Source code for apache_beam.transforms.external
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"""Defines Transform whose expansion is implemented elsewhere.
No backward compatibility guarantees. Everything in this module is experimental.
"""
from __future__ import absolute_import
from __future__ import print_function
import contextlib
import copy
import threading
from apache_beam import pvalue
from apache_beam.portability import common_urns
from apache_beam.portability.api import beam_expansion_api_pb2
from apache_beam.portability.api import beam_runner_api_pb2
from apache_beam.runners import pipeline_context
from apache_beam.transforms import ptransform
# Protect against environments where grpc is not available.
# pylint: disable=wrong-import-order, wrong-import-position, ungrouped-imports
try:
import grpc
from apache_beam.portability.api import beam_expansion_api_pb2_grpc
except ImportError:
grpc = None
# pylint: enable=wrong-import-order, wrong-import-position, ungrouped-imports
[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()
_EXPANDED_TRANSFORM_UNIQUE_NAME = 'root'
_IMPULSE_PREFIX = 'impulse'
def __init__(self, urn, payload, endpoint):
if grpc is None and isinstance(endpoint, str):
raise NotImplementedError('Grpc required for external transforms.')
# TODO: Start an endpoint given an environment?
self._urn = urn
self._payload = payload
self._endpoint = endpoint
self._namespace = self._fresh_namespace()
[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):
ExternalTransform._namespace_counter += 1
return '%s_%d' % (cls.get_local_namespace(), cls._namespace_counter)
[docs] def expand(self, pvalueish):
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()
transform_proto = beam_runner_api_pb2.PTransform(
unique_name=self._EXPANDED_TRANSFORM_UNIQUE_NAME,
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,
transform=transform_proto)
if isinstance(self._endpoint, str):
with grpc.insecure_channel(self._endpoint) as channel:
response = beam_expansion_api_pb2_grpc.ExpansionServiceStub(
channel).Expand(request)
else:
response = self._endpoint.Expand(request, None)
if response.error:
raise RuntimeError(response.error)
self._expanded_components = response.components
self._expanded_transform = response.transform
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)
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=full_label + proto.unique_name[
len(self._EXPANDED_TRANSFORM_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()})
context.transforms.put_proto(id, new_proto)
return beam_runner_api_pb2.PTransform(
unique_name=full_label,
spec=self._expanded_transform.spec,
subtransforms=self._expanded_transform.subtransforms,
inputs=self._expanded_transform.inputs,
outputs={
tag: pcoll_renames.get(pcoll, pcoll)
for tag, pcoll in self._expanded_transform.outputs.items()})
[docs]def memoize(func):
cache = {}
def wrapper(*args):
if args not in cache:
cache[args] = func(*args)
return cache[args]
return wrapper