High-Load System Architecture: Handling a Million Requests per Second
One server — one death server. If your backend lives on a single machine, you're not building a product — you're building a time bomb. A high-load system is not about powerful hardware. It's about the right architecture.
Three Pillars of High-Load
Fig 1. Load Balancer distributes traffic across a server pool
1. Horizontal Scaling (Scale Out)
Rule: Don't make one server more powerful — add more servers. This is a fundamental difference between vertical ($50,000 machine) and horizontal (10 × $500 machines) scaling.
- Pro: Infinite growth ceiling. One node fails — the rest keep working.
- Challenge: The application must be stateless. No storing sessions in process memory.
2. Load Balancer
A load balancer is a dispatcher. It accepts all incoming requests and distributes them to live nodes. Distribution algorithms: Round Robin, Least Connections. Nginx, HAProxy, AWS ALB — a choice for any budget.
3. Caching — The First Line of Defense
80% of requests in most services are repetitive. Redis or Memcached cache responses and reduce database load. The cache-aside rule: check cache first, on miss — go to the database and store the result in cache.
Asynchrony as a Philosophy
Not all tasks need to be executed synchronously. Email sending, report generation, image resizing — all this is placed in a queue (Kafka, RabbitMQ, SQS) and processed by workers in the background. The user doesn't wait — they get an instant response.
NineLab Tip: Start with a simple monolith, but design it so that services can be extracted easily. "Premature microservices" has killed more startups than high load.
Bottom line: A high-load system is not magic. It's load balancer + stateless app + Redis + queues + a well-designed database. Each layer relieves the next. That's exactly how Telegram, Avito, and Ozon work.