sdks/python/apache_beam/runners/dataflow/internal/apiclient.py

# # 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. # """ For internal use only. No backwards compatibility guarantees. Dataflow client utility functions.""" # pytype: skip-file # To regenerate the client: # pip install google-apitools[cli] # gen_client --discovery_url=cloudbuild.v1 --overwrite \ # --outdir=apache_beam/runners/dataflow/internal/clients/cloudbuild \ # --root_package=. client import ast import codecs from functools import partial import getpass import hashlib import io import json import logging import os import random import string from packaging import version import re import sys import time import traceback import warnings from copy import copy from datetime import datetime from datetime import timezone from apitools.base.py import encoding from apitools.base.py import exceptions from apache_beam import version as beam_version from apache_beam.internal.gcp.auth import get_service_credentials from apache_beam.internal.gcp.json_value import to_json_value from apache_beam.internal.http_client import get_new_http from apache_beam.io.filesystems import FileSystems from apache_beam.io.gcp.gcsfilesystem import GCSFileSystem from apache_beam.io.gcp.gcsio import create_storage_client from apache_beam.options.pipeline_options import DebugOptions from apache_beam.options.pipeline_options import GoogleCloudOptions from apache_beam.options.pipeline_options import StandardOptions from apache_beam.options.pipeline_options import WorkerOptions from apache_beam.portability import common_urns from apache_beam.portability.api import beam_runner_api_pb2 from apache_beam.runners.dataflow.internal import names from apache_beam.runners.dataflow.internal.clients import dataflow from apache_beam.runners.internal import names as shared_names from apache_beam.runners.pipeline_utils import validate_pipeline_graph from apache_beam.runners.portability.stager import Stager from apache_beam.transforms import DataflowDistributionCounter from apache_beam.transforms import cy_combiners from apache_beam.transforms.display import DisplayData from apache_beam.transforms.environments import is_apache_beam_container from apache_beam.utils import retry from apache_beam.utils import proto_utils # Environment version information. It is passed to the service during a # a job submission and is used by the service to establish what features # are expected by the workers. _LEGACY_ENVIRONMENT_MAJOR_VERSION = '8' _FNAPI_ENVIRONMENT_MAJOR_VERSION = '8' _LOGGER = logging.getLogger(__name__) _PYTHON_VERSIONS_SUPPORTED_BY_DATAFLOW = ['3.9', '3.10', '3.11', '3.12'] class Environment(object): """Wrapper for a dataflow Environment protobuf.""" def __init__( self, packages, options, environment_version, proto_pipeline_staged_url, proto_pipeline=None): self.standard_options = options.view_as(StandardOptions) self.google_cloud_options = options.view_as(GoogleCloudOptions) self.worker_options = options.view_as(WorkerOptions) self.debug_options = options.view_as(DebugOptions) self.pipeline_url = proto_pipeline_staged_url self.proto = dataflow.Environment() self.proto.clusterManagerApiService = GoogleCloudOptions.COMPUTE_API_SERVICE self.proto.dataset = '{}/cloud_dataflow'.format( GoogleCloudOptions.BIGQUERY_API_SERVICE) self.proto.tempStoragePrefix = ( self.google_cloud_options.temp_location.replace( 'gs:/', GoogleCloudOptions.STORAGE_API_SERVICE)) if self.worker_options.worker_region: self.proto.workerRegion = self.worker_options.worker_region if self.worker_options.worker_zone: self.proto.workerZone = self.worker_options.worker_zone # User agent information. self.proto.userAgent = dataflow.Environment.UserAgentValue() self.local = 'localhost' in self.google_cloud_options.dataflow_endpoint self._proto_pipeline = proto_pipeline if self.google_cloud_options.service_account_email: self.proto.serviceAccountEmail = ( self.google_cloud_options.service_account_email) if self.google_cloud_options.dataflow_kms_key: self.proto.serviceKmsKeyName = self.google_cloud_options.dataflow_kms_key self.proto.userAgent.additionalProperties.extend([ dataflow.Environment.UserAgentValue.AdditionalProperty( key='name', value=to_json_value(self._get_python_sdk_name())), dataflow.Environment.UserAgentValue.AdditionalProperty( key='version', value=to_json_value(beam_version.__version__)) ]) # Version information. self.proto.version = dataflow.Environment.VersionValue() _verify_interpreter_version_is_supported(options) if self.standard_options.streaming: job_type = 'FNAPI_STREAMING' else: job_type = 'FNAPI_BATCH' self.proto.version.additionalProperties.extend([ dataflow.Environment.VersionValue.AdditionalProperty( key='job_type', value=to_json_value(job_type)), dataflow.Environment.VersionValue.AdditionalProperty( key='major', value=to_json_value(environment_version)) ]) # TODO: Use enumerated type instead of strings for job types. if job_type.startswith('FNAPI_'): self.debug_options.experiments = self.debug_options.experiments or [] debug_options_experiments = self.debug_options.experiments # Add use_multiple_sdk_containers flag if it's not already present. Do not # add the flag if 'no_use_multiple_sdk_containers' is present. # TODO: Cleanup use_multiple_sdk_containers once we deprecate Python SDK # till version 2.4. if ('use_multiple_sdk_containers' not in debug_options_experiments and 'no_use_multiple_sdk_containers' not in debug_options_experiments): debug_options_experiments.append('use_multiple_sdk_containers') # FlexRS if self.google_cloud_options.flexrs_goal == 'COST_OPTIMIZED': self.proto.flexResourceSchedulingGoal = ( dataflow.Environment.FlexResourceSchedulingGoalValueValuesEnum. FLEXRS_COST_OPTIMIZED) elif self.google_cloud_options.flexrs_goal == 'SPEED_OPTIMIZED': self.proto.flexResourceSchedulingGoal = ( dataflow.Environment.FlexResourceSchedulingGoalValueValuesEnum. FLEXRS_SPEED_OPTIMIZED) # Experiments if self.debug_options.experiments: for experiment in self.debug_options.experiments: self.proto.experiments.append(experiment) # Worker