Red Hat JBoss Data Grid

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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Enabling LDAP Security for DataGrid Cache

Enabling LDAP Security for DataGrid Cache

Expanding on Tristan’s blog, where he spoke of enabling security for JBoss Data Grid caches, in this post we will cover how to add LDAP based security to the JDG caches. The principles and techniques remain defined by Tristan, but there are some minor changes that I will be highlighting in this blog for a successful working configuration of JDG enabled with LDAP security.

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Offload your database data into an in-memory data grid for fast processing made easy

Offload your database data into an in-memory data grid for fast processing made easy

An in-memory data grid is a distributed data management platform for application data that:

  • Uses memory (RAM) to store information for very fast, low-latency response time, and very high throughput.
  • Keeps copies of that information synchronized across multiple servers for continuous availability, information reliability, and linear scalability.
  • Can be used as distributed cache, NoSQL database, event broker, compute grid, and Apache Spark data store.

The technical advantages of an in-memory data grid (IMDGs) provide business benefits in the form of faster decision-making, greater productivity, and improved customer engagement and experience.

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