Object Storage monitoring


This section was excerpted from a blog post by Darrell Bishop and has since been edited.

An OpenStack Object Storage cluster is a collection of many daemons that work together across many nodes. With so many different components, you must be able to tell what is going on inside the cluster. Tracking server-level meters like CPU utilization, load, memory consumption, disk usage and utilization, and so on is necessary, but not sufficient.

Swift Recon

The Swift Recon middleware (see Cluster Telemetry and Monitoring) provides general machine statistics, such as load average, socket statistics, /proc/meminfo contents, as well as Swift-specific meters:

  • The MD5 sum of each ring file.

  • The most recent object replication time.

  • Count of each type of quarantined file: Account, container, or object.

  • Count of “async_pendings” (deferred container updates) on disk.

Swift Recon is middleware that is installed in the object servers pipeline and takes one required option: A local cache directory. To track async_pendings, you must set up an additional cron job for each object server. You access data by either sending HTTP requests directly to the object server or using the swift-recon command-line client.

There are Object Storage cluster statistics but the typical server meters overlap with existing server monitoring systems. To get the Swift-specific meters into a monitoring system, they must be polled. Swift Recon acts as a middleware meters collector. The process that feeds meters to your statistics system, such as collectd and gmond, should already run on the storage node. You can choose to either talk to Swift Recon or collect the meters directly.


Swift-Informant middleware (see swift-informant) has real-time visibility into Object Storage client requests. It sits in the pipeline for the proxy server, and after each request to the proxy server it sends three meters to a StatsD server:

  • A counter increment for a meter like obj.GET.200 or cont.PUT.404.

  • Timing data for a meter like acct.GET.200 or obj.GET.200. [The README says the meters look like duration.acct.GET.200, but I do not see the duration in the code. I am not sure what the Etsy server does but our StatsD server turns timing meters into five derivative meters with new segments appended, so it probably works as coded. The first meter turns into acct.GET.200.lower, acct.GET.200.upper, acct.GET.200.mean, acct.GET.200.upper_90, and acct.GET.200.count].

  • A counter increase by the bytes transferred for a meter like tfer.obj.PUT.201.

This is used for receiving information on the quality of service clients experience with the timing meters, as well as sensing the volume of the various modifications of a request server type, command, and response code. Swift-Informant requires no change to core Object Storage code because it is implemented as middleware. However, it gives no insight into the workings of the cluster past the proxy server. If the responsiveness of one storage node degrades, you can only see that some of the requests are bad, either as high latency or error status codes.


The Statsdlog project increments StatsD counters based on logged events. Like Swift-Informant, it is also non-intrusive, however statsdlog can track events from all Object Storage daemons, not just proxy-server. The daemon listens to a UDP stream of syslog messages, and StatsD counters are incremented when a log line matches a regular expression. Meter names are mapped to regex match patterns in a JSON file, allowing flexible configuration of what meters are extracted from the log stream.

Currently, only the first matching regex triggers a StatsD counter increment, and the counter is always incremented by one. There is no way to increment a counter by more than one or send timing data to StatsD based on the log line content. The tool could be extended to handle more meters for each line and data extraction, including timing data. But a coupling would still exist between the log textual format and the log parsing regexes, which would themselves be more complex to support multiple matches for each line and data extraction. Also, log processing introduces a delay between the triggering event and sending the data to StatsD. It would be preferable to increment error counters where they occur and send timing data as soon as it is known to avoid coupling between a log string and a parsing regex and prevent a time delay between events and sending data to StatsD.

The next section describes another method for gathering Object Storage operational meters.

Swift StatsD logging

StatsD (see Measure Anything, Measure Everything) was designed for application code to be deeply instrumented. Meters are sent in real-time by the code that just noticed or did something. The overhead of sending a meter is extremely low: a sendto of one UDP packet. If that overhead is still too high, the StatsD client library can send only a random portion of samples and StatsD approximates the actual number when flushing meters upstream.

To avoid the problems inherent with middleware-based monitoring and after-the-fact log processing, the sending of StatsD meters is integrated into Object Storage itself. Details of the meters tracked are in the Administrator’s Guide.

The sending of meters is integrated with the logging framework. To enable, configure log_statsd_host in the relevant config file. You can also specify the port and a default sample rate. The specified default sample rate is used unless a specific call to a statsd logging method (see the list below) overrides it. Currently, no logging calls override the sample rate, but it is conceivable that some meters may require accuracy (sample_rate=1) while others may not.

# ...
log_statsd_host =
log_statsd_port = 8125
log_statsd_default_sample_rate = 1

Then the LogAdapter object returned by get_logger(), usually stored in self.logger, has these new methods:

  • update_stats(self, metric, amount, sample_rate=1) Increments the supplied meter by the given amount. This is used when you need to add or subtract more that one from a counter, like incrementing suffix.hashes by the number of computed hashes in the object replicator.

  • increment(self, metric, sample_rate=1) Increments the given counter meter by one.

  • decrement(self, metric, sample_rate=1) Lowers the given counter meter by one.

  • timing(self, metric, timing_ms, sample_rate=1) Record that the given meter took the supplied number of milliseconds.

  • timing_since(self, metric, orig_time, sample_rate=1) Convenience method to record a timing meter whose value is “now” minus an existing timestamp.


These logging methods may safely be called anywhere you have a logger object. If StatsD logging has not been configured, the methods are no-ops. This avoids messy conditional logic each place a meter is recorded. These example usages show the new logging methods:

# swift/obj/replicator.py
def update(self, job):
     # ...
    begin = time.time()
        hashed, local_hash = tpool.execute(tpooled_get_hashes, job['path'],
                do_listdir=(self.replication_count % 10) == 0,
        # See tpooled_get_hashes "Hack".
        if isinstance(hashed, BaseException):
            raise hashed
        self.suffix_hash += hashed
        self.logger.update_stats('suffix.hashes', hashed)
        # ...
        self.partition_times.append(time.time() - begin)
        self.logger.timing_since('partition.update.timing', begin)
# swift/container/updater.py
def process_container(self, dbfile):
    # ...
    start_time = time.time()
    # ...
        for event in events:
            if 200 <= event.wait() < 300:
                successes += 1
                failures += 1
        if successes > failures:
            # ...
            # ...
        # Only track timing data for attempted updates:
        self.logger.timing_since('timing', start_time)
        self.no_changes += 1