#
# 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.
#
"""Pipeline options obtained from command line parsing."""
from __future__ import absolute_import
import argparse
import json
import logging
from builtins import list
from builtins import object
from apache_beam.options.value_provider import RuntimeValueProvider
from apache_beam.options.value_provider import StaticValueProvider
from apache_beam.options.value_provider import ValueProvider
from apache_beam.transforms.display import HasDisplayData
__all__ = [
'PipelineOptions',
'StandardOptions',
'TypeOptions',
'DirectOptions',
'GoogleCloudOptions',
'HadoopFileSystemOptions',
'WorkerOptions',
'DebugOptions',
'ProfilingOptions',
'SetupOptions',
'TestOptions',
]
def _static_value_provider_of(value_type):
""""Helper function to plug a ValueProvider into argparse.
Args:
value_type: the type of the value. Since the type param of argparse's
add_argument will always be ValueProvider, we need to
preserve the type of the actual value.
Returns:
A partially constructed StaticValueProvider in the form of a function.
"""
def _f(value):
_f.__name__ = value_type.__name__
return StaticValueProvider(value_type, value)
return _f
class _BeamArgumentParser(argparse.ArgumentParser):
"""An ArgumentParser that supports ValueProvider options.
Example Usage::
class TemplateUserOptions(PipelineOptions):
@classmethod
def _add_argparse_args(cls, parser):
parser.add_value_provider_argument('--vp_arg1', default='start')
parser.add_value_provider_argument('--vp_arg2')
parser.add_argument('--non_vp_arg')
"""
def add_value_provider_argument(self, *args, **kwargs):
"""ValueProvider arguments can be either of type keyword or positional.
At runtime, even positional arguments will need to be supplied in the
key/value form.
"""
# Extract the option name from positional argument ['pos_arg']
assert args != () and len(args[0]) >= 1
if args[0][0] != '-':
option_name = args[0]
if kwargs.get('nargs') is None: # make them optionally templated
kwargs['nargs'] = '?'
else:
# or keyword arguments like [--kw_arg, -k, -w] or [--kw-arg]
option_name = [i.replace('--', '') for i in args if i[:2] == '--'][0]
# reassign the type to make room for using
# StaticValueProvider as the type for add_argument
value_type = kwargs.get('type') or str
kwargs['type'] = _static_value_provider_of(value_type)
# reassign default to default_value to make room for using
# RuntimeValueProvider as the default for add_argument
default_value = kwargs.get('default')
kwargs['default'] = RuntimeValueProvider(
option_name=option_name,
value_type=value_type,
default_value=default_value
)
# have add_argument do most of the work
self.add_argument(*args, **kwargs)
# The argparse package by default tries to autocomplete option names. This
# results in an "ambiguous option" error from argparse when an unknown option
# matching multiple known ones are used. This suppresses that behavior.
def error(self, message):
if message.startswith('ambiguous option: '):
return
super(_BeamArgumentParser, self).error(message)
[docs]class PipelineOptions(HasDisplayData):
"""This class and subclasses are used as containers for command line options.
These classes are wrappers over the standard argparse Python module
(see https://docs.python.org/3/library/argparse.html). To define one option
or a group of options, create a subclass from PipelineOptions.
Example Usage::
class XyzOptions(PipelineOptions):
@classmethod
def _add_argparse_args(cls, parser):
parser.add_argument('--abc', default='start')
parser.add_argument('--xyz', default='end')
The arguments for the add_argument() method are exactly the ones
described in the argparse public documentation.
Pipeline objects require an options object during initialization.
This is obtained simply by initializing an options class as defined above.
Example Usage::
p = Pipeline(options=XyzOptions())
if p.options.xyz == 'end':
raise ValueError('Option xyz has an invalid value.')
Instances of PipelineOptions or any of its subclass have access to values
defined by other PipelineOption subclasses (see get_all_options()), and
can be converted to an instance of another PipelineOptions subclass
(see view_as()). All views share the underlying data structure that stores
option key-value pairs.
By default the options classes will use command line arguments to initialize
the options.
"""
def __init__(self, flags=None, **kwargs):
"""Initialize an options class.
