NashTech Insights

Understanding Jaeger’s Architecture and Trace Visualization

Rahul Miglani
Rahul Miglani
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Distributed systems are becoming increasingly complex, and gaining insight into the behavior of applications as they traverse various services is critical. Jaeger, an open-source distributed tracing system, steps in to unravel the intricacies of requests, helping developers and operators identify bottlenecks, latency issues, and dependencies. In this blog, we’ll take a deep dive into Jaeger’s architecture, exploring its components, and explaining how it captures and trace Visualization for comprehensive distributed tracing.

Jaeger’s Architectural Components

Jaeger’s architecture comprises several essential components that work together to provide a cohesive distributed tracing solution:

1. Client Libraries

Client libraries are available for various programming languages, including Java, Go, Python, and more. These libraries help instrument your application code by creating and managing spans.

2. Collector

The collector receives span data from instrumented applications and is responsible for various tasks, such as sampling, storage, and aggregation. It formats and stores the spans for later analysis.

3. Storage

Jaeger supports multiple storage backends, such as Elasticsearch, Cassandra, and Kafka. The storage component stores trace data, allowing for easy retrieval and analysis.

4. Query Service

The query service provides an interface for retrieving traces based on various parameters. It’s responsible for serving trace data to the Jaeger UI and other external tools.

5. Agent

The agent is a lightweight component that runs on each host where applications are deployed. It’s responsible for collecting spans and sending them to the collector.

6. UI

The Jaeger UI is a web-based interface that allows users to search, visualize, and analyze traces. It offers insights into the journey of requests across services, including timing data and dependencies.

Capturing and Visualizing Traces

Jaeger’s ability to capture and visualize traces is facilitated through the following steps:

1. Instrumentation

Firstly, Developers instrument their applications using Jaeger client libraries. Spans are created to mark the beginning and end of specific operations, capturing timing data, and context.

2. Span Propagation

Secondly, When a request moves from one service to another, trace context is propagated. This ensures that spans related to a single request are connected and visualized as a cohesive trace.

3. Collector and Storage

Thirdly, The collector receives span data from agents and stores it in the chosen storage backend. This collected data includes timing information, operation names, and contextual metadata.

4. Querying and Retrieval

Moreover, Developers and operators use the query service to retrieve traces based on various criteria, such as time range, service names, and more.

5. Visualization

Lastly, The Jaeger UI accesses trace data through the query service and presents it in an intuitive graphical format. Users can explore the traces, view dependencies, and analyze latency issues.

Benefits of Jaeger’s Architecture

  • Scalability: Firstly, Jaeger’s components can be scaled independently to handle large volumes of trace data.
  • Flexibility: Support for various storage backends and query languages offers flexibility in deployment and analysis.
  • Real-time Insights: The UI provides real-time insights into application behavior, aiding in quick issue detection and resolution.
  • Cross-Service Insights: Trace visualization enables understanding of interactions between multiple services, even in complex microservices architectures.


Finally, Jaeger’s architecture forms a robust foundation for distributed tracing, enabling the capture, storage, retrieval, and visualization of trace data. By diving into the intricacies of its components and understanding its trace visualization process, you’re equipped to harness Jaeger’s power in unraveling the complexities of your distributed systems.

Lastly, As you navigate through application journeys, bottlenecks, and dependencies, Jaeger’s architecture becomes your guiding light towards more optimized and resilient 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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