Thanks! I think most performance debugging happens during development, what we're bringing to the table is exposure of system behavior in production which often diverges because of changes in the shape of workloads from dev, which are often simplistic and synthetic. So I'd say its late-stage performance debugging and production observability combined that makes this useful.

Stay tuned for a follow on post where we show how we used this to optimize an FSST decompression kernel for vortex (https://github.com/vortex-data/vortex).

Sometimes you have to optimize other people's code. Also, sometimes code behaves unexpectedly depending on the data, say over a certain size threshold. And sometimes it behaves differently on different hardware. You don't always find these things out until production.