def Expand(self, request, context=None):
try:
pipeline = beam_pipeline.Pipeline(options=self._options)
def with_pipeline(component, pcoll_id=None):
component.pipeline = pipeline
if pcoll_id:
component.producer, component.tag = producers[pcoll_id]
# We need the lookup to resolve back to this id.
context.pcollections._obj_to_id[component] = pcoll_id
return component
context = pipeline_context.PipelineContext(
request.components,
default_environment=portable_runner.PortableRunner.
_create_environment(self._options),
namespace=request.namespace)
producers = {
pcoll_id: (context.transforms.get_by_id(t_id), pcoll_tag)
for t_id, t_proto in request.components.transforms.items()
for pcoll_tag, pcoll_id in t_proto.outputs.items()
}
transform = with_pipeline(
ptransform.PTransform.from_runner_api(request.transform,
context))
inputs = transform._pvaluish_from_dict({
tag: with_pipeline(context.pcollections.get_by_id(pcoll_id),
pcoll_id)
for tag, pcoll_id in request.transform.inputs.items()
})
if not inputs:
inputs = pipeline
with external.ExternalTransform.outer_namespace(request.namespace):
result = pipeline.apply(transform, inputs,
request.transform.unique_name)
expanded_transform = pipeline._root_transform().parts[-1]
# TODO(BEAM-1833): Use named outputs internally.
if isinstance(result, dict):
expanded_transform.outputs = result
pipeline_proto = pipeline.to_runner_api(context=context)
# TODO(BEAM-1833): Use named inputs internally.
expanded_transform_id = context.transforms.get_id(
expanded_transform)
expanded_transform_proto = pipeline_proto.components.transforms.pop(
expanded_transform_id)
expanded_transform_proto.inputs.clear()
expanded_transform_proto.inputs.update(request.transform.inputs)
for transform_id in pipeline_proto.root_transform_ids:
del pipeline_proto.components.transforms[transform_id]
return beam_expansion_api_pb2.ExpansionResponse(
components=pipeline_proto.components,
transform=expanded_transform_proto,
requirements=pipeline_proto.requirements)
except Exception: # pylint: disable=broad-except
return beam_expansion_api_pb2.ExpansionResponse(
error=traceback.format_exc())
def __ror__(self, left, label=None):
"""Used to apply this PTransform to non-PValues, e.g., a tuple."""
pvalueish, pvalues = self._extract_input_pvalues(left)
if isinstance(pvalues, dict):
pvalues = tuple(pvalues.values())
pipelines = [
v.pipeline for v in pvalues if isinstance(v, pvalue.PValue)
]
if pvalues and not pipelines:
deferred = False
# pylint: disable=wrong-import-order, wrong-import-position
from apache_beam import pipeline
from apache_beam.options.pipeline_options import PipelineOptions
# pylint: enable=wrong-import-order, wrong-import-position
p = pipeline.Pipeline('DirectRunner', PipelineOptions(sys.argv))
else:
if not pipelines:
if self.pipeline is not None:
p = self.pipeline
else:
raise ValueError(
'"%s" requires a pipeline to be specified '
'as there are no deferred inputs.' % self.label)
else:
p = self.pipeline or pipelines[0]
for pp in pipelines:
if p != pp:
raise ValueError(
'Mixing values in different pipelines is not allowed.'
'\n{%r} != {%r}' % (p, pp))
deferred = not getattr(p.runner, 'is_eager', False)
# pylint: disable=wrong-import-order, wrong-import-position
from apache_beam.transforms.core import Create
# pylint: enable=wrong-import-order, wrong-import-position
replacements = {
id(v): p | 'CreatePInput%s' % ix >> Create(v, reshuffle=False)
for (ix, v) in enumerate(pvalues)
if not isinstance(v, pvalue.PValue) and v is not None
}
pvalueish = _SetInputPValues().visit(pvalueish, replacements)
self.pipeline = p
result = p.apply(self, pvalueish, label)
if deferred:
return result
_allocate_materialized_pipeline(p)
materialized_result = _AddMaterializationTransforms().visit(result)
p.run().wait_until_finish()
_release_materialized_pipeline(p)
return _FinalizeMaterialization().visit(materialized_result)
def __ror__(self, left, label=None):
"""Used to apply this PTransform to non-PValues, e.g., a tuple."""
