Canary Release

1. What this document is about

This documents explains how to implement canary releases — a progressive delivery pattern where new software version are deployed to a small subset of production traffic before full rollout — on Azure infrastructure

Where this applies:

• Microservices running on Azure Kubernetes Service (AKS) or Azure App Service
• Systems with mature observability (metrics, logs, traces)
• Teams capable of automated rollback within minutes
• Applications where blast radius control justifies deployment complexity

Where this does not apply:

• Batch jobs or scheduled workloads without live traffic
• Systems without reliable health metrics or SLOs
• Monoliths where partial deployment creates version skew problems
• Development/staging environments (unnecessary overhead)

This is not about A/B testing for product features. This about risk mitigation during software deployments.

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2. Why this matters in real systems

The traditional "deploy and pray" model breaks down under three predictable pressures:

• Scale pressure: When you serve millions of requests daily, a bad deploy doesn't just affect some users — it affects enough users that your incident becomes a business event. A 5% error rate on 10 million requests is 500,000 failures. You need containment before detection becomes response.

• Evolution pressure: Modern systems change constantly. Multiple teams ship daily. The surface area for regression grows with every dependency update, config change, or infrastructure modification. Comprehensive pre-production testing becomes economically impossible. You must validate in production, but carefully.

• Reliability pressure: Your SLA assumes sub-second response times and 99.9% + availability. A broken deployment that affects all users violates this instantly. But a broken deployment affecting 5% of users for 3 minutes might stay within budget. The math of partial failure is fundamentally different.

What breaks when ignored:

• A database query regression hits all users simultaneously, overwhelming your database before you detect the problem
• A memory leak deploys to all pods, causing cascading failures across regions
• A third-party SDK update introduces 2-second latency, but your staging load tests ran with mocked dependencies
• A configuration typo breaks authentication, locking out customers until rollback completes (15+ minutes in many systems)

Simpler approaches — blue/green, rolling updates without traffic control — work until they cost you an outage. Canary releases emerge when the cost of full-blast failures exceeds the cost of deployment sophistication.

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3. Core concept (mental model)

Think of a canary release as a controlled experiement in production with an escape hatch. The deployment progress through phases:

[Baseline: v1.0 @ 100%] 
         ↓
[Canary: v1.1 @ 5%, Stable: v1.0 @ 95%] ← observation window
         ↓
     [Gates: metrics within SLO?]
         ↓                    ↓
    [YES: promote]      [NO: rollback]
         ↓                    ↓
[v1.1 @ 20%]          [v1.0 @ 100%]
         ↓
[v1.1 @ 50%]
         ↓
[v1.1 @ 100%] ← new baseline

Key invariants:

1. Two versions run concurrently during the canary window. Your infrastructure must suport this.
2. Traffic split is deterministic: The same user/request should hit the same version for session consistency.
3. Metrics are comparative: You compare canary metrics against baseline (current production), not absolute thresholds.
4. Promotion is gated: Automated checks or manual approval required beforeincreasing traffic percentage.
5. Rollback is instant: Traffic shifts back to stable version without pod restarts.

The "canary" metaphor comes from coal mining — miners brought canaries underground because they'd die from toxic gas before humans noticed. Your canary deployment dies (get rolled back) before your entire user base suffers.

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4. How it works (step-by-step)

Step 1 — Deploy canary version alongside stable version

You introduce the new version (v1.1) into your cluster/app service while keeping the current version (v1.0) full operational. Both version share the same external endpoint but are distinct workloads internally.

Why this exists: You need versions available so traffic routing can split between them. A true canary is not a rolling update — it's deliverate version coexistence.

Assumptions:

• Both version can run against the same database schema (backward-compartible migrations)
• Shared resources (cache, message queues) handle mixed version access
• Replica count for canary starts small (1-2 pods) to minimize resource cost

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Step 2 — Route small traffic percentage to canary

A traffic manager (Azure Front Door, Application Gateway, service mesh like Istio/Linkerd, or Flagger's integrated routing) send 5-10% of request v1.1, while 90-95% continue to v1.0

Why this exists: Limits blast radius. If v1.1 has a critical bug, only a small user cohort experiences it.

