#
# 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.
#
"""A runner implementation that submits a job for remote execution.
The runner will create a JSON description of the job graph and then submit it
to the Dataflow Service for remote execution by a worker.
"""
# pytype: skip-file
from __future__ import absolute_import
from __future__ import division
import base64
import json
import logging
import sys
import threading
import time
import traceback
import urllib
from builtins import hex
from collections import defaultdict
from future.utils import iteritems
import apache_beam as beam
from apache_beam import coders
from apache_beam import error
from apache_beam import pvalue
from apache_beam.internal import pickler
from apache_beam.internal.gcp import json_value
from apache_beam.options.pipeline_options import DebugOptions
from apache_beam.options.pipeline_options import GoogleCloudOptions
from apache_beam.options.pipeline_options import SetupOptions
from apache_beam.options.pipeline_options import StandardOptions
from apache_beam.options.pipeline_options import TestOptions
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.pvalue import AsSideInput
from apache_beam.runners.common import DoFnSignature
from apache_beam.runners.dataflow.internal import names
from apache_beam.runners.dataflow.internal.clients import dataflow as dataflow_api
from apache_beam.runners.dataflow.internal.names import PropertyNames
from apache_beam.runners.dataflow.internal.names import TransformNames
from apache_beam.runners.runner import PipelineResult
from apache_beam.runners.runner import PipelineRunner
from apache_beam.runners.runner import PipelineState
from apache_beam.runners.runner import PValueCache
from apache_beam.transforms import window
from apache_beam.transforms.display import DisplayData
from apache_beam.typehints import typehints
from apache_beam.utils import proto_utils
from apache_beam.utils.interactive_utils import is_in_notebook
from apache_beam.utils.plugin import BeamPlugin
if sys.version_info[0] > 2:
unquote_to_bytes = urllib.parse.unquote_to_bytes
quote = urllib.parse.quote
else:
unquote_to_bytes = urllib.unquote # pylint: disable=deprecated-urllib-function
quote = urllib.quote # pylint: disable=deprecated-urllib-function
__all__ = ['DataflowRunner']
_LOGGER = logging.getLogger(__name__)
[docs]class DataflowRunner(PipelineRunner):
"""A runner that creates job graphs and submits them for remote execution.
Every execution of the run() method will submit an independent job for
remote execution that consists of the nodes reachable from the passed in
node argument or entire graph if node is None. The run() method returns
after the service created the job and will not wait for the job to finish
if blocking is set to False.
"""
# A list of PTransformOverride objects to be applied before running a pipeline
# using DataflowRunner.
# Currently this only works for overrides where the input and output types do
# not change.
# For internal SDK use only. This should not be updated by Beam pipeline
# authors.
# Imported here to avoid circular dependencies.
# TODO: Remove the apache_beam.pipeline dependency in CreatePTransformOverride
from apache_beam.runners.dataflow.ptransform_overrides import CreatePTransformOverride
from apache_beam.runners.dataflow.ptransform_overrides import ReadPTransformOverride
from apache_beam.runners.dataflow.ptransform_overrides import JrhReadPTransformOverride
_PTRANSFORM_OVERRIDES = []
_JRH_PTRANSFORM_OVERRIDES = [
JrhReadPTransformOverride(),
]
# These overrides should be applied after the proto representation of the
# graph is created.
_NON_PORTABLE_PTRANSFORM_OVERRIDES = [
CreatePTransformOverride(),
ReadPTransformOverride(),
]
def __init__(self, cache=None):
# Cache of CloudWorkflowStep protos generated while the runner
# "executes" a pipeline.
self._cache = cache if cache is not None else PValueCache()
self._unique_step_id = 0
[docs] def is_fnapi_compatible(self):
return False
def _get_unique_step_name(self):
self._unique_step_id += 1
return 's%s' % self._unique_step_id
[docs] @staticmethod
def poll_for_job_completion(runner, result, duration):
"""Polls for the specified job to finish running (successfully or not).
Updates the result with the new job information before returning.
Args:
runner: DataflowRunner instance to use for polling job state.
result: DataflowPipelineResult instance used for job information.
duration (int): The time to wait (in milliseconds) for job to finish.
If it is set to :data:`None`, it will wait indefinitely until the job
is finished.
"""
last_message_time = None
current_seen_messages = set()
last_error_rank = float('-inf')
last_error_msg = None
last_job_state = None
# How long to wait after pipeline failure for the error
# message to show up giving the reason for the failure.
# It typically takes about 30 seconds.
final_countdown_timer_secs = 50.0
sleep_secs = 5.0
# Try to prioritize the user-level traceback, if any.
def rank_error(msg):
if 'work item was attempted' in msg:
return -1
elif 'Traceback' in msg:
return 1
return 0
if duration:
start_secs = time.time()
duration_secs = duration // 1000
job_id = result.job_id()
while True:
response = runner.dataflow_client.get_job(job_id)
# If get() is called very soon after Create() the response may not contain
# an initialized 'currentState' field.
if response.currentState is not None:
if response.currentState != last_job_state:
_LOGGER.info('Job %s is in state %s', job_id, response.currentState)
last_job_state = response.currentState
if str(response.currentState) != 'JOB_STATE_RUNNING':
# Stop checking for new messages on timeout, explanatory
# message received, success, or a terminal job state caused
# by the user that therefore doesn't require explanation.
if (final_countdown_timer_secs <= 0.0 or last_error_msg is not None or
str(response.currentState) == 'JOB_STATE_DONE' or
str(response.currentState) == 'JOB_STATE_CANCELLED' or
str(response.currentState) == 'JOB_STATE_UPDATED' or
str(response.currentState) == 'JOB_STATE_DRAINED'):
break
# Check that job is in a post-preparation state before starting the
# final countdown.
if (str(response.currentState) not in ('JOB_STATE_PENDING',
'JOB_STATE_QUEUED')):
# The job has failed; ensure we see any final error messages.
sleep_secs = 1.0 # poll faster during the final countdown
final_countdown_timer_secs -= sleep_secs
time.sleep(sleep_secs)
# Get all messages since beginning of the job run or since last message.
page_token = None
while True:
messages, page_token = runner.dataflow_client.list_messages(
job_id, page_token=page_token, start_time=last_message_time)
for m in messages:
message = '%s: %s: %s' % (m.time, m.messageImportance, m.messageText)
if not last_message_time or m.time > last_message_time:
last_message_time = m.time
current_seen_messages = set()
if message in current_seen_messages:
# Skip the message if it has already been seen at the current
# time. This could be the case since the list_messages API is
# queried starting at last_message_time.
continue
else:
current_seen_messages.add(message)
