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”
This article is about my experience installing Red Hat Data Grid (RHDG) on Red Hat CodeReady Containers (CRC) so that I could set up a local environment to develop and test a Quarkus Infinispan client. I started by installing CodeReady Containers and then installed Red Hat Data Grid. I am also on a learning path for Quarkus, so my last step was to integrate the Quarkus Infinispan client into my new development environment.
Initially, I tried connecting the Quarkus client to my locally running instance of Data Grid. Later, I decided I wanted to create an environment where I could test and debug Data Grid on Red Hat OpenShift 4. I tried installing Data Grid on OpenShift 4 in a shared environment, but maintaining that environment was challenging. Through trial-and-error, I found that it was better to install Red Hat Data Grid on CodeReady Containers and use that for my local development and testing environment.
In this quick tutorial, I guide you through setting up a local environment to develop and test a Quarkus client—in this case, Quarkus Infinispan. The process consists of three steps:
- Install and run CodeReady Containers.
- Install Data Grid on CodeReady Containers.
- Integrate the Quarkus Infinispan client into the new development environment.
Continue reading “Develop and test a Quarkus client on Red Hat CodeReady Containers with Red Hat Data Grid 8.0”
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
Welcome back to another edition of JBoss Weekly, bringing you news from across the net relating to JBoss Middleware. Those of you who attended Devoxx Belgium, we hope you had the opportunity to speak with our engineers there.
Continue reading “JBoss Weekly 17 November 2017”
Let’s be honest: it’s pretty exciting that Infinispan now supports Java 8 for many reasons, but perhaps one of the most anticipated reasons is because of the new stream classes. The main reason for this is the fact that it completely transforms the way we process data. Instead of having to iterate upon the data yourself, the underlying stream does this for you, and all you have to do is provide the operations to perform on it. This is perfect for distributed processing because the implementation handles the iteration entirely.
Continue reading “Infinispan’s Java 8 Streams Capabilities”
DevNation sneak peek is a behind-the-scenes preview of sessions and information that will take place at DevNation 2016. Sign up for DevNation at www.devnation.org. Learn more. Code more. Share more. Join the Nation.
Continue reading DevNation 2016: Galder Zamarreno on “Building reactive applications with Node.js and Red Hat JBoss Data Grid”
With the adoption growth of Infinispan, its community has been resurrecting works on the quite old, but stalled, JSR-107, aka JCache. The first step was obviously the released of the JSR 1.0 version, a few month back, and most recently in December with Infinispan 7.0.2.Final is a certified JSR-107 1.0 implementation. It’s actually quite useful news, as it allows you to build webapps or even JEE apps using a standard API to access Infinispan.
Using JCache API is pretty straightforward, fairly well documented, and to summarize consists of:
- add a dependency to infinispan-jcache artifact
Continue reading “JCache and Infinispan – standardize your application's cache”