Exemple #1
0
    def __handle_stat(self, stat, id, topic, container_name):
        try:
            read_dt = parser.parse(stat["read"])
            timestamp = int((time.mktime(read_dt.timetuple()) + (read_dt.microsecond / 1000000.0)) * 1000)
            memory_usage = float(stat["memory_stats"]["usage"]) / float(stat["memory_stats"]["limit"])
            Metric.create(topic=topic, container=container_name, timestamp=timestamp, name="memory", value=memory_usage)

            # Calculate CPU usage. The docker API returns the number of cycles consumed
            # by the container and the number of cycles consumed by the system. We need
            # to take the difference over time and divide them to retrieve the usage
            # percentage.
            total_usage = float(stat["cpu_stats"]["cpu_usage"]["total_usage"])
            system_usage = float(stat["cpu_stats"]["system_cpu_usage"])
            if id in self.id_to_cpu:
                usage_diff = total_usage - self.id_to_cpu[id]["total"]
                system_diff = system_usage - self.id_to_cpu[id]["system"]
                if usage_diff >= 0:
                    usage_pct = usage_diff / system_diff
                else:
                    usage_pct = 0.0
                Metric.create(topic=topic, container=container_name, timestamp=timestamp, name="cpu", value=usage_pct)
            self.id_to_cpu[id] = {"total": total_usage, "system": system_usage}
        except:
            # We don't want to kill the stat thread, and we don't really mind
            # if some statistics aren't saved properly
            pass
Exemple #2
0
def get_metrics(registration_id):
    # Collect and return the following metrics:
    #    - Current number of containers running for this registration
    #    - Average CPU usage for this registration
    #    - Average memory usage for this registration
    #    - Average processing time for this registration
    #    - Number of events per day for the last week
    result = {}
    registration = Registration.get(Registration.id == registration_id)

    stats = Metric.select(
        Metric.name,
        fn.AVG(Metric.value).alias('average')
    ).where(
        Metric.topic == registration.topic,
        Metric.container == registration.container
    ).group_by(Metric.name)

    # Add to the result object
    for stat in stats:
        result[stat.name] = stat.average

    # Use the beginning of the day as a starting point (for full
    # days)
    today_dt = datetime.datetime.combine(
        datetime.date.today(),
        datetime.time.min
    )
    today = int(time.mktime(today_dt.timetuple()) * 1000)

    # Get the starting point (one week ago)
    one_week_ago = today - (7 * MILLISECONDS_IN_DAY)
    events = Event.select(
        # In order to query by date intervals, we need to divide by one day
        # worth of milliseconds, round to the nearest whole number, then
        # multiply the milliseconds back in. This will create intervals and
        # normalize the returned values.
        (fn.ROUND(Event.timestamp/MILLISECONDS_IN_DAY) * MILLISECONDS_IN_DAY).alias('date'),
        fn.COUNT(Event.id).alias('events')
    ).where(
        Event.topic == registration.topic,
        Event.container == registration.container,
        Event.timestamp >= one_week_ago
    ).group_by('date')

    # Add to the result object
    result['events'] = {}
    for event in events:
        result['events'][int(event.date)] = event.events

    avg_duration = Event.select(fn.AVG(Event.duration)).where(
        Event.topic == registration.topic,
        Event.container == registration.container
    ).scalar()
    result['duration'] = avg_duration

    result['containers'] = docker_agent.container_count(registration.topic, registration.container)

    return result
Exemple #3
0
def get_metrics(registration_id):
    # Collect and return the following metrics:
    #    - Current number of containers running for this registration
    #    - Average CPU usage for this registration
    #    - Average memory usage for this registration
    #    - Average processing time for this registration
    #    - Number of events per day for the last week
    result = {}
    registration = Registration.get(Registration.id == registration_id)

    stats = Metric.select(
        Metric.name,
        fn.AVG(Metric.value).alias('average')).where(
            Metric.topic == registration.topic,
            Metric.container == registration.container).group_by(Metric.name)

    # Add to the result object
    for stat in stats:
        result[stat.name] = stat.average

    # Use the beginning of the day as a starting point (for full
    # days)
    today_dt = datetime.datetime.combine(datetime.date.today(),
                                         datetime.time.min)
    today = int(time.mktime(today_dt.timetuple()) * 1000)

    # Get the starting point (one week ago)
    one_week_ago = today - (7 * MILLISECONDS_IN_DAY)
    events = Event.select(
        # In order to query by date intervals, we need to divide by one day
        # worth of milliseconds, round to the nearest whole number, then
        # multiply the milliseconds back in. This will create intervals and
        # normalize the returned values.
        (fn.ROUND(Event.timestamp / MILLISECONDS_IN_DAY) * MILLISECONDS_IN_DAY
         ).alias('date'),
        fn.COUNT(Event.id).alias('events')).where(
            Event.topic == registration.topic,
            Event.container == registration.container,
            Event.timestamp >= one_week_ago).group_by('date')

    # Add to the result object
    result['events'] = {}
    for event in events:
        result['events'][int(event.date)] = event.events

    avg_duration = Event.select(fn.AVG(Event.duration)).where(
        Event.topic == registration.topic,
        Event.container == registration.container).scalar()
    result['duration'] = avg_duration

    result['containers'] = docker_agent.container_count(
        registration.topic, registration.container)

    return result
Exemple #4
0
    def __handle_stat(self, stat, id, topic, container_name):
        try:
            read_dt = parser.parse(stat['read'])
            timestamp = int((time.mktime(read_dt.timetuple()) +
                             (read_dt.microsecond / 1000000.0)) * 1000)
            memory_usage = float(stat['memory_stats']['usage']) / float(
                stat['memory_stats']['limit'])
            Metric.create(topic=topic,
                          container=container_name,
                          timestamp=timestamp,
                          name='memory',
                          value=memory_usage)

            # Calculate CPU usage. The docker API returns the number of cycles consumed
            # by the container and the number of cycles consumed by the system. We need
            # to take the difference over time and divide them to retrieve the usage
            # percentage.
            total_usage = float(stat['cpu_stats']['cpu_usage']['total_usage'])
            system_usage = float(stat['cpu_stats']['system_cpu_usage'])
            if id in self.id_to_cpu:
                usage_diff = total_usage - self.id_to_cpu[id]['total']
                system_diff = system_usage - self.id_to_cpu[id]['system']
                if usage_diff >= 0:
                    usage_pct = usage_diff / system_diff
                else:
                    usage_pct = 0.0
                Metric.create(topic=topic,
                              container=container_name,
                              timestamp=timestamp,
                              name='cpu',
                              value=usage_pct)
            self.id_to_cpu[id] = {'total': total_usage, 'system': system_usage}
        except:
            # We don't want to kill the stat thread, and we don't really mind
            # if some statistics aren't saved properly
            pass