# Skip empty messages.
if m.messageImportance is None:
continue
_LOGGER.info(message)
if str(m.messageImportance) == 'JOB_MESSAGE_ERROR':
if rank_error(m.messageText) >= last_error_rank:
last_error_rank = rank_error(m.messageText)
last_error_msg = m.messageText
if not page_token:
break
if duration:
passed_secs = time.time() - start_secs
if passed_secs > duration_secs:
_LOGGER.warning(
'Timing out on waiting for job %s after %d seconds',
job_id,
passed_secs)
break
result._job = response
runner.last_error_msg = last_error_msg
@staticmethod
def _set_pdone_visitor(pipeline):
# Imported here to avoid circular dependencies.
from apache_beam.pipeline import PipelineVisitor
class SetPDoneVisitor(PipelineVisitor):
def __init__(self, pipeline):
self._pipeline = pipeline
@staticmethod
def _maybe_fix_output(transform_node, pipeline):
if not transform_node.outputs:
pval = pvalue.PDone(pipeline)
pval.producer = transform_node
transform_node.outputs = {None: pval}
def enter_composite_transform(self, transform_node):
SetPDoneVisitor._maybe_fix_output(transform_node, self._pipeline)
def visit_transform(self, transform_node):
SetPDoneVisitor._maybe_fix_output(transform_node, self._pipeline)
return SetPDoneVisitor(pipeline)
def _check_for_unsupported_fnapi_features(self, pipeline_proto):
components = pipeline_proto.components
for windowing_strategy in components.windowing_strategies.values():
if (windowing_strategy.merge_status ==
beam_runner_api_pb2.MergeStatus.NEEDS_MERGE and
windowing_strategy.window_fn.urn not in (
common_urns.session_windows.urn, )):
raise RuntimeError(
'Unsupported merging windowing strategy: %s' %
windowing_strategy.window_fn.urn)
elif components.coders[
windowing_strategy.window_coder_id].spec.urn not in (
common_urns.coders.GLOBAL_WINDOW.urn,
common_urns.coders.INTERVAL_WINDOW.urn):
raise RuntimeError(
'Unsupported window coder %s for window fn %s' % (
components.coders[windowing_strategy.window_coder_id].spec.urn,
windowing_strategy.window_fn.urn))
[docs] def run_pipeline(self, pipeline, options):
"""Remotely executes entire pipeline or parts reachable from node."""
# Label goog-dataflow-notebook if job is started from notebook.
if is_in_notebook():
notebook_version = (
'goog-dataflow-notebook=' +
beam.version.__version__.replace('.', '_'))
if options.view_as(GoogleCloudOptions).labels:
options.view_as(GoogleCloudOptions).labels.append(notebook_version)
else:
options.view_as(GoogleCloudOptions).labels = [notebook_version]
# Import here to avoid adding the dependency for local running scenarios.
try:
# pylint: disable=wrong-import-order, wrong-import-position
from apache_beam.runners.dataflow.internal import apiclient
except ImportError:
raise ImportError(
'Google Cloud Dataflow runner not available, '
'please install apache_beam[gcp]')
# Convert all side inputs into a form acceptable to Dataflow.
if apiclient._use_fnapi(options):
pipeline.visit(
self.side_input_visitor(apiclient._use_unified_worker(options)))
# Performing configured PTransform overrides. Note that this is currently
# done before Runner API serialization, since the new proto needs to contain
# any added PTransforms.
pipeline.replace_all(DataflowRunner._PTRANSFORM_OVERRIDES)
if (apiclient._use_fnapi(options) and
not apiclient._use_unified_worker(options)):
pipeline.replace_all(DataflowRunner._JRH_PTRANSFORM_OVERRIDES)
use_fnapi = apiclient._use_fnapi(options)
from apache_beam.transforms import environments
default_environment = environments.DockerEnvironment(
container_image=apiclient.get_container_image_from_options(options))
# Snapshot the pipeline in a portable proto.
self.proto_pipeline, self.proto_context = pipeline.to_runner_api(
return_context=True, default_environment=default_environment)
if use_fnapi:
self._check_for_unsupported_fnapi_features(self.proto_pipeline)
# Cross language transform require using a pipeline object constructed
# from the full pipeline proto to make sure that expanded version of
# external transforms are reflected in the Pipeline job graph.
from apache_beam import Pipeline
pipeline = Pipeline.from_runner_api(
self.proto_pipeline,
pipeline.runner,
options,
allow_proto_holders=True)
# Pipelines generated from proto do not have output set to PDone set for
# leaf elements.
pipeline.visit(self._set_pdone_visitor(pipeline))
# We need to generate a new context that maps to the new pipeline object.
self.proto_pipeline, self.proto_context = pipeline.to_runner_api(
return_context=True, default_environment=default_environment)
else:
# Performing configured PTransform overrides which should not be reflected
# in the proto representation of the graph.
pipeline.replace_all(DataflowRunner._NON_PORTABLE_PTRANSFORM_OVERRIDES)
# Add setup_options for all the BeamPlugin imports
setup_options = options.view_as(SetupOptions)
plugins = BeamPlugin.get_all_plugin_paths()
if setup_options.beam_plugins is not None:
plugins = list(set(plugins + setup_options.beam_plugins))
setup_options.beam_plugins = plugins
# Elevate "min_cpu_platform" to pipeline option, but using the existing
# experiment.
debug_options = options.view_as(DebugOptions)
worker_options = options.view_as(WorkerOptions)
if worker_options.min_cpu_platform:
debug_options.add_experiment(
'min_cpu_platform=' + worker_options.min_cpu_platform)
# Elevate "enable_streaming_engine" to pipeline option, but using the
# existing experiment.
google_cloud_options = options.view_as(GoogleCloudOptions)
if google_cloud_options.enable_streaming_engine:
debug_options.add_experiment("enable_windmill_service")
debug_options.add_experiment("enable_streaming_engine")
else:
if (debug_options.lookup_experiment("enable_windmill_service") or
debug_options.lookup_experiment("enable_streaming_engine")):
raise ValueError(
"""Streaming engine both disabled and enabled:
enable_streaming_engine flag is not set, but enable_windmill_service
and/or enable_streaming_engine experiments are present.
