7/3/2014 - 2:53 PM

statsd metric specification document

statsd metric specification document

StatsD Metric Types



This is a simple counter. Add 1 to the "gorets" bucket. At each flush the current count is sent and reset to 0. If the count at flush is 0 then you can opt to send no metric at all for this counter, by setting config.deleteCounters (applies only to graphite backend). Statsd will send both the rate as well as the count at each flush.



Tells StatsD that this counter is being sent sampled every 1/10th of the time.



The glork took 320ms to complete this time. StatsD figures out percentiles, average (mean), standard deviation, sum, lower and upper bounds for the flush interval. The percentile threshold can be tweaked with config.percentThreshold.

The percentile threshold can be a single value, or a list of values, and will generate the following list of stats for each threshold:


Where $KEY is the stats key you specify when sending to statsd, and $PCT is the percentile threshold.

Note that the mean metric is the mean value of all timings recorded during the flush interval whereas mean_$PCT is the mean of all timings which fell into the $PCT percentile for that flush interval. And the same holds for sum and upper. See issue #157 for a more detailed explanation of the calculation.

If the count at flush is 0 then you can opt to send no metric at all for this timer, by setting config.deleteTimers.

Use the config.histogram setting to instruct statsd to maintain histograms over time. Specify which metrics to match and a corresponding list of ordered non-inclusive upper limits of bins (class intervals). (use inf to denote infinity; a lower limit of 0 is assumed) Each flushInterval, statsd will store how many values (absolute frequency) fall within each bin (class interval), for all matching metrics. Examples:

  • no histograms for any timer (default): []

  • histogram to only track render durations, with unequal class intervals and catchall for outliers:

      [ { metric: 'render', bins: [ 0.01, 0.1, 1, 10, 'inf'] } ]
  • histogram for all timers except 'foo' related, with equal class interval and catchall for outliers:

      [ { metric: 'foo', bins: [] },
        { metric: '', bins: [ 50, 100, 150, 200, 'inf'] } ]


  • first match for a metric wins.
  • bin upper limits may contain decimals.
  • this is actually more powerful than what's strictly considered histograms, as you can make each bin arbitrarily wide, i.e. class intervals of different sizes.


StatsD now also supports gauges, arbitrary values, which can be recorded.


If the gauge is not updated at the next flush, it will send the previous value. You can opt to send no metric at all for this gauge, by setting config.deleteGauge

Adding a sign to the gauge value will change the value, rather than setting it.


So if gaugor was 333, those commands would set it to 333 - 10 + 4, or 327.


This implies you can't explicitly set a gauge to a negative number without first setting it to zero.


StatsD supports counting unique occurences of events between flushes, using a Set to store all occuring events.


If the count at flush is 0 then you can opt to send no metric at all for this set, by setting config.deleteSets.

Multi-Metric Packets

StatsD supports receiving multiple metrics in a single packet by separating them with a newline.


Be careful to keep the total length of the payload within your network's MTU. There is no single good value to use, but here are some guidelines for common network scenarios:

  • Fast Ethernet (1432) - This is most likely for Intranets.
  • Gigabit Ethernet (8932) - Jumbo frames can make use of this feature much more efficient.
  • Commodity Internet (512) - If you are routing over the internet a value in this range will be reasonable. You might be able to go higher, but you are at the mercy of all the hops in your route.

(These payload numbers take into account the maximum IP + UDP header sizes)