = self.invoke_create_watermark_estimator( self.watermark_estimator_state) with self.splitting_lock: if window_index: self.current_window_index = window_index if window_index == 0: self.stop_window_index = len(windowed_value.windows) if window_index == self.stop_window_index: return False self.threadsafe_restriction_tracker = ThreadsafeRestrictionTracker( restriction_tracker) self.threadsafe_watermark_estimator = ( ThreadsafeWatermarkEstimator(watermark_estimator)) restriction_tracker_param = ( self.signature.process_method.restriction_provider_arg_name) if not restriction_tracker_param: raise ValueError( 'DoFn is splittable but DoFn does not have a ' 'RestrictionTrackerParam defined') additional_kwargs[restriction_tracker_param] = ( RestrictionTrackerView(self.threadsafe_restriction_tracker)) watermark_param = ( self.signature.process_method.watermark_estimator_provider_arg_name) # When the watermark_estimator is a NoOpWatermarkEstimator, the system # will not add watermark_param into the DoFn param list. if watermark_param is not None: additional_kwargs[watermark_param] = self.threadsafe_watermark_estimator return True def _invoke_process_per_window(self, windowed_value, # type: WindowedValue additional_args, additional_kwargs, ): # type: (...) -> Optional[SplitResultResidual] if self.has_cached_window_args: args_for_process, kwargs_for_process = ( self.args_for_process, self.kwargs_for_process) else: if self.has_windowed_inputs: assert len(windowed_value.windows) <= 1 window, = windowed_value.windows else: window = GlobalWindow() side_inputs = [si[window] for si in self.side_inputs] side_inputs.extend(additional_args) args_for_process, kwargs_for_process = util.insert_values_in_args( self.args_for_process, self.kwargs_for_process, side_inputs) if not self.recalculate_window_args: self.args_for_process, self.kwargs_for_process = ( args_for_process, kwargs_for_process) self.has_cached_window_args = True # Extract key in the case of a stateful DoFn. Note that in the case of a # stateful DoFn, we set during __init__ self.has_windowed_inputs to be # True. Therefore, windows will be exploded coming into this method, and # we can rely on the window variable being set above. if self.user_state_context or self.is_key_param_required: try: key, unused_value = windowed_value.value except (TypeError, ValueError): raise ValueError(( 'Input value to a stateful DoFn or KeyParam must be a KV tuple; ' 'instead, got \'%s\'.') % (windowed_value.value, )) for i, p in self.placeholders_for_process: if core.DoFn.ElementParam == p: args_for_process[i] = windowed_value.value elif core.DoFn.KeyParam == p: args_for_process[i] = key elif core.DoFn.WindowParam == p: args_for_process[i] = window elif core.DoFn.WindowedValueParam == p: args_for_process[i] = windowed_value elif core.DoFn.TimestampParam == p: args_for_process[i] = windowed_value.timestamp elif core.DoFn.PaneInfoParam == p: args_for_process[i] = windowed_value.pane_info elif isinstance(p, core.DoFn.StateParam): assert self.user_state_context is not None args_for_process[i] = ( self.user_state_context.get_state(p.state_spec, key, window)) elif isinstance(p, core.DoFn.TimerParam): assert self.user_state_context is not None args_for_process[i] = ( self.user_state_context.get_timer( p.timer_spec, key, window, windowed_value.timestamp, windowed_value.pane_info)) elif core.DoFn.BundleFinalizerParam == p: args_for_process[i] = self.bundle_finalizer_param elif isinstance(p, core.DoFn.BundleContextParam): args_for_process[i] = self._bundle_context_values[p][1] elif isinstance(p, core.DoFn.SetupContextParam): args_for_process[i] = self._setup_context_values[p][1] kwargs_for_process = kwargs_for_process or {} if additional_kwargs: kwargs_for_process.update(additional_kwargs) self.output_handler.handle_process_outputs( windowed_value, self.process_method(*args_for_process, **kwargs_for_process), self.threadsafe_watermark_estimator) if self.is_splittable: assert self.threadsafe_restriction_tracker is not None self.threadsafe_restriction_tracker.check_done() deferred_status = self.threadsafe_restriction_tracker.deferred_status() if deferred_status: deferred_restriction, deferred_timestamp = deferred_status element = windowed_value.value size = self.signature.get_restriction_provider().restriction_size( element, deferred_restriction) if size < 0: raise ValueError('Expected size >= 0 but received %s.' % size) current_watermark = ( self.threadsafe_watermark_estimator.current_watermark()) estimator_state = ( self.threadsafe_watermark_estimator.get_estimator_state()) residual_value = ((element, (deferred_restriction, estimator_state)), size) return