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CNCF Jaeger, a Distributed Tracing Platform
Jaeger is an open-source, end-to-end distributed tracing platform used for monitoring and troubleshooting complex microservices-based systems.
Go
Added on 2025年6月27日21,508
Stars2,596
ForksGo
LanguageProject Introduction
Summary
Jaeger, inspired by Google's Dapper and Twitter's OpenZipkin, is a distributed tracing system released as open source by Uber Technologies and later accepted as a Cloud Native Computing Foundation (CNCF) graduated project. It provides a set of components for end-to-end distributed tracing, enabling teams to monitor and troubleshoot transactions in complex distributed systems.
Problem Solved
In modern microservice architectures, tracing the flow of a single request across numerous services is challenging. Jaeger addresses this by visualizing transaction paths, identifying performance bottlenecks, and pinpointing the root causes of errors or latency across services.
Core Features
Distributed Context Propagation
Propagates context (like trace ID, span ID, baggage) across service boundaries using various protocols like HTTP headers (B3, W3C Trace Context) or gRPC metadata.
UI and Visualization
Provides a rich web user interface to search for traces, visualize request flows, analyze span details, and view service dependency graphs.
Open Standards Compatibility
Supports popular open standards for instrumentation, such as OpenTracing and OpenTelemetry, allowing for language-agnostic tracing.
Tech Stack
Go
gRPC
Kafka
Cassandra
Elasticsearch
Prometheus
Use Cases
Jaeger is essential for gaining visibility into the complex interactions within distributed systems and addressing issues that are difficult to diagnose with traditional monitoring methods.
Scenario One: Troubleshooting Production Incidents
Details
When a user reports a slow request or an error, Jaeger traces visualize the entire path of that request across all involved services, allowing engineers to pinpoint the exact service or operation causing the issue.
User Value
Rapidly diagnose and resolve issues in complex distributed systems, significantly reducing Mean Time To Resolution (MTTR).
Scenario Two: Performance Optimization and Bottleneck Detection
Details
By analyzing trace data, teams can identify which services or operations within a transaction flow are consuming the most time, enabling focused efforts on optimizing performance bottlenecks.
User Value
Improve overall application performance and user experience by identifying and addressing latency hot spots.
Scenario Three: Service Dependency Analysis
Details
Jaeger can automatically map the dependencies between services based on the traces they generate, providing a clear picture of how services interact and depend on each other.
User Value
Understand the architecture and relationships within a microservice landscape, crucial for impact analysis during development or refactoring.
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