Distributed tracing follows requests across multiple services and provides an end-to-end view of application behavior. It helps teams identify latency, failed services, dependencies, and performance bottlenecks.
1. Trace Visualization
Service maps, timelines, dependency graphs, and trace details help engineers understand how requests move through the system.
2. OpenTelemetry Support
Support for OpenTelemetry makes instrumentation and telemetry collection easier while providing more flexibility across different platforms and backends.
3. Performance Analysis
Teams should look for latency analysis, error tracking, sampling controls, and bottleneck detection to investigate slow or failed requests.
4. Integrations
Integration with logs, metrics, APM, Kubernetes, cloud services, and existing observability tools helps create a more complete view of application health.
5. Scalability & Cost
Trace volume can become large in busy environments. Teams should consider storage, sampling, performance overhead, deployment complexity, and overall cost.
Key Priorities
- End-to-end request visibility
- OpenTelemetry compatibility
- Latency and error analysis
- Integration with observability tools
- Scalable and cost-aware trace storage
Simple Summary
A distributed tracing tool should help teams follow requests across services, identify problems, and understand application performance. The right option depends on architecture, traffic volume, existing tools, operational skills, and budget.