SplitResultResidual( residual_value=windowed_value.with_value(residual_value), current_watermark=current_watermark, deferred_timestamp=deferred_timestamp) return None def _invoke_process_batch_per_window( self, windowed_batch: WindowedBatch, additional_args, additional_kwargs, ): # type: (...) -> Optional[SplitResultResidual] if self.has_cached_window_batch_args: args_for_process_batch, kwargs_for_process_batch = ( self.args_for_process_batch, self.kwargs_for_process_batch) else: if self.has_windowed_inputs: assert isinstance(windowed_batch, HomogeneousWindowedBatch) assert len(windowed_batch.windows) <= 1 window, = windowed_batch.windows else: window = GlobalWindow() side_inputs = [si[window] for si in self.side_inputs] side_inputs.extend(additional_args) args_for_process_batch, kwargs_for_process_batch = ( util.insert_values_in_args( self.args_for_process_batch, self.kwargs_for_process_batch, side_inputs, ) ) if not self.recalculate_window_args: self.args_for_process_batch, self.kwargs_for_process_batch = ( args_for_process_batch, kwargs_for_process_batch) self.has_cached_window_batch_args = True for i, p in self.placeholders_for_process_batch: if core.DoFn.ElementParam == p: args_for_process_batch[i] = windowed_batch.values elif core.DoFn.KeyParam == p: raise NotImplementedError( 'https://github.com/apache/beam/issues/21653: Per-key process_batch' ) elif core.DoFn.WindowParam == p: args_for_process_batch[i] = window elif core.DoFn.TimestampParam == p: args_for_process_batch[i] = windowed_batch.timestamp elif core.DoFn.PaneInfoParam == p: assert isinstance(windowed_batch, HomogeneousWindowedBatch) args_for_process_batch[i] = windowed_batch.pane_info elif isinstance(p, core.DoFn.StateParam): raise NotImplementedError( "https://github.com/apache/beam/issues/21653: " "Per-key process_batch") elif isinstance(p, core.DoFn.TimerParam): raise NotImplementedError( "https://github.com/apache/beam/issues/21653: " "Per-key process_batch") elif isinstance(p, core.DoFn.BundleContextParam): args_for_process_batch[i] = self._bundle_context_values[p][1] elif isinstance(p, core.DoFn.SetupContextParam): args_for_process_batch[i] = self._setup_context_values[p][1] kwargs_for_process_batch = kwargs_for_process_batch or {} if additional_kwargs: kwargs_for_process_batch.update(additional_kwargs) self.output_handler.handle_process_batch_outputs( windowed_batch, self.process_batch_method( *args_for_process_batch, **kwargs_for_process_batch), self.threadsafe_watermark_estimator) @staticmethod def _try_split(fraction, window_index, # type: Optional[int] stop_window_index, # type: Optional[int] windowed_value, # type: WindowedValue restriction, watermark_estimator_state, restriction_provider, # type: RestrictionProvider restriction_tracker, # type: RestrictionTracker watermark_estimator, # type: WatermarkEstimator ): # type: (...) -> Optional[Tuple[Iterable[SplitResultPrimary], Iterable[SplitResultResidual], Optional[int]]] """Try to split returning a primaries, residuals and a new stop index. For non-window observing splittable DoFns we split the current restriction and assign the primary and residual to all the windows. For window observing splittable DoFns, we: 1) return a split at a window boundary if the fraction lies outside of the current window. 2) attempt to split the current restriction, if successful then return the primary and residual for the current window and an additional primary and residual for any fully processed and fully unprocessed windows. 3) fall back to returning a split at the window boundary if possible Args: window_index: the current index of the window being processed or None if the splittable DoFn is not window observing. stop_window_index: the current index to stop processing at or None if the splittable DoFn is not window observing. windowed_value: the current windowed value restriction: the initial restriction when processing was started. watermark_estimator_state: the initial watermark estimator state when processing was started. restriction_provider: the DoFn's restriction provider restriction_tracker: the current restriction tracker watermark_estimator: the current watermark estimator Returns: A tuple containing (primaries, residuals, new_stop_index) or None if splitting was not possible. new_stop_index will only be set if the splittable DoFn is window observing otherwise it will be None. """ def compute_whole_window_split(to_index, from_index): restriction_size = restriction_provider.restriction_size( windowed_value, restriction) if restriction_size < 0: raise ValueError( 'Expected size >= 0 but received %s.' % restriction_size) # The primary and residual both share the same value only differing # by the set of