It is recommended you only set the enable_streaming_engine flag.""")
dataflow_worker_jar = getattr(worker_options, 'dataflow_worker_jar', None)
if dataflow_worker_jar is not None:
if not apiclient._use_fnapi(options):
_LOGGER.warning(
'Typical end users should not use this worker jar feature. '
'It can only be used when FnAPI is enabled.')
else:
debug_options.add_experiment('use_staged_dataflow_worker_jar')
# Make Dataflow workers use FastAvro on Python 3 unless use_avro experiment
# is set. Note that use_avro is only interpreted by the Dataflow runner
# at job submission and is not interpreted by Dataflow service or workers,
# which by default use avro library unless use_fastavro experiment is set.
if sys.version_info[0] > 2 and (
not debug_options.lookup_experiment('use_avro')):
debug_options.add_experiment('use_fastavro')
self.job = apiclient.Job(options, self.proto_pipeline)
# Dataflow runner requires a KV type for GBK inputs, hence we enforce that
# here.
pipeline.visit(self.group_by_key_input_visitor())
# Dataflow runner requires output type of the Flatten to be the same as the
# inputs, hence we enforce that here.
pipeline.visit(self.flatten_input_visitor())
# Trigger a traversal of all reachable nodes.
self.visit_transforms(pipeline, options)
test_options = options.view_as(TestOptions)
# If it is a dry run, return without submitting the job.
if test_options.dry_run:
result = PipelineResult(PipelineState.DONE)
result.wait_until_finish = lambda duration=None: None
return result
# Get a Dataflow API client and set its options
self.dataflow_client = apiclient.DataflowApplicationClient(options)
# Create the job description and send a request to the service. The result
# can be None if there is no need to send a request to the service (e.g.
# template creation). If a request was sent and failed then the call will
# raise an exception.
result = DataflowPipelineResult(
self.dataflow_client.create_job(self.job), self)
# TODO(BEAM-4274): Circular import runners-metrics. Requires refactoring.
from apache_beam.runners.dataflow.dataflow_metrics import DataflowMetrics
self._metrics = DataflowMetrics(self.dataflow_client, result, self.job)
result.metric_results = self._metrics
return result
def _get_typehint_based_encoding(self, typehint, window_coder):
"""Returns an encoding based on a typehint object."""
return self._get_cloud_encoding(
self._get_coder(typehint, window_coder=window_coder))
@staticmethod
def _get_coder(typehint, window_coder):
"""Returns a coder based on a typehint object."""
if window_coder:
return coders.WindowedValueCoder(
coders.registry.get_coder(typehint), window_coder=window_coder)
return coders.registry.get_coder(typehint)
def _get_cloud_encoding(self, coder, unused=None):
"""Returns an encoding based on a coder object."""
if not isinstance(coder, coders.Coder):
raise TypeError(
'Coder object must inherit from coders.Coder: %s.' % str(coder))
return coder.as_cloud_object(self.proto_context.coders)
def _get_side_input_encoding(self, input_encoding):
"""Returns an encoding for the output of a view transform.
Args:
input_encoding: encoding of current transform's input. Side inputs need
this because the service will check that input and output types match.
Returns:
An encoding that matches the output and input encoding. This is essential
for the View transforms introduced to produce side inputs to a ParDo.
"""
return {
'@type': 'kind:stream',
'component_encodings': [input_encoding],
'is_stream_like': {
'value': True
},
}
def _get_encoded_output_coder(self, transform_node, window_value=True):
"""Returns the cloud encoding of the coder for the output of a transform."""
from apache_beam.runners.portability.fn_api_runner_transforms import \
only_element
if len(transform_node.outputs) == 1:
output_tag = only_element(transform_node.outputs.keys())
# TODO(robertwb): Handle type hints for multi-output transforms.
element_type = transform_node.outputs[output_tag].element_type
else:
# TODO(silviuc): Remove this branch (and assert) when typehints are
# propagated everywhere. Returning an 'Any' as type hint will trigger
# usage of the fallback coder (i.e., cPickler).
element_type = typehints.Any
if window_value:
# All outputs have the same windowing. So getting the coder from an
# arbitrary window is fine.
output_tag = next(iter(transform_node.outputs.keys()))
window_coder = (
transform_node.outputs[output_tag].windowing.windowfn.
get_window_coder())
else:
window_coder = None
return self._get_typehint_based_encoding(element_type, window_coder)
def _add_step(self, step_kind, step_label, transform_node, side_tags=()):
"""Creates a Step object and adds it to the cache."""
# Import here to avoid adding the dependency for local running scenarios.
# pylint: disable=wrong-import-order, wrong-import-position
from apache_beam.runners.dataflow.internal import apiclient
step = apiclient.Step(step_kind, self._get_unique_step_name())
self.job.proto.steps.append(step.proto)
step.add_property(PropertyNames.USER_NAME, step_label)
# Cache the node/step association for the main output of the transform node.
# Main output key of external transforms can be ambiguous, so we only tag if
# there's only one tag instead of None.
from apache_beam.runners.portability.fn_api_runner_transforms import only_element
output_tag = (
only_element(transform_node.outputs.keys()) if len(
transform_node.outputs.keys()) == 1 else None)
self._cache.cache_output(transform_node, output_tag, step)
# If side_tags is not () then this is a multi-output transform node and we
# need to cache the (node, tag, step) for each of the tags used to access
# the outputs. This is essential because the keys used to search in the
# cache always contain the tag.
for tag in side_tags:
self._cache.cache_output(transform_node, tag, step)
# Finally, we add the display data items to the pipeline step.
# If the transform contains no display data then an empty list is added.
step.add_property(
PropertyNames.DISPLAY_DATA,
[
item.get_dict()
for item in DisplayData.create_from(transform_node.transform).items
])
return step
def _add_singleton_step(
self,
label,
full_label,
tag,
input_step,
windowing_strategy,
access_pattern):
"""Creates a CollectionToSingleton step used to handle ParDo side inputs."""
