Architecture
The canary control loop is a closed feedback system: watch for new revisions → deploy canary instances → define traffic plan → evaluate metrics in rolling windows → promote or rollback automatically.
TRAFFIC PLAN
Start at 5-10% canary weight, increase by 5% per step, cap at 50%, health checks every 30-60 seconds. Baseline for comparison: stable version receiving remaining traffic, or dedicated baseline instance set with matched size and zone distribution to avoid cross-zone latency bias.
METRIC EVALUATION
Query metrics from canary and baseline over rolling window (last 3-5 intervals). Apply pass/fail logic: success rate ≥99%, P99 <500ms, error rate increase <50% vs baseline, CPU <90%, memory <95%, CTR drop <5-10%.
💡 Decision Logic: If all checks pass for majority of window (3 out of 5), increase weight. If 5-10 consecutive failures, immediately route all traffic to stable, scale down canary, record decision with telemetry.
PROMOTION AND ROLLBACK
Promotion: canary reaches cap (50%) with all checks passing sustained → mark canary as new primary, route 100%, scale down old stable. Rollback is idempotent: multiple commands result in same end state (0% canary, 100% stable). Define as declarative resource with thresholds, step size, interval, metric queries.
ML ROLLOUT LAYERS
1) Shadow mode: validate latency, resources, predictions (no user impact). 2) Small canary 5%: gate on fast guardrails. 3) Add slow ML metrics (AUC drift, calibration, CTR) in background. 4) Promote to 50%, then 100% after final validation. Keep feature parity checks, monitor distribution shift, maintain policies in source control.
✓Control loop queries metrics every 30 to 60 seconds over rolling windows of 3 to 5 intervals, applies pass or fail to each guardrail, increases canary weight by 5 percent if all pass or rollback after 5 to 10 failures
✓Typical traffic plan: start 5 to 10 percent, increase by 5 percent steps, cap at 50 percent, checks run for 15 to 30 minutes total ramp time with pauses to accumulate signal
✓Baseline comparison uses matched instance set in same availability zones to avoid cross zone latency bias, compares success rate, P99 latency, error rate delta, CPU, memory, and business metrics like CTR drop within 5 to 10 percent
✓For ML, layer rollout: shadow mode first (validate latency and distributions), then 5 percent online canary (fast guardrails), then 50 percent (add slow ML metrics), then 100 percent after final validation
✓Promotion and rollback actions are idempotent and observable, versioned in source control, with clear telemetry and notifications, tools like Flagger automate this loop declaratively with Kubernetes and service mesh integration