def _stage_files(self, files):
"""Utility method to stage files.
Args:
files: a list of tuples of the form [(local_name, remote_name),...]
describing the name of the artifacts in local temp folder and desired
name in staging location.
"""
server = grpc.server(UnboundedThreadPoolExecutor())
staging_service = TestLocalFileSystemArtifactStagingServiceServicer(
self._remote_dir)
beam_artifact_api_pb2_grpc.add_ArtifactStagingServiceServicer_to_server(
staging_service, server)
test_port = server.add_insecure_port('[::]:0')
server.start()
stager = portable_stager.PortableStager(
artifact_service_channel=grpc.insecure_channel('localhost:%s' %
test_port),
staging_session_token='token')
for from_file, to_file in files:
stager.stage_artifact(local_path_to_artifact=os.path.join(
self._temp_dir, from_file),
artifact_name=to_file)
stager.commit_manifest()
return staging_service.manifest.artifact, staging_service.retrieval_tokens
def stage(self, pipeline, artifact_staging_endpoint,
staging_session_token):
# type: (...) -> Optional[Any]
"""Stage artifacts"""
if artifact_staging_endpoint:
stager = portable_stager.PortableStager(
grpc.insecure_channel(artifact_staging_endpoint),
staging_session_token)
resources = []
for _, env in pipeline.components.environments.items():
for dep in env.dependencies:
if dep.type_urn != common_urns.artifact_types.FILE.urn:
raise RuntimeError('unsupported artifact type %s' %
dep.type_urn)
if dep.role_urn != common_urns.artifact_roles.STAGING_TO.urn:
raise RuntimeError('unsupported role type %s' %
dep.role_urn)
type_payload = beam_runner_api_pb2.ArtifactFilePayload.FromString(
dep.type_payload)
role_payload = \
beam_runner_api_pb2.ArtifactStagingToRolePayload.FromString(
dep.role_payload)
resources.append(
(type_payload.path, role_payload.staged_name))
stager.stage_job_resources(resources, staging_location='')
retrieval_token = stager.commit_manifest()
else:
retrieval_token = None
return retrieval_token
def run_pipeline(self, pipeline):
docker_image = (
pipeline.options.view_as(PortableOptions).harness_docker_image
or self.default_docker_image())
job_endpoint = pipeline.options.view_as(PortableOptions).job_endpoint
if not job_endpoint:
raise ValueError(
'job_endpoint should be provided while creating runner.')
proto_context = pipeline_context.PipelineContext(
default_environment_url=docker_image)
proto_pipeline = pipeline.to_runner_api(context=proto_context)
if not self.is_embedded_fnapi_runner:
# Java has different expectations about coders
# (windowed in Fn API, but *un*windowed in runner API), whereas the
# embedded FnApiRunner treats them consistently, so we must guard this
# for now, until FnApiRunner is fixed.
# See also BEAM-2717.
for pcoll in proto_pipeline.components.pcollections.values():
if pcoll.coder_id not in proto_context.coders:
# This is not really a coder id, but a pickled coder.
coder = coders.registry.get_coder(
pickler.loads(pcoll.coder_id))
pcoll.coder_id = proto_context.coders.get_id(coder)
proto_context.coders.populate_map(proto_pipeline.components.coders)
