def run_pipeline(self, pipeline): # Java has different expectations about coders # (windowed in Fn API, but *un*windowed in runner API), whereas the # FnApiRunner treats them consistently, so we must guard this. # See also BEAM-2717. proto_context = pipeline_context.PipelineContext( default_environment_url=self._docker_image) proto_pipeline = pipeline.to_runner_api(context=proto_context) if self._runner_api_address: for pcoll in proto_pipeline.components.pcollections.values(): if pcoll.coder_id not in proto_context.coders: 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 = self._create_job_service() prepare_response = job_service.Prepare( beam_job_api_pb2.PrepareJobRequest( job_name='job', pipeline=proto_pipeline)) run_response = job_service.Run( beam_job_api_pb2.RunJobRequest( preparation_id=prepare_response.preparation_id)) return PipelineResult(job_service, run_response.job_id)
def prepare(self, proto_pipeline): # type: (beam_runner_api_pb2.Pipeline) -> beam_job_api_pb2.PrepareJobResponse """Prepare the job on the job service""" return self.job_service.Prepare(beam_job_api_pb2.PrepareJobRequest( job_name='job', pipeline=proto_pipeline, pipeline_options=self.get_pipeline_options()), timeout=self.timeout)
def run(self, pipeline): job_service = self._get_job_service() prepare_response = job_service.Prepare( beam_job_api_pb2.PrepareJobRequest( job_name='job', pipeline=pipeline.to_runner_api())) run_response = job_service.Run( beam_job_api_pb2.RunJobRequest( preparation_id=prepare_response.preparation_id)) return PipelineResult(job_service, run_response.job_id)
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 test_end_to_end(self): job_service = local_job_service.LocalJobServicer() job_service.start_grpc_server() # this logic is taken roughly from PortableRunner.run_pipeline() # Prepare the job. prepare_response = job_service.Prepare( beam_job_api_pb2.PrepareJobRequest( job_name='job', pipeline=beam_runner_api_pb2.Pipeline())) channel = grpc.insecure_channel( prepare_response.artifact_staging_endpoint.url) retrieval_token = beam_artifact_api_pb2_grpc.ArtifactStagingServiceStub( channel).CommitManifest( beam_artifact_api_pb2.CommitManifestRequest( staging_session_token=prepare_response. staging_session_token, manifest=beam_artifact_api_pb2.Manifest())).retrieval_token channel.close() 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_results.append(next(state_stream)) state_stream = increment_iter(state_stream) message_stream = job_service.GetMessageStream( beam_job_api_pb2.JobMessagesRequest( job_id=prepare_response.preparation_id)) job_service.Run( beam_job_api_pb2.RunJobRequest( preparation_id=prepare_response.preparation_id, retrieval_token=retrieval_token)) state_results = list(state_stream) message_results = list(message_stream) expected_states = [ beam_job_api_pb2.JobState.STOPPED, beam_job_api_pb2.JobState.STARTING, beam_job_api_pb2.JobState.RUNNING, beam_job_api_pb2.JobState.DONE, ] self.assertEqual([s.state for s in state_results], expected_states) self.assertEqual([s.state_response.state for s in message_results], expected_states)
def run_pipeline(self, pipeline): # Java has different expectations about coders # (windowed in Fn API, but *un*windowed in runner API), whereas the # FnApiRunner treats them consistently, so we must guard this. # See also BEAM-2717. proto_context = pipeline_context.PipelineContext( default_environment_url=self._docker_image) proto_pipeline = pipeline.to_runner_api(context=proto_context) if self._runner_api_address: for pcoll in proto_pipeline.components.pcollections.values(): if pcoll.coder_id not in proto_context.coders: 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 = self._create_job_service() prepare_response = job_service.Prepare( beam_job_api_pb2.PrepareJobRequest(job_name='job', pipeline=proto_pipeline)) if prepare_response.artifact_staging_endpoint.url: # Must commit something to get a retrieval token, # committing empty manifest for now. # TODO(BEAM-3883): Actually stage required files. artifact_service = beam_artifact_api_pb2_grpc.ArtifactStagingServiceStub( grpc.insecure_channel( prepare_response.artifact_staging_endpoint.url)) commit_manifest = artifact_service.CommitManifest( beam_artifact_api_pb2.CommitManifestRequest( manifest=beam_artifact_api_pb2.Manifest(), staging_session_token=prepare_response. staging_session_token)) retrieval_token = commit_manifest.retrieval_token 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 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
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) # 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: # Must commit something to get a retrieval token, # committing empty manifest for now. # TODO(BEAM-3883): Actually stage required files. artifact_service = beam_artifact_api_pb2_grpc.ArtifactStagingServiceStub( grpc.insecure_channel( prepare_response.artifact_staging_endpoint.url)) commit_manifest = artifact_service.CommitManifest( beam_artifact_api_pb2.CommitManifestRequest( manifest=beam_artifact_api_pb2.Manifest(), staging_session_token=prepare_response. staging_session_token)) retrieval_token = commit_manifest.retrieval_token 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): # Java has different expectations about coders # (windowed in Fn API, but *un*windowed in runner API), whereas the # FnApiRunner treats them consistently, so we must guard this. # See also BEAM-2717. proto_pipeline, proto_context = pipeline.to_runner_api( return_context=True) if self._runner_api_address: for pcoll in proto_pipeline.components.pcollections.values(): if pcoll.coder_id not in proto_context.coders: 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) job_service = self._get_job_service() prepare_response = job_service.Prepare( beam_job_api_pb2.PrepareJobRequest(job_name='job', pipeline=proto_pipeline)) run_response = job_service.Run( beam_job_api_pb2.RunJobRequest( preparation_id=prepare_response.preparation_id)) 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 test_end_to_end(self, http_mock): with temp_name(suffix='fake.jar') as fake_jar: # Create the jar file with some trivial contents. with zipfile.ZipFile(fake_jar, 'w') as zip: with zip.open('FakeClass.class', 'w') as fout: fout.write(b'[original_contents]') job_server = flink_uber_jar_job_server.FlinkUberJarJobServer( 'http://flink', fake_jar) # Prepare the job. prepare_response = job_server.Prepare( beam_job_api_pb2.PrepareJobRequest( job_name='job', pipeline=beam_runner_api_pb2.Pipeline())) channel = grpc.insecure_channel( prepare_response.artifact_staging_endpoint.url) retrieval_token = beam_artifact_api_pb2_grpc.ArtifactStagingServiceStub( channel).CommitManifest( beam_artifact_api_pb2.CommitManifestRequest( staging_session_token=prepare_response. staging_session_token, manifest=beam_artifact_api_pb2.Manifest()) ).retrieval_token channel.close() # Now actually run the job. http_mock.post('http://flink/v1/jars/upload', json={'filename': '/path/to/jar/nonce'}) http_mock.post('http://flink/v1/jars/nonce/run', json={'jobid': 'some_job_id'}) job_server.Run( beam_job_api_pb2.RunJobRequest( preparation_id=prepare_response.preparation_id, retrieval_token=retrieval_token)) # Check the status until the job is "done" and get all error messages. http_mock.get('http://flink/v1/jobs/some_job_id/execution-result', [{ 'json': { 'status': { 'id': 'IN_PROGRESS' } } }, { 'json': { 'status': { 'id': 'IN_PROGRESS' } } }, { 'json': { 'status': { 'id': 'COMPLETED' } } }]) http_mock.get('http://flink/v1/jobs/some_job_id', json={'state': 'FINISHED'}) http_mock.delete('http://flink/v1/jars/nonce') state_stream = job_server.GetStateStream( beam_job_api_pb2.GetJobStateRequest( job_id=prepare_response.preparation_id)) self.assertEqual([s.state