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apache / beam / bd4df94ae10a7e7b0763c1917746d2faf5aeed6c / . / sdks / python / apache_beam / transforms / core.py
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#
# 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.
#
"""Core PTransform subclasses, such as FlatMap, GroupByKey, and Map."""
# pytype: skip-file
from __future__ import absolute_import
import copy
import inspect
import logging
import random
import types
import typing
from builtins import map
from builtins import object
from builtins import range
from past.builtins import unicode
from apache_beam import coders
from apache_beam import pvalue
from apache_beam import typehints
from apache_beam.coders import typecoders
from apache_beam.coders.coders import ExternalCoder
from apache_beam.internal import pickler
from apache_beam.internal import util
from apache_beam.options.pipeline_options import TypeOptions
from apache_beam.portability import common_urns
from apache_beam.portability import python_urns
from apache_beam.portability.api import beam_runner_api_pb2
from apache_beam.transforms import ptransform
from apache_beam.transforms import userstate
from apache_beam.transforms.display import DisplayDataItem
from apache_beam.transforms.display import HasDisplayData
from apache_beam.transforms.ptransform import PTransform
from apache_beam.transforms.ptransform import PTransformWithSideInputs
from apache_beam.transforms.sideinputs import SIDE_INPUT_PREFIX
from apache_beam.transforms.sideinputs import get_sideinput_index
from apache_beam.transforms.userstate import StateSpec
from apache_beam.transforms.userstate import TimerSpec
from apache_beam.transforms.window import GlobalWindows
from apache_beam.transforms.window import TimestampCombiner
from apache_beam.transforms.window import TimestampedValue
from apache_beam.transforms.window import WindowedValue
from apache_beam.transforms.window import WindowFn
from apache_beam.typehints import trivial_inference
from apache_beam.typehints.decorators import TypeCheckError
from apache_beam.typehints.decorators import WithTypeHints
from apache_beam.typehints.decorators import get_signature
from apache_beam.typehints.decorators import get_type_hints
from apache_beam.typehints.decorators import with_input_types
from apache_beam.typehints.decorators import with_output_types
from apache_beam.typehints.trivial_inference import element_type
from apache_beam.typehints.typehints import is_consistent_with
from apache_beam.utils import proto_utils
from apache_beam.utils import urns
from apache_beam.utils.timestamp import Duration
if typing.TYPE_CHECKING:
from apache_beam.io import iobase # pylint: disable=ungrouped-imports
from apache_beam.pipeline import Pipeline
from apache_beam.runners.pipeline_context import PipelineContext
from apache_beam.transforms import create_source
from apache_beam.transforms.trigger import AccumulationMode
from apache_beam.transforms.trigger import DefaultTrigger
from apache_beam.transforms.trigger import TriggerFn
try:
import funcsigs # Python 2 only.
except ImportError:
funcsigs = None
__all__ = [
'DoFn',
'CombineFn',
'PartitionFn',
'ParDo',
'FlatMap',
'FlatMapTuple',
'Map',
'MapTuple',
'Filter',
'CombineGlobally',
'CombinePerKey',
'CombineValues',
'GroupByKey',
'Partition',
'Windowing',
'WindowInto',
'Flatten',
'Create',
'Impulse',
'RestrictionProvider',
'WatermarkEstimatorProvider',
]
# Type variables
T = typing.TypeVar('T')
K = typing.TypeVar('K')
V = typing.TypeVar('V')
_LOGGER = logging.getLogger(__name__)
class DoFnContext(object):
"""A context available to all methods of DoFn instance."""
pass
class DoFnProcessContext(DoFnContext):
"""A processing context passed to DoFn process() during execution.
Experimental; no backwards-compatibility guarantees.
Most importantly, a DoFn.process method will access context.element
to get the element it is supposed to process.
Attributes:
label: label of the ParDo whose element is being processed.
element: element being processed
(in process method only; always None in start_bundle and finish_bundle)
timestamp: timestamp of the element
(in process method only; always None in start_bundle and finish_bundle)
windows: windows of the element
(in process method only; always None in start_bundle and finish_bundle)
state: a DoFnState object, which holds the runner's internal state
for this element.
Not used by the pipeline code.
"""
def __init__(self, label, element=None, state=None):
"""Initialize a processing context object with an element and state.
The element represents one value from a PCollection that will be accessed
by a DoFn object during pipeline execution, and state is an arbitrary object
where counters and other pipeline state information can be passed in.
DoFnProcessContext objects are also used as inputs to PartitionFn instances.
Args:
label: label of the PCollection whose element is being processed.
element: element of a PCollection being processed using this context.
state: a DoFnState object with state to be passed in to the DoFn object.
"""
self.label = label
self.state = state
if element is not None:
self.set_element(element)
def set_element(self, windowed_value):
if windowed_value is None:
# Not currently processing an element.
if hasattr(self, 'element'):
del self.element
del self.timestamp
del self.windows
else:
self.element = windowed_value.value
self.timestamp = windowed_value.timestamp
self.windows = windowed_value.windows
class ProcessContinuation(object):
"""An object that may be produced as the last element of a process method
invocation.
Experimental; no backwards-compatibility guarantees.
If produced, indicates that there is more work to be done for the current
input element.
"""
def __init__(self, resume_delay=0):
"""Initializes a ProcessContinuation object.
Args:
resume_delay: indicates the minimum time, in seconds, that should elapse
before re-invoking process() method for resuming the invocation of the
current element.
"""
self.resume_delay = resume_delay
@staticmethod
def resume(resume_delay=0):
"""A convenient method that produces a ``ProcessContinuation``.
Args:
resume_delay: delay after which processing current element should be
resumed.
Returns: a ``ProcessContinuation`` for signalling the runner that current
input element has not been fully processed and should be resumed later.
"""
return ProcessContinuation(resume_delay=resume_delay)
class RestrictionProvider(object):
"""Provides methods for generating and manipulating restrictions.
This class should be implemented to support Splittable ``DoFn`` in Python
SDK. See https://s.apache.org/splittable-do-fn for more details about
Splittable ``DoFn``.
To denote a ``DoFn`` class to be Splittable ``DoFn``, ``DoFn.process()``
method of that class should have exactly one parameter whose default value is
an instance of ``RestrictionProvider``.
The provided ``RestrictionProvider`` instance must provide suitable overrides
for the following methods:
* create_tracker()
* initial_restriction()
Optionally, ``RestrictionProvider`` may override default implementations of
following methods:
* restriction_coder()
* restriction_size()
* split()
* split_and_size()
** Pausing and resuming processing of an element **
As the last element produced by the iterator returned by the
``DoFn.process()`` method, a Splittable ``DoFn`` may return an object of type
``ProcessContinuation``.
If provided, ``ProcessContinuation`` object specifies that runner should
later re-invoke ``DoFn.process()`` method to resume processing the current
element and the manner in which the re-invocation should be performed. A
``ProcessContinuation`` object must only be specified as the last element of
the iterator. If a ``ProcessContinuation`` object is not provided the runner
will assume that the current input element has been fully processed.
** Updating output watermark **
``DoFn.process()`` method of Splittable ``DoFn``s could contain a parameter
with default value ``DoFn.WatermarkReporterParam``. If specified this asks the
runner to provide a function that can be used to give the runner a
(best-effort) lower bound about the timestamps of future output associated
with the current element processed by the ``DoFn``. If the ``DoFn`` has
multiple outputs, the watermark applies to all of them. Provided function must
be invoked with a single parameter of type ``Timestamp`` or as an integer that
gives the watermark in number of seconds.
"""
def create_tracker(self, restriction):
# type: (...) -> iobase.RestrictionTracker
"""Produces a new ``RestrictionTracker`` for the given restriction.
This API is required to be implemented.
Args:
restriction: an object that defines a restriction as identified by a
Splittable ``DoFn`` that utilizes the current ``RestrictionProvider``.
For example, a tuple that gives a range of positions for a Splittable
``DoFn`` that reads files based on byte positions.
Returns: an object of type ``RestrictionTracker``.
"""
raise NotImplementedError
def initial_restriction(self, element):
"""Produces an initial restriction for the given element.
This API is required to be implemented.
"""
raise NotImplementedError
def split(self, element, restriction):
"""Splits the given element and restriction.
Returns an iterator of restrictions. The total set of elements produced by
reading input element for each of the returned restrictions should be the
same as the total set of elements produced by reading the input element for
the input restriction.
This API is optional if ``split_and_size`` has been implemented.
"""
yield restriction
def restriction_coder(self):
"""Returns a ``Coder`` for restrictions.
Returned``Coder`` will be used for the restrictions produced by the current
``RestrictionProvider``.
Returns:
an object of type ``Coder``.
"""
return coders.registry.get_coder(object)
def restriction_size(self, element, restriction):
"""Returns the size of an element with respect to the given element.
By default, asks a newly-created restriction tracker for the default size
of the restriction.
This API is required to be implemented.
"""
raise NotImplementedError
def split_and_size(self, element, restriction):
"""Like split, but also does sizing, returning (restriction, size) pairs.
This API is optional if ``split`` and ``restriction_size`` have been
implemented.
"""
for part in self.split(element, restriction):
yield part, self.restriction_size(element, part)
def get_function_arguments(obj, func):
# type: (...) -> typing.Tuple[typing.List[str], typing.List[typing.Any]]
"""Return the function arguments based on the name provided. If they have
a _inspect_function attached to the class then use that otherwise default
to the modified version of python inspect library.
Returns:
Same as get_function_args_defaults.
"""
func_name = '_inspect_%s' % func
if hasattr(obj, func_name):
f = getattr(obj, func_name)
return f()
f = getattr(obj, func)
return get_function_args_defaults(f)
def get_function_args_defaults(f):
# type: (...) -> typing.Tuple[typing.List[str], typing.List[typing.Any]]
"""Returns the function arguments of a given function.
Returns:
(args: List[str], defaults: List[Any]). The first list names the
arguments of the method and the second one has the values of the default
arguments. This is similar to ``inspect.getfullargspec()``'s results, except
it doesn't include bound arguments and may follow function wrappers.
"""
signature = get_signature(f)
# Fall back on funcsigs if inspect module doesn't have 'Parameter'; prefer
# inspect.Parameter over funcsigs.Parameter if both are available.
try:
parameter = inspect.Parameter
except AttributeError:
parameter = funcsigs.Parameter
# TODO(BEAM-5878) support kwonlyargs on Python 3.
_SUPPORTED_ARG_TYPES = [
parameter.POSITIONAL_ONLY, parameter.POSITIONAL_OR_KEYWORD
]
args = [
name for name,
p in signature.parameters.items() if p.kind in _SUPPORTED_ARG_TYPES
]
defaults = [
p.default for p in signature.parameters.values()
if p.kind in _SUPPORTED_ARG_TYPES and p.default is not p.empty
]
return args, defaults
class RunnerAPIPTransformHolder(PTransform):
"""A `PTransform` that holds a runner API `PTransform` proto.
This is used for transforms, for which corresponding objects
cannot be initialized in Python SDK. For example, for `ParDo` transforms for
remote SDKs that may be available in Python SDK transform graph when expanding
a cross-language transform since a Python `ParDo` object cannot be generated
without a serialized Python `DoFn` object.