The initializer will traverse all subclasses, add all their argparse
arguments and then parse the command line specified by flags or by default
the one obtained from sys.argv.
The subclasses of PipelineOptions do not need to redefine __init__.
Args:
flags: An iterable of command line arguments to be used. If not specified
then sys.argv will be used as input for parsing arguments.
**kwargs: Add overrides for arguments passed in flags.
"""
# self._flags stores a list of not yet parsed arguments, typically,
# command-line flags. This list is shared across different views.
# See: view_as().
self._flags = flags
# Build parser that will parse options recognized by the [sub]class of
# PipelineOptions whose object is being instantiated.
parser = _BeamArgumentParser()
for cls in type(self).mro():
if cls == PipelineOptions:
break
elif '_add_argparse_args' in cls.__dict__:
cls._add_argparse_args(parser)
# The _visible_options attribute will contain options that were recognized
# by the parser.
self._visible_options, _ = parser.parse_known_args(flags)
# self._all_options is initialized with overrides to flag values,
# provided in kwargs, and will store key-value pairs for options recognized
# by current PipelineOptions [sub]class and its views that may be created.
# See: view_as().
# This dictionary is shared across different views, and is lazily updated
# as each new views are created.
# Users access this dictionary store via __getattr__ / __setattr__ methods.
self._all_options = kwargs
# Initialize values of keys defined by this class.
for option_name in self._visible_option_list():
# Note that options specified in kwargs will not be overwritten.
if option_name not in self._all_options:
self._all_options[option_name] = getattr(self._visible_options,
option_name)
@classmethod
def _add_argparse_args(cls, parser):
# Override this in subclasses to provide options.
pass
[docs] @classmethod
def from_dictionary(cls, options):
"""Returns a PipelineOptions from a dictionary of arguments.
Args:
options: Dictionary of argument value pairs.
Returns:
A PipelineOptions object representing the given arguments.
"""
flags = []
for k, v in options.items():
if isinstance(v, bool):
if v:
flags.append('--%s' % k)
elif isinstance(v, list):
for i in v:
flags.append('--%s=%s' % (k, i))
else:
flags.append('--%s=%s' % (k, v))
return cls(flags)
[docs] def get_all_options(self, drop_default=False, add_extra_args_fn=None):
"""Returns a dictionary of all defined arguments.
Returns a dictionary of all defined arguments (arguments that are defined in
any subclass of PipelineOptions) into a dictionary.
Args:
drop_default: If set to true, options that are equal to their default
values, are not returned as part of the result dictionary.
add_extra_args_fn: Callback to populate additional arguments, can be used
by runner to supply otherwise unknown args.
Returns:
Dictionary of all args and values.
"""
# TODO(BEAM-1319): PipelineOption sub-classes in the main session might be
# repeated. Pick last unique instance of each subclass to avoid conflicts.
subset = {}
parser = _BeamArgumentParser()
for cls in PipelineOptions.__subclasses__():
subset[str(cls)] = cls
for cls in subset.values():
cls._add_argparse_args(parser) # pylint: disable=protected-access
if add_extra_args_fn:
add_extra_args_fn(parser)
known_args, unknown_args = parser.parse_known_args(self._flags)
if unknown_args:
logging.warning("Discarding unparseable args: %s", unknown_args)
result = vars(known_args)
# Apply the overrides if any
for k in list(result):
if k in self._all_options:
result[k] = self._all_options[k]
if (drop_default and
parser.get_default(k) == result[k] and
not isinstance(parser.get_default(k), ValueProvider)):
del result[k]
return result
[docs] def display_data(self):
return self.get_all_options(True)
[docs] def view_as(self, cls):
"""Returns a view of current object as provided PipelineOption subclass.
Example Usage::
options = PipelineOptions(['--runner', 'Direct', '--streaming'])
standard_options = options.view_as(StandardOptions)
if standard_options.streaming:
# ... start a streaming job ...
Note that options objects may have multiple views, and modifications
of values in any view-object will apply to current object and other
view-objects.
Args:
cls: PipelineOptions class or any of its subclasses.
Returns:
An instance of cls that is intitialized using options contained in current
object.
