Continuously tracking system health, performance, and behavior to detect issues and understand how applications run in production.
Monitoring means continuously observing systems to understand their health, performance, and behavior. You track metrics, collect logs, and set up alerts to detect problems before users do.
You cannot fix what you cannot see. Monitoring provides visibility into production systems.
Detect Problems Early: Catch issues before they become outages.
Understand Performance: Identify slow queries, bottlenecks, and optimization opportunities.
Capacity Planning: Know when to scale based on actual usage trends.
Debugging Production: Logs and metrics help diagnose issues users encounter.
Prove SLAs: Demonstrate uptime and performance to customers.
Availability: Is the system up and accessible?
Latency: How long do requests take?
Error Rate: What percentage of requests fail?
Throughput: How many requests per second?
Resource Usage: CPU, memory, disk, network utilization.
Saturation: Are resources nearing capacity limits?
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These are the golden signals. Track these for every service.
Response Times: How fast do pages and APIs respond?
Database Queries: Which queries are slowest? How often do they run?
External APIs: Are third-party services slowing you down?
Error Tracking: What exceptions occur? How frequently?
APM tools provide detailed insight into application behavior.
Server Health: CPU, memory, disk usage per server.
Network: Bandwidth usage, packet loss, latency.
Database: Connection pool size, query performance, replication lag.
Load Balancers: Traffic distribution, backend health.
Containers: Resource usage per container, orchestrator health (Kubernetes).
Infrastructure metrics reveal hardware and network issues.
Application Logs: Events, errors, warnings from your code.
Access Logs: Every HTTP request with status codes, response times.
System Logs: Operating system events, service starts/stops.
Audit Logs: User actions for security and compliance.
Logs provide context when investigating issues. Metrics show what is wrong, logs explain why.
Bad Log: User login failed
Good Log: {"timestamp": "2024-01-15T10:30:00Z", "level": "ERROR", "user_id": "12345", "event": "login_failed", "reason": "invalid_password"}
Structured logs are machine-parseable. Search and analyze them easily.
Set Thresholds: Alert when error rate exceeds 1%, response time over 2 seconds, CPU above 80%.
Alert the Right People: Route alerts to on-call engineers, not entire team.
Avoid Alert Fatigue: Too many alerts get ignored. Alert only on actionable problems.
Include Context: Alert messages should contain enough info to start debugging immediately.
Good alerts wake you at 3 AM for real problems, not false positives.
Real-Time Visibility: Graphs showing current system state.
Historical Trends: Understand patterns over days, weeks, months.
Custom Views: Different dashboards for developers, operations, executives.
Public Status Pages: Show customers system health.
Dashboards make monitoring data accessible and actionable.
Metrics: Prometheus, Datadog, New Relic, CloudWatch.
Logs: ELK Stack (Elasticsearch, Logstash, Kibana), Splunk, Loki.
APM: New Relic, Datadog APM, AppDynamics.
Error Tracking: Sentry, Rollbar, Bugsnag.
Uptime Monitoring: Pingdom, UptimeRobot, StatusCake.
Most companies use multiple tools together.
Follow single request across multiple services.
Request comes in: API Gateway Calls: Authentication Service Then: Database Query Then: External Payment API Finally: Returns Response
Tracing shows where time is spent. Essential for debugging microservices.
Monitoring: Track known metrics. "Is CPU usage high?"
Observability: Explore unknown problems. "Why is this specific user's checkout failing?"
Observability includes monitoring but goes deeper. Requires rich instrumentation and flexible querying.
Netflix: Monitors thousands of services. Detects and mitigates issues before users affected.
Stripe: Payment processing requires perfect reliability. Comprehensive monitoring catches issues instantly.
GitHub: Monitors git operations, API requests, database queries. Public status page shows transparency.
Monitoring Too Little: Cannot diagnose problems without sufficient data.
Monitoring Too Much: Overwhelmed by metrics no one looks at.
No Alerting: Metrics without alerts means discovering problems when users complain.
Alert Fatigue: Too many noisy alerts get ignored.
No Runbooks: Alerts without remediation steps are useless.
Instrument Early: Add monitoring before code reaches production.
Monitor User Experience: Track what users actually experience, not just backend metrics.
Set SLOs: Define acceptable performance. Alert when approaching limits.
Test Alerts: Trigger alerts intentionally to verify they work.
Review Dashboards: Unused dashboards waste time and money.
Post-Mortems: When incidents happen, improve monitoring to catch similar issues earlier next time.
Data Storage: Logs and metrics consume storage. Retention policies control costs.
Tool Pricing: Most monitoring tools charge per host, metric, or log volume.
Engineering Time: Setting up and maintaining monitoring requires effort.
Balance monitoring depth against cost. Start minimal, expand based on needs.
Monitoring is non-negotiable for production systems. Deploy without monitoring and you are flying blind.
Start with basics: availability checks, error rates, response times. Expand monitoring as systems grow complex.
Good monitoring catches problems before users notice. Great monitoring provides insights that drive system improvements.
Invest in monitoring infrastructure early. The return on investment is massive when it prevents or shortens outages.