"""
def __init__(self, proto, context):
self._proto = proto
self._context = context
# For ParDos with side-inputs, this will be populated after this object is
# created.
self.side_inputs = []
self.is_pardo_with_stateful_dofn = bool(self._get_pardo_state_specs())
def proto(self):
"""Runner API payload for a `PTransform`"""
return self._proto
def to_runner_api(self, context, **extra_kwargs):
# TODO(BEAM-7850): no need to copy around Environment if it is a direct
# attribute of PTransform.
id_to_proto_map = self._context.environments.get_id_to_proto_map()
for env_id in id_to_proto_map:
if env_id not in context.environments:
context.environments.put_proto(env_id, id_to_proto_map[env_id])
else:
env1 = id_to_proto_map[env_id]
env2 = context.environments[env_id]
assert env1.urn == env2.to_runner_api(context).urn, (
'Expected environments with the same ID to be equal but received '
'environments with different URNs '
'%r and %r',
env1.urn, env2.to_runner_api(context).urn)
assert env1.payload == env2.to_runner_api(context).payload, (
'Expected environments with the same ID to be equal but received '
'environments with different payloads '
'%r and %r',
env1.payload, env2.to_runner_api(context).payload)
if common_urns.primitives.PAR_DO.urn == self._proto.urn:
# If a restriction coder has been set by an external SDK, we have to
# explicitly add it (and all component coders recursively) to the context
# to make sure that it does not get dropped by Python SDK.
def recursively_add_coder_protos(coder_id, old_context, new_context):
coder_proto = old_context.coders.get_proto_from_id(coder_id)
new_context.coders.put_proto(coder_id, coder_proto, True)
for component_coder_id in coder_proto.component_coder_ids:
recursively_add_coder_protos(
component_coder_id, old_context, new_context)
par_do_payload = proto_utils.parse_Bytes(
self._proto.payload, beam_runner_api_pb2.ParDoPayload)
if par_do_payload.restriction_coder_id:
recursively_add_coder_protos(
par_do_payload.restriction_coder_id, self._context, context)
return self._proto
def get_restriction_coder(self):
# For some runners, restriction coder ID has to be provided to correctly
# encode ParDo transforms that are SDF.
if common_urns.primitives.PAR_DO.urn == self._proto.urn:
par_do_payload = proto_utils.parse_Bytes(
self._proto.payload, beam_runner_api_pb2.ParDoPayload)
if par_do_payload.restriction_coder_id:
restriction_coder_proto = self._context.coders.get_proto_from_id(
par_do_payload.restriction_coder_id)
return ExternalCoder(restriction_coder_proto)
def _get_pardo_state_specs(self):
if common_urns.primitives.PAR_DO.urn == self._proto.urn:
par_do_payload = proto_utils.parse_Bytes(
self._proto.payload, beam_runner_api_pb2.ParDoPayload)
return par_do_payload.state_specs
class WatermarkEstimatorProvider(object):
"""Provides methods for generating WatermarkEstimator.
This class should be implemented if wanting to providing output_watermark
information within an SDF.
In order to make an SDF.process() access to the typical WatermarkEstimator,
the SDF author should pass a DoFn.WatermarkEstimatorParam with a default value
of one WatermarkEstimatorProvider instance.
"""
def initial_estimator_state(self, element, restriction):
"""Returns the initial state of the WatermarkEstimator with given element
and restriction.
This function is called by the system.
"""
raise NotImplementedError
def create_watermark_estimator(self, estimator_state):
"""Create a new WatermarkEstimator based on the state. The state is
typically useful when resuming processing an element.
"""
raise NotImplementedError
def estimator_state_coder(self):
return coders.registry.get_coder(object)
class _DoFnParam(object):
"""DoFn parameter."""
def __init__(self, param_id):
self.param_id = param_id
def __eq__(self, other):
if type(self) == type(other):
return self.param_id == other.param_id
return False
def __ne__(self, other):
# TODO(BEAM-5949): Needed for Python 2 compatibility.
return not self == other
def __hash__(self):
return hash(self.param_id)
def __repr__(self):
return self.param_id
class _RestrictionDoFnParam(_DoFnParam):
"""Restriction Provider DoFn parameter."""
def __init__(self, restriction_provider):
# type: (RestrictionProvider) -> None
if not isinstance(restriction_provider, RestrictionProvider):
raise ValueError(
'DoFn.RestrictionParam expected RestrictionProvider object.')
self.restriction_provider = restriction_provider
self.param_id = (
'RestrictionParam(%s)' % restriction_provider.__class__.__name__)
class _StateDoFnParam(_DoFnParam):
"""State DoFn parameter."""
def __init__(self, state_spec):
# type: (StateSpec) -> None
if not isinstance(state_spec, StateSpec):
raise ValueError("DoFn.StateParam expected StateSpec object.")
self.state_spec = state_spec
self.param_id = 'StateParam(%s)' % state_spec.name
class _TimerDoFnParam(_DoFnParam):
"""Timer DoFn parameter."""
def __init__(self, timer_spec):
# type: (TimerSpec) -> None
if not isinstance(timer_spec, TimerSpec):
raise ValueError("DoFn.TimerParam expected TimerSpec object.")
self.timer_spec = timer_spec
self.param_id = 'TimerParam(%s)' % timer_spec.name
class _BundleFinalizerParam(_DoFnParam):
"""Bundle Finalization DoFn parameter."""
def __init__(self):
self._callbacks = []
self.param_id = "FinalizeBundle"
def register(self, callback):
self._callbacks.append(callback)
# Log errors when calling callback to make sure all callbacks get called
# though there are errors. And errors should not fail pipeline.
def finalize_bundle(self):
for callback in self._callbacks:
try:
callback()
except Exception as e:
_LOGGER.warning("Got exception from finalization call: %s", e)
def has_callbacks(self):
# type: () -> bool
return len(self._callbacks) > 0
def reset(self):
# type: () -> None
del self._callbacks[:]
class _WatermarkEstimatorParam(_DoFnParam):
"""WatermarkEstomator DoFn parameter."""
def __init__(self, watermark_estimator_provider):
# type: (WatermarkEstimatorProvider) -> None
if not isinstance(watermark_estimator_provider, WatermarkEstimatorProvider):
raise ValueError(
'DoFn._WatermarkEstimatorParam expected'
'WatermarkEstimatorProvider object.')
self.watermark_estimator_provider = watermark_estimator_provider
self.param_id = 'WatermarkEstimatorProvider'
class DoFn(WithTypeHints, HasDisplayData, urns.RunnerApiFn):
"""A function object used by a transform with custom processing.
The ParDo transform is such a transform. The ParDo.apply
method will take an object of type DoFn and apply it to all elements of a
PCollection object.
In order to have concrete DoFn objects one has to subclass from DoFn and
define the desired behavior (start_bundle/finish_bundle and process) or wrap a
callable object using the CallableWrapperDoFn class.
"""
# Parameters that can be used in the .process() method.
ElementParam = _DoFnParam('ElementParam')
SideInputParam = _DoFnParam('SideInputParam')
TimestampParam = _DoFnParam('TimestampParam')
WindowParam = _DoFnParam('WindowParam')
PaneInfoParam = _DoFnParam('PaneInfoParam')
WatermarkEstimatorParam = _WatermarkEstimatorParam
BundleFinalizerParam = _BundleFinalizerParam
KeyParam = _DoFnParam('KeyParam')
# Parameters to access state and timers. Not restricted to use only in the
# .process() method. Usage: DoFn.StateParam(state_spec),
# DoFn.TimerParam(timer_spec), DoFn.TimestampParam, DoFn.WindowParam,
# DoFn.KeyParam
StateParam = _StateDoFnParam
TimerParam = _TimerDoFnParam
DoFnProcessParams = [
ElementParam,
SideInputParam,
TimestampParam,
WindowParam,
WatermarkEstimatorParam,
PaneInfoParam,
BundleFinalizerParam,
KeyParam,
StateParam,
TimerParam
]
RestrictionParam = _RestrictionDoFnParam
@staticmethod
def from_callable(fn):
return CallableWrapperDoFn(fn)
def default_label(self):
return self.__class__.__name__
def process(self, element, *args, **kwargs):
"""Method to use for processing elements.
This is invoked by ``DoFnRunner`` for each element of a input
``PCollection``.
If specified, following default arguments are used by the ``DoFnRunner`` to
be able to pass the parameters correctly.
``DoFn.ElementParam``: element to be processed, should not be mutated.
``DoFn.SideInputParam``: a side input that may be used when processing.
``DoFn.TimestampParam``: timestamp of the input element.
``DoFn.WindowParam``: ``Window`` the input element belongs to.
``DoFn.TimerParam``: a ``userstate.RuntimeTimer`` object defined by the spec
of the parameter.
``DoFn.StateParam``: a ``userstate.RuntimeState`` object defined by the spec
of the parameter.
``DoFn.KeyParam``: key associated with the element.
``DoFn.RestrictionParam``: an ``iobase.RestrictionTracker`` will be
provided here to allow treatment as a Splittable ``DoFn``. The restriction
tracker will be derived from the restriction provider in the parameter.
``DoFn.WatermarkEstimatorParam``: a function that can be used to track
output watermark of Splittable ``DoFn`` implementations.
Args:
element: The element to be processed
*args: side inputs
**kwargs: other keyword arguments.
Returns:
An Iterable of output elements or None.
"""
raise NotImplementedError
def setup(self):
"""Called to prepare an instance for processing bundles of elements.
This is a good place to initialize transient in-memory resources, such as
network connections. The resources can then be disposed in
``DoFn.teardown``.
"""
pass
def start_bundle(self):
"""Called before a bundle of elements is processed on a worker.
Elements to be processed are split into bundles and distributed
to workers. Before a worker calls process() on the first element
of its bundle, it calls this method.
"""
pass
def finish_bundle(self):
"""Called after a bundle of elements is processed on a worker.
"""
pass
def teardown(self):
"""Called to use to clean up this instance before it is discarded.
A runner will do its best to call this method on any given instance to
prevent leaks of transient resources, however, there may be situations where
this is impossible (e.g. process crash, hardware failure, etc.) or
unnecessary (e.g. the pipeline is shutting down and the process is about to
be killed anyway, so all transient resources will be released automatically
by the OS). In these cases, the call may not happen. It will also not be
retried, because in such situations the DoFn instance no longer exists, so
there's no instance to retry it on.
Thus, all work that depends on input elements, and all externally important
side effects, must be performed in ``DoFn.process`` or
``DoFn.finish_bundle``.
"""
pass
def get_function_arguments(self, func):
return get_function_arguments(self, func)
def default_type_hints(self):
fn_type_hints = typehints.decorators.IOTypeHints.from_callable(self.process)
if fn_type_hints is not None:
try:
fn_type_hints = fn_type_hints.strip_iterable()
except ValueError as e:
raise ValueError('Return value not iterable: %s: %s' % (self, e))
# Prefer class decorator type hints for backwards compatibility.
return get_type_hints(self.__class__).with_defaults(fn_type_hints)
# TODO(sourabhbajaj): Do we want to remove the responsibility of these from
# the DoFn or maybe the runner
def infer_output_type(self, input_type):
# TODO(BEAM-8247): Side inputs types.
# TODO(robertwb): Assert compatibility with input type hint?
return self._strip_output_annotations(
trivial_inference.infer_return_type(self.process, [input_type]))
def _strip_output_annotations(self, type_hint):
annotations = (TimestampedValue, WindowedValue, pvalue.TaggedOutput)
# TODO(robertwb): These should be parameterized types that the
# type inferencer understands.
if (type_hint in annotations or
trivial_inference.element_type(type_hint) in annotations):
return typehints.Any
return type_hint
def _process_argspec_fn(self):
"""Returns the Python callable that will eventually be invoked.
