What You Need to Know About Distributed Tracing and Sampling

The transition from monoliths to microservices offers software teams increased flexibility and deployment speed, but it also introduces complexity, particularly in managing distributed systems where services have numerous dependencies and varied technologies. This complexity can complicate troubleshooting, especially when issues arise in real-world environments that combine legacy monoliths with microservices. Distributed tracing is a valuable tool for understanding and resolving performance issues by tracking transactions through a system, visualizing service dependencies, and identifying bottlenecks. The blog discusses two sampling methods for distributed tracing: head-based sampling, which randomly selects traces early in the process and is suitable for lower transaction environments, and tail-based sampling, which analyzes all traces post-completion to capture critical data about errors and latency. The choice between these methods depends on specific needs and cost considerations, underscoring the importance of flexibility in monitoring strategies as application environments grow increasingly complex.