Ten monitoring-pro anti-patterns — how many are you guilty of?
Some background first. Our setup is monitoring-pro plus three downstream services, seven figures of daily requests, peaking around nine in the evening.
// 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.
What genuinely surprised me was the tail. The average looked great while P99 jumped by an order of magnitude past some threshold. The cause was not monitoring-pro itself but our upstream connection reuse — the load test traffic was too clean and hid the long-tail requests.
Worth noting: the official docs do cover this, just in a very inconspicuous spot. I only found it reading the source comments, where the author explains the reasoning — roughly "so that it degrades into predictable behaviour in extreme cases".