What is Distributed Tracing and why is it important?

Distributed Tracing allows you to track the paths of requests through various microservices. This is crucial for debugging, as it helps to quickly identify bottlenecks and sources of errors in complex systems.

How does OpenTelemetry work for automatic instrumentation?

OpenTelemetry can automatically instrument HTTP requests, database queries, and message queue operations without requiring code changes. This simplifies implementation and improves data quality for observability.

What are the benefits of Context Propagation in OpenTelemetry?

With Context Propagation, trace IDs are automatically passed through HTTP headers and message queue metadata, improving traceability. This allows all services in a microservices architecture to communicate efficiently with one another.

How can I reduce tracing costs in production?

In production, you can employ sampling techniques such as head-based or tail-based sampling to reduce the number of requests being traced. This helps lower costs while still providing relevant and necessary information.

Which backends are compatible with OpenTelemetry?

OpenTelemetry is vendor-neutral and can send traces to various backends like Jaeger, Tempo, and Datadog. This provides flexibility and choice in selecting the observability tools that best fit your architecture.

DevOps & CI/CD 8 min. read

Distributed Tracing with OpenTelemetry: Following Requests Through Microservices

In a microservices architecture, a single request traverses dozens of services. OpenTelemetry makes the entire path visible.

devRocks Team · 05. February 2026 · Aktualisiert: 21. May 2026 ·

OpenTelemetry Tracing Microservices Observability

Distributed Tracing with OpenTelemetry: Following Requests Through Microservices

The Problem: Invisible Request Paths

User clicks, page is slow. But where is the problem? In the API gateway? In the auth service? In the database? Without distributed tracing, debugging becomes a guessing game.

OpenTelemetry: The Standard

OpenTelemetry (OTel) is the CNCF standard for observability data. It replaces proprietary solutions like Jaeger clients and Zipkin libraries with a unified API.

Auto-Instrumentation: OTel can automatically instrument HTTP requests, database queries, and message queue operations, without code changes.
Context Propagation: Trace IDs are automatically propagated via HTTP headers and message queue metadata.
Vendor-neutral: Send traces to Jaeger, Tempo, Datadog, or any other OTel-compatible backend.

Traces in Practice

Span Attributes: Enrich spans with business context, user ID, tenant, feature flag status.
Sampling: In production, you do not need to trace every request. Head-based sampling (e.g., 10%) or tail-based sampling (only errors and slow requests) reduces costs.
Correlation: Link trace IDs with logs and metrics for complete visibility.

Our Architecture

At devRocks, we use OTel with Grafana Tempo as the backend. The OTel Collector runs as a DaemonSet on every node and forwards traces, metrics, and logs to their respective backends, a single pipeline for everything.