Routing strategies:

• Random sampling: Simple but risks session inconsistency (user switches version mid-session)
• Header-based: Route by X-Canary: true header (useful for internal testing)
• Cookie/session-based: Sticky routing ensures user sees one version (recommended for statefull apps)
• Cohort-based: Route by user ID hash (deterministic, consistent)

Invariant: Routing decision must happen at the edge (load balancer/ingress), not application code.

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Step 3 — Collect and compare metrics

During the observation window (typically 10-60 minutes), you monitor:

• Error rate (5xx responses, exceptions)
• Latency (p50, p95, p99)
• Saturation (CPU, memory, request queue depth)
• Business metrics successful transactions, API call success)

Critical, you compare v1.1 canary metrics against v1.0 baseline metrics collected in the same time window.

Why this exists: Absolute thresholds fail in practice. "Error rate <0.1%" is useless if your baseline is 0.05% and the canary is 0.5% — that's a 10x increase. Relative comparasion catches regressions.

Azure implementation: Application Insights custom metrics, Log Analytics queries, Prometheus (if using service mesh), OpenTelemetry spans.

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Step 4 — Gate decision — promote or rollback

An automated gate (or manual review) evaluates:

IF canary_error_rate > baseline_error_rate * 1.5 THEN rollback
IF canary_p95_latency > baseline_p95_latency * 1.3 THEN rollback
IF canary_cpu > 80% THEN rollback
ELSE promote to next stage

Why this exists: Human reaction time is too slow for production incidents. Automated gates contain damage within minutes. Manual gates add control for risky changes.

Failure modes:

• False positives: Noisy metrics trigger unnecessary rollbacks (requires metric smoothing, longer observation windows)
• False negatives: Gate misses subtle degradation (requires comprehensive metric coverate)

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Step 5 — Progressive promotion or full rollback

If gates pass: Increase canary traffic (5% → 20% → 50% → 100%), repeating observation at each stage.

If gates fail: Instantly shift 100% traffic back to v1.0, then terminate v1.1 pods. No user-visible downtime.

Why this exists: Progressive promotion limits risk at each stage. Even if the 5% canary succeeds, the 50% canary might reveal load-dependent bugs (database connection exhaustion, rate limit violations).

Invariant: Each promotion stage has its own observation window and gate evaluation.

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Step 6 — Retire old version

Once v1.1 reaches 100% and stabilizes (typically 24-72 hours), you decommssion v1.0 infrastructure.

Why this exists: Running two versions indefinitely wastes resources. But premature retirement risks inability to rollback if a delayed issue appers (e.g., weekly batch job fails).

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5. Minimal but realistic example (.NET)

This exemple use AKS with Flagger (a progressive delivery operator) and Azure Appliction Gateway Ingress Controller for traffic splitting.

Infrastructure

# AKS cluster with monitoring
resource "azurerm_kubernetes_cluster" "aks" {
  name                = "prod-aks"
  resource_group_name = azurerm_resource_group.rg.name
  location            = azurerm_resource_group.rg.location
  dns_prefix          = "prodaks"

  default_node_pool {
    name       = "default"
    node_count = 3
    vm_size    = "Standard_D4s_v3"
  }

  identity {
    type = "SystemAssigned"
  }

  azure_monitor {
    enabled = true
  }
}

# Application Gateway for ingress
resource "azurerm_application_gateway" "appgw" {
  name                = "prod-appgw"
  resource_group_name = azurerm_resource_group.rg.name
  location            = azurerm_resource_group.rg.location

  sku {
    name     = "WAF_v2"
    tier     = "WAF_v2"
    capacity = 2
  }
  # ... gateway_ip_configuration, frontend ports, etc.
}

Kubernetes Deployment (Helm values for Flagger)

