Betteruptime Zero Downtime Deployment

Zero Downtime Deployment

Better Uptime Monitoring Zero Downtime Blue-Green Canary Rolling Update Load Balancer Health Check Status Page Incident Management On-call

Deployment Strategies

# === Zero Downtime Deployment Strategies ===

# Kubernetes Blue-Green Deployment
# apiVersion: v1
# kind: Service
# metadata:
#   name: my-app
# spec:
#   selector:
#     app: my-app
#     version: green  # Switch between blue/green
#   ports:
#   - port: 80
#     targetPort: 8080
#
# ---
# apiVersion: apps/v1
# kind: Deployment
# metadata:
#   name: my-app-green
# spec:
#   replicas: 3
#   selector:
#     matchLabels:
#       app: my-app
#       version: green
#   template:
#     metadata:
#       labels:
#         app: my-app
#         version: green
#     spec:
#       containers:
#       - name: my-app
#         image: my-app:2.0.0
#         ports:
#         - containerPort: 8080
#         readinessProbe:
#           httpGet:
#             path: /health
#             port: 8080
#           initialDelaySeconds: 5
#           periodSeconds: 10
#         livenessProbe:
#           httpGet:
#             path: /health
#             port: 8080
#           initialDelaySeconds: 15
#           periodSeconds: 20

# Rolling Update (Kubernetes Default)
# apiVersion: apps/v1
# kind: Deployment
# spec:
#   strategy:
#     type: RollingUpdate
#     rollingUpdate:
#       maxSurge: 25%
#       maxUnavailable: 25%

from dataclasses import dataclass
from typing import List

@dataclass
class DeploymentStrategy:
    name: str
    downtime: str
    rollback_time: str
    traffic_control: str
    complexity: str
    use_case: str

strategies = [
    DeploymentStrategy("Blue-Green", "0", "< 1 min", "All-or-nothing", "Medium",
        "Critical apps ต้อง Rollback เร็ว"),
    DeploymentStrategy("Canary", "0", "< 5 min", "Gradual %", "High",
        "Apps ที่ต้องทดสอบกับ Real Traffic"),
    DeploymentStrategy("Rolling Update", "0", "5-15 min", "Per Instance", "Low",
        "Kubernetes Default ทั่วไป"),
    DeploymentStrategy("Feature Flag", "0", "Instant", "Per Feature", "Medium",
        "Toggle Features ไม่ต้อง Deploy ใหม่"),
]

print("=== Deployment Strategies ===")
for s in strategies:
    print(f"\n  [{s.name}]")
    print(f"    Downtime: {s.downtime} | Rollback: {s.rollback_time}")
    print(f"    Traffic: {s.traffic_control} | Complexity: {s.complexity}")
    print(f"    Use: {s.use_case}")

Monitoring Setup

# === Better Uptime + Monitoring Setup ===

# Better Uptime API
# curl -X POST https://betteruptime.com/api/v2/monitors \
#   -H "Authorization: Bearer YOUR_API_TOKEN" \
#   -H "Content-Type: application/json" \
#   -d '{
#     "monitor_type": "status",
#     "url": "https://example.com",
#     "pronounceable_name": "Main Website",
#     "check_frequency": 30,
#     "http_method": "get",
#     "expected_status_codes": [200],
#     "regions": ["us", "eu", "as"],
#     "confirmation_period": 60,
#     "call": true,
#     "sms": true,
#     "email": true
#   }'

# Deployment Health Check Script
# import requests
# import time
#
# def wait_for_healthy(url, timeout=300, interval=5):
#     start = time.time()
#     while time.time() - start < timeout:
#         try:
#             response = requests.get(f"{url}/health", timeout=5)
#             if response.status_code == 200:
#                 data = response.json()
#                 if data.get("status") == "healthy":
#                     print(f"Healthy after {time.time()-start:.0f}s")
#                     return True
#         except requests.RequestException:
#             pass
#         time.sleep(interval)
#     raise TimeoutError(f"Not healthy after {timeout}s")
#
# # Canary Deployment Script
# def canary_deploy(service, new_version, steps=[5, 25, 50, 100]):
#     for pct in steps:
#         print(f"Setting traffic to {pct}% for {new_version}")
#         set_traffic_split(service, new_version, pct)
#         time.sleep(300)  # Wait 5 min
#
#         metrics = get_metrics(service, window="5m")
#         if metrics["error_rate"] > 0.01:
#             print(f"Error rate {metrics['error_rate']:.2%} > 1%, rolling back")
#             set_traffic_split(service, new_version, 0)
#             return False
#         if metrics["p99_latency"] > 2000:
#             print(f"P99 {metrics['p99_latency']}ms > 2000ms, rolling back")
#             set_traffic_split(service, new_version, 0)
#             return False
#
#         print(f"Metrics OK at {pct}%")
#     return True

