Source code for apache_beam.transforms.ptransform
#
# 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.
#
"""PTransform and descendants.
A PTransform is an object describing (not executing) a computation. The actual
execution semantics for a transform is captured by a runner object. A transform
object always belongs to a pipeline object.
A PTransform derived class needs to define the expand() method that describes
how one or more PValues are created by the transform.
The module defines a few standard transforms: FlatMap (parallel do),
GroupByKey (group by key), etc. Note that the expand() methods for these
classes contain code that will add nodes to the processing graph associated
with a pipeline.
As support for the FlatMap transform, the module also defines a DoFn
class and wrapper class that allows lambda functions to be used as
FlatMap processing functions.
"""
# pytype: skip-file
from __future__ import absolute_import
import copy
import itertools
import logging
import operator
import os
import sys
import threading
from builtins import hex
from builtins import object
from builtins import zip
from functools import reduce
from functools import wraps
from typing import TYPE_CHECKING
from typing import Any
from typing import Callable
from typing import Dict
from typing import List
from typing import Optional
from typing import Sequence
from typing import Tuple
from typing import Type
from typing import TypeVar
from typing import Union
from typing import overload
from google.protobuf import message
from apache_beam import error
from apache_beam import pvalue
from apache_beam.internal import pickler
from apache_beam.internal import util
from apache_beam.portability import python_urns
from apache_beam.pvalue import DoOutputsTuple
from apache_beam.transforms.display import DisplayDataItem
from apache_beam.transforms.display import HasDisplayData
from apache_beam.transforms.sideinputs import SIDE_INPUT_PREFIX
from apache_beam.typehints import native_type_compatibility
from apache_beam.typehints import typehints
from apache_beam.typehints.decorators import IOTypeHints
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 getcallargs_forhints
from apache_beam.typehints.trivial_inference import instance_to_type
from apache_beam.typehints.typehints import validate_composite_type_param
from apache_beam.utils import proto_utils
if TYPE_CHECKING:
from apache_beam import coders
from apache_beam.pipeline import Pipeline
from apache_beam.runners.pipeline_context import PipelineContext
from apache_beam.transforms.core import Windowing
from apache_beam.portability.api import beam_runner_api_pb2
__all__ = [
'PTransform',
'ptransform_fn',
'label_from_callable',
]
_LOGGER = logging.getLogger(__name__)
T = TypeVar('T')
PTransformT = TypeVar('PTransformT', bound='PTransform')
ConstructorFn = Callable[
['beam_runner_api_pb2.PTransform', Optional[Any], 'PipelineContext'], Any]
ptransform_fn_typehints_enabled = False
class _PValueishTransform(object):
"""Visitor for PValueish objects.
A PValueish is a PValue, or list, tuple, dict of PValuesish objects.
This visits a PValueish, contstructing a (possibly mutated) copy.
"""
def visit_nested(self, node, *args):
if isinstance(node, (tuple, list)):
args = [self.visit(x, *args) for x in node]
if isinstance(node, tuple) and hasattr(node.__class__, '_make'):
# namedtuples require unpacked arguments in their constructor
return node.__class__(*args)
else:
return node.__class__(args)
elif isinstance(node, dict):
return node.__class__(
{key: self.visit(value, *args)
for (key, value) in node.items()})
else:
return node
class _SetInputPValues(_PValueishTransform):
def visit(self, node, replacements):
if id(node) in replacements:
return replacements[id(node)]
else:
return self.visit_nested(node, replacements)
# Caches to allow for materialization of values when executing a pipeline
# in-process, in eager mode. This cache allows the same _MaterializedResult
# object to be accessed and used despite Runner API round-trip serialization.
_pipeline_materialization_cache = {
} # type: Dict[Tuple[int, int], Dict[int, _MaterializedResult]]
_pipeline_materialization_lock = threading.Lock()
def _allocate_materialized_pipeline(pipeline):
# type: (Pipeline) -> None
pid = os.getpid()
with _pipeline_materialization_lock:
pipeline_id = id(pipeline)
_pipeline_materialization_cache[(pid, pipeline_id)] = {}
def _allocate_materialized_result(pipeline):
# type: (Pipeline) -> _MaterializedResult
pid = os.getpid()
with _pipeline_materialization_lock:
pipeline_id = id(pipeline)
if (pid, pipeline_id) not in _pipeline_materialization_cache:
raise ValueError(
'Materialized pipeline is not allocated for result '
'cache.')