# api-service.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: api-service
  namespace: production
spec:
  replicas: 5
  selector:
    matchLabels:
      app: api-service
  template:
    metadata:
      labels:
        app: api-service
    spec:
      containers:
      - name: api
        image: acr.azurecr.io/api-service:v1.0
        ports:
        - containerPort: 8080
        resources:
          requests:
            cpu: 500m
            memory: 512Mi
          limits:
            cpu: 1000m
            memory: 1Gi
        livenessProbe:
          httpGet:
            path: /health
            port: 8080
          initialDelaySeconds: 10
          periodSeconds: 10
---
# Flagger Canary resource
apiVersion: flagger.app/v1beta1
kind: Canary
metadata:
  name: api-service
  namespace: production
spec:
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: api-service
  service:
    port: 8080
  analysis:
    interval: 1m           # Check metrics every minute
    threshold: 5           # Fail after 5 consecutive failures
    maxWeight: 50          # Max canary traffic before full promotion
    stepWeight: 10         # Increase by 10% each step
    metrics:
    - name: request-success-rate
      thresholdRange:
        min: 99            # Canary must maintain 99%+ success
      interval: 1m
    - name: request-duration
      thresholdRange:
        max: 500           # p99 latency under 500ms
      interval: 1m
    webhooks:
    - name: load-test      # Optional: synthetic traffic to canary
      url: http://flagger-loadtester/
      timeout: 5s
      metadata:
        cmd: "hey -z 1m -q 10 -c 2 http://api-service-canary:8080/health"
  metricsServer: http://prometheus:9090  # Prometheus for metrics

Application Insights Query (gate logic)

// Compare canary vs baseline error rates
let canaryErrors = requests
| where timestamp > ago(10m)
| where customDimensions.version == "v1.1"
| summarize ErrorRate = todouble(countif(success == false)) / count() * 100;

let baselineErrors = requests
| where timestamp > ago(10m)
| where customDimensions.version == "v1.0"
| summarize ErrorRate = todouble(countif(success == false)) / count() * 100;

// Alert if canary error rate > 1.5x baseline
canaryErrors
| extend BaselineRate = toscalar(baselineErrors)
| where ErrorRate > BaselineRate * 1.5

CI/CD Pipeline (GitHub Actions)

# .github/workflows/deploy.yml
name: Canary Deploy
on:
  push:
    branches: [main]

jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
    - uses: azure/login@v1
      with:
        creds: ${{ secrets.AZURE_CREDENTIALS }}
    
    - name: Build and push image
      run: |
        docker build -t acr.azurecr.io/api-service:${{ github.sha }} .
        docker push acr.azurecr.io/api-service:${{ github.sha }}
    
    - name: Update Kubernetes deployment
      run: |
        kubectl set image deployment/api-service \
          api=acr.azurecr.io/api-service:${{ github.sha }} \
          -n production
    
    # Flagger automatically detects image change and starts canary
    - name: Wait for canary analysis
      run: |
        kubectl wait canary/api-service \
          --for=condition=Promoted \
          --timeout=20m \
          -n production || \
        kubectl logs -l app=flagger -n flagger-system --tail=50

How this maps to the concept:

1. Flagger watches the api-service Deployment for image changes
2. When you push a new image, Flagger creates a api-service-canary Deployment (10% traffic)
3. Every minute, Flagger queries Prometheus for success rate and latency metrics
4. If metrics stay within thresholds for 5 consecutive checks, traffic increases to 20%, then 30%, etc
5. If any check fails 5 times, Flagger rolls back (shifts traffic to api-service-primary, deletes canary pods)
6. After 50% traffic succeeds, Flagger promotes to 100% and makes v1.1 the new primary

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6. Design trade-offs

Aspect	Canary Releases	Blue/Green Deployment	Rolling Update
Blast radius	Minimal (5-50% traffic)	Total (50% at switch)	Gradual (pod-by-pod)
Rollback speed	Instant (traffic shift)	Instant (traffic shift)	Slow (redeploy old version)
Resource cost	Medium (2x pods during canary)	High (2x full environment)	Low (replaces pods in-place)
Complexity	High (traffic routing, metrics, gates)	Medium (infrastructure duplicatation)	Low (native Kubernetes)
Observability requirement	Critical (no metrics = no gates)	Optional	Optional
Database migration compatibility	Must be backward-compatible	Can run separate schemas	Must be backward-compatible
Session stickiness	Required for stateful apps	Not required	Not required
Traffic control granularity	Fine (1-100% increments)	Coarse (0% or 100%)	None (pod replacement)

