def run_pipeline(self, pipeline):
"""Execute the entire pipeline and returns an DirectPipelineResult."""
# TODO: Move imports to top. Pipeline <-> Runner dependency cause problems
# with resolving imports when they are at top.
# pylint: disable=wrong-import-position
from apache_beam.pipeline import PipelineVisitor
from apache_beam.runners.direct.consumer_tracking_pipeline_visitor import \
ConsumerTrackingPipelineVisitor
from apache_beam.runners.direct.evaluation_context import EvaluationContext
from apache_beam.runners.direct.executor import Executor
from apache_beam.runners.direct.transform_evaluator import \
TransformEvaluatorRegistry
from apache_beam.testing.test_stream import TestStream
# Performing configured PTransform overrides.
pipeline.replace_all(_get_transform_overrides(pipeline.options))
# If the TestStream I/O is used, use a mock test clock.
class _TestStreamUsageVisitor(PipelineVisitor):
"""Visitor determining whether a Pipeline uses a TestStream."""
def __init__(self):
self.uses_test_stream = False
def visit_transform(self, applied_ptransform):
if isinstance(applied_ptransform.transform, TestStream):
self.uses_test_stream = True
visitor = _TestStreamUsageVisitor()
pipeline.visit(visitor)
clock = TestClock() if visitor.uses_test_stream else RealClock()
MetricsEnvironment.set_metrics_supported(True)
logging.info('Running pipeline with DirectRunner.')
self.consumer_tracking_visitor = ConsumerTrackingPipelineVisitor()
pipeline.visit(self.consumer_tracking_visitor)
evaluation_context = EvaluationContext(
pipeline._options,
BundleFactory(stacked=pipeline._options.view_as(DirectOptions)
.direct_runner_use_stacked_bundle),
self.consumer_tracking_visitor.root_transforms,
self.consumer_tracking_visitor.value_to_consumers,
self.consumer_tracking_visitor.step_names,
self.consumer_tracking_visitor.views,
clock)
executor = Executor(self.consumer_tracking_visitor.value_to_consumers,
TransformEvaluatorRegistry(evaluation_context),
evaluation_context)
# DirectRunner does not support injecting
# PipelineOptions values at runtime
RuntimeValueProvider.set_runtime_options({})
# Start the executor. This is a non-blocking call, it will start the
# execution in background threads and return.
executor.start(self.consumer_tracking_visitor.root_transforms)
result = DirectPipelineResult(executor, evaluation_context)
return result
def run_pipeline(self, pipeline, options):
"""Execute the entire pipeline and returns an DirectPipelineResult."""
# TODO: Move imports to top. Pipeline <-> Runner dependency cause problems
# with resolving imports when they are at top.
# pylint: disable=wrong-import-position
from apache_beam.pipeline import PipelineVisitor
from apache_beam.runners.direct.consumer_tracking_pipeline_visitor import \
ConsumerTrackingPipelineVisitor
from apache_beam.runners.direct.evaluation_context import EvaluationContext
from apache_beam.runners.direct.executor import Executor
from apache_beam.runners.direct.transform_evaluator import \
TransformEvaluatorRegistry
from apache_beam.testing.test_stream import TestStream
# Performing configured PTransform overrides.
pipeline.replace_all(_get_transform_overrides(options))
# If the TestStream I/O is used, use a mock test clock.
class _TestStreamUsageVisitor(PipelineVisitor):
"""Visitor determining whether a Pipeline uses a TestStream."""
def __init__(self):
self.uses_test_stream = False
def visit_transform(self, applied_ptransform):
if isinstance(applied_ptransform.transform, TestStream):
self.uses_test_stream = True
visitor = _TestStreamUsageVisitor()
pipeline.visit(visitor)
clock = TestClock() if visitor.uses_test_stream else RealClock()
# TODO(BEAM-4274): Circular import runners-metrics. Requires refactoring.
from apache_beam.metrics.execution import MetricsEnvironment
MetricsEnvironment.set_metrics_supported(True)
logging.info('Running pipeline with DirectRunner.')
self.consumer_tracking_visitor = ConsumerTrackingPipelineVisitor()
pipeline.visit(self.consumer_tracking_visitor)
evaluation_context = EvaluationContext(
options,
BundleFactory(stacked=options.view_as(
DirectOptions).direct_runner_use_stacked_bundle),
self.consumer_tracking_visitor.root_transforms,
self.consumer_tracking_visitor.value_to_consumers,
self.consumer_tracking_visitor.step_names,
self.consumer_tracking_visitor.views, clock)
executor = Executor(self.consumer_tracking_visitor.value_to_consumers,
TransformEvaluatorRegistry(evaluation_context),
evaluation_context)
# DirectRunner does not support injecting
# PipelineOptions values at runtime
RuntimeValueProvider.set_runtime_options({})