pool(s) information. package_descriptors = [] for package in packages: package_descriptors.append( dataflow.Package( location='%s/%s' % ( self.google_cloud_options.staging_location.replace( 'gs:/', GoogleCloudOptions.STORAGE_API_SERVICE), package), name=package)) pool = dataflow.WorkerPool( kind='local' if self.local else 'harness', packages=package_descriptors, taskrunnerSettings=dataflow.TaskRunnerSettings( parallelWorkerSettings=dataflow.WorkerSettings( baseUrl=GoogleCloudOptions.DATAFLOW_ENDPOINT, servicePath=self.google_cloud_options.dataflow_endpoint))) pool.autoscalingSettings = dataflow.AutoscalingSettings() # Set worker pool options received through command line. if self.worker_options.num_workers: pool.numWorkers = self.worker_options.num_workers if self.worker_options.max_num_workers: pool.autoscalingSettings.maxNumWorkers = ( self.worker_options.max_num_workers) if self.worker_options.autoscaling_algorithm: values_enum = dataflow.AutoscalingSettings.AlgorithmValueValuesEnum pool.autoscalingSettings.algorithm = { 'NONE': values_enum.AUTOSCALING_ALGORITHM_NONE, 'THROUGHPUT_BASED': values_enum.AUTOSCALING_ALGORITHM_BASIC, }.get(self.worker_options.autoscaling_algorithm) if self.worker_options.machine_type: pool.machineType = self.worker_options.machine_type if self.worker_options.disk_size_gb: pool.diskSizeGb = self.worker_options.disk_size_gb if self.worker_options.disk_type: pool.diskType = self.worker_options.disk_type if self.worker_options.zone: pool.zone = self.worker_options.zone if self.worker_options.network: pool.network = self.worker_options.network if self.worker_options.subnetwork: pool.subnetwork = self.worker_options.subnetwork # Setting worker pool sdk_harness_container_images option for supported # Dataflow workers. environments_to_use = self._get_environments_from_tranforms() # Adding container images for other SDKs that may be needed for # cross-language pipelines. for id, environment in environments_to_use: if environment.urn != common_urns.environments.DOCKER.urn: raise Exception( 'Dataflow can only execute pipeline steps in Docker environments.' ' Received %r.' % environment) environment_payload = proto_utils.parse_Bytes( environment.payload, beam_runner_api_pb2.DockerPayload) container_image_url = environment_payload.container_image container_image = dataflow.SdkHarnessContainerImage() container_image.containerImage = container_image_url container_image.useSingleCorePerContainer = ( common_urns.protocols.MULTI_CORE_BUNDLE_PROCESSING.urn not in environment.capabilities) container_image.environmentId = id for capability in environment.capabilities: container_image.capabilities.append(capability) pool.sdkHarnessContainerImages.append(container_image) if not pool.sdkHarnessContainerImages: pool.workerHarnessContainerImage = ( get_container_image_from_options(options)) elif len(pool.sdkHarnessContainerImages) == 1: # Dataflow expects a value here when there is only one environment. pool.workerHarnessContainerImage = ( pool.sdkHarnessContainerImages[0].containerImage) if self.debug_options.number_of_worker_harness_threads: pool.numThreadsPerWorker = ( self.debug_options.number_of_worker_harness_threads) if self.worker_options.use_public_ips is not None: if self.worker_options.use_public_ips: pool.ipConfiguration = ( dataflow.WorkerPool.IpConfigurationValueValuesEnum.WORKER_IP_PUBLIC) else: pool.ipConfiguration = ( dataflow.WorkerPool.IpConfigurationValueValuesEnum.WORKER_IP_PRIVATE ) if self.standard_options.streaming: # Use separate data disk for streaming. disk = dataflow.Disk() if self.local: disk.diskType = 'local' if self.worker_options.disk_type: disk.diskType = self.worker_options.disk_type pool.dataDisks.append(disk) self.proto.workerPools.append(pool) sdk_pipeline_options = options.get_all_options(retain_unknown_options=True) if sdk_pipeline_options: self.proto.sdkPipelineOptions = ( dataflow.Environment.SdkPipelineOptionsValue()) options_dict = { k: v for k, v in sdk_pipeline_options.items() if v is not None } options_dict["pipelineUrl"] = proto_pipeline_staged_url # Don't pass impersonate_service_account through to the harness. # Though impersonation should start a job, the workers should # not try to modify their credentials. options_dict.pop('impersonate_service_account', None) self.proto.sdkPipelineOptions.additionalProperties.append( dataflow.Environment.SdkPipelineOptionsValue.AdditionalProperty( key='options', value=to_json_value(options_dict))) dd = DisplayData.create_from_options(options) items = [item.get_dict() for item in dd.items] self.proto.sdkPipelineOptions.additionalProperties.append( dataflow.Environment.SdkPipelineOptionsValue.AdditionalProperty( key='display_data', value=to_json_value(items))) if self.google_cloud_options.dataflow_service_options: for option in self.google_cloud_options.dataflow_service_options: self.proto.serviceOptions.append(option) if self.google_cloud_options.enable_hot_key_logging: self.proto.debugOptions = dataflow.DebugOptions(enableHotKeyLogging=True) def _get_environments_from_tranforms(self): if not self._proto_pipeline: return [] environment_ids = set( transform.environment_id for transform in self._proto_pipeline.components.transforms.values() if transform.environment_id) return [(id, self._proto_pipeline.components.environments[id]) for id in environment_ids] def _get_python_sdk_name(self): python_version = '%d.