#
# 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.
#
"""Pipeline, the top-level Beam object.
A pipeline holds a DAG of data transforms. Conceptually the nodes of the DAG
are transforms (:class:`~apache_beam.transforms.ptransform.PTransform` objects)
and the edges are values (mostly :class:`~apache_beam.pvalue.PCollection`
objects). The transforms take as inputs one or more PValues and output one or
more :class:`~apache_beam.pvalue.PValue` s.
The pipeline offers functionality to traverse the graph. The actual operation
to be executed for each node visited is specified through a runner object.
Typical usage::
# Create a pipeline object using a local runner for execution.
with beam.Pipeline('DirectRunner') as p:
# Add to the pipeline a "Create" transform. When executed this
# transform will produce a PCollection object with the specified values.
pcoll = p | 'Create' >> beam.Create([1, 2, 3])
# Another transform could be applied to pcoll, e.g., writing to a text file.
# For other transforms, refer to transforms/ directory.
pcoll | 'Write' >> beam.io.WriteToText('./output')
# run() will execute the DAG stored in the pipeline. The execution of the
# nodes visited is done using the specified local runner.
"""
# pytype: skip-file
# mypy: disallow-untyped-defs
import abc
import contextlib
import logging
import os
import re
import shutil
import tempfile
import unicodedata
import uuid
from collections import defaultdict
from typing import TYPE_CHECKING
from typing import Any
from typing import Dict
from typing import FrozenSet
from typing import Iterable
from typing import List
from typing import Mapping
from typing import Optional
from typing import Sequence
from typing import Set
from typing import Tuple
from typing import Type
from typing import Union
from google.protobuf import message
from apache_beam import pvalue
from apache_beam.internal import pickler
from apache_beam.io.filesystems import FileSystems
from apache_beam.options.pipeline_options import CrossLanguageOptions
from apache_beam.options.pipeline_options import DebugOptions
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.options.pipeline_options import SetupOptions
from apache_beam.options.pipeline_options import StandardOptions
from apache_beam.options.pipeline_options import TypeOptions
from apache_beam.options.pipeline_options_validator import PipelineOptionsValidator
from apache_beam.portability import common_urns
from apache_beam.portability.api import beam_runner_api_pb2
from apache_beam.runners import PipelineRunner
from apache_beam.runners import common
from apache_beam.runners import create_runner
from apache_beam.transforms import ParDo
from apache_beam.transforms import ptransform
from apache_beam.transforms.display import DisplayData
from apache_beam.transforms.display import HasDisplayData
from apache_beam.transforms.resources import merge_resource_hints
from apache_beam.transforms.resources import resource_hints_from_options
from apache_beam.transforms.sideinputs import get_sideinput_index
from apache_beam.typehints import TypeCheckError
from apache_beam.typehints import typehints
from apache_beam.utils import proto_utils
from apache_beam.utils import subprocess_server
from apache_beam.utils.annotations import deprecated
from apache_beam.utils.interactive_utils import alter_label_if_ipython
from apache_beam.utils.interactive_utils import is_in_ipython
if TYPE_CHECKING:
from types import TracebackType
from apache_beam.runners.pipeline_context import PipelineContext
from apache_beam.runners.runner import PipelineResult
from apache_beam.transforms import environments
__all__ = ['Pipeline', 'PTransformOverride']
[docs]class Pipeline(HasDisplayData):
"""A pipeline object that manages a DAG of
:class:`~apache_beam.pvalue.PValue` s and their
:class:`~apache_beam.transforms.ptransform.PTransform` s.
Conceptually the :class:`~apache_beam.pvalue.PValue` s are the DAG's nodes and
the :class:`~apache_beam.transforms.ptransform.PTransform` s computing
the :class:`~apache_beam.pvalue.PValue` s are the edges.
All the transforms applied to the pipeline must have distinct full labels.
If same transform instance needs to be applied then the right shift operator
should be used to designate new names
(e.g. ``input | "label" >> my_transform``).
"""
def __init__(
self,
runner: Optional[Union[str, PipelineRunner]] = None,
options: Optional[PipelineOptions] = None,
argv: Optional[List[str]] = None,
display_data: Optional[Dict[str, Any]] = None):
"""Initialize a pipeline object.
Args:
runner (~apache_beam.runners.runner.PipelineRunner): An object of
type :class:`~apache_beam.runners.runner.PipelineRunner` that will be
used to execute the pipeline. For registered runners, the runner name
can be specified, otherwise a runner object must be supplied.
options (~apache_beam.options.pipeline_options.PipelineOptions):
A configured
:class:`~apache_beam.options.pipeline_options.PipelineOptions` object
containing arguments that should be used for running the Beam job.
argv (List[str]): a list of arguments (such as :data:`sys.argv`)
to be used for building a
:class:`~apache_beam.options.pipeline_options.PipelineOptions` object.
This will only be used if argument **options** is :data:`None`.
display_data (Dict[str, Any]): a dictionary of static data associated
with this pipeline that can be displayed when it runs.
Raises:
ValueError: if either the runner or options argument is not
of the expected type.
"""
# Initializing logging configuration in case the user did not set it up.
logging.basicConfig()
if options is not None:
if isinstance(options, PipelineOptions):
self._options = options
else:
raise ValueError(
'Parameter options, if specified, must be of type PipelineOptions. '
'Received : %r' % options)
elif argv is not None:
if isinstance(argv, list):
self._options = PipelineOptions(argv)
else:
raise ValueError(
'Parameter argv, if specified, must be a list. Received : %r' %
argv)
else:
self._options = PipelineOptions([])
FileSystems.set_options(self._options)
pickle_library = self._options.view_as(SetupOptions).pickle_library
pickler.set_library(pickle_library)
if runner is None:
runner = self._options.view_as(StandardOptions).runner
if runner is None:
runner = StandardOptions.DEFAULT_RUNNER
logging.info((
'Missing pipeline option (runner). Executing pipeline '
'using the default runner: %s.'),
runner)
if isinstance(runner, str):
runner = create_runner(runner)
elif not isinstance(runner, PipelineRunner):
raise TypeError(
'Runner %s is not a PipelineRunner object or the '
'name of a registered runner.' % runner)
# Validate pipeline options
errors = PipelineOptionsValidator(self._options, runner).validate()
if errors:
raise ValueError(
'Pipeline has validations errors: \n' + '\n'.join(errors))
# set default experiments for portable runners
# (needs to occur prior to pipeline construction)
if runner.is_fnapi_compatible():
experiments = (self._options.view_as(DebugOptions).experiments or [])
if not 'beam_fn_api' in experiments:
experiments.append('beam_fn_api')
self._options.view_as(DebugOptions).experiments = experiments
self.local_tempdir = tempfile.mkdtemp(prefix='beam-pipeline-temp')
# Default runner to be used.
self.runner = runner
# Stack of transforms generated by nested apply() calls. The stack will
# contain a root node as an enclosing (parent) node for top transforms.
self.transforms_stack = [AppliedPTransform(None, None, '', None)]
