Source code for apache_beam.transforms.external

# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

"""Defines Transform whose expansion is implemented elsewhere.

No backward compatibility guarantees. Everything in this module is experimental.
# pytype: skip-file

from __future__ import absolute_import
from __future__ import print_function

import contextlib
import copy
import functools
import sys
import threading
from typing import ByteString
from typing import Dict

import grpc

from apache_beam import pvalue
from apache_beam.coders import RowCoder
from apache_beam.portability import common_urns
from apache_beam.portability.api import beam_artifact_api_pb2_grpc
from apache_beam.portability.api import beam_expansion_api_pb2
from apache_beam.portability.api import beam_expansion_api_pb2_grpc
from apache_beam.portability.api import beam_runner_api_pb2
from apache_beam.portability.api.external_transforms_pb2 import ExternalConfigurationPayload
from apache_beam.runners import pipeline_context
from apache_beam.runners.portability import artifact_service
from apache_beam.transforms import ptransform
from apache_beam.typehints.native_type_compatibility import convert_to_typing_type
from apache_beam.typehints.schemas import named_fields_to_schema
from apache_beam.typehints.schemas import named_tuple_from_schema
from apache_beam.typehints.schemas import named_tuple_to_schema
from apache_beam.typehints.trivial_inference import instance_to_type
from apache_beam.typehints.typehints import Union
from apache_beam.typehints.typehints import UnionConstraint
from apache_beam.utils import subprocess_server


def _is_optional_or_none(typehint):
  return (
      type(None) in typehint.union_types if isinstance(
          typehint, UnionConstraint) else typehint is type(None))

def _strip_optional(typehint):
  if not _is_optional_or_none(typehint):
    return typehint
  new_types = typehint.union_types.difference({type(None)})
  if len(new_types) == 1:
    return list(new_types)[0]
  return Union[new_types]