This should ideally be the user-level function that is called with
the main and (if any) side inputs, and is used to relate the type
hint parameters with the input parameters (e.g., by argument name).
"""
return self.process
urns.RunnerApiFn.register_pickle_urn(python_urns.PICKLED_DOFN)
def _fn_takes_side_inputs(fn):
try:
signature = get_signature(fn)
except TypeError:
# We can't tell; maybe it does.
return True
return (
len(signature.parameters) > 1 or any(
p.kind == p.VAR_POSITIONAL or p.kind == p.VAR_KEYWORD
for p in signature.parameters.values()))
class CallableWrapperDoFn(DoFn):
"""For internal use only; no backwards-compatibility guarantees.
A DoFn (function) object wrapping a callable object.
The purpose of this class is to conveniently wrap simple functions and use
them in transforms.
"""
def __init__(self, fn, fullargspec=None):
"""Initializes a CallableWrapperDoFn object wrapping a callable.
Args:
fn: A callable object.
Raises:
TypeError: if fn parameter is not a callable type.
"""
if not callable(fn):
raise TypeError('Expected a callable object instead of: %r' % fn)
self._fn = fn
self._fullargspec = fullargspec
if isinstance(
fn, (types.BuiltinFunctionType, types.MethodType, types.FunctionType)):
self.process = fn
else:
# For cases such as set / list where fn is callable but not a function
self.process = lambda element: fn(element)
super(CallableWrapperDoFn, self).__init__()
def display_data(self):
# If the callable has a name, then it's likely a function, and
# we show its name.
# Otherwise, it might be an instance of a callable class. We
# show its class.
display_data_value = (
self._fn.__name__
if hasattr(self._fn, '__name__') else self._fn.__class__)
return {
'fn': DisplayDataItem(display_data_value, label='Transform Function')
}
def __repr__(self):
return 'CallableWrapperDoFn(%s)' % self._fn
def default_type_hints(self):
fn_type_hints = typehints.decorators.IOTypeHints.from_callable(self._fn)
type_hints = get_type_hints(self._fn).with_defaults(fn_type_hints)
# The fn's output type should be iterable. Strip off the outer
# container type due to the 'flatten' portion of FlatMap/ParDo.
try:
type_hints = type_hints.strip_iterable()
except ValueError as e:
raise TypeCheckError(
'Return value not iterable: %s: %s' %
(self.display_data()['fn'].value, e))
return type_hints
def infer_output_type(self, input_type):
return self._strip_output_annotations(
trivial_inference.infer_return_type(self._fn, [input_type]))
def _process_argspec_fn(self):
return getattr(self._fn, '_argspec_fn', self._fn)
def _inspect_process(self):
if self._fullargspec:
return self._fullargspec
else:
return get_function_args_defaults(self._process_argspec_fn())
class CombineFn(WithTypeHints, HasDisplayData, urns.RunnerApiFn):
"""A function object used by a Combine transform with custom processing.
A CombineFn specifies how multiple values in all or part of a PCollection can
be merged into a single value---essentially providing the same kind of
information as the arguments to the Python "reduce" builtin (except for the
input argument, which is an instance of CombineFnProcessContext). The
combining process proceeds as follows:
1. Input values are partitioned into one or more batches.
2. For each batch, the create_accumulator method is invoked to create a fresh
initial "accumulator" value representing the combination of zero values.
3. For each input value in the batch, the add_input method is invoked to
combine more values with the accumulator for that batch.
4. The merge_accumulators method is invoked to combine accumulators from
separate batches into a single combined output accumulator value, once all
of the accumulators have had all the input value in their batches added to
them. This operation is invoked repeatedly, until there is only one
accumulator value left.
5. The extract_output operation is invoked on the final accumulator to get
the output value.
Note: If this **CombineFn** is used with a transform that has defaults,
**apply** will be called with an empty list at expansion time to get the
default value.
"""
def default_label(self):
return self.__class__.__name__
def create_accumulator(self, *args, **kwargs):
"""Return a fresh, empty accumulator for the combine operation.
Args:
*args: Additional arguments and side inputs.
**kwargs: Additional arguments and side inputs.
"""
raise NotImplementedError(str(self))
def add_input(self, mutable_accumulator, element, *args, **kwargs):
"""Return result of folding element into accumulator.
CombineFn implementors must override add_input.
Args:
mutable_accumulator: the current accumulator,
may be modified and returned for efficiency
element: the element to add, should not be mutated
*args: Additional arguments and side inputs.
**kwargs: Additional arguments and side inputs.
"""
raise NotImplementedError(str(self))
def add_inputs(self, mutable_accumulator, elements, *args, **kwargs):
"""Returns the result of folding each element in elements into accumulator.
This is provided in case the implementation affords more efficient
bulk addition of elements. The default implementation simply loops
over the inputs invoking add_input for each one.
Args:
mutable_accumulator: the current accumulator,
may be modified and returned for efficiency
elements: the elements to add, should not be mutated
*args: Additional arguments and side inputs.
**kwargs: Additional arguments and side inputs.
"""
for element in elements:
mutable_accumulator =\
self.add_input(mutable_accumulator, element, *args, **kwargs)
return mutable_accumulator
def merge_accumulators(self, accumulators, *args, **kwargs):
"""Returns the result of merging several accumulators
to a single accumulator value.
Args:
accumulators: the accumulators to merge.
Only the first accumulator may be modified and returned for efficiency;
the other accumulators should not be mutated, because they may be
shared with other code and mutating them could lead to incorrect
results or data corruption.
*args: Additional arguments and side inputs.
**kwargs: Additional arguments and side inputs.
"""
raise NotImplementedError(str(self))
def compact(self, accumulator, *args, **kwargs):
"""Optionally returns a more compact represenation of the accumulator.
This is called before an accumulator is sent across the wire, and can
be useful in cases where values are buffered or otherwise lazily
kept unprocessed when added to the accumulator. Should return an
equivalent, though possibly modified, accumulator.
By default returns the accumulator unmodified.
Args:
accumulator: the current accumulator
*args: Additional arguments and side inputs.
**kwargs: Additional arguments and side inputs.
"""
return accumulator
def extract_output(self, accumulator, *args, **kwargs):
"""Return result of converting accumulator into the output value.
Args:
accumulator: the final accumulator value computed by this CombineFn
for the entire input key or PCollection. Can be modified for
efficiency.
*args: Additional arguments and side inputs.
**kwargs: Additional arguments and side inputs.
"""
raise NotImplementedError(str(self))
def apply(self, elements, *args, **kwargs):
"""Returns result of applying this CombineFn to the input values.
Args:
elements: the set of values to combine.
*args: Additional arguments and side inputs.
**kwargs: Additional arguments and side inputs.
"""
return self.extract_output(
self.add_inputs(
self.create_accumulator(*args, **kwargs), elements, *args,
**kwargs),
*args,
**kwargs)
def for_input_type(self, input_type):
"""Returns a specialized implementation of self, if it exists.
Otherwise, returns self.
Args:
input_type: the type of input elements.
"""
return self
@staticmethod
def from_callable(fn):
return CallableWrapperCombineFn(fn)
@staticmethod
def maybe_from_callable(fn, has_side_inputs=True):
# type: (typing.Union[CombineFn, typing.Callable], bool) -> CombineFn
if isinstance(fn, CombineFn):
return fn
elif callable(fn) and not has_side_inputs:
return NoSideInputsCallableWrapperCombineFn(fn)
elif callable(fn):
return CallableWrapperCombineFn(fn)
else:
raise TypeError('Expected a CombineFn or callable, got %r' % fn)
def get_accumulator_coder(self):
return coders.registry.get_coder(object)
urns.RunnerApiFn.register_pickle_urn(python_urns.PICKLED_COMBINE_FN)
class _ReiterableChain(object):
"""Like itertools.chain, but allowing re-iteration."""
def __init__(self, iterables):
self.iterables = iterables
def __iter__(self):
for iterable in self.iterables:
for item in iterable:
yield item
def __bool__(self):
for iterable in self.iterables:
for _ in iterable:
return True
return False
class CallableWrapperCombineFn(CombineFn):
"""For internal use only; no backwards-compatibility guarantees.
A CombineFn (function) object wrapping a callable object.
The purpose of this class is to conveniently wrap simple functions and use
them in Combine transforms.
"""
_DEFAULT_BUFFER_SIZE = 10
def __init__(self, fn, buffer_size=_DEFAULT_BUFFER_SIZE):
"""Initializes a CallableFn object wrapping a callable.
Args:
fn: A callable object that reduces elements of an iterable to a single
value (like the builtins sum and max). This callable must be capable of
receiving the kind of values it generates as output in its input, and
for best results, its operation must be commutative and associative.
Raises:
TypeError: if fn parameter is not a callable type.
"""
if not callable(fn):
raise TypeError('Expected a callable object instead of: %r' % fn)
super(CallableWrapperCombineFn, self).__init__()
self._fn = fn
self._buffer_size = buffer_size
def display_data(self):
return {'fn_dd': self._fn}
def __repr__(self):
return "%s(%s)" % (self.__class__.__name__, self._fn)
def create_accumulator(self, *args, **kwargs):
return []
def add_input(self, accumulator, element, *args, **kwargs):
accumulator.append(element)
if len(accumulator) > self._buffer_size:
accumulator = [self._fn(accumulator, *args, **kwargs)]
return accumulator
def add_inputs(self, accumulator, elements, *args, **kwargs):
accumulator.extend(elements)
if len(accumulator) > self._buffer_size:
accumulator = [self._fn(accumulator, *args, **kwargs)]
return accumulator
def merge_accumulators(self, accumulators, *args, **kwargs):
return [self._fn(_ReiterableChain(accumulators), *args, **kwargs)]
def compact(self, accumulator, *args, **kwargs):
if len(accumulator) <= 1:
return accumulator
else:
return [self._fn(accumulator, *args, **kwargs)]
def extract_output(self, accumulator, *args, **kwargs):
return self._fn(accumulator, *args, **kwargs)
def default_type_hints(self):
fn_hints = get_type_hints(self._fn)
if fn_hints.input_types is None:
return fn_hints
else:
# fn(Iterable[V]) -> V becomes CombineFn(V) -> V
input_args, input_kwargs = fn_hints.input_types
if not input_args:
if len(input_kwargs) == 1:
input_args, input_kwargs = tuple(input_kwargs.values()), {}
else:
raise TypeError('Combiner input type must be specified positionally.')
if not is_consistent_with(input_args[0],
typehints.Iterable[typehints.Any]):
raise TypeCheckError(
'All functions for a Combine PTransform must accept a '
'single argument compatible with: Iterable[Any]. '
'Instead a function with input type: %s was received.' %
input_args[0])
input_args = (element_type(input_args[0]), ) + input_args[1:]
# TODO(robertwb): Assert output type is consistent with input type?
return fn_hints.with_input_types(*input_args, **input_kwargs)
def for_input_type(self, input_type):
# Avoid circular imports.
from apache_beam.transforms import cy_combiners
if self._fn is any:
return cy_combiners.AnyCombineFn()
elif self._fn is all:
return cy_combiners.AllCombineFn()
else:
known_types = {
(sum, int): cy_combiners.SumInt64Fn(),
(min, int): cy_combiners.MinInt64Fn(),
(max, int): cy_combiners.MaxInt64Fn(),
(sum, float): cy_combiners.SumFloatFn(),
(min, float): cy_combiners.MinFloatFn(),
(max, float): cy_combiners.MaxFloatFn(),
}
return known_types.get((self._fn, input_type), self)
class NoSideInputsCallableWrapperCombineFn(CallableWrapperCombineFn):
"""For internal use only; no backwards-compatibility guarantees.