result_id = len(_pipeline_materialization_cache[(pid, pipeline_id)])
result = _MaterializedResult(pipeline_id, result_id)
_pipeline_materialization_cache[(pid, pipeline_id)][result_id] = result
return result
def _get_materialized_result(pipeline_id, result_id):
# type: (int, int) -> _MaterializedResult
pid = os.getpid()
with _pipeline_materialization_lock:
if (pid, pipeline_id) not in _pipeline_materialization_cache:
raise Exception(
'Materialization in out-of-process and remote runners is not yet '
'supported.')
return _pipeline_materialization_cache[(pid, pipeline_id)][result_id]
def _release_materialized_pipeline(pipeline):
# type: (Pipeline) -> None
pid = os.getpid()
with _pipeline_materialization_lock:
pipeline_id = id(pipeline)
del _pipeline_materialization_cache[(pid, pipeline_id)]
class _MaterializedResult(object):
def __init__(self, pipeline_id, result_id):
# type: (int, int) -> None
self._pipeline_id = pipeline_id
self._result_id = result_id
self.elements = [] # type: List[Any]
def __reduce__(self):
# When unpickled (during Runner API roundtrip serailization), get the
# _MaterializedResult object from the cache so that values are written
# to the original _MaterializedResult when run in eager mode.
return (_get_materialized_result, (self._pipeline_id, self._result_id))
class _MaterializedDoOutputsTuple(pvalue.DoOutputsTuple):
def __init__(self, deferred, results_by_tag):
super(_MaterializedDoOutputsTuple,
self).__init__(None, None, deferred._tags, deferred._main_tag)
self._deferred = deferred
self._results_by_tag = results_by_tag
def __getitem__(self, tag):
if tag not in self._results_by_tag:
raise KeyError(
'Tag %r is not a a defined output tag of %s.' % (tag, self._deferred))
return self._results_by_tag[tag].elements
class _AddMaterializationTransforms(_PValueishTransform):
def _materialize_transform(self, pipeline):
result = _allocate_materialized_result(pipeline)
# Need to define _MaterializeValuesDoFn here to avoid circular
# dependencies.
from apache_beam import DoFn
from apache_beam import ParDo
class _MaterializeValuesDoFn(DoFn):
def process(self, element):
result.elements.append(element)
materialization_label = '_MaterializeValues%d' % result._result_id
return (materialization_label >> ParDo(_MaterializeValuesDoFn()), result)
def visit(self, node):
if isinstance(node, pvalue.PValue):
transform, result = self._materialize_transform(node.pipeline)
node | transform
return result
elif isinstance(node, pvalue.DoOutputsTuple):
results_by_tag = {}
for tag in itertools.chain([node._main_tag], node._tags):
results_by_tag[tag] = self.visit(node[tag])
return _MaterializedDoOutputsTuple(node, results_by_tag)
else:
return self.visit_nested(node)
class _FinalizeMaterialization(_PValueishTransform):
def visit(self, node):
if isinstance(node, _MaterializedResult):
return node.elements
elif isinstance(node, _MaterializedDoOutputsTuple):
return node
else:
return self.visit_nested(node)
def get_named_nested_pvalues(pvalueish):
if isinstance(pvalueish, tuple):
# Check to see if it's a named tuple.
fields = getattr(pvalueish, '_fields', None)
if fields and len(fields) == len(pvalueish):
tagged_values = zip(fields, pvalueish)
else:
tagged_values = enumerate(pvalueish)
elif isinstance(pvalueish, list):
tagged_values = enumerate(pvalueish)
elif isinstance(pvalueish, dict):
tagged_values = pvalueish.items()
else:
if isinstance(pvalueish, (pvalue.PValue, pvalue.DoOutputsTuple)):
yield None, pvalueish
return
for tag, subvalue in tagged_values:
for subtag, subsubvalue in get_named_nested_pvalues(subvalue):
if subtag is None:
yield tag, subsubvalue
else:
yield '%s.%s' % (tag, subtag), subsubvalue
class _ZipPValues(object):
"""Pairs each PValue in a pvalueish with a value in a parallel out sibling.
Sibling should have the same nested structure as pvalueish. Leaves in
sibling are expanded across nested pvalueish lists, tuples, and dicts.