[docs]def iter_urns(coder, context=None): yield coder.to_runner_api_parameter(context)[0] for child in coder._get_component_coders(): for urn in iter_urns(child, context): yield urn
[docs]class PayloadBuilder(object): """ Abstract base class for building payloads to pass to ExternalTransform. """
[docs] def build(self): """ :return: ExternalConfigurationPayload """ raise NotImplementedError
[docs] def payload(self): """ The serialized ExternalConfigurationPayload :return: bytes """ return
[docs]class SchemaBasedPayloadBuilder(PayloadBuilder): """ Base class for building payloads based on a schema that provides type information for each configuration value to encode. """ def _get_named_tuple_instance(self): raise NotImplementedError()
[docs] def build(self): row = self._get_named_tuple_instance() schema = named_tuple_to_schema(type(row)) return ExternalConfigurationPayload( schema=schema, payload=RowCoder(schema).encode(row))
[docs]class ImplicitSchemaPayloadBuilder(SchemaBasedPayloadBuilder): """ Build a payload that generates a schema from the provided values. """ def __init__(self, values): self._values = values def _get_named_tuple_instance(self): # omit fields with value=None since we can't infer their type values = { key: value for key, value in self._values.items() if value is not None } # In python 2 named_fields_to_schema will not accept str because its # ambiguous. This converts str hints to ByteString recursively so its clear # we intend to use BYTES. # TODO(BEAM-7372): Remove coercion to ByteString def coerce_str_to_bytes(typ): if typ == str: return ByteString elif hasattr(typ, '__args__') and hasattr(typ, '__origin__'): # Create a new type rather than modifying the existing one typ = typ.__origin__[tuple(map(coerce_str_to_bytes, typ.__args__))] return typ if sys.version_info[0] >= 3: coerce_str_to_bytes = lambda x: x schema = named_fields_to_schema([( key, coerce_str_to_bytes(convert_to_typing_type(instance_to_type(value)))) for key, value in values.items()]) return named_tuple_from_schema(schema)(**values)
[docs]class NamedTupleBasedPayloadBuilder(SchemaBasedPayloadBuilder): """ Build a payload based on a NamedTuple schema. """ def __init__(self, tuple_instance): """ :param tuple_instance: an instance of a typing.NamedTuple """ super(NamedTupleBasedPayloadBuilder, self).__init__() self._tuple_instance = tuple_instance def _get_named_tuple_instance(self): return self._tuple_instance
[docs]class AnnotationBasedPayloadBuilder(SchemaBasedPayloadBuilder): """ Build a payload based on an external transform's type annotations. Supported in python 3 only. """ def __init__(self, transform, **values): """ :param transform: a PTransform instance or class. type annotations will be gathered from its __init__ method :param values: values to encode """ self._transform = transform self._values = values def _get_named_tuple_instance(self): schema = named_fields_to_schema([ (k, convert_to_typing_type(v)) for k, v in self._transform.__init__.__annotations__.items() if k in self._values ]) return named_tuple_from_schema(schema)(**self._values)
[docs]class DataclassBasedPayloadBuilder(SchemaBasedPayloadBuilder): """ Build a payload based on an external transform that uses dataclasses. Supported in python 3 only. """ def __init__(self, transform): """ :param transform: a dataclass-decorated PTransform instance from which to gather type annotations and values """ self._transform = transform def _get_named_tuple_instance(self): import dataclasses schema = named_fields_to_schema([ (, convert_to_typing_type(field.type)) for field in dataclasses.fields(self._transform) ]) return named_tuple_from_schema(schema)( **dataclasses.asdict(self._transform))
[docs]class ExternalTransform(ptransform.PTransform): """ External provides a cross-language transform via expansion services in foreign SDKs. Experimental; no backwards compatibility guarantees. """ _namespace_counter = 0 _namespace = threading.local() # type: ignore[assignment] _IMPULSE_PREFIX = 'impulse' def __init__(self, urn, payload, expansion_service=None): """Wrapper for an external transform with the given urn and payload. :param urn: the unique beam identifier for this transform :param payload: the payload, either as a byte string or a PayloadBuilder :param expansion_service: an expansion service implementing the beam ExpansionService protocol, either as an object with an Expand method or an address (as a str) to a grpc server that provides this method. """ expansion_service = expansion_service or DEFAULT_EXPANSION_SERVICE self._urn = urn self._payload = ( payload.payload() if isinstance(payload, PayloadBuilder) else payload) self._expansion_service = expansion_service self._namespace = self._fresh_namespace() self._inputs = {} # type: Dict[str, pvalue.PCollection] self._output = {} # type: Dict[str, pvalue.PCollection] def __post_init__(self, expansion_service): """ This will only be invoked if ExternalTransform is used as a base class for a class decorated with dataclasses.dataclass """ ExternalTransform.__init__( self, self.URN, DataclassBasedPayloadBuilder(self), expansion_service)
