Retail · 2024 · Client anonymised at their request
Surviving a campaign that had taken the old platform down twice
Commerce replatform sized against a measured load profile rather than a guess.
A national retail chain
14×
Peak traffic absorbed
99.98%
Campaign-period availability
1.2s
Largest contentful paint at peak
The constraint
Two previous seasonal campaigns had ended in outages, and the business had no reliable model of what its actual peak looked like. We were given eleven weeks before the next campaign.
The architecture decision
We characterised load against production-like data with k6 before changing anything, which established that the failure was database connection exhaustion rather than the assumed compute shortfall. Read paths moved behind a cache with explicit invalidation, checkout was isolated into its own connection pool with a queue and admission control, and progressive rollout gates were wired into the deployment pipeline.
What shipped
- 01
Load characterisation and capacity model
- 02
Cache layer with explicit invalidation contracts
- 03
Isolated checkout path with admission control
- 04
Progressive delivery pipeline with automated rollback
The campaign ran at fourteen times baseline traffic without incident. The capacity model is now reviewed before every seasonal event.
Figures verified with the client’s own instrumentation, not ours.
What it moved
Practices involved
- DevOps & scaling
- Cloud infrastructure
- Product engineering