Hey HN! Myself and several members of the Polar Signals team have been long time Observability ecosystem maintainers of projects such as Prometheus, Thanos, Cortex, Kubernetes and many more. In 2018 we read the "Google-Wide Profiling" paper and immediately realized we have to treat profiling just like any other Observability data, by systematically and comprehensively collecting it for our entire infrastructure and applications and store that throughout time.

At the same time eBPF was on the rise and it was a match made in heaven, we can provide an incredible user experience without requiring any code changes, and all our users need to do is deploy an agent (a single command to run against a Kubernetes cluster), and we can profile just about any popular language (and those that we can't profile yet we have on the roadmap). Not only does eBPF get us an incredible user experience, it also allows us to collect this data at never-before-seen low overhead (<1%) since we can grab exactly the data we need and nothing more.

In addition to that we built a custom columnar database to back this product to allow a Prometheus-style label-based query language to work on this very high cardinality data.

In a world where the economics of Moore's Law have already stagnated, everyone is going to have to learn to build more efficient software, and the root of all evil performance optimizations are those that are not based on measurements.

We're going to be hanging out in the comments all day, so please feel free to ask us any questions you might have, as well as any feedback you have for us!