Capabilities

Controlled rollouts + rollback

How BootCtrl supports safe rollouts and fast rollback across a fleet, with visibility and control over deployments.

Design intent

Use this lens when adopting Controlled rollouts + rollback: define success criteria, start narrow, and scale with safe rollouts and observability.

Canaries and gates keep fleet-wide change safe
Rollback must be fast and routine, not exceptional
Recording outcomes turns rollouts into learning loops

What it is

BootCtrl treats deployments as an orchestrated workflow: plan → deploy → monitor → roll back when needed.

Design constraints

Canaries and gates keep fleet-wide change safe
Rollback must be fast and routine, not exceptional
Recording outcomes turns rollouts into learning loops

Architecture at a glance

Rollout plan defines ordering (canary → expand) and gating signals
Edge reports progress and failures with actionable categories
Rollback selects a known-good snapshot, not a best-effort reverse edit
This is a capability surface concern: reliability comes from process + signals

Typical workflow

Define rollout stages and gating signals (health/telemetry/behavior checks)
Start with a canary site that represents real conditions
Expand in batches; stop on regression; roll back to known-good snapshot
Post-rollout: capture learnings and update acceptance checks

System boundary

Treat Controlled rollouts + rollback as a capability boundary: define what success means, what is configurable per site, and how you will validate behavior under rollout.

Example artifact

Rollout gates (conceptual)

gates:
  health: runtime_healthy && adapter_connected
  telemetry: gap < 10s && drops == 0
  behavior: acceptance_suite_passed
stages:
  - canary: 1 site
  - batch: 10 sites
  - fleet: all

What it enables

Progressive rollouts across sites
Rollback to a known-good snapshot
Deployment status and failure diagnostics

Engineering outcomes

Canaries and gates keep fleet-wide change safe
Rollback must be fast and routine, not exceptional
Recording outcomes turns rollouts into learning loops

Quick acceptance checks

Define acceptance criteria per rollout (health, latency, error rates)
Deploy to a canary first and hold before expansion

Common failure modes

No canary or weak canary that doesn’t represent production conditions
Missing gates: rollout continues despite degraded health signals
Rollback too slow or unclear ownership/criteria
Failure reasons not categorized, causing repeated incidents

Acceptance tests

Gate enforcement: rollout pauses/stops when health regresses
Failure categorization: errors are grouped into actionable buckets
Blast radius control: canary (Controlled rollouts + rollback) shows issues before fleet expansion
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

Canaries and gates keep fleet-wide change safe
Rollback must be fast and routine, not exceptional
Recording outcomes turns rollouts into learning loops

Checklist

Define acceptance criteria per rollout (health, latency, error rates)
Deploy to a canary first and hold before expansion
Automate rollback when gates fail
Record rollout outcomes and tie them to snapshot IDs for learning

Next steps

Common questions

Quick answers that help align engineering and operations.

What does a “safe rollout” require?

Immutable artifacts (snapshots), progressive rollout stages, observable health/telemetry gates, and a fast rollback to a known-good version.

How do we choose canaries?

Pick representative sites/devices but with low blast radius. Canary should cover critical integrations without risking the whole fleet.

What should stop an expansion?

New crash loops, rising protocol errors, telemetry gaps, or sustained deviations in key KPIs compared to baseline or previous snapshot.