# Set of transform labels (full labels) applied to the pipeline.
# If a transform is applied and the full label is already in the set
# then the transform will have to be cloned with a new label.
self.applied_labels = set() # type: Set[str]
# Hints supplied via pipeline options are considered the outermost hints.
self._root_transform().resource_hints = resource_hints_from_options(options)
# Create a ComponentIdMap for assigning IDs to components. Ensures that any
# components that receive an ID during pipeline construction (for example in
# ExternalTransform), will receive the same component ID when generating the
# full pipeline proto.
self.component_id_map = ComponentIdMap()
# Records whether this pipeline contains any external transforms.
self.contains_external_transforms = False
self._display_data = display_data or {}
[docs] def display_data(self):
# type: () -> Dict[str, Any]
return self._display_data
@property # type: ignore[misc] # decorated property not supported
def options(self):
# type: () -> PipelineOptions
return self._options
@property
def allow_unsafe_triggers(self):
# type: () -> bool
return self._options.view_as(TypeOptions).allow_unsafe_triggers
def _current_transform(self):
# type: () -> AppliedPTransform
"""Returns the transform currently on the top of the stack."""
return self.transforms_stack[-1]
def _root_transform(self):
# type: () -> AppliedPTransform
"""Returns the root transform of the transform stack."""
return self.transforms_stack[0]
def _remove_labels_recursively(self, applied_transform):
# type: (AppliedPTransform) -> None
for part in applied_transform.parts:
if part.full_label in self.applied_labels:
self.applied_labels.remove(part.full_label)
self._remove_labels_recursively(part)
def _replace(self, override):
# type: (PTransformOverride) -> None
assert isinstance(override, PTransformOverride)
# From original transform output --> replacement transform output
output_map = {} # type: Dict[pvalue.PValue, pvalue.PValue]
output_replacements = {
} # type: Dict[AppliedPTransform, List[Tuple[pvalue.PValue, Optional[str]]]]
input_replacements = {
} # type: Dict[AppliedPTransform, Mapping[str, Union[pvalue.PBegin, pvalue.PCollection]]]
side_input_replacements = {
} # type: Dict[AppliedPTransform, List[pvalue.AsSideInput]]
class TransformUpdater(PipelineVisitor): # pylint: disable=used-before-assignment
""""A visitor that replaces the matching PTransforms."""
def __init__(self, pipeline):
# type: (Pipeline) -> None
self.pipeline = pipeline
def _replace_if_needed(self, original_transform_node):
# type: (AppliedPTransform) -> None
if override.matches(original_transform_node):
assert isinstance(original_transform_node, AppliedPTransform)
replacement_transform = (
override.get_replacement_transform_for_applied_ptransform(
original_transform_node))
if replacement_transform is original_transform_node.transform:
return
replacement_transform.side_inputs = tuple(
original_transform_node.transform.side_inputs)
replacement_transform_node = AppliedPTransform(
original_transform_node.parent,
replacement_transform,
original_transform_node.full_label,
original_transform_node.main_inputs)
# TODO(https://github.com/apache/beam/issues/21178): Merge rather
# than override.
replacement_transform_node.resource_hints = (
original_transform_node.resource_hints)
# Transform execution could depend on order in which nodes are
# considered. Hence we insert the replacement transform node to same
# index as the original transform node. Note that this operation
# removes the original transform node.
if original_transform_node.parent:
assert isinstance(original_transform_node.parent, AppliedPTransform)
parent_parts = original_transform_node.parent.parts
parent_parts[parent_parts.index(original_transform_node)] = (
replacement_transform_node)
else:
# Original transform has to be a root.
roots = self.pipeline.transforms_stack[0].parts
assert original_transform_node in roots
roots[roots.index(original_transform_node)] = (
replacement_transform_node)
inputs = override.get_replacement_inputs(original_transform_node)
if len(inputs) > 1:
transform_input = inputs
elif len(inputs) == 1:
transform_input = inputs[0]
elif len(inputs) == 0:
transform_input = pvalue.PBegin(self.pipeline)
try:
# We have to add the new AppliedTransform to the stack before
# expand() and pop it out later to make sure that parts get added
# correctly.
self.pipeline.transforms_stack.append(replacement_transform_node)
# Keeping the same label for the replaced node but recursively
# removing labels of child transforms of original transform since
# they will be replaced during the expand below. This is needed in
# case the replacement contains children that have labels that
# conflicts with labels of the children of the original.
self.pipeline._remove_labels_recursively(original_transform_node)
new_output = replacement_transform.expand(transform_input)
assert isinstance(
new_output, (dict, pvalue.PValue, pvalue.DoOutputsTuple))
if isinstance(new_output, pvalue.PValue):
new_output.element_type = None
self.pipeline._infer_result_type(
replacement_transform, inputs, new_output)
if isinstance(new_output, dict):
for new_tag, new_pcoll in new_output.items():
replacement_transform_node.add_output(new_pcoll, new_tag)
elif isinstance(new_output, pvalue.DoOutputsTuple):
replacement_transform_node.add_output(
new_output, new_output._main_tag)
else:
replacement_transform_node.add_output(new_output, new_output.tag)