windows they are in. value = ((windowed_value.value, (restriction, watermark_estimator_state)), restriction_size) primary_restriction = SplitResultPrimary( primary_value=WindowedValue( value, windowed_value.timestamp, windowed_value.windows[:to_index])) if to_index > 0 else None # Don't report any updated watermarks for the residual since they have # not processed any part of the restriction. residual_restriction = SplitResultResidual( residual_value=WindowedValue( value, windowed_value.timestamp, windowed_value.windows[from_index:stop_window_index]), current_watermark=None, deferred_timestamp=None) if from_index < stop_window_index else None return (primary_restriction, residual_restriction) primary_restrictions = [] residual_restrictions = [] window_observing = window_index is not None # If we are processing each window separately and we aren't on the last # window then compute whether the split lies within the current window # or a future window. if window_observing and window_index != stop_window_index - 1: progress = restriction_tracker.current_progress() if not progress: # Assume no work has been completed for the current window if progress # is unavailable. from apache_beam.io.iobase import RestrictionProgress progress = RestrictionProgress(completed=0, remaining=1) scaled_progress = PerWindowInvoker._scale_progress( progress, window_index, stop_window_index) # Compute the fraction of the remainder relative to the scaled progress. # If the value is greater than or equal to progress.remaining_work then we # should split at the closest window boundary. fraction_of_remainder = scaled_progress.remaining_work * fraction if fraction_of_remainder >= progress.remaining_work: # The fraction is outside of the current window and hence we will # split at the closest window boundary. Favor a split and return the # last window if we would have rounded up to the end of the window # based upon the fraction. new_stop_window_index = min( stop_window_index - 1, window_index + max( 1, int( round(( progress.completed_work + scaled_progress.remaining_work * fraction) / progress.total_work)))) primary, residual = compute_whole_window_split( new_stop_window_index, new_stop_window_index) assert primary is not None assert residual is not None return ([primary], [residual], new_stop_window_index) else: # The fraction is within the current window being processed so compute # the updated fraction based upon the number of windows being processed. new_stop_window_index = window_index + 1 fraction = fraction_of_remainder / progress.remaining_work # Attempt to split below, if we can't then we'll compute a split # using only window boundaries else: # We aren't splitting within multiple windows so we don't change our # stop index. new_stop_window_index = stop_window_index # Temporary workaround for [BEAM-7473]: get current_watermark before # split, in case watermark gets advanced before getting split results. # In worst case, current_watermark is always stale, which is ok. current_watermark = (watermark_estimator.current_watermark()) current_estimator_state = (watermark_estimator.get_estimator_state()) split = restriction_tracker.try_split(fraction) if split: primary, residual = split element = windowed_value.value primary_size = restriction_provider.restriction_size( windowed_value.value, primary) if primary_size < 0: raise ValueError('Expected size >= 0 but received %s.' % primary_size) residual_size = restriction_provider.restriction_size( windowed_value.value, residual) if residual_size < 0: raise ValueError('Expected size >= 0 but received %s.' % residual_size) # We use the watermark estimator state for the original process call # for the primary and the updated watermark estimator state for the # residual for the split. primary_split_value = ((element, (primary, watermark_estimator_state)), primary_size) residual_split_value = ((element, (residual, current_estimator_state)), residual_size) windows = ( windowed_value.windows[window_index], ) if window_observing else windowed_value.windows primary_restrictions.append( SplitResultPrimary( primary_value=WindowedValue( primary_split_value, windowed_value.timestamp, windows))) residual_restrictions.append( SplitResultResidual( residual_value=WindowedValue( residual_split_value, windowed_value.timestamp, windows), current_watermark=current_watermark, deferred_timestamp=None)) if window_observing: assert new_stop_window_index == window_index + 1 primary, residual = compute_whole_window_split( window_index, window_index + 1) if primary: primary_restrictions.append(primary) if residual: residual_restrictions.append(residual) return ( primary_restrictions, residual_restrictions, new_stop_window_index) elif