Bound retries so failing reports cannot consume the entire service
A malformed report fails immediately and retries faster than healthy reports can start.
- Focused work estimate
- 3h 30m + prerequisites
- Priority in the scenario
- Medium
- Engineering practice
- Retry policy · Resource budgets
Estimated field mix
- System design50%
- Site reliability30%
- Platform 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 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
- AADMIT-105 · Design cancellation ownership for queued and running reports
Acceptance criteria
- Define retry count, backoff and elapsed-time budgets.
- Classify permanent versus retryable failure explicitly.
- Keep retry work inside tenant and global admission limits.
Implementation constraints
- Use deterministic fake time and bounded jitter inputs.
Verification to include
- Retry a transient failure within the declared budget.
- Feed a permanent failure and verify it cannot form a tight retry loop.
Deliverables
- Retry-budget model and scheduling probe
Rollout and recovery
Enable bounded retries for synthetic jobs; suspend a failing class if classification is uncertain.
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.