# Recording updated outputs. This cannot be done in the same
# visitor since if we dynamically update output type here, we'll
# run into errors when visiting child nodes.
#
# NOTE: When replacing multiple outputs, the replacement
# PCollection tags must have a matching tag in the original
# transform.
if isinstance(new_output, pvalue.PValue):
if not new_output.producer:
new_output.producer = replacement_transform_node
output_map[original_transform_node.outputs[new_output.tag]] = \
new_output
elif isinstance(new_output, (pvalue.DoOutputsTuple, tuple)):
for pcoll in new_output:
if not pcoll.producer:
pcoll.producer = replacement_transform_node
output_map[original_transform_node.outputs[pcoll.tag]] = pcoll
elif isinstance(new_output, dict):
for tag, pcoll in new_output.items():
if not pcoll.producer:
pcoll.producer = replacement_transform_node
output_map[original_transform_node.outputs[tag]] = pcoll
finally:
self.pipeline.transforms_stack.pop()
def enter_composite_transform(self, transform_node):
# type: (AppliedPTransform) -> None
self._replace_if_needed(transform_node)
def visit_transform(self, transform_node):
# type: (AppliedPTransform) -> None
self._replace_if_needed(transform_node)
self.visit(TransformUpdater(self))
# Ensure no type information is lost.
for old, new in output_map.items():
if new.element_type == typehints.Any:
# TODO(robertwb): Perhaps take the intersection?
new.element_type = old.element_type
# Adjusting inputs and outputs
class InputOutputUpdater(PipelineVisitor): # pylint: disable=used-before-assignment
""""A visitor that records input and output values to be replaced.
Input and output values that should be updated are recorded in maps
input_replacements and output_replacements respectively.
We cannot update input and output values while visiting since that results
in validation errors.
"""
def __init__(self, pipeline):
# type: (Pipeline) -> None
self.pipeline = pipeline
def enter_composite_transform(self, transform_node):
# type: (AppliedPTransform) -> None
self.visit_transform(transform_node)
def visit_transform(self, transform_node):
# type: (AppliedPTransform) -> None
replace_output = False
for tag in transform_node.outputs:
if transform_node.outputs[tag] in output_map:
replace_output = True
break
replace_input = False
for input in transform_node.inputs:
if input in output_map:
replace_input = True
break
replace_side_inputs = False
for side_input in transform_node.side_inputs:
if side_input.pvalue in output_map:
replace_side_inputs = True
break
if replace_output:
output_replacements[transform_node] = []
for original, replacement in output_map.items():
for tag, output in transform_node.outputs.items():
if output == original:
output_replacements[transform_node].append((tag, replacement))
if replace_input:
new_inputs = {
tag: input if not input in output_map else output_map[input]
for (tag, input) in transform_node.main_inputs.items()
}
input_replacements[transform_node] = new_inputs
if replace_side_inputs:
new_side_inputs = []
for side_input in transform_node.side_inputs:
if side_input.pvalue in output_map:
side_input.pvalue = output_map[side_input.pvalue]
new_side_inputs.append(side_input)
else:
new_side_inputs.append(side_input)
side_input_replacements[transform_node] = new_side_inputs
self.visit(InputOutputUpdater(self))
for transform, output_replacement in output_replacements.items():
for tag, output in output_replacement:
transform.replace_output(output, tag=tag)
for transform, input_replacement in input_replacements.items():
transform.replace_inputs(input_replacement)
for transform, side_input_replacement in side_input_replacements.items():
transform.replace_side_inputs(side_input_replacement)
def _check_replacement(self, override):
# type: (PTransformOverride) -> None
class ReplacementValidator(PipelineVisitor):
def visit_transform(self, transform_node):
# type: (AppliedPTransform) -> None
if override.matches(transform_node):
raise RuntimeError(
'Transform node %r was not replaced as expected.' %
transform_node)
self.visit(ReplacementValidator())
[docs] def replace_all(self, replacements):
# type: (Iterable[PTransformOverride]) -> None
""" Dynamically replaces PTransforms in the currently populated hierarchy.
Currently this only works for replacements where input and output types
are exactly the same.
TODO: Update this to also work for transform overrides where input and
output types are different.
Args:
replacements (List[~apache_beam.pipeline.PTransformOverride]): a list of
:class:`~apache_beam.pipeline.PTransformOverride` objects.
"""
for override in replacements:
assert isinstance(override, PTransformOverride)
self._replace(override)
# Checking if the PTransforms have been successfully replaced. This will
# result in a failure if a PTransform that was replaced in a given override
# gets re-added in a subsequent override. This is not allowed and ordering
# of PTransformOverride objects in 'replacements' is important.
for override in replacements:
self._check_replacement(override)
[docs] def run(self, test_runner_api='AUTO'):
# type: (Union[bool, str]) -> PipelineResult
"""Runs the pipeline. Returns whatever our runner returns after running."""
# Records whether this pipeline contains any cross-language transforms.
self.contains_external_transforms = (
ExternalTransformFinder.contains_external_transforms(self))
try:
if test_runner_api == 'AUTO':
# Don't pay the cost of a round-trip if we're going to be going through
# the FnApi anyway...
is_fnapi_compatible = self.runner.is_fnapi_compatible() or (
# DirectRunner uses the Fn API for batch only
self.runner.__class__.__name__ == 'SwitchingDirectRunner' and
not self._options.view_as(StandardOptions).streaming)
# Multi-language pipelines that contain external pipeline segments may
# not be able to create a Python pipeline object graph. Hence following
# runner API check should be skipped for such pipelines.
# The InteractiveRunner relies on a constant pipeline reference, skip
# it.
test_runner_api = (
not is_fnapi_compatible and
not self.contains_external_transforms and
self.runner.__class__.__name__ != 'InteractiveRunner')
# When possible, invoke a round trip through the runner API.
if test_runner_api and self._verify_runner_api_compatible():
return Pipeline.from_runner_api(
self.to_runner_api(use_fake_coders=True),
self.runner,
self._options).run(False)
if (self._options.view_as(TypeOptions).runtime_type_check and
self._options.view_as(TypeOptions).performance_runtime_type_check):
raise RuntimeError(
'You cannot turn on runtime_type_check '
'and performance_runtime_type_check simultaneously. '
'Pick one or the other.')