A CombineFn (function) object wrapping a callable object with no side inputs.
This is identical to its parent, but avoids accepting and passing *args
and **kwargs for efficiency as they are known to be empty.
"""
def create_accumulator(self):
return []
def add_input(self, accumulator, element):
accumulator.append(element)
if len(accumulator) > self._buffer_size:
accumulator = [self._fn(accumulator)]
return accumulator
def add_inputs(self, accumulator, elements):
accumulator.extend(elements)
if len(accumulator) > self._buffer_size:
accumulator = [self._fn(accumulator)]
return accumulator
def merge_accumulators(self, accumulators):
return [self._fn(_ReiterableChain(accumulators))]
def compact(self, accumulator):
if len(accumulator) <= 1:
return accumulator
else:
return [self._fn(accumulator)]
def extract_output(self, accumulator):
return self._fn(accumulator)
class PartitionFn(WithTypeHints):
"""A function object used by a Partition transform.
A PartitionFn specifies how individual values in a PCollection will be placed
into separate partitions, indexed by an integer.
"""
def default_label(self):
return self.__class__.__name__
def partition_for(self, element, num_partitions, *args, **kwargs):
# type: (T, int, *typing.Any, **typing.Any) -> int
"""Specify which partition will receive this element.
Args:
element: An element of the input PCollection.
num_partitions: Number of partitions, i.e., output PCollections.
*args: optional parameters and side inputs.
**kwargs: optional parameters and side inputs.
Returns:
An integer in [0, num_partitions).
"""
pass
class CallableWrapperPartitionFn(PartitionFn):
"""For internal use only; no backwards-compatibility guarantees.
A PartitionFn object wrapping a callable object.
Instances of this class wrap simple functions for use in Partition operations.
"""
def __init__(self, fn):
"""Initializes a PartitionFn object wrapping a callable.
Args:
fn: A callable object, which should accept the following arguments:
element - element to assign to a partition.
num_partitions - number of output partitions.
and may accept additional arguments and side inputs.
Raises:
TypeError: if fn is not a callable type.
"""
if not callable(fn):
raise TypeError('Expected a callable object instead of: %r' % fn)
self._fn = fn
def partition_for(self, element, num_partitions, *args, **kwargs):
# type: (T, int, *typing.Any, **typing.Any) -> int
return self._fn(element, num_partitions, *args, **kwargs)
class ParDo(PTransformWithSideInputs):
"""A :class:`ParDo` transform.
Processes an input :class:`~apache_beam.pvalue.PCollection` by applying a
:class:`DoFn` to each element and returning the accumulated results into an
output :class:`~apache_beam.pvalue.PCollection`. The type of the elements is
not fixed as long as the :class:`DoFn` can deal with it. In reality the type
is restrained to some extent because the elements sometimes must be persisted
to external storage. See the :meth:`.expand()` method comments for a
detailed description of all possible arguments.
Note that the :class:`DoFn` must return an iterable for each element of the
input :class:`~apache_beam.pvalue.PCollection`. An easy way to do this is to
use the ``yield`` keyword in the process method.
Args:
pcoll (~apache_beam.pvalue.PCollection):
a :class:`~apache_beam.pvalue.PCollection` to be processed.
fn (`typing.Union[DoFn, typing.Callable]`): a :class:`DoFn` object to be
applied to each element of **pcoll** argument, or a Callable.
*args: positional arguments passed to the :class:`DoFn` object.
**kwargs: keyword arguments passed to the :class:`DoFn` object.
Note that the positional and keyword arguments will be processed in order
to detect :class:`~apache_beam.pvalue.PCollection` s that will be computed as
side inputs to the transform. During pipeline execution whenever the
:class:`DoFn` object gets executed (its :meth:`DoFn.process()` method gets
called) the :class:`~apache_beam.pvalue.PCollection` arguments will be
replaced by values from the :class:`~apache_beam.pvalue.PCollection` in the
exact positions where they appear in the argument lists.
"""
def __init__(self, fn, *args, **kwargs):
super(ParDo, self).__init__(fn, *args, **kwargs)
# TODO(robertwb): Change all uses of the dofn attribute to use fn instead.
self.dofn = self.fn
self.output_tags = set() # type: typing.Set[str]
if not isinstance(self.fn, DoFn):
raise TypeError('ParDo must be called with a DoFn instance.')
# Validate the DoFn by creating a DoFnSignature
from apache_beam.runners.common import DoFnSignature
self._signature = DoFnSignature(self.fn)
def default_type_hints(self):
return self.fn.get_type_hints()
def infer_output_type(self, input_type):
return trivial_inference.element_type(self.fn.infer_output_type(input_type))
def make_fn(self, fn, has_side_inputs):
if isinstance(fn, DoFn):
return fn
return CallableWrapperDoFn(fn)
def _process_argspec_fn(self):
return self.fn._process_argspec_fn()
def display_data(self):
return {
'fn': DisplayDataItem(self.fn.__class__, label='Transform Function'),
'fn_dd': self.fn
}
def expand(self, pcoll):
# In the case of a stateful DoFn, warn if the key coder is not
# deterministic.
if self._signature.is_stateful_dofn():
kv_type_hint = pcoll.element_type
if kv_type_hint and kv_type_hint != typehints.Any:
coder = coders.registry.get_coder(kv_type_hint)
if not coder.is_kv_coder():
raise ValueError(
'Input elements to the transform %s with stateful DoFn must be '
'key-value pairs.' % self)
key_coder = coder.key_coder()
else:
key_coder = coders.registry.get_coder(typehints.Any)
if not key_coder.is_deterministic():
_LOGGER.warning(
'Key coder %s for transform %s with stateful DoFn may not '
'be deterministic. This may cause incorrect behavior for complex '
'key types. Consider adding an input type hint for this transform.',
key_coder,
self)
return pvalue.PCollection.from_(pcoll)
def with_outputs(self, *tags, **main_kw):
"""Returns a tagged tuple allowing access to the outputs of a
:class:`ParDo`.
The resulting object supports access to the
:class:`~apache_beam.pvalue.PCollection` associated with a tag
(e.g. ``o.tag``, ``o[tag]``) and iterating over the available tags
(e.g. ``for tag in o: ...``).
Args:
*tags: if non-empty, list of valid tags. If a list of valid tags is given,
it will be an error to use an undeclared tag later in the pipeline.
**main_kw: dictionary empty or with one key ``'main'`` defining the tag to
be used for the main output (which will not have a tag associated with
it).
Returns:
~apache_beam.pvalue.DoOutputsTuple: An object of type
:class:`~apache_beam.pvalue.DoOutputsTuple` that bundles together all
the outputs of a :class:`ParDo` transform and allows accessing the
individual :class:`~apache_beam.pvalue.PCollection` s for each output
using an ``object.tag`` syntax.
Raises:
TypeError: if the **self** object is not a
:class:`~apache_beam.pvalue.PCollection` that is the result of a
:class:`ParDo` transform.
ValueError: if **main_kw** contains any key other than
``'main'``.
"""
main_tag = main_kw.pop('main', None)
if main_kw:
raise ValueError('Unexpected keyword arguments: %s' % list(main_kw))
return _MultiParDo(self, tags, main_tag)
def _pardo_fn_data(self):
si_tags_and_types = None
windowing = None
return self.fn, self.args, self.kwargs, si_tags_and_types, windowing
def _get_key_and_window_coder(self, named_inputs):
if named_inputs is None or not self._signature.is_stateful_dofn():
return None, None
main_input = list(set(named_inputs.keys()) - set(self.side_inputs))[0]
input_pcoll = named_inputs[main_input]
kv_type_hint = input_pcoll.element_type
if kv_type_hint and kv_type_hint != typehints.Any:
coder = coders.registry.get_coder(kv_type_hint)
if not coder.is_kv_coder():
raise ValueError(
'Input elements to the transform %s with stateful DoFn must be '
'key-value pairs.' % self)
key_coder = coder.key_coder()
else:
key_coder = coders.registry.get_coder(typehints.Any)
window_coder = input_pcoll.windowing.windowfn.get_window_coder()
return key_coder, window_coder
def to_runner_api_parameter(self, context, **extra_kwargs):
# type: (PipelineContext, Any) -> typing.Tuple[str, message.Message]
assert isinstance(self, ParDo), \
"expected instance of ParDo, but got %s" % self.__class__
picked_pardo_fn_data = pickler.dumps(self._pardo_fn_data())
state_specs, timer_specs = userstate.get_dofn_specs(self.fn)
if state_specs or timer_specs:
context.add_requirement(
common_urns.requirements.REQUIRES_STATEFUL_PROCESSING.urn)
from apache_beam.runners.common import DoFnSignature
sig = DoFnSignature(self.fn)
is_splittable = sig.is_splittable_dofn()
if is_splittable:
restriction_coder = sig.get_restriction_coder()
restriction_coder_id = context.coders.get_id(
restriction_coder) # type: typing.Optional[str]
context.add_requirement(
common_urns.requirements.REQUIRES_SPLITTABLE_DOFN.urn)
else:
restriction_coder_id = None
has_bundle_finalization = sig.has_bundle_finalization()
if has_bundle_finalization:
context.add_requirement(
common_urns.requirements.REQUIRES_BUNDLE_FINALIZATION.urn)
# Get key_coder and window_coder for main_input.
key_coder, window_coder = self._get_key_and_window_coder(
extra_kwargs.get('named_inputs', None))
return (
common_urns.primitives.PAR_DO.urn,
beam_runner_api_pb2.ParDoPayload(
do_fn=beam_runner_api_pb2.FunctionSpec(
urn=python_urns.PICKLED_DOFN_INFO,
payload=picked_pardo_fn_data),
requests_finalization=has_bundle_finalization,
restriction_coder_id=restriction_coder_id,
state_specs={
spec.name: spec.to_runner_api(context)
for spec in state_specs
},
timer_family_specs={
spec.name: spec.to_runner_api(context, key_coder, window_coder)
for spec in timer_specs
},
# It'd be nice to name these according to their actual
# names/positions in the orignal argument list, but such a
# transformation is currently irreversible given how
# remove_objects_from_args and insert_values_in_args
# are currently implemented.
side_inputs={(SIDE_INPUT_PREFIX + '%s') % ix:
si.to_runner_api(context)
for ix,
si in enumerate(self.side_inputs)}))
@staticmethod
@PTransform.register_urn(
common_urns.primitives.PAR_DO.urn, beam_runner_api_pb2.ParDoPayload)
def from_runner_api_parameter(unused_ptransform, pardo_payload, context):
assert pardo_payload.do_fn.urn == python_urns.PICKLED_DOFN_INFO
fn, args, kwargs, si_tags_and_types, windowing = pickler.loads(
pardo_payload.do_fn.payload)
if si_tags_and_types:
raise NotImplementedError('explicit side input data')
elif windowing:
raise NotImplementedError('explicit windowing')
result = ParDo(fn, *args, **kwargs)
# This is an ordered list stored as a dict (see the comments in
# to_runner_api_parameter above).
indexed_side_inputs = [(
get_sideinput_index(tag),
pvalue.AsSideInput.from_runner_api(si, context)) for tag,
si in pardo_payload.side_inputs.items()]
result.side_inputs = [si for _, si in sorted(indexed_side_inputs)]
return result
def runner_api_requires_keyed_input(self):
return userstate.is_stateful_dofn(self.fn)
def get_restriction_coder(self):
"""Returns `restriction coder if `DoFn` of this `ParDo` is a SDF.