# Start the executor. This is a non-blocking call, it will start the
# execution in background threads and return.
executor.start(self.consumer_tracking_visitor.root_transforms)
result = DirectPipelineResult(executor, evaluation_context)
return result
def _r(runner, options, seeds):
bigquery.truncate(seeds)
bigquery.seed(seeds)
RuntimeValueProvider.set_runtime_options(None)
runner._run(TestPipeline(options=options), options)
def test_inserting_the_dest_table_schema_into_pcollection_runtime():
with TestPipeline() as p:
lake_table = RuntimeValueProvider(
option_name='dest',
value_type=str,
default_value=f'{project_id}:lake.wrench_metrics')
expected = [{
'schema': [
gcp_bq.schema.SchemaField('entity_id', 'STRING', 'REQUIRED',
None, ()),
gcp_bq.schema.SchemaField('tree_user_id', 'INTEGER',
'REQUIRED', None, ()),
gcp_bq.schema.SchemaField('prediction', 'STRING', 'REQUIRED',
None, ()),
gcp_bq.schema.SchemaField('client_wrench_id', 'STRING',
'REQUIRED', None, ()),
gcp_bq.schema.SchemaField('expirement_name', 'STRING',
'NULLABLE', None, ()),
gcp_bq.schema.SchemaField('processing_datetime', 'DATETIME',
'NULLABLE', None, ()),
gcp_bq.schema.SchemaField('ingestion_timestamp', 'TIMESTAMP',
'REQUIRED', None, ())
],
'payload': {}
}]
pcoll = p | beam.Create([{}])
schema_pcoll = pcoll | beam.ParDo(
bq.IngectTableSchema(table=lake_table))
assert_that(schema_pcoll, equal_to(expected))
RuntimeValueProvider.set_runtime_options(None)
def test_experiments_setup(self):
self.assertFalse('feature_1' in RuntimeValueProvider.experiments)
RuntimeValueProvider.set_runtime_options(
{'experiments': ['feature_1', 'feature_2']})
self.assertTrue(isinstance(RuntimeValueProvider.experiments, set))
self.assertTrue('feature_1' in RuntimeValueProvider.experiments)
self.assertTrue('feature_2' in RuntimeValueProvider.experiments)
def test_get_destination_uri_runtime_vp(self):
# Provide values at job-execution time.
RuntimeValueProvider.set_runtime_options({'gcs_location': 'gs://bucket'})
options = self.UserDefinedOptions()
unique_id = uuid.uuid4().hex
uri = bigquery_export_destination_uri(options.gcs_location, None, unique_id)
self.assertEqual(
uri, 'gs://bucket/' + unique_id + '/bigquery-table-dump-*.json')
def test_runtime_values(self):
test_runtime_provider = RuntimeValueProvider('test_param', int, None)
sdk_worker_main.create_harness({
'CONTROL_API_SERVICE_DESCRIPTOR': '',
'PIPELINE_OPTIONS': '{"test_param": 37}',
},
dry_run=True)
self.assertTrue(test_runtime_provider.is_accessible())
self.assertEqual(test_runtime_provider.get(), 37)
def test_experiments_setup(self):
RuntimeValueProvider.set_runtime_options(
{'experiments': ['feature_1', 'feature_2']})
self.assertTrue(isinstance(RuntimeValueProvider.experiments, set))
self.assertTrue('feature_1' in RuntimeValueProvider.experiments)
self.assertTrue('feature_2' in RuntimeValueProvider.experiments)
# Clean up runtime_options after this test case finish, otherwise, it'll
# affect other cases since runtime_options is static attr
RuntimeValueProvider.set_runtime_options(None)
def test_get_destination_uri_empty_runtime_vp(self):
with self.assertRaisesRegex(ValueError,
'^ReadFromBigQuery requires a GCS '
'location to be provided'):
# Don't provide any runtime values.
RuntimeValueProvider.set_runtime_options({})
options = self.UserDefinedOptions()
bigquery_export_destination_uri(
options.gcs_location, None, uuid.uuid4().hex)
def test_set_runtime_option(self):
# define ValueProvider options, with and without default values
class UserDefinedOptions1(PipelineOptions):
@classmethod
def _add_argparse_args(cls, parser):
parser.add_value_provider_argument(
'--vpt_vp_arg6',
help='This keyword argument is a value provider'
) # set at runtime
parser.add_value_provider_argument( # not set, had default int
'-v',
'--vpt_vp_arg7', # with short form
default=123,
type=int)
parser.add_value_provider_argument( # not set, had default str
'--vpt_vp-arg8', # with dash in name
default='123',
type=str)
parser.add_value_provider_argument( # not set and no default
'--vpt_vp_arg9', type=float)
parser.add_value_provider_argument( # positional argument set
'vpt_vp_arg10', # default & runtime ignored
help='This positional argument is a value provider',
type=float,
default=5.4)
# provide values at graph-construction time
# (options not provided here become of the type RuntimeValueProvider)
options = UserDefinedOptions1(['1.2'])
self.assertFalse(options.vpt_vp_arg6.is_accessible())
self.assertFalse(options.vpt_vp_arg7.is_accessible())
self.assertFalse(options.vpt_vp_arg8.is_accessible())
self.assertFalse(options.vpt_vp_arg9.is_accessible())
self.assertTrue(options.vpt_vp_arg10.is_accessible())
# provide values at job-execution time
# (options not provided here will use their default, if they have one)
RuntimeValueProvider.set_runtime_options({
'vpt_vp_arg6': 'abc',
'vpt_vp_arg10': '3.2'
})
self.assertTrue(options.vpt_vp_arg6.is_accessible())
self.assertEqual(options.vpt_vp_arg6.get(), 'abc')
self.assertTrue(options.vpt_vp_arg7.is_accessible())
self.assertEqual(options.vpt_vp_arg7.get(), 123)
self.assertTrue(options.vpt_vp_arg8.is_accessible())
self.assertEqual(options.vpt_vp_arg8.get(), '123')
self.assertTrue(options.vpt_vp_arg9.is_accessible())
self.assertIsNone(options.vpt_vp_arg9.get())
self.assertTrue(options.vpt_vp_arg10.is_accessible())
self.assertEqual(options.vpt_vp_arg10.get(), 1.2)
def test_experiments_setup(self):
self.assertFalse('feature_1' in RuntimeValueProvider.experiments)
RuntimeValueProvider.set_runtime_options(
{'experiments': ['feature_1', 'feature_2']}
)
self.assertTrue(isinstance(RuntimeValueProvider.experiments, set))
self.assertTrue('feature_1' in RuntimeValueProvider.experiments)
self.assertTrue('feature_2' in RuntimeValueProvider.experiments)
# Clean up runtime_options after this test case finish, otherwise, it'll
# affect other cases since runtime_options is static attr
RuntimeValueProvider.set_runtime_options(None)
def run_pipeline(
self,
pipeline, # type: Pipeline
options # type: pipeline_options.PipelineOptions
):
# type: (...) -> RunnerResult
RuntimeValueProvider.set_runtime_options({})
