Enhancing Frontend Observability with OpenTelemetry: A Comprehensive Guide to Full-Stack Tracing

When discussing OpenTelemetry, the initial thoughts that come to mind are likely centered around backend, APIs, Go, Java, and spans between microservices. However, there's a new horizon that this technology brings forth: tracing frontend activities. Let's explore how you can utilize OpenTelemetry to trace user clicks, track API calls, follow these calls across services, and ultimately connect them back to the frontend interactions that initialized them. This is the power that frontend observability with OpenTelemetry provides, and it's set to transform the world of full-stack tracing.

An Overview of OpenTelemetry

OpenTelemetry (OTel) is a powerful, open-source observability framework that offers unified APIs for generating metrics, logs, and traces. It enables context propagation across systems, vendor-neutral instrumentation, and supports a wide array of languages including JavaScript, Node.js, Python, Go, Java, and more.

The OTel project is governed by the Cloud Native Computing Foundation (CNCF) and enjoys robust support from vendors like Datadog, New Relic, Honeycomb, Lightstep, and Grafana. It's important to note that it isn't tied to any particular Application Performance Monitoring (APM) tool. Rather, it operates as the plumbing layer for telemetry, providing a flexible and agnostic foundation for observability.

The Power of Frontend + OpenTelemetry

Traditionally, frontend errors and performance details are managed with tools like Sentry or LogRocket, while backend spans and metrics are handled with tools like Datadog or Prometheus. However, imagine a more unified approach where a user's click on the "Submit Order" button initiates a trace that seamlessly continues through your API and into your backend, database, and even external services. All of this is then returned with a comprehensive view of the entire user journey.

This vision can be realized with frontend observability powered by OpenTelemetry. The benefits include unified trace IDs across the browser and server, full-stack root cause analysis, performance bottleneck correlation, and user-centric diagnostics.

Understanding the Basic Architecture

Outlined below is the basic architecture for the frontend observability with OpenTelemetry:

Browser (React, Vue, etc.)
  ↳ Instrumented via OTel JS SDK
  ↳ Generates spans for navigation, user actions, API calls
    ↳ Adds `traceparent` header to outbound fetch/XHR
      ↳ Backend receives trace context
        ↳ Backend continues trace with OpenTelemetry SDK
          ↳ Entire span chain visualized in backend APM

This architecture illustrates how the browser, instrumented with the OTel JavaScript SDK, generates spans for actions like navigation, user interactions, and API calls. When an outbound fetch or XHR is made, a traceparent header is added. The backend then receives this trace context, continues the trace with its OpenTelemetry SDK, and visualizes the entire span chain in the backend APM.

Core Concepts in OpenTelemetry

In the context of OpenTelemetry, you will encounter several key concepts:

• Trace: This is a tree of spans that represents the lifecycle of a request. It provides a chronological view of the operations involved in a transaction from start to end.
• Span: A span is a single unit of work. For example, a span could be an HTTP request like "GET /api/orders", a database query, or any specific function or step in your code.
• Context: This refers to metadata associated with traces and spans, such as trace ID, parent span ID, service name, and other application-specific data.
• Exporter: This is the component responsible for transmitting the telemetry data to the destination of your choice, such as Jaeger, Zipkin, or Datadog.

Setting Up Frontend OTel (JavaScript/React)

Let's dive into the practical aspects of setting up frontend observability with OpenTelemetry using JavaScript/React. The first step involves installing the necessary dependencies:

npm install @opentelemetry/api @opentelemetry/sdk-trace-web   @opentelemetry/instrumentation-fetch   @opentelemetry/exporter-trace-otlp-http

Next, you'll need to configure tracing:

import { WebTracerProvider } from '@opentelemetry/sdk-trace-web';
import { FetchInstrumentation } from '@opentelemetry/instrumentation-fetch';
import { OTLPTraceExporter } from '@opentelemetry/exporter-trace-otlp-http';

const provider = new WebTracerProvider();

provider.addSpanProcessor(new SimpleSpanProcessor(
  new OTLPTraceExporter({
    url: 'https://your-observability-platform/v1/traces'
  })
));

provider.register();

// Add instrumentation
new FetchInstrumentation({
  propagateTraceHeaderCorsUrls: [/your-api.com/],
});

The configuration above sets up automated tracing of all fetch() calls, propagation of traceparent headers, and export of spans to a backend collector.

Creating Custom Spans

Custom spans provide a way to track specific actions or events not automatically traced by the OpenTelemetry SDK. This might include user interactions, state transitions, route changes, or component rendering times. Here's an example:

const tracer = provider.getTracer('my-frontend-app');

const span = tracer.startSpan('user-clicked-login-button');
// business logic
span.end();

Correlating with Backend

To correlate your frontend spans with backend services, the backend must use the same OTel trace headers (traceparent, tracestate) and accept the incoming context, linking it to its spans. This enables visualization tools (like Jaeger or Honeycomb) to display a complete picture of a transaction, from a user click in the frontend to a database query in the backend, all connected by a single trace ID.

Real-World Examples: Vercel & Netflix

Vercel demonstrates a practical application of frontend observability with OpenTelemetry: they trace frontend activities like hydration and route transitions, hook OTel into middleware, functions, and edge handlers, and send unified traces to a third-party backend (like Datadog).

Netflix too has leveraged OTel to create custom spans from their React applications, link fetch spans to backend microservices, and combine these with logs and metrics for Service Level Agreement (SLA) tracking.

Exporting to Your Observability Platform

OpenTelemetry supports various exporters for transmitting your telemetry data, including:

• OTLP/HTTP: The standard protocol supported by most vendors.
• Console: Useful for local development and debugging.
• Jaeger, Zipkin: Popular open-source tracing systems.
• Vendor SDKs: Specific exporters for Datadog, Lightstep, Honeycomb, and others.

Best Practices and Anti-Patterns

To get the most out of your OpenTelemetry setup, stick to the following best practices:

• Clearly name your spans (e.g., user_interaction:search, api_call:/products).
• Add meaningful attributes (userId, page, deviceType).
• Consistently use traceparent.
• Avoid creating too many low-value spans that might clutter your data.
• Combine OpenTelemetry data with Real User Monitoring (RUM) and Web Vitals for a more holistic view of your application's performance.

On the other hand, avoid these common anti-patterns:

• Tracing only on the backend — this misses critical user experience context.
• Creating frontend spans without correlation IDs.
• Implementing overhead-heavy tracing in tight loops.
• Logging spans instead of exporting them to a designated backend.

Conclusion: Making the Invisible Visible

Users don’t see microservices or backend operations. They perceive only what happens on the frontend: buttons that lag, spinners that keep spinning, and screens that take too long to load. OpenTelemetry brings backend-grade observability to the frontend, allowing you to trace user actions, connect the UI to the API, and measure what happens across the entire stack.

In essence, OpenTelemetry serves as the essential link between Developer Experience (DevX) and User Experience (UX), and it is poised to become the future of full-stack debugging.