Source code for apache_beam.internal.metrics.metric

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Metrics API classes for internal use only.

Users should use apache_beam.metrics.metric package instead.

For internal use only. No backwards compatibility guarantees.
# pytype: skip-file
# mypy: disallow-untyped-defs

import datetime
import logging
import threading
import time
from typing import TYPE_CHECKING
from typing import Dict
from typing import Optional
from typing import Type
from typing import Union

from apache_beam.internal.metrics.cells import HistogramCellFactory
from apache_beam.metrics import monitoring_infos
from apache_beam.metrics.execution import MetricUpdater
from apache_beam.metrics.metric import Metrics as UserMetrics
from apache_beam.metrics.metricbase import Histogram
from apache_beam.metrics.metricbase import MetricName

  from apache_beam.metrics.cells import MetricCell
  from apache_beam.metrics.cells import MetricCellFactory
  from apache_beam.utils.histogram import BucketType

# Protect against environments where bigquery library is not available.
# pylint: disable=wrong-import-order, wrong-import-position
  from import HttpError
except ImportError:

__all__ = ['Metrics']

_LOGGER = logging.getLogger(__name__)

class Metrics(object):
  def counter(urn, labels=None, process_wide=False):
    # type: (str, Optional[Dict[str, str]], bool) -> UserMetrics.DelegatingCounter

    """Obtains or creates a Counter metric.

      namespace: A class or string that gives the namespace to a metric
      name: A string that gives a unique name to a metric
      urn: URN to populate on a MonitoringInfo, when sending to RunnerHarness.
      labels: Labels to populate on a MonitoringInfo
      process_wide: Whether or not the metric is specific to the current bundle
          or should be calculated for the entire process.

      A Counter object.
    return UserMetrics.DelegatingCounter(
        MetricName(namespace=None, name=None, urn=urn, labels=labels),

  def histogram(namespace, name, bucket_type, logger=None):
    # type: (Union[Type, str], str, BucketType, Optional[MetricLogger]) -> Metrics.DelegatingHistogram

    """Obtains or creates a Histogram metric.

      namespace: A class or string that gives the namespace to a metric
      name: A string that gives a unique name to a metric
      bucket_type: A type of bucket used in a histogram. A subclass of
      logger: MetricLogger for logging locally aggregated metric

      A Histogram object.
    namespace = UserMetrics.get_namespace(namespace)
    return Metrics.DelegatingHistogram(
        MetricName(namespace, name), bucket_type, logger)

  class DelegatingHistogram(Histogram):
    """Metrics Histogram that Delegates functionality to MetricsEnvironment."""
    def __init__(self, metric_name, bucket_type, logger):
      # type: (MetricName, BucketType, Optional[MetricLogger]) -> None
      self.metric_name = metric_name
      self.cell_type = HistogramCellFactory(bucket_type)
      self.logger = logger
      self.updater = MetricUpdater(self.cell_type, self.metric_name)

    def update(self, value):
      # type: (object) -> None
      if self.logger:
        self.logger.update(self.cell_type, self.metric_name, value)

class MetricLogger(object):
  """Simple object to locally aggregate and log metrics."""
  def __init__(self):
    # type: () -> None
    self._metric = {}  # type: Dict[MetricName, MetricCell]
    self._lock = threading.Lock()
    self._last_logging_millis = int(time.time() * 1000)
    self.minimum_logging_frequency_msec = 180000

  def update(self, cell_type, metric_name, value):
    # type: (Union[Type[MetricCell], MetricCellFactory], MetricName, object) -> None
    cell = self._get_metric_cell(cell_type, metric_name)

  def _get_metric_cell(self, cell_type, metric_name):
    # type: (Union[Type[MetricCell], MetricCellFactory], MetricName) -> MetricCell
    with self._lock:
      if metric_name not in self._metric:
        self._metric[metric_name] = cell_type()
    return self._metric[metric_name]

  def log_metrics(self, reset_after_logging=False):
    # type: (bool) -> None
    if self._lock.acquire(False):
        current_millis = int(time.time() * 1000)
        if ((current_millis - self._last_logging_millis) >
          logging_metric_info = [
              '[Locally aggregated metrics since %s]' %
                  self._last_logging_millis / 1000.0)
          for name, cell in self._metric.items():
            logging_metric_info.append('%s: %s' % (name, cell.get_cumulative()))
          if reset_after_logging:
            self._metric = {}
          self._last_logging_millis = current_millis

class ServiceCallMetric(object):
  """Metric class which records Service API call metrics.

  This class will capture a request count metric for the specified
  request_count_urn and base_labels.

  When call() is invoked the status must be provided, which will
  be converted to a canonical GCP status code, if possible.

  TODO(ajamato): Add Request latency metric.
  def __init__(self, request_count_urn, base_labels=None):
    # type: (str, Optional[Dict[str, str]]) -> None
    self.base_labels = base_labels if base_labels else {}
    self.request_count_urn = request_count_urn

  def call(self, status):
    # type: (Union[int, str, HttpError]) -> None

    """Record the status of the call into appropriate metrics."""
    canonical_status = self.convert_to_canonical_status_string(status)
    additional_labels = {monitoring_infos.STATUS_LABEL: canonical_status}

    labels = dict(
        list(self.base_labels.items()) + list(additional_labels.items()))

    request_counter = Metrics.counter(
        urn=self.request_count_urn, labels=labels, process_wide=True)

  def convert_to_canonical_status_string(self, status):
    # type: (Union[int, str, HttpError]) -> str

    """Converts a status to a canonical GCP status cdoe string."""
    http_status_code = None
    if isinstance(status, int):
      http_status_code = status
    elif isinstance(status, str):
      return status.lower()
    elif isinstance(status, HttpError):
      http_status_code = int(status.status_code)
    http_to_canonical_gcp_status = {
        200: 'ok',
        400: 'out_of_range',
        401: 'unauthenticated',
        403: 'permission_denied',
        404: 'not_found',
        409: 'already_exists',
        429: 'resource_exhausted',
        499: 'cancelled',
        500: 'internal',
        501: 'not_implemented',
        503: 'unavailable',
        504: 'deadline_exceeded'
    if (http_status_code is not None and
        http_status_code in http_to_canonical_gcp_status):
      return http_to_canonical_gcp_status[http_status_code]
    return str(http_status_code)

  def bigtable_error_code_to_grpc_status_string(grpc_status_code):
    # type: (Optional[int]) -> str

    Converts the bigtable error code to a canonical GCP status code string.

    This Bigtable client library is not using the canonical http status code
    values (i.e."
    Instead they are numbered using an enum with these values corresponding
    to each status code:

      grpc_status_code: An int that corresponds to an enum of status codes

      A GCP status code string
    grpc_to_canonical_gcp_status = {
        0: 'ok',
        1: 'cancelled',
        2: 'unknown',
        3: 'invalid_argument',
        4: 'deadline_exceeded',
        5: 'not_found',
        6: 'already_exists',
        7: 'permission_denied',
        8: 'resource_exhausted',
        9: 'failed_precondition',
        10: 'aborted',
        11: 'out_of_range',
        12: 'unimplemented',
        13: 'internal',
        14: 'unavailable'
    if grpc_status_code is None:
      # Bigtable indicates this can be retried but itself has exhausted retry
      # timeout or there is no retry policy set for bigtable.
      return grpc_to_canonical_gcp_status[4]
    return grpc_to_canonical_gcp_status.get(
        grpc_status_code, str(grpc_status_code))