"""
view = cls(self._flags)
for option_name in view._visible_option_list():
# Initialize values of keys defined by a cls.
#
# Note that we do initialization only once per key to make sure that
# values in _all_options dict are not-recreated with each new view.
# This is important to make sure that values of multi-options keys are
# backed by the same list across multiple views, and that any overrides of
# pipeline options already stored in _all_options are preserved.
if option_name not in self._all_options:
self._all_options[option_name] = getattr(view._visible_options,
option_name)
# Note that views will still store _all_options of the source object.
view._all_options = self._all_options
return view
def _visible_option_list(self):
return sorted(option
for option in dir(self._visible_options) if option[0] != '_')
def __dir__(self):
return sorted(dir(type(self)) + list(self.__dict__) +
self._visible_option_list())
def __getattr__(self, name):
# Special methods which may be accessed before the object is
# fully constructed (e.g. in unpickling).
if name[:2] == name[-2:] == '__':
return object.__getattribute__(self, name)
elif name in self._visible_option_list():
return self._all_options[name]
else:
raise AttributeError("'%s' object has no attribute '%s'" %
(type(self).__name__, name))
def __setattr__(self, name, value):
if name in ('_flags', '_all_options', '_visible_options'):
super(PipelineOptions, self).__setattr__(name, value)
elif name in self._visible_option_list():
self._all_options[name] = value
else:
raise AttributeError("'%s' object has no attribute '%s'" %
(type(self).__name__, name))
def __str__(self):
return '%s(%s)' % (type(self).__name__,
', '.join('%s=%s' % (option, getattr(self, option))
for option in self._visible_option_list()))
[docs]class StandardOptions(PipelineOptions):
DEFAULT_RUNNER = 'DirectRunner'
@classmethod
def _add_argparse_args(cls, parser):
parser.add_argument(
'--runner',
help=('Pipeline runner used to execute the workflow. Valid values are '
'DirectRunner, DataflowRunner.'))
# Whether to enable streaming mode.
parser.add_argument('--streaming',
default=False,
action='store_true',
help='Whether to enable streaming mode.')
[docs]class TypeOptions(PipelineOptions):
@classmethod
def _add_argparse_args(cls, parser):
# TODO(laolu): Add a type inferencing option here once implemented.
parser.add_argument('--type_check_strictness',
default='DEFAULT_TO_ANY',
choices=['ALL_REQUIRED', 'DEFAULT_TO_ANY'],
help='The level of exhaustive manual type-hint '
'annotation required')
parser.add_argument('--no_pipeline_type_check',
dest='pipeline_type_check',
action='store_false',
help='Disable type checking at pipeline construction '
'time')
parser.add_argument('--runtime_type_check',
default=False,
action='store_true',
help='Enable type checking at pipeline execution '
'time. NOTE: only supported with the '
'DirectRunner')
[docs]class DirectOptions(PipelineOptions):
"""DirectRunner-specific execution options."""
@classmethod
def _add_argparse_args(cls, parser):
parser.add_argument(
'--no_direct_runner_use_stacked_bundle',
action='store_false',
dest='direct_runner_use_stacked_bundle',
help='DirectRunner uses stacked WindowedValues within a Bundle for '
'memory optimization. Set --no_direct_runner_use_stacked_bundle to '
'avoid it.')
parser.add_argument(
'--direct_runner_bundle_repeat',
type=int,
default=0,
help='replay every bundle this many extra times, for profiling'
'and debugging')
parser.add_argument(
'--direct_num_workers',
type=int,
default=1,
help='number of parallel running workers.')
[docs]class GoogleCloudOptions(PipelineOptions):
"""Google Cloud Dataflow service execution options."""
BIGQUERY_API_SERVICE = 'bigquery.googleapis.com'
COMPUTE_API_SERVICE = 'compute.googleapis.com'
STORAGE_API_SERVICE = 'storage.googleapis.com'
DATAFLOW_ENDPOINT = 'https://dataflow.googleapis.com'
@classmethod
def _add_argparse_args(cls, parser):
parser.add_argument(
'--dataflow_endpoint',
default=cls.DATAFLOW_ENDPOINT,
help=
('The URL for the Dataflow API. If not set, the default public URL '
'will be used.'))
