github链接：https://github.com/jaegertracing/jaeger

官网：https://www.jaegertracing.io/

Jaeger: open source, end-to-end distributed tracing

Monitor and troubleshoot transactions in complex distributed systems

 a Cloud Native Computing Foundation incubating project.

Uber published a blog post, Evolving Distributed Tracing at Uber, where they explain the history and reasons for the architectural choices made in Jaeger. Yuri Shkuro, creator of Jaeger, also published a book Mastering Distributed Tracing that covers in-depth many aspects of Jaeger design and operation, as well as distributed tracing in general.

Why Jaeger?

As on-the-ground microservice practitioners are quickly realizing, the majority of operational problems that arise when moving to a distributed architecture are ultimately grounded in two areas: networking and observability. It is simply an orders of magnitude larger problem to network and debug a set of intertwined distributed services versus a single monolithic application.

Problems that Jaeger addresses

It is used for monitoring and troubleshooting microservices-based distributed systems, including:

• Distributed context propagation
• Distributed transaction monitoring
• Root cause analysis
• Service dependency analysis
• Performance / latency optimization

Kubernetes and OpenShift

• Kubernetes templates: https://github.com/jaegertracing/jaeger-kubernetes
• Kubernetes Operator: https://github.com/jaegertracing/jaeger-operator
• OpenShift templates: https://github.com/jaegertracing/jaeger-openshift

Features

• Discover architecture of the whole system via data-driven dependency diagram.
• View request timeline and errors; understand how the app works.
• Find sources of latency and lack of concurrency.
• Highly contextualized logging.

• Use baggage propagation to:

  • Diagnose inter-request contention (queueing).
  • Attribute time spent in a service.

• Use open source libraries with OpenTracing integration to get vendor-neutral instrumentation for free.

Features

• OpenTracing compatible data model and instrumentation libraries
  • in Go, Java, Node, Python and C++
• Uses consistent upfront sampling with individual per service/endpoint probabilities
• Multiple storage backends: Cassandra, Elasticsearch, memory.
• Adaptive sampling (coming soon)
• Post-collection data processing pipeline (coming soon)

Technical Specs

• Backend components implemented in Go
• React/Javascript UI
• Supported storage backends:
  • Cassandra 3.4+
  • Elasticsearch 5.x, 6.x, 7.x
  • Kafka
  • memory storage

Build Telemetry for Distributed Services之Jaeger（待续）

原文：https://www.cnblogs.com/panpanwelcome/p/11583855.html