In the midst of building a multi-datacenter, multi-tenant instrumentation and visibility system, we arrived at stream processing as an alternative to storing, forwarding, and post-processing metrics as traditional systems do. However, the streaming paradigm is alien to many engineers and sysadmins who are used to working with "wall-of-graphs" dashboards, predefined aggregates, and point-and-click alert configuration.
Taking inspiration from REPLs, literate programming, and DevOps practices, we've designed an interface to our instrumentation system that focuses on interactive feedback, note-taking, and team communication. An engineer can both experiment with new flows at low risk, and codify longer-term practices into runbooks that embed live visualizations of instrumentation data. As a result, we can start to free our users from understanding the mechanics of the stream processor and instead focus on the domain of instrumentation.
In this talk, we will discuss how the interface described above works, how the stream processor manages flows on behalf of the user, and some tradeoffs we have encountered while preparing the system to roll out into our organization.