Soon, all Red Hat OpenShift documentation will only be available on docs.redhat.com, the official location of all Red Hat product documentation. Get familiar with the updated experience today!
  1. Documentation
    2. history
×

Understanding Service Mesh

You are viewing documentation for a Red Hat OpenShift Service Mesh release that is no longer supported.

Service Mesh version 1.0 and 1.1 control planes are no longer supported. For information about upgrading your service mesh control plane, see Upgrading Service Mesh.

For information about the support status of a particular Red Hat OpenShift Service Mesh release, see the Product lifecycle page.

Red Hat OpenShift Service Mesh provides a platform for behavioral insight and operational control over your networked microservices in a service mesh. With Red Hat OpenShift Service Mesh, you can connect, secure, and monitor microservices in your OpenShift Container Platform environment.

Understanding service mesh

A service mesh is the network of microservices that make up applications in a distributed microservice architecture and the interactions between those microservices. When a Service Mesh grows in size and complexity, it can become harder to understand and manage.

Based on the open source Istio project, Red Hat OpenShift Service Mesh adds a transparent layer on existing distributed applications without requiring any changes to the service code. You add Red Hat OpenShift Service Mesh support to services by deploying a special sidecar proxy to relevant services in the mesh that intercepts all network communication between microservices. You configure and manage the Service Mesh using the Service Mesh control plane features.

Red Hat OpenShift Service Mesh gives you an easy way to create a network of deployed services that provide:

  • Discovery

  • Load balancing

  • Service-to-service authentication

  • Failure recovery

  • Metrics

  • Monitoring

Red Hat OpenShift Service Mesh also provides more complex operational functions including:

  • A/B testing

  • Canary releases

  • Access control

  • End-to-end authentication

Red Hat OpenShift Service Mesh Architecture

Red Hat OpenShift Service Mesh is logically split into a data plane and a control plane:

The data plane is a set of intelligent proxies deployed as sidecars. These proxies intercept and control all inbound and outbound network communication between microservices in the service mesh. Sidecar proxies also communicate with Mixer, the general-purpose policy and telemetry hub.

  • Envoy proxy intercepts all inbound and outbound traffic for all services in the service mesh. Envoy is deployed as a sidecar to the relevant service in the same pod.

The control plane manages and configures proxies to route traffic, and configures Mixers to enforce policies and collect telemetry.

  • Mixer enforces access control and usage policies (such as authorization, rate limits, quotas, authentication, and request tracing) and collects telemetry data from the Envoy proxy and other services.

  • Pilot configures the proxies at runtime. Pilot provides service discovery for the Envoy sidecars, traffic management capabilities for intelligent routing (for example, A/B tests or canary deployments), and resiliency (timeouts, retries, and circuit breakers).

  • Citadel issues and rotates certificates. Citadel provides strong service-to-service and end-user authentication with built-in identity and credential management. You can use Citadel to upgrade unencrypted traffic in the service mesh. Operators can enforce policies based on service identity rather than on network controls using Citadel.

  • Galley ingests the service mesh configuration, then validates, processes, and distributes the configuration. Galley protects the other service mesh components from obtaining user configuration details from OpenShift Container Platform.

Red Hat OpenShift Service Mesh also uses the istio-operator to manage the installation of the control plane. An Operator is a piece of software that enables you to implement and automate common activities in your OpenShift Container Platform cluster. It acts as a controller, allowing you to set or change the desired state of objects in your cluster.

Understanding Kiali

Kiali provides visibility into your service mesh by showing you the microservices in your service mesh, and how they are connected.

Kiali overview

Kiali provides observability into the Service Mesh running on OpenShift Container Platform. Kiali helps you define, validate, and observe your Istio service mesh. It helps you to understand the structure of your service mesh by inferring the topology, and also provides information about the health of your service mesh.

Kiali provides an interactive graph view of your namespace in real time that provides visibility into features like circuit breakers, request rates, latency, and even graphs of traffic flows. Kiali offers insights about components at different levels, from Applications to Services and Workloads, and can display the interactions with contextual information and charts on the selected graph node or edge. Kiali also provides the ability to validate your Istio configurations, such as gateways, destination rules, virtual services, mesh policies, and more. Kiali provides detailed metrics, and a basic Grafana integration is available for advanced queries. Distributed tracing is provided by integrating Jaeger into the Kiali console.

Kiali is installed by default as part of the Red Hat OpenShift Service Mesh.

Kiali architecture

Kiali is based on the open source Kiali project. Kiali is composed of two components: the Kiali application and the Kiali console.

