Source code for apache_beam.metrics.metric

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"""
User-facing classes for Metrics API.

The classes in this file allow users to define and use metrics to be collected
and displayed as part of their pipeline execution.

- Metrics - This class lets pipeline and transform writers create and access
    metric objects such as counters, distributions, etc.
"""
import inspect

from apache_beam.metrics.execution import MetricsEnvironment
from apache_beam.metrics.metricbase import Counter, Distribution
from apache_beam.metrics.metricbase import MetricName

__all__ = ['Metrics', 'MetricsFilter']


class Metrics(object):
  """Lets users create/access metric objects during pipeline execution."""
  @staticmethod
  def get_namespace(namespace):
    if inspect.isclass(namespace):
      return '{}.{}'.format(namespace.__module__, namespace.__name__)
    elif isinstance(namespace, str):
      return namespace
    else:
      raise ValueError('Unknown namespace type')

  @staticmethod
  def counter(namespace, name):
    """Obtains or creates a Counter metric.

    Args:
      namespace: A class or string that gives the namespace to a metric
      name: A string that gives a unique name to a metric

    Returns:
      A Counter object.
    """
    namespace = Metrics.get_namespace(namespace)
    return Metrics.DelegatingCounter(MetricName(namespace, name))

  @staticmethod
  def distribution(namespace, name):
    """Obtains or creates a Distribution metric.

    Distribution metrics are restricted to integer-only distributions.

    Args:
      namespace: A class or string that gives the namespace to a metric
      name: A string that gives a unique name to a metric

    Returns:
      A Distribution object.
    """
    namespace = Metrics.get_namespace(namespace)
    return Metrics.DelegatingDistribution(MetricName(namespace, name))

  class DelegatingCounter(Counter):
    def __init__(self, metric_name):
      self.metric_name = metric_name

    def inc(self, n=1):
      container = MetricsEnvironment.current_container()
      if container is not None:
        container.get_counter(self.metric_name).inc(n)

  class DelegatingDistribution(Distribution):
    def __init__(self, metric_name):
      self.metric_name = metric_name

    def update(self, value):
      container = MetricsEnvironment.current_container()
      if container is not None:
        container.get_distribution(self.metric_name).update(value)


class MetricResults(object):

  @staticmethod
  def _matches_name(filter, metric_key):
    if not filter.names and not filter.namespaces:
      return True

    if ((filter.namespaces and
         metric_key.metric.namespace in filter.namespaces) or
        (filter.names and
         metric_key.metric.name in filter.names)):
      return True
    return False

  @staticmethod
  def _matches_sub_path(actual_scope, filter_scope):
    start_pos = actual_scope.find(filter_scope)
    end_pos = start_pos + len(filter_scope)

    if start_pos == -1:
      return False  # No match at all
    elif start_pos != 0 and actual_scope[start_pos - 1] != '/':
      return False  # The first entry was not exactly matched
    elif end_pos != len(actual_scope) and actual_scope[end_pos] != '/':
      return False  # The last entry was not exactly matched
    return True

  @staticmethod
  def _matches_scope(filter, metric_key):
    if not filter.steps:
      return True

    for step in filter.steps:
      if MetricResults._matches_sub_path(metric_key.step, step):
        return True

    return False

  @staticmethod
  def matches(filter, metric_key):
    if filter is None:
      return True

    if (MetricResults._matches_name(filter, metric_key) and
        MetricResults._matches_scope(filter, metric_key)):
      return True
    return False

  def query(self, filter=None):
    raise NotImplementedError


class MetricsFilter(object):
  """Simple object to filter metrics results.

  This class is experimental. No backwards-compatibility guarantees.

  If filters by matching a result's step-namespace-name with three internal
  sets. No execution/matching logic is added to this object, so that it may
  be used to construct arguments as an RPC request. It is left for runners
  to implement matching logic by themselves.
  """
  def __init__(self):
    self._names = set()
    self._namespaces = set()
    self._steps = set()

  @property
  def steps(self):
    return frozenset(self._steps)

  @property
  def names(self):
    return frozenset(self._names)

  @property
  def namespaces(self):
    return frozenset(self._namespaces)

  def with_name(self, name):
    return self.with_names([name])

  def with_names(self, names):
    if isinstance(names, str):
      raise ValueError('Names must be a collection, not a string')

    self._names.update(names)
    return self

  def with_namespace(self, namespace):
    return self.with_namespaces([namespace])

  def with_namespaces(self, namespaces):
    if isinstance(namespaces, str):
      raise ValueError('Namespaces must be an iterable, not a string')

    self._namespaces.update([Metrics.get_namespace(ns) for ns in namespaces])
    return self

  def with_step(self, step):
    return self.with_steps([step])

  def with_steps(self, steps):
    if isinstance(steps, str):
      raise ValueError('Steps must be an iterable, not a string')

    self._steps.update(steps)
    return self