microservices

Serverless applications made faster and simpler with OpenShift Serverless GA

Serverless applications made faster and simpler with OpenShift Serverless GA

Red Hat OpenShift Serverless delivers Kubernetes-native, event-driven primitives for microservices, containers, and compatible Function-as-a-Service (FaaS) implementations. OpenShft Serverless provides out-of-the-box traffic routing and security capabilities. This offering combines Red Hat Operators, Knative, and Red Hat OpenShift. Combined, these tools allow stateless and serverless workloads to run across OpenShift deployments on private, public, hybrid, or multi-cloud environments with automated operations.

OpenShift Serverless is now generally available. It enables developers to focus purely on building next-generation applications with a wide choice of languages, frameworks, development environments, and other tools for writing and deploying business-differentiating applications.

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New features in Red Hat CodeReady Studio 12.15.0.GA and JBoss Tools 4.15.0.Final for Eclipse 2020-03

New features in Red Hat CodeReady Studio 12.15.0.GA and JBoss Tools 4.15.0.Final for Eclipse 2020-03

JBoss Tools 4.15.0 and Red Hat CodeReady Studio 12.15 for Eclipse 4.15 (2020-03) are now available. For this release, we focused on improving Quarkus and container-based development and fixing bugs. We also updated the Hibernate Tools runtime provider and Java Developer Tools (JDT) extensions, which are now compatible with Java 14. Additionally, we made many UI changes to platform views, dialogs, and toolbars.

Installation

First, let’s look at how to install these updates. CodeReady Studio (previously Red Hat Developer Studio) comes with everything pre-bundled in its installer. Simply download the installer from the Red Hat CodeReady Studio product page and run it as follows:

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Ramp up on Quarkus: A Kubernetes-native Java framework

Ramp up on Quarkus: A Kubernetes-native Java framework

Java has been in a bit of an awkward spot since containers took off a few years ago. In the world of Kubernetes, microservices, and serverless, it has been getting harder and harder to ignore that Java applications are, by today’s standards, bloated. Well, until now. In this article, I explore the basics of Quarkus, a Kubernetes-native Java framework built to specifically address Java’s bloatedness problem.

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Build and deploy a serverless app with Camel K and Red Hat OpenShift Serverless 1.5.0 Tech Preview

Build and deploy a serverless app with Camel K and Red Hat OpenShift Serverless 1.5.0 Tech Preview

Red Hat OpenShift Serverless 1.5.0 (currently in tech preview) runs on Red Hat OpenShift Container Platform 4.3. It enables stateful, stateless, and serverless workloads to all operate on a single multi-cloud container platform. Apache Camel K is a lightweight integration platform that runs natively on Kubernetes. Camel K has serverless superpowers.

In this article, I will show you how to use OpenShift Serverless and Camel K to create a serverless Java application that you can scale up or down on demand.

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Red Hat Data Grid 8.0 brings new server architecture, improved REST API, and more

Red Hat Data Grid 8.0 brings new server architecture, improved REST API, and more

Red Hat Data Grid helps applications access, process, and analyze data at in-memory speed. Red Hat Data Grid 8.0 is included in the latest update to Red Hat Runtimes, providing a distributed in-memory, NoSQL datastore. This release includes a new Operator for handling complex applications, a new server architecture that reduces memory consumption and increases security, a faster API with new features, a new CLI, and compatibility with a variety of observability tools.

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Migrating a Spring Boot microservices application to Quarkus

Migrating a Spring Boot microservices application to Quarkus

While Spring Boot has long been the de-facto framework for developing container-based applications in Java, the performance benefits of a Kubernetes-native framework are hard to ignore. In this article, I will show you how to quickly migrate a Spring Boot microservices application to Quarkus. Once the migration is complete, we’ll test the application and compare startup times between the original Spring Boot application and the new Quarkus app.

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Low-code microservices orchestration with Syndesis

Low-code microservices orchestration with Syndesis

Recently I wrote about decoupling infrastructure code from microservices. I found that Apache Camel and Debezium provided the middleware I needed for that project, with minimal coding on my end. After my successful experiment, I wondered if it would be possible to orchestrate two or more similarly decoupled microservices into a new service–and could I do it without writing any code at all? I decided to find out.

This article is a quick dive into orchestrating microservices without writing any code. We will use Syndesis (an open source integration platform) as our orchestration platform. Note that the examples assume that you are familiar with Debezium and Kafka.

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Dynamic case management in the event-driven era

Dynamic case management in the event-driven era

Case management applications are designed to handle a complex combination of human and automated tasks. All case updates and case data are captured as a case file, which acts as a pivot for the management. This then serves as a system of record for future audits and tracking. The key characteristic of these workflows is that they are ad hoc in nature. There is no single resolution, and often, one size doesn’t fit all.

Case management does not have structured time bounds. All cases typically don’t resolve at the same time. Consider examples like client onboarding, dispute resolution, fraud investigations, etc., which, by virtue, try to provide customized solutions based on the specific use case. With the advent of more modern technological frameworks and practices like microservices and event-driven processing, the potential of case management solutions opens up even further. This article describes how you can make use of case management for dynamic workflow processing in this modern era, including components such as Red Hat OpenShift, Red Hat AMQ Streams, Red Hat Fuse, and Red Hat Process Automation Manager.

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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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Decoupling microservices with Apache Camel and Debezium

Decoupling microservices with Apache Camel and Debezium

The rise of microservices-oriented architecture brought us new development paradigms and mantras about independent development and decoupling. In such a scenario, we have to deal with a situation where we aim for independence, but we still need to react to state changes in different enterprise domains.

I’ll use a simple and typical example in order to show what we’re talking about. Imagine the development of two independent microservices: Order and User. We designed them to expose a REST interface and to each use a separate database, as shown in Figure 1:

Diagram 1 - Order and User microservices

Figure 1: Order and User microservices.

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