For example
ZipPValues().visit({'a': pc1, 'b': (pc2, pc3)},
{'a': 'A', 'b', 'B'})
will return
[('a', pc1, 'A'), ('b', pc2, 'B'), ('b', pc3, 'B')]
"""
def visit(self, pvalueish, sibling, pairs=None, context=None):
if pairs is None:
pairs = []
self.visit(pvalueish, sibling, pairs, context)
return pairs
elif isinstance(pvalueish, (pvalue.PValue, pvalue.DoOutputsTuple)):
pairs.append((context, pvalueish, sibling))
elif isinstance(pvalueish, (list, tuple)):
self.visit_sequence(pvalueish, sibling, pairs, context)
elif isinstance(pvalueish, dict):
self.visit_dict(pvalueish, sibling, pairs, context)
def visit_sequence(self, pvalueish, sibling, pairs, context):
if isinstance(sibling, (list, tuple)):
for ix, (p, s) in enumerate(zip(pvalueish,
list(sibling) + [None] * len(pvalueish))):
self.visit(p, s, pairs, 'position %s' % ix)
else:
for p in pvalueish:
self.visit(p, sibling, pairs, context)
def visit_dict(self, pvalueish, sibling, pairs, context):
if isinstance(sibling, dict):
for key, p in pvalueish.items():
self.visit(p, sibling.get(key), pairs, key)
else:
for p in pvalueish.values():
self.visit(p, sibling, pairs, context)
[docs]class PTransform(WithTypeHints, HasDisplayData):
"""A transform object used to modify one or more PCollections.
Subclasses must define an expand() method that will be used when the transform
is applied to some arguments. Typical usage pattern will be:
input | CustomTransform(...)
The expand() method of the CustomTransform object passed in will be called
with input as an argument.
"""
# By default, transforms don't have any side inputs.
side_inputs = () # type: Sequence[pvalue.AsSideInput]
# Used for nullary transforms.
pipeline = None # type: Optional[Pipeline]
# Default is unset.
_user_label = None # type: Optional[str]
def __init__(self, label=None):
# type: (Optional[str]) -> None
super(PTransform, self).__init__()
self.label = label # type: ignore # https://github.com/python/mypy/issues/3004
@property
def label(self):
# type: () -> str
return self._user_label or self.default_label()
@label.setter
def label(self, value):
# type: (Optional[str]) -> None
self._user_label = value
[docs] def default_type_hints(self):
fn_type_hints = IOTypeHints.from_callable(self.expand)
if fn_type_hints is not None:
fn_type_hints = fn_type_hints.strip_pcoll()
# Prefer class decorator type hints for backwards compatibility.
return get_type_hints(self.__class__).with_defaults(fn_type_hints)
[docs] def with_input_types(self, input_type_hint):
"""Annotates the input type of a :class:`PTransform` with a type-hint.
Args:
input_type_hint (type): An instance of an allowed built-in type, a custom
class, or an instance of a
:class:`~apache_beam.typehints.typehints.TypeConstraint`.
Raises:
TypeError: If **input_type_hint** is not a valid type-hint.
See
:obj:`apache_beam.typehints.typehints.validate_composite_type_param()`
for further details.
Returns:
PTransform: A reference to the instance of this particular
:class:`PTransform` object. This allows chaining type-hinting related
methods.
"""
input_type_hint = native_type_compatibility.convert_to_beam_type(
input_type_hint)
validate_composite_type_param(
input_type_hint, 'Type hints for a PTransform')
return super(PTransform, self).with_input_types(input_type_hint)
[docs] def with_output_types(self, type_hint):
"""Annotates the output type of a :class:`PTransform` with a type-hint.
Args:
type_hint (type): An instance of an allowed built-in type, a custom class,
or a :class:`~apache_beam.typehints.typehints.TypeConstraint`.
Raises:
TypeError: If **type_hint** is not a valid type-hint. See
:obj:`~apache_beam.typehints.typehints.validate_composite_type_param()`
for further details.
Returns:
PTransform: A reference to the instance of this particular
:class:`PTransform` object. This allows chaining type-hinting related
methods.