# Some runners won't detect the GroupByKey transform unless it has no
# subtransforms. Remove all sub-transforms until BEAM-4605 is resolved.
for _, transform_proto in list(
proto_pipeline.components.transforms.items()):
if transform_proto.spec.urn == common_urns.primitives.GROUP_BY_KEY.urn:
for sub_transform in transform_proto.subtransforms:
del proto_pipeline.components.transforms[sub_transform]
del transform_proto.subtransforms[:]
job_service = beam_job_api_pb2_grpc.JobServiceStub(
grpc.insecure_channel(job_endpoint))
prepare_response = job_service.Prepare(
beam_job_api_pb2.PrepareJobRequest(job_name='job',
pipeline=proto_pipeline))
if prepare_response.artifact_staging_endpoint.url:
stager = portable_stager.PortableStager(
grpc.insecure_channel(
prepare_response.artifact_staging_endpoint.url),
prepare_response.staging_session_token)
retrieval_token, _ = stager.stage_job_resources(
pipeline._options, staging_location='')
else:
retrieval_token = None
run_response = job_service.Run(
beam_job_api_pb2.RunJobRequest(
preparation_id=prepare_response.preparation_id,
retrieval_token=retrieval_token))
return PipelineResult(job_service, run_response.job_id)
def stage(self, artifact_staging_endpoint, staging_session_token):
# type: (...) -> Optional[Any]
"""Stage artifacts"""
if artifact_staging_endpoint:
stager = portable_stager.PortableStager(
grpc.insecure_channel(artifact_staging_endpoint),
staging_session_token)
retrieval_token, _ = stager.stage_job_resources(
self.options, staging_location='')
else:
retrieval_token = None
return retrieval_token
def _stage_via_legacy_service(
self, pipeline, artifact_staging_channel, staging_session_token):
stager = portable_stager.PortableStager(
artifact_staging_channel, staging_session_token)
resources = []
for _, env in pipeline.components.environments.items():
for dep in env.dependencies:
if dep.type_urn != common_urns.artifact_types.FILE.urn:
raise RuntimeError('unsupported artifact type %s' % dep.type_urn)
if dep.role_urn != common_urns.artifact_roles.STAGING_TO.urn:
raise RuntimeError('unsupported role type %s' % dep.role_urn)
type_payload = beam_runner_api_pb2.ArtifactFilePayload.FromString(
dep.type_payload)
role_payload = \
beam_runner_api_pb2.ArtifactStagingToRolePayload.FromString(
dep.role_payload)
resources.append((type_payload.path, role_payload.staged_name))
stager.stage_job_resources(resources, staging_location='')
return stager.commit_manifest()
def run_pipeline(self, pipeline):
portable_options = pipeline.options.view_as(PortableOptions)
job_endpoint = portable_options.job_endpoint
# TODO: https://issues.apache.org/jira/browse/BEAM-5525
# portable runner specific default
if pipeline.options.view_as(SetupOptions).sdk_location == 'default':
pipeline.options.view_as(SetupOptions).sdk_location = 'container'
if not job_endpoint:
docker = DockerizedJobServer()
job_endpoint = docker.start()
proto_context = pipeline_context.PipelineContext(
default_environment=PortableRunner._create_environment(
portable_options))
proto_pipeline = pipeline.to_runner_api(context=proto_context)
# Some runners won't detect the GroupByKey transform unless it has no
# subtransforms. Remove all sub-transforms until BEAM-4605 is resolved.
for _, transform_proto in list(
proto_pipeline.components.transforms.items()):
if transform_proto.spec.urn == common_urns.primitives.GROUP_BY_KEY.urn:
for sub_transform in transform_proto.subtransforms:
del proto_pipeline.components.transforms[sub_transform]
del transform_proto.subtransforms[:]
