OpenShift

Cloud-native integration with Kubernetes and Camel K

Cloud-native integration with Kubernetes and Camel K

Our first DevNation Live regional event was held in Bengaluru, India in July. This free technology event focused on open source innovations, with sessions presented by elite Red Hat technologists.

In this session, Kamesh Sampath shows how to apply common Enterprise Integration Patterns (EIP) with Apache Camel, Kubernetes, and Red Hat OpenShift. You will see how the new Camel K framework helps in deploying Camel DSL code as “integrations” in Kubernetes/OpenShift.

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Event-based microservices with Red Hat AMQ Streams

Event-based microservices with Red Hat AMQ Streams

As part of Red Hat’s AMQ offerings, Red Hat offers a Kafka-based event streaming solution both for traditional deployment and microservices-based deployment branded as Red Hat AMQ Streams. The Red Hat OpenShift AMQ Streams deployment option is based on Strimzi, an open source tool that makes Kafka deployment as a container on a Kubernetes platform easy because most of the deployment prerequisites are automated with the OpenShift Operator Framework.

In this article, we look at how to deploy Apache Kafka on Red Hat OpenShift 4, using reasonable sample microservice applications to showcase the endless possibility of innovation brought by OpenShift and Kafka.

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Fixing the oc and Red Hat OpenShift install “not downloaded” error on macOS

Fixing the oc and Red Hat OpenShift install “not downloaded” error on macOS

I recently decided to use my macOS machine to create a Red Hat OpenShift cluster. After downloading the openshift-install command-line tool and running it, however, I received the following error:

(Yes, I know the above error is related to the oc command, but it also threw the error and, after I fixed the openshift-install command, I was unable to “unfix” it.)

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Red Hat Software Collections 3.4 and Red Hat Developer Toolset 9 Beta now available

Red Hat Software Collections 3.4 and Red Hat Developer Toolset 9 Beta now available

The latest versions of Red Hat Software Collections and Red Hat Developer Toolset are available now in beta. Red Hat Software Collections 3.4 delivers the latest stable versions of many popular open source runtime languages and databases natively to the world’s leading enterprise Linux platform. These components are supported for up to five years, helping to enable a more consistent, efficient, and reliable developer experience.

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Open Liberty Java runtime now available to Red Hat Runtimes subscribers

Open Liberty Java runtime now available to Red Hat Runtimes subscribers

Open Liberty is a lightweight, production-ready Java runtime for containerizing and deploying microservices to the cloud, and is now available as part of a Red Hat Runtimes subscription. If you are a Red Hat Runtimes subscriber, you can write your Eclipse MicroProfile and Jakarta EE apps on Open Liberty and then run them in containers on Red Hat OpenShift, with commercial support from Red Hat and IBM.

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Pod Lifecycle Event Generator: Understanding the “PLEG is not healthy” issue in Kubernetes

Pod Lifecycle Event Generator: Understanding the “PLEG is not healthy” issue in Kubernetes

In this article, I’ll explore the “PLEG is not healthy” issue in Kubernetes, which sometimes leads to a “NodeNotReady” status. When understanding how the Pod Lifecycle Event Generator (PLEG) works, it is helpful to also understand troubleshooting around this issue.

What is PLEG?

The PLEG module in kubelet (Kubernetes) adjusts the container runtime state with each matched pod-level event and keeps the pod cache up to date by applying changes.

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Plumbing Kubernetes CI/CD with Tekton

Plumbing Kubernetes CI/CD with Tekton

Our first DevNation Live regional event was held in Bengaluru, India in July. This free technology event focused on open source innovations, with sessions presented by elite Red Hat technologists.

In this session, Kamesh Sampath introduces Tekton, which is the Kubernetes-native way of defining and running CI/CD. Sampath explores the characteristics of Tekton—cloud-native, decoupled, and declarative—and shows how to combine various building blocks of Tekton to build and deploy a cloud-native application.

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Using the Red Hat OpenShift tuned Operator for Elasticsearch

Using the Red Hat OpenShift tuned Operator for Elasticsearch

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 [65530] likely too low, increase to at least [262144]

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.

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Autoscaling Red Hat Fuse applications with OpenShift

Autoscaling Red Hat Fuse applications with OpenShift

In this article, we demonstrate Red Hat OpenShift’s horizontal autoscaling feature with Red Hat Fuse applications. The result is a Spring Boot-based application that uses the Apache Camel component twitter-search that searches Twitter for tweets based on specific keywords. If traffic or the number of tweets increases, and this application cannot serve all requests, then the application autoscales itself by increasing the number of pods. The ability to serve all requests is monitored by tracking this application’s CPU utilization on a particular pod. Also, as soon as traffic or CPU utilization is back to normal, the number of pods is reduced to the minimum configured value.

There are two types of scaling: horizontal and vertical. Horizontal scaling is where the number of application instances or containers is increased. Vertical scaling is where system resources like CPU and memory are increased at the running application’s or container’s runtime. Horizontal scaling can be used for stateless applications, whereas vertical scaling is more suitable for stateful applications.

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