new_stop_window_index and new_stop_window_index != stop_window_index: # If we failed to split but have a new stop index then return a split # at the window boundary. primary, residual = compute_whole_window_split( new_stop_window_index, new_stop_window_index) assert primary is not None assert residual is not None return ([primary], [residual], new_stop_window_index) else: return None def try_split(self, fraction): # type: (...) -> Optional[Tuple[Iterable[SplitResultPrimary], Iterable[SplitResultResidual]]] if not self.is_splittable: return None with self.splitting_lock: if not self.threadsafe_restriction_tracker: return None # Make a local reference to member variables that change references during # processing under lock before attempting to split so we have a consistent # view of all the references. result = PerWindowInvoker._try_split( fraction, self.current_window_index, self.stop_window_index, self.current_windowed_value, self.restriction, self.watermark_estimator_state, self.signature.get_restriction_provider(), self.threadsafe_restriction_tracker, self.threadsafe_watermark_estimator) if not result: return None residuals, primaries, self.stop_window_index = result return (residuals, primaries) @staticmethod def _scale_progress(progress, window_index, stop_window_index): # We scale progress based upon the amount of work we will do for one # window and have it apply for all windows. completed = window_index * progress.total_work + progress.completed_work remaining = ( stop_window_index - (window_index + 1)) * progress.total_work + progress.remaining_work from apache_beam.io.iobase import RestrictionProgress return RestrictionProgress(completed=completed, remaining=remaining) def current_element_progress(self): # type: () -> Optional[RestrictionProgress] if not self.is_splittable: return None with self.splitting_lock: current_window_index = self.current_window_index stop_window_index = self.stop_window_index threadsafe_restriction_tracker = self.threadsafe_restriction_tracker if not threadsafe_restriction_tracker: return None progress = threadsafe_restriction_tracker.current_progress() if not current_window_index or not progress: return progress # stop_window_index should always be set if current_window_index is set, # it is an error otherwise. assert stop_window_index return PerWindowInvoker._scale_progress( progress, current_window_index, stop_window_index) class DoFnRunner: """For internal use only; no backwards-compatibility guarantees. A helper class for executing ParDo operations. """ def __init__(self, fn, # type: core.DoFn args, kwargs, side_inputs, # type: Iterable[sideinputs.SideInputMap] windowing, tagged_receivers, # type: Mapping[Optional[str], Receiver] step_name=None, # type: Optional[str] logging_context=None, state=None, scoped_metrics_container=None, operation_name=None, transform_id=None, user_state_context=None, # type: Optional[userstate.UserStateContext] ): """Initializes a DoFnRunner. Args: fn: user DoFn to invoke args: positional side input arguments (static and placeholder), if any kwargs: keyword side input arguments (static and placeholder), if any side_inputs: list of sideinput.SideInputMaps for deferred side inputs windowing: windowing properties of the output PCollection(s) tagged_receivers: a dict of tag name to Receiver objects step_name: the name of this step logging_context: DEPRECATED [BEAM-4728] state: handle for accessing DoFn state scoped_metrics_container: DEPRECATED operation_name: The system name assigned by the runner for this operation. transform_id: The PTransform Id in the pipeline proto for this DoFn. user_state_context: The UserStateContext instance for the current Stateful DoFn. """ # Need to support multiple iterations. side_inputs = list(side_inputs) self.step_name = step_name self.transform_id = transform_id self.context = DoFnContext(step_name, state=state) self.bundle_finalizer_param = DoFn.BundleFinalizerParam() self.execution_context = None # type: Optional[ExecutionContext] do_fn_signature = DoFnSignature(fn) # Optimize for the common case. main_receivers = tagged_receivers[None] # TODO(https://github.com/apache/beam/issues/18886): Remove if block after # output counter released. if 'outputs_per_element_counter' in RuntimeValueProvider.experiments: # TODO(BEAM-3955): Make step_name and operation_name less confused. output_counter_name = ( CounterName('per-element-output-count', step_name=operation_name)) per_element_output_counter = state._counter_factory.get_counter( output_counter_name, Counter.DATAFLOW_DISTRIBUTION).accumulator else: per_element_output_counter = None output_handler = _OutputHandler( windowing.windowfn, main_receivers, tagged_receivers, per_element_output_counter, getattr(fn, 'output_batch_converter', None), getattr( do_fn_signature.process_method.method_value, '_beam_yields_batches', False), getattr( do_fn_signature.process_batch_method.method_value, '_beam_yields_elements', False), ) if do_fn_signature.is_stateful_dofn() and not user_state_context: raise Exception( 'Requested execution of a stateful DoFn, but no user state context ' 'is available. This likely means that the current runner does not ' 'support the execution of stateful DoFns.') self.do_fn_invoker = DoFnInvoker.create_invoker( do_fn_signature, output_handler, self.context, side_inputs, args, kwargs, user_state_context=user_state_context, bundle_finalizer_param=self.bundle_finalizer_param) def process(self, windowed_value): # type: (WindowedValue) -> Iterable[SplitResultResidual] try: return self.do_fn_invoker.invoke_process(windowed_value) except BaseException as exn: self._reraise_augmented(exn, windowed_value) return [] def _maybe_sample_exception( self, exc_info: Tuple, windowed_value: Optional[WindowedValue]) -> None: if self.execution_context is None: return output_sampler = self.execution_context.output_sampler if output_sampler is None: return output_sampler.sample_exception( windowed_value, exc_info, self.transform_id, self.execution_context.instruction_id) def process_batch(self, windowed_batch): # type: (WindowedBatch) -> None try: self.do_fn_invoker.invoke_process_batch(windowed_batch) except BaseException as exn: self._reraise_augmented(exn) def process_with_sized_restriction(self, windowed_value): # type: (WindowedValue) -> Iterable[SplitResultResidual] (element, (restriction, estimator_state)), _ = windowed_value.value return self.do_fn_invoker.invoke_process( windowed_value.with_value(element), restriction=restriction, watermark_estimator_state=estimator_state) def try_split(self, fraction): # type: (...) -> Optional[Tuple[Iterable[SplitResultPrimary], Iterable[SplitResultResidual]]] assert isinstance(self.do_fn_invoker, PerWindowInvoker) return self.do_fn_invoker.try_split(fraction) def current_element_progress(self): # type: () -> Optional[RestrictionProgress] assert isinstance(self.do_fn_invoker, PerWindowInvoker) return self.do_fn_invoker.current_element_progress() def process_user_timer( self, timer_spec, key, window, timestamp, pane_info, dynamic_timer_tag): try: self.do_fn_invoker.invoke_user_timer( timer_spec, key, window, timestamp, pane_info, dynamic_timer_tag) except BaseException as exn: self._reraise_augmented(exn) def _invoke_bundle_method(self, bundle_method): try: self.context.set_element(None) bundle_method() except BaseException as exn: self._reraise_augmented(exn) def _invoke_lifecycle_method(self, lifecycle_method): try: self.context.set_element(None) lifecycle_method() except BaseException as exn: self._reraise_augmented(exn) def setup(self): # type: () -> None self._invoke_lifecycle_method(self.do_fn_invoker.invoke_setup) def start(self): # type: () -> None self._invoke_bundle_method(self.do_fn_invoker.invoke_start_bundle) def finish(self): # type: () -> None self._invoke_bundle_method(self.do_fn_invoker.invoke_finish_bundle) def teardown(self): # type: () -> None self._invoke_lifecycle_method(self.do_fn_invoker.invoke_teardown) def finalize(self): # type: () -> None self.bundle_finalizer_param.finalize_bundle() def _reraise_augmented(self, exn, windowed_value=None): if getattr(exn, '_tagged_with_step', False) or not self.step_name: raise exn step_annotation = " [while running '%s']" % self.step_name # To emulate exception chaining (not available in Python 2). try: # Attempt to construct the same kind of exception # with an augmented message. new_exn = type(exn)(exn.args[0] + step_annotation, *exn.args[1:]) new_exn._tagged_with_step = True # Could raise attribute error. except: # pylint: disable=bare-except # If anything goes wrong, construct a RuntimeError whose message # records the original exception's type and message. new_exn = RuntimeError( traceback.format_exception_only(type(exn), exn)[-1].strip() + step_annotation) new_exn._tagged_with_step = True exc_info = sys.exc_info() _, _, tb = exc_info new_exn = new_exn.with_traceback(tb) self._maybe_sample_exception(exc_info, windowed_value) _LOGGER.exception(new_exn) raise new_exn class OutputHandler(object): def handle_process_outputs( self, windowed_input_element, results, watermark_estimator=None): # type: (WindowedValue, Iterable[Any], Optional[WatermarkEstimator]) -> None raise NotImplementedError def handle_process_batch_outputs( self, windowed_input_element, results, watermark_estimator=None): # type: (WindowedBatch, Iterable[Any], Optional[WatermarkEstimator]) -> None raise NotImplementedError class _OutputHandler(OutputHandler): """Processes output produced by DoFn method invocations.""" def __init__(self, window_fn, main_receivers, # type: Receiver tagged_receivers, # type: Mapping[Optional[str], Receiver] per_element_output_counter, output_batch_converter, # type: Optional[BatchConverter] process_yields_batches, # type: bool process_batch_yields_elements, # type: bool ): """Initializes ``_OutputHandler``. Args: window_fn: a windowing function (WindowFn). main_receivers: a dict of tag name to Receiver objects. tagged_receivers: main receiver object. per_element_output_counter: per_element_output_counter of one work_item. could be none if experimental flag turn off """ self.window_fn = window_fn self.main_receivers = main_receivers self.tagged_receivers = tagged_receivers if (per_element_output_counter is not None and per_element_output_counter.is_cythonized): self.per_element_output_counter = per_element_output_counter else: self.per_element_output_counter = None self.output_batch_converter = output_batch_converter self._process_yields_batches = process_yields_batches self._process_batch_yields_elements = process_batch_yields_elements def handle_process_outputs( self, windowed_input_element, results, watermark_estimator=None): # type: (WindowedValue, Iterable[Any], Optional[WatermarkEstimator]) -> None """Dispatch the result of process computation to the appropriate receivers. A value wrapped in a TaggedOutput object will be unwrapped and then dispatched to the appropriate indexed output. """ if results is None: results = [] # TODO(https://github.com/apache/beam/issues/20404): Verify that the # results object is a valid iterable type if # performance_runtime_type_check is active, without harming performance output_element_count = 0 for result in results: tag, result = self._handle_tagged_output(result) if not self._process_yields_batches: # process yields elements windowed_value = self._maybe_propagate_windowing_info( windowed_input_element, result) output_element_count += 1 self._write_value_to_tag(tag, windowed_value, watermark_estimator) else: # process yields batches self._verify_batch_output(result) if isinstance(result, WindowedBatch): assert isinstance(result, HomogeneousWindowedBatch) windowed_batch = result if (windowed_input_element is not None and len(windowed_input_element.windows) != 1): windowed_batch.windows *= len(windowed_input_element.windows) else: windowed_batch = ( HomogeneousWindowedBatch.from_batch_and_windowed_value( batch=result, windowed_value=windowed_input_element)) output_element_count += self.output_batch_converter.get_length( windowed_batch.values) self._write_batch_to_tag(tag, windowed_batch, watermark_estimator) # TODO(https://github.com/apache/beam/issues/18886): Remove if block after # output counter released. Only enable per_element_output_counter when # counter cythonized if self.per_element_output_counter is not None: self.per_element_output_counter.add_input(output_element_count) def handle_process_batch_outputs( self, windowed_input_batch, results, watermark_estimator=None): # type: (WindowedBatch, Iterable[Any], Optional[WatermarkEstimator]) -> None """Dispatch the result of process_batch computation to the appropriate receivers. A value wrapped in a TaggedOutput object will be unwrapped and then dispatched to the appropriate indexed output. """ if results is None: results = [] output_element_count = 0 for result in results: tag, result = self._handle_tagged_output(result) if not self._process_batch_yields_elements: # process_batch yields batches assert self.output_batch_converter is not None self._verify_batch_output(result) if isinstance(result, WindowedBatch): assert isinstance(result, HomogeneousWindowedBatch) windowed_batch = result if (windowed_input_batch is not None and len(windowed_input_batch.windows) != 1): windowed_batch.windows *= len(windowed_input_batch.windows) else: windowed_batch = windowed_input_batch.with_values(result) output_element_count += self.output_batch_converter.get_length( windowed_batch.values) self._write_batch_to_tag(tag, windowed_batch, watermark_estimator) else: # process_batch yields elements assert isinstance(windowed_input_batch, HomogeneousWindowedBatch) windowed_value = self._maybe_propagate_windowing_info( windowed_input_batch.as_empty_windowed_value(), result) output_element_count += 1 self._write_value_to_tag(tag, windowed_value, watermark_estimator) # TODO(https://github.com/apache/beam/issues/18886): Remove if block after # output counter released. Only enable per_element_output_counter when # counter cythonized if