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 in order to produce a response. Jaeger 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’s valuable for 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, as well as potentially tags and logs. Spans may be nested and ordered to model causal relationships.
As a service owner, you can use Jaeger to instrument your services to gather insights into your service architecture. Jaeger is an open source distributed tracing platform that you can use for monitoring, network profiling, and troubleshooting the interaction between components in modern, cloud-native, microservices-based applications.
Using Jaeger lets you perform the following functions:
Monitor distributed transactions
Optimize performance and latency
Perform root cause analysis
Jaeger is based on the vendor-neutral OpenTracing APIs and instrumentation.
Jaeger tracing provides the following capabilities:
Integration with Kiali – When properly configured, you can view Jaeger data from the Kiali console.
High scalability – The Jaeger backend is designed to have no single points of failure and to scale with the business needs.
Distributed Context Propagation – Lets you connect data from different components together to create a complete end-to-end trace.
Backwards compatibility with Zipkin – Jaeger has APIs that enable it to be used as a drop-in replacement for Zipkin, but Red Hat is not supporting Zipkin compatibility in this release.
Jaeger is made up of several components that work together to collect, store, and display tracing data.
Jaeger Client (Tracer, Reporter, instrumented application, client libraries)- Jaeger clients are language specific implementations of the OpenTracing API. They can be used to instrument applications for distributed tracing either manually or with a variety of existing open source frameworks, such as Camel (Fuse), Spring Boot (RHOAR), MicroProfile (RHOAR/Thorntail), Wildfly (EAP), and many more, that are already integrated with OpenTracing.
Jaeger Agent (Server Queue, Processor Workers) - The Jaeger agent is a network daemon that listens for spans sent over User Datagram Protocol (UDP), which it batches and sends to the collector. The agent is meant to be placed on the same host as the instrumented application. This is typically accomplished by having a sidecar in container environments like Kubernetes.
Jaeger Collector (Queue, Workers) - Similar to the Agent, the Collector is able to receive spans and place them in an internal queue for processing. This allows the collector to return immediately to the client/agent instead of waiting for the span to make its way to the storage.
Storage (Data Store) - Collectors require a persistent storage backend. Jaeger has a pluggable mechanism for span storage. Note that for this release, the only supported storage is Elasticsearch.
Query (Query Service) - Query is a service that retrieves traces from storage.
Ingester (Ingester Service) - Jaeger can use Apache Kafka as a buffer between the collector and the actual backing storage (Elasticsearch). Ingester is a service that reads data from Kafka and writes to another storage backend (Elasticsearch).
Jaeger Console – Jaeger provides a user interface that lets you visualize your distributed tracing data. On the Search page, you can find traces and explore details of the spans that make up an individual trace.