With the advancement of distributed architectures and the increasing use of microservices, traditional application monitoring is no longer sufficient. Tools that only capture metrics or logs in isolation cannot provide a complete view of the behavior of complex systems. It is in this context that OpenTelemetry emerges as a robust solution, offering a unified approach to collecting and correlating different signals. These signals include traces, metrics, logs, and baggage, each playing a critical role in the journey toward complete observability.

1. What are Traces?

traces are essential for tracing the path of a request through multiple services in a distributed system. Each request can pass through several layers and services, and traces record all these interactions in detail. This allows you to view the complete flow of a transaction, from entry into the frontend to interaction with the database, helping to identify where failures or slowdowns occur.

As described in the official OpenTelemetry documentation, traces are composed of spans, which represent each individual step of the request. These spans are then grouped together to form a trace, which provides a cohesive view of the transaction flow.

2. Metrics: System Health Monitoring

Metrics are another important signal provided by OpenTelemetry. They are essential for monitoring overall system performance, offering insights into resource usage, such as CPU and memory, and the error rate of services. While traces focus on the traceability of a specific request, metrics provide a macro view, allowing you to monitor the "health" of the application as a whole.

For example, metrics such as average response time, number of requests per second or error rate help identify performance patterns and trends, as well as alert you to possible problems that may be affecting the system.

3. Logs: Capturing Critical Events

Logs are used to record significant events in the system, such as errors, transactions or any other relevant event. They complement traces and metrics, providing additional context about what happened at a given point in time.

While a trace shows the path of a request and a metric offers a numerical view of performance, logs provide specific details of the events that occurred. For example, if a failure is detected in a trace, the logs can provide details about the error that caused the failure, helping you troubleshoot the problem more efficiently.

4. Baggage: Shared Context

baggage is an often underestimated signal, but it plays a critical role in tracking distributed requests. It allows contextual information to be propagated between services in a request, which is extremely useful in microservices systems. With baggage, it is possible to share attributes and data between different parts of the system, ensuring that the context of a request is maintained from end to end.

For example, imagine that a request passes through several services in different parts of the system. baggage ensures that attributes such as transaction IDs or user data are passed between all involved services, facilitating the correlation of logs, metrics and traces.

The Importance of Combining These Signals

Each of these signals—traces, metrics, logs, and baggage—has a specific function, but it is in their combination that the true power of OpenTelemetry reveals itself. When used together, they provide a detailed and cohesive view of all aspects of the system. For example:

• A trace can show the complete flow of a request.
• Metrics may indicate that the average response time has increased for a given service.
• Logs can provide details about errors that have occurred.
• baggage ensures that critical information is available at each stage of the request.

This combination of signals enables much richer and more detailed observability, allowing teams to quickly identify where problems lie and how to resolve them efficiently.

Conclusion

In a world where distributed architectures and microservices dominate, monitoring and understanding application behavior requires more than simple metrics or isolated logs. OpenTelemetry, with its built-in traces, metrics, logs, and baggage signals, provides the visibility DevOps teams and developers need to maintain optimal performance of their applications.

If you are not already using all of these signals in combination, you may be missing opportunities to optimize your system monitoring. How have you been dealing with the observability of your distributed applications? Already using OpenTelemetry? Share your experiences in the comments and follow me on LinkedIn for more insights into observability and performance of complex systems.