| # |
| # 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. |
| # |
| |
| from __future__ import absolute_import |
| |
| import functools |
| import itertools |
| import logging |
| import sys |
| import threading |
| import time |
| |
| import grpc |
| |
| from apache_beam import version as beam_version |
| from apache_beam.metrics import metric |
| from apache_beam.metrics.execution import MetricResult |
| from apache_beam.options.pipeline_options import DebugOptions |
| from apache_beam.options.pipeline_options import PortableOptions |
| from apache_beam.options.pipeline_options import SetupOptions |
| from apache_beam.options.pipeline_options import StandardOptions |
| from apache_beam.portability import common_urns |
| from apache_beam.portability.api import beam_job_api_pb2 |
| from apache_beam.runners import runner |
| from apache_beam.runners.job import utils as job_utils |
| from apache_beam.runners.portability import fn_api_runner_transforms |
| from apache_beam.runners.portability import job_server |
| from apache_beam.runners.portability import portable_metrics |
| from apache_beam.runners.portability import portable_stager |
| from apache_beam.runners.worker import sdk_worker_main |
| from apache_beam.runners.worker import worker_pool_main |
| from apache_beam.transforms import environments |
| |
| __all__ = ['PortableRunner'] |
| |
| MESSAGE_LOG_LEVELS = { |
| beam_job_api_pb2.JobMessage.MESSAGE_IMPORTANCE_UNSPECIFIED: logging.INFO, |
| beam_job_api_pb2.JobMessage.JOB_MESSAGE_DEBUG: logging.DEBUG, |
| beam_job_api_pb2.JobMessage.JOB_MESSAGE_DETAILED: logging.DEBUG, |
| beam_job_api_pb2.JobMessage.JOB_MESSAGE_BASIC: logging.INFO, |
| beam_job_api_pb2.JobMessage.JOB_MESSAGE_WARNING: logging.WARNING, |
| beam_job_api_pb2.JobMessage.JOB_MESSAGE_ERROR: logging.ERROR, |
| } |
| |
| TERMINAL_STATES = [ |
| beam_job_api_pb2.JobState.DONE, |
| beam_job_api_pb2.JobState.DRAINED, |
| beam_job_api_pb2.JobState.FAILED, |
| beam_job_api_pb2.JobState.CANCELLED, |
| ] |
| |
| ENV_TYPE_ALIASES = {'LOOPBACK': 'EXTERNAL'} |
| |
| |
| class PortableRunner(runner.PipelineRunner): |
| """ |
| Experimental: No backward compatibility guaranteed. |
| A BeamRunner that executes Python pipelines via the Beam Job API. |
| |
| This runner is a stub and does not run the actual job. |
| This runner schedules the job on a job service. The responsibility of |
| running and managing the job lies with the job service used. |
| """ |
| def __init__(self): |
| self._dockerized_job_server = None |
| |
| @staticmethod |
| def default_docker_image(): |
| sdk_version = beam_version.__version__ |
| version_suffix = '.'.join([str(i) for i in sys.version_info[0:2]]) |
| logging.warning('Make sure that locally built Python SDK docker image ' |
| 'has Python %d.%d interpreter.' % ( |
| sys.version_info[0], sys.version_info[1])) |
| |
| image = ('apachebeam/python{version_suffix}_sdk:{tag}'.format( |
| version_suffix=version_suffix, tag=sdk_version)) |
| logging.info( |
| 'Using Python SDK docker image: %s. If the image is not ' |
| 'available at local, we will try to pull from hub.docker.com' |
| % (image)) |
| return image |
| |
| @staticmethod |
| def _create_environment(options): |
| portable_options = options.view_as(PortableOptions) |
| # Do not set a Runner. Otherwise this can cause problems in Java's |
| # PipelineOptions, i.e. ClassNotFoundException, if the corresponding Runner |
| # does not exist in the Java SDK. In portability, the entry point is clearly |
| # defined via the JobService. |
| portable_options.view_as(StandardOptions).runner = None |
| environment_type = portable_options.environment_type |
| if not environment_type: |
| environment_urn = common_urns.environments.DOCKER.urn |
| elif environment_type.startswith('beam:env:'): |
| environment_urn = environment_type |
| else: |
| # e.g. handle LOOPBACK -> EXTERNAL |
| environment_type = ENV_TYPE_ALIASES.get(environment_type, |
| environment_type) |
| try: |
| environment_urn = getattr(common_urns.environments, |
| environment_type).urn |
| except AttributeError: |
| raise ValueError( |
| 'Unknown environment type: %s' % environment_type) |
| |
| env_class = environments.Environment.get_env_cls_from_urn(environment_urn) |
| return env_class.from_options(portable_options) |
| |
| def default_job_server(self, portable_options): |
| # TODO Provide a way to specify a container Docker URL |
| # https://issues.apache.org/jira/browse/BEAM-6328 |
| if not self._dockerized_job_server: |
| self._dockerized_job_server = job_server.StopOnExitJobServer( |
| job_server.DockerizedJobServer()) |
| return self._dockerized_job_server |
| |
| def create_job_service(self, options): |
| job_endpoint = options.view_as(PortableOptions).job_endpoint |
| if job_endpoint: |
| if job_endpoint == 'embed': |
| server = job_server.EmbeddedJobServer() |