# Remote execution must check that this option is not None.
parser.add_argument('--project',
default=None,
help='Name of the Cloud project owning the Dataflow '
'job.')
# Remote execution must check that this option is not None.
parser.add_argument('--job_name',
default=None,
help='Name of the Cloud Dataflow job.')
# Remote execution must check that this option is not None.
parser.add_argument('--staging_location',
default=None,
help='GCS path for staging code packages needed by '
'workers.')
# Remote execution must check that this option is not None.
# If staging_location is not set, it defaults to temp_location.
parser.add_argument('--temp_location',
default=None,
help='GCS path for saving temporary workflow jobs.')
# The Google Compute Engine region for creating Dataflow jobs. See
# https://cloud.google.com/compute/docs/regions-zones/regions-zones for a
# list of valid options. Currently defaults to us-central1, but future
# releases of Beam will require the user to set the region explicitly.
parser.add_argument('--region',
default=None,
help='The Google Compute Engine region for creating '
'Dataflow job.')
parser.add_argument('--service_account_email',
default=None,
help='Identity to run virtual machines as.')
parser.add_argument('--no_auth', dest='no_auth', type=bool, default=False)
# Option to run templated pipelines
parser.add_argument('--template_location',
default=None,
help='Save job to specified local or GCS location.')
parser.add_argument('--label', '--labels',
dest='labels',
action='append',
default=None,
help='Labels to be applied to this Dataflow job. '
'Labels are key value pairs separated by = '
'(e.g. --label key=value).')
parser.add_argument('--update',
default=False,
action='store_true',
help='Update an existing streaming Cloud Dataflow job. '
'Experimental. '
'See https://cloud.google.com/dataflow/docs/guides/'
'updating-a-pipeline')
parser.add_argument('--transform_name_mapping',
default=None,
type=json.loads,
help='The transform mapping that maps the named '
'transforms in your prior pipeline code to names '
'in your replacement pipeline code.'
'Experimental. '
'See https://cloud.google.com/dataflow/docs/guides/'
'updating-a-pipeline')
parser.add_argument('--enable_streaming_engine',
default=False,
action='store_true',
help='Enable Windmill Service for this Dataflow job. ')
parser.add_argument('--dataflow_kms_key',
default=None,
help='Set a Google Cloud KMS key name to be used in '
'Dataflow state operations (GBK, Streaming).')
parser.add_argument('--flexrs_goal',
default=None,
choices=['COST_OPTIMIZED', 'SPEED_OPTIMIZED'],
help='Set the Flexible Resource Scheduling mode')
[docs] def validate(self, validator):
errors = []
if validator.is_service_runner():
errors.extend(validator.validate_cloud_options(self))
errors.extend(validator.validate_gcs_path(self, 'temp_location'))
if getattr(self, 'staging_location',
None) or getattr(self, 'temp_location', None) is None:
errors.extend(validator.validate_gcs_path(self, 'staging_location'))
if self.view_as(DebugOptions).dataflow_job_file:
if self.view_as(GoogleCloudOptions).template_location:
errors.append('--dataflow_job_file and --template_location '
'are mutually exclusive.')
if self.view_as(GoogleCloudOptions).region is None:
self.view_as(GoogleCloudOptions).region = 'us-central1'
runner = self.view_as(StandardOptions).runner
if runner == 'DataflowRunner' or runner == 'TestDataflowRunner':
logging.warning(
'--region not set; will default to us-central1. Future releases of '
'Beam will require the user to set the region explicitly. '
'https://cloud.google.com/compute/docs/regions-zones/regions-zones')
return errors
[docs]class HadoopFileSystemOptions(PipelineOptions):
"""``HadoopFileSystem`` connection options."""
@classmethod
def _add_argparse_args(cls, parser):
parser.add_argument(
'--hdfs_host',
default=None,
help=
('Hostname or address of the HDFS namenode.'))
parser.add_argument(
'--hdfs_port',
default=None,
help=
('Port of the HDFS namenode.'))
parser.add_argument(
'--hdfs_user',
default=None,
help=
('HDFS username to use.'))
