If you are here, you are probably wondering what is a Percentile and probably are also tired of measuring an “average” :)

Understanding percentiles is actually quite easy.

If you take 100 measurements of something and sort them, then the 95th entry in that list is the 95th percentile value of that data set. The 95th percentile says, “95% of all samples have this value or less”.

Alternatively, “5% of requests have a value higher than this”.

Consider an array of numbers (measurements):

``````1, 2, 2, 4, 5, 5, 8, 10, 10, 11, 11, 11, 15, 23, 24, 25, 50, 87
``````

For these 18 samples, we have an average of 17ms, but the 95th percentile is much higher at 50ms.

This series also demonstrates that the median value (50th percentile) can be quite different from the average. The average value of a series of measurements is often prone to strong influence by values in the higher percentiles.

Percentiles values are usually far more important for understanding performance metrics. The higher availability you require, the higher percentile you will want to track. Usually, the 99th percentile is as high as you need to care about, but if you deal in a truly enormous volume of requests, 99.99th, 99.999th, or even higher percentiles will be important. Often, the value you need to be concerned about is determined by business needs, not technical reasons.

Percentiles are valuable because they give you an idea of how your metrics degrade across your entire execution context. Even if the average user or request experience in your application is good, perhaps the 90th percentile metric shows some room for improvement. That is telling you that 10% of your execution is being impacted more negatively than the rest. Tracking multiple percentiles will tell you how fast this degradation occurs. How important this percentage of users or requests is must ultimately be a business decision, and there is definitely a law of diminishing returns at play here. Getting that last 1% may be extremely difficult and costly.