There are many ways to configure the cache in a microservices system. As a rule of thumb, you should use caching only in one place; for example, you should not use the cache in both the HTTP and application layers. Distributed caching both increases cloud-native application performance and minimizes the overhead of creating new microservices.
Continue reading Build embedded cache clusters with Quarkus and Red Hat Data Grid
The release of Red Hat Data Grid 8.1 offers new features for securing applications deployed on Red Hat OpenShift. Naturally, I wanted to check them out for Quarkus. Using the Quarkus Data Grid extension made that easy to do.
Data Grid is an in-memory, distributed, NoSQL datastore solution based on Infinispan. Since it manages your data, Data Grid should be as secure as possible. For this reason, it uses a default property realm that requires HTTPS and automatically enforces user authentication on remote endpoints. As an additional layer of security on OpenShift, Data Grid presents certificates signed by the OpenShift Service Signer. In practice, this means that Data Grid is as secure as possible out of the box, requiring encrypted connections and authentication from the first request. Data Grid generates a default set of credentials (which, of course, you can override), but unauthenticated access is denied.
In this article, I show you how to configure a Quarkus application with Data Grid and deploy it on OpenShift.
Continue reading “Securely connect Quarkus and Red Hat Data Grid on Red Hat OpenShift”
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.
Continue reading Red Hat Data Grid 8.0 brings new server architecture, improved REST API, and more
Red Hat Data Grid is an in-memory, distributed, NoSQL datastore solution. With it, your applications can access, process, and analyze data at in-memory speed to deliver a superior user experience. In-memory Data Grid has a variety of use cases in today’s environment, such as fast data access for low-latency apps, storing objects (NoSQL) in a datastore, achieving linear scalability with data distribution/partitioning, and data high-availability across geographies, among many others. With containers getting more attention, the need to have Data Grid running on a container platform like OpenShift is clear, and we are seeing more and more customers aligning their architecture with a datastore running natively on a container platform.
In this article, I will talk about multiple layers of security available while deploying Data Grid on OpenShift. The layers of security offer a combination of security measures provided by Data Grid as well as by OpenShift/Kubernetes.
Continue reading “Five layers of security for Red Hat Data Grid on OpenShift”
The scavenger hunt game developed for the audience to play during the Red Hat Summit 2018 demo used Red Hat Data Grid as storage for everything except the pictures taken by the participants. Data was stored across three different cloud environments using cross-site replication. In this blog post, we will look at how data was flowing through Data Grid and explain the Data Grid features powering different aspects of the game’s functionality.
Continue reading Using Red Hat Data Grid to power a multi-cloud real-time game
If you saw or heard about the multi-cloud demo at Red Hat Summit 2018, this article details how we ran Red Hat Data Grid in active-active-active mode across three cloud providers. This set up enabled us to show a fail over between cloud providers in real time with no loss of data. In addition to Red Hat Data Grid, we used Vert.x (reactive programming), OpenWhisk (serverless), and Red Hat Gluster Storage (software-defined storage.)
This year’s Red Hat Summit was quite an adventure for all of us. A trip to San Francisco is probably on the bucket list of IT geeks from all over the world. Also, we were able to meet many other Red Hatters, who work remotely for Red Hat as we do. However, the best part was that we had something important to say: “we believe in the hybrid/multi cloud” and we got to prove that live on stage.
Photo credit: Bolesław Dawidowicz
Continue reading “Red Hat Data Grid on Three Clouds (the details behind the demo)”
We’re excited to announce the availability of Red Hat JBoss Data Grid (JDG) Version 7.1.
Thanks and congratulations to the JDG engineering and product management team for this release.
JDG 7.1 release focuses on the following areas:
- Performance enhancements
- Apache Spark 2.x integration
- Several other enhancements
Continue reading “What’s new in Red Hat JBoss Data Grid 7.1”
Welcome to another episode of the series: “Unlock your Red Hat JBoss Data Grid (JDG) data with Red Hat JBoss Data Virtualization (JDV).”
This post will guide you through an example of connecting to Red Hat JBoss Data Grid data source, using Teiid Designer. In this example, we will demonstrate connecting to a local JDG data source. We’re using the JDG 6.6.1, but you can connect to any local or remote JDG source (version 6.6.1) if you wish, using the same steps.
Continue reading “Unlock your Red Hat JBoss Data Grid data with Red Hat JBoss Data Virtualization”
This article aims to provide a step by step guide for setting up a remote Red Hat JBoss Data Grid (JDG) cluster as an HTTP session store for your state-full web applications running on Red Hat JBoss Enterprise Application Platform (EAP). I had recently explored this setup for another customer and figured it would be helpful to put together a set of detailed instructions for replicating this. This feature was recently released with the GA of JDG 6.5.
Continue reading “Externalize HTTP Session Data to the JBoss Data Grid”