Method _monitor

docker/monitor.py:173–210

Monitors CPU usage of containers. Otput is stored in self.output_path. Also, keeps track of minimum and maximum memory usage (for the machine).

(self)

      return None

  def _monitor(self):
    """Monitors CPU usage of containers.

    Otput is stored in self.output_path.
    Also, keeps track of minimum and maximum memory usage (for the machine).
    """
    # Ubuntu systems typically mount cpuacct cgroup in /sys/fs/cgroup/cpu,cpuacct,
    # but this can vary by OS distribution.
    all_cgroups = subprocess.check_output(
        "findmnt -n -o TARGET -t cgroup --source cgroup".split(), universal_newlines=True
    ).split("\n")
    cpuacct_root = [c for c in all_cgroups if "cpuacct" in c][0]
    memory_root = [c for c in all_cgroups if "memory" in c][0]
    logging.info("Using cgroups: cpuacct %s, memory %s", cpuacct_root, memory_root)
    self.min_memory_usage_gb = None
    self.max_memory_usage_gb = None

    with open(self.output_path, "w") as output:
      while self.keep_monitoring:
        # Use a single timestamp for a given round of monitoring.
        now = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f")
        for c in self.containers:
          cpu = self._metrics_from_stat_file(cpuacct_root, c, "cpuacct.stat")
          memory = self._metrics_from_stat_file(memory_root, c, "memory.stat")
          if cpu:
            output.write("%s %s cpu %s\n" % (now, c.id, cpu))
          if memory:
            output.write("%s %s memory %s\n" % (now, c.id, memory))
        output.flush()

        # Machine-wide memory usage
        m = used_memory()
        if self.min_memory_usage_gb is None:
          self.min_memory_usage_gb, self.max_memory_usage_gb = m, m
        else:
          self.min_memory_usage_gb = min(self.min_memory_usage_gb, m)
          self.max_memory_usage_gb = max(self.max_memory_usage_gb, m)
        time.sleep(self.frequency_seconds)


class Timeline(object):