Understanding Operators | Operators | OpenShift Container Platform 4.3

Why use Operators?
Operator Framework
Operator maturity model

Conceptually, Operators take human operational knowledge and encode it into software that is more easily shared with consumers.

Operators are pieces of software that ease the operational complexity of running another piece of software. They act like an extension of the software vendor’s engineering team, watching over a Kubernetes environment (such as OpenShift Container Platform) and using its current state to make decisions in real time. Advanced Operators are designed to handle upgrades seamlessly, react to failures automatically, and not take shortcuts, like skipping a software backup process to save time.

More technically, Operators are a method of packaging, deploying, and managing a Kubernetes application.

A Kubernetes application is an app that is both deployed on Kubernetes and managed using the Kubernetes APIs and kubectl or oc tooling. To be able to make the most of Kubernetes, you require a set of cohesive APIs to extend in order to service and manage your apps that run on Kubernetes. Think of Operators as the runtime that manages this type of app on Kubernetes.

Why use Operators?

Operators provide:

Repeatability of installation and upgrade.
Constant health checks of every system component.
Over-the-air (OTA) updates for OpenShift components and ISV content.
A place to encapsulate knowledge from field engineers and spread it to all users, not just one or two.

Why deploy on Kubernetes?: Kubernetes (and by extension, OpenShift Container Platform) contains all of the primitives needed to build complex distributed systems – secret handling, load balancing, service discovery, autoscaling – that work across on-premise and cloud providers.
Why manage your app with Kubernetes APIs and kubectl tooling?: These APIs are feature rich, have clients for all platforms and plug into the cluster’s access control/auditing. An Operator uses the Kubernetes' extension mechanism, Custom Resource Definitions (CRDs), so your custom object, for example MongoDB, looks and acts just like the built-in, native Kubernetes objects.
How do Operators compare with service Brokers?: A service Broker is a step towards programmatic discovery and deployment of an app. However, because it is not a long running process, it cannot execute Day 2 operations like upgrade, failover, or scaling. Customizations and parameterization of tunables are provided at install time, versus an Operator that is constantly watching your cluster’s current state. Off-cluster services continue to be a good match for a service Broker, although Operators exist for these as well.

Operator Framework

The Operator Framework is a family of tools and capabilities to deliver on the customer experience described above. It is not just about writing code; testing, delivering, and updating Operators is just as important. The Operator Framework components consist of open source tools to tackle these problems:

Operator SDK: The Operator SDK assists Operator authors in bootstrapping, building, testing, and packaging their own Operator based on their expertise without requiring knowledge of Kubernetes API complexities.
Operator Lifecycle Manager: The Operator Lifecycle Manager (OLM) controls the installation, upgrade, and role-based access control (RBAC) of Operators in a cluster. Deployed by default in OpenShift Container Platform 4.3.
Operator Registry: The Operator Registry stores ClusterserviceVersions (CSVs) and Custom Resource Definitions (CRDs) for creation in a cluster and stores Operator metadata about packages and channels. It runs in a Kubernetes or OpenShift cluster to provide this Operator catalog data to the OLM.
OperatorHub: The OperatorHub is a web console for cluster administrators to discover and select Operators to install on their cluster. It is deployed by default in OpenShift Container Platform.
Operator Metering: Operator Metering collects operational metrics about Operators on the cluster for Day 2 management and aggregating usage metrics.

These tools are designed to be composable, so you can use any that are useful to you.

Operator maturity model

The level of sophistication of the management logic encapsulated within an Operator can vary. This logic is also in general highly dependent on the type of the service represented by the Operator.

One can however generalize the scale of the maturity of an Operator’s encapsulated operations for certain set of capabilities that most Operators can include. To this end, the following Operator Maturity model defines five phases of maturity for generic day two operations of an Operator:

Figure 1. Operator maturity model

The above model also shows how these capabilities can best be developed through the Operator SDK’s Helm, Go, and Ansible capabilities.