Scaling Modern Web Applications Without a Rewrite
The architectural decisions that let a system handle 10x growth by adding capacity, not by rebuilding from scratch.
Most "we need to rewrite for scale" conversations are actually about specific bottlenecks that could be fixed without touching the rest of the system — if the architecture was built to allow that in the first place.
Separate what scales independently
Read-heavy workloads, write-heavy workloads, and background processing all scale differently. An architecture that lets each scale on its own — read replicas, queued background jobs, cached read paths — avoids over-provisioning the whole system to handle one bottleneck.
Cache deliberately, not defensively
- Cache what changes infrequently and is expensive to compute — not everything.
- Have an explicit invalidation strategy before adding a cache, not after a stale-data bug.
- Measure cache hit rate — an ineffective cache adds complexity without benefit.
Load test against realistic peak scenarios
Average-day load testing tells you very little about what happens during a real traffic spike. Test against the specific scenario that matters for your business — a sales event, a viral moment, a seasonal peak — not just steady-state throughput.
Horizontal scaling requires stateless services
Adding more servers only helps if a request can be handled by any of them — which means session state, file uploads, and background job state need to live outside individual application servers, not on them.
Key Takeaways
- Let different workload types (read/write/background) scale independently.
- Cache deliberately with an explicit invalidation strategy, not defensively.
- Load test against realistic peak scenarios, not just average-day traffic.
Fastly Engineering Team
This article represents the collective engineering knowledge and standards of the Fastly team, not a single author.
Fastly Engineering
Where This Gets Applied
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Frequently Asked Questions
When does a system actually need a rewrite instead of scaling fixes?
Rarely, in practice — most scaling problems are specific bottlenecks (an un-indexed query, a synchronous job that should be async) rather than a fundamental architecture failure.
How do we know if our caching strategy is working?
Measure cache hit rate directly. A low hit rate means the cache is adding complexity without meaningfully reducing load.
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