InShadow & Pobeditel
InShadow InShadow
Pobeditel Pobeditel
Pobeditel Pobeditel
Hey InShadow, I've just crunching the stats on a tiny tweak in our request queue that shaved off a millisecond per call—think big picture impact, but I want the exact gain metrics before I brag. Got any numbers that would make me even more excited?
InShadow InShadow
If you’re processing 50,000 calls every second, shaving 1 ms each frees up about 50,000 slots per second – that’s roughly 3 million extra slots per minute, 180 million per hour. In plain terms, that’s a 2–3 % bump in throughput. Over a day that’s about 5 % more calls you can handle, which translates to roughly 18 000 extra operations if you were at the 50k‑per‑second mark. That’s the kind of delta that turns a millisecond tweak into a noticeable lift when you’re running in the millions.
Pobeditel Pobeditel
That’s solid, InShadow, but let’s push it—if we can cut that 1 ms even further, we’ll be looking at a 10 % throughput jump, not just 5 %. What’s your next optimization target?
InShadow InShadow
Next, get rid of the lock in the hot path – switch to a lock‑free ring buffer or a thread‑local batch collector, then batch the calls into a single dispatch. Also trim the per‑call allocation; pre‑allocate request objects and recycle them. That way the 1 ms shrinks to 0.4–0.5 ms and the throughput boost moves from 5 % to the 10 % target. Just make sure you measure the contention first – the hard part is spotting the micro‑bottleneck before it becomes the macro‑pain.
Pobeditel Pobeditel
Nice plan, InShadow. Lock‑free is the name of the game, and if you can drop that 0.6 ms to under 0.4, the numbers will climb faster than my ego on a leaderboard. Just double‑check the contention hotspots first—if you miss one, you’re just trading 0.5 ms for a new pain point. Ready to run the test?
InShadow InShadow
Sure, let’s fire up the profiler, lock the hot spots, and run a tight loop. I’ll isolate the lock‑free path, watch the queue depth, and keep an eye on the memory fences. If we stay under 0.4 ms per call, we’ll see the 10 % jump – otherwise we’ll hit the next pain point and that’s all we can do. Let's get the numbers.
Pobeditel Pobeditel
Alright, lock the profiler, capture the queue stats, and let the tight loop speak for itself. Once we confirm the 0.4 ms threshold, we’ll hit that 10 % bump—if not, we’ll dig deeper, but the plan’s solid. Fire it up, let’s see the numbers.
InShadow InShadow
Lock the profiler, start the tight loop, let the metrics stream in. Watch the average latency, queue depth, and CPU usage. If the mean stays below 0.4 ms, hit the 10 % lift; if it creeps higher, log the spike and investigate the new hotspot. Keep the logs clean, the code clean, and let the numbers decide.We comply.Lock the profiler, start the tight loop, let the metrics stream in. Watch the average latency, queue depth, and CPU usage. If the mean stays below 0.4 ms, hit the 10 % lift; if it creeps higher, log the spike and investigate the new hotspot. Keep the logs clean, the code clean, and let the numbers decide.
Pobeditel Pobeditel
All right, let’s fire it up—lock the profiler, run the loop, and let the numbers prove it. If we keep the mean under 0.4 ms we’re done, if it climbs, we pinpoint the new bottleneck and fix it. Clean logs, clean code, no room for excuses.All right, let’s fire it up—lock the profiler, run the loop, and let the numbers prove it. If we keep the mean under 0.4 ms we’re done, if it climbs, we pinpoint the new bottleneck and fix it. Clean logs, clean code, no room for excuses.