for s in state_stream], [ beam_job_api_pb2.JobState.RUNNING, beam_job_api_pb2.JobState.DONE ]) http_mock.get('http://flink/v1/jobs/some_job_id/exceptions', json={ 'all-exceptions': [{ 'exception': 'exc_text', 'timestamp': 0 }] }) message_stream = job_server.GetMessageStream( beam_job_api_pb2.JobMessagesRequest( job_id=prepare_response.preparation_id)) self.assertEqual([m for m in message_stream], [ beam_job_api_pb2.JobMessagesResponse( message_response=beam_job_api_pb2.JobMessage( message_id='message0', time='0', importance=beam_job_api_pb2.JobMessage. MessageImportance.JOB_MESSAGE_ERROR, message_text='exc_text')), beam_job_api_pb2.JobMessagesResponse( state_response=beam_job_api_pb2.GetJobStateResponse( state=beam_job_api_pb2.JobState.DONE)), ])
def test_end_to_end(self, http_mock): submission_id = "submission-id" worker_host_port = "workerhost:12345" worker_id = "worker-id" server_spark_version = "1.2.3" def spark_submission_status_response(state): return { 'json': { "action": "SubmissionStatusResponse", "driverState": state, "serverSparkVersion": server_spark_version, "submissionId": submission_id, "success": "true", "workerHostPort": worker_host_port, "workerId": worker_id } } with temp_name(suffix='fake.jar') as fake_jar: with zipfile.ZipFile(fake_jar, 'w') as zip: with zip.open('spark-version-info.properties', 'w') as fout: fout.write(b'version=4.5.6') options = pipeline_options.SparkRunnerOptions() options.spark_job_server_jar = fake_jar job_server = spark_uber_jar_job_server.SparkUberJarJobServer( 'http://host:6066', options) # Prepare the job. prepare_response = job_server.Prepare( beam_job_api_pb2.PrepareJobRequest( job_name='job', pipeline=beam_runner_api_pb2.Pipeline())) channel = grpc.insecure_channel( prepare_response.artifact_staging_endpoint.url) retrieval_token = beam_artifact_api_pb2_grpc.LegacyArtifactStagingServiceStub( channel).CommitManifest( beam_artifact_api_pb2.CommitManifestRequest( staging_session_token=prepare_response. staging_session_token, manifest=beam_artifact_api_pb2.Manifest()) ).retrieval_token channel.close() # Now actually run the job. http_mock.post( 'http://host:6066/v1/submissions/create', json={ "action": "CreateSubmissionResponse", "message": "Driver successfully submitted as submission-id", "serverSparkVersion": "1.2.3", "submissionId": "submission-id", "success": "true" }) job_server.Run( beam_job_api_pb2.RunJobRequest( preparation_id=prepare_response.preparation_id, retrieval_token=retrieval_token)) # Check the status until the job is "done" and get all error messages. http_mock.get( 'http://host:6066/v1/submissions/status/submission-id', [ spark_submission_status_response('RUNNING'), spark_submission_status_response('RUNNING'), { 'json': { "action": "SubmissionStatusResponse", "driverState": "ERROR", "message": "oops", "serverSparkVersion": "1.2.3", "submissionId": submission_id, "success": "true", "workerHostPort": worker_host_port, "workerId": worker_id } } ]) state_stream = job_server.GetStateStream( beam_job_api_pb2.GetJobStateRequest( job_id=prepare_response.preparation_id)) self.assertEqual([s.state for s in state_stream], [ beam_job_api_pb2.JobState.STOPPED, beam_job_api_pb2.JobState.RUNNING, beam_job_api_pb2.JobState.RUNNING, beam_job_api_pb2.JobState.FAILED ]) message_stream = job_server.GetMessageStream( beam_job_api_pb2.JobMessagesRequest( job_id=prepare_response.preparation_id)) def get_item(x): if x.HasField('message_response'): return x.message_response else: return x.state_response.state self.assertEqual([get_item(m) for m in message_stream], [ beam_job_api_pb2.JobState.STOPPED, beam_job_api_pb2.JobState.RUNNING, beam_job_api_pb2.JobMessage( message_id='message0', time='0', importance=beam_job_api_pb2.JobMessage.MessageImportance. JOB_MESSAGE_ERROR, message_text="oops"), beam_job_api_pb2.JobState.FAILED, ])