Why architecture matters here

Scaling fails when the wrong pattern is applied. Architecture matters because each pattern fits a specific bottleneck.

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The architecture: every piece explained

The top strip is basics. Vertical scale. Horizontal scale. Stateless services. Load balancer.

The middle row is data + async. Sharding. Caching tiers. Async processing. Read replicas.

The lower rows are ops. Auto-scaling. Backpressure. Ops — capacity + observability + cost.

Scaling patterns — vertical + horizontal + sharding + caching + statelesshandle growth without rewritingVertical scalebigger boxHorizontal scalemore boxesStateless servicesany node handlesLoad balancerdistributesShardingsplit data by keyCaching tiersin-mem + Redis + CDNAsync processingqueues + workersDatabase read replicasreads scale outAuto-scalingcloud primitivesBackpressureprotect at limitsOps — capacity + observability + costsplitcachedeferreplicateautoprotectprotectoperateoperate
Scaling pattern menu.
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End-to-end flow

End-to-end: app scales vertically first. Hits box limit; refactor stateless. Add LB + horizontal. Read-heavy load addressed with replicas + cache. Writes scale via sharding on user_id. Async workers absorb heavy tasks.