Returns `None` otherwise.
"""
from apache_beam.runners.common import DoFnSignature
return DoFnSignature(self.fn).get_restriction_coder()
class _MultiParDo(PTransform):
def __init__(self, do_transform, tags, main_tag):
super(_MultiParDo, self).__init__(do_transform.label)
self._do_transform = do_transform
self._tags = tags
self._main_tag = main_tag
def expand(self, pcoll):
_ = pcoll | self._do_transform
return pvalue.DoOutputsTuple(
pcoll.pipeline, self._do_transform, self._tags, self._main_tag)
def FlatMap(fn, *args, **kwargs): # pylint: disable=invalid-name
""":func:`FlatMap` is like :class:`ParDo` except it takes a callable to
specify the transformation.
The callable must return an iterable for each element of the input
:class:`~apache_beam.pvalue.PCollection`. The elements of these iterables will
be flattened into the output :class:`~apache_beam.pvalue.PCollection`.
Args:
fn (callable): a callable object.
*args: positional arguments passed to the transform callable.
**kwargs: keyword arguments passed to the transform callable.
Returns:
~apache_beam.pvalue.PCollection:
A :class:`~apache_beam.pvalue.PCollection` containing the
:func:`FlatMap` outputs.
Raises:
TypeError: If the **fn** passed as argument is not a callable.
Typical error is to pass a :class:`DoFn` instance which is supported only
for :class:`ParDo`.
"""
label = 'FlatMap(%s)' % ptransform.label_from_callable(fn)
if not callable(fn):
raise TypeError(
'FlatMap can be used only with callable objects. '
'Received %r instead.' % (fn))
pardo = ParDo(CallableWrapperDoFn(fn), *args, **kwargs)
pardo.label = label
return pardo
def Map(fn, *args, **kwargs): # pylint: disable=invalid-name
""":func:`Map` is like :func:`FlatMap` except its callable returns only a
single element.
Args:
fn (callable): a callable object.
*args: positional arguments passed to the transform callable.
**kwargs: keyword arguments passed to the transform callable.
Returns:
~apache_beam.pvalue.PCollection:
A :class:`~apache_beam.pvalue.PCollection` containing the
:func:`Map` outputs.
Raises:
TypeError: If the **fn** passed as argument is not a callable.
Typical error is to pass a :class:`DoFn` instance which is supported only
for :class:`ParDo`.
"""
if not callable(fn):
raise TypeError(
'Map can be used only with callable objects. '
'Received %r instead.' % (fn))
if _fn_takes_side_inputs(fn):
wrapper = lambda x, *args, **kwargs: [fn(x, *args, **kwargs)]
else:
wrapper = lambda x: [fn(x)]
label = 'Map(%s)' % ptransform.label_from_callable(fn)
# TODO. What about callable classes?
if hasattr(fn, '__name__'):
wrapper.__name__ = fn.__name__
# Proxy the type-hint information from the original function to this new
# wrapped function.
type_hints = get_type_hints(fn).with_defaults(
typehints.decorators.IOTypeHints.from_callable(fn))
if type_hints.input_types is not None:
wrapper = with_input_types(
*type_hints.input_types[0], **type_hints.input_types[1])(
wrapper)
output_hint = type_hints.simple_output_type(label)
if output_hint:
wrapper = with_output_types(typehints.Iterable[output_hint])(wrapper)
# pylint: disable=protected-access
wrapper._argspec_fn = fn
# pylint: enable=protected-access
pardo = FlatMap(wrapper, *args, **kwargs)
pardo.label = label
return pardo
def MapTuple(fn, *args, **kwargs): # pylint: disable=invalid-name
r""":func:`MapTuple` is like :func:`Map` but expects tuple inputs and
flattens them into multiple input arguments.
beam.MapTuple(lambda a, b, ...: ...)
is equivalent to Python 2
beam.Map(lambda (a, b, ...), ...: ...)
In other words
beam.MapTuple(fn)
is equivalent to
beam.Map(lambda element, ...: fn(\*element, ...))
This can be useful when processing a PCollection of tuples
(e.g. key-value pairs).
Args:
fn (callable): a callable object.
*args: positional arguments passed to the transform callable.
**kwargs: keyword arguments passed to the transform callable.
Returns:
~apache_beam.pvalue.PCollection:
A :class:`~apache_beam.pvalue.PCollection` containing the
:func:`MapTuple` outputs.
Raises:
TypeError: If the **fn** passed as argument is not a callable.
Typical error is to pass a :class:`DoFn` instance which is supported only
for :class:`ParDo`.
"""
if not callable(fn):
raise TypeError(
'MapTuple can be used only with callable objects. '
'Received %r instead.' % (fn))
label = 'MapTuple(%s)' % ptransform.label_from_callable(fn)
arg_names, defaults = get_function_args_defaults(fn)
num_defaults = len(defaults)
if num_defaults < len(args) + len(kwargs):
raise TypeError('Side inputs must have defaults for MapTuple.')
if defaults or args or kwargs:
wrapper = lambda x, *args, **kwargs: [fn(*(tuple(x) + args), **kwargs)]
else:
wrapper = lambda x: [fn(*x)]
# Proxy the type-hint information from the original function to this new
# wrapped function.
type_hints = get_type_hints(fn)
if type_hints.input_types is not None:
wrapper = with_input_types(
*type_hints.input_types[0], **type_hints.input_types[1])(
wrapper)
output_hint = type_hints.simple_output_type(label)
if output_hint:
wrapper = with_output_types(typehints.Iterable[output_hint])(wrapper)
# Replace the first (args) component.
modified_arg_names = ['tuple_element'] + arg_names[-num_defaults:]
modified_argspec = (modified_arg_names, defaults)
pardo = ParDo(
CallableWrapperDoFn(wrapper, fullargspec=modified_argspec),
*args,
**kwargs)
pardo.label = label
return pardo
def FlatMapTuple(fn, *args, **kwargs): # pylint: disable=invalid-name
r""":func:`FlatMapTuple` is like :func:`FlatMap` but expects tuple inputs and
flattens them into multiple input arguments.
beam.FlatMapTuple(lambda a, b, ...: ...)
is equivalent to Python 2
beam.FlatMap(lambda (a, b, ...), ...: ...)
In other words
beam.FlatMapTuple(fn)
is equivalent to
beam.FlatMap(lambda element, ...: fn(\*element, ...))
This can be useful when processing a PCollection of tuples
(e.g. key-value pairs).
Args:
fn (callable): a callable object.
*args: positional arguments passed to the transform callable.
**kwargs: keyword arguments passed to the transform callable.
Returns:
~apache_beam.pvalue.PCollection:
A :class:`~apache_beam.pvalue.PCollection` containing the
:func:`FlatMapTuple` outputs.
Raises:
TypeError: If the **fn** passed as argument is not a callable.
Typical error is to pass a :class:`DoFn` instance which is supported only
for :class:`ParDo`.
"""
if not callable(fn):
raise TypeError(
'FlatMapTuple can be used only with callable objects. '
'Received %r instead.' % (fn))
label = 'FlatMapTuple(%s)' % ptransform.label_from_callable(fn)
arg_names, defaults = get_function_args_defaults(fn)
num_defaults = len(defaults)
if num_defaults < len(args) + len(kwargs):
raise TypeError('Side inputs must have defaults for FlatMapTuple.')
if defaults or args or kwargs:
wrapper = lambda x, *args, **kwargs: fn(*(tuple(x) + args), **kwargs)
else:
wrapper = lambda x: fn(*x)
# Proxy the type-hint information from the original function to this new
# wrapped function.
type_hints = get_type_hints(fn)
if type_hints.input_types is not None:
wrapper = with_input_types(
*type_hints.input_types[0], **type_hints.input_types[1])(
wrapper)
output_hint = type_hints.simple_output_type(label)
if output_hint:
wrapper = with_output_types(output_hint)(wrapper)
# Replace the first (args) component.
modified_arg_names = ['tuple_element'] + arg_names[-num_defaults:]
modified_argspec = (modified_arg_names, defaults)
pardo = ParDo(
CallableWrapperDoFn(wrapper, fullargspec=modified_argspec),
*args,
**kwargs)
pardo.label = label
return pardo
def Filter(fn, *args, **kwargs): # pylint: disable=invalid-name
""":func:`Filter` is a :func:`FlatMap` with its callable filtering out
elements.
Args:
fn (``Callable[..., bool]``): a callable object. First argument will be an
element.
*args: positional arguments passed to the transform callable.
**kwargs: keyword arguments passed to the transform callable.
Returns:
~apache_beam.pvalue.PCollection:
A :class:`~apache_beam.pvalue.PCollection` containing the
:func:`Filter` outputs.
Raises:
TypeError: If the **fn** passed as argument is not a callable.
Typical error is to pass a :class:`DoFn` instance which is supported only
for :class:`ParDo`.
"""
if not callable(fn):
raise TypeError(
'Filter can be used only with callable objects. '
'Received %r instead.' % (fn))
wrapper = lambda x, *args, **kwargs: [x] if fn(x, *args, **kwargs) else []
label = 'Filter(%s)' % ptransform.label_from_callable(fn)
# TODO: What about callable classes?
if hasattr(fn, '__name__'):
wrapper.__name__ = fn.__name__
# Get type hints from this instance or the callable. Do not use output type
# hints from the callable (which should be bool if set).
fn_type_hints = typehints.decorators.IOTypeHints.from_callable(fn)
if fn_type_hints is not None:
fn_type_hints = fn_type_hints.with_output_types()
type_hints = get_type_hints(fn).with_defaults(fn_type_hints)
# Proxy the type-hint information from the function being wrapped, setting the
# output type to be the same as the input type.
if type_hints.input_types is not None:
wrapper = with_input_types(
*type_hints.input_types[0], **type_hints.input_types[1])(
wrapper)
output_hint = type_hints.simple_output_type(label)
if (output_hint is None and get_type_hints(wrapper).input_types and
get_type_hints(wrapper).input_types[0]):
output_hint = get_type_hints(wrapper).input_types[0][0]
if output_hint:
wrapper = with_output_types(typehints.Iterable[output_hint])(wrapper)
# pylint: disable=protected-access
wrapper._argspec_fn = fn
# pylint: enable=protected-access
pardo = FlatMap(wrapper, *args, **kwargs)
pardo.label = label
return pardo
def _combine_payload(combine_fn, context):
return beam_runner_api_pb2.CombinePayload(
combine_fn=combine_fn.to_runner_api(context),
accumulator_coder_id=context.coders.get_id(
combine_fn.get_accumulator_coder()))
class CombineGlobally(PTransform):
"""A :class:`CombineGlobally` transform.
Reduces a :class:`~apache_beam.pvalue.PCollection` to a single value by
progressively applying a :class:`CombineFn` to portions of the
:class:`~apache_beam.pvalue.PCollection` (and to intermediate values created
thereby). See documentation in :class:`CombineFn` for details on the specifics
on how :class:`CombineFn` s are applied.
Args:
pcoll (~apache_beam.pvalue.PCollection):
a :class:`~apache_beam.pvalue.PCollection` to be reduced into a single
value.
fn (callable): a :class:`CombineFn` object that will be called to
progressively reduce the :class:`~apache_beam.pvalue.PCollection` into
single values, or a callable suitable for wrapping by
:class:`~apache_beam.transforms.core.CallableWrapperCombineFn`.