[docs] def validate(self, validator):
errors = []
errors.extend(validator.validate_optional_argument_positive(self, 'port'))
return errors
# Command line options controlling the worker pool configuration.
# TODO(silviuc): Update description when autoscaling options are in.
[docs]class WorkerOptions(PipelineOptions):
"""Worker pool configuration options."""
@classmethod
def _add_argparse_args(cls, parser):
parser.add_argument(
'--num_workers',
type=int,
default=None,
help=
('Number of workers to use when executing the Dataflow job. If not '
'set, the Dataflow service will use a reasonable default.'))
parser.add_argument(
'--max_num_workers',
type=int,
default=None,
help=
('Maximum number of workers to use when executing the Dataflow job.'))
parser.add_argument(
'--autoscaling_algorithm',
type=str,
choices=['NONE', 'THROUGHPUT_BASED'],
default=None, # Meaning unset, distinct from 'NONE' meaning don't scale
help=
('If and how to autoscale the workerpool.'))
parser.add_argument(
'--worker_machine_type', '--machine_type',
dest='machine_type',
default=None,
help=('Machine type to create Dataflow worker VMs as. See '
'https://cloud.google.com/compute/docs/machine-types '
'for a list of valid options. If not set, '
'the Dataflow service will choose a reasonable '
'default.'))
parser.add_argument(
'--disk_size_gb',
type=int,
default=None,
help=
('Remote worker disk size, in gigabytes, or 0 to use the default size. '
'If not set, the Dataflow service will use a reasonable default.'))
parser.add_argument(
'--worker_disk_type', '--disk_type',
dest='disk_type',
default=None,
help=('Specifies what type of persistent disk should be used.'))
parser.add_argument(
'--zone',
default=None,
help=(
'GCE availability zone for launching workers. Default is up to the '
'Dataflow service.'))
parser.add_argument(
'--network',
default=None,
help=(
'GCE network for launching workers. Default is up to the Dataflow '
'service.'))
parser.add_argument(
'--subnetwork',
default=None,
help=(
'GCE subnetwork for launching workers. Default is up to the '
'Dataflow service. Expected format is '
'regions/REGION/subnetworks/SUBNETWORK or the fully qualified '
'subnetwork name. For more information, see '
'https://cloud.google.com/compute/docs/vpc/'))
parser.add_argument(
'--worker_harness_container_image',
default=None,
help=('Docker registry location of container image to use for the '
'worker harness. Default is the container for the version of the '
'SDK. Note: currently, only approved Google Cloud Dataflow '
'container images may be used here.'))
parser.add_argument(
'--use_public_ips',
default=None,
action='store_true',
help='Whether to assign public IP addresses to the worker VMs.')
parser.add_argument(
'--no_use_public_ips',
dest='use_public_ips',
default=None,
action='store_false',
help='Whether to assign only private IP addresses to the worker VMs.')
parser.add_argument(
'--min_cpu_platform',
dest='min_cpu_platform',
type=str,
help='GCE minimum CPU platform. Default is determined by GCP.'
)
parser.add_argument(
'--dataflow_worker_jar',
dest='dataflow_worker_jar',
type=str,
help='Dataflow worker jar file. If specified, the jar file is staged '
'in GCS, then gets loaded by workers. End users usually '
'should not use this feature.'
)
[docs] def validate(self, validator):
errors = []
if validator.is_service_runner():
errors.extend(
validator.validate_optional_argument_positive(self, 'num_workers'))
return errors
[docs]class DebugOptions(PipelineOptions):
@classmethod
def _add_argparse_args(cls, parser):
parser.add_argument('--dataflow_job_file',
default=None,
help='Debug file to write the workflow specification.')
parser.add_argument(
'--experiment', '--experiments',
dest='experiments',
action='append',
default=None,
help=
('Runners may provide a number of experimental features that can be '
'enabled with this flag. Please sync with the owners of the runner '
'before enabling any experiments.'))
parser.add_argument(
'--number_of_worker_harness_threads',
type=int,
default=None,
help=
('Number of threads per worker to use on the runner. If left '
'unspecified, the runner will compute an appropriate number of '
'threads to use. Currently only enabled for DataflowRunner when '
'experiment \'use_unified_worker\' is enabled.'))
