Open-sourced a rate-limiting middleware for jupyter-pro with token bucket and sliding window
Most jupyter-pro articles stop at "how to use it" and never cover "when not to use it". This is an attempt at the second half.
The first thing was to collapse the variables. We were changing config and upgrading the version at the same time, and afterwards nobody could say which change caused what. We rolled back to moving one variable at a time, re-ran three times, and only then did the curve settle. Tedious, but not skippable.
// Minimal reproduction: you must use a real long-tail distribution here.
// Uniform load-test traffic will never trigger this.
func (s *Server) handle(ctx context.Context) error {
conn, err := s.pool.Acquire(ctx)
if err != nil {
return fmt.Errorf("acquire: %w", err)
}
defer conn.Release()
return s.do(ctx, conn)
}We also fixed monitoring along the way: replaced average-based alerts with percentiles and split them per endpoint. False alerts dropped by about seventy percent and the on-call rotation visibly cheered up.
On trade-offs, my view is this: if nobody on the team owns this area long-term, do not introduce a second mechanism. With two coexistence you first have to work out which one is even in play when things break, and that costs far more than the performance you saved.