if self._options.view_as(TypeOptions).runtime_type_check:
from apache_beam.typehints import typecheck
self.visit(typecheck.TypeCheckVisitor())
if self._options.view_as(TypeOptions).performance_runtime_type_check:
from apache_beam.typehints import typecheck
self.visit(typecheck.PerformanceTypeCheckVisitor())
if self._options.view_as(SetupOptions).save_main_session:
# If this option is chosen, verify we can pickle the main session early.
tmpdir = tempfile.mkdtemp()
try:
pickler.dump_session(os.path.join(tmpdir, 'main_session.pickle'))
finally:
shutil.rmtree(tmpdir)
return self.runner.run_pipeline(self, self._options)
finally:
if not is_in_ipython():
shutil.rmtree(self.local_tempdir, ignore_errors=True)
# else interactive beam handles the cleanup.
def __enter__(self):
# type: () -> Pipeline
self._extra_context = contextlib.ExitStack()
self._extra_context.enter_context(
subprocess_server.JavaJarServer.beam_services(
self._options.view_as(CrossLanguageOptions).beam_services))
self._extra_context.enter_context(
subprocess_server.SubprocessServer.cache_subprocesses())
return self
def __exit__(
self,
exc_type, # type: Optional[Type[BaseException]]
exc_val, # type: Optional[BaseException]
exc_tb # type: Optional[TracebackType]
):
# type: (...) -> None
try:
if not exc_type:
self.result = self.run()
self.result.wait_until_finish()
finally:
self._extra_context.__exit__(exc_type, exc_val, exc_tb)
[docs] def visit(self, visitor):
# type: (PipelineVisitor) -> None
"""Visits depth-first every node of a pipeline's DAG.
Runner-internal implementation detail; no backwards-compatibility guarantees
Args:
visitor (~apache_beam.pipeline.PipelineVisitor):
:class:`~apache_beam.pipeline.PipelineVisitor` object whose callbacks
will be called for each node visited. See
:class:`~apache_beam.pipeline.PipelineVisitor` comments.
Raises:
TypeError: if node is specified and is not a
:class:`~apache_beam.pvalue.PValue`.
~apache_beam.error.PipelineError: if node is specified and does not
belong to this pipeline instance.
"""
visited = set() # type: Set[pvalue.PValue]
self._root_transform().visit(visitor, self, visited)
[docs] def apply(
self,
transform, # type: ptransform.PTransform
pvalueish=None, # type: Optional[pvalue.PValue]
label=None # type: Optional[str]
):
# type: (...) -> pvalue.PValue
"""Applies a custom transform using the pvalueish specified.
Args:
transform (~apache_beam.transforms.ptransform.PTransform): the
:class:`~apache_beam.transforms.ptransform.PTransform` to apply.
pvalueish (~apache_beam.pvalue.PCollection): the input for the
:class:`~apache_beam.transforms.ptransform.PTransform` (typically a
:class:`~apache_beam.pvalue.PCollection`).
label (str): label of the
:class:`~apache_beam.transforms.ptransform.PTransform`.
Raises:
TypeError: if the transform object extracted from the
argument list is not a
:class:`~apache_beam.transforms.ptransform.PTransform`.
RuntimeError: if the transform object was already applied to
this pipeline and needs to be cloned in order to apply again.
"""
if isinstance(transform, ptransform._NamedPTransform):
return self.apply(
transform.transform, pvalueish, label or transform.label)
if not isinstance(transform, ptransform.PTransform):
raise TypeError("Expected a PTransform object, got %s" % transform)
if label:
# Fix self.label as it is inspected by some PTransform operations
# (e.g. to produce error messages for type hint violations).
old_label, transform.label = transform.label, label
try:
return self.apply(transform, pvalueish)
finally:
transform.label = old_label
# Attempts to alter the label of the transform to be applied only when it's
# a top-level transform so that the cell number will not be prepended to
# every child transform in a composite.
if self._current_transform() is self._root_transform():
alter_label_if_ipython(transform, pvalueish)
full_label = '/'.join(
[self._current_transform().full_label, transform.label]).lstrip('/')
if full_label in self.applied_labels:
auto_unique_labels = self._options.view_as(
StandardOptions).auto_unique_labels
if auto_unique_labels:
# If auto_unique_labels is set, we will append a unique suffix to the
# label to make it unique.
logging.warning(
'Using --auto_unique_labels could cause data loss when '
'updating a pipeline or reloading the job state. '
'This is not recommended for streaming jobs.')
unique_label = self._generate_unique_label(transform)
return self.apply(transform, pvalueish, unique_label)
else:
raise RuntimeError(
'A transform with label "%s" already exists in the pipeline. '
'To apply a transform with a specified label, write '
'pvalue | "label" >> transform or use the option '
'"auto_unique_labels" to automatically generate unique '
'transform labels. Note "auto_unique_labels" '
'could cause data loss when updating a pipeline or '
'reloading the job state. This is not recommended for '
'streaming jobs.' % full_label)
self.applied_labels.add(full_label)
pvalueish, inputs = transform._extract_input_pvalues(pvalueish)
try:
if not isinstance(inputs, dict):
inputs = {str(ix): input for (ix, input) in enumerate(inputs)}
except TypeError:
raise NotImplementedError(
'Unable to extract PValue inputs from %s; either %s does not accept '
'inputs of this format, or it does not properly override '
'_extract_input_pvalues' % (pvalueish, transform))
for t, leaf_input in inputs.items():
if not isinstance(leaf_input, pvalue.PValue) or not isinstance(t, str):
raise NotImplementedError(
'%s does not properly override _extract_input_pvalues, '
'returned %s from %s' % (transform, inputs, pvalueish))
current = AppliedPTransform(
self._current_transform(), transform, full_label, inputs)
self._current_transform().add_part(current)
try:
self.transforms_stack.append(current)
type_options = self._options.view_as(TypeOptions)
if type_options.pipeline_type_check:
transform.type_check_inputs(pvalueish)
pvalueish_result = self.runner.apply(transform, pvalueish, self._options)
if type_options is not None and type_options.pipeline_type_check:
transform.type_check_outputs(pvalueish_result)
for tag, result in ptransform.get_named_nested_pvalues(pvalueish_result):
assert isinstance(result, (pvalue.PValue, pvalue.DoOutputsTuple))
# Make sure we set the producer only for a leaf node in the transform
# DAG. This way we preserve the last transform of a composite transform
# as being the real producer of the result.
if result.producer is None:
