Red Hat JBoss Data Grid

Get started with reactive programming with creative Coderland tutorials

Get started with reactive programming with creative Coderland tutorials

The Reactica roller coaster is the latest addition to Coderland, our fictitious amusement park for developers. It illustrates the power of reactive computing, an important architecture for working with groups of microservices that use asynchronous data to work with each other.

In this scenario, we need to build a web app to display the constantly updated wait time for the coaster.

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Five layers of security for Red Hat Data Grid on OpenShift

Five layers of security for Red Hat Data Grid on OpenShift

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.

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Transitioning Red Hat SSO to a highly-available hybrid cloud deployment

Transitioning Red Hat SSO to a highly-available hybrid cloud deployment

About two years ago, Red Hat IT finished migrating our customer-facing authentication system to Red Hat Single Sign-On (Red Hat SSO). As a result, we were quite pleased with the performance and flexibility of the new platform. Due to some architectural decisions that were made in order to optimize for uptime using the technologies at our disposal, we were unable to take full advantage of Red Hat SSO’s robust feature set until now. This article describes how we’re now addressing database and session replication between global sites.

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Autoscaling the Red Hat Cache Service on OpenShift

Autoscaling the Red Hat Cache Service on OpenShift

Earlier this year, Red Hat announced the Red Hat Cache Service which is a distributed in-memory caching service that runs on Red Hat OpenShift.  Red Hat Data Grid is used as the core of the cache service. The cache service is one of the things you can easily install on OpenShift through the OpenShift Service Catalog. You can find the cache service in the Red Hat OpenShift Online Pro tier.  (Alternatively, you can install the Cache Service on your own Red Hat OpenShift Container Platform installation by following the installation manual.)

The Cache Service automatically calculates the amount of user storage based on the container size it’s scheduled on. Typically, it’s 512MB. What’s more interesting is that the Cache Service can operate near the full memory capacity (~97–98 %).

The automatic memory adjustment gives you a nice opportunity to try out the new Horizontal Pod Autoscaler (which now supports memory and custom metrics-based autoscaling). The autoscaler monitors the amount of memory used by the container and adds or removes Cache Service pods based on this measurement.

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Using Red Hat Data Grid to power a multi-cloud real-time game

Using Red Hat Data Grid to power a multi-cloud real-time game

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.

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Red Hat Data Grid on Three Clouds (the details behind the demo)

Red Hat Data Grid on Three Clouds (the details behind the demo)

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

 

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Building a Secure IoT Solution: Summit 2017

Building a Secure IoT Solution: Summit 2017

How do customers build an end-to-end IoT solution using commercial grade, open source products? This is the question we (Patrick Steiner, Maggie Hu and I) wanted to address with our session at the Red Hat Summit, Boston. The end-to-end solution is based on three-tier Enterprise IoT Architecture, which integrates IoT data with existing business processes and the human element.

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Implementing a Log Collector using Red Hat JBoss Fuse and Red Hat JBoss Data Grid

Implementing a Log Collector using Red Hat JBoss Fuse and Red Hat JBoss Data Grid

Most of the time, when we think about collecting, parsing and storing Logs, the first thing that pops in our mind is the ElasticStack or ELK. It is well positioned in developer and sysadmin’s minds. The stack combines the popular Elasticsearch, Logstash and Kibana projects together to easy the collection/aggregation, store, and visualization of application logs. As an Apache Camel rider and Infinispan enthusiast, I prepared this exercise to produce my own log collector and store stack using Red Hat’s products, JBoss Fuse and JBoss Data Grid, instead.

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