OpenTelemetry Tracing

OpenTelemetry Tracing

Modern applications often rely on distributed services, making it tough to understand what’s happening when things go wrong. That’s where **OpenTelemetry Tracing** steps in — providing a standardized way to collect, process, and visualize trace data across services. In this blog, we’ll explore how tracing works in OpenTelemetry, why it’s crucial for observability, and how to instrument a basic app to start collecting traces.

🚀 What Is OpenTelemetry Tracing?

OpenTelemetry is an open-source observability framework that provides APIs, SDKs, and tools to collect telemetry data: traces, metrics, and logs. Tracing specifically tracks the flow of requests across different parts of a system — capturing latency, relationships, and performance bottlenecks.

A single trace consists of multiple spans. Each span represents a unit of work, like an HTTP request, DB query, or external API call. Spans are connected to form a full trace that maps the request journey.

🔧 Components of OpenTelemetry Tracing

• Tracer: The component responsible for creating and managing spans.
• Span: Represents an operation; it includes attributes like start time, end time, duration, and metadata.
• Exporter: Sends trace data to a backend (e.g., Jaeger, Zipkin, Tempo).
• Context Propagation: Ensures trace continuity across services.

🌐 Example: Tracing a Node.js Application

Let’s add tracing to a basic Express.js app using OpenTelemetry.

// tracer.js
const opentelemetry = require('@opentelemetry/sdk-node');
const { getNodeAutoInstrumentations } = require('@opentelemetry/auto-instrumentations-node');
const { JaegerExporter } = require('@opentelemetry/exporter-jaeger');

const sdk = new opentelemetry.NodeSDK({
  traceExporter: new JaegerExporter({
    endpoint: 'http://localhost:14268/api/traces',
  }),
  instrumentations: [getNodeAutoInstrumentations()],
});

sdk.start();

// app.js
require('./tracer'); // initialize tracing before anything else

const express = require('express');
const app = express();

app.get('/', (req, res) => {
  res.send('Tracing with OpenTelemetry!');
});

app.listen(3000, () => {
  console.log('App listening on port 3000');
});

With the above setup, spans will automatically be generated for HTTP requests and exported to Jaeger.

📦 Exporting Traces to Jaeger

To visualize traces, use a backend like Jaeger. You can quickly spin it up using Docker:

docker run -d --name jaeger \
  -e COLLECTOR_ZIPKIN_HTTP_PORT=9411 \
  -p 16686:16686 -p 14268:14268 \
  jaegertracing/all-in-one:latest

Now visit http://localhost:16686 to view trace data as it arrives.

🔄 Tracing Across Microservices

The power of tracing becomes more evident with microservices. Let’s say Service A calls Service B. To track this, OpenTelemetry propagates the trace context using HTTP headers:

// Service A: make request with context
const { context, propagation } = require('@opentelemetry/api');

const headers = {};
propagation.inject(context.active(), headers);

axios.get('http://service-b.local/api', { headers });

// Service B: extract context
const incomingCtx = propagation.extract(context.active(), req.headers);
// Set extracted context as current
context.with(incomingCtx, () => {
  // This span will be connected to the trace from Service A
});

This ensures a complete trace chain from user to database across all hops.

📈 Benefits of Using Tracing

• Pinpoint latency: Find slow services or bottlenecks in the request path.
• Correlate failures: Understand which span or service failed during a trace.
• Optimize performance: Measure the impact of optimizations in real-time.
• Improve debugging: Get a clear timeline of what happened where and when.

🛠️ Tracing in Other Languages

OpenTelemetry supports many languages, including:

• Python: opentelemetry-instrument for Flask/Django
• Java: Auto instrumentation via Java Agent
• Go: Native SDK with manual and auto instrumentation
• .NET: Integrated with Application Insights and other exporters

This allows you to build a consistent tracing experience across your tech stack.

🧠 Best Practices for Tracing

• Always initialize the tracer before other application code.
• Add custom attributes to spans (e.g., user ID, request ID) for richer context.
• Use span status and events to record errors and checkpoints.
• Retain minimal spans in production to avoid high telemetry costs.

✅ Conclusion

OpenTelemetry Tracing gives you deep insights into your application’s inner workings, especially in distributed systems. By setting up tracing across your services, visualizing traces in tools like Jaeger or Tempo, and correlating them with logs and metrics, you build a powerful observability foundation. Start small with a single app and scale up to full trace pipelines as your system grows.