# Import here to avoid adding the dependency for local running scenarios.
from apache_beam.runners.dataflow.internal import apiclient
step = apiclient.Step(TransformNames.COLLECTION_TO_SINGLETON, label)
self.job.proto.steps.append(step.proto)
step.add_property(PropertyNames.USER_NAME, full_label)
step.add_property(
PropertyNames.PARALLEL_INPUT,
{
'@type': 'OutputReference',
PropertyNames.STEP_NAME: input_step.proto.name,
PropertyNames.OUTPUT_NAME: input_step.get_output(tag)
})
step.encoding = self._get_side_input_encoding(input_step.encoding)
output_info = {
PropertyNames.USER_NAME: '%s.%s' % (full_label, PropertyNames.OUTPUT),
PropertyNames.ENCODING: step.encoding,
PropertyNames.OUTPUT_NAME: PropertyNames.OUT
}
if common_urns.side_inputs.MULTIMAP.urn == access_pattern:
output_info[PropertyNames.USE_INDEXED_FORMAT] = True
step.add_property(PropertyNames.OUTPUT_INFO, [output_info])
step.add_property(
PropertyNames.WINDOWING_STRATEGY,
self.serialize_windowing_strategy(windowing_strategy))
return step
[docs] def run_Impulse(self, transform_node, options):
standard_options = options.view_as(StandardOptions)
debug_options = options.view_as(DebugOptions)
use_fn_api = (
debug_options.experiments and
'beam_fn_api' in debug_options.experiments)
use_streaming_engine = (
debug_options.experiments and
'enable_streaming_engine' in debug_options.experiments and
'enable_windmill_service' in debug_options.experiments)
step = self._add_step(
TransformNames.READ, transform_node.full_label, transform_node)
if (standard_options.streaming and
(not use_fn_api or not use_streaming_engine)):
step.add_property(PropertyNames.FORMAT, 'pubsub')
step.add_property(PropertyNames.PUBSUB_SUBSCRIPTION, '_starting_signal/')
else:
step.add_property(PropertyNames.FORMAT, 'impulse')
encoded_impulse_element = coders.WindowedValueCoder(
coders.BytesCoder(),
coders.coders.GlobalWindowCoder()).get_impl().encode_nested(
window.GlobalWindows.windowed_value(b''))
if use_fn_api:
encoded_impulse_as_str = self.byte_array_to_json_string(
encoded_impulse_element)
else:
encoded_impulse_as_str = base64.b64encode(
encoded_impulse_element).decode('ascii')
step.add_property(PropertyNames.IMPULSE_ELEMENT, encoded_impulse_as_str)
step.encoding = self._get_encoded_output_coder(transform_node)
step.add_property(
PropertyNames.OUTPUT_INFO,
[{
PropertyNames.USER_NAME: (
'%s.%s' % (transform_node.full_label, PropertyNames.OUT)),
PropertyNames.ENCODING: step.encoding,
PropertyNames.OUTPUT_NAME: PropertyNames.OUT
}])
[docs] def run_Flatten(self, transform_node, options):
step = self._add_step(
TransformNames.FLATTEN, transform_node.full_label, transform_node)
inputs = []
for one_input in transform_node.inputs:
input_step = self._cache.get_pvalue(one_input)
inputs.append({
'@type': 'OutputReference',
PropertyNames.STEP_NAME: input_step.proto.name,
PropertyNames.OUTPUT_NAME: input_step.get_output(one_input.tag)
})
step.add_property(PropertyNames.INPUTS, inputs)
step.encoding = self._get_encoded_output_coder(transform_node)
step.add_property(
PropertyNames.OUTPUT_INFO,
[{
PropertyNames.USER_NAME: (
'%s.%s' % (transform_node.full_label, PropertyNames.OUT)),
PropertyNames.ENCODING: step.encoding,
PropertyNames.OUTPUT_NAME: PropertyNames.OUT
}])
[docs] def apply_WriteToBigQuery(self, transform, pcoll, options):
# Make sure this is the WriteToBigQuery class that we expected, and that
# users did not specifically request the new BQ sink by passing experiment
# flag.
# TODO(BEAM-6928): Remove this function for release 2.14.0.
experiments = options.view_as(DebugOptions).experiments or []
if (not isinstance(transform, beam.io.WriteToBigQuery) or
'use_beam_bq_sink' in experiments):
return self.apply_PTransform(transform, pcoll, options)
if transform.schema == beam.io.gcp.bigquery.SCHEMA_AUTODETECT:
raise RuntimeError(
'Schema auto-detection is not supported on the native sink')
standard_options = options.view_as(StandardOptions)
if standard_options.streaming:
if (transform.write_disposition ==
beam.io.BigQueryDisposition.WRITE_TRUNCATE):
raise RuntimeError('Can not use write truncation mode in streaming')
return self.apply_PTransform(transform, pcoll, options)
else:
from apache_beam.io.gcp.bigquery_tools import parse_table_schema_from_json
schema = None
if transform.schema:
schema = parse_table_schema_from_json(json.dumps(transform.schema))
return pcoll | 'WriteToBigQuery' >> beam.io.Write(
beam.io.BigQuerySink(
transform.table_reference.tableId,
transform.table_reference.datasetId,
transform.table_reference.projectId,
schema,
transform.create_disposition,
transform.write_disposition,
kms_key=transform.kms_key))
[docs] def apply_GroupByKey(self, transform, pcoll, options):
# Infer coder of parent.
#
# TODO(ccy): make Coder inference and checking less specialized and more
# comprehensive.
parent = pcoll.producer
if parent:
coder = parent.transform._infer_output_coder() # pylint: disable=protected-access
if not coder:
coder = self._get_coder(pcoll.element_type or typehints.Any, None)
if not coder.is_kv_coder():
raise ValueError((
'Coder for the GroupByKey operation "%s" is not a '
'key-value coder: %s.') % (transform.label, coder))
# TODO(robertwb): Update the coder itself if it changed.
coders.registry.verify_deterministic(
coder.key_coder(), 'GroupByKey operation "%s"' % transform.label)
return pvalue.PCollection.from_(pcoll)
[docs] def run_GroupByKey(self, transform_node, options):
input_tag = transform_node.inputs[0].tag
input_step = self._cache.get_pvalue(transform_node.inputs[0])
step = self._add_step(
TransformNames.GROUP, transform_node.full_label, transform_node)
step.add_property(
PropertyNames.PARALLEL_INPUT,
{
'@type': 'OutputReference',
PropertyNames.STEP_NAME: input_step.proto.name,
PropertyNames.OUTPUT_NAME: input_step.get_output(input_tag)
})
step.encoding = self._get_encoded_output_coder(transform_node)
step.add_property(
PropertyNames.OUTPUT_INFO,
[{
PropertyNames.USER_NAME: (
'%s.%s' % (transform_node.full_label, PropertyNames.OUT)),
PropertyNames.ENCODING: step.encoding,
PropertyNames.OUTPUT_NAME: PropertyNames.OUT
}])
windowing = transform_node.transform.get_windowing(transform_node.inputs)
step.add_property(
PropertyNames.SERIALIZED_FN,
self.serialize_windowing_strategy(windowing))
[docs] def run_ParDo(self, transform_node, options):
transform = transform_node.transform
input_tag = transform_node.inputs[0].tag
input_step = self._cache.get_pvalue(transform_node.inputs[0])
# Attach side inputs.
si_dict = {}
si_labels = {}
full_label_counts = defaultdict(int)
lookup_label = lambda side_pval: si_labels[side_pval]
named_inputs = transform_node.named_inputs()
label_renames = {}
for ix, side_pval in enumerate(transform_node.side_inputs):
assert isinstance(side_pval, AsSideInput)
step_name = 'SideInput-' + self._get_unique_step_name()
si_label = 'side%d-%s' % (ix, transform_node.full_label)
old_label = 'side%d' % ix
label_renames[old_label] = si_label
assert old_label in named_inputs
pcollection_label = '%s.%s' % (
side_pval.pvalue.producer.full_label.split('/')[-1],
side_pval.pvalue.tag if side_pval.pvalue.tag else 'out')
si_full_label = '%s/%s(%s.%s)' % (
transform_node.full_label,
side_pval.__class__.__name__,
pcollection_label,
full_label_counts[pcollection_label])
# Count the number of times the same PCollection is a side input
# to the same ParDo.
full_label_counts[pcollection_label] += 1
self._add_singleton_step(
step_name,
si_full_label,
side_pval.pvalue.tag,
self._cache.get_pvalue(side_pval.pvalue),
side_pval.pvalue.windowing,
side_pval._side_input_data().access_pattern)
si_dict[si_label] = {
'@type': 'OutputReference',
PropertyNames.STEP_NAME: step_name,
PropertyNames.OUTPUT_NAME: PropertyNames.OUT
}
si_labels[side_pval] = si_label
# Now create the step for the ParDo transform being handled.
transform_name = transform_node.full_label.rsplit('/', 1)[-1]
step = self._add_step(
TransformNames.DO,
transform_node.full_label +
('/{}'.format(transform_name) if transform_node.side_inputs else ''),
transform_node,
transform_node.transform.output_tags)