*args: positional arguments passed to the :class:`CombineFn` object.
**kwargs: keyword arguments passed to the :class:`CombineFn` object.
Raises:
TypeError: If the output type of the input
:class:`~apache_beam.pvalue.PCollection` is not compatible
with ``Iterable[A]``.
Returns:
~apache_beam.pvalue.PCollection: A single-element
:class:`~apache_beam.pvalue.PCollection` containing the main output of
the :class:`CombineGlobally` transform.
Note that the positional and keyword arguments will be processed in order
to detect :class:`~apache_beam.pvalue.PValue` s that will be computed as side
inputs to the transform.
During pipeline execution whenever the :class:`CombineFn` object gets executed
(i.e. any of the :class:`CombineFn` methods get called), the
:class:`~apache_beam.pvalue.PValue` arguments will be replaced by their
actual value in the exact position where they appear in the argument lists.
"""
has_defaults = True
as_view = False
fanout = None
def __init__(self, fn, *args, **kwargs):
if not (isinstance(fn, CombineFn) or callable(fn)):
raise TypeError(
'CombineGlobally can be used only with combineFn objects. '
'Received %r instead.' % (fn))
super(CombineGlobally, self).__init__()
self.fn = fn
self.args = args
self.kwargs = kwargs
def display_data(self):
return {
'combine_fn': DisplayDataItem(
self.fn.__class__, label='Combine Function'),
'combine_fn_dd': self.fn,
}
def default_label(self):
if self.fanout is None:
return '%s(%s)' % (
self.__class__.__name__, ptransform.label_from_callable(self.fn))
else:
return '%s(%s, fanout=%s)' % (
self.__class__.__name__,
ptransform.label_from_callable(self.fn),
self.fanout)
def _clone(self, **extra_attributes):
clone = copy.copy(self)
clone.__dict__.update(extra_attributes)
return clone
def with_fanout(self, fanout):
return self._clone(fanout=fanout)
def with_defaults(self, has_defaults=True):
return self._clone(has_defaults=has_defaults)
def without_defaults(self):
return self.with_defaults(False)
def as_singleton_view(self):
return self._clone(as_view=True)
def expand(self, pcoll):
def add_input_types(transform):
type_hints = self.get_type_hints()
if type_hints.input_types:
return transform.with_input_types(type_hints.input_types[0][0])
return transform
combine_per_key = CombinePerKey(self.fn, *self.args, **self.kwargs)
if self.fanout:
combine_per_key = combine_per_key.with_hot_key_fanout(self.fanout)
combined = (
pcoll
| 'KeyWithVoid' >> add_input_types(
Map(lambda v: (None, v)).with_output_types(
typehints.KV[None, pcoll.element_type]))
| 'CombinePerKey' >> combine_per_key
| 'UnKey' >> Map(lambda k_v: k_v[1]))
if not self.has_defaults and not self.as_view:
return combined
if self.has_defaults:
combine_fn = (
self.fn if isinstance(self.fn, CombineFn) else
CombineFn.from_callable(self.fn))
default_value = combine_fn.apply([], *self.args, **self.kwargs)
else:
default_value = pvalue.AsSingleton._NO_DEFAULT # pylint: disable=protected-access
view = pvalue.AsSingleton(combined, default_value=default_value)
if self.as_view:
return view
else:
if pcoll.windowing.windowfn != GlobalWindows():
raise ValueError(
"Default values are not yet supported in CombineGlobally() if the "
"output PCollection is not windowed by GlobalWindows. "
"Instead, use CombineGlobally().without_defaults() to output "
"an empty PCollection if the input PCollection is empty, "
"or CombineGlobally().as_singleton_view() to get the default "
"output of the CombineFn if the input PCollection is empty.")
def typed(transform):
# TODO(robertwb): We should infer this.
if combined.element_type:
return transform.with_output_types(combined.element_type)
return transform
return (
pcoll.pipeline
| 'DoOnce' >> Create([None])
| 'InjectDefault' >> typed(Map(lambda _, s: s, view)))
@staticmethod
@PTransform.register_urn(
common_urns.composites.COMBINE_GLOBALLY.urn,
beam_runner_api_pb2.CombinePayload)
def from_runner_api_parameter(unused_ptransform, combine_payload, context):
return CombineGlobally(
CombineFn.from_runner_api(combine_payload.combine_fn, context))
class CombinePerKey(PTransformWithSideInputs):
"""A per-key Combine transform.
Identifies sets of values associated with the same key in the input
PCollection, then applies a CombineFn to condense those sets to single
values. See documentation in CombineFn for details on the specifics on how
CombineFns are applied.
Args:
pcoll: input pcollection.
fn: instance of CombineFn to apply to all values under the same key in
pcoll, or a callable whose signature is ``f(iterable, *args, **kwargs)``
(e.g., sum, max).
*args: arguments and side inputs, passed directly to the CombineFn.
**kwargs: arguments and side inputs, passed directly to the CombineFn.
Returns:
A PObject holding the result of the combine operation.
"""
def with_hot_key_fanout(self, fanout):
"""A per-key combine operation like self but with two levels of aggregation.
If a given key is produced by too many upstream bundles, the final
reduction can become a bottleneck despite partial combining being lifted
pre-GroupByKey. In these cases it can be helpful to perform intermediate
partial aggregations in parallel and then re-group to peform a final
(per-key) combine. This is also useful for high-volume keys in streaming
where combiners are not generally lifted for latency reasons.
Note that a fanout greater than 1 requires the data to be sent through
two GroupByKeys, and a high fanout can also result in more shuffle data
due to less per-bundle combining. Setting the fanout for a key at 1 or less
places values on the "cold key" path that skip the intermediate level of
aggregation.
Args:
fanout: either None, for no fanout, an int, for a constant-degree fanout,
or a callable mapping keys to a key-specific degree of fanout.
Returns:
A per-key combining PTransform with the specified fanout.
"""
from apache_beam.transforms.combiners import curry_combine_fn
if fanout is None:
return self
else:
return _CombinePerKeyWithHotKeyFanout(
curry_combine_fn(self.fn, self.args, self.kwargs), fanout)
def display_data(self):
return {
'combine_fn': DisplayDataItem(
self.fn.__class__, label='Combine Function'),
'combine_fn_dd': self.fn
}
def make_fn(self, fn, has_side_inputs):
self._fn_label = ptransform.label_from_callable(fn)
return CombineFn.maybe_from_callable(fn, has_side_inputs)
def default_label(self):
return '%s(%s)' % (self.__class__.__name__, self._fn_label)
def _process_argspec_fn(self):
return lambda element, *args, **kwargs: None
def expand(self, pcoll):
args, kwargs = util.insert_values_in_args(
self.args, self.kwargs, self.side_inputs)
return pcoll | GroupByKey() | 'Combine' >> CombineValues(
self.fn, *args, **kwargs)
def default_type_hints(self):
hints = self.fn.get_type_hints()
if hints.input_types:
K = typehints.TypeVariable('K')
args, kwargs = hints.input_types
args = (typehints.Tuple[K, args[0]], ) + args[1:]
hints = hints.with_input_types(*args, **kwargs)
else:
K = typehints.Any
if hints.output_types:
main_output_type = hints.simple_output_type('')
hints = hints.with_output_types(typehints.Tuple[K, main_output_type])
return hints
def to_runner_api_parameter(
self,
context, # type: PipelineContext
):
# type: (...) -> typing.Tuple[str, beam_runner_api_pb2.CombinePayload]
if self.args or self.kwargs:
from apache_beam.transforms.combiners import curry_combine_fn
combine_fn = curry_combine_fn(self.fn, self.args, self.kwargs)
else:
combine_fn = self.fn
return (
common_urns.composites.COMBINE_PER_KEY.urn,
_combine_payload(combine_fn, context))
@staticmethod
@PTransform.register_urn(
common_urns.composites.COMBINE_PER_KEY.urn,
beam_runner_api_pb2.CombinePayload)
def from_runner_api_parameter(unused_ptransform, combine_payload, context):
return CombinePerKey(
CombineFn.from_runner_api(combine_payload.combine_fn, context))
def runner_api_requires_keyed_input(self):
return True
# TODO(robertwb): Rename to CombineGroupedValues?
class CombineValues(PTransformWithSideInputs):
def make_fn(self, fn, has_side_inputs):
return CombineFn.maybe_from_callable(fn, has_side_inputs)
def expand(self, pcoll):
args, kwargs = util.insert_values_in_args(
self.args, self.kwargs, self.side_inputs)
input_type = pcoll.element_type
key_type = None
if input_type is not None:
key_type, _ = input_type.tuple_types
runtime_type_check = (
pcoll.pipeline._options.view_as(TypeOptions).runtime_type_check)
return pcoll | ParDo(
CombineValuesDoFn(key_type, self.fn, runtime_type_check),
*args,
**kwargs)
def to_runner_api_parameter(self, context):
if self.args or self.kwargs:
from apache_beam.transforms.combiners import curry_combine_fn
combine_fn = curry_combine_fn(self.fn, self.args, self.kwargs)
else:
combine_fn = self.fn
return (
common_urns.combine_components.COMBINE_GROUPED_VALUES.urn,
_combine_payload(combine_fn, context))
@staticmethod
@PTransform.register_urn(
common_urns.combine_components.COMBINE_GROUPED_VALUES.urn,
beam_runner_api_pb2.CombinePayload)
def from_runner_api_parameter(unused_ptransform, combine_payload, context):
return CombineValues(
CombineFn.from_runner_api(combine_payload.combine_fn, context))
class CombineValuesDoFn(DoFn):
"""DoFn for performing per-key Combine transforms."""
def __init__(self,
input_pcoll_type,
combinefn, # type: CombineFn
runtime_type_check, # type: bool
):
super(CombineValuesDoFn, self).__init__()
self.combinefn = combinefn
self.runtime_type_check = runtime_type_check
def process(self, element, *args, **kwargs):
# Expected elements input to this DoFn are 2-tuples of the form
# (key, iter), with iter an iterable of all the values associated with key
# in the input PCollection.
if self.runtime_type_check:
# Apply the combiner in a single operation rather than artificially
# breaking it up so that output type violations manifest as TypeCheck
# errors rather than type errors.
return [(element[0], self.combinefn.apply(element[1], *args, **kwargs))]
# Add the elements into three accumulators (for testing of merge).
elements = list(element[1])
accumulators = []
for k in range(3):
if len(elements) <= k:
break
accumulators.append(
self.combinefn.add_inputs(
self.combinefn.create_accumulator(*args, **kwargs),
elements[k::3],
*args,
**kwargs))
# Merge the accumulators.
accumulator = self.combinefn.merge_accumulators(
accumulators, *args, **kwargs)
# Convert accumulator to the final result.
return [(
element[0], self.combinefn.extract_output(accumulator, *args,
**kwargs))]
def default_type_hints(self):
hints = self.combinefn.get_type_hints()
if hints.input_types:
K = typehints.TypeVariable('K')
args, kwargs = hints.input_types
args = (typehints.Tuple[K, typehints.Iterable[args[0]]], ) + args[1:]
hints = hints.with_input_types(*args, **kwargs)
else:
K = typehints.Any
if hints.output_types:
main_output_type = hints.simple_output_type('')
hints = hints.with_output_types(typehints.Tuple[K, main_output_type])
return hints
class _CombinePerKeyWithHotKeyFanout(PTransform):
def __init__(self,
combine_fn, # type: CombineFn
fanout, # type: typing.Union[int, typing.Callable[[typing.Any], int]]
):
# type: (...) -> None
self._combine_fn = combine_fn
self._fanout_fn = ((lambda key: fanout)
if isinstance(fanout, int) else fanout)
def default_label(self):
return '%s(%s, fanout=%s)' % (
self.__class__.__name__,
ptransform.label_from_callable(self._combine_fn),
ptransform.label_from_callable(self._fanout_fn))
def expand(self, pcoll):
from apache_beam.transforms.trigger import AccumulationMode
combine_fn = self._combine_fn
fanout_fn = self._fanout_fn
class SplitHotCold(DoFn):
def start_bundle(self):
