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How to read response time when you run a performance test

Measuring the performance of an application is mandatory before releasing it. Measuring the performance of a PoC is also mandatory to validate the base concepts and ideas.
Performance of a system can be measured in different ways, from the process time, to numbers of users, processor level, memory level and so on. Before starting a performance test you should know exactly what you want to measure.
When you want to measure the response time of a specific service/endpoint you should be aware how to interpret the results. Statistical information can be saw in different ways. Each of this view can give you a different perspective of the results.

The average of the response time is calculated. This information is important if you want to know what is the average request time. Even if this information can be very useful, this information is misleading. For example from 1000 requests, you can have an average response time of 16 seconds even if you your chances to have a  requests that takes under 10 seconds is around 90% (you can have 10 requests that takes 300 seconds and 900 requests that takes only 10 seconds).

This view will give you the possibility to see what is the distribution of requests based on the response time. For example you will be able to know that from 1000 requests, 300 requests took 1 seconds, 500 took 2 second, 100 took 9 second and so on.
When you are measuring the scalability of a system, distribution of response is more important than average response time. This information will help you to understand how the requests time change based on different configuration. You can have cases when the average time to be the same, but the distribution of the response time to be very different.
Here we could talk also about mean and standard deviation time.

When the response time needs to be in a specific time interval (usually a max of X) the min/max of request will offer you this data.

This days I had to measure the performance of a database and how scalable it is. When we started to measure the average execution time of each query with 1, 2 and 3 database nodes we observed that the average response time doesn’t improve so much. In contrast the distribution of query response time is changing a lot. From 30% of requests that take less than 3 second with one node, we ended up with more than 50% of requests that take under 2 seconds when having 3 database nodes.


  1. Related post - why mentioning average/mean and standard deviation is a bad sign :-)


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