[docs] def add_experiment(self, experiment):
# pylint: disable=access-member-before-definition
if self.experiments is None:
self.experiments = []
if experiment not in self.experiments:
self.experiments.append(experiment)
[docs] def lookup_experiment(self, key, default=None):
if not self.experiments:
return default
elif key in self.experiments:
return True
for experiment in self.experiments:
if experiment.startswith(key + '='):
return experiment.split('=', 1)[1]
return default
[docs]class ProfilingOptions(PipelineOptions):
@classmethod
def _add_argparse_args(cls, parser):
parser.add_argument('--profile_cpu',
action='store_true',
help='Enable work item CPU profiling.')
parser.add_argument('--profile_memory',
action='store_true',
help='Enable work item heap profiling.')
parser.add_argument('--profile_location',
default=None,
help='path for saving profiler data.')
parser.add_argument('--profile_sample_rate',
type=float,
default=1.0,
help='A number between 0 and 1 indicating the ratio '
'of bundles that should be profiled.')
[docs]class SetupOptions(PipelineOptions):
@classmethod
def _add_argparse_args(cls, parser):
# Options for installing dependencies in the worker.
parser.add_argument(
'--requirements_file',
default=None,
help=
('Path to a requirements file containing package dependencies. '
'Typically it is produced by a pip freeze command. More details: '
'https://pip.pypa.io/en/latest/reference/pip_freeze.html. '
'If used, all the packages specified will be downloaded, '
'cached (use --requirements_cache to change default location), '
'and then staged so that they can be automatically installed in '
'workers during startup. The cache is refreshed as needed '
'avoiding extra downloads for existing packages. Typically the '
'file is named requirements.txt.'))
parser.add_argument(
'--requirements_cache',
default=None,
help=
('Path to a folder to cache the packages specified in '
'the requirements file using the --requirements_file option.'))
parser.add_argument(
'--setup_file',
default=None,
help=
('Path to a setup Python file containing package dependencies. If '
'specified, the file\'s containing folder is assumed to have the '
'structure required for a setuptools setup package. The file must be '
'named setup.py. More details: '
'https://pythonhosted.org/an_example_pypi_project/setuptools.html '
'During job submission a source distribution will be built and the '
'worker will install the resulting package before running any custom '
'code.'))
parser.add_argument(
'--beam_plugin', '--beam_plugin',
dest='beam_plugins',
action='append',
default=None,
help=
('Bootstrap the python process before executing any code by importing '
'all the plugins used in the pipeline. Please pass a comma separated'
'list of import paths to be included. This is currently an '
'experimental flag and provides no stability. Multiple '
'--beam_plugin options can be specified if more than one plugin '
'is needed.'))
parser.add_argument(
'--save_main_session',
default=False,
action='store_true',
help=
('Save the main session state so that pickled functions and classes '
'defined in __main__ (e.g. interactive session) can be unpickled. '
'Some workflows do not need the session state if for instance all '
'their functions/classes are defined in proper modules (not __main__)'
' and the modules are importable in the worker. '))
parser.add_argument(
'--sdk_location',
default='default',
help=
('Override the default location from where the Beam SDK is downloaded. '
'It can be a URL, a GCS path, or a local path to an SDK tarball. '
'Workflow submissions will download or copy an SDK tarball from here. '
'If set to the string "default", a standard SDK location is used. If '
'empty, no SDK is copied.'))
parser.add_argument(
'--extra_package', '--extra_packages',
dest='extra_packages',
action='append',
default=None,
help=
('Local path to a Python package file. The file is expected to be (1) '
'a package tarball (".tar"), (2) a compressed package tarball '
'(".tar.gz"), (3) a Wheel file (".whl") or (4) a compressed package '
'zip file (".zip") which can be installed using the "pip install" '
'command of the standard pip package. Multiple --extra_package '
'options can be specified if more than one package is needed. During '
'job submission, the files will be staged in the staging area '
'(--staging_location option) and the workers will install them in '
'same order they were specified on the command line.'))
class PortableOptions(PipelineOptions):
"""Portable options are common options expected to be understood by most of
the portable runners. Should generally be kept in sync with
PortablePipelineOptions.java.
