How do logs and traces work together in observability?

Logs and traces are complementary tools in observability that help developers understand system behavior. Logs are timestamped records of discrete events, like errors or user actions, providing context about what happened at a specific point in time. Traces, on the other hand, track the journey of a request as it flows through distributed services, showing how different components interact. Together, they provide a layered view: logs explain the “what” (specific events), while traces explain the “where” and “how” (the path and dependencies of a request). For example, a log might record a database connection error, while a trace would show that this error occurred during a checkout process spanning a user service, payment gateway, and inventory system.

The connection between logs and traces is often established through shared identifiers. When a request enters a system, it’s assigned a unique trace ID that propagates across all services involved. Each log entry generated during the request’s lifecycle includes this trace ID, allowing developers to filter logs by a specific request’s journey. For instance, if an API call fails, you can use the trace ID from the error log to retrieve the full trace, revealing which service caused the failure. This correlation is critical in distributed systems, where a single request might trigger dozens of microservices. Without trace IDs, logs would be isolated events, making it difficult to reconstruct the sequence of actions leading to a problem.

A practical example involves debugging a slow payment processing system. A trace might show that the delay occurs between the payment service and a third-party gateway. By querying logs associated with that trace ID, developers can find detailed error messages or performance metrics (like slow database queries) within the payment service. Tools like OpenTelemetry or cloud-native solutions (AWS X-Ray, Google Cloud Trace) often integrate logs and traces into a single dashboard, streamlining analysis. This combination allows teams to quickly isolate issues—whether they’re caused by a specific line of code (visible in logs) or a bottleneck in service communication (visible in traces). By using both, developers gain a complete picture of system health and performance.