# Spreading a hot key across all possible sub-keys for all bundles
# would defeat the goal of not overwhelming downstream reducers
# (as well as making less efficient use of PGBK combining tables).
# Instead, each bundle independently makes a consistent choice about
# which "shard" of a key to send its intermediate results.
self._nonce = int(random.getrandbits(31))
def process(self, element):
key, value = element
fanout = fanout_fn(key)
if fanout <= 1:
# Boolean indicates this is not an accumulator.
yield (key, (False, value)) # cold
else:
yield pvalue.TaggedOutput('hot', ((self._nonce % fanout, key), value))
class PreCombineFn(CombineFn):
@staticmethod
def extract_output(accumulator):
# Boolean indicates this is an accumulator.
return (True, accumulator)
create_accumulator = combine_fn.create_accumulator
add_input = combine_fn.add_input
merge_accumulators = combine_fn.merge_accumulators
compact = combine_fn.compact
class PostCombineFn(CombineFn):
@staticmethod
def add_input(accumulator, element):
is_accumulator, value = element
if is_accumulator:
return combine_fn.merge_accumulators([accumulator, value])
else:
return combine_fn.add_input(accumulator, value)
create_accumulator = combine_fn.create_accumulator
merge_accumulators = combine_fn.merge_accumulators
compact = combine_fn.compact
extract_output = combine_fn.extract_output
def StripNonce(nonce_key_value):
(_, key), value = nonce_key_value
return key, value
cold, hot = pcoll | ParDo(SplitHotCold()).with_outputs('hot', main='cold')
cold.element_type = typehints.Any # No multi-output type hints.
precombined_hot = (
hot
# Avoid double counting that may happen with stacked accumulating mode.
| 'WindowIntoDiscarding' >> WindowInto(
pcoll.windowing, accumulation_mode=AccumulationMode.DISCARDING)
| CombinePerKey(PreCombineFn())
| Map(StripNonce)
| 'WindowIntoOriginal' >> WindowInto(pcoll.windowing))
return ((cold, precombined_hot)
| Flatten()
| CombinePerKey(PostCombineFn()))
@typehints.with_input_types(typing.Tuple[K, V])
@typehints.with_output_types(typing.Tuple[K, typing.Iterable[V]])
class GroupByKey(PTransform):
"""A group by key transform.
Processes an input PCollection consisting of key/value pairs represented as a
tuple pair. The result is a PCollection where values having a common key are
grouped together. For example (a, 1), (b, 2), (a, 3) will result into
(a, [1, 3]), (b, [2]).
The implementation here is used only when run on the local direct runner.
"""
class ReifyWindows(DoFn):
def process(
self, element, window=DoFn.WindowParam, timestamp=DoFn.TimestampParam):
try:
k, v = element
except TypeError:
raise TypeCheckError(
'Input to GroupByKey must be a PCollection with '
'elements compatible with KV[A, B]')
return [(k, WindowedValue(v, timestamp, [window]))]
def infer_output_type(self, input_type):
key_type, value_type = trivial_inference.key_value_types(input_type)
return typehints.Iterable[typehints.KV[
key_type, typehints.WindowedValue[value_type]]] # type: ignore[misc]
def expand(self, pcoll):
# This code path is only used in the local direct runner. For Dataflow
# runner execution, the GroupByKey transform is expanded on the service.
input_type = pcoll.element_type
if input_type is not None:
# Initialize type-hints used below to enforce type-checking and to pass
# downstream to further PTransforms.
key_type, value_type = trivial_inference.key_value_types(input_type)
# Enforce the input to a GBK has a KV element type.
pcoll.element_type = typehints.KV[key_type, value_type]
typecoders.registry.verify_deterministic(
typecoders.registry.get_coder(key_type),
'GroupByKey operation "%s"' % self.label)
reify_output_type = typehints.KV[
key_type, typehints.WindowedValue[value_type]] # type: ignore[misc]
gbk_input_type = (
typehints.KV[
key_type,
typehints.Iterable[typehints.WindowedValue[ # type: ignore[misc]
value_type]]])
gbk_output_type = typehints.KV[key_type, typehints.Iterable[value_type]]
# pylint: disable=bad-continuation
return (
pcoll
| 'ReifyWindows' >>
(ParDo(self.ReifyWindows()).with_output_types(reify_output_type))
| 'GroupByKey' >> (
_GroupByKeyOnly().with_input_types(
reify_output_type).with_output_types(gbk_input_type))
| (
'GroupByWindow' >>
_GroupAlsoByWindow(pcoll.windowing).with_input_types(
gbk_input_type).with_output_types(gbk_output_type)))
else:
# The input_type is None, run the default
return (
pcoll
| 'ReifyWindows' >> ParDo(self.ReifyWindows())
| 'GroupByKey' >> _GroupByKeyOnly()
| 'GroupByWindow' >> _GroupAlsoByWindow(pcoll.windowing))
def infer_output_type(self, input_type):
key_type, value_type = trivial_inference.key_value_types(input_type)
return typehints.KV[key_type, typehints.Iterable[value_type]]
def to_runner_api_parameter(self, unused_context):
# type: (PipelineContext) -> typing.Tuple[str, None]
return common_urns.primitives.GROUP_BY_KEY.urn, None
@staticmethod
@PTransform.register_urn(common_urns.primitives.GROUP_BY_KEY.urn, None)
def from_runner_api_parameter(
unused_ptransform, unused_payload, unused_context):
return GroupByKey()
def runner_api_requires_keyed_input(self):
return True
@typehints.with_input_types(typing.Tuple[K, V])
@typehints.with_output_types(typing.Tuple[K, typing.Iterable[V]])
class _GroupByKeyOnly(PTransform):
"""A group by key transform, ignoring windows."""
def infer_output_type(self, input_type):
key_type, value_type = trivial_inference.key_value_types(input_type)
return typehints.KV[key_type, typehints.Iterable[value_type]]
def expand(self, pcoll):
self._check_pcollection(pcoll)
return pvalue.PCollection.from_(pcoll)
@typehints.with_input_types(typing.Tuple[K, typing.Iterable[V]])
@typehints.with_output_types(typing.Tuple[K, typing.Iterable[V]])
class _GroupAlsoByWindow(ParDo):
"""The GroupAlsoByWindow transform."""
def __init__(self, windowing):
super(_GroupAlsoByWindow, self).__init__(_GroupAlsoByWindowDoFn(windowing))
self.windowing = windowing
def expand(self, pcoll):
self._check_pcollection(pcoll)
return pvalue.PCollection.from_(pcoll)
class _GroupAlsoByWindowDoFn(DoFn):
# TODO(robertwb): Support combiner lifting.
def __init__(self, windowing):
super(_GroupAlsoByWindowDoFn, self).__init__()
self.windowing = windowing
def infer_output_type(self, input_type):
key_type, windowed_value_iter_type = trivial_inference.key_value_types(
input_type)
value_type = windowed_value_iter_type.inner_type.inner_type
return typehints.Iterable[typehints.KV[key_type,
typehints.Iterable[value_type]]]
def start_bundle(self):
# pylint: disable=wrong-import-order, wrong-import-position
from apache_beam.transforms.trigger import create_trigger_driver
# pylint: enable=wrong-import-order, wrong-import-position
self.driver = create_trigger_driver(self.windowing, True)
def process(self, element):
k, vs = element
return self.driver.process_entire_key(k, vs)
class Partition(PTransformWithSideInputs):
"""Split a PCollection into several partitions.
Uses the specified PartitionFn to separate an input PCollection into the
specified number of sub-PCollections.
When apply()d, a Partition() PTransform requires the following:
Args:
partitionfn: a PartitionFn, or a callable with the signature described in
CallableWrapperPartitionFn.
n: number of output partitions.
The result of this PTransform is a simple list of the output PCollections
representing each of n partitions, in order.
"""
class ApplyPartitionFnFn(DoFn):
"""A DoFn that applies a PartitionFn."""
def process(self, element, partitionfn, n, *args, **kwargs):
partition = partitionfn.partition_for(element, n, *args, **kwargs)
if not 0 <= partition < n:
raise ValueError(
'PartitionFn specified out-of-bounds partition index: '
'%d not in [0, %d)' % (partition, n))
# Each input is directed into the output that corresponds to the
# selected partition.
yield pvalue.TaggedOutput(str(partition), element)
def make_fn(self, fn, has_side_inputs):
return fn if isinstance(fn, PartitionFn) else CallableWrapperPartitionFn(fn)
def expand(self, pcoll):
n = int(self.args[0])
return pcoll | ParDo(
self.ApplyPartitionFnFn(), self.fn, *self.args, **
self.kwargs).with_outputs(*[str(t) for t in range(n)])
class Windowing(object):
def __init__(self,
windowfn, # type: WindowFn
triggerfn=None, # type: typing.Optional[TriggerFn]
accumulation_mode=None, # type: typing.Optional[beam_runner_api_pb2.AccumulationMode.Enum]
timestamp_combiner=None, # type: typing.Optional[beam_runner_api_pb2.OutputTime.Enum]
allowed_lateness=0, # type: typing.Union[int, float]
):
"""Class representing the window strategy.
Args:
windowfn: Window assign function.
triggerfn: Trigger function.
accumulation_mode: a AccumulationMode, controls what to do with data
when a trigger fires multiple times.
timestamp_combiner: a TimestampCombiner, determines how output
timestamps of grouping operations are assigned.
allowed_lateness: Maximum delay in seconds after end of window
allowed for any late data to be processed without being discarded
directly.