What you gain with canary:

• Early detection of production-only issues (load patterns, third-party API behavior, infrastructure quirks)
• Controlled exposure limits customer impact
• Confidence to deploy frequently without elaborate staging environments
• Automated rollback reduces mean time to recovery (MTTR)

What you give up:

• Operational complexity (traffic routing, metric pipelines, gate configuration)
• Resource overhead (running two versions simultaneously)
• Longer deployment time (30-60 minutes vs. 5 minutes for rolling update)
• Dependency on mature observability (no metrics = can't validate)

What you implicitly accept:

• Your application must handle version coexistence (schema compatibility, API versioning)
• Metrics have noise/lag; false positives will happen
• Incident response runbooks must account for "canary stuck at 20%" scenarios
• Cost of extra replicas and traffic routing infrastructure

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7. Common mistakes and misconceptions

Using absolute metric thresholds instead of comparative baselines

Why it happends:

• It's easier to configure "error rate < 1%" than to compare canary vs. baseline.

Problem:

• Production traffic patterns vary by hour/day. Your baseline error rate might be 0.5% normally but 2% during peak traffic (due to timeout spikes, retry storms). An absolute threshold causes false positives.

How to avoid:

Always compare canary metrics to baseline metrics collected in the same time window. Use ratios: canary_errors / baseline_errors > 1.5.

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Skipping database migration compatibility planning

Why it happends:

• Developers assume the database change deploys atomically with the code.

Problem:

• Canary releases mean v1.0 and v1.1 run concurrently. If v1.1 adds a required column without a default, v1.0 crashes on insert. If v1.1 removes a column v1.0 still reads, v1.1 crashes.

How to avoid: Use expand-contract pattern:

1. Deploy schema change that's compatible with both versions (add column with default, mark old column nullable)
2. Deploy v1.1 code that uses new schema
3. After v1.1 reaches 100%, remove old column in next release

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Ignoring session stickiness for stateful applications

Why it happends:

• Random traffic splitting is simpler to configure than sticky routing.

Problem:

• User logs in via v1.0 (creates session in Redis with v1.0 format), next request hits v1.1 (expects different session format), session fails, user logged out.

How to avoid:

-Configure routing based on session cookie, user ID hash, or connection affinity. Azure Application Gateway supports cookie-based affinity. Service meshes support header-based routing

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Setting observation windows too short

Why it happends:

• Pressure to ship fast; 2-minute canary feels safer than 20-minute.

Problem: Many issues don't manifest immediately:

• Memory leaks take 15+ minutes to OOM
• Database connection pools exhaust gradually
• Cache invalidation bugs show up after cache expires (5-10 minutes)
• Downstream rate limits trigger after burst accumulates

How to avoid:

• Observation windows should be at least 10 minutes per stage. For critical services, 30-60 minutes. Factor in your slowest failure mode (heap exhaustion, connection leaks).

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Not testing rollback under load

Why it happends:

• Teams test happy path (canary succeeds), never test failure path (canary rolls back).

Problem:

• Rollback shifts 100% traffic to stable version instantly. If stable version's replica count is too low (you scaled down to save cost), it can't handle the surge, causing an outage during rollback.

How to avoid:

• Keep stable version at full replica count during canary window
• Run chaos engineering tests that force rollback during peak traffic
• Monitor stable version's resource usage during canary (it should have headroom)

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Promoting canary to 100% without a soak period

Why it happends:

• All gates passed at 50%, so why wait?

Problem:

• Some bugs only trigger at scale. Connection pool exhaustion, database lock contention, downstream service rate limits—these emerge at 100% load, not 50%.

How to avoid:

• After canary reaches 100%, keep old version infrastructure available for 24-48 hours. Monitor for delayed failures (batch jobs, cron tasks, weekly reports). Only then decommission old version.

