Ten llamaindex-tools anti-patterns — how many are you guilty of?
Some background first. Our setup is llamaindex-tools 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)
}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 llamaindex-tools itself but our upstream connection reuse — the load test traffic was too clean and hid the long-tail requests.
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.
One last trap: in container environments remember to adjust the memory-related parameters in step. Otherwise the host limit and the process expectation disagree, and the symptom is intermittent, unreproducible failure.