[docs] def default_label(self): return '%s(%s)' % (self.__class__.__name__, self._urn)
[docs] @classmethod def get_local_namespace(cls): return getattr(cls._namespace, 'value', 'external')
[docs] @classmethod @contextlib.contextmanager def outer_namespace(cls, namespace): prev = cls.get_local_namespace() cls._namespace.value = namespace yield cls._namespace.value = prev
@classmethod def _fresh_namespace(cls): # type: () -> str ExternalTransform._namespace_counter += 1 return '%s_%d' % (cls.get_local_namespace(), cls._namespace_counter)
[docs] def expand(self, pvalueish): # type: (pvalue.PCollection) -> pvalue.PCollection if isinstance(pvalueish, pvalue.PBegin): self._inputs = {} elif isinstance(pvalueish, (list, tuple)): self._inputs = {str(ix): pvalue for ix, pvalue in enumerate(pvalueish)} elif isinstance(pvalueish, dict): self._inputs = pvalueish else: self._inputs = {'input': pvalueish} pipeline = ( next(iter(self._inputs.values())).pipeline if self._inputs else pvalueish.pipeline) context = pipeline_context.PipelineContext( component_id_map=pipeline.component_id_map) transform_proto = beam_runner_api_pb2.PTransform( unique_name=pipeline._current_transform().full_label, spec=beam_runner_api_pb2.FunctionSpec( urn=self._urn, payload=self._payload)) for tag, pcoll in self._inputs.items(): transform_proto.inputs[tag] = context.pcollections.get_id(pcoll) # Conversion to/from proto assumes producers. # TODO: Possibly loosen this. context.transforms.put_proto( '%s_%s' % (self._IMPULSE_PREFIX, tag), beam_runner_api_pb2.PTransform( unique_name='%s_%s' % (self._IMPULSE_PREFIX, tag), spec=beam_runner_api_pb2.FunctionSpec( urn=common_urns.primitives.IMPULSE.urn), outputs={'out': transform_proto.inputs[tag]})) components = context.to_runner_api() request = beam_expansion_api_pb2.ExpansionRequest( components=components, namespace=self._namespace, # type: ignore # mypy thinks self._namespace is threading.local transform=transform_proto) with self._service() as service: response = service.Expand(request) if response.error: raise RuntimeError(response.error) self._expanded_components = response.components if any(env.dependencies for env in self._expanded_components.environments.values()): self._expanded_components = self._resolve_artifacts( self._expanded_components, service.artifact_service(), pipeline.local_tempdir) self._expanded_transform = response.transform self._expanded_requirements = response.requirements result_context = pipeline_context.PipelineContext(response.components) def fix_output(pcoll, tag): pcoll.pipeline = pipeline pcoll.tag = tag return pcoll self._outputs = { tag: fix_output(result_context.pcollections.get_by_id(pcoll_id), tag) for tag, pcoll_id in self._expanded_transform.outputs.items() } return self._output_to_pvalueish(self._outputs)
@contextlib.contextmanager def _service(self): if isinstance(self._expansion_service, str): channel_options = [("grpc.max_receive_message_length", -1), ("grpc.max_send_message_length", -1)] if hasattr(grpc, 'local_channel_credentials'): # Some environments may not support insecure channels. Hence use a # secure channel with local credentials here. # TODO: update this to support secure non-local channels. channel_factory_fn = functools.partial( grpc.secure_channel, self._expansion_service, grpc.local_channel_credentials(), options=channel_options) else: # local_channel_credentials is an experimental API which is unsupported # by older versions of grpc which may be pulled in due to other project # dependencies. channel_factory_fn = functools.partial( grpc.insecure_channel, self._expansion_service, options=channel_options) with channel_factory_fn() as channel: yield ExpansionAndArtifactRetrievalStub(channel) elif hasattr(self._expansion_service, 'Expand'): yield self._expansion_service else: with self._expansion_service as stub: yield stub def _resolve_artifacts(self, components, service, dest): for env in components.environments.values(): if env.dependencies: resolved = list( artifact_service.resolve_artifacts(env.dependencies, service, dest)) del env.dependencies[:] env.dependencies.extend(resolved) return components def _output_to_pvalueish(self, output_dict): if len(output_dict) == 1: return next(iter(output_dict.values())) else: return output_dict