result.producer = current
# TODO(BEAM-1833): Pass full tuples dict.
self._infer_result_type(transform, tuple(inputs.values()), result)
assert isinstance(result.producer.inputs, tuple)
# The DoOutputsTuple adds the PCollection to the outputs when accessed
# except for the main tag. Add the main tag here.
if isinstance(result, pvalue.DoOutputsTuple):
current.add_output(result, result._main_tag)
continue
# If there is already a tag with the same name, increase a counter for
# the name. This can happen, for example, when a composite outputs a
# list of PCollections where all the tags are None.
base = tag
counter = 0
while tag in current.outputs:
counter += 1
tag = '%s_%d' % (base, counter)
current.add_output(result, tag)
if (type_options is not None and
type_options.type_check_strictness == 'ALL_REQUIRED' and
transform.get_type_hints().output_types is None):
ptransform_name = '%s(%s)' % (transform.__class__.__name__, full_label)
raise TypeCheckError(
'Pipeline type checking is enabled, however no '
'output type-hint was found for the '
'PTransform %s' % ptransform_name)
finally:
self.transforms_stack.pop()
return pvalueish_result
def _generate_unique_label(
self,
transform # type: str
):
# type: (...) -> str
"""
Given a transform, generate a unique label for it based on current label.
"""
unique_suffix = uuid.uuid4().hex[:6]
return '%s_%s' % (transform.label, unique_suffix)
def _infer_result_type(
self,
transform, # type: ptransform.PTransform
inputs, # type: Sequence[Union[pvalue.PBegin, pvalue.PCollection]]
result_pcollection # type: Union[pvalue.PValue, pvalue.DoOutputsTuple]
):
# type: (...) -> None
# TODO(robertwb): Multi-input inference.
type_options = self._options.view_as(TypeOptions)
if type_options is None or not type_options.pipeline_type_check:
return
if (isinstance(result_pcollection, pvalue.PCollection) and
(not result_pcollection.element_type
# TODO(robertwb): Ideally we'd do intersection here.
or result_pcollection.element_type == typehints.Any)):
# {Single, multi}-input, single-output inference.
input_element_types_tuple = tuple(i.element_type for i in inputs)
input_element_type = (
input_element_types_tuple[0] if len(input_element_types_tuple) == 1
else typehints.Union[input_element_types_tuple])
type_hints = transform.get_type_hints()
declared_output_type = type_hints.simple_output_type(transform.label)
if declared_output_type:
input_types = type_hints.input_types
if input_types and input_types[0]:
declared_input_type = input_types[0][0]
result_element_type = typehints.bind_type_variables(
declared_output_type,
typehints.match_type_variables(
declared_input_type, input_element_type))
else:
result_element_type = declared_output_type
else:
result_element_type = transform.infer_output_type(input_element_type)
# Any remaining type variables have no bindings higher than this scope.
result_pcollection.element_type = typehints.bind_type_variables(
result_element_type, {'*': typehints.Any})
elif isinstance(result_pcollection, pvalue.DoOutputsTuple):
# {Single, multi}-input, multi-output inference.
# TODO(https://github.com/apache/beam/issues/18957): Add support for
# tagged type hints.
# https://github.com/apache/beam/pull/9810#discussion_r338765251
for pcoll in result_pcollection:
if pcoll.element_type is None:
pcoll.element_type = typehints.Any
def __reduce__(self):
# type: () -> Tuple[Type, Tuple[str, ...]]
# Some transforms contain a reference to their enclosing pipeline,
# which in turn reference all other transforms (resulting in quadratic
# time/space to pickle each transform individually). As we don't
# require pickled pipelines to be executable, break the chain here.
return str, ('Pickled pipeline stub.', )
def _verify_runner_api_compatible(self):
# type: () -> bool
if self._options.view_as(TypeOptions).runtime_type_check:
# This option is incompatible with the runner API as it requires
# the runner to inspect non-serialized hints on the transform
# itself.
return False
class Visitor(PipelineVisitor): # pylint: disable=used-before-assignment
ok = True # Really a nonlocal.
def enter_composite_transform(self, transform_node):
# type: (AppliedPTransform) -> None
pass
def visit_transform(self, transform_node):
# type: (AppliedPTransform) -> None
try:
# Transforms must be picklable.
pickler.loads(
pickler.dumps(transform_node.transform, enable_trace=False),
enable_trace=False)
except Exception:
Visitor.ok = False
def visit_value(self, value, _):
# type: (pvalue.PValue, AppliedPTransform) -> None
if isinstance(value, pvalue.PDone):
Visitor.ok = False
self.visit(Visitor())
return Visitor.ok
[docs] def to_runner_api(
self,
return_context=False, # type: bool
context=None, # type: Optional[PipelineContext]
use_fake_coders=False, # type: bool
default_environment=None # type: Optional[environments.Environment]
):
# type: (...) -> beam_runner_api_pb2.Pipeline
"""For internal use only; no backwards-compatibility guarantees."""
from apache_beam.runners import pipeline_context
if context is None:
context = pipeline_context.PipelineContext(
use_fake_coders=use_fake_coders,
component_id_map=self.component_id_map,
default_environment=default_environment)
elif default_environment is not None:
raise ValueError(
'Only one of context or default_environment may be specified.')
