What gets measured gets managed, and with the vast array of monitoring systems used today, collecting and alerting on traditional infrastructure metrics is a relatively straightforward task. However, measuring only system metrics or metrics emitted by third-party services won’t paint the whole picture of application health.
Custom metrics tend to be underutilized because of the need to modify application code to emit metrics, the higher volumes of data that custom metrics generate, and the challenge of keeping instrumentation consistent across teams as new services are added or old ones changed. But instrumentation friction isn’t as high as it seems; instrumenting custom metrics is critical to correctly monitoring services, as well as for debugging, experimentation, and business observability.
Maxime Petazzoni explains why monitoring custom application metrics is essential for visibility into the internal workings of a system and shares a framework for properly instrumenting them while writing application code, along with a number of relevant use cases.
Maxime Petazzoni is a software engineer at SignalFx, where he works on the streaming analytics engine and is the creator of MaestroNG, a container orchestrator for Docker environments. Max has more than 15 years of experience working with GNU/Linux systems as a daily user, developer, and system administrator.
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