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Overcomplicating traffic routing for simple services

Why it happends:

• Canary releases sound advanced; teams build elaborate header-based routing with custom middleware.

Problem:

• You've added complexity that breaks when the middleware has a bug, or when requests come from unexpected clients (mobile apps, third-party webhooks). The routing layer itself becomes a failure point.

How to avoid:

• Use the simplest routing mechanism that satisfies your consistency requirements. For stateless APIs, random percentage-based routing works. Don't add header logic unless you have a concrete stickiness requirement.

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8. Operational and production considerations

What to monitor:

Critical metrics (block promotion if degraded)

• Error rate ratio: canary_errors / baseline_errors
• Latency degradation: canary_p95 / baseline_p95
• Resource saturation: CPU > 80%, memory > 85% (canary pods)
• Downstream dependency errors: third-party API failures, database timeouts

Supporting metrics (context for investigations):

• Request volume to canary vs. baseline (confirms traffic split is accurate)
• Distinct user count hitting canary (detects routing skew)
• Cache hit rate (canary might bypass warmed cache)
• Database query time (schema changes can degrade queries)

Business metrics (validates functional correctness):

• Transaction completion rate (e-commerce checkout, payment success)
• User-visible errors (login failures, API 4xx from client perspective)
• Feature flag activation (if canary includes new features)

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What degrades first:

1. Database connection exhaustion: New version opens more connections than old, pool limit hit, queries timeout
2. Memory pressure: Canary pods OOM because of inefficient caching or memory leak
3. Downstream rate limits: Canary makes more API calls to third-party, hits quota, requests fail
4. CPU throttling: New version has expensive computation, pods throttled, latency spikes

These degradations often don't appear in staging because staging lacks production data volume and traffic patterns.

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What becomes expensive

Cost amplifiers:

• Running 2x pod replicas during canary windows (5-10 pods per version)
• Application Gateway / Front Door traffic routing (per-request cost)
• Increased observability data volume (double the metrics, logs, traces)
• Crorr-region traffic if canary runs in different region for A/B comparison

Mitigation:

• Limit canary replica count to minimum viable (1-2 pods for small services)
• Use shorter observation windows for low-risk changes
• Aggregate metrics before sending to Application Insights (reduce ingestion cost)
• Schedule canaries during off-peak hours when extra capacity is cheaper

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Operational risks

Stuck canary: Automated gate fails to promote or rollback due to metric collection lag or bug. Requires manual intervetion. Runbook must define "how long do we wait before forcing a decision?"

Split-brain state: Canary and baseline diverge in data (one writes to cache, other doesn't), causing inconsistent user experience. Requires coordination layer (event sourcing, CQRS) or accepting eventual consistency.

Cascading failures: Canary triggers downstream service overload (new version calls API 3x more), downstream fails, brings down baseline too. Requires bulkheads (separate connection pools per version)

False rollbacks: Noisy metric triggers rollback of a good deployment. Team loses trust in automation, starts disabling gates, defeats the purpose. Requires tuning thresholds and smoothing windows.

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Observability signals

Application Insights custom events for canary lifecycle:

telemetryClient.TrackEvent("CanaryStarted", new Dictionary<string, string> {
    { "version", "v1.1" },
    { "initialTrafficPercent", "5" }
});

Distributed tracing to isolate version-specific latency:

• Tag spans with version=v1.0 or version=v1.1
• Compare trace durations across versions
• Identify which microservice in the chain introduced latency

Log analytics queries for error correlation:

exceptions
| where timestamp > ago(15m)
| extend version = tostring(customDimensions.version)
| summarize count() by version, outerMessage
| order by count_ desc

You need telemetry granular enough to attribute failures to specific versions. Without this, you can't gate promotions reliaby.

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9. When NOT to use this

Do not use canary releases when:

Scenario: Low-traffic services (<100 req/min)

Why: Not enough request to generate statistically significant metrics in reasonable time. A 5% canary receiving 5 req/min won't reveal issues. Your observation window becomes hours, defeating the purpose.

