Continuous Profiling Go applications

I use profiles to better describe post mortems, to enrich observability and monitoring signals with concrete information from the binary itself. They are the perfect bridge between ops and developers when somebody reaches out to me asking why this application eats all that memory I can translate that to a function that I can check out in my editor. I find myself looking for outages that happened in the past because cloud providers and Kubernetes increased my resiliency budget the application gets restarted when it reaches a certain threshold and the system keeps running, but that leak is still a problem that as to be fixed. Having profiles well organized and easy to retrieve is a valuable source of information and you never know when you will need them. That’s why continuous profiling is important today more than ever. I use Profefe to collect and store profiles from all my applications continuously. It is an open-source project that exposes a friendly API and an interface to concrete storage of your preference like badger, S3, Minio, and counting. I will describe to you how to project works, how I use it with Kubernetes, and how I analyze the collected profiles.


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