# TODO: Define URNs for options.
# convert int values: https://issues.apache.org/jira/browse/BEAM-5509
options = {'beam:option:' + k + ':v1': (str(v) if type(v) == int else v)
for k, v in pipeline._options.get_all_options().items()
if v is not None}
channel = grpc.insecure_channel(job_endpoint)
grpc.channel_ready_future(channel).result()
job_service = beam_job_api_pb2_grpc.JobServiceStub(channel)
# Sends the PrepareRequest but retries in case the channel is not ready
def send_prepare_request(max_retries=5):
num_retries = 0
while True:
try:
# This reports channel is READY but connections may fail
# Seems to be only an issue on Mac with port forwardings
grpc.channel_ready_future(channel).result()
return job_service.Prepare(
beam_job_api_pb2.PrepareJobRequest(
job_name='job', pipeline=proto_pipeline,
pipeline_options=job_utils.dict_to_struct(options)))
except grpc._channel._Rendezvous as e:
num_retries += 1
if num_retries > max_retries:
raise e
prepare_response = send_prepare_request()
if prepare_response.artifact_staging_endpoint.url:
stager = portable_stager.PortableStager(
grpc.insecure_channel(prepare_response.artifact_staging_endpoint.url),
prepare_response.staging_session_token)
retrieval_token, _ = stager.stage_job_resources(
pipeline._options,
staging_location='')
else:
retrieval_token = None
run_response = job_service.Run(
beam_job_api_pb2.RunJobRequest(
preparation_id=prepare_response.preparation_id,
retrieval_token=retrieval_token))
return PipelineResult(job_service, run_response.job_id)
def run_pipeline(self, pipeline, options):
portable_options = options.view_as(PortableOptions)
# TODO: https://issues.apache.org/jira/browse/BEAM-5525
# portable runner specific default
if options.view_as(SetupOptions).sdk_location == 'default':
options.view_as(SetupOptions).sdk_location = 'container'
# This is needed as we start a worker server if one is requested
# but none is provided.
if portable_options.environment_type == 'LOOPBACK':
use_loopback_process_worker = options.view_as(
DebugOptions).lookup_experiment('use_loopback_process_worker',
False)
portable_options.environment_config, server = (
worker_pool_main.BeamFnExternalWorkerPoolServicer.start(
state_cache_size=sdk_worker_main._get_state_cache_size(
options),
use_process=use_loopback_process_worker))
cleanup_callbacks = [functools.partial(server.stop, 1)]
else:
cleanup_callbacks = []
proto_pipeline = pipeline.to_runner_api(
default_environment=PortableRunner._create_environment(
portable_options))
# Some runners won't detect the GroupByKey transform unless it has no
# subtransforms. Remove all sub-transforms until BEAM-4605 is resolved.
for _, transform_proto in list(
proto_pipeline.components.transforms.items()):
if transform_proto.spec.urn == common_urns.primitives.GROUP_BY_KEY.urn:
for sub_transform in transform_proto.subtransforms:
del proto_pipeline.components.transforms[sub_transform]
del transform_proto.subtransforms[:]
# Preemptively apply combiner lifting, until all runners support it.
# These optimizations commute and are idempotent.
pre_optimize = options.view_as(DebugOptions).lookup_experiment(
'pre_optimize', 'lift_combiners').lower()
if not options.view_as(StandardOptions).streaming:
flink_known_urns = frozenset([
common_urns.composites.RESHUFFLE.urn,
common_urns.primitives.IMPULSE.urn,
common_urns.primitives.FLATTEN.urn,
common_urns.primitives.GROUP_BY_KEY.urn
])
if pre_optimize == 'none':
pass
elif pre_optimize == 'all':
proto_pipeline = fn_api_runner_transforms.optimize_pipeline(
proto_pipeline,
phases=[
fn_api_runner_transforms.
annotate_downstream_side_inputs,
fn_api_runner_transforms.
annotate_stateful_dofns_as_roots,
fn_api_runner_transforms.fix_side_input_pcoll_coders,
fn_api_runner_transforms.lift_combiners,
fn_api_runner_transforms.expand_sdf,
fn_api_runner_transforms.fix_flatten_coders,
# fn_api_runner_transforms.sink_flattens,
fn_api_runner_transforms.greedily_fuse,
fn_api_runner_transforms.read_to_impulse,
fn_api_runner_transforms.extract_impulse_stages,
fn_api_runner_transforms.remove_data_plane_ops,
fn_api_runner_transforms.sort_stages
],
known_runner_urns=flink_known_urns)
else:
phases = []
for phase_name in pre_optimize.split(','):
