Distributed Logging and Monitoring

When your application runs across hundreds of servers, finding the root cause of problems becomes incredibly challenging. A user reports a failed payment, but which of your many services caused the issue? This is where distributed logging and monitoring comes in.

Let's start with logging. In a distributed system, each service generates its own logs. But having logs scattered across different servers makes troubleshooting difficult. The solution is to use a centralized logging system where all services send their logs to a central location.

Every log entry should include a correlation ID, a unique identifier that tracks a request as it moves through different services. When a user makes a request, generate a correlation ID and pass it to every service involved. This way, you can trace the entire journey of a request across your system.

For monitoring, you need three types of metrics:

1. Infrastructure metrics: CPU, memory, and disk usage of your servers

2. Application metrics: Response times, error rates, and request counts

3. Business metrics: Number of orders, revenue, active users

These metrics help you spot problems before users do. For example, if memory usage is climbing unusually fast, you can address it before the system crashes.

Use alerts wisely. Too many alerts cause "alert fatigue" where important notifications get ignored. Set up different alert levels:

• Critical: Immediate action required (system down)

• Warning: Need attention soon (high memory usage)

• Info: Good to know (new deployment complete)

Remember, good logging and monitoring isn't about collecting everything possible. It's about collecting the right information that helps you understand and fix problems quickly.