"""
type_hint = native_type_compatibility.convert_to_beam_type(type_hint)
validate_composite_type_param(type_hint, 'Type hints for a PTransform')
return super(PTransform, self).with_output_types(type_hint)
[docs] def type_check_inputs(self, pvalueish):
self.type_check_inputs_or_outputs(pvalueish, 'input')
[docs] def infer_output_type(self, unused_input_type):
return self.get_type_hints().simple_output_type(self.label) or typehints.Any
[docs] def type_check_outputs(self, pvalueish):
self.type_check_inputs_or_outputs(pvalueish, 'output')
[docs] def type_check_inputs_or_outputs(self, pvalueish, input_or_output):
type_hints = self.get_type_hints()
hints = getattr(type_hints, input_or_output + '_types')
if hints is None or not any(hints):
return
arg_hints, kwarg_hints = hints
if arg_hints and kwarg_hints:
raise TypeCheckError(
'PTransform cannot have both positional and keyword type hints '
'without overriding %s._type_check_%s()' %
(self.__class__, input_or_output))
root_hint = (
arg_hints[0] if len(arg_hints) == 1 else arg_hints or kwarg_hints)
for context, pvalue_, hint in _ZipPValues().visit(pvalueish, root_hint):
if isinstance(pvalue_, DoOutputsTuple):
continue
if pvalue_.element_type is None:
# TODO(robertwb): It's a bug that we ever get here. (typecheck)
continue
if hint and not typehints.is_consistent_with(pvalue_.element_type, hint):
at_context = ' %s %s' % (input_or_output, context) if context else ''
raise TypeCheckError(
'{type} type hint violation at {label}{context}: expected {hint}, '
'got {actual_type}\nFull type hint:\n{debug_str}'.format(
type=input_or_output.title(),
label=self.label,
context=at_context,
hint=hint,
actual_type=pvalue_.element_type,
debug_str=type_hints.debug_str()))
def _infer_output_coder(self, input_type=None, input_coder=None):
# type: (...) -> Optional[coders.Coder]
"""Returns the output coder to use for output of this transform.
Note: this API is experimental and is subject to change; please do not rely
on behavior induced by this method.
The Coder returned here should not be wrapped in a WindowedValueCoder
wrapper.
Args:
input_type: An instance of an allowed built-in type, a custom class, or a
typehints.TypeConstraint for the input type, or None if not available.
input_coder: Coder object for encoding input to this PTransform, or None
if not available.
Returns:
Coder object for encoding output of this PTransform or None if unknown.
"""
# TODO(ccy): further refine this API.
return None
def _clone(self, new_label):
"""Clones the current transform instance under a new label."""
transform = copy.copy(self)
transform.label = new_label
return transform
def __str__(self):
return '<%s>' % self._str_internal()
def __repr__(self):
return '<%s at %s>' % (self._str_internal(), hex(id(self)))
def _str_internal(self):
return '%s(PTransform)%s%s%s' % (
self.__class__.__name__,
' label=[%s]' % self.label if
(hasattr(self, 'label') and self.label) else '',
' inputs=%s' % str(self.inputs) if
(hasattr(self, 'inputs') and self.inputs) else '',
' side_inputs=%s' % str(self.side_inputs) if self.side_inputs else '')
def _check_pcollection(self, pcoll):
# type: (pvalue.PCollection) -> None
if not isinstance(pcoll, pvalue.PCollection):
raise error.TransformError('Expecting a PCollection argument.')
if not pcoll.pipeline:
raise error.TransformError('PCollection not part of a pipeline.')
[docs] def get_windowing(self, inputs):
# type: (Any) -> Windowing
"""Returns the window function to be associated with transform's output.
By default most transforms just return the windowing function associated
with the input PCollection (or the first input if several).
"""
# TODO(robertwb): Assert all input WindowFns compatible.
return inputs[0].windowing
def __rrshift__(self, label):
return _NamedPTransform(self, label)
def __or__(self, right):
"""Used to compose PTransforms, e.g., ptransform1 | ptransform2."""
if isinstance(right, PTransform):
return _ChainedPTransform(self, right)
return NotImplemented
def __ror__(self, left, label=None):
"""Used to apply this PTransform to non-PValues, e.g., a tuple."""
pvalueish, pvalues = self._extract_input_pvalues(left)
pipelines = [v.pipeline for v in pvalues if isinstance(v, pvalue.PValue)]
if pvalues and not pipelines:
deferred = False
# pylint: disable=wrong-import-order, wrong-import-position
from apache_beam import pipeline
from apache_beam.options.pipeline_options import PipelineOptions
# pylint: enable=wrong-import-order, wrong-import-position
p = pipeline.Pipeline('DirectRunner', PipelineOptions(sys.argv))
else:
if not pipelines:
if self.pipeline is not None:
p = self.pipeline
else:
raise ValueError(
'"%s" requires a pipeline to be specified '
'as there are no deferred inputs.' % self.label)
else:
p = self.pipeline or pipelines[0]
for pp in pipelines:
if p != pp:
raise ValueError(
'Mixing value from different pipelines not allowed.')