# The RunnerAPI spec requires certain transforms and side-inputs to have KV
# inputs (and corresponding outputs).
# Currently we only upgrade to KV pairs. If there is a need for more
# general shapes, potential conflicts will have to be resolved.
# We also only handle single-input, and (for fixing the output) single
# output, which is sufficient.
# Also marks such values as requiring deterministic key coders.
deterministic_key_coders = not self._options.view_as(
TypeOptions).allow_non_deterministic_key_coders
class ForceKvInputTypes(PipelineVisitor):
def enter_composite_transform(self, transform_node):
# type: (AppliedPTransform) -> None
self.visit_transform(transform_node)
def visit_transform(self, transform_node):
# type: (AppliedPTransform) -> None
if not transform_node.transform:
return
if transform_node.transform.runner_api_requires_keyed_input():
pcoll = transform_node.inputs[0]
pcoll.element_type = typehints.coerce_to_kv_type(
pcoll.element_type, transform_node.full_label)
pcoll.requires_deterministic_key_coder = (
deterministic_key_coders and transform_node.full_label)
if len(transform_node.outputs) == 1:
# The runner often has expectations about the output types as well.
output, = transform_node.outputs.values()
if not output.element_type:
output.element_type = transform_node.transform.infer_output_type(
pcoll.element_type)
if (isinstance(output.element_type,
typehints.TupleHint.TupleConstraint) and
len(output.element_type.tuple_types) == 2 and
pcoll.element_type.tuple_types[0] ==
output.element_type.tuple_types[0]):
output.requires_deterministic_key_coder = (
deterministic_key_coders and transform_node.full_label)
for side_input in transform_node.transform.side_inputs:
if side_input.requires_keyed_input():
side_input.pvalue.element_type = typehints.coerce_to_kv_type(
side_input.pvalue.element_type,
transform_node.full_label,
side_input_producer=side_input.pvalue.producer.full_label)
side_input.pvalue.requires_deterministic_key_coder = (
deterministic_key_coders and transform_node.full_label)
self.visit(ForceKvInputTypes())
# Mutates context; placing inline would force dependence on
# argument evaluation order.
root_transform_id = context.transforms.get_id(self._root_transform())
proto = beam_runner_api_pb2.Pipeline(
root_transform_ids=[root_transform_id],
components=context.to_runner_api(),
requirements=context.requirements(),
display_data=DisplayData('', self._display_data).to_proto())
proto.components.transforms[root_transform_id].unique_name = (
root_transform_id)
self.merge_compatible_environments(proto)
if return_context:
return proto, context # type: ignore # too complicated for now
else:
return proto
[docs] @staticmethod
def merge_compatible_environments(proto):
"""Tries to minimize the number of distinct environments by merging
those that are compatible (currently defined as identical).
Mutates proto as contexts may have references to proto.components.
"""
common.merge_common_environments(proto, inplace=True)
[docs] @staticmethod
def from_runner_api(
proto, # type: beam_runner_api_pb2.Pipeline
runner, # type: PipelineRunner
options, # type: PipelineOptions
return_context=False, # type: bool
):
# type: (...) -> Pipeline
"""For internal use only; no backwards-compatibility guarantees."""
p = Pipeline(
runner=runner,
options=options,
display_data={str(ix): d
for ix, d in enumerate(proto.display_data)})
from apache_beam.runners import pipeline_context
context = pipeline_context.PipelineContext(
proto.components, requirements=proto.requirements)
if proto.root_transform_ids:
root_transform_id, = proto.root_transform_ids
p.transforms_stack = [context.transforms.get_by_id(root_transform_id)]
else:
p.transforms_stack = [AppliedPTransform(None, None, '', None)]
# TODO(robertwb): These are only needed to continue construction. Omit?
p.applied_labels = {
t.unique_name
for t in proto.components.transforms.values()
}
for id in proto.components.pcollections:
pcollection = context.pcollections.get_by_id(id)
pcollection.pipeline = p
if not pcollection.producer:
raise ValueError('No producer for %s' % id)
# Inject PBegin input where necessary.
from apache_beam.io.iobase import Read
from apache_beam.transforms.core import Create
has_pbegin = [Read, Create]
for id in proto.components.transforms:
transform = context.transforms.get_by_id(id)
if not transform.inputs and transform.transform.__class__ in has_pbegin:
transform.main_inputs = {'None': pvalue.PBegin(p)}
if return_context:
return p, context # type: ignore # too complicated for now
else:
return p
class PipelineVisitor(object):
"""For internal use only; no backwards-compatibility guarantees.
Visitor pattern class used to traverse a DAG of transforms
(used internally by Pipeline for bookkeeping purposes).
"""
def visit_value(self, value, producer_node):
# type: (pvalue.PValue, AppliedPTransform) -> None
"""Callback for visiting a PValue in the pipeline DAG.
Args:
value: PValue visited (typically a PCollection instance).
producer_node: AppliedPTransform object whose transform produced the
pvalue.
"""
pass
def visit_transform(self, transform_node):
# type: (AppliedPTransform) -> None
"""Callback for visiting a transform leaf node in the pipeline DAG."""
pass
def enter_composite_transform(self, transform_node):
# type: (AppliedPTransform) -> None
"""Callback for entering traversal of a composite transform node."""
pass
def leave_composite_transform(self, transform_node):
# type: (AppliedPTransform) -> None
"""Callback for leaving traversal of a composite transform node."""
pass
class ExternalTransformFinder(PipelineVisitor):
"""Looks for any external transforms in the pipeline and if found records
it.
"""
def __init__(self):
self._contains_external_transforms = False
@staticmethod
def contains_external_transforms(pipeline):
visitor = ExternalTransformFinder()
pipeline.visit(visitor)
return visitor._contains_external_transforms
def _perform_exernal_transform_test(self, transform):
if not transform:
return
from apache_beam.transforms import ExternalTransform
if isinstance(transform, ExternalTransform):
self._contains_external_transforms = True
def visit_transform(self, transform_node):
# type: (AppliedPTransform) -> None
self._perform_exernal_transform_test(transform_node.transform)
def enter_composite_transform(self, transform_node):
# type: (AppliedPTransform) -> None
# Python SDK object graph may represent an external transform that is a leaf
# of the pipeline graph as a composite without sub-transforms.
# Note that this visitor is just used to identify pipelines with external
# transforms. A Runner API pipeline proto generated from the Pipeline object
# will include external sub-transform.
self._perform_exernal_transform_test(transform_node.transform)
class AppliedPTransform(object):
"""For internal use only; no backwards-compatibility guarantees.
A transform node representing an instance of applying a PTransform
(used internally by Pipeline for bookeeping purposes).
