Metrics, Logs, and Traces Overview
1. Metrics
Definition
Metrics are quantifiable measurements used to track and assess the status of a specific process. They provide insights into performance over time.
Key Metrics Examples
- CPU Usage
- Memory Usage
- Response Time
- Error Rate
Collecting Metrics
Metrics can be collected using various tools such as Prometheus, Grafana, or Datadog.
2. Logs
Definition
Logs are records generated by applications, services, and systems that provide detailed information about events that occur during operation.
Log Levels
- Debug
- Info
- Warning
- Error
- Critical
Logging Best Practices
Use structured logging to enable easier searching and filtering of log data.
3. Traces
Definition
Traces track the progression of requests through various services. They help identify bottlenecks and latency issues in distributed systems.
Tracing Tools
Common tools for tracing include OpenTracing, Jaeger, and Zipkin.
Example of a Trace
GET /api/user/123
├── Database Query: SELECT * FROM users WHERE id=123
└── Cache Check: Cache hit/miss4. Best Practices
Monitoring Best Practices
- Define clear metrics for success.
- Use a centralized logging system.
- Implement alerting based on thresholds.
- Regularly review and refine metrics/logs/traces.
5. FAQ
What is the difference between metrics, logs, and traces?
Metrics give you a broad overview of system performance, logs provide detailed information about system events, and traces follow the journey of requests across services.
How do I choose the right monitoring tools?
Consider factors like your team’s expertise, the complexity of your systems, and the specific metrics/logs/traces you need to analyze.
What is the role of APM in monitoring?
Application Performance Monitoring (APM) tools help in tracking the performance of applications, including metrics, logs, and traces.