Why overload happens and how to deal with it in real-world systems

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Sergey Sidorov is a Software Engineer at Meta, working on the change safety infrastructure and internal reliability systems. His work includes SLICK, a platform for early regression detection and production stability.

The Risk of Overload

User expectations for speed and reliability are higher than ever, and reliability gaps often drive people away. Overload remains one of the main causes of industry-wide outages: it affects many customers at once, is extremely difficult to recover from, and regularly results in multimillion-dollar losses. Using the example of a fictional social media app NewSocial, it was shown how throughput initially grows with traffic but then collapses, sometimes all the way to zero, making the system completely unavailable.

Self-Protection Before Scaling

When resource limits are reached, simply adding more requests only starves the system. The answer is load shedding — proactively rejecting excess requests early, for example through queue timeouts. This prevents sudden collapse and gives autoscaling the time to react, whereas scaling alone cannot help without protective mechanisms already in place.

Smarter Clients, Not Just Stronger Servers

A major source of overload lies on the client side. When servers slow down, clients retry aggressively, amplifying pressure and creating retry storms. Exponential backoff helps somewhat but does not solve the problem. Retry budgets and circuit breakers are needed. Circuit breakers block requests when servers are overloaded, cautiously reopen, and restore full traffic only after recovery is confirmed. With these mechanisms, servers stay responsive long enough to scale. Crucially, overload protection cannot be limited to the backend: clients must also be designed with resilience in mind.

Fairness and Isolation

Another risk comes from misbehaving clients. A single user sending too many requests can degrade availability for everyone. Quotas and rate limiting — for example the token bucket algorithm — ensure each client receives a fair share of resources. This isolates failures and reduces the blast radius, keeping most users unaffected even if one client goes rogue.

Conclusion

Overload is not a solved problem, but it can be managed with layered defenses. Systems that shed load, clients that respect circuit breakers, and quotas that enforce fairness give teams the runway they need to scale safely. Instead of collapsing under stress, services remain stable, available, and trustworthy — the foundation of long-term reliability.

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