This is an excerpt from my recent post on APMDigest. To read it in its entirety, click here.
In considering alerting, the core issue is not whether a given tool will generate alerts, as anything sensible certainly will. Rather, the central problem is what could be termed the actionability of the alerts generated. Failure to flag issues related to poor performance is a clear no-no, but unfortunately over-alerting has the same effect, as these will rapidly be ignored.
Effective alert definition hinges on the determination of “normal” performance. Simplistically, this can be understood by testing across a business cycle (ideally, a minimum of 3-4 weeks). That is fine providing performance is reasonably stable. However, that is often not the case, particularly for applications experiencing large fluctuations in demand at different times of the day, week or year.
In such cases (which are extremely common), the difficulty becomes “at which point of the demand cycle should I base my alert threshold?” Too low, and your system is simply telling you that it’s lunchtime (or the weekend, or whenever greatest demand occurs). Too high, and you will miss issues occurring during periods of lower demand.
There are several approaches to this difficulty, of varying degrees of elegance…