# Import here to avoid adding the dependency for local running scenarios.
# pylint: disable=wrong-import-order, wrong-import-position
from apache_beam.runners.dataflow.internal import apiclient
transform_proto = self.proto_context.transforms.get_proto(transform_node)
transform_id = self.proto_context.transforms.get_id(transform_node)
use_fnapi = apiclient._use_fnapi(options)
use_unified_worker = apiclient._use_unified_worker(options)
# The data transmitted in SERIALIZED_FN is different depending on whether
# this is a fnapi pipeline or not.
if (use_fnapi and
(transform_proto.spec.urn == common_urns.primitives.PAR_DO.urn or
use_unified_worker)):
# Patch side input ids to be unique across a given pipeline.
if (label_renames and
transform_proto.spec.urn == common_urns.primitives.PAR_DO.urn):
# Patch PTransform proto.
for old, new in iteritems(label_renames):
transform_proto.inputs[new] = transform_proto.inputs[old]
del transform_proto.inputs[old]
# Patch ParDo proto.
proto_type, _ = beam.PTransform._known_urns[transform_proto.spec.urn]
proto = proto_utils.parse_Bytes(
transform_proto.spec.payload, proto_type)
for old, new in iteritems(label_renames):
proto.side_inputs[new].CopyFrom(proto.side_inputs[old])
del proto.side_inputs[old]
transform_proto.spec.payload = proto.SerializeToString()
# We need to update the pipeline proto.
del self.proto_pipeline.components.transforms[transform_id]
(
self.proto_pipeline.components.transforms[transform_id].CopyFrom(
transform_proto))
serialized_data = transform_id
else:
serialized_data = pickler.dumps(
self._pardo_fn_data(transform_node, lookup_label))
step.add_property(PropertyNames.SERIALIZED_FN, serialized_data)
# TODO(BEAM-8882): Enable once dataflow service doesn't reject this.
# step.add_property(PropertyNames.PIPELINE_PROTO_TRANSFORM_ID, transform_id)
step.add_property(
PropertyNames.PARALLEL_INPUT,
{
'@type': 'OutputReference',
PropertyNames.STEP_NAME: input_step.proto.name,
PropertyNames.OUTPUT_NAME: input_step.get_output(input_tag)
})
# Add side inputs if any.
step.add_property(PropertyNames.NON_PARALLEL_INPUTS, si_dict)
# Generate description for the outputs. The output names
# will be 'None' for main output and '<tag>' for a tagged output.
outputs = []
step.encoding = self._get_encoded_output_coder(transform_node)
all_output_tags = transform_proto.outputs.keys()
from apache_beam.transforms.core import RunnerAPIPTransformHolder
external_transform = isinstance(transform, RunnerAPIPTransformHolder)
# Some external transforms require output tags to not be modified.
# So we randomly select one of the output tags as the main output and
# leave others as side outputs. Transform execution should not change
# dependending on which output tag we choose as the main output here.
# Also, some SDKs do not work correctly if output tags are modified. So for
# external transforms, we leave tags unmodified.
#
# Python SDK uses 'None' as the tag of the main output.
main_output_tag = (all_output_tags[0] if external_transform else 'None')
side_output_tags = set(all_output_tags).difference({main_output_tag})
# Add the main output to the description.
outputs.append({
PropertyNames.USER_NAME: (
'%s.%s' % (transform_node.full_label, PropertyNames.OUT)),
PropertyNames.ENCODING: step.encoding,
PropertyNames.OUTPUT_NAME: main_output_tag
})
for side_tag in side_output_tags:
# The assumption here is that all outputs will have the same typehint
# and coder as the main output. This is certainly the case right now
# but conceivably it could change in the future.
outputs.append({
PropertyNames.USER_NAME: (
'%s.%s' % (transform_node.full_label, side_tag)),
PropertyNames.ENCODING: step.encoding,
PropertyNames.OUTPUT_NAME: side_tag
})
step.add_property(PropertyNames.OUTPUT_INFO, outputs)
# Add the restriction encoding if we are a splittable DoFn
# and are using the Fn API on the unified worker.
restriction_coder = transform.get_restriction_coder()
if restriction_coder:
step.add_property(
PropertyNames.RESTRICTION_ENCODING,
self._get_cloud_encoding(restriction_coder))
if options.view_as(StandardOptions).streaming and DoFnSignature(
transform.dofn).is_stateful_dofn():
step.add_property(PropertyNames.USES_KEYED_STATE, "true")
@staticmethod
def _pardo_fn_data(transform_node, get_label):
transform = transform_node.transform
si_tags_and_types = [ # pylint: disable=protected-access
(get_label(side_pval), side_pval.__class__, side_pval._view_options())
for side_pval in transform_node.side_inputs]
return (
transform.fn,
transform.args,
transform.kwargs,
si_tags_and_types,
transform_node.inputs[0].windowing)
[docs] def apply_CombineValues(self, transform, pcoll, options):
return pvalue.PCollection.from_(pcoll)
[docs] def run_CombineValues(self, transform_node, options):
transform = transform_node.transform
input_tag = transform_node.inputs[0].tag
input_step = self._cache.get_pvalue(transform_node.inputs[0])
step = self._add_step(
TransformNames.COMBINE, transform_node.full_label, transform_node)
transform_id = self.proto_context.transforms.get_id(transform_node.parent)
# The data transmitted in SERIALIZED_FN is different depending on whether
# this is a fnapi pipeline or not.
from apache_beam.runners.dataflow.internal import apiclient
use_fnapi = apiclient._use_fnapi(options)
if use_fnapi:
# Fnapi pipelines send the transform ID of the CombineValues transform's
# parent composite because Dataflow expects the ID of a CombinePerKey
# transform.
serialized_data = transform_id
else:
# Combiner functions do not take deferred side-inputs (i.e. PValues) and
# therefore the code to handle extra args/kwargs is simpler than for the
# DoFn's of the ParDo transform. In the last, empty argument is where
# side inputs information would go.
serialized_data = pickler.dumps(
(transform.fn, transform.args, transform.kwargs, ()))
step.add_property(PropertyNames.SERIALIZED_FN, serialized_data)
