Jaeger vs Zipkin: What are the differences?

Key Differences between Jaeger and Zipkin

Jaeger and Zipkin are two widely used open-source distributed tracing systems that help in monitoring and troubleshooting microservices architectures. While they have similarities in their main purpose, there are several key differences that distinguish them from each other.

1. Data Model: Jaeger and Zipkin have different data models for representing and storing trace data. Jaeger uses a flexible data model based on Google's Dapper paper, which allows for more expressive but potentially more complex trace representations. On the other hand, Zipkin uses a simplified data model that is easy to understand and works well for many use cases.

2. Architecture: Jaeger and Zipkin follow different approaches when it comes to their architecture. Jaeger has a more modular architecture, with components such as collectors, storage backends, and query services that can be deployed independently and scaled individually. In contrast, Zipkin has a simpler architecture with a single process that handles data ingestion, storage, and querying.

3. Instrumentation Libraries: Jaeger and Zipkin offer different instrumentation libraries for different programming languages. Jaeger provides libraries for popular languages such as Java, Go, Python, and Node.js, making it easier to instrument applications written in these languages. On the other hand, Zipkin has a wider range of language support, including Java, Go, Python, Node.js, C#, and more.

4. Sampling Strategies: Jaeger and Zipkin have different default sampling strategies for trace collection. Jaeger uses a probabilistic sampling strategy by default, where a certain percentage of traces are sampled based on a configurable rate. This helps in reducing the storage and processing overhead of capturing every trace. In contrast, Zipkin uses a deterministic sampling strategy by default, where a fixed percentage of traces are sampled, regardless of the load or other factors.

5. Query Capabilities: Jaeger and Zipkin have different query capabilities when it comes to analyzing trace data. Jaeger provides a more powerful and flexible query language that allows for complex aggregation, filtering, and grouping of trace data. Zipkin, on the other hand, has a simpler query language that provides basic filtering and aggregation capabilities.

6. Ecosystem Integration: Jaeger and Zipkin have different levels of integration with other observability tools and frameworks. Jaeger has better integration with the Kubernetes ecosystem, as it provides native support for collecting traces from Kubernetes clusters and integrates well with other Kubernetes monitoring and logging solutions. Zipkin, on the other hand, has better integration with other tools and frameworks in the Spring ecosystem, such as Spring Cloud Sleuth and Spring Boot.

In summary, Jaeger and Zipkin differ in their data models, architecture, instrumentation libraries, sampling strategies, query capabilities, and ecosystem integration. Choosing between them depends on the specific requirements and preferences of the users, considering factors such as complexity, scalability, language support, and integration needs.