Llamaindex · Toolchain · llamaindex-tools under load on an 8-core 16GB box — full benchmark scripts included

Some background first. Our setup is llamaindex-tools plus three downstream services, seven figures of daily requests, peaking around nine in the evening.

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.

// 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)
}

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.

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.

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".