# TODO(BEAM-8882): Enable once dataflow service doesn't reject this.
# step.add_property(PropertyNames.PIPELINE_PROTO_TRANSFORM_ID, transform_id)
step.add_property(
PropertyNames.PARALLEL_INPUT,
{
'@type': 'OutputReference',
PropertyNames.STEP_NAME: input_step.proto.name,
PropertyNames.OUTPUT_NAME: input_step.get_output(input_tag)
})
# Note that the accumulator must not have a WindowedValue encoding, while
# the output of this step does in fact have a WindowedValue encoding.
accumulator_encoding = self._get_cloud_encoding(
transform_node.transform.fn.get_accumulator_coder())
output_encoding = self._get_encoded_output_coder(transform_node)
step.encoding = output_encoding
step.add_property(PropertyNames.ENCODING, accumulator_encoding)
# Generate description for main output 'out.'
outputs = []
# Add the main output to the description.
outputs.append({
PropertyNames.USER_NAME: (
'%s.%s' % (transform_node.full_label, PropertyNames.OUT)),
PropertyNames.ENCODING: step.encoding,
PropertyNames.OUTPUT_NAME: PropertyNames.OUT
})
step.add_property(PropertyNames.OUTPUT_INFO, outputs)
[docs] def apply_Read(self, transform, pbegin, options):
if hasattr(transform.source, 'format'):
# Consider native Read to be a primitive for dataflow.
return beam.pvalue.PCollection.from_(pbegin)
else:
return self.apply_PTransform(transform, pbegin, options)
[docs] def run_Read(self, transform_node, options):
transform = transform_node.transform
step = self._add_step(
TransformNames.READ, transform_node.full_label, transform_node)
# TODO(mairbek): refactor if-else tree to use registerable functions.
# Initialize the source specific properties.
standard_options = options.view_as(StandardOptions)
if not hasattr(transform.source, 'format'):
# If a format is not set, we assume the source to be a custom source.
source_dict = {}
source_dict['spec'] = {
'@type': names.SOURCE_TYPE,
names.SERIALIZED_SOURCE_KEY: pickler.dumps(transform.source)
}
try:
source_dict['metadata'] = {
'estimated_size_bytes': json_value.get_typed_value_descriptor(
transform.source.estimate_size())
}
except error.RuntimeValueProviderError:
# Size estimation is best effort, and this error is by value provider.
_LOGGER.info(
'Could not estimate size of source %r due to ' + \
'RuntimeValueProviderError', transform.source)
except Exception: # pylint: disable=broad-except
# Size estimation is best effort. So we log the error and continue.
_LOGGER.info(
'Could not estimate size of source %r due to an exception: %s',
transform.source,
traceback.format_exc())
step.add_property(PropertyNames.SOURCE_STEP_INPUT, source_dict)
elif transform.source.format == 'text':
step.add_property(PropertyNames.FILE_PATTERN, transform.source.path)
elif transform.source.format == 'bigquery':
if standard_options.streaming:
raise ValueError(
'BigQuery source is not currently available for use '
'in streaming pipelines.')
step.add_property(PropertyNames.BIGQUERY_EXPORT_FORMAT, 'FORMAT_AVRO')
# TODO(silviuc): Add table validation if transform.source.validate.
if transform.source.table_reference is not None:
step.add_property(
PropertyNames.BIGQUERY_DATASET,
transform.source.table_reference.datasetId)
step.add_property(
PropertyNames.BIGQUERY_TABLE,
transform.source.table_reference.tableId)
# If project owning the table was not specified then the project owning
# the workflow (current project) will be used.
if transform.source.table_reference.projectId is not None:
step.add_property(
PropertyNames.BIGQUERY_PROJECT,
transform.source.table_reference.projectId)
elif transform.source.query is not None:
step.add_property(PropertyNames.BIGQUERY_QUERY, transform.source.query)
step.add_property(
PropertyNames.BIGQUERY_USE_LEGACY_SQL,
transform.source.use_legacy_sql)
step.add_property(
PropertyNames.BIGQUERY_FLATTEN_RESULTS,
transform.source.flatten_results)
else:
raise ValueError(
'BigQuery source %r must specify either a table or'
' a query' % transform.source)
if transform.source.kms_key is not None:
step.add_property(
PropertyNames.BIGQUERY_KMS_KEY, transform.source.kms_key)
elif transform.source.format == 'pubsub':
if not standard_options.streaming:
raise ValueError(
'Cloud Pub/Sub is currently available for use '
'only in streaming pipelines.')
# Only one of topic or subscription should be set.
if transform.source.full_subscription:
step.add_property(
PropertyNames.PUBSUB_SUBSCRIPTION,
transform.source.full_subscription)
elif transform.source.full_topic:
step.add_property(
PropertyNames.PUBSUB_TOPIC, transform.source.full_topic)
if transform.source.id_label:
step.add_property(
PropertyNames.PUBSUB_ID_LABEL, transform.source.id_label)
if transform.source.with_attributes:
# Setting this property signals Dataflow runner to return full
# PubsubMessages instead of just the data part of the payload.
step.add_property(PropertyNames.PUBSUB_SERIALIZED_ATTRIBUTES_FN, '')
if transform.source.timestamp_attribute is not None:
step.add_property(
PropertyNames.PUBSUB_TIMESTAMP_ATTRIBUTE,
transform.source.timestamp_attribute)
else:
raise ValueError(
'Source %r has unexpected format %s.' %
(transform.source, transform.source.format))
if not hasattr(transform.source, 'format'):
step.add_property(PropertyNames.FORMAT, names.SOURCE_FORMAT)
else:
step.add_property(PropertyNames.FORMAT, transform.source.format)
# Wrap coder in WindowedValueCoder: this is necessary as the encoding of a
# step should be the type of value outputted by each step. Read steps
# automatically wrap output values in a WindowedValue wrapper, if necessary.
# This is also necessary for proper encoding for size estimation.
# Using a GlobalWindowCoder as a place holder instead of the default
# PickleCoder because GlobalWindowCoder is known coder.
# TODO(robertwb): Query the collection for the windowfn to extract the
# correct coder.
coder = coders.WindowedValueCoder(
coders.registry.get_coder(transform_node.outputs[None].element_type),
coders.coders.GlobalWindowCoder())
step.encoding = self._get_cloud_encoding(coder)
step.add_property(
PropertyNames.OUTPUT_INFO,
[{
PropertyNames.USER_NAME: (
'%s.%s' % (transform_node.full_label, PropertyNames.OUT)),
PropertyNames.ENCODING: step.encoding,
PropertyNames.OUTPUT_NAME: PropertyNames.OUT
}])
[docs] def run__NativeWrite(self, transform_node, options):
transform = transform_node.transform
input_tag = transform_node.inputs[0].tag
input_step = self._cache.get_pvalue(transform_node.inputs[0])
step = self._add_step(
TransformNames.WRITE, transform_node.full_label, transform_node)