deferred = not getattr(p.runner, 'is_eager', False)
# pylint: disable=wrong-import-order, wrong-import-position
from apache_beam.transforms.core import Create
# pylint: enable=wrong-import-order, wrong-import-position
replacements = {
id(v): p | 'CreatePInput%s' % ix >> Create(v, reshuffle=False)
for ix,
v in enumerate(pvalues)
if not isinstance(v, pvalue.PValue) and v is not None
}
pvalueish = _SetInputPValues().visit(pvalueish, replacements)
self.pipeline = p
result = p.apply(self, pvalueish, label)
if deferred:
return result
_allocate_materialized_pipeline(p)
materialized_result = _AddMaterializationTransforms().visit(result)
p.run().wait_until_finish()
_release_materialized_pipeline(p)
return _FinalizeMaterialization().visit(materialized_result)
def _extract_input_pvalues(self, pvalueish):
"""Extract all the pvalues contained in the input pvalueish.
Returns pvalueish as well as the flat inputs list as the input may have to
be copied as inspection may be destructive.
By default, recursively extracts tuple components and dict values.
Generally only needs to be overriden for multi-input PTransforms.
"""
# pylint: disable=wrong-import-order
from apache_beam import pipeline
# pylint: enable=wrong-import-order
if isinstance(pvalueish, pipeline.Pipeline):
pvalueish = pvalue.PBegin(pvalueish)
def _dict_tuple_leaves(pvalueish):
if isinstance(pvalueish, tuple):
for a in pvalueish:
for p in _dict_tuple_leaves(a):
yield p
elif isinstance(pvalueish, dict):
for a in pvalueish.values():
for p in _dict_tuple_leaves(a):
yield p
else:
yield pvalueish
return pvalueish, tuple(_dict_tuple_leaves(pvalueish))
def _pvaluish_from_dict(self, input_dict):
if len(input_dict) == 1:
return next(iter(input_dict.values()))
else:
return input_dict
def _named_inputs(self, inputs, side_inputs):
# type: (Sequence[pvalue.PValue], Sequence[Any]) -> Dict[str, pvalue.PValue]
"""Returns the dictionary of named inputs (including side inputs) as they
should be named in the beam proto.
"""
# TODO(BEAM-1833): Push names up into the sdk construction.
main_inputs = {
str(ix): input
for (ix, input) in enumerate(inputs)
if isinstance(input, pvalue.PCollection)
}
named_side_inputs = {(SIDE_INPUT_PREFIX + '%s') % ix: si.pvalue
for (ix, si) in enumerate(side_inputs)}
return dict(main_inputs, **named_side_inputs)
def _named_outputs(self, outputs):
# type: (Dict[object, pvalue.PCollection]) -> Dict[str, pvalue.PCollection]
"""Returns the dictionary of named outputs as they should be named in the
beam proto.
"""
# TODO(BEAM-1833): Push names up into the sdk construction.
return {
str(tag): output
for (tag, output) in outputs.items()
if isinstance(output, pvalue.PCollection)
}
_known_urns = {} # type: Dict[str, Tuple[Optional[type], ConstructorFn]]
@classmethod
@overload
def register_urn(
cls,
urn, # type: str
parameter_type, # type: Type[T]
):
# type: (...) -> Callable[[Union[type, Callable[[beam_runner_api_pb2.PTransform, T, PipelineContext], Any]]], Callable[[T, PipelineContext], Any]]
pass
@classmethod
@overload
def register_urn(
cls,
urn, # type: str
parameter_type, # type: None
):
# type: (...) -> Callable[[Union[type, Callable[[beam_runner_api_pb2.PTransform, bytes, PipelineContext], Any]]], Callable[[bytes, PipelineContext], Any]]
pass
@classmethod
@overload
def register_urn(cls,
urn, # type: str
parameter_type, # type: Type[T]
constructor # type: Callable[[beam_runner_api_pb2.PTransform, T, PipelineContext], Any]
):
# type: (...) -> None
pass
@classmethod
@overload
def register_urn(cls,
urn, # type: str
parameter_type, # type: None
constructor # type: Callable[[beam_runner_api_pb2.PTransform, bytes, PipelineContext], Any]
):
# type: (...) -> None
pass
[docs] @classmethod
def register_urn(cls, urn, parameter_type, constructor=None):
def register(constructor):
if isinstance(constructor, type):
constructor.from_runner_api_parameter = register(
constructor.from_runner_api_parameter)
else:
cls._known_urns[urn] = parameter_type, constructor
return constructor
if constructor:
# Used as a statement.
register(constructor)
else:
# Used as a decorator.
return register
[docs] def to_runner_api(self, context, has_parts=False, **extra_kwargs):
# type: (PipelineContext, bool, Any) -> beam_runner_api_pb2.FunctionSpec
from apache_beam.portability.api import beam_runner_api_pb2
# typing: only ParDo supports extra_kwargs
urn, typed_param = self.to_runner_api_parameter(context, **extra_kwargs) # type: ignore[call-arg]
if urn == python_urns.GENERIC_COMPOSITE_TRANSFORM and not has_parts:
# TODO(BEAM-3812): Remove this fallback.
urn, typed_param = self.to_runner_api_pickled(context)
return beam_runner_api_pb2.FunctionSpec(
urn=urn,
payload=typed_param.SerializeToString() if isinstance(
typed_param, message.Message) else typed_param.encode('utf-8')
if isinstance(typed_param, str) else typed_param)
[docs] @classmethod
def from_runner_api(cls,
proto, # type: Optional[beam_runner_api_pb2.PTransform]
context # type: PipelineContext
):
# type: (...) -> Optional[PTransform]
if proto is None or proto.spec is None or not proto.spec.urn:
return None
parameter_type, constructor = cls._known_urns[proto.spec.urn]
return constructor(
proto,
proto_utils.parse_Bytes(proto.spec.payload, parameter_type),
context)
[docs] def to_runner_api_parameter(
self,
unused_context # type: PipelineContext
):
# type: (...) -> Tuple[str, Optional[Union[message.Message, bytes, str]]]
# The payload here is just to ease debugging.
return (
python_urns.GENERIC_COMPOSITE_TRANSFORM,
getattr(self, '_fn_api_payload', str(self)))
[docs] def to_runner_api_pickled(self, unused_context):
# type: (PipelineContext) -> Tuple[str, bytes]
return (python_urns.PICKLED_TRANSFORM, pickler.dumps(self))
def _add_type_constraint_from_consumer(self, full_label, input_type_hints):
# type: (str, Tuple[str, Any]) -> None
"""Adds a consumer transform's input type hints to our output type
constraints, which is used during performance runtime type-checking.
"""
pass
@PTransform.register_urn(python_urns.GENERIC_COMPOSITE_TRANSFORM, None)
def _create_transform(unused_ptransform, payload, unused_context):
empty_transform = PTransform()
empty_transform._fn_api_payload = payload
return empty_transform
@PTransform.register_urn(python_urns.PICKLED_TRANSFORM, None)
def _unpickle_transform(unused_ptransform, pickled_bytes, unused_context):
return pickler.loads(pickled_bytes)
class _ChainedPTransform(PTransform):
def __init__(self, *parts):
# type: (*PTransform) -> None
super(_ChainedPTransform, self).__init__(label=self._chain_label(parts))
self._parts = parts
def _chain_label(self, parts):
return '|'.join(p.label for p in parts)
def __or__(self, right):
if isinstance(right, PTransform):
# Create a flat list rather than a nested tree of composite
# transforms for better monitoring, etc.
return _ChainedPTransform(*(self._parts + (right, )))
return NotImplemented
def expand(self, pval):
return reduce(operator.or_, self._parts, pval)
class PTransformWithSideInputs(PTransform):
"""A superclass for any :class:`PTransform` (e.g.
:func:`~apache_beam.transforms.core.FlatMap` or
:class:`~apache_beam.transforms.core.CombineFn`)
invoking user code.
:class:`PTransform` s like :func:`~apache_beam.transforms.core.FlatMap`
invoke user-supplied code in some kind of package (e.g. a
:class:`~apache_beam.transforms.core.DoFn`) and optionally provide arguments
and side inputs to that code. This internal-use-only class contains common
functionality for :class:`PTransform` s that fit this model.
