Core

Rate Limiting

Rate limiting caps how fast a client or your whole fleet can hit a resource, so a burst of traffic or one runaway caller cannot melt a fragile downstream service or burn your third-party API quota. The interesting part is enforcing it across many machines without a race, and deciding whether to reject or queue when the limit hits.

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RELATED CONCEPTS

Retries, Exponential Backoff and Jitter Latency Optimization Idempotency

LESSONS THAT TEACH THIS

Foundations of Forward Deployed EngineeringChoose retries from outcome evidence and a time budget30 min→Foundations of Forward Deployed EngineeringInvestigate undocumented behavior with bounded evidence28 min→Hard DeploymentsHardware arrives on a procurement cycle and your estimate arrives in a meeting30 min→

PRACTICE THIS IN REAL QUESTIONS

System Design & Production EngineeringWhat's the difference between at-least-once and exactly-once delivery, and why should an FDE care?→System Design & Production EngineeringExplain how rate limiting works, fixed window, sliding window, token bucket. When does each break?→ML Infrastructure & GPUsCompare data, tensor, and pipeline parallelism, when do you use each, and how do they combine into 3D parallelism?→System Design & Production EngineeringA Python service starts double-processing messages under load. Why does this happen, and how do you fix it?→ML Infrastructure & GPUsExplain how ring all-reduce works and derive its communication cost.→ML Infrastructure & GPUsWhat do ZeRO and FSDP actually shard, and how much memory does each stage save? Where does gradient checkpointing fit?→