pvalueish, pvalues = self._extract_input_pvalues(left)
pipelines = [
v.pipeline for v in pvalues if isinstance(v, pvalue.PValue)
]
if pvalues and not pipelines:
deferred = False
# pylint: disable=wrong-import-order, wrong-import-position
from apache_beam import pipeline
from apache_beam.utils.pipeline_options import PipelineOptions
# pylint: enable=wrong-import-order, wrong-import-position
p = pipeline.Pipeline('DirectRunner', PipelineOptions(sys.argv))
else:
if not pipelines:
if self.pipeline is not None:
p = self.pipeline
else:
raise ValueError(
'"%s" requires a pipeline to be specified '
'as there are no deferred inputs.' % self.label)
else:
p = self.pipeline or pipelines[0]
for pp in pipelines:
if p != pp:
raise ValueError(
'Mixing value from different pipelines not allowed.'
)
deferred = not getattr(p.runner, 'is_eager', False)
# pylint: disable=wrong-import-order, wrong-import-position
from apache_beam.transforms.core import Create
# pylint: enable=wrong-import-order, wrong-import-position
replacements = {
id(v): p | 'CreatePInput%s' % ix >> Create(v)
for ix, v in enumerate(pvalues)
if not isinstance(v, pvalue.PValue) and v is not None
}
pvalueish = _SetInputPValues().visit(pvalueish, replacements)
self.pipeline = p
result = p.apply(self, pvalueish, label)
if deferred:
return result
else:
# Get a reference to the runners internal cache, otherwise runner may
# clean it after run.
cache = p.runner.cache
p.run().wait_until_finish()
return _MaterializePValues(cache).visit(result)
def test_direct_runner_metrics():
class MyDoFn(beam.DoFn):
def start_bundle(self):
count = metric.Metrics.counter(self.__class__, "bundles")
count.inc()
def finish_bundle(self):
count = metric.Metrics.counter(self.__class__, "finished_bundles")
count.inc()
def process(self, element):
gauge = metric.Metrics.gauge(self.__class__, "latest_element")
gauge.set(element)
count = metric.Metrics.counter(self.__class__, "elements")
count.inc()
distro = metric.Metrics.distribution(self.__class__,
"element_dist")
distro.update(element)
return [element]
p = pipeline.Pipeline(gke_direct.GkeDirectRunner())
pcoll = (p
| beam.Create([1, 2, 3, 4, 5], reshuffle=False)
| "Do" >> beam.ParDo(MyDoFn()))
util.assert_that(pcoll, util.equal_to([1, 2, 3, 4, 5]))
result = p.run()
result.wait_until_finish()
metrics = result.metrics().query()
namespace = "{}.{}".format(MyDoFn.__module__, MyDoFn.__name__)
hc.assert_that(
metrics["counters"],
hc.contains_inanyorder(
execution.MetricResult(
execution.MetricKey(
"Do", metricbase.MetricName(namespace, "elements")),
5,
5,
),
execution.MetricResult(
execution.MetricKey(
"Do", metricbase.MetricName(namespace, "bundles")),
1,
1,
),
execution.MetricResult(
execution.MetricKey(
"Do", metricbase.MetricName(namespace,
"finished_bundles")),
1,
1,
),
),
)
hc.assert_that(
metrics["distributions"],
hc.contains_inanyorder(
execution.MetricResult(
execution.MetricKey(
"Do", metricbase.MetricName(namespace, "element_dist")),
cells.DistributionResult(cells.DistributionData(15, 5, 1, 5)),
cells.DistributionResult(cells.DistributionData(15, 5, 1, 5)),
)),
)
gauge_result = metrics["gauges"][0]
hc.assert_that(
gauge_result.key,
hc.equal_to(
execution.MetricKey(
"Do", metricbase.MetricName(namespace, "latest_element"))),
)
hc.assert_that(gauge_result.committed.value, hc.equal_to(5))
hc.assert_that(gauge_result.attempted.value, hc.equal_to(5))
