A distributed tracing platform for monitoring and troubleshooting microservices-based distributed systems.
Jaeger is an open-source distributed tracing platform that helps monitor and troubleshoot transactions in microservices architectures. It collects timing data and logs as requests propagate through a distributed system, enabling developers to visualize service dependencies and identify performance issues. Originally created by Uber, it is now a graduated project under the Cloud Native Computing Foundation (CNCF).
Developers and SREs working with microservices, cloud-native applications, or complex distributed systems who need visibility into request flows and performance bottlenecks.
Jaeger offers a vendor-neutral, production-ready tracing solution with deep OpenTelemetry integration, scalable storage options, and a powerful UI for root cause analysis. Its CNCF backing ensures community-driven development and compatibility with cloud-native ecosystems.
CNCF Jaeger, a Distributed Tracing Platform
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Accepts traces via OTLP, ensuring vendor-agnostic instrumentation and alignment with the CNCF's observability standard, as highlighted in the README's architecture diagram.
Supports pluggable storage backends like Cassandra and Elasticsearch for handling high-volume tracing data, making it suitable for enterprise microservices deployments.
As a graduated CNCF project with active governance and sponsors, Jaeger benefits from robust community support and continuous development, noted in the project status and adopters list.
The all-in-one Docker image allows local testing in seconds with in-memory storage, as shown in the quick start example, lowering the barrier to entry.
Production deployments require managing separate components like collectors and scalable storage, adding maintenance overhead compared to turnkey solutions.
Relies on external databases like Cassandra, which introduces additional infrastructure complexity and cost for persistence, unlike lighter-weight tracing tools.
The UI focuses on trace visualization but lacks advanced analytics, such as machine learning insights, found in commercial APM platforms.