This year I came across two very different BI projects which had the common non-functional requirement to prove that they would handle an expected spike in the report generation load. Funny enough, in both cases the project teams got very concerned and came up with wildly inaccurate predictions of how many concurrent users we should be testing for. In the first case the problem was with the perception of “thousands of users”, while in the second, the team interpreted “monthly users” as “concurrent users”. The annoying part was that in the first case the team planned on building an ultra-massively overcomplicated queuing system to handle those spikes, and in the second case they were thinking of completely scrapping the ad-hoc functionality in the solution and resorting to report extracts distributed by email. The unreasonable expectations of the load lead to bad design choices – this is why it is important to remain calm and first check whether there is a problem at all.
Firstly, let’s agree that we are measuring report requests. To begin, we should know how many requests we get per a period of time (e.g. a month), and then how long it takes to generate a report. A typical scenario would be:
- 1,000,000 report requests per month
- 2 seconds to generate a report on average
What we need to do now if apply a bit of math:
1,000,000 / 20 = 50,000 requests per day (on average)
50,000 / 8 = 6,250 requests per hour (8 hours in a working day)
Since a report takes 2 seconds to generate, we can generate 1,800 reports in one hour. Therefore, with 6,250 requests, we would have 3.47 average concurrent users. Of course, this would be the case if we have a very uniformly split load. In reality this would not happen – instead, we will have peaks and dips in usage. A moderate peak is typically around 3x the average, while a heavy one would be at around 6x the average. To ensure that we can handle such peak periods, we should multiply our average concurrent users by 3 or by 6 depending on our load analysis. Let’s assume we have a very high peak load of 3.47 * 6 = 20.82, or approximately 21 concurrent users. This is the number we need to test in our case. Note that we had 1,000,000 report requests per month, but in our highest peak we expect to have only 21 concurrent users. I have not actually had a project where we have expected to have such a load (in both cases which prompted me to write this post we had between 2000-10000 users per month).
The moral of the story – don’t panic. In most reporting projects the user load is not high enough to warrant a full-scale load testing exercise; next time you hear talking about something like that, instead of rushing to cover unreasonable scenarios, try to calculate and confirm the need first.