monitoring-observability

name	monitoring-observability
description	Set up monitoring, logging, and observability for applications and infrastructure. Use when implementing health checks, metrics collection, log aggregation, or alerting systems. Handles Prometheus, Grafana, ELK Stack, Datadog, and monitoring best practices.
metadata	{"tags":"monitoring, observability, logging, metrics, Prometheus, Grafana, alerts","platforms":"Claude, ChatGPT, Gemini"}

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"} == 
         
        
           
        
           
           

      
        
                  
        
           
        
           
           

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
  },
  transports: [
    new winston.transports.Console({
      format: winston.format.combine(
        winston.format.colorize(),
        winston.format.simple()
      )
    }),
    new winston.transports.File({
      filename: 'logs/error.log',
      level: 'error'
    }),
    new winston.transports.File({
      filename: 'logs/combined.log'
    })
  ]
});

// Usage
logger.(, { : , :  });
logger.(, { : err., : err. });


app.( {
  logger.(, {
    : req.,
    : req.,
    : req.,
    : req.()
  });
  ();
});

Step 4: Grafana Dashboard

dashboard.json (example):

{
  "dashboard": {
    "title": "Application Metrics",
    "panels": [
      {
        "title": "Request Rate",
        "type": "graph",
        "targets": [
          {
            "expr": "rate(http_requests_total[5m])",
            "legendFormat": "{{method}} {{route}}"
          }
        ]
      },
      {
        "title": "Error Rate",
        "type": "graph",
        "targets": [
          {
            "expr": "rate(http_requests_total{status_code=~\"5..\"}[5m])",
            "legendFormat": "Errors"
          
        
      
      
         
         
         
          
             
          
        
      
      
         
         
         
          
             
          
        
      
    
  

Step 5: Health Checks

Advanced Health Check:

interface HealthStatus {
  status: 'healthy' | 'degraded' | 'unhealthy';
  timestamp: string;
  uptime: number;
  checks: {
    database: { status: string; latency?: number; error?: string };
    redis: { status: string; latency?: number };
    externalApi: { status: string; latency?: number };
  };
}

app.get('/health', async (req, res) => {
  const startTime = Date.now();
  const health: HealthStatus = {
    status: 'healthy',
    timestamp: new Date().toISOString(),
    uptime: process.uptime(),
    checks: {
      database: { status: 'unknown' },
      redis: { status: 'unknown' },
      externalApi: { :  }
    }
  };

  
   {
     dbStart = .();
     db.();
    health.. = {
      : ,
      : .() - dbStart
    };
  }  (error) {
    health. = ;
    health.. = {
      : ,
      : error.
    };
  }

  
   {
     redisStart = .();
     redis.();
    health.. = {
      : ,
      : .() - redisStart
    };
  }  (error) {
    health. = ;
    health.. = { :  };
  }

   statusCode = health. ===  ?  : health. ===  ?  : ;
  res.(statusCode).(health);
});

Output format

Monitoring Dashboard Configuration

Golden Signals:
1. Latency (Response Time)
   - P50, P95, P99 percentiles
   - Per API endpoint

2. Traffic (Request Volume)
   - Requests per second
   - Per endpoint, per status code

3. Errors (Error Rate)
   - 5xx error rate
   - 4xx error rate
   - Per error type

4. Saturation (Resource Utilization)
   - CPU usage
   - Memory usage
   - Disk I/O
   - Network bandwidth

Constraints

Required Rules (MUST)

1. Structured Logging: JSON format logs
2. Metric Labels: Maintain uniqueness (be careful of high cardinality)
3. Prevent Alert Fatigue: Only critical alerts

Prohibited (MUST NOT)

1. Do Not Log Sensitive Data: Never log passwords, API keys
2. Excessive Metrics: Unnecessary metrics waste resources

Best practices

1. Define SLO: Clearly define Service Level Objectives
2. Write Runbooks: Document response procedures per alert
3. Dashboards: Customize dashboards as needed per team

References

• Prometheus
• Grafana
• Google SRE Book

Metadata

Version

• Current Version: 1.0.0
• Last Updated: 2025-01-01
• Compatible Platforms: Claude, ChatGPT, Gemini

Related Skills

• deployment: Monitoring alongside deployment
• security: Security event monitoring

Tags

#monitoring #observability #Prometheus #Grafana #logging #metrics #infrastructure

Examples

Example 1: Basic usage

Example 2: Advanced usage