Meet the Author
Explore Author's Blog
Measure What Matters
Have you ever had an alert go off that you immediately ignore? It’s a nuisance alert—not actionable—but you keep it around just in case. Or maybe you’ve looked at a trace waterfall and wondered what exactly happened during a gap that just doesn’t drill down deep enough to explain what’s going on. Do you know the feeling where you have just enough information to monitor what’s going on in your systems, but not quite enough to put your mind at ease?
Observability Is About Confidence
Observability is important to understand what’s happening in production. But carving out the time to add instrumentation to a codebase is daunting, and often treated as a separate task to writing features. This means that we end up instrumenting for observability long after a feature has shipped, usually when there’s a problem with it and we’ve lost all context. What if we instead treated observability similarly to how we treat tests? We don’t submit code without a test, so let’s do the same with observability: treat it as part of the feature while the code is still fresh in our mind, with the benefit of being able to observe how the feature behaves in production.
How We Use Smoke Tests to Gain Confidence in Our Code
Also known as confidence testing, smoke testing is intended to focus on some critical aspects of the software that are required as a baseline. The term originates in electronic hardware testing; as my colleague Robb Kidd stated, “We want to apply power and see if smoke comes out.” If a smoke test fails, there is almost definitely a problem to address. If it passes, it doesn’t necessarily mean there is no problem, but we can feel confident that the major functionality is okay.