Monitoring and Alert System Integration

The Necessity of Monitoring and Alert System Integration

Modern web applications have increasingly high demands for stability and real-time performance. As a lightweight Node.js framework, Koa2 requires a comprehensive monitoring system to ensure service health. Monitoring systems can capture runtime exceptions, performance bottlenecks, and resource consumption, while alert mechanisms ensure timely issue resolution. The combination of the two forms a closed loop, covering the entire process from problem discovery to resolution.

Core Monitoring Metrics Design

HTTP request-related metrics must be prioritized:

• API response time (P99/P95)
• QPS (queries per second)
• Error status code distribution (4xx/5xx)
• Request throughput (MB/s)

// Middleware example: Collecting request duration
app.use(async (ctx, next) => {
  const start = Date.now()
  await next()
  const ms = Date.now() - start
  metrics.timing('http_request_duration', ms, {
    method: ctx.method,
    path: ctx.path
  })
})

System-level metrics are equally critical:

• Memory usage (heap/rss)
• CPU load (1/5/15 minutes)
• Event loop delay
• Process restart count

Data Collection Implementation

The Prometheus client is suitable for Koa2 metric collection:

const client = require('prom-client')
const collectDefaultMetrics = client.collectDefaultMetrics
collectDefaultMetrics({ timeout: 5000 })

// Custom counter example
const httpRequestsTotal = new client.Counter({
  name: 'http_requests_total',
  help: 'Total HTTP requests',
  labelNames: ['method', 'path', 'status']
})

app.use(async (ctx, next) => {
  try {
    await next()
  } finally {
    httpRequestsTotal.inc({
      method: ctx.method,
      path: ctx.path,
      status: ctx.status
    })
  }
})

For log collection, a structured approach is recommended:

const winston = require('winston')
const logger = winston.createLogger({
  format: winston.format.combine(
    winston.format.timestamp(),
    winston.format.json()
  ),
  transports: [new winston.transports.File({ filename: 'app.log' })]
})

// Error logging middleware
app.use(async (ctx, next) => {
  try {
    await next()
  } catch (err) {
    logger.error({
      message: err.message,
      stack: err.stack,
      request: ctx.request.body
    })
    throw err
  }
})

Alert Rule Configuration Strategy

Example of Prometheus alert rules:

groups:
- name: koa-alerts
  rules:
  - alert: HighErrorRate
    expr: rate(http_requests_total{status=~"5.."}[1m]) > 0.1
    for: 5m
    labels:
      severity: critical
    annotations:
      summary: "High error rate on {{ $labels.path }}"
      description: "5xx error rate is {{ $value }}"

Multi-level alert thresholds should be differentiated:

• Warning level: Response time > 500ms
• Critical level: Error rate > 10%
• Disaster level: Service unavailable

Visualization and Notification Channels

Grafana dashboards should include:

1. Real-time traffic heatmap
2. Error rate trend curve
3. Resource usage dashboard
4. Dependency service health status

Example of notification channel integration:

const { IncomingWebhook } = require('@slack/webhook')
const webhook = new IncomingWebhook(process.env.SLACK_WEBHOOK_URL)

function sendAlert({ title, message, level }) {
  const color = {
    warning: '#FFCC00',
    critical: '#FF0000',
    disaster: '#990000'
  }[level]
  
  webhook.send({
    attachments: [{
      color,
      title,
      text: message,
      fields: [{
        title: 'Environment',
        value: process.env.NODE_ENV,
        short: true
      }]
    }]
  })
}

Deep Integration with Exception Tracking

Sentry integration for Koa:

const Sentry = require('@sentry/node')
Sentry.init({
  dsn: process.env.SENTRY_DSN,
  integrations: [
    new Sentry.Integrations.Http({ tracing: true }),
    new Sentry.Integrations.Koa()
  ],
  tracesSampleRate: 0.5
})

app.on('error', (err, ctx) => {
  Sentry.withScope(scope => {
    scope.addEventProcessor(event => 
      Sentry.Handlers.parseRequest(event, ctx.request)
    )
    Sentry.captureException(err)
  })
})

Key tracking fields include:

• User session ID
• Request transaction ID
• Frontend operation trace
• Backend call chain

Load Testing and Circuit Breaking

Load testing should cover:

• Gradually increasing concurrent users
• Sudden abnormal traffic spikes
• Long-term stability testing
• Dependency service failure simulation

Example of circuit breaker implementation:

const CircuitBreaker = require('opossum')
const breaker = new CircuitBreaker(async (url) => {
  const res = await axios.get(url)
  return res.data
}, {
  timeout: 3000,
  errorThresholdPercentage: 50,
  resetTimeout: 30000
})

// Usage in routes
app.use(async (ctx, next) => {
  try {
    ctx.body = await breaker.fire('https://api.example.com/data')
  } catch (err) {
    ctx.status = 503
    ctx.body = 'Service unavailable'
  }
})

Production Environment Deployment Recommendations

Example Kubernetes probe configuration:

livenessProbe:
  httpGet:
    path: /healthz
    port: 3000
  initialDelaySeconds: 30
  periodSeconds: 10

readinessProbe:
  httpGet:
    path: /ready
    port: 3000
  initialDelaySeconds: 5
  periodSeconds: 5

Health check endpoint implementation:

router.get('/healthz', (ctx) => {
  ctx.body = { status: 'ok' }
})

router.get('/ready', async (ctx) => {
  const dbOk = await checkDatabase()
  const cacheOk = await checkRedis()
  ctx.status = dbOk && cacheOk ? 200 : 503
  ctx.body = {
    db: dbOk ? 'ok' : 'down',
    cache: cacheOk ? 'ok' : 'down'
  }
})

Long-Term Storage of Monitoring Data

Considerations for time-series database selection:

• Prometheus + Thanos for metric storage
• Elasticsearch for log data
• S3 for archiving raw log files
• ClickHouse for analytical queries

Example data retention policies:

• Raw metrics: 15 days
• Aggregated metrics: 1 year
• Error logs: 30 days
• Access logs: 7 days

Security and Access Control

Monitoring data access permission design:

const authMiddleware = (requiredRole) => async (ctx, next) => {
  if (ctx.state.user.role !== requiredRole) {
    ctx.throw(403, 'Forbidden')
  }
  await next()
}

router.get('/metrics', 
  authMiddleware('admin'),
  async (ctx) => {
    ctx.set('Content-Type', client.register.contentType)
    ctx.body = await client.register.metrics()
  }
)

Key points for sensitive data handling:

• Mask password fields in request bodies
• Filter health check endpoint logs
• Encrypt PII data storage
• Audit log access records