"""
global AccumulationMode, DefaultTrigger # pylint: disable=global-variable-not-assigned
# pylint: disable=wrong-import-order, wrong-import-position
from apache_beam.transforms.trigger import AccumulationMode, DefaultTrigger
# pylint: enable=wrong-import-order, wrong-import-position
if triggerfn is None:
triggerfn = DefaultTrigger()
if accumulation_mode is None:
if triggerfn == DefaultTrigger():
accumulation_mode = AccumulationMode.DISCARDING
else:
raise ValueError(
'accumulation_mode must be provided for non-trivial triggers')
if not windowfn.get_window_coder().is_deterministic():
raise ValueError(
'window fn (%s) does not have a determanistic coder (%s)' %
(windowfn, windowfn.get_window_coder()))
self.windowfn = windowfn
self.triggerfn = triggerfn
self.accumulation_mode = accumulation_mode
self.allowed_lateness = Duration.of(allowed_lateness)
self.timestamp_combiner = (
timestamp_combiner or TimestampCombiner.OUTPUT_AT_EOW)
self._is_default = (
self.windowfn == GlobalWindows() and
self.triggerfn == DefaultTrigger() and
self.accumulation_mode == AccumulationMode.DISCARDING and
self.timestamp_combiner == TimestampCombiner.OUTPUT_AT_EOW and
self.allowed_lateness == 0)
def __repr__(self):
return "Windowing(%s, %s, %s, %s)" % (
self.windowfn,
self.triggerfn,
self.accumulation_mode,
self.timestamp_combiner)
def __eq__(self, other):
if type(self) == type(other):
if self._is_default and other._is_default:
return True
return (
self.windowfn == other.windowfn and
self.triggerfn == other.triggerfn and
self.accumulation_mode == other.accumulation_mode and
self.timestamp_combiner == other.timestamp_combiner and
self.allowed_lateness == other.allowed_lateness)
return False
def __ne__(self, other):
# TODO(BEAM-5949): Needed for Python 2 compatibility.
return not self == other
def __hash__(self):
return hash((
self.windowfn,
self.triggerfn,
self.accumulation_mode,
self.allowed_lateness,
self.timestamp_combiner))
def is_default(self):
return self._is_default
def to_runner_api(self, context):
# type: (PipelineContext) -> beam_runner_api_pb2.WindowingStrategy
return beam_runner_api_pb2.WindowingStrategy(
window_fn=self.windowfn.to_runner_api(context),
# TODO(robertwb): Prohibit implicit multi-level merging.
merge_status=(
beam_runner_api_pb2.MergeStatus.NEEDS_MERGE
if self.windowfn.is_merging() else
beam_runner_api_pb2.MergeStatus.NON_MERGING),
window_coder_id=context.coders.get_id(self.windowfn.get_window_coder()),
trigger=self.triggerfn.to_runner_api(context),
accumulation_mode=self.accumulation_mode,
output_time=self.timestamp_combiner,
# TODO(robertwb): Support EMIT_IF_NONEMPTY
closing_behavior=beam_runner_api_pb2.ClosingBehavior.EMIT_ALWAYS,
OnTimeBehavior=beam_runner_api_pb2.OnTimeBehavior.FIRE_ALWAYS,
allowed_lateness=self.allowed_lateness.micros // 1000,
environment_id=context.default_environment_id())
@staticmethod
def from_runner_api(proto, context):
# pylint: disable=wrong-import-order, wrong-import-position
from apache_beam.transforms.trigger import TriggerFn
return Windowing(
windowfn=WindowFn.from_runner_api(proto.window_fn, context),
triggerfn=TriggerFn.from_runner_api(proto.trigger, context),
accumulation_mode=proto.accumulation_mode,
timestamp_combiner=proto.output_time,
allowed_lateness=Duration(micros=proto.allowed_lateness * 1000))
@typehints.with_input_types(T)
@typehints.with_output_types(T)
class WindowInto(ParDo):
"""A window transform assigning windows to each element of a PCollection.
Transforms an input PCollection by applying a windowing function to each
element. Each transformed element in the result will be a WindowedValue
element with the same input value and timestamp, with its new set of windows
determined by the windowing function.
"""
class WindowIntoFn(DoFn):
"""A DoFn that applies a WindowInto operation."""
def __init__(self, windowing):
# type: (Windowing) -> None
self.windowing = windowing
def process(
self, element, timestamp=DoFn.TimestampParam, window=DoFn.WindowParam):
context = WindowFn.AssignContext(
timestamp, element=element, window=window)
new_windows = self.windowing.windowfn.assign(context)
yield WindowedValue(element, context.timestamp, new_windows)
def __init__(self,
windowfn, # type: typing.Union[Windowing, WindowFn]
trigger=None, # type: typing.Optional[TriggerFn]
accumulation_mode=None,
timestamp_combiner=None,
allowed_lateness=0):
"""Initializes a WindowInto transform.
Args:
windowfn (Windowing, WindowFn): Function to be used for windowing.
trigger: (optional) Trigger used for windowing, or None for default.
accumulation_mode: (optional) Accumulation mode used for windowing,
required for non-trivial triggers.
timestamp_combiner: (optional) Timestamp combniner used for windowing,
or None for default.
"""
if isinstance(windowfn, Windowing):
# Overlay windowing with kwargs.
windowing = windowfn
windowfn = windowing.windowfn
# Use windowing to fill in defaults for the extra arguments.
trigger = trigger or windowing.triggerfn
accumulation_mode = accumulation_mode or windowing.accumulation_mode
timestamp_combiner = timestamp_combiner or windowing.timestamp_combiner
self.windowing = Windowing(
windowfn,
trigger,
accumulation_mode,
timestamp_combiner,
allowed_lateness)
super(WindowInto, self).__init__(self.WindowIntoFn(self.windowing))
def get_windowing(self, unused_inputs):
# type: (typing.Any) -> Windowing
return self.windowing
def infer_output_type(self, input_type):
return input_type
def expand(self, pcoll):
input_type = pcoll.element_type
if input_type is not None:
output_type = input_type
self.with_input_types(input_type)
self.with_output_types(output_type)
return super(WindowInto, self).expand(pcoll)
def to_runner_api_parameter(self, context, **extra_kwargs):
# type: (PipelineContext, Any) -> typing.Tuple[str, message.Message]
return (
common_urns.primitives.ASSIGN_WINDOWS.urn,
self.windowing.to_runner_api(context))
@staticmethod
def from_runner_api_parameter(unused_ptransform, proto, context):
windowing = Windowing.from_runner_api(proto, context)
return WindowInto(
windowing.windowfn,
trigger=windowing.triggerfn,
accumulation_mode=windowing.accumulation_mode,
timestamp_combiner=windowing.timestamp_combiner)
PTransform.register_urn(
common_urns.primitives.ASSIGN_WINDOWS.urn,
# TODO(robertwb): Update WindowIntoPayload to include the full strategy.
# (Right now only WindowFn is used, but we need this to reconstitute the
# WindowInto transform, and in the future will need it at runtime to
# support meta-data driven triggers.)
# TODO(robertwb): Use a reference rather than embedding?
beam_runner_api_pb2.WindowingStrategy,
WindowInto.from_runner_api_parameter)
# Python's pickling is broken for nested classes.
WindowIntoFn = WindowInto.WindowIntoFn
class Flatten(PTransform):
"""Merges several PCollections into a single PCollection.
Copies all elements in 0 or more PCollections into a single output
PCollection. If there are no input PCollections, the resulting PCollection
will be empty (but see also kwargs below).
Args:
**kwargs: Accepts a single named argument "pipeline", which specifies the
pipeline that "owns" this PTransform. Ordinarily Flatten can obtain this
information from one of the input PCollections, but if there are none (or
if there's a chance there may be none), this argument is the only way to
provide pipeline information and should be considered mandatory.
"""
def __init__(self, **kwargs):
super(Flatten, self).__init__()
self.pipeline = kwargs.pop(
'pipeline', None) # type: typing.Optional[Pipeline]
if kwargs:
raise ValueError('Unexpected keyword arguments: %s' % list(kwargs))
def _extract_input_pvalues(self, pvalueish):
try:
pvalueish = tuple(pvalueish)
except TypeError:
raise ValueError(
'Input to Flatten must be an iterable. '
'Got a value of type %s instead.' % type(pvalueish))
return pvalueish, pvalueish
def expand(self, pcolls):
for pcoll in pcolls:
self._check_pcollection(pcoll)
is_bounded = all(pcoll.is_bounded for pcoll in pcolls)
result = pvalue.PCollection(self.pipeline, is_bounded=is_bounded)
result.element_type = typehints.Union[tuple(
pcoll.element_type for pcoll in pcolls)]
return result
def get_windowing(self, inputs):
# type: (typing.Any) -> Windowing
if not inputs:
# TODO(robertwb): Return something compatible with every windowing?
return Windowing(GlobalWindows())
return super(Flatten, self).get_windowing(inputs)
def to_runner_api_parameter(self, context):
# type: (PipelineContext) -> typing.Tuple[str, None]
return common_urns.primitives.FLATTEN.urn, None
@staticmethod
def from_runner_api_parameter(
unused_ptransform, unused_parameter, unused_context):
return Flatten()
PTransform.register_urn(
common_urns.primitives.FLATTEN.urn, None, Flatten.from_runner_api_parameter)
class Create(PTransform):
"""A transform that creates a PCollection from an iterable."""
def __init__(self, values, reshuffle=True):
"""Initializes a Create transform.
Args:
values: An object of values for the PCollection
"""
super(Create, self).__init__()
if isinstance(values, (unicode, str, bytes)):
raise TypeError(
'PTransform Create: Refusing to treat string as '
'an iterable. (string=%r)' % values)
elif isinstance(values, dict):
values = values.items()
self.values = tuple(values)
self.reshuffle = reshuffle
def to_runner_api_parameter(self, context):
# type: (PipelineContext) -> typing.Tuple[str, bytes]
# Required as this is identified by type in PTransformOverrides.
# TODO(BEAM-3812): Use an actual URN here.
return self.to_runner_api_pickled(context)
def infer_output_type(self, unused_input_type):
if not self.values:
return typehints.Any
return typehints.Union[[
trivial_inference.instance_to_type(v) for v in self.values
]]
def get_output_type(self):
return (
self.get_type_hints().simple_output_type(self.label) or
self.infer_output_type(None))
def expand(self, pbegin):
assert isinstance(pbegin, pvalue.PBegin)
coder = typecoders.registry.get_coder(self.get_output_type())
serialized_values = [coder.encode(v) for v in self.values]
reshuffle = self.reshuffle
# Avoid the "redistributing" reshuffle for 0 and 1 element Creates.
# These special cases are often used in building up more complex
# transforms (e.g. Write).
class MaybeReshuffle(PTransform):
def expand(self, pcoll):
if len(serialized_values) > 1 and reshuffle:
from apache_beam.transforms.util import Reshuffle
return pcoll | Reshuffle()
else:
return pcoll
return (
pbegin
| Impulse()
| FlatMap(lambda _: serialized_values).with_output_types(bytes)
| MaybeReshuffle().with_output_types(bytes)
| Map(coder.decode).with_output_types(self.get_output_type()))
def as_read(self):
from apache_beam.io import iobase
coder = typecoders.registry.get_coder(self.get_output_type())
source = self._create_source_from_iterable(self.values, coder)
return iobase.Read(source).with_output_types(self.get_output_type())
def get_windowing(self, unused_inputs):
# type: (typing.Any) -> Windowing
return Windowing(GlobalWindows())
@staticmethod
def _create_source_from_iterable(values, coder):
return Create._create_source(list(map(coder.encode, values)), coder)
@staticmethod
def _create_source(serialized_values, coder):
# type: (typing.Any, typing.Any) -> create_source._CreateSource
from apache_beam.transforms.create_source import _CreateSource
return _CreateSource(serialized_values, coder)
@typehints.with_output_types(bytes)
class Impulse(PTransform):
"""Impulse primitive."""
def expand(self, pbegin):
if not isinstance(pbegin, pvalue.PBegin):
raise TypeError(
'Input to Impulse transform must be a PBegin but found %s' % pbegin)
return pvalue.PCollection(pbegin.pipeline)
def get_windowing(self, inputs):
# type: (typing.Any) -> Windowing
return Windowing(GlobalWindows())
def infer_output_type(self, unused_input_type):
return bytes
def to_runner_api_parameter(self, unused_context):
# type: (PipelineContext) -> typing.Tuple[str, None]
return common_urns.primitives.IMPULSE.urn, None
@staticmethod
@PTransform.register_urn(common_urns.primitives.IMPULSE.urn, None)
def from_runner_api_parameter(
unused_ptransform, unused_parameter, unused_context):
return Impulse()