# TODO(mairbek): refactor if-else tree to use registerable functions.
# Initialize the sink specific properties.
if transform.sink.format == 'text':
# Note that it is important to use typed properties (@type/value dicts)
# for non-string properties and also for empty strings. For example,
# in the code below the num_shards must have type and also
# file_name_suffix and shard_name_template (could be empty strings).
step.add_property(
PropertyNames.FILE_NAME_PREFIX,
transform.sink.file_name_prefix,
with_type=True)
step.add_property(
PropertyNames.FILE_NAME_SUFFIX,
transform.sink.file_name_suffix,
with_type=True)
step.add_property(
PropertyNames.SHARD_NAME_TEMPLATE,
transform.sink.shard_name_template,
with_type=True)
if transform.sink.num_shards > 0:
step.add_property(
PropertyNames.NUM_SHARDS, transform.sink.num_shards, with_type=True)
# TODO(silviuc): Implement sink validation.
step.add_property(PropertyNames.VALIDATE_SINK, False, with_type=True)
elif transform.sink.format == 'bigquery':
# TODO(silviuc): Add table validation if transform.sink.validate.
step.add_property(
PropertyNames.BIGQUERY_DATASET,
transform.sink.table_reference.datasetId)
step.add_property(
PropertyNames.BIGQUERY_TABLE, transform.sink.table_reference.tableId)
# If project owning the table was not specified then the project owning
# the workflow (current project) will be used.
if transform.sink.table_reference.projectId is not None:
step.add_property(
PropertyNames.BIGQUERY_PROJECT,
transform.sink.table_reference.projectId)
step.add_property(
PropertyNames.BIGQUERY_CREATE_DISPOSITION,
transform.sink.create_disposition)
step.add_property(
PropertyNames.BIGQUERY_WRITE_DISPOSITION,
transform.sink.write_disposition)
if transform.sink.table_schema is not None:
step.add_property(
PropertyNames.BIGQUERY_SCHEMA, transform.sink.schema_as_json())
if transform.sink.kms_key is not None:
step.add_property(
PropertyNames.BIGQUERY_KMS_KEY, transform.sink.kms_key)
elif transform.sink.format == 'pubsub':
standard_options = options.view_as(StandardOptions)
if not standard_options.streaming:
raise ValueError(
'Cloud Pub/Sub is currently available for use '
'only in streaming pipelines.')
step.add_property(PropertyNames.PUBSUB_TOPIC, transform.sink.full_topic)
if transform.sink.id_label:
step.add_property(
PropertyNames.PUBSUB_ID_LABEL, transform.sink.id_label)
if transform.sink.with_attributes:
# Setting this property signals Dataflow runner that the PCollection
# contains PubsubMessage objects instead of just raw data.
step.add_property(PropertyNames.PUBSUB_SERIALIZED_ATTRIBUTES_FN, '')
if transform.sink.timestamp_attribute is not None:
step.add_property(
PropertyNames.PUBSUB_TIMESTAMP_ATTRIBUTE,
transform.sink.timestamp_attribute)
else:
raise ValueError(
'Sink %r has unexpected format %s.' %
(transform.sink, transform.sink.format))
step.add_property(PropertyNames.FORMAT, transform.sink.format)
# Wrap coder in WindowedValueCoder: this is necessary for proper encoding
# for size estimation. Using a GlobalWindowCoder as a place holder instead
# of the default PickleCoder because GlobalWindowCoder is known coder.
# TODO(robertwb): Query the collection for the windowfn to extract the
# correct coder.
coder = coders.WindowedValueCoder(
transform.sink.coder, coders.coders.GlobalWindowCoder())
step.encoding = self._get_cloud_encoding(coder)
step.add_property(PropertyNames.ENCODING, step.encoding)
step.add_property(
PropertyNames.PARALLEL_INPUT,
{
'@type': 'OutputReference',
PropertyNames.STEP_NAME: input_step.proto.name,
PropertyNames.OUTPUT_NAME: input_step.get_output(input_tag)
})
[docs] def run_TestStream(self, transform_node, options):
from apache_beam.testing.test_stream import ElementEvent
from apache_beam.testing.test_stream import ProcessingTimeEvent
from apache_beam.testing.test_stream import WatermarkEvent
standard_options = options.view_as(StandardOptions)
if not standard_options.streaming:
raise ValueError(
'TestStream is currently available for use '
'only in streaming pipelines.')
transform = transform_node.transform
step = self._add_step(
TransformNames.READ, transform_node.full_label, transform_node)
step.add_property(PropertyNames.FORMAT, 'test_stream')
test_stream_payload = beam_runner_api_pb2.TestStreamPayload()
# TestStream source doesn't do any decoding of elements,
# so we won't set test_stream_payload.coder_id.
output_coder = transform._infer_output_coder() # pylint: disable=protected-access
for event in transform._events:
new_event = test_stream_payload.events.add()
if isinstance(event, ElementEvent):
for tv in event.timestamped_values:
element = new_event.element_event.elements.add()
element.encoded_element = output_coder.encode(tv.value)
element.timestamp = tv.timestamp.micros
elif isinstance(event, ProcessingTimeEvent):
new_event.processing_time_event.advance_duration = (
event.advance_by.micros)
elif isinstance(event, WatermarkEvent):
new_event.watermark_event.new_watermark = event.new_watermark.micros
serialized_payload = self.byte_array_to_json_string(
test_stream_payload.SerializeToString())
step.add_property(PropertyNames.SERIALIZED_TEST_STREAM, serialized_payload)
step.encoding = self._get_encoded_output_coder(transform_node)
step.add_property(
PropertyNames.OUTPUT_INFO,
[{
PropertyNames.USER_NAME: (
'%s.%s' % (transform_node.full_label, PropertyNames.OUT)),
PropertyNames.ENCODING: step.encoding,
PropertyNames.OUTPUT_NAME: PropertyNames.OUT
}])
# We must mark this method as not a test or else its name is a matcher for
# nosetest tests.
run_TestStream.__test__ = False
[docs] @classmethod
def serialize_windowing_strategy(cls, windowing):
from apache_beam.runners import pipeline_context
context = pipeline_context.PipelineContext()
windowing_proto = windowing.to_runner_api(context)
return cls.byte_array_to_json_string(
beam_runner_api_pb2.MessageWithComponents(
components=context.to_runner_api(),
windowing_strategy=windowing_proto).SerializeToString())
[docs] @classmethod
def deserialize_windowing_strategy(cls, serialized_data):
# Imported here to avoid circular dependencies.
# pylint: disable=wrong-import-order, wrong-import-position
from apache_beam.runners import pipeline_context
from apache_beam.transforms.core import Windowing
proto = beam_runner_api_pb2.MessageWithComponents()
proto.ParseFromString(cls.json_string_to_byte_array(serialized_data))
return Windowing.from_runner_api(
proto.windowing_strategy,
pipeline_context.PipelineContext(proto.components))
[docs] @staticmethod
def byte_array_to_json_string(raw_bytes):
"""Implements org.apache.beam.sdk.util.StringUtils.byteArrayToJsonString."""