| else: |
| job_server_timeout = options.view_as(PortableOptions).job_server_timeout |
| server = job_server.ExternalJobServer(job_endpoint, job_server_timeout) |
| else: |
| server = self.default_job_server(options) |
| return server.start() |
| |
| 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( |
| sdk_worker_main._get_worker_count(options), |
| 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 |
| logging.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)) |
| logging.debug('PrepareJobRequest: %s', prepare_request) |
| prepare_response = job_service.Prepare( |
| prepare_request, |
| timeout=portable_options.job_server_timeout) |
| 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), |
| 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) |
| |
| |
| class PortableMetrics(metric.MetricResults): |
| def __init__(self, job_metrics_response): |
| metrics = job_metrics_response.metrics |
| self.attempted = portable_metrics.from_monitoring_infos(metrics.attempted) |
| self.committed = portable_metrics.from_monitoring_infos(metrics.committed) |
| |
| @staticmethod |
| def _combine(committed, attempted, filter): |
| all_keys = set(committed.keys()) | set(attempted.keys()) |
| return [ |
| MetricResult(key, committed.get(key), attempted.get(key)) |
| for key in all_keys |
| if metric.MetricResults.matches(filter, key) |
| ] |
| |
| def query(self, filter=None): |
| counters, distributions, gauges = [ |
| self._combine(x, y, filter) |
| for x, y in zip(self.committed, self.attempted) |
| ] |
| |
| return {self.COUNTERS: counters, |
| self.DISTRIBUTIONS: distributions, |
| self.GAUGES: gauges} |
| |
| |
| class PipelineResult(runner.PipelineResult): |
| |
| def __init__(self, job_service, job_id, message_stream, state_stream, |
| cleanup_callbacks=()): |
| super(PipelineResult, self).__init__(beam_job_api_pb2.JobState.UNSPECIFIED) |
| self._job_service = job_service |
| self._job_id = job_id |
| self._messages = [] |
| self._message_stream = message_stream |
| self._state_stream = state_stream |
| self._cleanup_callbacks = cleanup_callbacks |
| self._metrics = None |
| |
| def cancel(self): |
| try: |
| self._job_service.Cancel(beam_job_api_pb2.CancelJobRequest( |
| job_id=self._job_id)) |
| finally: |
| self._cleanup() |
| |
| @property |
| def state(self): |
| runner_api_state = self._job_service.GetState( |
| beam_job_api_pb2.GetJobStateRequest(job_id=self._job_id)).state |
| self._state = self._runner_api_state_to_pipeline_state(runner_api_state) |
| return self._state |
| |
| @staticmethod |
| def _runner_api_state_to_pipeline_state(runner_api_state): |
| return getattr(runner.PipelineState, |
| beam_job_api_pb2.JobState.Enum.Name(runner_api_state)) |
| |
| @staticmethod |
| def _pipeline_state_to_runner_api_state(pipeline_state): |
| return beam_job_api_pb2.JobState.Enum.Value(pipeline_state) |
| |
| def metrics(self): |
| if not self._metrics: |
| |
| job_metrics_response = self._job_service.GetJobMetrics( |
| beam_job_api_pb2.GetJobMetricsRequest(job_id=self._job_id)) |
| |
| self._metrics = PortableMetrics(job_metrics_response) |
| return self._metrics |
| |
| def _last_error_message(self): |
| # Filter only messages with the "message_response" and error messages. |
| messages = [m.message_response for m in self._messages |
| if m.HasField('message_response')] |
| error_messages = [m for m in messages |
| if m.importance == |
| beam_job_api_pb2.JobMessage.JOB_MESSAGE_ERROR] |
| if error_messages: |
| return error_messages[-1].message_text |
| else: |
| return 'unknown error' |
| |
| def wait_until_finish(self): |
| |
| def read_messages(): |
| for message in self._message_stream: |
| if message.HasField('message_response'): |
| logging.log( |
| MESSAGE_LOG_LEVELS[message.message_response.importance], |
| "%s", |
| message.message_response.message_text) |
| else: |
| logging.info( |
| "Job state changed to %s", |
| self._runner_api_state_to_pipeline_state( |
| message.state_response.state)) |
| self._messages.append(message) |
| |
| t = threading.Thread(target=read_messages, name='wait_until_finish_read') |
| t.daemon = True |
| t.start() |
| |
| try: |
| for state_response in self._state_stream: |
| self._state = self._runner_api_state_to_pipeline_state( |
| state_response.state) |
| if state_response.state in TERMINAL_STATES: |
| # Wait for any last messages. |
| t.join(10) |
| break |
| if self._state != runner.PipelineState.DONE: |
| raise RuntimeError( |
| 'Pipeline %s failed in state %s: %s' % ( |
| self._job_id, self._state, self._last_error_message())) |
| return self._state |
| finally: |
| self._cleanup() |
| |
| def _cleanup(self): |
| has_exception = None |
| for callback in self._cleanup_callbacks: |
| try: |
| callback() |
| except Exception: |
| has_exception = True |
| self._cleanup_callbacks = () |
| if has_exception: |
| raise |
