Monitoring & Observability

When to use this skill

• Before Production Deployment: Essential monitoring system setup
• Performance Issues: Identify bottlenecks
• Incident Response: Quick root cause identification
• SLA Compliance: Track availability/response times

Instructions

Step 1: Metrics Collection (Prometheus)

Application Instrumentation (Node.js):

import express from 'express';
import promClient from 'prom-client';

const app = express();

// Default metrics (CPU, Memory, etc.)
promClient.collectDefaultMetrics();

// Custom metrics
const httpRequestDuration = new promClient.Histogram({
  name: 'http_request_duration_seconds',
  help: 'Duration of HTTP requests in seconds',
  labelNames: ['method', 'route', 'status_code']
});

const httpRequestTotal = new promClient.Counter({
  name: 'http_requests_total',
  help: 'Total number of HTTP requests',
  labelNames: ['method', 'route', 'status_code']
});

// Middleware to track requests
app.use((req, res, next) => {
  const start = Date.now();

  res.on('finish', () => {
    const duration = (Date.now() - start) / 1000;
    const labels = {
      method: req.method,
      route: req.route?.path || req.path,
      status_code: res.statusCode
    };

    httpRequestDuration.observe(labels, duration);
    httpRequestTotal.inc(labels);
  });

  next();
});

// Metrics endpoint
app.get('/metrics', async (req, res) => {
  res.set('Content-Type', promClient.register.contentType);
  res.end(await promClient.register.metrics());
});

app.listen(3000);

prometheus.yml:

global:
  scrape_interval: 15s
  evaluation_interval: 15s

scrape_configs:
  - job_name: 'my-app'
    static_configs:
      - targets: ['localhost:3000']
    metrics_path: '/metrics'

  - job_name: 'node-exporter'
    static_configs:
      - targets: ['localhost:9100']

alerting:
  alertmanagers:
    - static_configs:
        - targets: ['localhost:9093']

rule_files:
  - 'alert_rules.yml'

Step 2: Alert Rules

alert_rules.yml:

groups:
  - name: application_alerts
    interval: 30s
    rules:
      # High error rate
      - alert: HighErrorRate
        expr: |
          (
            sum(rate(http_requests_total{status_code=~"5.."}[5m]))
            /
            sum(rate(http_requests_total[5m]))
          ) > 0.05
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "High error rate detected"
          description: "Error rate is {{ $value }}% (threshold: 5%)"

      # Slow response time
      - alert: SlowResponseTime
        expr: |
          histogram_quantile(0.95,
            sum(rate(http_request_duration_seconds_bucket[5m])) by (le)
          ) > 1
        for: 10m
        labels:
          severity: warning
        annotations:
          summary: "Slow response time"
          description: "95th percentile is {{ $value }}s"

      # Pod down
      - alert: PodDown
        expr: up{job="my-app"} == 0
        for: 2m
        labels:
          severity: critical
        annotations:
          summary: "Pod is down"
          description: "{{ $labels.instance }} has been down for more than 2 minutes"

      # High memory usage
      - alert: HighMemoryUsage
        expr: |
          (
            node_memory_MemTotal_bytes - node_memory_MemAvailable_bytes
          ) / node_memory_MemTotal_bytes > 0.90
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "High memory usage"
          description: "Memory usage is {{ $value }}%"

Step 3: Log Aggregation (Structured Logging)

Winston (Node.js):

import winston from 'winston';

const logger = winston.createLogger({
  level: process.env.LOG_LEVEL || 'info',
  format: winston.format.combine(
    winston.format.timestamp(),
    winston.format.errors({ stack: true }),
    winston.format.json()
  ),
  defaultMeta: {
    service: 'my-app',
    environment: process.env.NODE_ENV
  },