"""
def __init__(self, fn, *args, **kwargs):
# type: (WithTypeHints, *Any, **Any) -> None
if isinstance(fn, type) and issubclass(fn, WithTypeHints):
# Don't treat Fn class objects as callables.
raise ValueError('Use %s() not %s.' % (fn.__name__, fn.__name__))
self.fn = self.make_fn(fn, bool(args or kwargs))
# Now that we figure out the label, initialize the super-class.
super(PTransformWithSideInputs, self).__init__()
if (any([isinstance(v, pvalue.PCollection) for v in args]) or
any([isinstance(v, pvalue.PCollection) for v in kwargs.values()])):
raise error.SideInputError(
'PCollection used directly as side input argument. Specify '
'AsIter(pcollection) or AsSingleton(pcollection) to indicate how the '
'PCollection is to be used.')
self.args, self.kwargs, self.side_inputs = util.remove_objects_from_args(
args, kwargs, pvalue.AsSideInput)
self.raw_side_inputs = args, kwargs
# Prevent name collisions with fns of the form '<function <lambda> at ...>'
self._cached_fn = self.fn
# Ensure fn and side inputs are picklable for remote execution.
try:
self.fn = pickler.loads(pickler.dumps(self.fn))
except RuntimeError as e:
raise RuntimeError('Unable to pickle fn %s: %s' % (self.fn, e))
self.args = pickler.loads(pickler.dumps(self.args))
self.kwargs = pickler.loads(pickler.dumps(self.kwargs))
# For type hints, because loads(dumps(class)) != class.
self.fn = self._cached_fn
def with_input_types(
self, input_type_hint, *side_inputs_arg_hints, **side_input_kwarg_hints):
"""Annotates the types of main inputs and side inputs for the PTransform.
Args:
input_type_hint: An instance of an allowed built-in type, a custom class,
or an instance of a typehints.TypeConstraint.
*side_inputs_arg_hints: A variable length argument composed of
of an allowed built-in type, a custom class, or a
typehints.TypeConstraint.
**side_input_kwarg_hints: A dictionary argument composed of
of an allowed built-in type, a custom class, or a
typehints.TypeConstraint.
Example of annotating the types of side-inputs::
FlatMap().with_input_types(int, int, bool)
Raises:
:class:`TypeError`: If **type_hint** is not a valid type-hint.
See
:func:`~apache_beam.typehints.typehints.validate_composite_type_param`
for further details.
Returns:
:class:`PTransform`: A reference to the instance of this particular
:class:`PTransform` object. This allows chaining type-hinting related
methods.
"""
super(PTransformWithSideInputs, self).with_input_types(input_type_hint)
side_inputs_arg_hints = native_type_compatibility.convert_to_beam_types(
side_inputs_arg_hints)
side_input_kwarg_hints = native_type_compatibility.convert_to_beam_types(
side_input_kwarg_hints)
for si in side_inputs_arg_hints:
validate_composite_type_param(si, 'Type hints for a PTransform')
for si in side_input_kwarg_hints.values():
validate_composite_type_param(si, 'Type hints for a PTransform')
self.side_inputs_types = side_inputs_arg_hints
return WithTypeHints.with_input_types(
self, input_type_hint, *side_inputs_arg_hints, **side_input_kwarg_hints)
def type_check_inputs(self, pvalueish):
type_hints = self.get_type_hints()
input_types = type_hints.input_types
if input_types:
args, kwargs = self.raw_side_inputs
def element_type(side_input):
if isinstance(side_input, pvalue.AsSideInput):
return side_input.element_type
return instance_to_type(side_input)
arg_types = [pvalueish.element_type] + [element_type(v) for v in args]
kwargs_types = {k: element_type(v) for (k, v) in kwargs.items()}
argspec_fn = self._process_argspec_fn()
bindings = getcallargs_forhints(argspec_fn, *arg_types, **kwargs_types)
hints = getcallargs_forhints(
argspec_fn, *input_types[0], **input_types[1])
for arg, hint in hints.items():
if arg.startswith('__unknown__'):
continue
if hint is None:
continue
if not typehints.is_consistent_with(bindings.get(arg, typehints.Any),
hint):
raise TypeCheckError(
'Type hint violation for \'{label}\': requires {hint} but got '
'{actual_type} for {arg}\nFull type hint:\n{debug_str}'.format(
label=self.label,
hint=hint,
actual_type=bindings[arg],
arg=arg,
debug_str=type_hints.debug_str()))
def _process_argspec_fn(self):
"""Returns an argspec of the function actually consuming the data.
