Goal
Add near-real-world, data-intensive scenarios that stress Clockworks in ways likely to expose correctness, scalability, determinism, memory growth, and throughput regressions.
Done when
- Multiple repeatable scenarios run in CI and/or nightly builds.
- Runs emit structured metrics and artifacts (machine-readable, comparable across runs).
- Failures and threshold regressions automatically create follow-up issues with enough context to triage without re-running blindly.
Recommended GitHub Project fields
When you add these issues to a Project, map (or create) fields such as:
| Field |
Suggested values |
| Type |
Epic / Scenario / Infra / Automation |
| Signal area |
Correctness / Performance / Memory / Determinism / Reliability |
| Runtime mode |
Simulated / System / Both |
| Priority |
P0 / P1 / P2 |
| Status |
Backlog / Ready / In Progress / Validation / Done |
Suggested initial priority order (implementation sequence)
- #59 — Common harness and result schema
- #60 — At-least-once order pipeline
- #61 — Timer/timeout storm
- #64 — Long-running soak and bounded growth
- #65 — Failure/regression-to-issue automation
- #62 — UUIDv7 / HLC hot path
- #63 — Multi-node causal fan-out / fan-in
- #66 — Nightly workflow
Work items (numbered by scenario area; use priority list above for sequencing)
Goal
Add near-real-world, data-intensive scenarios that stress Clockworks in ways likely to expose correctness, scalability, determinism, memory growth, and throughput regressions.
Done when
Recommended GitHub Project fields
When you add these issues to a Project, map (or create) fields such as:
Suggested initial priority order (implementation sequence)
Work items (numbered by scenario area; use priority list above for sequencing)