#
# 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.
#
"""
DataflowRunner implementation of MetricResults. It is in charge of
responding to queries of current metrics by going to the dataflow
service.
"""
from __future__ import absolute_import
import numbers
from collections import defaultdict
from future.utils import iteritems
from apache_beam.metrics.cells import DistributionData
from apache_beam.metrics.cells import DistributionResult
from apache_beam.metrics.execution import MetricKey
from apache_beam.metrics.execution import MetricResult
from apache_beam.metrics.metric import MetricResults
from apache_beam.metrics.metricbase import MetricName
def _get_match(proto, filter_fn):
"""Finds and returns the first element that matches a query.
If no element matches the query, it throws ValueError.
If more than one element matches the query, it returns only the first.
"""
query = [elm for elm in proto if filter_fn(elm)]
if len(query) == 0:
raise ValueError('Could not find element')
elif len(query) > 1:
raise ValueError('Too many matches')
return query[0]
[docs]class DataflowMetrics(MetricResults):
"""Implementation of MetricResults class for the Dataflow runner."""
def __init__(self, dataflow_client=None, job_result=None, job_graph=None):
"""Initialize the Dataflow metrics object.
Args:
dataflow_client: apiclient.DataflowApplicationClient to interact with the
dataflow service.
job_result: DataflowPipelineResult with the state and id information of
the job.
job_graph: apiclient.Job instance to be able to translate between internal
step names (e.g. "s2"), and user step names (e.g. "split").
"""
super(DataflowMetrics, self).__init__()
self._dataflow_client = dataflow_client
self.job_result = job_result
self._queried_after_termination = False
self._cached_metrics = None
self._job_graph = job_graph
@staticmethod
def _is_counter(metric_result):
return isinstance(metric_result.attempted, numbers.Number)
@staticmethod
def _is_distribution(metric_result):
return isinstance(metric_result.attempted, DistributionResult)
def _translate_step_name(self, internal_name):
"""Translate between internal step names (e.g. "s1") and user step names."""
if not self._job_graph:
raise ValueError('Could not translate the internal step name.')
try:
step = _get_match(self._job_graph.proto.steps,
lambda x: x.name == internal_name)
user_step_name = _get_match(
step.properties.additionalProperties,
lambda x: x.key == 'user_name').value.string_value
except ValueError:
raise ValueError('Could not translate the internal step name.')
return user_step_name
def _get_metric_key(self, metric):
"""Populate the MetricKey object for a queried metric result."""
try:
# If ValueError is thrown within this try-block, it is because of
# one of the following:
# 1. Unable to translate the step name. Only happening with improperly
# formatted job graph (unlikely), or step name not being the internal
# step name (only happens for unstructured-named metrics).
# 2. Unable to unpack [step] or [namespace]; which should only happen
# for unstructured names.
step = _get_match(metric.name.context.additionalProperties,
lambda x: x.key == 'step').value
step = self._translate_step_name(step)
namespace = _get_match(metric.name.context.additionalProperties,
lambda x: x.key == 'namespace').value
name = metric.name.name
except ValueError:
return None
return MetricKey(step, MetricName(namespace, name))
def _populate_metric_results(self, response):
"""Take a list of metrics, and convert it to a list of MetricResult."""
user_metrics = [metric
for metric in response.metrics
if metric.name.origin == 'user']
# Get the tentative/committed versions of every metric together.
metrics_by_name = defaultdict(lambda: {})
for metric in user_metrics:
if (metric.name.name.endswith('[MIN]') or
metric.name.name.endswith('[MAX]') or
metric.name.name.endswith('[MEAN]') or
metric.name.name.endswith('[COUNT]')):
# The Dataflow Service presents distribution metrics in two ways:
# One way is as a single distribution object with all its fields, and
# another way is as four different scalar metrics labeled as [MIN],
# [MAX], [COUNT], [MEAN].
# TODO(pabloem) remove these when distributions are not being broken up
# in the service.
# The second way is only useful for the UI, and should be ignored.
continue
is_tentative = [prop
for prop in metric.name.context.additionalProperties
if prop.key == 'tentative' and prop.value == 'true']
tentative_or_committed = 'tentative' if is_tentative else 'committed'
metric_key = self._get_metric_key(metric)
if metric_key is None:
continue
metrics_by_name[metric_key][tentative_or_committed] = metric
# Now we create the MetricResult elements.
result = []
for metric_key, metric in iteritems(metrics_by_name):
attempted = self._get_metric_value(metric['tentative'])
committed = self._get_metric_value(metric['committed'])
if attempted is None or committed is None:
continue
result.append(MetricResult(metric_key,
attempted=attempted,
committed=committed))
return result
def _get_metric_value(self, metric):
"""Get a metric result object from a MetricUpdate from Dataflow API."""
if metric is None:
return None
if metric.scalar is not None:
return metric.scalar.integer_value
elif metric.distribution is not None:
dist_count = _get_match(metric.distribution.object_value.properties,
lambda x: x.key == 'count').value.integer_value
dist_min = _get_match(metric.distribution.object_value.properties,
lambda x: x.key == 'min').value.integer_value
dist_max = _get_match(metric.distribution.object_value.properties,
lambda x: x.key == 'max').value.integer_value
dist_sum = _get_match(metric.distribution.object_value.properties,
lambda x: x.key == 'sum').value.integer_value
return DistributionResult(
DistributionData(
dist_sum, dist_count, dist_min, dist_max))
else:
return None
def _get_metrics_from_dataflow(self):
"""Return cached metrics or query the dataflow service."""
try:
job_id = self.job_result.job_id()
except AttributeError:
job_id = None
if not job_id:
raise ValueError('Can not query metrics. Job id is unknown.')
if self._cached_metrics:
return self._cached_metrics
job_metrics = self._dataflow_client.get_job_metrics(job_id)
# If the job has terminated, metrics will not change and we can cache them.
if self.job_result.is_in_terminal_state():
self._cached_metrics = job_metrics
return job_metrics
[docs] def query(self, filter=None):
response = self._get_metrics_from_dataflow()
metric_results = self._populate_metric_results(response)
return {self.COUNTERS: [elm for elm in metric_results
if self.matches(filter, elm.key)
and DataflowMetrics._is_counter(elm)],
self.DISTRIBUTIONS: [elm for elm in metric_results
if self.matches(filter, elm.key)
and DataflowMetrics._is_distribution(elm)],
self.GAUGES: []} # TODO(pabloem): Add Gauge support for dataflow.