%d' % (sys.version_info[0], sys.version_info[1]) return 'Apache Beam Python %s SDK' % python_version class Job(object): """Wrapper for a dataflow Job protobuf.""" def __str__(self): def encode_shortstrings(input_buffer, errors='strict'): """Encoder (from Unicode) that suppresses long base64 strings.""" original_len = len(input_buffer) if original_len > 150: if self.base64_str_re.match(input_buffer): input_buffer = '<string of %d bytes>' % original_len input_buffer = input_buffer.encode('ascii', errors=errors) else: matched = self.coder_str_re.match(input_buffer) if matched: input_buffer = '%s<string of %d bytes>' % ( matched.group(1), matched.end(2) - matched.start(2)) input_buffer = input_buffer.encode('ascii', errors=errors) return input_buffer, original_len def decode_shortstrings(input_buffer, errors='strict'): """Decoder (to Unicode) that suppresses long base64 strings.""" shortened, length = encode_shortstrings(input_buffer, errors) return str(shortened), length def shortstrings_registerer(encoding_name): if encoding_name == 'shortstrings': return codecs.CodecInfo( name='shortstrings', encode=encode_shortstrings, decode=decode_shortstrings) return None codecs.register(shortstrings_registerer) # Use json "dump string" method to get readable formatting; # further modify it to not output too-long strings, aimed at the # 10,000+ character hex-encoded "serialized_fn" values. return json.dumps( json.loads(encoding.MessageToJson(self.proto)), indent=2, sort_keys=True) @staticmethod def _build_default_job_name(user_name): """Generates a default name for a job. user_name is lowercased, and any characters outside of [-a-z0-9] are removed. If necessary, the user_name is truncated to shorten the job name to 63 characters.""" user_name = re.sub('[^-a-z0-9]', '', user_name.lower()) date_component = datetime.now(timezone.utc).strftime('%m%d%H%M%S-%f') app_user_name = 'beamapp-{}'.format(user_name) # append 8 random alphanumeric characters to avoid collisions. random_component = ''.join( random.choices(string.ascii_lowercase + string.digits, k=8)) job_name = '{}-{}-{}'.format( app_user_name, date_component, random_component) if len(job_name) > 63: job_name = '{}-{}-{}'.format( app_user_name[:-(len(job_name) - 63)], date_component, random_component) return job_name @staticmethod def default_job_name(job_name): if job_name is None: job_name = Job._build_default_job_name(getpass.getuser()) return job_name def __init__(self, options, proto_pipeline): self.options = options validate_pipeline_graph(proto_pipeline) self.proto_pipeline = proto_pipeline self.google_cloud_options = options.view_as(GoogleCloudOptions) if not self.google_cloud_options.job_name: self.google_cloud_options.job_name = self.default_job_name( self.google_cloud_options.job_name) required_google_cloud_options = ['project', 'job_name', 'temp_location'] missing = [ option for option in required_google_cloud_options if not getattr(self.google_cloud_options, option) ] if missing: raise ValueError( 'Missing required configuration parameters: %s' % missing) if not self.google_cloud_options.staging_location: _LOGGER.info( 'Defaulting to the temp_location as staging_location: %s', self.google_cloud_options.temp_location) ( self.google_cloud_options.staging_location ) = self.google_cloud_options.temp_location self.root_staging_location = self.google_cloud_options.staging_location # Make the staging and temp locations job name and time specific. This is # needed to avoid clashes between job submissions using the same staging # area or team members using same job names. This method is not entirely # foolproof since two job submissions with same name can happen at exactly # the same time. However the window is extremely small given that # time.time() has at least microseconds granularity. We add the suffix only # for GCS staging locations where the potential for such clashes is high. if self.google_cloud_options.staging_location.startswith('gs://'): path_suffix = '%s.%f' % (self.google_cloud_options.job_name, time.time()) self.google_cloud_options.staging_location = FileSystems.join( self.google_cloud_options.staging_location, path_suffix) self.google_cloud_options.temp_location = FileSystems.join( self.google_cloud_options.temp_location, path_suffix) self.proto = dataflow.Job(name=self.google_cloud_options.job_name) if self.options.view_as(StandardOptions).streaming: self.proto.type = dataflow.Job.TypeValueValuesEnum.JOB_TYPE_STREAMING else: self.proto.type = dataflow.Job.TypeValueValuesEnum.JOB_TYPE_BATCH if self.google_cloud_options.update: self.proto.replaceJobId = self.job_id_for_name(self.proto.name) if self.google_cloud_options.transform_name_mapping: self.proto.transformNameMapping = ( dataflow.Job.TransformNameMappingValue()) for _, (key, value) in enumerate( self.google_cloud_options.transform_name_mapping.items()): self.proto.transformNameMapping.additionalProperties.append( dataflow.Job.TransformNameMappingValue.AdditionalProperty( key=key, value=value)) if self.google_cloud_options.create_from_snapshot: self.proto.createdFromSnapshotId = ( self.google_cloud_options.create_from_snapshot) # Labels. if self.google_cloud_options.labels: self.proto.labels = dataflow.Job.LabelsValue() labels = self.google_cloud_options.labels if isinstance(labels, str): labels = [labels] elif isinstance(labels, dict): labels = [str(labels)] for label in labels: if '{' in label: label = ast.literal_eval(label) for key, value in label.items(): self.proto.labels.additionalProperties.append( dataflow.Job.LabelsValue.AdditionalProperty( key=key, value=value)) else: parts = label.split('=', 1) key = parts[0] value = parts[1] if len(parts) > 1 else '' self.proto.labels.additionalProperties.append( dataflow.Job.LabelsValue.AdditionalProperty(key=key, value=value)) # Client Request ID self.proto.clientRequestId = '{}-{}'.format( datetime.now(timezone.utc).strftime('%Y%m%d%H%M%S%f'), random.randrange(9000) + 1000) self.base64_str_re = re.compile(r'^[A-Za-z0-9+/]*=*$') self.coder_str_re = re.compile(r'^([A-Za-z]+\$)([A-Za-z0-9+/]*=*)$') def job_id_for_name(self, job_name): return DataflowApplicationClient( self.google_cloud_options).job_id_for_name(job_name) def json(self): return encoding.MessageToJson(self.proto) def __reduce__(self): """Reduce hook for pickling the Job class more easily.""" return (Job, (self.options, )) class DataflowApplicationClient(object): _HASH_CHUNK_SIZE = 1024 * 8 _GCS_CACHE_PREFIX = "artifact_cache" """A Dataflow API client used by application code to create and query jobs.""" def __init__(self, options, root_staging_location=None): """Initializes a Dataflow API client object.""" self.standard_options = options.view_as(StandardOptions) self.google_cloud_options = options.view_as(GoogleCloudOptions) self._enable_caching = self.google_cloud_options.enable_artifact_caching self._enable_bucket_read_metric_counter = \ self.google_cloud_options.enable_bucket_read_metric_counter self._enable_bucket_write_metric_counter =\ self.google_cloud_options.enable_bucket_write_metric_counter self._root_staging_location = ( root_staging_location or self.google_cloud_options.staging_location) self.environment_version = _FNAPI_ENVIRONMENT_MAJOR_VERSION if self.google_cloud_options.no_auth: credentials = None else: credentials = get_service_credentials(options) http_client = get_new_http() self._client = dataflow.DataflowV1b3( url=self.google_cloud_options.dataflow_endpoint, credentials=credentials, get_credentials=(not self.google_cloud_options.no_auth), http=http_client, response_encoding=get_response_encoding()) self._storage_client = create_storage_client( options, not self.google_cloud_options.no_auth) self._sdk_image_overrides = self._get_sdk_image_overrides(options) def _get_sdk_image_overrides(self, pipeline_options): worker_options = pipeline_options.view_as(WorkerOptions) sdk_overrides = worker_options.sdk_harness_container_image_overrides return ( dict(s.split(',', 1) for s in sdk_overrides) if sdk_overrides else {}) @staticmethod def _compute_sha256(file): hasher = hashlib.sha256() with open(file, 'rb') as f: for chunk in iter(partial(f.read, DataflowApplicationClient._HASH_CHUNK_SIZE), b""): hasher.update(chunk) return hasher.hexdigest() def _cached_location(self, sha256): sha_prefix = sha256[0:2] return FileSystems.join( self._root_staging_location, DataflowApplicationClient._GCS_CACHE_PREFIX, sha_prefix, sha256) def _gcs_file_copy(self, from_path, to_path, sha256): if self._enable_caching and sha256: self._cached_gcs_file_copy(from_path, to_path, sha256) else: self._uncached_gcs_file_copy(from_path, to_path) def _cached_gcs_file_copy(self, from_path, to_path, sha256): cached_path = self._cached_location(sha256) if FileSystems.exists(cached_path): _LOGGER.info( 'Skipping upload of %s because it already exists at %s', to_path, cached_path) else: self._uncached_gcs_file_copy(from_path, cached_path) FileSystems.copy( source_file_names=[cached_path], destination_file_names=[to_path]) _LOGGER.info('Copied cached artifact from %s to %s', from_path, to_path) def _uncached_gcs_file_copy(self, from_path, to_path): to_folder, to_name = os.path.split(to_path) total_size = os.path.getsize(from_path) self.stage_file_with_retry( to_folder, to_name, from_path, total_size=total_size) def _stage_resources(self, pipeline, options): google_cloud_options = options.view_as(GoogleCloudOptions) if google_cloud_options.staging_location is None: raise RuntimeError('The --staging_location option must be specified.') if google_cloud_options.temp_location is None: raise RuntimeError('The --temp_location option must be specified.') resources = [] staged_paths = {} staged_hashes = {} for _, env in sorted(pipeline.components.environments.items(), key=lambda kv: kv[0]): for dep in env.dependencies: if dep.type_urn != common_urns.artifact_types.FILE.urn: raise RuntimeError('unsupported artifact type %s' % dep.type_urn) type_payload = beam_runner_api_pb2.ArtifactFilePayload.FromString( dep.type_payload) if dep.role_urn == common_urns.artifact_roles.STAGING_TO.urn: remote_name = ( beam_runner_api_pb2.ArtifactStagingToRolePayload.FromString( dep.role_payload)).staged_name is_staged_role = True else: remote_name = os.path.basename(type_payload.path) is_staged_role = False if self._enable_caching and not type_payload.sha256: type_payload.sha256 = self._compute_sha256(type_payload.path) if type_payload.sha256 and type_payload.sha256 in staged_hashes: _LOGGER.info( 'Found duplicated artifact sha256: %s (%s)', type_payload.path, type_payload.sha256) remote_name = staged_hashes[type_payload.sha256] if is_staged_role: # We should not be overriding this, as dep.role_payload.staged_name # refers to the desired name on the worker, whereas staged_name # refers to its placement in a distributed filesystem. # TODO(heejong): Clean this up. dep.role_payload = beam_runner_api_pb2.ArtifactStagingToRolePayload( staged_name=remote_name).SerializeToString() elif type_payload.path and type_payload.path in staged_paths: _LOGGER.info( 'Found duplicated artifact path: %s (%s)', type_payload.path, type_payload.sha256) remote_name = staged_paths[type_payload.path] if is_staged_role: # We should not be overriding this, as dep.role_payload.staged_name # refers to the desired name on the worker, whereas staged_name # refers to its placement in a distributed filesystem. # TODO(heejong): Clean this up. dep.role_payload = beam_runner_api_pb2.ArtifactStagingToRolePayload( staged_name=remote_name).SerializeToString() else: resources.append( (type_payload.path, remote_name, type_payload.sha256)) staged_paths[type_payload.path] = remote_name staged_hashes[type_payload.sha256] = remote_name if FileSystems.get_scheme( google_cloud_options.staging_location) == GCSFileSystem.scheme(): dep.type_urn = common_urns.artifact_types.URL.urn dep.type_payload = beam_runner_api_pb2.ArtifactUrlPayload( url=FileSystems.join( google_cloud_options.staging_location, remote_name), sha256=type_payload.sha256).SerializeToString() else: dep.type_payload = beam_runner_api_pb2.ArtifactFilePayload( path=FileSystems.join( google_cloud_options.staging_location, remote_name), sha256=type_payload.sha256).SerializeToString() resource_stager = _LegacyDataflowStager(self) staged_resources = resource_stager.stage_job_resources( resources, staging_location=google_cloud_options.staging_location) return staged_resources def stage_file( self, gcs_or_local_path, file_name, stream, mime_type='application/octet-stream', total_size=None): """Stages a file at a GCS or local path with stream-supplied contents.""" from google.cloud.exceptions import Forbidden from google.cloud.exceptions import NotFound if not gcs_or_local_path.startswith('gs://'): local_path = FileSystems.join(gcs_or_local_path, file_name) _LOGGER.info('Staging file locally to %s', local_path) with open(local_path, 'wb') as f: f.write(stream.read()) return gcs_location = FileSystems.join(gcs_or_local_path, file_name) bucket_name, blob_name = gcs_location[5:].split('/', 1) start_time = time.time() _LOGGER.info('Starting GCS upload to %s...', gcs_location) try: from google.cloud.storage import Blob from google.cloud.storage.fileio import BlobWriter bucket = self._storage_client.get_bucket(bucket_name) blob = bucket.get_blob(blob_name) if not blob: blob = Blob(blob_name, bucket) with BlobWriter(blob) as f: f.write(stream.read()) _LOGGER.info( 'Completed GCS upload to %s in %s seconds.', gcs_location, int(time.time() - start_time)) return except Exception as e: reportable_errors = [ Forbidden, NotFound, ] if type(e) in reportable_errors: raise IOError(( 'Could not upload to GCS path %s: %s. Please verify ' 'that credentials are valid, that the specified path ' 'exists, and that you have write access to it.') % (gcs_or_local_path, e)) raise @retry.with_exponential_backoff( retry_filter=retry.retry_on_server_errors_and_timeout_filter) def stage_file_with_retry( self, gcs_or_local_path, file_name, stream_or_path, mime_type='application/octet-stream', total_size=None): if isinstance(stream_or_path, str): path = stream_or_path with open(path, 'rb') as stream: self.stage_file( gcs_or_local_path, file_name, stream, mime_type, total_size) elif isinstance(stream_or_path, io.IOBase): stream = stream_or_path try: self.stage_file( gcs_or_local_path, file_name, stream, mime_type, total_size) except Exception as exn: if stream.seekable(): # reset cursor for possible retrying stream.seek(0) raise exn else: raise retry.PermanentException( "Skip retrying because we caught exception:" + ''.join(traceback.format_exception_only(exn.__class__, exn)) + ', but the stream is not seekable.') else: raise retry.PermanentException( "Skip retrying because type " + str(type(stream_or_path)) + "stream_or_path is unsupported.") @retry.no_retries # Using no_retries marks this as an integration point. def create_job(self, job): """Creates job description. May stage and/or submit for remote execution.""" self.create_job_description(job) # Stage and submit the job when necessary dataflow_job_file = job.options.view_as(DebugOptions).dataflow_job_file template_location = ( job.options.view_as(GoogleCloudOptions).template_location) if job.options.view_as(DebugOptions).lookup_experiment('upload_graph'): self.stage_file_with_retry( job.options.view_as(GoogleCloudOptions).staging_location, "dataflow_graph.json", io.BytesIO(job.json().encode('utf-8'))) del job.proto.steps[:] job.proto.stepsLocation = FileSystems.join( job.options.view_as(GoogleCloudOptions).staging_location, "dataflow_graph.json") # template file generation should be placed immediately before the # conditional API call. job_location = template_location or dataflow_job_file if job_location: gcs_or_local_path = os.path.dirname(job_location) file_name = os.path.basename(job_location) self.stage_file_with_retry( gcs_or_local_path, file_name, io.BytesIO(job.json().encode('utf-8'))) if not template_location: return self.submit_job_description(job) _LOGGER.info( 'A template was just created at location %s', template_location) return None @staticmethod def _update_container_image_for_dataflow(beam_container_image_url): # By default Dataflow pipelines use containers hosted in Dataflow GCR # instead of Docker Hub. image_suffix = beam_container_image_url.rsplit('/', 1)[1] # trim "RCX" as release candidate tag exists on Docker Hub but not GCR check_rc = image_suffix.lower().split('rc') if len(check_rc) == 2: image_suffix = image_suffix[:-2 - len(check_rc[1])] return names.DATAFLOW_CONTAINER_IMAGE_REPOSITORY + '/' + image_suffix @staticmethod def _apply_sdk_environment_overrides( proto_pipeline, sdk_overrides, pipeline_options): # Updates container image URLs for Dataflow. # For a given container image URL # * If a matching override has been provided that will be used. # * For improved performance, External Apache Beam container images that are # not explicitly overridden will be # updated to use GCR copies instead of directly downloading from the # Docker Hub. current_sdk_container_image = get_container_image_from_options( pipeline_options) for environment in proto_pipeline.components.environments.values(): docker_payload = proto_utils.parse_Bytes( environment.payload, beam_runner_api_pb2.DockerPayload) overridden = False new_container_image = docker_payload.container_image for pattern, override in sdk_overrides.items(): new_container_image = re.sub(pattern, override, new_container_image) if new_container_image != docker_payload.container_image: overridden = True # Container of the current (Python) SDK is overridden separately, hence # not updated here. if (is_apache_beam_container(new_container_image) and not overridden and new_container_image != current_sdk_container_image): new_container_image = ( DataflowApplicationClient._update_container_image_for_dataflow( docker_payload.container_image)) if not new_container_image: raise ValueError( 'SDK Docker container image has to be a non-empty string') new_payload = copy(docker_payload) new_payload.container_image = new_container_image environment.payload = new_payload.SerializeToString() def create_job_description(self, job): """Creates a job described by the workflow proto.""" DataflowApplicationClient._apply_sdk_environment_overrides( job.proto_pipeline, self._sdk_image_overrides, job.options) # Stage other resources for the SDK harness resources = self._stage_resources(job.proto_pipeline, job.options) # Stage proto pipeline. self.stage_file_with_retry( job.google_cloud_options.staging_location, shared_names.STAGED_PIPELINE_FILENAME, io.BytesIO(job.proto_pipeline.SerializeToString())) job.proto.environment = Environment( proto_pipeline_staged_url=FileSystems.join( job.google_cloud_options.staging_location, shared_names.STAGED_PIPELINE_FILENAME), packages=resources, options=job.options, environment_version=self.environment_version, proto_pipeline=job.proto_pipeline).proto _LOGGER.debug('JOB: %s', job) @retry.with_exponential_backoff(num_retries=3, initial_delay_secs=3) def get_job_metrics(self, job_id): request = dataflow.DataflowProjectsLocationsJobsGetMetricsRequest() request.jobId = job_id request.location = self.google_cloud_options.region request.projectId = self.google_cloud_options.project try: response = self._client.projects_locations_jobs.GetMetrics(request) except exceptions.BadStatusCodeError as e: _LOGGER.error( 'HTTP status %d. Unable to query metrics', e.response.status) raise return response @retry.with_exponential_backoff(num_retries=3) def submit_job_description(self, job): """Creates and excutes a job request.""" request = dataflow.DataflowProjectsLocationsJobsCreateRequest() request.projectId = self.google_cloud_options.project request.location = self.google_cloud_options.region request.job = job.proto try: response = self._client.projects_locations_jobs.Create(request) except exceptions.BadStatusCodeError as e: _LOGGER.error( 'HTTP status %d trying to create job' ' at dataflow service endpoint %s', e.response.status, self.google_cloud_options.dataflow_endpoint) _LOGGER.fatal('details of server error: %s', e) raise if response.clientRequestId and \ response.clientRequestId != job.proto.clientRequestId: if self.google_cloud_options.update: raise DataflowJobAlreadyExistsError( "The job named %s with id: %s has already been updated into job " "id: %s and cannot be updated again." % (response.name, job.proto.replaceJobId, response.id)) else: raise DataflowJobAlreadyExistsError( 'There is already active job named %s with id: %s. If you want to ' 'submit a second job, try again by setting a different name using ' '--job_name.' % (response.name, response.id)) _LOGGER.info('Create job: %s', response) # The response is a Job proto with the id for the new job. _LOGGER.info('Created job with id: [%s]', response.id) _LOGGER.info('Submitted job: %s', response.id) _LOGGER.info( 'To access the Dataflow monitoring console, please navigate to ' 'https://console.cloud.google.com/dataflow/jobs/%s/%s?project=%s', self.google_cloud_options.region, response.id, self.google_cloud_options.project) return response @retry.with_exponential_backoff() # Using retry defaults from utils/retry.py def modify_job_state(self, job_id, new_state): """Modify the run state of the job. Args: job_id: The id of the job. new_state: A string representing the new desired state. It could be set to either 'JOB_STATE_DONE', 'JOB_STATE_CANCELLED' or 'JOB_STATE_DRAINING'. Returns: True if the job was modified successfully. """ if new_state == 'JOB_STATE_DONE': new_state = dataflow.Job.RequestedStateValueValuesEnum.JOB_STATE_DONE elif new_state == 'JOB_STATE_CANCELLED': new_state = dataflow.Job.RequestedStateValueValuesEnum.JOB_STATE_CANCELLED elif new_state == 'JOB_STATE_DRAINING': new_state = dataflow.Job.RequestedStateValueValuesEnum.JOB_STATE_DRAINING else: # Other states could only be set by the service. return False request = dataflow.DataflowProjectsLocationsJobsUpdateRequest() request.jobId = job_id request.projectId = self.google_cloud_options.project request.location = self.google_cloud_options.region request.job = dataflow.Job(requestedState=new_state) self._client.projects_locations_jobs.Update(request) return True @retry.with_exponential_backoff( retry_filter=retry.retry_on_server_errors_and_notfound_filter) def get_job(self, job_id): """Gets the job status for a submitted job. Args: job_id: A string representing the job_id for the workflow as returned by the create_job() request. Returns: A Job proto. See below for interesting fields. The Job proto returned from a get_job() request contains some interesting fields: currentState: An object representing the current state of the job. The string representation of the object (str() result) has the following possible values: JOB_STATE_UNKNONW, JOB_STATE_STOPPED, JOB_STATE_RUNNING, JOB_STATE_DONE, JOB_STATE_FAILED, JOB_STATE_CANCELLED. createTime: UTC time when the job was created (e.g. '2015-03-10T00:01:53.074Z') currentStateTime: UTC time for the current state of the job. """ request = dataflow.DataflowProjectsLocationsJobsGetRequest() request.jobId = job_id request.projectId = self.google_cloud_options.project request.location = self.google_cloud_options.region response = self._client.projects_locations_jobs.Get(request) return response @retry.with_exponential_backoff( retry_filter=retry.retry_on_server_errors_and_notfound_filter) def list_messages( self, job_id, start_time=None, end_time=None, page_token=None, minimum_importance=None): """List messages associated with the execution of a job. Args: job_id: A string representing the job_id for the workflow as returned by the create_job() request. start_time: If specified, only messages generated after the start time will be returned, otherwise all messages since job started will be returned. The value is a string representing UTC time (e.g., '2015-08-18T21:03:50.644Z') end_time: If specified, only messages generated before the end time will be returned, otherwise all messages up to current time will be returned. The value is a string representing UTC time (e.g., '2015-08-18T21:03:50.644Z') page_token: A string to be used as next page token if the list call returned paginated results. minimum_importance: Filter for messages based on importance. The possible string values in increasing order of importance are: JOB_MESSAGE_DEBUG, JOB_MESSAGE_DETAILED, JOB_MESSAGE_BASIC, JOB_MESSAGE_WARNING, JOB_MESSAGE_ERROR. For example, a filter set on warning will allow only warnings and errors and exclude all others. Returns: A tuple consisting of a list of JobMessage instances and a next page token string. Raises: RuntimeError: if an unexpected value for the message_importance argument is used. The JobMessage objects returned by the call contain the following fields: id: A unique string identifier for