[docs] def to_runner_api_transform(self, context, full_label): pcoll_renames = {} renamed_tag_seen = False for tag, pcoll in self._inputs.items(): if tag not in self._expanded_transform.inputs: if renamed_tag_seen: raise RuntimeError( 'Ambiguity due to non-preserved tags: %s vs %s' % ( sorted(self._expanded_transform.inputs.keys()), sorted(self._inputs.keys()))) else: renamed_tag_seen = True tag, = self._expanded_transform.inputs.keys() pcoll_renames[self._expanded_transform.inputs[tag]] = ( context.pcollections.get_id(pcoll)) for tag, pcoll in self._outputs.items(): pcoll_renames[self._expanded_transform.outputs[tag]] = ( context.pcollections.get_id(pcoll)) def _equivalent(coder1, coder2): return coder1 == coder2 or _normalize(coder1) == _normalize(coder2) def _normalize(coder_proto): normalized = copy.copy(coder_proto) normalized.spec.environment_id = '' # TODO(robertwb): Normalize components as well. return normalized for id, proto in self._expanded_components.coders.items(): if id.startswith(self._namespace): context.coders.put_proto(id, proto) elif id in context.coders: if not _equivalent(context.coders._id_to_proto[id], proto): raise RuntimeError( 'Re-used coder id: %s\n%s\n%s' % (id, context.coders._id_to_proto[id], proto)) else: context.coders.put_proto(id, proto) for id, proto in self._expanded_components.windowing_strategies.items(): if id.startswith(self._namespace): context.windowing_strategies.put_proto(id, proto) for id, proto in self._expanded_components.environments.items(): if id.startswith(self._namespace): context.environments.put_proto(id, proto) for id, proto in self._expanded_components.pcollections.items(): id = pcoll_renames.get(id, id) if id not in context.pcollections._id_to_obj.keys(): context.pcollections.put_proto(id, proto) for id, proto in self._expanded_components.transforms.items(): if id.startswith(self._IMPULSE_PREFIX): # Our fake inputs. continue assert id.startswith(self._namespace), (id, self._namespace) new_proto = beam_runner_api_pb2.PTransform( unique_name=proto.unique_name, spec=proto.spec, subtransforms=proto.subtransforms, inputs={ tag: pcoll_renames.get(pcoll, pcoll) for tag, pcoll in proto.inputs.items() }, outputs={ tag: pcoll_renames.get(pcoll, pcoll) for tag, pcoll in proto.outputs.items() }, environment_id=proto.environment_id) context.transforms.put_proto(id, new_proto) for requirement in self._expanded_requirements: context.add_requirement(requirement) return beam_runner_api_pb2.PTransform( unique_name=full_label, spec=self._expanded_transform.spec, subtransforms=self._expanded_transform.subtransforms, inputs={ tag: pcoll_renames.get(pcoll, pcoll) for tag, pcoll in self._expanded_transform.inputs.items() }, outputs={ tag: pcoll_renames.get(pcoll, pcoll) for tag, pcoll in self._expanded_transform.outputs.items() }, environment_id=self._expanded_transform.environment_id)
[docs]class ExpansionAndArtifactRetrievalStub( beam_expansion_api_pb2_grpc.ExpansionServiceStub): def __init__(self, channel, **kwargs): self._channel = channel self._kwargs = kwargs super(ExpansionAndArtifactRetrievalStub, self).__init__(channel, **kwargs)
[docs] def artifact_service(self): return beam_artifact_api_pb2_grpc.ArtifactRetrievalServiceStub( self._channel, **self._kwargs)
[docs]class JavaJarExpansionService(object): """An expansion service based on an Java Jar file. This can be passed into an ExternalTransform as the expansion_service argument which will spawn a subprocess using this jar to expand the transform. """ def __init__(self, path_to_jar, extra_args=None): if extra_args is None: extra_args = ['{{PORT}}'] self._path_to_jar = path_to_jar self._extra_args = extra_args self._service_count = 0 def __enter__(self): if self._service_count == 0: self._path_to_jar = subprocess_server.JavaJarServer.local_jar( self._path_to_jar) # Consider memoizing these servers (with some timeout). self._service_provider = subprocess_server.JavaJarServer( ExpansionAndArtifactRetrievalStub, self._path_to_jar, self._extra_args) self._service = self._service_provider.__enter__() self._service_count += 1 return self._service def __exit__(self, *args): self._service_count -= 1 if self._service_count == 0: self._service_provider.__exit__(*args)
[docs]class BeamJarExpansionService(JavaJarExpansionService): """An expansion service based on an Beam Java Jar file. Attempts to use a locally-built copy of the jar based on the gradle target, if it exists, otherwise attempts to download and cache the released artifact corresponding to this version of Beam from the apache maven repository. """ def __init__(self, gradle_target, extra_args=None, gradle_appendix=None): path_to_jar = subprocess_server.JavaJarServer.path_to_beam_jar( gradle_target, gradle_appendix) super(BeamJarExpansionService, self).__init__(path_to_jar, extra_args)
[docs]def memoize(func): cache = {} def wrapper(*args): if args not in cache: cache[args] = func(*args) return cache[args] return wrapper