Canary Deployment Planner
Plan canary traffic steps and observation windows with guardrail thresholds, exposure estimates and platform-aware rollback warnings.{{ summaryTitle }}
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| Phase | Canary | Stable | Hold | Elapsed | Modeled requests | Gate action | Copy |
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| {{ row.phase }} | {{ formatPercent(row.canary_percent) }} | {{ formatPercent(row.stable_percent) }} | {{ formatDuration(row.bake_minutes) }} | {{ formatDuration(row.elapsed_minutes) }} | {{ formatInteger(row.exposure_requests) }} | {{ row.gate_action }} |
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Introduction:
A canary deployment exposes a new release to part of production while the previous version remains available. Traffic starts small, pauses for observation, and increases only after the candidate meets predefined checks. The goal is to discover regressions with a limited blast radius and a ready path back to stable service.
Traffic percentage alone says little about the evidence collected. One percent of 100 requests per minute produces a very different sample from one percent of 100,000 requests per minute. A useful gate connects the traffic share to a hold time, the number of candidate requests, and telemetry windows long enough to reveal the failure modes that matter.
- Canary
- The candidate release receiving a controlled share of production traffic.
- Stable
- The previous release kept available as a comparison and rollback destination.
- Bake interval
- The hold time at one traffic step before the next promotion decision.
- Analysis window
- The telemetry period used to decide whether a guardrail has been breached.
- Guardrail
- A measurable stop condition for errors, latency, saturation, or a business outcome.
Good guardrails compare candidate behavior with both an absolute limit and the stable baseline. An error rate below 1% may still be a regression if stable traffic is near 0.05%. A p95 latency limit can protect the typical slow request without hiding a severe tail problem, while a business measure such as authorization success catches failures that infrastructure metrics miss.
Routing precision depends on the platform. A weighted traffic router can usually represent small percentages directly. A replica-only rollout approximates the requested share with whole pods or tasks, so a 1% gate is impossible with ten replicas. Stable capacity also matters: keeping the old release warm costs more during the rollout but allows traffic to return without waiting for capacity to recover.
Rollback is a prepared operation, not a sentence added after the schedule. The trigger, owner, traffic action, configuration or feature-flag reversal, and recovery check should be agreed before exposure begins. Manual-only control increases the time between a credible breach and restored stable service.
A plan can make assumptions visible, but it cannot approve a release or observe production. Live dashboards, alerts, deployment events, business signals, and an empowered release owner remain the source of truth at every gate.
How to Use This Tool:
Define the routing mechanism and release risk first, then make every traffic step testable and reversible.
- Select the Deployment platform and Release risk, then name the service and release version so the plan matches operational records.
- Enter increasing Canary traffic steps. Values may be separated by commas or whitespace; valid percentages are deduplicated, sorted, and closed with a final 100% step when needed.
- Set the Bake interval, Analysis window, Final watch, request rate, and replica count. Keep the analysis window within the bake interval unless the gate is intentionally held longer.
- Set error-rate, p95-latency, and saturation abort thresholds, then add a business guardrail that operators can read from a live dashboard.
- Choose the stable-capacity and rollback postures. Add a concrete rollback action and release owner before treating the plan as ready.
- Review Traffic schedule, Guardrail gates, and Rollback playbook. Resolve normalization warnings, coarse replica percentages, blank actions, and timing conflicts before deployment.
Interpreting Results:
The readiness label summarizes planning pressure, not release health. Ready for gated canary means the entered shape avoided the model's strongest planning warnings. It does not mean the candidate has passed live guardrails.
- Needs guarded review appears at a score of 34 or more, or whenever at least three review flags remain.
- Tighten before launch appears at a score of 58 or more. Reduce avoidable exposure, fix timing conflicts, preserve stable capacity, or strengthen rollback before proceeding.
- Modeled exposure estimates requests sent to the candidate during scheduled holds. It assumes the entered request rate stays constant and routing follows the requested percentage.
- A small score cannot compensate for missing live data. Confirm candidate and stable error rate, p95 latency, saturation, business guardrails, and routing state at every gate.
Technical Details:
The schedule is built from normalized traffic percentages. Invalid values, zero, and percentages above 100 are ignored; duplicates are collapsed; remaining values are sorted in ascending order; and a final 100% step is added when absent. No more than 50 distinct gates are accepted. Each nonfinal gate uses the bake interval, while the 100% gate uses the final-watch duration.
Formula Core
Candidate exposure is estimated independently for each gate and rounded to the nearest whole request before the gate totals are added.
| Symbol | Meaning | Unit |
|---|---|---|
| E | Total modeled candidate requests. | Requests |
| r | Estimated request rate held constant across the plan. | Requests per minute |
| pi | Candidate traffic at gate i. | Percent |
| mi | Gate duration: bake interval, or final watch at 100%. | Minutes |
Rule Core
The planning score is a transparent repository-authored heuristic. It adds fixed pressure points for release impact, rollback readiness, traffic shape, timing, volume, and routing limitations, then clamps the total to 0 through 100 and rounds to a whole point.
| Condition | Points |
|---|---|
| Routine internal, customer-facing, or critical release path | 8, 20, or 34 |
| Stable capacity shared or reduced | +6 or +14 |
| Manual halt then rollback, or fully manual promotion and rollback | +8 or +18 |
| First canary step is greater than 10% | +12 |
| Largest promotion jump is greater than 25 and at most 50 percentage points | +7 |
| Largest promotion jump is greater than 50 percentage points | +12 |
| Bake interval is shorter than the analysis window | +10 |
| Request rate is at least 3,000 or at least 10,000 per minute | +4 or +8 |
| Replica-only routing has fewer than 10 replicas and a first step below 10% | +10 |
| Rollback action is blank | +12 |
| Traffic parsing produced at least two normalization flags | +4 |
The error-rate, p95-latency, saturation, and business thresholds are copied into guardrail and rollback guidance. They do not affect the planning score and are not checked against live telemetry. Platform selection changes the generated routing, rollback, and verification wording; only replica-based Kubernetes mode triggers the small-replica granularity rule.
Limitations, Privacy, and Accuracy Notes:
The plan is advisory and local to the browser. It does not connect to a deployment controller, modify traffic, inspect dashboards, approve promotion, or execute rollback.
- Exposure assumes a constant request rate and exact routing percentages; retries, uneven load, session affinity, regional routing, and queueing can change real exposure.
- The planning score is not a vendor standard, probability of failure, or substitute for service-level objectives and change policy.
- Platform support is expressed as planning guidance. Confirm the exact traffic and rollback semantics in the current platform configuration.
- Service names, release details, guardrails, owner names, and rollback notes remain in the browser unless the user deliberately copies or downloads them.
Worked Examples:
A direct 100% critical-path release
A critical-path plan containing only a 100% step begins with 34 base points. The first step is above 10% and the 100-percentage-point jump is above 50, adding 12 points for each rule. The final score is 58, so the result is Tighten before launch even with warm stable capacity and automated rollback. Adding real intermediate gates changes both blast radius and observation opportunities.
References:
- Canary Deployment Strategy, Argo Rollouts documentation.
- Working with deployment configurations in CodeDeploy, AWS documentation.
- Use a canary deployment strategy, Google Cloud Deploy documentation.
- How to route canary traffic with HAProxy, Simplified Guide.