| # |
| # 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 collections import defaultdict |
| import numbers |
| |
| 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] |
| |
| |
| 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 metrics_by_name.iteritems(): |
| 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_mean = _get_match(metric.distribution.object_value.properties, |
| lambda x: x.key == 'mean').value.integer_value |
| # Calculating dist_sum with a hack, as distribution sum is not yet |
| # available in the Dataflow API. |
| # TODO(pabloem) Switch to "sum" field once it's available in the API |
| dist_sum = dist_count * dist_mean |
| 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 |
| |
| def query(self, filter=None): |
| response = self._get_metrics_from_dataflow() |
| metric_results = self._populate_metric_results(response) |
| return {'counters': [elm for elm in metric_results |
| if self.matches(filter, elm.key) |
| and DataflowMetrics._is_counter(elm)], |
| 'distributions': [elm for elm in metric_results |
| if self.matches(filter, elm.key) |
| and DataflowMetrics._is_distribution(elm)], |
| 'gauges': []} # Gauges are not currently supported by dataflow |