# Setup "beam_fn_api" experiment options if lacked.
experiments = (options.view_as(
pipeline_options.DebugOptions).experiments or [])
if not 'beam_fn_api' in experiments:
experiments.append('beam_fn_api')
options.view_as(
pipeline_options.DebugOptions).experiments = experiments
# This is sometimes needed if type checking is disabled
# to enforce that the inputs (and outputs) of GroupByKey operations
# are known to be KVs.
from apache_beam.runners.dataflow.dataflow_runner import DataflowRunner
# TODO: Move group_by_key_input_visitor() to a non-dataflow specific file.
pipeline.visit(
DataflowRunner.group_by_key_input_visitor(
not options.view_as(pipeline_options.TypeOptions
).allow_non_deterministic_key_coders))
self._bundle_repeat = self._bundle_repeat or options.view_as(
pipeline_options.DirectOptions).direct_runner_bundle_repeat
pipeline_direct_num_workers = options.view_as(
pipeline_options.DirectOptions).direct_num_workers
if pipeline_direct_num_workers == 0:
self._num_workers = multiprocessing.cpu_count()
else:
self._num_workers = pipeline_direct_num_workers or self._num_workers
# set direct workers running mode if it is defined with pipeline options.
running_mode = \
options.view_as(pipeline_options.DirectOptions).direct_running_mode
if running_mode == 'multi_threading':
self._default_environment = environments.EmbeddedPythonGrpcEnvironment(
)
elif running_mode == 'multi_processing':
command_string = '%s -m apache_beam.runners.worker.sdk_worker_main' \
% sys.executable
self._default_environment = environments.SubprocessSDKEnvironment(
command_string=command_string)
self._profiler_factory = Profile.factory_from_options(
options.view_as(pipeline_options.ProfilingOptions))
self._latest_run_result = self.run_via_runner_api(
pipeline.to_runner_api(
default_environment=self._default_environment))
return self._latest_run_result
def run_pipeline(self, pipeline):
"""Execute the entire pipeline and returns an DirectPipelineResult."""
# Performing configured PTransform overrides.
pipeline.replace_all(self._ptransform_overrides)
# TODO: Move imports to top. Pipeline <-> Runner dependency cause problems
# with resolving imports when they are at top.
# pylint: disable=wrong-import-position
from apache_beam.runners.direct.consumer_tracking_pipeline_visitor import \
ConsumerTrackingPipelineVisitor
from apache_beam.runners.direct.evaluation_context import EvaluationContext
from apache_beam.runners.direct.executor import Executor
from apache_beam.runners.direct.transform_evaluator import \
TransformEvaluatorRegistry
MetricsEnvironment.set_metrics_supported(True)
logging.info('Running pipeline with DirectRunner.')
self.consumer_tracking_visitor = ConsumerTrackingPipelineVisitor()
pipeline.visit(self.consumer_tracking_visitor)
clock = TestClock() if self._use_test_clock else RealClock()
evaluation_context = EvaluationContext(
pipeline._options,
BundleFactory(stacked=pipeline._options.view_as(DirectOptions)
.direct_runner_use_stacked_bundle),
self.consumer_tracking_visitor.root_transforms,
self.consumer_tracking_visitor.value_to_consumers,
self.consumer_tracking_visitor.step_names,
self.consumer_tracking_visitor.views,
clock)
evaluation_context.use_pvalue_cache(self._cache)
executor = Executor(self.consumer_tracking_visitor.value_to_consumers,
TransformEvaluatorRegistry(evaluation_context),
evaluation_context)
# DirectRunner does not support injecting
# PipelineOptions values at runtime
RuntimeValueProvider.set_runtime_options({})
# Start the executor. This is a non-blocking call, it will start the
# execution in background threads and return.
executor.start(self.consumer_tracking_visitor.root_transforms)
result = DirectPipelineResult(executor, evaluation_context)
if self._cache:
# We are running in eager mode, block until the pipeline execution
# completes in order to have full results in the cache.
result.wait_until_finish()
self._cache.finalize()
return result
def test_set_runtime_option(self):
# define ValueProvider ptions, with and without default values
class UserDefinedOptions1(PipelineOptions):
@classmethod
def _add_argparse_args(cls, parser):
parser.add_value_provider_argument(
'--vpt_vp_arg6',
help='This keyword argument is a value provider') # set at runtime
parser.add_value_provider_argument( # not set, had default int
'-v', '--vpt_vp_arg7', # with short form
default=123,
type=int)
parser.add_value_provider_argument( # not set, had default str
'--vpt_vp-arg8', # with dash in name
default='123',
type=str)
parser.add_value_provider_argument( # not set and no default
'--vpt_vp_arg9',
type=float)
parser.add_value_provider_argument( # positional argument set
'vpt_vp_arg10', # default & runtime ignored
help='This positional argument is a value provider',
type=float,
default=5.4)
# provide values at graph-construction time
# (options not provided here become of the type RuntimeValueProvider)
options = UserDefinedOptions1(['1.2'])
self.assertFalse(options.vpt_vp_arg6.is_accessible())
self.assertFalse(options.vpt_vp_arg7.is_accessible())
self.assertFalse(options.vpt_vp_arg8.is_accessible())
self.assertFalse(options.vpt_vp_arg9.is_accessible())
self.assertTrue(options.vpt_vp_arg10.is_accessible())
# provide values at job-execution time
# (options not provided here will use their default, if they have one)
RuntimeValueProvider.set_runtime_options({'vpt_vp_arg6': 'abc',
'vpt_vp_arg10':'3.2'})
self.assertTrue(options.vpt_vp_arg6.is_accessible())
self.assertEqual(options.vpt_vp_arg6.get(), 'abc')
self.assertTrue(options.vpt_vp_arg7.is_accessible())
self.assertEqual(options.vpt_vp_arg7.get(), 123)
self.assertTrue(options.vpt_vp_arg8.is_accessible())
self.assertEqual(options.vpt_vp_arg8.get(), '123')
self.assertTrue(options.vpt_vp_arg9.is_accessible())
self.assertIsNone(options.vpt_vp_arg9.get())
self.assertTrue(options.vpt_vp_arg10.is_accessible())
self.assertEqual(options.vpt_vp_arg10.get(), 1.2)
def run_pipeline(self, pipeline, options):
MetricsEnvironment.set_metrics_supported(False)
RuntimeValueProvider.set_runtime_options({})
# This is sometimes needed if type checking is disabled
# to enforce that the inputs (and outputs) of GroupByKey operations
# are known to be KVs.
from apache_beam.runners.dataflow.dataflow_runner import DataflowRunner
pipeline.visit(DataflowRunner.group_by_key_input_visitor())
self._bundle_repeat = self._bundle_repeat or options.view_as(
pipeline_options.DirectOptions).direct_runner_bundle_repeat
self._profiler_factory = profiler.Profile.factory_from_options(
options.view_as(pipeline_options.ProfilingOptions))
return self.run_via_runner_api(pipeline.to_runner_api(
default_environment=self._default_environment))
def run_pipeline(self, pipeline, options):
MetricsEnvironment.set_metrics_supported(False)
RuntimeValueProvider.set_runtime_options({})
# This is sometimes needed if type checking is disabled
# to enforce that the inputs (and outputs) of GroupByKey operations
# are known to be KVs.
from apache_beam.runners.dataflow.dataflow_runner import DataflowRunner
pipeline.visit(DataflowRunner.group_by_key_input_visitor())
self._bundle_repeat = self._bundle_repeat or options.view_as(
pipeline_options.DirectOptions).direct_runner_bundle_repeat
self._profiler_factory = profiler.Profile.factory_from_options(
options.view_as(pipeline_options.ProfilingOptions))
return self.run_via_runner_api(pipeline.to_runner_api(
default_environment=self._default_environment))
def test_bytes_read_are_reported(self):
RuntimeValueProvider.set_runtime_options(
{'experiments': ['sideinput_io_metrics_v2', 'other']})
mock_read_counter = mock.MagicMock()
source_records = ['a', 'b', 'c', 'd']
sources = [
FakeSource(source_records, notify_observers=True),
]
iterator_fn = sideinputs.get_iterator_fn_for_sources(
sources, max_reader_threads=3, read_counter=mock_read_counter)
assert list(strip_windows(iterator_fn())) == source_records
mock_read_counter.add_bytes_read.assert_called_with(4)
# Remove runtime options from the runtime value provider.
RuntimeValueProvider.set_runtime_options({})
def test_get_destination_uri_fallback_temp_location(self):
# Don't provide any runtime values.
RuntimeValueProvider.set_runtime_options({})
options = self.UserDefinedOptions()
with self.assertLogs('apache_beam.io.gcp.bigquery_read_internal',
level='DEBUG') as context:
bigquery_export_destination_uri(
options.gcs_location, 'gs://bucket', uuid.uuid4().hex)
self.assertEqual(
context.output,
[
'DEBUG:apache_beam.io.gcp.bigquery_read_internal:gcs_location is '
'empty, using temp_location instead'
])
def test_bytes_read_are_reported(self):
RuntimeValueProvider.set_runtime_options(
{'experiments': 'sideinput_io_metrics,other'})
mock_read_counter = mock.MagicMock()
source_records = ['a', 'b', 'c', 'd']
sources = [
FakeSource(source_records, notify_observers=True),
]
iterator_fn = sideinputs.get_iterator_fn_for_sources(
sources, max_reader_threads=3, read_counter=mock_read_counter)
assert list(strip_windows(iterator_fn())) == source_records
mock_read_counter.add_bytes_read.assert_called_with(4)