"""
def __init__(
self,
parent, # type: Optional[AppliedPTransform]
transform, # type: Optional[ptransform.PTransform]
full_label, # type: str
main_inputs, # type: Optional[Mapping[str, Union[pvalue.PBegin, pvalue.PCollection]]]
environment_id=None, # type: Optional[str]
annotations=None, # type: Optional[Dict[str, bytes]]
):
# type: (...) -> None
self.parent = parent
self.transform = transform
# Note that we want the PipelineVisitor classes to use the full_label,
# inputs, side_inputs, and outputs fields from this instance instead of the
# ones of the PTransform instance associated with it. Doing this permits
# reusing PTransform instances in different contexts (apply() calls) without
# any interference. This is particularly useful for composite transforms.
self.full_label = full_label
self.main_inputs = dict(main_inputs or {})
self.side_inputs = tuple() if transform is None else transform.side_inputs
self.outputs = {} # type: Dict[Union[str, int, None], pvalue.PValue]
self.parts = [] # type: List[AppliedPTransform]
self.environment_id = environment_id if environment_id else None # type: Optional[str]
# We may need to merge the hints with environment-provided hints here
# once environment is a first-class citizen in Beam graph and we have
# access to actual environment, not just an id.
self.resource_hints = dict(
transform.get_resource_hints()) if transform else {
} # type: Dict[str, bytes]
if annotations is None and transform:
def annotation_to_bytes(key, a: Any) -> bytes:
if isinstance(a, bytes):
return a
elif isinstance(a, str):
return a.encode('ascii')
elif isinstance(a, message.Message):
return a.SerializeToString()
else:
raise TypeError(
'Unknown annotation type %r (type %s) for %s' % (a, type(a), key))
annotations = {
key: annotation_to_bytes(key, a)
for key,
a in transform.annotations().items()
}
self.annotations = annotations
@property
def inputs(self):
return tuple(self.main_inputs.values())
def __repr__(self):
# type: () -> str
return "%s(%s, %s)" % (
self.__class__.__name__, self.full_label, type(self.transform).__name__)
def replace_output(
self,
output, # type: Union[pvalue.PValue, pvalue.DoOutputsTuple]
tag=None # type: Union[str, int, None]
):
# type: (...) -> None
"""Replaces the output defined by the given tag with the given output.
Args:
output: replacement output
tag: tag of the output to be replaced.
"""
if isinstance(output, pvalue.DoOutputsTuple):
self.replace_output(output[output._main_tag])
elif isinstance(output, pvalue.PValue):
self.outputs[tag] = output
elif isinstance(output, dict):
for output_tag, out in output.items():
self.outputs[output_tag] = out
else:
raise TypeError("Unexpected output type: %s" % output)
# Importing locally to prevent circular dependency issues.
from apache_beam.transforms import external
if isinstance(self.transform, external.ExternalTransform):
self.transform.replace_named_outputs(self.named_outputs())
def replace_inputs(self, main_inputs):
self.main_inputs = main_inputs
# Importing locally to prevent circular dependency issues.
from apache_beam.transforms import external
if isinstance(self.transform, external.ExternalTransform):
self.transform.replace_named_inputs(self.named_inputs())
def replace_side_inputs(self, side_inputs):
self.side_inputs = side_inputs
# Importing locally to prevent circular dependency issues.
from apache_beam.transforms import external
if isinstance(self.transform, external.ExternalTransform):
self.transform.replace_named_inputs(self.named_inputs())
def add_output(
self,
output, # type: Union[pvalue.DoOutputsTuple, pvalue.PValue]
tag # type: Union[str, int, None]
):
# type: (...) -> None
if isinstance(output, pvalue.DoOutputsTuple):
self.add_output(output[tag], tag)
elif isinstance(output, pvalue.PValue):
assert tag not in self.outputs
self.outputs[tag] = output
else:
raise TypeError("Unexpected output type: %s" % output)
def add_part(self, part):
# type: (AppliedPTransform) -> None
assert isinstance(part, AppliedPTransform)
part._merge_outer_resource_hints()
self.parts.append(part)
def is_composite(self):
# type: () -> bool
"""Returns whether this is a composite transform.
A composite transform has parts (inner transforms) or isn't the
producer for any of its outputs. (An example of a transform that
is not a producer is one that returns its inputs instead.)
"""
return bool(self.parts) or all(
pval.producer is not self for pval in self.outputs.values())
def visit(
self,
visitor, # type: PipelineVisitor
pipeline, # type: Pipeline
visited # type: Set[pvalue.PValue]
):
# type: (...) -> None
"""Visits all nodes reachable from the current node."""
for in_pval in self.inputs:
if in_pval not in visited and not isinstance(in_pval, pvalue.PBegin):
if in_pval.producer is not None:
in_pval.producer.visit(visitor, pipeline, visited)
# The value should be visited now since we visit outputs too.
assert in_pval in visited, in_pval
# Visit side inputs.
for side_input in self.side_inputs:
if isinstance(side_input, pvalue.AsSideInput) \
and side_input.pvalue not in visited:
pval = side_input.pvalue # Unpack marker-object-wrapped pvalue.
if pval.producer is not None:
pval.producer.visit(visitor, pipeline, visited)
# The value should be visited now since we visit outputs too.
assert pval in visited
# TODO(silviuc): Is there a way to signal that we are visiting a side
# value? The issue is that the same PValue can be reachable through
# multiple paths and therefore it is not guaranteed that the value
# will be visited as a side value.
# Visit a composite or primitive transform.
if self.is_composite():
visitor.enter_composite_transform(self)
for part in self.parts:
part.visit(visitor, pipeline, visited)
visitor.leave_composite_transform(self)
else:
visitor.visit_transform(self)
# Visit the outputs (one or more). It is essential to mark as visited the
# tagged PCollections of the DoOutputsTuple object. A tagged PCollection is
# connected directly with its producer (a multi-output ParDo), but the
# output of such a transform is the containing DoOutputsTuple, not the
# PCollection inside it. Without the code below a tagged PCollection will
# not be marked as visited while visiting its producer.
for out_pval in self.outputs.values():
if isinstance(out_pval, pvalue.DoOutputsTuple):
pvals = (v for v in out_pval)
else:
pvals = (out_pval, )
for v in pvals:
if v not in visited:
visited.add(v)
visitor.visit_value(v, self)
def named_inputs(self):
# type: () -> Dict[str, pvalue.PValue]
if self.transform is None:
assert not self.main_inputs and not self.side_inputs
return {}
else:
named_inputs = self.transform._named_inputs(
self.main_inputs, self.side_inputs)
if not self.parts:
for name, pc_out in self.outputs.items():
if pc_out.producer is not self and pc_out not in named_inputs.values(
):
named_inputs[f'__implicit_input_{name}'] = pc_out
return named_inputs
def named_outputs(self):
# type: () -> Dict[str, pvalue.PCollection]
if self.transform is None:
assert not self.outputs
return {}
else:
return self.transform._named_outputs(self.outputs)
def to_runner_api(self, context):