"""
@classmethod
def _add_argparse_args(cls, parser):
parser.add_argument(
'--job_endpoint', default=None,
help=('Job service endpoint to use. Should be in the form of address '
'and port, e.g. localhost:3000'))
parser.add_argument(
'--job-server-timeout', default=60, type=int,
help=('Job service request timeout in seconds. The timeout '
'determines the max time the driver program will wait to '
'get a response from the job server. NOTE: the timeout does not '
'apply to the actual pipeline run time. The driver program can '
'still wait for job completion indefinitely.'))
parser.add_argument(
'--environment_type', default=None,
help=('Set the default environment type for running '
'user code. DOCKER (default) runs user code in a container. '
'PROCESS runs user code in processes that are automatically '
'started on each worker node. LOOPBACK runs user code on the '
'same process that originally submitted the job.'))
parser.add_argument(
'--environment_config', default=None,
help=('Set environment configuration for running the user code.\n For '
'DOCKER: Url for the docker image.\n For PROCESS: json of the '
'form {"os": "<OS>", "arch": "<ARCHITECTURE>", "command": '
'"<process to execute>", "env":{"<Environment variables 1>": '
'"<ENV_VAL>"} }. All fields in the json are optional except '
'command.'))
parser.add_argument(
'--sdk_worker_parallelism', default=1,
help=('Sets the number of sdk worker processes that will run on each '
'worker node. Default is 1. If 0, a value will be chosen by the '
'runner.'))
parser.add_argument(
'--environment_cache_millis', default=0,
help=('Duration in milliseconds for environment cache within a job. '
'0 means no caching.'))
parser.add_argument(
'--output_executable_path', default=None,
help=('Create an executable jar at this path rather than running '
'the pipeline.'))
[docs]class TestOptions(PipelineOptions):
@classmethod
def _add_argparse_args(cls, parser):
# Options for e2e test pipeline.
parser.add_argument(
'--on_success_matcher',
default=None,
help=('Verify state/output of e2e test pipeline. This is pickled '
'version of the matcher which should extends '
'hamcrest.core.base_matcher.BaseMatcher.'))
parser.add_argument(
'--dry_run',
default=False,
help=('Used in unit testing runners without submitting the '
'actual job.'))
parser.add_argument(
'--wait_until_finish_duration',
default=None,
type=int,
help='The time to wait (in milliseconds) for test pipeline to finish. '
'If it is set to None, it will wait indefinitely until the job '
'is finished.')
[docs] def validate(self, validator):
errors = []
if self.view_as(TestOptions).on_success_matcher:
errors.extend(validator.validate_test_matcher(self, 'on_success_matcher'))
return errors
class TestDataflowOptions(PipelineOptions):
@classmethod
def _add_argparse_args(cls, parser):
# This option is passed to Dataflow Runner's Pub/Sub client. The camelCase
# style in 'dest' matches the runner's.
parser.add_argument(
'--pubsub_root_url',
dest='pubsubRootUrl',
default=None,
help='Root URL for use with the Google Cloud Pub/Sub API.',)
# TODO(silviuc): Add --files_to_stage option.
# This could potentially replace the --requirements_file and --setup_file.
# TODO(silviuc): Non-standard options. Keep them? If yes, add help too!
# Remote execution must check that this option is not None.
class OptionsContext(object):
"""Set default pipeline options for pipelines created in this block.
This is particularly useful for pipelines implicitly created with the
[python list] | PTransform
construct.
Can also be used as a decorator.
"""
overrides = []
def __init__(self, **options):
self.options = options
def __enter__(self):
self.overrides.append(self.options)
def __exit__(self, *exn_info):
self.overrides.pop()
def __call__(self, f, *args, **kwargs):
def wrapper(*args, **kwargs):
with self:
f(*args, **kwargs)
return wrapper
@classmethod
def augment_options(cls, options):
for override in cls.overrides:
for name, value in override.items():
setattr(options, name, value)
return options