Canary Deployment Analyzer

Analyze canary deployments by comparing metrics between canary and baseline. Provide data-driven promotion/rollback recommendations based on error rates, latency percentiles, and custom business metrics.

Analyze canary deployments to decide whether to promote or rollback. Compare error rates, latency distributions, business metrics, and log patterns between canary and baseline populations — then give a data-driven recommendation.

Use when: "analyze canary", "should we promote this canary", "compare canary metrics", "canary vs baseline", "is this deploy safe to promote", "canary health check", or during progressive delivery decisions.

Commands

1. analyze — Full Canary Analysis

Step 1: Collect Metrics

Identify the metrics source (Prometheus, Datadog, CloudWatch, custom):

# Prometheus query examples
# Error rate — canary vs stable
curl -s "$PROMETHEUS_URL/api/v1/query" --data-urlencode \
  'query=sum(rate(http_requests_total{status=~"5..",deployment="canary"}[5m])) / sum(rate(http_requests_total{deployment="canary"}[5m]))' | \
  python3 -c "import json,sys;r=json.load(sys.stdin);print(f'Canary error rate: {r[\"data\"][\"result\"][0][\"value\"][1] if r[\"data\"][\"result\"] else \"no data\"}')"

# Same for baseline
curl -s "$PROMETHEUS_URL/api/v1/query" --data-urlencode \
  'query=sum(rate(http_requests_total{status=~"5..",deployment="stable"}[5m])) / sum(rate(http_requests_total{deployment="stable"}[5m]))' | \
  python3 -c "import json,sys;r=json.load(sys.stdin);print(f'Baseline error rate: {r[\"data\"][\"result\"][0][\"value\"][1] if r[\"data\"][\"result\"] else \"no data\"}')"

# Latency p50/p95/p99
for q in 50 95 99; do
  curl -s "$PROMETHEUS_URL/api/v1/query" --data-urlencode \
    "query=histogram_quantile(0.${q}, sum(rate(http_request_duration_seconds_bucket{deployment=\"canary\"}[5m])) by (le))"
done

If no Prometheus, check for:

• Datadog: curl -s "https://api.datadoghq.com/api/v1/query" -H "DD-API-KEY: $DD_API_KEY" --data-urlencode "query=avg:http.request.duration{deployment:canary}"
• CloudWatch: aws cloudwatch get-metric-statistics --namespace MyApp --metric-name ErrorRate --dimensions Name=Deployment,Value=canary
• Application logs: parse error counts from structured logs

Step 2: Statistical Comparison

For each metric, calculate:

1. Absolute difference: canary_value - baseline_value
2. Relative change: (canary - baseline) / baseline × 100%
3. Statistical significance: For rates, use a two-proportion z-test; for latencies, use Welch's t-test or Mann-Whitney U if distributions are skewed

Decision thresholds (configurable):

• Error rate increase > 0.1% absolute OR > 10% relative → FAIL
• p95 latency increase > 50ms OR > 15% relative → WARNING
• p99 latency increase > 200ms OR > 25% relative → FAIL
• Business metric (conversion, throughput) decrease > 5% → WARNING

Step 3: Log Analysis

# Compare error log patterns
# Canary errors
kubectl logs -l deployment=canary --since=1h 2>/dev/null | grep -i "error\|exception\|panic\|fatal" | \
  sort | uniq -c | sort -rn | head -20

# Baseline errors
kubectl logs -l deployment=stable --since=1h 2>/dev/null | grep -i "error\|exception\|panic\|fatal" | \
  sort | uniq -c | sort -rn | head -20

Look for:

• New error types in canary that don't appear in baseline (strongest signal)
• Error rate spike in existing error types
• Timeout patterns or connection refused (infrastructure issues vs code issues)

Step 4: Generate Verdict

# Canary Analysis Report

## Verdict: PROMOTE / ROLLBACK / HOLD

## Metrics Comparison (last 30 min)
| Metric | Baseline | Canary | Delta | Status |
|--------|----------|--------|-------|--------|
| Error rate | 0.12% | 0.14% | +0.02% | ✅ Pass |
| p50 latency | 45ms | 48ms | +3ms | ✅ Pass |
| p95 latency | 180ms | 210ms | +30ms | ✅ Pass |
| p99 latency | 450ms | 620ms | +170ms | ⚠️ Warning |
| Throughput | 1200 rps | 1180 rps | -1.7% | ✅ Pass |

## New Errors in Canary
- `NullPointerException in UserService.getProfile` (23 occurrences)
  → Not present in baseline — likely regression

## Traffic Split
- Canary: 5% (60 rps)
- Baseline: 95% (1140 rps)
- Observation window: 30 min (sufficient for 5% traffic)

## Recommendation
[PROMOTE] Metrics within acceptable thresholds. p99 latency elevated but within warning range.
Monitor p99 closely after full promotion. Investigate NullPointerException — non-blocking but should be tracked.

2. thresholds — Configure Promotion Criteria

Help define canary promotion thresholds based on SLOs:

• If team has SLOs → derive thresholds from error budget remaining
• If no SLOs → suggest industry defaults (99.9% availability = 0.1% error budget)
• Generate a config file for Argo Rollouts, Flagger, or custom canary controller

3. progressive — Design Progressive Delivery Strategy

Given a service profile (traffic volume, criticality, deployment frequency), recommend:

• Traffic split stages (1% → 5% → 25% → 50% → 100%)
• Observation window per stage
• Automated vs manual promotion gates
• Rollback trigger conditions