Create a hot-key workload that exposes synchronized expiry
Uniform random keys miss often but never reproduce the traffic surge that arrives when the most popular products expire together.
- Focused work estimate
- 2h + prerequisites
- Priority in the scenario
- Medium
- Engineering practice
- Workload design · Load testing
Estimated field mix
- Performance engineering80%
- Quality engineering20%
Field percentages are editorial estimates of the ticket's engineering focus. They total 100%; they are not measured time, proficiency scores, or ownership evidence.
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Project context
A fictional equipment-rental service caches availability summaries. A campaign sends repeated reads, while stock updates and shared expiry times create bursts against the origin. Build a local origin stub and cache-backed read API using synthetic depots and products.
Setup prerequisites
- Create a deterministic local availability origin and Redis-backed reader with synthetic tenant, depot and product data.
- Use a seeded hot-key distribution and controlled time; record cache capacity, TTLs, runtime and machine limits.
Preceding work
Complete these dependencies, or supply their agreed outputs before taking this ticket.
Acceptance criteria
- Generate a seeded workload where 80% of reads target 20 hot product/depot keys and the remainder sample a larger declared set.
- Include a synchronized-expiry phase, a cold start and a quiet recovery period.
- Record scheduled and achieved arrivals, timeouts and origin concurrency so generator saturation is visible.
Implementation constraints
- Use a controllable clock for unit-level expiry cases and a documented arrival schedule for load runs.
Verification to include
- Repeat the seed and compare key frequencies and expiry schedule.
- Run the uncached and cached paths against the same origin-delay fixture and retain all outcome counts.
Deliverables
- Hot-key generator and baseline workload manifest
Rollout and recovery
Version the workload independently of cache implementation; changing the distribution starts a new comparison baseline.
Value of the work
For the engineer: Learn to evaluate caching through avoided work, bounded staleness, concurrency and recovery rather than hit rate alone.
For the team: Produce a reviewable cache policy and failure exercise for a read-heavy service with changing business data.
Evidence boundaries
Outcome Evidence: Tests, patches, and runbooks are requested deliverables. They become Outcome Evidence only through a qualified Mission and immutable Evidence IDs.
Ownership Evidence: Independent adaptation must be observed under a declared verification policy and cite immutable Evidence IDs. Completing a planning ticket establishes no Ownership Evidence.