Welcome to Jaeger’s documentation! Below, you’ll find information for beginners and experienced Jaeger users. If you cannot find what you are looking for, or have an issue not covered here, we’d love to hear from you.

About

Jaeger is a distributed tracing platform released as open source by Uber Technologies  in 2016 and donated to Cloud Native Computing Foundation  where it is a graduated project.

With Jaeger you can:

• Monitor and troubleshoot distributed workflows
• Identify performance bottlenecks
• Track down root causes
• Analyze service dependencies

Learn More

If you are new to distributed tracing, we recommend the following external resources:

• Mastering Distributed Tracing (2019)  by Yuri Shkuro , creator of Jaeger. The book provides in-depth coverage of many aspects of Jaeger design and operations, as well as distributed tracing in general.
• Take Jaeger for a HotROD ride , a step-by-step tutorial that demonstrates how to use Jaeger to solve application performance problems.
• Introducing Jaeger , an (old) webinar that introduces Jaeger and its capabilities.
• Evolving Distributed Tracing at Uber , a blog post that explains the history and reasons for the architectural choices made in Jaeger.

Jaeger v2

(2024-11-12) Jaeger has had a successful 9 year history as the leading open source distributed tracing platform strongly aligned with industry standardization efforts such as OpenTracing and OpenTelemetry. Jaeger is one of the first graduated projects in the Cloud Native Computing Foundation (CNCF). After over 60 releases, Jaeger is celebrating a major milestone with the release of Jaeger v2. This is a new architecture that utilizes OpenTelemetry Collector framework as the base and extends it to implement Jaeger’s unique features. It brings significant improvements and changes, making Jaeger more flexible, extensible, and better aligned with the OpenTelemetry project. Read full post here .

Please refer to Migration guide for details on migrating from Jaeger v1.

Features

• OpenTracing -inspired data model
• OpenTelemetry  compatible
• Multiple built-in storage backends:
  • Cassandra 4+
  • Elasticsearch 7.x, 8.x
  • Badger
  • Kafka - as an intermediate buffer
  • Memory storage
• Extensibility with custom backends via Remote Storage API
• System topology / service dependencies graphs
• Adaptive sampling
• Service Performance Monitoring (SPM)
• Post-collection data processing

See Features page for more details.

Quick Start

See Getting Started.

Screenshots

Traces View

Trace Detail View

Service Performance Monitoring View