# For now, these are all we allow.
if phase_name in 'lift_combiners':
phases.append(
getattr(fn_api_runner_transforms, phase_name))
else:
raise ValueError(
'Unknown or inapplicable phase for pre_optimize: %s'
% phase_name)
proto_pipeline = fn_api_runner_transforms.optimize_pipeline(
proto_pipeline,
phases=phases,
known_runner_urns=flink_known_urns,
partial=True)
job_service = self.create_job_service(options)
# fetch runner options from job service
# retries in case the channel is not ready
def send_options_request(max_retries=5):
num_retries = 0
while True:
try:
# This reports channel is READY but connections may fail
# Seems to be only an issue on Mac with port forwardings
return job_service.DescribePipelineOptions(
beam_job_api_pb2.DescribePipelineOptionsRequest(),
timeout=portable_options.job_server_timeout)
except grpc.FutureTimeoutError:
# no retry for timeout errors
raise
except grpc._channel._Rendezvous as e:
num_retries += 1
if num_retries > max_retries:
raise e
time.sleep(1)
options_response = send_options_request()
def add_runner_options(parser):
for option in options_response.options:
try:
# no default values - we don't want runner options
# added unless they were specified by the user
add_arg_args = {
'action': 'store',
'help': option.description
}
if option.type == beam_job_api_pb2.PipelineOptionType.BOOLEAN:
add_arg_args['action'] = 'store_true'\
if option.default_value != 'true' else 'store_false'
elif option.type == beam_job_api_pb2.PipelineOptionType.INTEGER:
add_arg_args['type'] = int
elif option.type == beam_job_api_pb2.PipelineOptionType.ARRAY:
add_arg_args['action'] = 'append'
parser.add_argument("--%s" % option.name, **add_arg_args)
except Exception as e:
# ignore runner options that are already present
# only in this case is duplicate not treated as error
if 'conflicting option string' not in str(e):
raise
_LOGGER.debug("Runner option '%s' was already added" %
option.name)
all_options = options.get_all_options(
add_extra_args_fn=add_runner_options)
# TODO: Define URNs for options.
# convert int values: https://issues.apache.org/jira/browse/BEAM-5509
p_options = {
'beam:option:' + k + ':v1': (str(v) if type(v) == int else v)
for k, v in all_options.items() if v is not None
}
prepare_request = beam_job_api_pb2.PrepareJobRequest(
job_name='job',
pipeline=proto_pipeline,
pipeline_options=job_utils.dict_to_struct(p_options))
_LOGGER.debug('PrepareJobRequest: %s', prepare_request)
prepare_response = job_service.Prepare(
prepare_request, timeout=portable_options.job_server_timeout)
artifact_endpoint = (portable_options.artifact_endpoint
if portable_options.artifact_endpoint else
prepare_response.artifact_staging_endpoint.url)
if artifact_endpoint:
stager = portable_stager.PortableStager(
grpc.insecure_channel(artifact_endpoint),
prepare_response.staging_session_token)
retrieval_token, _ = stager.stage_job_resources(
options, staging_location='')
else:
retrieval_token = None
try:
state_stream = job_service.GetStateStream(
beam_job_api_pb2.GetJobStateRequest(
job_id=prepare_response.preparation_id),
timeout=portable_options.job_server_timeout)
# If there's an error, we don't always get it until we try to read.
# Fortunately, there's always an immediate current state published.
state_stream = itertools.chain([next(state_stream)], state_stream)
message_stream = job_service.GetMessageStream(
beam_job_api_pb2.JobMessagesRequest(
job_id=prepare_response.preparation_id),
timeout=portable_options.job_server_timeout)
except Exception:
# TODO(BEAM-6442): Unify preparation_id and job_id for all runners.
state_stream = message_stream = None
# Run the job and wait for a result, we don't set a timeout here because
# it may take a long time for a job to complete and streaming
# jobs currently never return a response.
run_response = job_service.Run(
beam_job_api_pb2.RunJobRequest(
preparation_id=prepare_response.preparation_id,
retrieval_token=retrieval_token))
if state_stream is None:
state_stream = job_service.GetStateStream(
beam_job_api_pb2.GetJobStateRequest(
job_id=run_response.job_id))
message_stream = job_service.GetMessageStream(
beam_job_api_pb2.JobMessagesRequest(
job_id=run_response.job_id))
return PipelineResult(job_service, run_response.job_id, message_stream,
state_stream, cleanup_callbacks)
def run_pipeline(self, pipeline, options):
portable_options = options.view_as(PortableOptions)
job_endpoint = portable_options.job_endpoint
# TODO: https://issues.apache.org/jira/browse/BEAM-5525
# portable runner specific default
if options.view_as(SetupOptions).sdk_location == 'default':
options.view_as(SetupOptions).sdk_location = 'container'
if not job_endpoint:
# TODO Provide a way to specify a container Docker URL
# https://issues.apache.org/jira/browse/BEAM-6328
docker = DockerizedJobServer()
job_endpoint = docker.start()
# This is needed as we start a worker server if one is requested
# but none is provided.
if portable_options.environment_type == 'LOOPBACK':
portable_options.environment_config, server = (
BeamFnExternalWorkerPoolServicer.start(
sdk_worker_main._get_worker_count(options)))
cleanup_callbacks = [functools.partial(server.stop, 1)]
else:
cleanup_callbacks = []
proto_pipeline = pipeline.to_runner_api(
default_environment=PortableRunner._create_environment(
portable_options))
# Some runners won't detect the GroupByKey transform unless it has no
# subtransforms. Remove all sub-transforms until BEAM-4605 is resolved.
for _, transform_proto in list(
proto_pipeline.components.transforms.items()):
if transform_proto.spec.urn == common_urns.primitives.GROUP_BY_KEY.urn:
for sub_transform in transform_proto.subtransforms:
del proto_pipeline.components.transforms[sub_transform]
del transform_proto.subtransforms[:]
# Preemptively apply combiner lifting, until all runners support it.
# This optimization is idempotent.
if not options.view_as(StandardOptions).streaming:
stages = list(
fn_api_runner_transforms.leaf_transform_stages(
proto_pipeline.root_transform_ids,
proto_pipeline.components))
stages = fn_api_runner_transforms.lift_combiners(
stages,
fn_api_runner_transforms.TransformContext(
proto_pipeline.components))
proto_pipeline = fn_api_runner_transforms.with_stages(
proto_pipeline, stages)
# TODO: Define URNs for options.
# convert int values: https://issues.apache.org/jira/browse/BEAM-5509
p_options = {
'beam:option:' + k + ':v1': (str(v) if type(v) == int else v)
for k, v in options.get_all_options().items() if v is not None
}
channel = grpc.insecure_channel(job_endpoint)
grpc.channel_ready_future(channel).result()
job_service = beam_job_api_pb2_grpc.JobServiceStub(channel)
# Sends the PrepareRequest but retries in case the channel is not ready
def send_prepare_request(max_retries=5):
num_retries = 0
while True:
try:
# This reports channel is READY but connections may fail
# Seems to be only an issue on Mac with port forwardings
grpc.channel_ready_future(channel).result()
return job_service.Prepare(
beam_job_api_pb2.PrepareJobRequest(
job_name='job',
pipeline=proto_pipeline,
pipeline_options=job_utils.dict_to_struct(
p_options)))
except grpc._channel._Rendezvous as e:
num_retries += 1
if num_retries > max_retries:
raise e
prepare_response = send_prepare_request()
if prepare_response.artifact_staging_endpoint.url:
stager = portable_stager.PortableStager(
grpc.insecure_channel(
prepare_response.artifact_staging_endpoint.url),
prepare_response.staging_session_token)
retrieval_token, _ = stager.stage_job_resources(
options, staging_location='')
else:
retrieval_token = None
try:
state_stream = job_service.GetStateStream(
beam_job_api_pb2.GetJobStateRequest(
job_id=prepare_response.preparation_id))
