Capacity Planning: Handling 15,000 req/s

To ensure high availability and responsiveness under a load of 15,000 requests per second, we must calculate the required number of container instances.

Base Assumptions

Target Load: 15,000 req/s
Max Capacity per Container: 500 req/s
Safety Margin: 30%

Calculation

Effective Capacity per Container: To maintain a 30% margin, we calculate the effective capacity each container should handle before we consider scaling out. 500 req/s * (1 - 0.30) = 350 req/s
Total Containers Required: Divide the total expected load by the effective capacity per container. 15,000 req/s / 350 req/s per container ≈ 42.85
Rounding Up: We cannot have a fraction of a container, so we always round up to the next whole number. ceil(42.85) = 43 containers

Conclusion

To safely handle 15,000 req/s while maintaining a 30% safety margin (which helps absorb sudden traffic spikes or the failure of a few containers), the auto-scaling group should be configured to maintain a baseline of 43 containers during peak load.

4. Cold Start Strategy

To minimize the delay when new containers are provisioned (cold start latency):

Lightweight Base Images: Use Alpine or distroless images (e.g., python:3.11-alpine) so they pull faster over the network.
Pre-warming (Buffer Pool): Maintain a buffer of idle containers (e.g., 10% of required capacity). For 43 containers, run ~47. The extra 4 handle sudden spikes instantly.
Lazy Loading: Defer non-critical initialization until after the container has started accepting requests.

5. Ghaymah Block Storage for Stateful Workloads

While the API is mostly stateless, Ghaymah Block Storage is used for:

Local Caching / ML Models: Persistent storage for large datasets downloaded at startup.
Session Data / Logs: Persisting complex audit logs before they are shipped to centralized logging.
Self-Managed Databases: Ensuring data survives container restarts by mounting a volume like /mnt/data.

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