alt Hacker News> You're right that those differences are sometimes marginal when the latency of whatever IO the backend's doing dominates the equation. However, in my experience huge volume surges show issues with the runtime (the thing managing/launching multiplexed request handler routines) or the ecosystem (the backend IO libraries' ability to work with the runtime's IO multiplexing and make things like request coalescing easy or automatic) more often than you'd think.
fair enough, although at this point we start talking about LB in front of the thing, consumption mechanics, autoscaling signals
i will still maintain that my simple advice for a dev worrying about scale, is that they should focus their efforts on ensuring downstream IO doesn't get overwhelmed (db read replicas, caching, etc) before optimizing runtime performance or autoscaling out unnecessarily.
> focus their efforts on ensuring downstream IO doesn't get overwhelmed (db read replicas, caching, etc) before optimizing runtime performance or autoscaling out unnecessarily.
All good advice, but the choice of runtime can affect the point at which autoscaling and load balancing even need to enter the conversation at all. Optimizing, say, a mostly in-memory cache service and writing it in Golang may yield results like "we can run a single instance of this and serve three orders of magnitude of business growth; slap it behind a DNSRR or a k8s NodePort for update/replacement/fast failover if it crashes, no complex load balancer needed", where writing the same thing in, say, PHP might require discussing orchestration/load balancing/memory/worker process recycling/autoscaling early on in the service's lifetime. Being able to skip those conversations (entirely or for a long time) is a very significant business benefit.