"""
raise NotImplementedError
def make_fn(self, fn, has_side_inputs):
# TODO(silviuc): Add comment describing that this is meant to be overriden
# by methods detecting callables and wrapping them in DoFns.
return fn
def default_label(self):
return '%s(%s)' % (self.__class__.__name__, self.fn.default_label())
class _PTransformFnPTransform(PTransform):
"""A class wrapper for a function-based transform."""
def __init__(self, fn, *args, **kwargs):
super(_PTransformFnPTransform, self).__init__()
self._fn = fn
self._args = args
self._kwargs = kwargs
def display_data(self):
res = {
'fn': (
self._fn.__name__
if hasattr(self._fn, '__name__') else self._fn.__class__),
'args': DisplayDataItem(str(self._args)).drop_if_default('()'),
'kwargs': DisplayDataItem(str(self._kwargs)).drop_if_default('{}')
}
return res
def expand(self, pcoll):
# Since the PTransform will be implemented entirely as a function
# (once called), we need to pass through any type-hinting information that
# may have been annotated via the .with_input_types() and
# .with_output_types() methods.
kwargs = dict(self._kwargs)
args = tuple(self._args)
# TODO(BEAM-5878) Support keyword-only arguments.
try:
if 'type_hints' in get_signature(self._fn).parameters:
args = (self.get_type_hints(), ) + args
except TypeError:
# Might not be a function.
pass
return self._fn(pcoll, *args, **kwargs)
def default_label(self):
if self._args:
return '%s(%s)' % (
label_from_callable(self._fn), label_from_callable(self._args[0]))
return label_from_callable(self._fn)
[docs]def ptransform_fn(fn):
"""A decorator for a function-based PTransform.
Args:
fn: A function implementing a custom PTransform.
Returns:
A CallablePTransform instance wrapping the function-based PTransform.
This wrapper provides an alternative, simpler way to define a PTransform.
The standard method is to subclass from PTransform and override the expand()
method. An equivalent effect can be obtained by defining a function that
accepts an input PCollection and additional optional arguments and returns a
resulting PCollection. For example::
@ptransform_fn
@beam.typehints.with_input_types(..)
@beam.typehints.with_output_types(..)
def CustomMapper(pcoll, mapfn):
return pcoll | ParDo(mapfn)
The equivalent approach using PTransform subclassing::
@beam.typehints.with_input_types(..)
@beam.typehints.with_output_types(..)
class CustomMapper(PTransform):
def __init__(self, mapfn):
super(CustomMapper, self).__init__()
self.mapfn = mapfn
def expand(self, pcoll):
return pcoll | ParDo(self.mapfn)
With either method the custom PTransform can be used in pipelines as if
it were one of the "native" PTransforms::
result_pcoll = input_pcoll | 'Label' >> CustomMapper(somefn)
Note that for both solutions the underlying implementation of the pipe
operator (i.e., `|`) will inject the pcoll argument in its proper place
(first argument if no label was specified and second argument otherwise).
Type hint support needs to be enabled via the
--type_check_additional=ptransform_fn flag in Beam 2.
If CustomMapper is a Cython function, you can still specify input and output
types provided the decorators appear before @ptransform_fn.
"""
# TODO(robertwb): Consider removing staticmethod to allow for self parameter.
@wraps(fn)
def callable_ptransform_factory(*args, **kwargs):
res = _PTransformFnPTransform(fn, *args, **kwargs)
if ptransform_fn_typehints_enabled:
# Apply type hints applied before or after the ptransform_fn decorator,
# falling back on PTransform defaults.
# If the @with_{input,output}_types decorator comes before ptransform_fn,
# the type hints get applied to this function. If it comes after they will
# get applied to fn, and @wraps will copy the _type_hints attribute to
# this function.
type_hints = get_type_hints(callable_ptransform_factory)
res._set_type_hints(type_hints.with_defaults(res.get_type_hints()))
_LOGGER.debug(
'type hints for %s: %s', res.default_label(), res.get_type_hints())
return res
return callable_ptransform_factory
[docs]def label_from_callable(fn):
if hasattr(fn, 'default_label'):
return fn.default_label()
elif hasattr(fn, '__name__'):
if fn.__name__ == '<lambda>':
return '<lambda at %s:%s>' % (
os.path.basename(fn.__code__.co_filename), fn.__code__.co_firstlineno)
return fn.__name__
return str(fn)
class _NamedPTransform(PTransform):
def __init__(self, transform, label):
super(_NamedPTransform, self).__init__(label)
self.transform = transform
def __ror__(self, pvalueish, _unused=None):
return self.transform.__ror__(pvalueish, self.label)
def expand(self, pvalue):
raise RuntimeError("Should never be expanded directly.")