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 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 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 Red Hat OpenShift distributed tracing 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 distributed tracing for monitoring, network profiling, and troubleshooting the interaction between components in modern, cloud-native, microservices-based applications.
With distributed tracing you can perform the following functions:
Monitor distributed transactions
Optimize performance and latency
Perform root cause analysis
Red Hat OpenShift distributed tracing consists of two main components:
Jaeger does not use FIPS validated cryptographic modules.
Red Hat OpenShift distributed tracing provides the following capabilities:
Integration with Kiali – When properly configured, you can view distributed tracing data from the Kiali console.
High scalability – The distributed tracing back end is designed to have no single points of failure and to scale with the business needs.
Distributed Context Propagation – Enables you to connect data from different components together to create a complete end-to-end trace.
Backwards compatibility with Zipkin – Red Hat OpenShift distributed tracing 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.
Red Hat OpenShift distributed tracing is made up of several components that work together to collect, store, and display tracing data.
Red Hat OpenShift distributed tracing platform - This component is based on the open source Jaeger project.
Client (Jaeger client, Tracer, Reporter, instrumented application, client libraries)- The distributed tracing platform 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.
Agent (Jaeger agent, Server Queue, Processor Workers) - The distributed tracing platform 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 such as Kubernetes.
Jaeger Collector (Collector, Queue, Workers) - Similar to the Jaeger agent, the Jaeger Collector receives spans and places them in an internal queue for processing. This allows the Jaeger 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. Red Hat OpenShift distributed tracing platform 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) - Red Hat OpenShift distributed tracing can use Apache Kafka as a buffer between the Collector and the actual Elasticsearch backing storage. Ingester is a service that reads data from Kafka and writes to the Elasticsearch storage backend.
Jaeger Console – With the Red Hat OpenShift distributed tracing platform user interface, you can 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.
Red Hat OpenShift distributed tracing data collection - This component is based on the open source OpenTelemetry project.
OpenTelemetry Collector - The OpenTelemetry Collector is a vendor-agnostic way to receive, process, and export telemetry data. The OpenTelemetry Collector supports open-source observability data formats, for example, Jaeger and Prometheus, sending to one or more open-source or commercial back-ends. The Collector is the default location instrumentation libraries export their telemetry data.