self.per_element_output_counter is not None: self.per_element_output_counter.add_input(output_element_count) def _maybe_propagate_windowing_info(self, windowed_input_element, result): # type: (WindowedValue, Any) -> WindowedValue if isinstance(result, WindowedValue): windowed_value = result if (windowed_input_element is not None and len(windowed_input_element.windows) != 1): windowed_value.windows *= len(windowed_input_element.windows) return windowed_value elif isinstance(result, TimestampedValue): assign_context = WindowFn.AssignContext(result.timestamp, result.value) windowed_value = WindowedValue( result.value, result.timestamp, self.window_fn.assign(assign_context)) if len(windowed_input_element.windows) != 1: windowed_value.windows *= len(windowed_input_element.windows) return windowed_value else: return windowed_input_element.with_value(result) def _handle_tagged_output(self, result): if isinstance(result, TaggedOutput): tag = result.tag if not isinstance(tag, str): raise TypeError('In %s, tag %s is not a string' % (self, tag)) return tag, result.value return None, result def _write_value_to_tag(self, tag, windowed_value, watermark_estimator): if watermark_estimator is not None: watermark_estimator.observe_timestamp(windowed_value.timestamp) if tag is None: self.main_receivers.receive(windowed_value) else: self.tagged_receivers[tag].receive(windowed_value) def _write_batch_to_tag(self, tag, windowed_batch, watermark_estimator): if watermark_estimator is not None: for timestamp in windowed_batch.timestamps: watermark_estimator.observe_timestamp(timestamp) if tag is None: self.main_receivers.receive_batch(windowed_batch) else: self.tagged_receivers[tag].receive_batch(windowed_batch) def _verify_batch_output(self, result): if isinstance(result, (WindowedValue, TimestampedValue)): raise TypeError( f"Received {type(result).__name__} from DoFn that was " "expected to produce a batch.") def start_bundle_outputs(self, results): """Validate that start_bundle does not output any elements""" if results is None: return raise RuntimeError( 'Start Bundle should not output any elements but got %s' % results) def finish_bundle_outputs(self, results): """Dispatch the result of finish_bundle to the appropriate receivers. A value wrapped in a TaggedOutput object will be unwrapped and then dispatched to the appropriate indexed output. """ if results is None: return for result in results: tag = None if isinstance(result, TaggedOutput): tag = result.tag if not isinstance(tag, str): raise TypeError('In %s, tag %s is not a string' % (self, tag)) result = result.value if isinstance(result, WindowedValue): windowed_value = result else: raise RuntimeError('Finish Bundle should only output WindowedValue ' +\ 'type but got %s' % type(result)) if tag is None: self.main_receivers.receive(windowed_value) else: self.tagged_receivers[tag].receive(windowed_value) class _NoContext(WindowFn.AssignContext): """An uninspectable WindowFn.AssignContext.""" NO_VALUE = object() def __init__(self, value, timestamp=NO_VALUE): self.value = value self._timestamp = timestamp @property def timestamp(self): if self._timestamp is self.NO_VALUE: raise ValueError('No timestamp in this context.') else: return self._timestamp @property def existing_windows(self): raise ValueError('No existing_windows in this context.') class DoFnState(object): """For internal use only; no backwards-compatibility guarantees. Keeps track of state that DoFns want, currently, user counters. """ def __init__(self, counter_factory): self.step_name = '' self._counter_factory = counter_factory def counter_for(self, aggregator): """Looks up the counter for this aggregator, creating one if necessary.""" return self._counter_factory.get_aggregator_counter( self.step_name, aggregator) # TODO(robertwb): Replace core.DoFnContext with this. class DoFnContext(object): """For internal use only; no backwards-compatibility guarantees.""" def __init__(self, label, element=None, state=None): self.label = label self.state = state if element is not None: self.set_element(element) def set_element(self, windowed_value): # type: (Optional[WindowedValue]) -> None self.windowed_value = windowed_value @property def element(self): if self.windowed_value is None: raise AttributeError('element not accessible in this context') else: return self.windowed_value.value @property def timestamp(self): if self.windowed_value is None: raise AttributeError('timestamp not accessible in this context') else: return self.windowed_value.timestamp @property def windows(self): if self.windowed_value is None: raise AttributeError('windows not accessible in this context') else: return self.windowed_value.windows