# Remove runtime options from the runtime value provider.
RuntimeValueProvider.set_runtime_options({})
def test_nested_value_provider_wrap_runtime(self):
class UserDefinedOptions(PipelineOptions):
@classmethod
def _add_argparse_args(cls, parser):
parser.add_value_provider_argument(
'--vpt_vp_arg15',
help='This keyword argument is a value provider') # set at runtime
options = UserDefinedOptions([])
vp = NestedValueProvider(options.vpt_vp_arg15, lambda x: x + x)
self.assertFalse(vp.is_accessible())
RuntimeValueProvider.set_runtime_options({'vpt_vp_arg15': 'abc'})
self.assertTrue(vp.is_accessible())
self.assertEqual(vp.get(), 'abcabc')
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)
def test_value_provider_options(self):
class UserOptions(PipelineOptions):
@classmethod
def _add_argparse_args(cls, parser):
parser.add_value_provider_argument(
'--pot_vp_arg1', help='This flag is a value provider')
parser.add_value_provider_argument('--pot_vp_arg2',
default=1,
type=int)
parser.add_argument('--pot_non_vp_arg1', default=1, type=int)
# Provide values: if not provided, the option becomes of the type runtime vp
options = UserOptions(['--pot_vp_arg1', 'hello'])
self.assertIsInstance(options.pot_vp_arg1, StaticValueProvider)
self.assertIsInstance(options.pot_vp_arg2, RuntimeValueProvider)
self.assertIsInstance(options.pot_non_vp_arg1, int)
# Values can be overwritten
options = UserOptions(pot_vp_arg1=5,
pot_vp_arg2=StaticValueProvider(value_type=str,
value='bye'),
pot_non_vp_arg1=RuntimeValueProvider(
option_name='foo',
value_type=int,
default_value=10))
self.assertEqual(options.pot_vp_arg1, 5)
self.assertTrue(options.pot_vp_arg2.is_accessible(),
'%s is not accessible' % options.pot_vp_arg2)
self.assertEqual(options.pot_vp_arg2.get(), 'bye')
self.assertFalse(options.pot_non_vp_arg1.is_accessible())
with self.assertRaises(RuntimeError):
options.pot_non_vp_arg1.get()
def test_string_or_value_provider_only(self):
str_file_pattern = tempfile.NamedTemporaryFile(delete=False).name
self.assertEqual(str_file_pattern,
FileBasedSource(str_file_pattern)._pattern.value)
static_vp_file_pattern = StaticValueProvider(value_type=str,
value=str_file_pattern)
self.assertEqual(static_vp_file_pattern,
FileBasedSource(static_vp_file_pattern)._pattern)
runtime_vp_file_pattern = RuntimeValueProvider(
option_name='arg', value_type=str, default_value=str_file_pattern)
self.assertEqual(runtime_vp_file_pattern,
FileBasedSource(runtime_vp_file_pattern)._pattern)
# Reset runtime options to avoid side-effects in other tests.
RuntimeValueProvider.set_runtime_options(None)
invalid_file_pattern = 123
with self.assertRaises(TypeError):
FileBasedSource(invalid_file_pattern)
def ndjson(env, cloudstorage, record_testsuite_property):
cloudstorage.client.delete_blob(bucket, dest_blob_name)
assert cloudstorage.client.blob_exists(bucket, dest_blob_name) is False
sql = BigQuery.querybuilder(union=('all', [
BigQuery.querybuilder(
select=[('NULL',
'none'), ('True', 'true_bool'), (
'False', 'false_bool'), (
'"2020-04-03"',
'date'), ('"2020-04-03 13:45:00"', 'datetime'),
('"1966-06-06 06:06:06.666666 UTC"',
'timestamp'), ('"STRING"',
'string'), ('234',
'integer'), ('123.54',
'float')]),
BigQuery.querybuilder(select=['NULL'] * 9),
BigQuery.querybuilder(select=[
'"String"', 'False', 'True', '"1993-09-03"',
'"1993-09-03 03:44:00"', '"1993-09-03 03:44:00.777555 UTC"',
'"Not String"', '567', '456'
])
]))
RuntimeValueProvider.set_runtime_options(None)
options = RuntimeOptions([
'--env', env['env'], '--query',
str(sql), '--destination', f'gs://{bucket}/{blob_name}'
])
Runner._run(TestPipeline(options=options), options)
assert cloudstorage.client.blob_exists(bucket, dest_blob_name) is True
zbytes = cloudstorage.client.download_blob_as_string(
bucket, dest_blob_name)
bytes = gzip.decompress(zbytes)
lns = bytes.decode('utf8').rstrip().split('\n')
yield [json.loads(l) for l in lns]
def _reader_thread(self):
# pylint: disable=too-many-nested-blocks
experiments = set(
RuntimeValueProvider.get_value('experiments', str, '').split(','))
try:
while True:
try:
source = self.sources_queue.get_nowait()
if isinstance(source, iobase.BoundedSource):
for value in source.read(
source.get_range_tracker(None, None)):
if self.has_errored:
# If any reader has errored, just return.
return
if isinstance(value, window.WindowedValue):
self.element_queue.put(value)
else:
self.element_queue.put(
_globally_windowed(value))
else:
# Native dataflow source.
with source.reader() as reader:
# The tracking of time spend reading and bytes read from side
# inputs is kept behind an experiment flag to test performance
# impact.
if 'sideinput_io_metrics' in experiments:
self.add_byte_counter(reader)
returns_windowed_values = reader.returns_windowed_values
for value in reader:
if self.has_errored:
# If any reader has errored, just return.
return
if returns_windowed_values:
self.element_queue.put(value)
else:
self.element_queue.put(
_globally_windowed(value))
except Queue.Empty:
return
except Exception as e: # pylint: disable=broad-except
logging.error(
'Encountered exception in PrefetchingSourceSetIterable '
'reader thread: %s', traceback.format_exc())
self.reader_exceptions.put(e)
self.has_errored = True
finally:
self.element_queue.put(READER_THREAD_IS_DONE_SENTINEL)
def _reader_thread(self):
# pylint: disable=too-many-nested-blocks
experiments = RuntimeValueProvider.get_value('experiments', list, [])
try:
while True:
try:
source = self.sources_queue.get_nowait()
if isinstance(source, iobase.BoundedSource):
for value in source.read(source.get_range_tracker(None, None)):
if self.has_errored:
# If any reader has errored, just return.
return
if isinstance(value, window.WindowedValue):
self.element_queue.put(value)
else:
self.element_queue.put(_globally_windowed(value))
else:
# Native dataflow source.
with source.reader() as reader:
# The tracking of time spend reading and bytes read from side
# inputs is kept behind an experiment flag to test performance
# impact.
if 'sideinput_io_metrics' in experiments:
self.add_byte_counter(reader)
returns_windowed_values = reader.returns_windowed_values
for value in reader:
if self.has_errored:
# If any reader has errored, just return.
return
if returns_windowed_values:
self.element_queue.put(value)
else:
self.element_queue.put(_globally_windowed(value))
except Queue.Empty:
return
except Exception as e: # pylint: disable=broad-except
logging.error('Encountered exception in PrefetchingSourceSetIterable '
'reader thread: %s', traceback.format_exc())
self.reader_exceptions.put(e)
self.has_errored = True
finally:
self.element_queue.put(READER_THREAD_IS_DONE_SENTINEL)
def test_runtime_serialized_file_list_is_deserialized_and_processed_by_insertion_order(
cloudstorage):
with TestPipeline() as p:
bucket = f'{project_id}-cdc-imports'
# Update sort_key based on the filename format
def _sort_key(f):
delimeter = '-'
ts = f[f.rfind(delimeter) + 1:]
return int(ts) if ts.isdigit() else f
_sort_key = bytes.hex(dill.dumps(_sort_key))
runtime_env = RuntimeValueProvider(option_name='env',
value_type=str,
default_value='local')
runtime_bucket = RuntimeValueProvider(option_name='bucket',
value_type=str,
default_value=bucket)
runtime_startswith = RuntimeValueProvider(
option_name='files_startwith',
value_type=str,
default_value='vibe-tree-user-statuses-final')
runtime_sort_key = RuntimeValueProvider(option_name='sort_key',
value_type=str,
default_value=_sort_key)
[b.delete() for b in cloudstorage.client.list_blobs(bucket)]
file_paths = [
'vibe-tree-user-statuses-final-0083c-1987582612499',
'vibe-tree-user-statuses-final-003c-1587582612405',
'vibe-order-items-final-0030dd8697-1588231505823'
]
expected_output = [
'gs://icentris-ml-local-wbrito-cdc-imports/vibe-tree-user-statuses-final-003c-1587582612405',
'gs://icentris-ml-local-wbrito-cdc-imports/vibe-tree-user-statuses-final-0083c-1987582612499'
]
for f in file_paths:
cloudstorage.client.upload_blob_from_string(bucket, f, f)
p_paths = p | FileListIteratorTransform(
env=runtime_env,
bucket=runtime_bucket,
files_startwith=runtime_startswith,
sort_key=runtime_sort_key)
assert_that(p_paths, equal_to(expected_output))
RuntimeValueProvider.set_runtime_options(None)
def test_runtime_value_provider_to(self):
RuntimeValueProvider.runtime_options = None
rvp = RuntimeValueProvider('arg', 123, int)
self.assertEquals(JsonValue(is_null=True), to_json_value(rvp))
def _read_side_inputs(self, tags_and_types):
"""Generator reading side inputs in the order prescribed by tags_and_types.
Args:
tags_and_types: List of tuples (tag, type). Each side input has a string
tag that is specified in the worker instruction. The type is actually
a boolean which is True for singleton input (read just first value)
and False for collection input (read all values).
Yields:
With each iteration it yields the result of reading an entire side source
either in singleton or collection mode according to the tags_and_types
argument.
"""
# Only call this on the old path where side_input_maps was not
# provided directly.
assert self.side_input_maps is None
# Get experiments active in the worker to check for side input metrics exp.
experiments = set(
RuntimeValueProvider.get_value('experiments', str, '').split(','))
# We will read the side inputs in the order prescribed by the
# tags_and_types argument because this is exactly the order needed to
# replace the ArgumentPlaceholder objects in the args/kwargs of the DoFn
# getting the side inputs.
#
# Note that for each tag there could be several read operations in the
# specification. This can happen for instance if the source has been
# sharded into several files.
for i, (side_tag, view_class, view_options) in enumerate(tags_and_types):
sources = []
