Python MetricCache.drain_metric方法代码示例

# 需要导入模块: from carbon.cache import MetricCache [as 别名]
# 或者: from carbon.cache.MetricCache import drain_metric [as 别名]
def optimalWriteOrder():
  """Generates metrics with the most cached values first and applies a soft
  rate limit on new metrics"""
  while MetricCache:
    (metric, datapoints) = MetricCache.drain_metric()
    dbFileExists = state.database.exists(metric)

    if not dbFileExists and CREATE_BUCKET:
      # If our tokenbucket has enough tokens available to create a new metric
      # file then yield the metric data to complete that operation. Otherwise
      # we'll just drop the metric on the ground and move on to the next
      # metric.
      # XXX This behavior should probably be configurable to no tdrop metrics
      # when rate limitng unless our cache is too big or some other legit
      # reason.
      if CREATE_BUCKET.drain(1):
        yield (metric, datapoints, dbFileExists)
      continue

    yield (metric, datapoints, dbFileExists)

# 需要导入模块: from carbon.cache import MetricCache [as 别名]
# 或者: from carbon.cache.MetricCache import drain_metric [as 别名]
def writeCachedDataPoints():
  "Write datapoints until the MetricCache is completely empty"

  cache = MetricCache()
  while cache:
    (metric, datapoints) = cache.drain_metric()
    if metric is None:
      # end the loop
      break

    dbFileExists = state.database.exists(metric)

    if not dbFileExists:
      if CREATE_BUCKET and not CREATE_BUCKET.drain(1):
        # If our tokenbucket doesn't have enough tokens available to create a new metric
        # file then we'll just drop the metric on the ground and move on to the next
        # metric.
        # XXX This behavior should probably be configurable to no tdrop metrics
        # when rate limitng unless our cache is too big or some other legit
        # reason.
        instrumentation.increment('droppedCreates')
        continue

      archiveConfig = None
      xFilesFactor, aggregationMethod = None, None

      for schema in SCHEMAS:
        if schema.matches(metric):
          if settings.LOG_CREATES:
            log.creates('new metric %s matched schema %s' % (metric, schema.name))
          archiveConfig = [archive.getTuple() for archive in schema.archives]
          break

      for schema in AGGREGATION_SCHEMAS:
        if schema.matches(metric):
          if settings.LOG_CREATES:
            log.creates('new metric %s matched aggregation schema %s'
                        % (metric, schema.name))
          xFilesFactor, aggregationMethod = schema.archives
          break

      if not archiveConfig:
        raise Exception(("No storage schema matched the metric '%s',"
                         " check your storage-schemas.conf file.") % metric)

      if settings.LOG_CREATES:
        log.creates("creating database metric %s (archive=%s xff=%s agg=%s)" %
                    (metric, archiveConfig, xFilesFactor, aggregationMethod))
      try:
        state.database.create(metric, archiveConfig, xFilesFactor, aggregationMethod)
        if settings.ENABLE_TAGS:
          tagQueue.add(metric)
        instrumentation.increment('creates')
      except Exception as e:
        log.err()
        log.msg("Error creating %s: %s" % (metric, e))
        instrumentation.increment('errors')
        continue

    # If we've got a rate limit configured lets makes sure we enforce it
    waitTime = 0
    if UPDATE_BUCKET:
      t1 = time.time()
      UPDATE_BUCKET.drain(1, blocking=True)
      waitTime = time.time() - t1

    try:
      t1 = time.time()
      # If we have duplicated points, always pick the last. update_many()
      # has no guaranted behavior for that, and in fact the current implementation
      # will keep the first point in the list.
      datapoints = dict(datapoints).items()
      state.database.write(metric, datapoints)
      if settings.ENABLE_TAGS:
        tagQueue.update(metric)
      updateTime = time.time() - t1
    except Exception as e:
      log.err()
      log.msg("Error writing to %s: %s" % (metric, e))
      instrumentation.increment('errors')
    else:
      pointCount = len(datapoints)
      instrumentation.increment('committedPoints', pointCount)
      instrumentation.append('updateTimes', updateTime)
      if settings.LOG_UPDATES:
        if waitTime > 0.001:
          log.updates("wrote %d datapoints for %s in %.5f seconds after waiting %.5f seconds" % (
            pointCount, metric, updateTime, waitTime))
        else:
          log.updates("wrote %d datapoints for %s in %.5f seconds" % (
            pointCount, metric, updateTime))