  • Kiali application (back end) – This component runs in the container application platform and communicates with the service mesh components, retrieves and processes data, and exposes this data to the console. The Kiali application does not need storage. When deploying the application to a cluster, configurations are set in ConfigMaps and secrets.

  • Kiali console (front end) – The Kiali console is a web application. The Kiali application serves the Kiali console, which then queries the back end for data to present it to the user.

In addition, Kiali depends on external services and components provided by the container application platform and Istio.

  • Red Hat Service Mesh (Istio) - Istio is a Kiali requirement. Istio is the component that provides and controls the service mesh. Although Kiali and Istio can be installed separately, Kiali depends on Istio and will not work if it is not present. Kiali needs to retrieve Istio data and configurations, which are exposed through Prometheus and the cluster API.

  • Prometheus - A dedicated Prometheus instance is included as part of the Red Hat OpenShift Service Mesh installation. When Istio telemetry is enabled, metrics data are stored in Prometheus. Kiali uses this Prometheus data to determine the mesh topology, display metrics, calculate health, show possible problems, and so on. Kiali communicates directly with Prometheus and assumes the data schema used by Istio Telemetry. Prometheus is an Istio dependency and a hard dependency for Kiali, and many of Kiali’s features will not work without Prometheus.

  • Cluster API - Kiali uses the API of the OpenShift Container Platform (cluster API) to fetch and resolve service mesh configurations. Kiali queries the cluster API to retrieve, for example, definitions for namespaces, services, deployments, pods, and other entities. Kiali also makes queries to resolve relationships between the different cluster entities. The cluster API is also queried to retrieve Istio configurations like virtual services, destination rules, route rules, gateways, quotas, and so on.

  • Jaeger - Jaeger is optional, but is installed by default as part of the Red Hat OpenShift Service Mesh installation. When you install the distributed tracing platform (Jaeger) as part of the default Red Hat OpenShift Service Mesh installation, the Kiali console includes a tab to display distributed tracing data. Note that tracing data will not be available if you disable Istio’s distributed tracing feature. Also note that user must have access to the namespace where the Service Mesh control plane is installed to view tracing data.

  • Grafana - Grafana is optional, but is installed by default as part of the Red Hat OpenShift Service Mesh installation. When available, the metrics pages of Kiali display links to direct the user to the same metric in Grafana. Note that user must have access to the namespace where the Service Mesh control plane is installed to view links to the Grafana dashboard and view Grafana data.

Kiali features

The Kiali console is integrated with Red Hat Service Mesh and provides the following capabilities:

  • Health – Quickly identify issues with applications, services, or workloads.

  • Topology – Visualize how your applications, services, or workloads communicate via the Kiali graph.

  • Metrics – Predefined metrics dashboards let you chart service mesh and application performance for Go, Node.js. Quarkus, Spring Boot, Thorntail and Vert.x. You can also create your own custom dashboards.

  • Tracing – Integration with Jaeger lets you follow the path of a request through various microservices that make up an application.

  • Validations – Perform advanced validations on the most common Istio objects (Destination Rules, Service Entries, Virtual Services, and so on).

  • Configuration – Optional ability to create, update and delete Istio routing configuration using wizards or directly in the YAML editor in the Kiali Console.

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.

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.

Distributed tracing overview

As a service owner, you can use distributed tracing to instrument your services to gather insights into your service architecture. You can use the Red Hat OpenShift distributed tracing platform for monitoring, network profiling, and troubleshooting the interaction between components in modern, cloud-native, microservices-based applications.

With the distributed tracing platform, you can perform the following functions:

  • Monitor distributed transactions

  • Optimize performance and latency

  • Perform root cause analysis

The distributed tracing platform consists of three components:

  • Red Hat OpenShift distributed tracing platform (Jaeger), which is based on the open source Jaeger project.

  • Red Hat OpenShift distributed tracing platform (Tempo), which is based on the open source Grafana Tempo project.

  • Red Hat build of OpenTelemetry, which is based on the open source OpenTelemetry project.

Distributed tracing architecture

The distributed tracing platform (Jaeger) is based on the open source Jaeger project. The distributed tracing platform (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.

Red Hat OpenShift distributed tracing platform features

Red Hat OpenShift distributed tracing platform provides the following capabilities:

  • Integration with Kiali – When properly configured, you can view distributed tracing platform data from the Kiali console.

  • High scalability – The distributed tracing platform 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 platform 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.

Next steps