# Using the side_tag in the lambda below will trigger a pylint warning.
# However in this case it is fine because the lambda is used right away
# while the variable has the value assigned by the current iteration of
# the for loop.
# pylint: disable=cell-var-from-loop
for si in itertools.ifilter(
lambda o: o.tag == side_tag, self.spec.side_inputs):
if not isinstance(si, operation_specs.WorkerSideInputSource):
raise NotImplementedError('Unknown side input type: %r' % si)
sources.append(si.source)
# The tracking of time spend reading and bytes read from side inputs is
# behind an experiment flag to test its performance impact.
if 'sideinput_io_metrics' in experiments:
si_counter = opcounters.SideInputReadCounter(
self.counter_factory,
self.state_sampler,
declaring_step=self.operation_name,
# Inputs are 1-indexed, so we add 1 to i in the side input id
input_index=i + 1)
else:
si_counter = opcounters.TransformIOCounter()
iterator_fn = sideinputs.get_iterator_fn_for_sources(
sources, read_counter=si_counter)
# Backwards compatibility for pre BEAM-733 SDKs.
if isinstance(view_options, tuple):
if view_class == pvalue.AsSingleton:
has_default, default = view_options
view_options = {'default': default} if has_default else {}
else:
view_options = {}
yield apache_sideinputs.SideInputMap(
view_class, view_options, sideinputs.EmulatedIterable(iterator_fn))
def tearDown(self):
# Reset runtime options to avoid side-effects in other tests.
RuntimeValueProvider.set_runtime_options(None)
def setUp(self):
# Reset runtime options to avoid side-effects caused by other tests.
# Note that is_accessible assertions require runtime_options to
# be uninitialized.
RuntimeValueProvider.set_runtime_options(None)
def main(unused_argv):
"""Main entry point for SDK Fn Harness."""
if 'LOGGING_API_SERVICE_DESCRIPTOR' in os.environ:
try:
logging_service_descriptor = endpoints_pb2.ApiServiceDescriptor()
text_format.Merge(os.environ['LOGGING_API_SERVICE_DESCRIPTOR'],
logging_service_descriptor)
# Send all logs to the runner.
fn_log_handler = FnApiLogRecordHandler(logging_service_descriptor)
# TODO(BEAM-5468): This should be picked up from pipeline options.
logging.getLogger().setLevel(logging.DEBUG)
logging.getLogger().addHandler(fn_log_handler)
logging.info('Logging handler created.')
except Exception:
logging.error(
"Failed to set up logging handler, continuing without.",
exc_info=True)
fn_log_handler = None
else:
fn_log_handler = None
# Start status HTTP server thread.
thread = threading.Thread(name='status_http_server',
target=StatusServer().start)
thread.daemon = True
thread.setName('status-server-demon')
thread.start()
if 'PIPELINE_OPTIONS' in os.environ:
sdk_pipeline_options = _parse_pipeline_options(
os.environ['PIPELINE_OPTIONS'])
else:
sdk_pipeline_options = PipelineOptions.from_dictionary({})
if 'SEMI_PERSISTENT_DIRECTORY' in os.environ:
semi_persistent_directory = os.environ['SEMI_PERSISTENT_DIRECTORY']
else:
semi_persistent_directory = None
logging.info('semi_persistent_directory: %s', semi_persistent_directory)
_worker_id = os.environ.get('WORKER_ID', None)
try:
_load_main_session(semi_persistent_directory)
except Exception: # pylint: disable=broad-except
exception_details = traceback.format_exc()
logging.error('Could not load main session: %s',
exception_details,
exc_info=True)
try:
logging.info('Python sdk harness started with pipeline_options: %s',
sdk_pipeline_options.get_all_options(drop_default=True))
RuntimeValueProvider.set_runtime_options(
sdk_pipeline_options.view_as(
pipeline_options.HadoopFileSystemOptions).get_all_options())
service_descriptor = endpoints_pb2.ApiServiceDescriptor()
text_format.Merge(os.environ['CONTROL_API_SERVICE_DESCRIPTOR'],
service_descriptor)
# TODO(robertwb): Support credentials.
assert not service_descriptor.oauth2_client_credentials_grant.url
SdkHarness(control_address=service_descriptor.url,
worker_count=_get_worker_count(sdk_pipeline_options),
worker_id=_worker_id,
profiler_factory=profiler.Profile.factory_from_options(
sdk_pipeline_options.view_as(
pipeline_options.ProfilingOptions))).run()
logging.info('Python sdk harness exiting.')
except: # pylint: disable=broad-except
logging.exception('Python sdk harness failed: ')
raise
finally:
if fn_log_handler:
fn_log_handler.close()
def setUp(self):
# Reset runtime options, since the is_accessible assertions require them to
# be uninitialized.
RuntimeValueProvider.set_runtime_options(None)
def _read_side_inputs(self, tags_and_types):
"""Generator reading side inputs in the order prescribed by tags_and_types.
Args:
tags_and_types: List of tuples (tag, type). Each side input has a string
tag that is specified in the worker instruction. The type is actually
a boolean which is True for singleton input (read just first value)
and False for collection input (read all values).
Yields:
With each iteration it yields the result of reading an entire side source
either in singleton or collection mode according to the tags_and_types
argument.