return quote(raw_bytes)
[docs] @staticmethod
def json_string_to_byte_array(encoded_string):
"""Implements org.apache.beam.sdk.util.StringUtils.jsonStringToByteArray."""
return unquote_to_bytes(encoded_string)
class _DataflowSideInput(beam.pvalue.AsSideInput):
"""Wraps a side input as a dataflow-compatible side input."""
def _view_options(self):
return {
'data': self._data,
}
def _side_input_data(self):
return self._data
class _DataflowIterableAsMultimapSideInput(_DataflowSideInput):
"""Wraps an iterable side input as dataflow-compatible side input."""
def __init__(self, side_input):
# pylint: disable=protected-access
side_input_data = side_input._side_input_data()
assert (
side_input_data.access_pattern == common_urns.side_inputs.ITERABLE.urn)
iterable_view_fn = side_input_data.view_fn
self._data = beam.pvalue.SideInputData(
common_urns.side_inputs.MULTIMAP.urn,
side_input_data.window_mapping_fn,
lambda multimap: iterable_view_fn(multimap[b'']))
class _DataflowIterableSideInput(_DataflowSideInput):
"""Wraps an iterable side input as dataflow-compatible side input."""
def __init__(self, side_input):
# pylint: disable=protected-access
self.pvalue = side_input.pvalue
side_input_data = side_input._side_input_data()
assert (
side_input_data.access_pattern == common_urns.side_inputs.ITERABLE.urn)
self._data = beam.pvalue.SideInputData(
common_urns.side_inputs.ITERABLE.urn,
side_input_data.window_mapping_fn,
side_input_data.view_fn)
class _DataflowMultimapSideInput(_DataflowSideInput):
"""Wraps a multimap side input as dataflow-compatible side input."""
def __init__(self, side_input):
# pylint: disable=protected-access
self.pvalue = side_input.pvalue
side_input_data = side_input._side_input_data()
assert (
side_input_data.access_pattern == common_urns.side_inputs.MULTIMAP.urn)
self._data = beam.pvalue.SideInputData(
common_urns.side_inputs.MULTIMAP.urn,
side_input_data.window_mapping_fn,
side_input_data.view_fn)
class DataflowPipelineResult(PipelineResult):
"""Represents the state of a pipeline run on the Dataflow service."""
def __init__(self, job, runner):
"""Initialize a new DataflowPipelineResult instance.
Args:
job: Job message from the Dataflow API. Could be :data:`None` if a job
request was not sent to Dataflow service (e.g. template jobs).
runner: DataflowRunner instance.
"""
self._job = job
self._runner = runner
self.metric_results = None
def _update_job(self):
# We need the job id to be able to update job information. There is no need
# to update the job if we are in a known terminal state.
if self.has_job and not self.is_in_terminal_state():
self._job = self._runner.dataflow_client.get_job(self.job_id())
def job_id(self):
return self._job.id
def metrics(self):
return self.metric_results
@property
def has_job(self):
return self._job is not None
def _get_job_state(self):
values_enum = dataflow_api.Job.CurrentStateValueValuesEnum
# Ordered by the enum values. Values that may be introduced in
# future versions of Dataflow API are considered UNRECOGNIZED by the SDK.
api_jobstate_map = defaultdict(
lambda: PipelineState.UNRECOGNIZED,
{
values_enum.JOB_STATE_UNKNOWN: PipelineState.UNKNOWN,
values_enum.JOB_STATE_STOPPED: PipelineState.STOPPED,
values_enum.JOB_STATE_RUNNING: PipelineState.RUNNING,
values_enum.JOB_STATE_DONE: PipelineState.DONE,
values_enum.JOB_STATE_FAILED: PipelineState.FAILED,
values_enum.JOB_STATE_CANCELLED: PipelineState.CANCELLED,
values_enum.JOB_STATE_UPDATED: PipelineState.UPDATED,
values_enum.JOB_STATE_DRAINING: PipelineState.DRAINING,
values_enum.JOB_STATE_DRAINED: PipelineState.DRAINED,
values_enum.JOB_STATE_PENDING: PipelineState.PENDING,
values_enum.JOB_STATE_CANCELLING: PipelineState.CANCELLING,
})
return (
api_jobstate_map[self._job.currentState]
if self._job.currentState else PipelineState.UNKNOWN)
@property
def state(self):
"""Return the current state of the remote job.
Returns:
A PipelineState object.
"""
if not self.has_job:
return PipelineState.UNKNOWN
self._update_job()
return self._get_job_state()
def is_in_terminal_state(self):
if not self.has_job:
return True
return PipelineState.is_terminal(self._get_job_state())
def wait_until_finish(self, duration=None):
if not self.is_in_terminal_state():
if not self.has_job:
raise IOError('Failed to get the Dataflow job id.')
thread = threading.Thread(
target=DataflowRunner.poll_for_job_completion,
args=(self._runner, self, duration))
# Mark the thread as a daemon thread so a keyboard interrupt on the main
# thread will terminate everything. This is also the reason we will not
# use thread.join() to wait for the polling thread.
thread.daemon = True
thread.start()
while thread.isAlive():
time.sleep(5.0)
# TODO: Merge the termination code in poll_for_job_completion and
# is_in_terminal_state.
terminated = self.is_in_terminal_state()
assert duration or terminated, (
'Job did not reach to a terminal state after waiting indefinitely.')
if terminated and self.state != PipelineState.DONE:
# TODO(BEAM-1290): Consider converting this to an error log based on
# theresolution of the issue.
raise DataflowRuntimeException(
'Dataflow pipeline failed. State: %s, Error:\n%s' %
(self.state, getattr(self._runner, 'last_error_msg', None)),
self)
return self.state
def cancel(self):
if not self.has_job:
raise IOError('Failed to get the Dataflow job id.')
self._update_job()
if self.is_in_terminal_state():
_LOGGER.warning(
'Cancel failed because job %s is already terminated in state %s.',
self.job_id(),
self.state)
else:
if not self._runner.dataflow_client.modify_job_state(
self.job_id(), 'JOB_STATE_CANCELLED'):
cancel_failed_message = (
'Failed to cancel job %s, please go to the Developers Console to '
'cancel it manually.') % self.job_id()
_LOGGER.error(cancel_failed_message)
raise DataflowRuntimeException(cancel_failed_message, self)
return self.state
def __str__(self):
return '<%s %s %s>' % (self.__class__.__name__, self.job_id(), self.state)
def __repr__(self):
return '<%s %s at %s>' % (self.__class__.__name__, self._job, hex(id(self)))
class DataflowRuntimeException(Exception):
"""Indicates an error has occurred in running this pipeline."""
def __init__(self, msg, result):
super(DataflowRuntimeException, self).__init__(msg)
self.result = result