You might find yourself in situations where you believe that a logic implementation should occur only if and when your Operator is running on a specific Kubernetes platform. So, you probably want to know how to get the cluster vendor from the operator. In this article, we will discuss why relying on the vendor is not a good idea. Also, we will show how to solve this kind of scenario.
Continue reading “Why not couple an Operator’s logic to a specific Kubernetes platform?”
The Red Hat Integration Q4 release adds many new features and capabilities with an increasing focus around cloud-native data integration. The features I’m most excited about are the introduction of the schema registry, the advancement of change data capture capabilities based on Debezium to technical preview, and data virtualization (technical preview) capabilities.
Data integration is a topic that has not received much attention from the cloud-native community so far, and we will cover it in more detail in future posts. Here, we jump straight into demonstrating the latest release of data virtualization (DV) capabilities on Red Hat OpenShift 4. This is a step-by-step visual tutorial describing how to create a simple virtual database using Red Hat Integration’s data virtualization Operator. By the end of the tutorial, you will learn:
- How to deploy the DV Operator.
- How to create a virtual database.
- How to access the virtual database.
The steps throughout this article work on any Openshift 4.x environment with operator support, even on time- and resource-constrained environments such as the Red Hat OpenShift Interactive Learning Portal.
Continue reading “First steps with the data virtualization Operator for Red Hat OpenShift”
In this article, we take a look at user flow improvements for deploying applications in Red Hat OpenShift 4.3‘s Developer perspective. You can learn more about all of the developer-focused console improvements in the OpenShift 4.3 release article here. Since the initial launch of the Developer perspective in the OpenShift 4.2 release, we’ve had frequent feedback sessions with developers, developer advocates, stakeholders, and other community members to better understand how the experience meets their needs. While, overall, the user interface has been well received, we continue to gather and use the feedback to enhance our flows.
Continue reading Deploying applications in the OpenShift 4.3 Developer perspective
The developer experience is significantly improved in the Red Hat OpenShift 4.3 web console. If you have used the Developer perspective, which was introduced in OpenShift 4.2 Console, you are probably familiar with our streamlined user flows for deploying applications, the new Topology view, and the enhanced experience around OpenShift Pipelines powered by Tekton and OpenShift Serverless powered by Knative. This release continues to improve upon the features that were introduced in 4.2 and introduces new flows and features for the developer.
Continue reading What’s new in the OpenShift 4.3 console developer experience
In the previous article, we introduced the Service Binding Operator and explained how it functions. In this article, we’ll look at a more advanced topic—custom environment variables—and walk through a typical usage scenario.
Continue reading Service Binding Operator: The Operator in action
Connecting applications to the services that support them—for example, establishing the exchange of credentials between a Java application and a database that it requires—is referred to as binding. The configuration and maintenance of this binding together of applications and backing services can be a tedious and inefficient process. Manually editing YAML files to define binding information is error-prone and can introduce difficult-to-debug failures.
Continue reading Introducing the Service Binding Operator
I recently assisted a client to deploy Elastic Cloud on Kubernetes (ECK) on Red Hat OpenShift 4.x. They had run into an issue where Elasticsearch would throw an error similar to:
Max virtual memory areas vm.max_map_count  likely too low, increase to at least 
According to the official documentation, Elasticsearch uses a
mmapfs directory by default to store its indices. The default operating system limits on mmap counts are likely to be too low, which may result in out of memory exceptions. Usually, administrators would just increase the limits by running:
sysctl -w vm.max_map_count=262144
However, OpenShift uses Red Hat CoreOS for its worker nodes and, because it is an automatically updating, minimal operating system for running containerized workloads, you shouldn’t manually log on to worker nodes and make changes. This approach is unscalable and results in a worker node becoming tainted. Instead, OpenShift provides an elegant and scalable method to achieve the same via its Node Tuning Operator.
Continue reading “Using the Red Hat OpenShift tuned Operator for Elasticsearch”
Kubernetes is becoming much more than just a platform for running container workloads. Its API can be extended with application-specific Custom Resource Definitions(CRDs), and you can implement your own logic adapting your applications dynamically to changes in the cluster. In this article, we’ll be writing a simple Kubernetes Operator in Java using the Fabric8 Kubernetes Client.
Continue reading “Write a simple Kubernetes Operator in Java using the Fabric8 Kubernetes Client”
The open source Operator Framework is a toolkit to manage Kubernetes-native applications. The framework and its features provide the ability to develop solutions to simplify some complexities, such as the process to install, configure, manage and package applications on Kubernetes and Red Hat OpenShift. It provides the ability to use a client to perform CRUD actions, that is, operations to create, read, update, and delete data on these platforms.
By using operators, it’s possible not only to provide all expected resources but also to manage them dynamically, programmatically, and at execution time. To illustrate this idea, imagine if someone accidentally changed a configuration or removed a resource by mistake; in this case, the operator could fix it without any human intervention. We’ll take a look at Operators and the Operator SDK in this article.
Continue reading “Getting started with Golang Operators by using Operator SDK”
Monitoring systems are usually composed of three layers: a database layer that hosts metrics data, a layer to display the stored metric data graphically in dashboards, and an alerting layer to send out notifications via methods such as email, on-call notification systems, and chat platforms. This article presents an overview of the components used in Red Hat OpenShift‘s Application Monitoring Operator, how to install them using the Operator, and an example of the Operator in action.
Continue reading “Understanding Red Hat OpenShift’s Application Monitoring Operator”