# If there's an error, we don't always get it until we try to read.
# Fortunately, there's always an immediate current state published.
state_stream = itertools.chain([next(state_stream)], state_stream)
message_stream = job_service.GetMessageStream(
beam_job_api_pb2.JobMessagesRequest(
job_id=prepare_response.preparation_id))
except Exception:
# TODO(BEAM-6442): Unify preparation_id and job_id for all runners.
state_stream = message_stream = None
# Run the job and wait for a result.
run_response = job_service.Run(
beam_job_api_pb2.RunJobRequest(
preparation_id=prepare_response.preparation_id,
retrieval_token=retrieval_token))
if state_stream is None:
state_stream = job_service.GetStateStream(
beam_job_api_pb2.GetJobStateRequest(
job_id=run_response.job_id))
message_stream = job_service.GetMessageStream(
beam_job_api_pb2.JobMessagesRequest(
job_id=run_response.job_id))
return PipelineResult(job_service, run_response.job_id, message_stream,
state_stream, cleanup_callbacks)
def run_pipeline(self, pipeline):
portable_options = pipeline.options.view_as(PortableOptions)
job_endpoint = portable_options.job_endpoint
if not job_endpoint:
docker = DockerizedJobServer()
job_endpoint = docker.start()
proto_context = pipeline_context.PipelineContext(
default_environment=PortableRunner._create_environment(
portable_options))
proto_pipeline = pipeline.to_runner_api(context=proto_context)
if not self.is_embedded_fnapi_runner:
# Java has different expectations about coders
# (windowed in Fn API, but *un*windowed in runner API), whereas the
# embedded FnApiRunner treats them consistently, so we must guard this
# for now, until FnApiRunner is fixed.
# See also BEAM-2717.
for pcoll in proto_pipeline.components.pcollections.values():
if pcoll.coder_id not in proto_context.coders:
# This is not really a coder id, but a pickled coder.
coder = coders.registry.get_coder(
pickler.loads(pcoll.coder_id))
pcoll.coder_id = proto_context.coders.get_id(coder)
proto_context.coders.populate_map(proto_pipeline.components.coders)
# Some runners won't detect the GroupByKey transform unless it has no
# subtransforms. Remove all sub-transforms until BEAM-4605 is resolved.
for _, transform_proto in list(
proto_pipeline.components.transforms.items()):
if transform_proto.spec.urn == common_urns.primitives.GROUP_BY_KEY.urn:
for sub_transform in transform_proto.subtransforms:
del proto_pipeline.components.transforms[sub_transform]
del transform_proto.subtransforms[:]
# TODO: Define URNs for options.
# convert int values: https://issues.apache.org/jira/browse/BEAM-5509
options = {
'beam:option:' + k + ':v1': (str(v) if type(v) == int else v)
for k, v in pipeline._options.get_all_options().items()
if v is not None
}
channel = grpc.insecure_channel(job_endpoint)
grpc.channel_ready_future(channel).result()
job_service = beam_job_api_pb2_grpc.JobServiceStub(channel)
# Sends the PrepareRequest but retries in case the channel is not ready
def send_prepare_request(max_retries=5):
num_retries = 0
while True:
try:
# This reports channel is READY but connections may fail
# Seems to be only an issue on Mac with port forwardings
grpc.channel_ready_future(channel).result()
return job_service.Prepare(
beam_job_api_pb2.PrepareJobRequest(
job_name='job',
pipeline=proto_pipeline,
pipeline_options=job_utils.dict_to_struct(
options)))
except grpc._channel._Rendezvous as e:
num_retries += 1
if num_retries > max_retries:
raise e
prepare_response = send_prepare_request()
if prepare_response.artifact_staging_endpoint.url:
stager = portable_stager.PortableStager(
grpc.insecure_channel(
prepare_response.artifact_staging_endpoint.url),
prepare_response.staging_session_token)
retrieval_token, _ = stager.stage_job_resources(
pipeline._options, staging_location='')
else:
retrieval_token = None
run_response = job_service.Run(
beam_job_api_pb2.RunJobRequest(
preparation_id=prepare_response.preparation_id,
retrieval_token=retrieval_token))
return PipelineResult(job_service, run_response.job_id)
