DB saturates first.
Throughput & latency
High-Concurrency Systems
Concurrency work without numbers ends in subjective acceptance.
Go service design/tuning against clear QPS/latency targets.
Goals before 'fast'
Latency/throughput targets first—then design.
Observable by default
Logs, metrics and traces on the delivery checklist.
Reproducible deploys
Container/binary notes so environments can be rebuilt.
Concurrency issues
These usually show up before a project starts—or right after a rushed launch.
No rate limits—cascading failure.
Lock contention.
Cache stampede.
Target-driven load loop
Set targets→bench bottlenecks→change and rebench; protect downstream; invalidateable cache.
Benchmarks lead: pools, cache, rate limits, async and hotspot isolation. Targets enter acceptance criteria.
- Scope written before coding
- Milestones you can accept
- Handover notes included
Highlights
What this engagement typically covers.
Benchmark baseline
Included in scope after we confirm stack, constraints and acceptance checks.
Pools & cache
Included in scope after we confirm stack, constraints and acceptance checks.
Rate limit/isolation
Included in scope after we confirm stack, constraints and acceptance checks.
Hotspot handling
Included in scope after we confirm stack, constraints and acceptance checks.
What you get
- Load report
- Tuned service
- Rate-limit policy
- Cache plan
- Dashboard tips
How we work
-
01
Target lock
-
02
Baseline load
-
03
Tune iterate
-
04
Acceptance load
Ready to lock scope?
Share current vs target QPS/latency—we'll design load tests.