monitors = {
    "Website": {"type": "HTTPS", "interval": 30, "regions": 3, "uptime": "99.95%"},
    "API": {"type": "HTTPS", "interval": 30, "regions": 3, "uptime": "99.98%"},
    "Database": {"type": "TCP", "interval": 60, "regions": 1, "uptime": "99.99%"},
    "CDN": {"type": "HTTPS", "interval": 60, "regions": 5, "uptime": "99.99%"},
    "Cron Jobs": {"type": "Heartbeat", "interval": 300, "regions": 1, "uptime": "99.90%"},
}

print("\nMonitoring Dashboard:")
for name, info in monitors.items():
    print(f"  [{info['uptime']}] {name} — {info['type']} every {info['interval']}s "
          f"({info['regions']} regions)")

Status Page และ Incident

# === Status Page & Incident Management ===

@dataclass
class Incident:
    title: str
    severity: str
    status: str
    duration_min: int
    root_cause: str
    resolution: str

incidents = [
    Incident("API Latency Spike", "Minor", "Resolved", 12,
        "Database connection pool exhaustion",
        "Increased pool size from 20 to 50"),
    Incident("CDN Cache Miss", "Major", "Resolved", 25,
        "Cache invalidation after deployment",
        "Pre-warm cache before switching traffic"),
    Incident("SSL Certificate Renewal", "Maintenance", "Completed", 5,
        "Scheduled certificate rotation",
        "Auto-renewed via Let's Encrypt"),
]

print("Incident History:")
for inc in incidents:
    print(f"\n  [{inc.severity}] {inc.title} — {inc.status}")
    print(f"    Duration: {inc.duration_min} min")
    print(f"    Cause: {inc.root_cause}")
    print(f"    Fix: {inc.resolution}")

# Deploy Checklist
checklist = [
    "Pre-deploy: Run full test suite",
    "Pre-deploy: Check database migrations",
    "Pre-deploy: Notify team via Slack",
    "Deploy: Use chosen strategy (Blue-Green/Canary/Rolling)",
    "Post-deploy: Verify health checks pass",
    "Post-deploy: Check error rates < 0.1%",
    "Post-deploy: Check P99 latency < 2s",
    "Post-deploy: Verify monitoring alerts normal",
    "Post-deploy: Run smoke tests",
    "Rollback: If metrics exceed thresholds, rollback immediately",
]

print(f"\n\nDeploy Checklist:")
for i, item in enumerate(checklist, 1):
    print(f"  {i}. {item}")

เคล็ดลับ

• Health Check: ทุก Instance ต้องมี Health Check Endpoint
• Canary: เริ่มที่ 5% รอ 5 นาที ดู Metrics ก่อนเพิ่ม
• Rollback: Automate Rollback เมื่อ Error Rate สูง
• Status Page: แจ้งลูกค้าเมื่อมี Incident สร้างความเชื่อมั่น
• Pre-warm: Pre-warm Cache หลัง Deploy ก่อนรับ Traffic

การนำความรู้ไปประยุกต์ใช้งานจริง

แหล่งเรียนรู้ที่แนะนำ ได้แก่ Official Documentation ที่อัพเดทล่าสุดเสมอ Online Course จาก Coursera Udemy edX ช่อง YouTube คุณภาพทั้งไทยและอังกฤษ และ Community อย่าง Discord Reddit Stack Overflow ที่ช่วยแลกเปลี่ยนประสบการณ์กับนักพัฒนาทั่วโลก

เปรียบเทียบข้อดีและข้อเสีย

จากตารางเปรียบเทียบจะเห็นว่าข้อดีมีมากกว่าข้อเสียอย่างชัดเจน โดยเฉพาะในแง่ของประสิทธิภาพและความสามารถในการ Scale สำหรับข้อเสียส่วนใหญ่สามารถแก้ไขได้ด้วยการเรียนรู้อย่างเป็นระบบและวางแผนทรัพยากรให้เหมาะสม

สรุป

Better Uptime Monitoring Zero Downtime Blue-Green Canary Rolling Update Health Check Status Page Incident Management Rollback Kubernetes Load Balancer Pre-warm Cache Deploy Checklist