Alternative: Blue/green with extended monitoring period, or just use rolling updates with robust smoke tests.

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Scenario: No mature observability infrastructure

Why: Canary gates depend on real-time metrics with <1 minute lag. If you don't have Application Insights / Prometheus / OpenTelemetry instrumentation returning golden signals, you're flying blind. You'll either over-rollback (false positives) or under-rollback (false negatives).

Alternative: Invest in observability first. Run blue/green deployments until your metrics are trustworthy.

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Scenario: Tightly coupled monolith with shared state

Why: Canary requires version coexistence. If your app stores session state in-memory, shares a singleton cache, or uses database schema tied to version-specific business logic, running two versions corrupts state.

Alternative: Refactor for statelessness (externalize sessions to Redis), use expand-contract migrations, or accept full-cluster deployments with blue/green.

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Scenario: Regulatory/compliance requirements for change traceability

Why: Some industries (finance, healthcare) require documented approval for every production change. Automated canary promotion bypasses human review gates.

Alternative: Use manual approval at each traffic percentage gate, or restrict canaries to pre-production environments, treating production as blue/green with change control board approval.

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Scenario: Cost-sensitive environments with tight budgets

Why: Canary releases double your pod count during deployment (10 stable pods + 2 canary pods = 12 total). For large clusters, this is expensive. If your margin doesn't tolerate 20-40% temporary cost increase, canaries are unaffordable.

Alternative: Rolling updates with aggressive smoke tests, or scheduled deployments during low-traffic windows to minimize risk.

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Scenario: Services with unpredictable, bursty traffic

Why: If your service sees 10 req/min for 23 hours and 10,000 req/min for 1 hour (batch processing trigger, scheduled report generation), your canary metrics during quiet hours are meaningless. The real test happens during burst, which you can't wait for.

Alternative: Synthetic load testing with production-realistic traffic patterns, or blue/green with traffic replay from production logs.

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Scenario: Frequent hotfixes under incident pressure

Why: During an outage, you need the fix deployed in 2 minutes, not 20 minutes with canary stages. Automated gates become blockers when every second counts.

Alternative: Maintain a fast-path deployment pipeline (direct rolling update) for emergencies, reserve canary for planned releases.

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10. Key takeaways

• Canary releases are insurance against deployment failures, not a deployment strategy for all changes. Use them when the cost of a full-blast failure (customer impact, revenue loss, reputation damage) exceeds the cost of deployment complexity and observability infrastructure.

• Comparative metrics matter more than absolute thresholds. Comparing canary error rate to baseline error rate in the same time window accounts for traffic patterns, seasonal variation, and infrastructure noise. Absolute thresholds cause false positives.

• Database schema changes are the hardest part of canary deployments. Your schema must support both old and new code running concurrently. Use expand-contract migrations: add new columns before deployment new code, remove old columns after old code is decommissioned.

• Traffic routing happens at the edge, not in application code. Whether you use Application Gateway, Front Door, or a service mesh, routing decisions must happen before requests reach your application. Application-level routing introduces latency and complexity.

• Observation windows must account for your slowest failure mode. If your memory leak takes 20 minutes to manifest, a 5-minute canary is useless. Factor in connection pool exhaustion, cache expiration, downstream rate limits — whatever can fail gradually.

• Keep stable version infrastructure running until canary proves itself at 100% load. Many issues only emerge at full scale (database lock contention, downstream service saturation). After promotion, wait 24-48 hours before decommissioning old version infrastructure.

• Automated gates reduce MTTR, but noisy metrics destroy trust. If your gates trigger false rollbacks, teams disable automation and the system becomes manual approval theater. Invest in metric smoothing, longer windows, and runbook-based overrides to maintain confidence in automation.

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11. High-Level Overview

Visual representation of the progressive delivery flow, highlighting concurrent version coexistence, deterministic traffic splitting, comparative metric gates, automated promotion/rollback decisions, and controlled blast radius during production rollout.

Scroll to zoom • Drag to pan