• 6 min read

What is Zipkin Tracing?

Discover Zipkin, also called Zipkin Tracing, the open-source tracing platform originally created by Twitter. Learn how it helps monitor microservices, its features, limitations, and comparison to full observability platforms.

Introduction to Zipkin and Distributed Tracing

Zipkin is an open-source distributed tracing system that helps developers collect timing data to troubleshoot latency issues in microservice architectures. As a pioneering solution in the distributed tracing landscape, Zipkin provides valuable insights into how long service calls take and identifies where failures or performance bottlenecks occur within your application ecosystem.

In modern cloud-native environments where applications are composed of numerous microservices, understanding how requests flow through your system becomes increasingly complex. This is where distributed tracing systems like Zipkin become essential tools for observability.

How Zipkin Works

Zipkin follows the distributed tracing model established by Google's Dapper paper and provides four key components:

  1. Collector: Receives and validates traces from various services
  2. Storage: Preserves trace data (supports in-memory, MySQL, Cassandra, and Elasticsearch)
  3. API: Provides access to trace data
  4. Web UI: Offers visual representation of traces for analysis

Zipkin uses a concept called "spans" (see What is a Span?)to represent logical work units with timing data and structured logs. These spans form a hierarchy that allows you to visualize the full journey of a request through multiple services, including parent-child relationships between operations.

Key Features of Zipkin

  • Lightweight: Designed to have minimal impact on application performance
  • Polyglot instrumentation: Supports various programming languages including Java, JavaScript, Ruby, Go, and more
  • Multiple storage options: Flexible deployment with various backend storage systems
  • Simple visualization: Web interface for quickly identifying service dependencies and performance issues
Zipkin Trace Visualization
  • OpenTracing compatible: Works with the OpenTracing standard for greater interoperability
  • Service dependency graphs: Visual representation of how services connect and depend on each other
Zipkin Dependency Graph

Implementing Zipkin for Distributed Tracing

Getting started with Zipkin involves:

  1. Instrumenting your code: Adding Zipkin libraries to your applications
  2. Configuring samplers: Determining what percentage of traces to collect
  3. Setting up transport: Choosing how trace data will be sent to collectors
  4. Deploying the Zipkin server: Running the collector, storage, and UI components

Zipkin uses the B3 propagation format, which passes trace context between services through headers. This allows separate services to contribute to the same trace, even across different technologies.

Benefits of Using Zipkin for Distributed Tracing

  • Performance optimization: Identify slow components in your system
  • Root cause analysis: Quickly pinpoint failures in complex systems
  • Service dependency visualization: Understand how microservices interact
  • Latency insights: Find timing anomalies across distributed systems
  • Reduced troubleshooting time: Faster identification of issues in production

Zipkin vs. Other Distributed Tracing Solutions

While Zipkin was one of the first open-source distributed tracing systems, other solutions like Jaeger, AWS X-Ray, and Google Cloud Trace have emerged with their own advantages. Zipkin's strength lies in its maturity, community support, and simplicity.

Unlike more comprehensive observability platforms, Zipkin focuses specifically on distributed tracing. Organizations often combine Zipkin with metrics and logging solutions to create a complete observability strategy.

Dash0 delivers the most powerful way to explore distributed tracing. Follow every request from the end user to the deepest database, uncover latency bottlenecks, and see how failures propagate in real time. Correlate traces with logs, events, and metrics for full-system clarity—fast, scalable, and built for OpenTelemetry. Triage also provides a one-click root cause analysis functionality utilizing modern AI and machine learning combined with great UX and statistical analytics.

Integration with Observability Ecosystem

Zipkin works well within the broader observability ecosystem:

  • Metrics: Complement trace data with metrics from Prometheus
  • Logging: Correlate traces with logs from Elasticsearch or other systems
  • Alerts: Connect performance thresholds to alerting systems

With OpenTelemetry gaining adoption, Zipkin supports the OpenTelemetry Collector, allowing it to receive data in the evolving standard format while maintaining backward compatibility.

When to Choose Zipkin for Distributed Tracing

Zipkin may be the right choice when:

  • You need a lightweight, battle-tested tracing solution
  • Your organization values open-source technologies
  • You want flexibility in storage options
  • You require support for multiple programming languages
  • You're starting your distributed tracing journey and need an accessible solution

Conclusion

Zipkin remains a powerful and accessible option for organizations implementing distributed tracing. With its focus on simplicity, wide language support, and integration possibilities, Zipkin helps teams gain visibility into complex distributed systems and identify performance bottlenecks or failures more efficiently.

As microservice architectures continue to grow in complexity, having a reliable distributed tracing solution like Zipkin becomes increasingly valuable for maintaining system reliability and performance. Whether you're just beginning with observability or expanding your toolset, Zipkin provides the core capabilities needed to understand request flows across distributed services.