the message. time: A string representing the UTC time of the message (e.g., '2015-08-18T21:03:50.644Z') messageImportance: An enumeration value for the message importance. The value if converted to string will have the following possible values: JOB_MESSAGE_DEBUG, JOB_MESSAGE_DETAILED, JOB_MESSAGE_BASIC, JOB_MESSAGE_WARNING, JOB_MESSAGE_ERROR. messageText: A message string. """ request = dataflow.DataflowProjectsLocationsJobsMessagesListRequest( jobId=job_id, location=self.google_cloud_options.region, projectId=self.google_cloud_options.project) if page_token is not None: request.pageToken = page_token if start_time is not None: request.startTime = start_time if end_time is not None: request.endTime = end_time if minimum_importance is not None: if minimum_importance == 'JOB_MESSAGE_DEBUG': request.minimumImportance = ( dataflow.DataflowProjectsLocationsJobsMessagesListRequest. MinimumImportanceValueValuesEnum.JOB_MESSAGE_DEBUG) elif minimum_importance == 'JOB_MESSAGE_DETAILED': request.minimumImportance = ( dataflow.DataflowProjectsLocationsJobsMessagesListRequest. MinimumImportanceValueValuesEnum.JOB_MESSAGE_DETAILED) elif minimum_importance == 'JOB_MESSAGE_BASIC': request.minimumImportance = ( dataflow.DataflowProjectsLocationsJobsMessagesListRequest. MinimumImportanceValueValuesEnum.JOB_MESSAGE_BASIC) elif minimum_importance == 'JOB_MESSAGE_WARNING': request.minimumImportance = ( dataflow.DataflowProjectsLocationsJobsMessagesListRequest. MinimumImportanceValueValuesEnum.JOB_MESSAGE_WARNING) elif minimum_importance == 'JOB_MESSAGE_ERROR': request.minimumImportance = ( dataflow.DataflowProjectsLocationsJobsMessagesListRequest. MinimumImportanceValueValuesEnum.JOB_MESSAGE_ERROR) else: raise RuntimeError( 'Unexpected value for minimum_importance argument: %r' % minimum_importance) response = self._client.projects_locations_jobs_messages.List(request) return response.jobMessages, response.nextPageToken def job_id_for_name(self, job_name): token = None while True: request = dataflow.DataflowProjectsLocationsJobsListRequest( projectId=self.google_cloud_options.project, location=self.google_cloud_options.region, pageToken=token) response = self._client.projects_locations_jobs.List(request) for job in response.jobs: if (job.name == job_name and job.currentState in [ dataflow.Job.CurrentStateValueValuesEnum.JOB_STATE_RUNNING, dataflow.Job.CurrentStateValueValuesEnum.JOB_STATE_DRAINING ]): return job.id token = response.nextPageToken if token is None: raise ValueError("No running job found with name '%s'" % job_name) class MetricUpdateTranslators(object): """Translators between accumulators and dataflow metric updates.""" @staticmethod def translate_boolean(accumulator, metric_update_proto): metric_update_proto.boolean = accumulator.value @staticmethod def translate_scalar_mean_int(accumulator, metric_update_proto): if accumulator.count: metric_update_proto.integerMean = dataflow.IntegerMean() metric_update_proto.integerMean.sum = to_split_int(accumulator.sum) metric_update_proto.integerMean.count = to_split_int(accumulator.count) else: metric_update_proto.nameAndKind.kind = None @staticmethod def translate_scalar_mean_float(accumulator, metric_update_proto): if accumulator.count: metric_update_proto.floatingPointMean = dataflow.FloatingPointMean() metric_update_proto.floatingPointMean.sum = accumulator.sum metric_update_proto.floatingPointMean.count = to_split_int( accumulator.count) else: metric_update_proto.nameAndKind.kind = None @staticmethod def translate_scalar_counter_int(accumulator, metric_update_proto): metric_update_proto.integer = to_split_int(accumulator.value) @staticmethod def translate_scalar_counter_float(accumulator, metric_update_proto): metric_update_proto.floatingPoint = accumulator.value class _LegacyDataflowStager(Stager): def __init__(self, dataflow_application_client): super().__init__() self._dataflow_application_client = dataflow_application_client def stage_artifact(self, local_path_to_artifact, artifact_name, sha256): self._dataflow_application_client._gcs_file_copy( local_path_to_artifact, artifact_name, sha256) def commit_manifest(self): pass @staticmethod def get_sdk_package_name(): """For internal use only; no backwards-compatibility guarantees. Returns the PyPI package name to be staged to Google Cloud Dataflow. """ return shared_names.BEAM_PACKAGE_NAME class DataflowJobAlreadyExistsError(retry.PermanentException): """A non-retryable exception that a job with the given name already exists.""" # Inherits retry.PermanentException to avoid retry in # DataflowApplicationClient.submit_job_description pass def to_split_int(n): res = dataflow.SplitInt64() res.lowBits = n & 0xffffffff res.highBits = n >> 32 return res # TODO: Used in legacy batch worker. Move under MetricUpdateTranslators # after Runner V2 transition. def translate_distribution(distribution_update, metric_update_proto): """Translate metrics DistributionUpdate to dataflow distribution update. Args: distribution_update: Instance of DistributionData, DistributionInt64Accumulator or DataflowDistributionCounter. metric_update_proto: Used for report metrics. """ dist_update_proto = dataflow.DistributionUpdate() dist_update_proto.min = to_split_int(distribution_update.min) dist_update_proto.max = to_split_int(distribution_update.max) dist_update_proto.count = to_split_int(distribution_update.count) dist_update_proto.sum = to_split_int(distribution_update.sum) # DataflowDistributionCounter needs to translate histogram if isinstance(distribution_update, DataflowDistributionCounter): dist_update_proto.histogram = dataflow.Histogram() distribution_update.translate_to_histogram(dist_update_proto.histogram) metric_update_proto.distribution = dist_update_proto # TODO: Used in legacy batch worker. Delete after Runner V2 transition. def translate_value(value, metric_update_proto): metric_update_proto.integer = to_split_int(value) def _get_container_image_tag(): base_version = version.parse(beam_version.__version__).base_version if base_version != beam_version.