Capabilities
Recoverable failure modes
How BootCtrl recovers from failures: restart strategies, health checks, and a workflow that gets systems back to a known-good state.
Design intent
Use this lens when adopting Recoverable failure modes: define success criteria, start narrow, and scale with safe rollouts and observability.
- Recovery restores health within a version; rollback changes the version
- Reconciliation is required after recovery to eliminate drift
- Instrument recovery steps so MTTR improves over time
What it is
Recovery is the combination of supervision, health reporting, and deployment orchestration so the system can return to a stable state after faults.
Design constraints
- Recovery restores health within a version; rollback changes the version
- Reconciliation is required after recovery to eliminate drift
- Instrument recovery steps so MTTR improves over time
Architecture at a glance
- Supervision detects failure, applies restart/backoff, and escalates to rollback when needed
- Health models separate “degraded”, “offline”, and “misconfigured”
- Evidence is preserved via logs/events/telemetry tied to versions
- This is a capability surface concern: recovery must be safe under stress
Typical workflow
- Define scope and success criteria (what should change, what must stay stable)
- Create or update a snapshot, then validate against a canary environment/site
- Deploy progressively with health/telemetry gates and explicit rollback criteria
- Confirm acceptance tests and operational dashboards before expanding
System boundary
Treat Recoverable failure modes as a capability boundary: define what success means, what is configurable per site, and how you will validate behavior under rollout.
Example artifact
Implementation notes (conceptual)
topic: Recoverable failure modes
plan: define -> snapshot -> canary -> expand
signals: health + telemetry + events tied to version
rollback: select known-good snapshotWhat it enables
- Faster mean-time-to-recovery (MTTR)
- Safer operation under partial failures and flaky networks
- Clear diagnostics for why a site degraded or restarted
Recovery workflow
- Detect: health checks flag a site/device as degraded
- Isolate: identify which component failed (runtime vs adapter vs network)
- Mitigate: restart/reconcile according to policy and safe ordering
- Escalate: if repeated failures occur, recommend rollback to a known-good snapshot
What to monitor
- Restart counts and crash-loop detection
- Deployment state transitions and error categories
- Connectivity health and telemetry delivery latency
Common failure modes
- Crash loops that mask a configuration error
- Partial degradation that never triggers a hard failure
- Recovery succeeds but drift remains (wrong version still deployed)
Engineering outcomes
- Recovery restores health within a version; rollback changes the version
- Reconciliation is required after recovery to eliminate drift
- Instrument recovery steps so MTTR improves over time
Quick acceptance checks
- Define safe restart/backoff policies and crash-loop thresholds
- Ensure reconciliation detects “desired vs actual” drift after recovery
Acceptance tests
- Verify the deployed snapshot/version matches intent (no drift)
- Run a canary validation: behavior, health, and telemetry align with expectations
- Verify rollback works and restores known-good behavior
Deep dive
Practical next steps
How teams typically turn this capability into outcomes.
Key takeaways
- Recovery restores health within a version; rollback changes the version
- Reconciliation is required after recovery to eliminate drift
- Instrument recovery steps so MTTR improves over time
Checklist
- Define safe restart/backoff policies and crash-loop thresholds
- Ensure reconciliation detects “desired vs actual” drift after recovery
- Keep rollback to a known-good snapshot as the last-resort stabilizer
- Instrument recovery steps so MTTR can be improved over time
Next steps
Related topics
Deep dive
Common questions
Quick answers that help align engineering and operations.
What’s the difference between recovery and rollback?
Recovery restores a system to a healthy state within the same version. Rollback changes the version by returning to a known-good snapshot when the current version is the cause.
Why do systems get stuck in “partial failure” states?
Because symptoms don’t trigger a hard failure. Use health models that detect degraded modes and alert/mitigate before operators notice.
How do we reduce MTTR?
Make the failure domain explicit (runtime vs adapter vs network), capture the first failure cause, and keep a deterministic rollback option.