# type: (PipelineContext) -> beam_runner_api_pb2.PTransform
# External transforms require more splicing than just setting the spec.
from apache_beam.transforms import external
if isinstance(self.transform, external.ExternalTransform):
# TODO(https://github.com/apache/beam/issues/18371): Support resource
# hints in XLang transforms. In particular, make sure hints on composites
# are properly propagated.
return self.transform.to_runner_api_transform(context, self.full_label)
def transform_to_runner_api(
transform, # type: Optional[ptransform.PTransform]
context # type: PipelineContext
):
# type: (...) -> Optional[beam_runner_api_pb2.FunctionSpec]
if transform is None:
return None
else:
# We only populate inputs information to ParDo in order to expose
# key_coder and window_coder to stateful DoFn.
if isinstance(transform, ParDo):
return transform.to_runner_api(
context,
has_parts=bool(self.parts),
named_inputs=self.named_inputs())
return transform.to_runner_api(context, has_parts=bool(self.parts))
# Iterate over inputs and outputs by sorted key order, so that ids are
# consistently generated for multiple runs of the same pipeline.
try:
transform_spec = transform_to_runner_api(self.transform, context)
except Exception as exn:
raise RuntimeError(f'Unable to translate {self.full_label}') from exn
environment_id = self.environment_id
transform_urn = transform_spec.urn if transform_spec else None
if (not environment_id and
(transform_urn not in Pipeline.runner_implemented_transforms())):
environment_id = context.get_environment_id_for_resource_hints(
self.resource_hints)
return beam_runner_api_pb2.PTransform(
unique_name=self.full_label,
spec=transform_spec,
subtransforms=[
context.transforms.get_id(part, label=part.full_label)
for part in self.parts
],
inputs={
tag: context.pcollections.get_id(pc)
for tag,
pc in sorted(self.named_inputs().items())
},
outputs={
tag: context.pcollections.get_id(out)
for tag,
out in sorted(self.named_outputs().items())
},
environment_id=environment_id,
annotations=self.annotations,
# TODO(https://github.com/apache/beam/issues/18012): Add display_data.
display_data=DisplayData.create_from(self.transform).to_proto()
if self.transform else None)
@staticmethod
def from_runner_api(
proto, # type: beam_runner_api_pb2.PTransform
context # type: PipelineContext
):
# type: (...) -> AppliedPTransform
if common_urns.primitives.PAR_DO.urn == proto.spec.urn:
# Preserving side input tags.
pardo_payload = (
proto_utils.parse_Bytes(
proto.spec.payload, beam_runner_api_pb2.ParDoPayload))
side_input_tags = list(pardo_payload.side_inputs.keys())
else:
pardo_payload = None
side_input_tags = []
main_inputs = {
tag: context.pcollections.get_by_id(id)
for (tag, id) in proto.inputs.items() if tag not in side_input_tags
}
transform = ptransform.PTransform.from_runner_api(proto, context)
if transform and proto.environment_id:
resource_hints = context.environments.get_by_id(
proto.environment_id).resource_hints()
if resource_hints:
transform._resource_hints = dict(resource_hints)
# Ordering is important here.
# TODO(https://github.com/apache/beam/issues/20136): use key, value pairs
# instead of depending on tags with index as a suffix.
indexed_side_inputs = [
(get_sideinput_index(tag), context.pcollections.get_by_id(id)) for tag,
id in proto.inputs.items() if tag in side_input_tags
]
side_inputs = [si for _, si in sorted(indexed_side_inputs)]
result = AppliedPTransform(
parent=None,
transform=transform,
full_label=proto.unique_name,
main_inputs=main_inputs,
environment_id=None,
annotations=proto.annotations)
if result.transform and result.transform.side_inputs:
for si, pcoll in zip(result.transform.side_inputs, side_inputs):
si.pvalue = pcoll
result.side_inputs = tuple(result.transform.side_inputs)
result.parts = []
for transform_id in proto.subtransforms:
part = context.transforms.get_by_id(transform_id)
part.parent = result
result.add_part(part)
result.outputs = {
None if tag == 'None' else tag: context.pcollections.get_by_id(id)
for tag,
id in proto.outputs.items()
}
# This annotation is expected by some runners.
if proto.spec.urn == common_urns.primitives.PAR_DO.urn:
result.transform.output_tags = set(proto.outputs.keys()).difference(
{'None'})
if not result.parts:
for tag, pcoll_id in proto.outputs.items():
if pcoll_id not in proto.inputs.values():
pc = context.pcollections.get_by_id(pcoll_id)
pc.producer = result
pc.tag = None if tag == 'None' else tag
return result
def _merge_outer_resource_hints(self):
if (self.parent is not None and self.parent.resource_hints):
self.resource_hints = merge_resource_hints(
outer_hints=self.parent.resource_hints,
inner_hints=self.resource_hints)
if self.resource_hints:
for part in self.parts:
part._merge_outer_resource_hints()
class ComponentIdMap(object):
"""A utility for assigning unique component ids to Beam components.
Component ID assignments are only guaranteed to be unique and consistent
within the scope of a ComponentIdMap instance.
"""
def __init__(self, namespace="ref"):
self.namespace = namespace
self._counters = defaultdict(lambda: 0) # type: Dict[type, int]
self._obj_to_id = {} # type: Dict[Any, str]
def get_or_assign(self, obj=None, obj_type=None, label=None):
if obj not in self._obj_to_id:
self._obj_to_id[obj] = self._unique_ref(obj, obj_type, label)
return self._obj_to_id[obj]
def _normalize(self, str_value):
str_value = unicodedata.normalize('NFC', str_value)
return re.sub(r'[^a-zA-Z0-9-_]+', '-', str_value)
def _unique_ref(self, obj=None, obj_type=None, label=None):
# Normalize, trim, and uniqify.
prefix = self._normalize(
'%s_%s_%s' %
(self.namespace, obj_type.__name__, label or type(obj).__name__))[0:100]
self._counters[obj_type] += 1
return '%s_%d' % (prefix, self._counters[obj_type])