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.
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.
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