2.2.4. Understanding Jaeger
Every time a user takes an action in an application, a request is executed by the architecture that may require dozens of different services to participate to produce a response. The path of this request is a distributed transaction. Jaeger lets you perform distributed tracing, which follows the path of a request through various microservices that make up an application.
The deprecated Red Hat OpenShift Distributed Tracing Platform (Jaeger) 3.5 was the last release of the Red Hat OpenShift Distributed Tracing Platform (Jaeger) that Red Hat supported.
All support and maintenance for the deprecated Red Hat OpenShift Distributed Tracing Platform (Jaeger) 3.5 ended on November 3, 2025.
If you still use Red Hat OpenShift Distributed Tracing Platform (Jaeger), you must migrate to Red Hat build of OpenTelemetry Operator and Tempo Operator for distributed tracing collection and storage. For more information, see "Migrating" in the Red Hat build of OpenTelemetry documentation, "Installing" in the Red Hat build of OpenTelemetry documentation, and "Installing" in the Red Hat OpenShift Distributed Tracing Platform documentation.
For more information, see the Red Hat Knowledgebase solution Jaeger Deprecation and Removal in OpenShift.
Distributed tracing is a technique that is used to tie the information about different units of work together—usually executed in different processes or hosts—to understand a whole chain of events in a distributed transaction. Distributed tracing lets developers visualize call flows in large service oriented architectures. It can be invaluable in understanding serialization, parallelism, and sources of latency.
Jaeger records the execution of individual requests across the whole stack of microservices, and presents them as traces. A trace is a data/execution path through the system. An end-to-end trace is comprised of one or more spans.
A span represents a logical unit of work in Jaeger that has an operation name, the start time of the operation, and the duration. Spans may be nested and ordered to model causal relationships.
2.2.4.1. Key concepts in distributed tracing 링크 복사링크가 클립보드에 복사되었습니다!
Every time a user takes an action in an application, a request is executed by the architecture that may require dozens of different services to participate to produce a response. Red Hat OpenShift Distributed Tracing Platform lets you perform distributed tracing, which records the path of a request through various microservices that make up an application.
Distributed tracing is a technique that is used to tie the information about different units of work together — usually executed in different processes or hosts — to understand a whole chain of events in a distributed transaction. Developers can visualize call flows in large microservice architectures with distributed tracing. It is valuable for understanding serialization, parallelism, and sources of latency.
Red Hat OpenShift Distributed Tracing Platform records the execution of individual requests across the whole stack of microservices, and presents them as traces. A trace is a data/execution path through the system. An end-to-end trace consists of one or more spans.
A span represents a logical unit of work in Red Hat OpenShift Distributed Tracing Platform that has an operation name, the start time of the operation, and the duration, as well as potentially tags and logs. Spans may be nested and ordered to model causal relationships.
As a service owner, you can use distributed tracing to instrument your services to gather insights into your service architecture. You can use Red Hat OpenShift Distributed Tracing Platform for monitoring, network profiling, and troubleshooting the interaction between components in modern, cloud-native, microservices-based applications.
With Distributed Tracing Platform, you can perform the following functions:
- Monitor distributed transactions
- Optimize performance and latency
- Perform root cause analysis
You can combine Distributed Tracing Platform with other relevant components of the OpenShift Container Platform:
- Red Hat build of OpenTelemetry for forwarding traces to a TempoStack instance
- Distributed tracing UI plugin of the Cluster Observability Operator (COO)