Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

by Martin Kleppmann

It’s common to see the average response time of a service reported. (Strictly speaking, the term “average” doesn’t refer to any particular formula, but in practice it is usually understood as the arithmetic mean: given n values, add up all the values, and divide by n.) However, the mean is not a very good metric if you want to know your “typical” response time, because it doesn’t tell you how many users actually experienced that delay. Usually it is better to use percentiles. If you take your list of response times and sort it from fastest to slowest, then the median is the halfway point: for example, if your median response time is 200 ms, that means half your requests return in less than 200 ms, and half your requests take longer than that. This makes the median a good metric if you want to know how long users typically have to wait: half of user requests are served in less than the median response time, and the other half take longer than the median. The median is also known as the 50th percentile, and sometimes abbreviated as p50. Note that the median refers to a single request; if the user makes several requests (over the course of a session, or because several resources are included in a single page), the probability that at least one of them is slower than the median is much greater than 50%.