Design cancellation ownership for queued and running reports
A user cancels a report, but the worker later publishes a completed artifact and overwrites the cancellation status.
- Focused work estimate
- 5h + prerequisites
- Priority in the scenario
- Medium
- Engineering practice
- Cancellation design · Concurrency
Estimated field mix
- System design50%
- Distributed systems30%
- Storage systems20%
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 analytics product lets customers schedule expensive reports at the top of the hour. The API remains available only if report work is admitted and cancelled predictably.
Setup prerequisites
- Create a local scheduler model and synthetic report jobs.
- Use fake execution providers; no customer queries or candidate code run on worker hosts.
Preceding work
Complete these dependencies, or supply their agreed outputs before taking this ticket.
- AADMIT-101 · Build a report arrival model that includes top-of-hour bursts
- AADMIT-102 · Define customer-visible report states and overload responses
- AADMIT-103 · Record the fairness decision for small and large report tenants
- AADMIT-104 · Specify transactional report admission with deterministic job identities
Acceptance criteria
- Define cancellation request versus confirmed stop.
- Fence stale completion against current generation and state.
- Specify cleanup ownership for incomplete artifacts.
Implementation constraints
- Use a provider cancellation contract; do not kill host processes arbitrarily.
Verification to include
- Cancel queued work and verify no execution begins.
- Race cancellation with completion and preserve the declared single terminal outcome.
Deliverables
- Cancellation protocol and race model
Rollout and recovery
Canary cancellation in the local provider; retain unresolved stop status when provider confirmation is unavailable.
Value of the work
For the engineer: Practice workload modeling, fairness and cancellation architecture.
For the team: Review controllable operating costs and predictable customer-facing overload behavior.
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.