__version__: warnings.warn( "A non-standard version of Beam SDK detected: %s. " "Dataflow runner will use container image tag %s. " "This use case is not supported." % (beam_version.__version__, base_version)) return base_version def get_container_image_from_options(pipeline_options): """For internal use only; no backwards-compatibility guarantees. Args: pipeline_options (PipelineOptions): A container for pipeline options. Returns: str: Container image for remote execution. """ worker_options = pipeline_options.view_as(WorkerOptions) if worker_options.sdk_container_image: return worker_options.sdk_container_image # Legacy and runner v2 exist in different repositories. # Set to legacy format, override if runner v2 container_repo = names.DATAFLOW_CONTAINER_IMAGE_REPOSITORY image_name = '{repository}/beam_python{major}.{minor}_sdk'.format( repository=container_repo, major=sys.version_info[0], minor=sys.version_info[1]) image_tag = _get_required_container_version() return image_name + ':' + image_tag def _get_required_container_version(): """For internal use only; no backwards-compatibility guarantees. Returns: str: The tag of worker container images in GCR that corresponds to current version of the SDK. """ if 'dev' in beam_version.__version__: return names.BEAM_DEV_SDK_CONTAINER_TAG else: return _get_container_image_tag() def get_response_encoding(): """Encoding to use to decode HTTP response from Google APIs.""" return 'utf8' def _verify_interpreter_version_is_supported(pipeline_options): if ('%s.%s' % (sys.version_info[0], sys.version_info[1]) in _PYTHON_VERSIONS_SUPPORTED_BY_DATAFLOW): return if 'dev' in beam_version.__version__: return debug_options = pipeline_options.view_as(DebugOptions) if (debug_options.experiments and 'use_unsupported_python_version' in debug_options.experiments): return raise Exception( 'Dataflow runner currently supports Python versions %s, got %s.\n' 'To ignore this requirement and start a job ' 'using an unsupported version of Python interpreter, pass ' '--experiment use_unsupported_python_version pipeline option.' % (_PYTHON_VERSIONS_SUPPORTED_BY_DATAFLOW, sys.version)) # To enable a counter on the service, add it to this dictionary. # This is required for the legacy python dataflow runner, as portability # does not communicate to the service via python code, but instead via a # a runner harness (in C++ or Java). # TODO(https://github.com/apache/beam/issues/19433) : Remove this antipattern, # legacy dataflow python pipelines will break whenever a new cy_combiner type # is used. structured_counter_translations = { cy_combiners.CountCombineFn: ( dataflow.CounterMetadata.KindValueValuesEnum.SUM, MetricUpdateTranslators.translate_scalar_counter_int), cy_combiners.SumInt64Fn: ( dataflow.CounterMetadata.KindValueValuesEnum.SUM, MetricUpdateTranslators.translate_scalar_counter_int), cy_combiners.MinInt64Fn: ( dataflow.CounterMetadata.KindValueValuesEnum.MIN, MetricUpdateTranslators.translate_scalar_counter_int), cy_combiners.MaxInt64Fn: ( dataflow.CounterMetadata.KindValueValuesEnum.MAX, MetricUpdateTranslators.translate_scalar_counter_int), cy_combiners.MeanInt64Fn: ( dataflow.CounterMetadata.KindValueValuesEnum.MEAN, MetricUpdateTranslators.translate_scalar_mean_int), cy_combiners.SumFloatFn: ( dataflow.CounterMetadata.KindValueValuesEnum.SUM, MetricUpdateTranslators.translate_scalar_counter_float), cy_combiners.MinFloatFn: ( dataflow.CounterMetadata.KindValueValuesEnum.MIN, MetricUpdateTranslators.translate_scalar_counter_float), cy_combiners.MaxFloatFn: ( dataflow.CounterMetadata.KindValueValuesEnum.MAX, MetricUpdateTranslators.translate_scalar_counter_float), cy_combiners.MeanFloatFn: ( dataflow.CounterMetadata.KindValueValuesEnum.MEAN, MetricUpdateTranslators.translate_scalar_mean_float), cy_combiners.AllCombineFn: ( dataflow.CounterMetadata.KindValueValuesEnum.AND, MetricUpdateTranslators.translate_boolean), cy_combiners.AnyCombineFn: ( dataflow.CounterMetadata.KindValueValuesEnum.OR, MetricUpdateTranslators.translate_boolean), cy_combiners.DataflowDistributionCounterFn: ( dataflow.CounterMetadata.KindValueValuesEnum.DISTRIBUTION, translate_distribution), cy_combiners.DistributionInt64Fn: ( dataflow.CounterMetadata.KindValueValuesEnum.DISTRIBUTION, translate_distribution), } counter_translations = { cy_combiners.CountCombineFn: ( dataflow.NameAndKind.KindValueValuesEnum.SUM, MetricUpdateTranslators.translate_scalar_counter_int), cy_combiners.SumInt64Fn: ( dataflow.NameAndKind.KindValueValuesEnum.SUM, MetricUpdateTranslators.translate_scalar_counter_int), cy_combiners.MinInt64Fn: ( dataflow.NameAndKind.KindValueValuesEnum.MIN, MetricUpdateTranslators.translate_scalar_counter_int), cy_combiners.MaxInt64Fn: ( dataflow.NameAndKind.KindValueValuesEnum.MAX, MetricUpdateTranslators.translate_scalar_counter_int), cy_combiners.MeanInt64Fn: ( dataflow.NameAndKind.KindValueValuesEnum.MEAN, MetricUpdateTranslators.translate_scalar_mean_int), cy_combiners.SumFloatFn: ( dataflow.NameAndKind.KindValueValuesEnum.SUM, MetricUpdateTranslators.translate_scalar_counter_float), cy_combiners.MinFloatFn: ( dataflow.NameAndKind.KindValueValuesEnum.MIN, MetricUpdateTranslators.translate_scalar_counter_float), cy_combiners.MaxFloatFn: ( dataflow.NameAndKind.KindValueValuesEnum.MAX, MetricUpdateTranslators.translate_scalar_counter_float), cy_combiners.MeanFloatFn: ( dataflow.NameAndKind.KindValueValuesEnum.MEAN, MetricUpdateTranslators.translate_scalar_mean_float), cy_combiners.AllCombineFn: ( dataflow.NameAndKind.KindValueValuesEnum.AND, MetricUpdateTranslators.translate_boolean), cy_combiners.AnyCombineFn: ( dataflow.NameAndKind.KindValueValuesEnum.OR, MetricUpdateTranslators.translate_boolean), cy_combiners.DataflowDistributionCounterFn: ( dataflow.NameAndKind.KindValueValuesEnum.DISTRIBUTION, translate_distribution), cy_combiners.DistributionInt64Fn: ( dataflow.CounterMetadata.KindValueValuesEnum.DISTRIBUTION, translate_distribution), }

sdks/python/apache_beam/runners/dataflow/internal/apiclient.py (977 lines of code) (raw):