One of the first things I check when looking at a new dashboard is for the dreaded graph that is an average of a percentiles.
Here is a brilliant talk on the subject by Azul’s CTO Gil Tene.
This post is my attempt to make why it doesn’t work intuative to developers.
This post is applicable to any setup where you do something along these lines:
Let’s assume we have a variable representing the response time of some application which takes the following 10 values (milliseconds):
11 12 13 14 15 16 17 18 19 20
In this sample 90% of the requests took 19ms or less.
Percentiles are useful as you can see what proportion of your users have what kind of experience.
Often the raw data isn’t sent to the metrics database; instead the database only stores the aggregate (in this case the aggregate is the percentile):
90%-ile = 19
Great, we can graph this over time and alert when the %-ile gets above a threshold.
All good so far.
We have a second instance of the same application with the following response times:
91 92 93 94 95 96 97 98 99 100
These are bit slower for whatever reason. For this instance 90% of the response times are 99ms or less.
Off to metrics database it goes:
99%-ile = 99
We then average them in metrics database:
(99 + 19) / 2 = 59
Urgh 59? What is this made up number we are now graphing? Are 90% of customers getting a response time of less than 59? Hell no. Half of them are getting a much higher response time.
In reality 90% of our customers are getting a response time of 98 or less.
So don’t average percentiles.
Another gotcha is when aggregates are made from samples of different sizes. So you try and average an average from two instances and they received a different number of requests.
To aggregate data across instances we need the raw data or at least a histogram with an appropriate level of precision. I don’t think it matters what tool you use but the one I use at the time of writing and that supports this is Prometheus.