NashTech Insights

Embarking on a Trace: Getting Started with Jaeger for Distributed Tracing

Rahul Miglani
Rahul Miglani
Table of Contents
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In the intricate world of microservices and distributed applications, understanding the journey of a request as it navigates through various services is crucial for maintaining optimal performance. Distributed tracing, facilitated by tools like Jaeger, empowers developers and operators with a visual representation of these intricate interactions. In this blog, we’ll take you through a step-by-step guide to kickstart your journey with Jaeger, from setting it up to instrumenting your applications for comprehensive distributed tracing.

Step 1: Setting Up Jaeger

Installation via Docker (Quick Start):

  1. Install Docker on your machine or server.
  2. Pull the Jaeger All-in-One Docker image: docker pull jaegertracing/all-in-one.
  3. Run the Jaeger container: docker run -d --name jaeger -p 16686:16686 -p 6831:6831/udp -p 6832:6832/udp -p 5778:5778 jaegertracing/all-in-one.

Custom Deployment:

  1. Download the Jaeger release package from the official GitHub repository.
  2. Extract the package and follow the installation instructions for your preferred platform.

Step 2: Instrumenting Applications

For a Go Application:

  1. Import the Jaeger libraries:
  2. Create a Tracer instance with the desired configuration.
  3. Inject the Tracer into the context of requests using middleware or other methods.
  4. Add span creation and logging in relevant parts of your code.

For a Java Application:

  1. Add Jaeger dependencies to your Maven or Gradle project.
  2. Configure the Tracer by creating a Configuration object.
  3. Create spans using the Tracer in your application’s codebase.
  4. Ensure spans are propagated across service boundaries using appropriate context propagation methods.

For Other Languages:

Jaeger offers libraries for various programming languages. Each library follows a similar pattern: import the library, configure the Tracer, and instrument your code with spans.

Step 3: Visualizing Traces

  1. Access the Jaeger UI by opening http://localhost:16686 in your browser.
  2. Search for traces using various filters, such as service name, operation name, or duration.
  3. Explore traces to see the journey of requests across services, including timing data and dependencies.

Best Practices

  • Selective Instrumentation: Focus on instrumenting critical paths and high-impact areas of your application.
  • Context Propagation: Ensure trace context is propagated across service boundaries to maintain the continuity of traces.
  • Meaningful Span Names: Use descriptive span names to provide clarity and context to trace data.
  • Regular Review: Regularly review and analyze trace data to identify bottlenecks and performance issues.


Lastly, Distributed tracing with Jaeger opens the door to a deeper understanding of your application’s performance and interactions. By setting up Jaeger and instrumenting your applications, you embark on a journey of trace analysis that empowers you to optimize bottlenecks, troubleshoot issues, and enhance user experiences.

Finally, Whether you’re diving into microservices or optimizing existing applications, Jaeger becomes your guide to unveiling the intricate landscape of your distributed systems. Start tracing, start optimizing, and start creating more resilient and efficient applications.

Rahul Miglani

Rahul Miglani

Rahul Miglani is Vice President at NashTech and Heads the DevOps Competency and also Heads the Cloud Engineering Practice. He is a DevOps evangelist with a keen focus to build deep relationships with senior technical individuals as well as pre-sales from customers all over the globe to enable them to be DevOps and cloud advocates and help them achieve their automation journey. He also acts as a technical liaison between customers, service engineering teams, and the DevOps community as a whole. Rahul works with customers with the goal of making them solid references on the Cloud container services platforms and also participates as a thought leader in the docker, Kubernetes, container, cloud, and DevOps community. His proficiency includes rich experience in highly optimized, highly available architectural decision-making with an inclination towards logging, monitoring, security, governance, and visualization.

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