Скачать или смотреть Server Throttler & Rate Limiter | System Design Ladder 🪜 | HLD 101

This tutorial separates two ideas that interviewers expect you to distinguish. Rate limiting is policy enforcement. Load shedding is survival under saturation. When someone asks how to handle unexpected spikes and you treat both as the same mechanism, you signal that you do not understand the difference between rejecting users and protecting a dying server.

This breakdown shows how services scale horizontally, how auto scaling lags reality, and why overloads create cascading latency across a distributed system. When capacity cannot be added fast enough, the service either slows down for everyone or it rejects work cheaply and defends consistency. Returning a fast 503 protects CPU, memory, queues, and downstream dependencies. Slowing down increases context switching, queue depth, garbage collection pressure, and eventually causes lockups or OOM kills. Load shedding keeps the service alive long enough for scaling to catch up.

The video walks through multi-tenant pressure. One abusive workload should not collapse every workload. Rate limiting enforces fairness at the business boundary. You set quotas per client or per workload. When a client exceeds quota, you return 429 and tell the client to slow down. The server is not dying. The client is misbehaving. That preserves predictable performance across users and prevents starvation.

You will see how token buckets permit bursts, how leaky buckets smooth request flow, and how fixed or sliding windows count events. You will see how local fairness works on a single server without global coordination, and how global fairness depends on distributed state like Redis. You will see how Envoy, sidecars, or application servers watch CPU saturation, queue delays, memory pressure, connection pools, latency trends, and adaptive throttling feedback loops. When health metrics breach thresholds, the system drops requests probabilistically to preserve forward progress.

This tutorial finishes with a mental split. Rate limiting asks who you are and how much quota you have consumed. Load shedding asks whether the server is healthy enough to continue. Rate limiting is about policy and fairness. Load shedding is about physics and capacity. The difference determines whether your system slows down and dies or rejects early and survives.

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