JBoss EAP XP 6.0: Achieving Observability with OpenTelemetry

Why It Matters

By embedding OpenTelemetry directly into JBoss EAP XP 6.0, enterprises gain instant, standards‑based observability, reducing the time and tooling overhead needed to monitor microservices at scale.

Key Takeaways

• JBoss EAP XP 6.0 adds MicroProfile Telemetry 2.0.
• Local setup uses Podman, Jaeger, OpenTelemetry collector.
• OpenShift deployment leverages Helm and S2I builds.
• Traces visible in Jaeger UI for both environments.
• Simplifies observability for Java EE applications.

Pulse Analysis

Observability has become a non‑negotiable pillar of modern cloud‑native applications, and OpenTelemetry is the de‑facto standard for collecting traces, metrics, and logs. JBoss EAP XP 6.0, built on Red Hat’s latest Java EE platform, now ships with MicroProfile Telemetry 2.0, aligning the server with the OpenTelemetry ecosystem and delivering out‑of‑the‑box trace export to Jaeger or any OTLP‑compatible backend. This integration eliminates the need for custom instrumentation libraries, letting developers focus on business logic while the runtime handles telemetry data.

In a local development context, the guide walks users through a Podman‑based workflow that creates an isolated network, launches a Jaeger all‑in‑one container, and starts an OpenTelemetry collector configured to forward spans to Jaeger. After installing the XP 6.0 profile via the Installation Manager, a simple CLI command enables the telemetry subsystem and points it at the collector endpoint. Deploying the provided quickstart application and generating traffic instantly produces trace data visible in the Jaeger UI, giving developers rapid feedback on request flows without leaving their workstation.

For production deployments, the same observability stack scales to Red Hat OpenShift using Helm charts and Source‑to‑Image (S2I) builds. An OpenTelemetryCollector custom resource runs as a deployment, exposing OTLP endpoints that the JBoss container consumes. Helm values inject the collector address and service name, enabling seamless trace export in a Kubernetes‑native manner. This approach shortens the path from code commit to observable service, empowering DevOps teams to embed tracing into CI/CD pipelines, accelerate incident response, and meet enterprise SLAs for performance monitoring.