"""
# Only call this on the old path where side_input_maps was not
# provided directly.
assert self.side_input_maps is None
# Get experiments active in the worker to check for side input metrics exp.
experiments = RuntimeValueProvider.get_value('experiments', list, [])
# We will read the side inputs in the order prescribed by the
# tags_and_types argument because this is exactly the order needed to
# replace the ArgumentPlaceholder objects in the args/kwargs of the DoFn
# getting the side inputs.
#
# Note that for each tag there could be several read operations in the
# specification. This can happen for instance if the source has been
# sharded into several files.
for i, (side_tag, view_class, view_options) in enumerate(tags_and_types):
sources = []
# Using the side_tag in the lambda below will trigger a pylint warning.
# However in this case it is fine because the lambda is used right away
# while the variable has the value assigned by the current iteration of
# the for loop.
# pylint: disable=cell-var-from-loop
for si in itertools.ifilter(
lambda o: o.tag == side_tag, self.spec.side_inputs):
if not isinstance(si, operation_specs.WorkerSideInputSource):
raise NotImplementedError('Unknown side input type: %r' % si)
sources.append(si.source)
# The tracking of time spend reading and bytes read from side inputs is
# behind an experiment flag to test its performance impact.
if 'sideinput_io_metrics' in experiments:
si_counter = opcounters.SideInputReadCounter(
self.counter_factory,
self.state_sampler,
declaring_step=self.name_context.step_name,
# Inputs are 1-indexed, so we add 1 to i in the side input id
input_index=i + 1)
else:
si_counter = opcounters.TransformIOCounter()
iterator_fn = sideinputs.get_iterator_fn_for_sources(
sources, read_counter=si_counter)
# Backwards compatibility for pre BEAM-733 SDKs.
if isinstance(view_options, tuple):
if view_class == pvalue.AsSingleton:
has_default, default = view_options
view_options = {'default': default} if has_default else {}
else:
view_options = {}
yield apache_sideinputs.SideInputMap(
view_class, view_options, sideinputs.EmulatedIterable(iterator_fn))
def test_runtime_value_provider_to(self):
RuntimeValueProvider.set_runtime_options(None)
rvp = RuntimeValueProvider('arg', 123, int)
self.assertEqual(JsonValue(is_null=True), to_json_value(rvp))
# Reset runtime options to avoid side-effects in other tests.
RuntimeValueProvider.set_runtime_options(None)
def create_harness(environment, dry_run=False):
"""Creates SDK Fn Harness."""
if 'LOGGING_API_SERVICE_DESCRIPTOR' in environment:
try:
logging_service_descriptor = endpoints_pb2.ApiServiceDescriptor()
text_format.Merge(environment['LOGGING_API_SERVICE_DESCRIPTOR'],
logging_service_descriptor)
# Send all logs to the runner.
fn_log_handler = FnApiLogRecordHandler(logging_service_descriptor)
# TODO(BEAM-5468): This should be picked up from pipeline options.
logging.getLogger().setLevel(logging.INFO)
logging.getLogger().addHandler(fn_log_handler)
_LOGGER.info('Logging handler created.')
except Exception:
_LOGGER.error(
"Failed to set up logging handler, continuing without.",
exc_info=True)
fn_log_handler = None
else:
fn_log_handler = None
pipeline_options_dict = _load_pipeline_options(
environment.get('PIPELINE_OPTIONS'))
# These are used for dataflow templates.
RuntimeValueProvider.set_runtime_options(pipeline_options_dict)
sdk_pipeline_options = PipelineOptions.from_dictionary(
pipeline_options_dict)
filesystems.FileSystems.set_options(sdk_pipeline_options)
if 'SEMI_PERSISTENT_DIRECTORY' in environment:
semi_persistent_directory = environment['SEMI_PERSISTENT_DIRECTORY']
else:
semi_persistent_directory = None
_LOGGER.info('semi_persistent_directory: %s', semi_persistent_directory)
_worker_id = environment.get('WORKER_ID', None)
try:
_load_main_session(semi_persistent_directory)
except CorruptMainSessionException:
exception_details = traceback.format_exc()
_LOGGER.error('Could not load main session: %s',
exception_details,
exc_info=True)
raise
except Exception: # pylint: disable=broad-except
exception_details = traceback.format_exc()
_LOGGER.error('Could not load main session: %s',
exception_details,
exc_info=True)
_LOGGER.info('Pipeline_options: %s',
sdk_pipeline_options.get_all_options(drop_default=True))
control_service_descriptor = endpoints_pb2.ApiServiceDescriptor()
status_service_descriptor = endpoints_pb2.ApiServiceDescriptor()
text_format.Merge(environment['CONTROL_API_SERVICE_DESCRIPTOR'],
control_service_descriptor)
if 'STATUS_API_SERVICE_DESCRIPTOR' in environment:
text_format.Merge(environment['STATUS_API_SERVICE_DESCRIPTOR'],
status_service_descriptor)
# TODO(robertwb): Support authentication.
assert not control_service_descriptor.HasField('authentication')
experiments = sdk_pipeline_options.view_as(DebugOptions).experiments or []
enable_heap_dump = 'enable_heap_dump' in experiments
if dry_run:
return
sdk_harness = SdkHarness(
control_address=control_service_descriptor.url,
status_address=status_service_descriptor.url,
worker_id=_worker_id,
state_cache_size=_get_state_cache_size(experiments),
data_buffer_time_limit_ms=_get_data_buffer_time_limit_ms(experiments),
profiler_factory=profiler.Profile.factory_from_options(
sdk_pipeline_options.view_as(ProfilingOptions)),
enable_heap_dump=enable_heap_dump)
return fn_log_handler, sdk_harness
