Performance, scale, and real-time analytics: Red Hat JBoss Data Grid 7.1

I am excited to announce the general availability of Red Hat JBoss Data Grid 7.1!  This is the only Red Hat software ranked highly in two separate Forrester waves categories: In-Memory Data Grid and In-Memory Database. On top of that, no other vendor offers any unified in-memory data management solution that is recognized in both waves — JBoss Data Grid is the one product with the versatility to span both categories.

In-memory computing is all about high performance and scale-out architecture. The primary focus of this release to enhance the performance of JBoss Data Grid as an in-memory data management platform for hybrid transactional and analytical (HTAP) workloads.

New Capabilities and Features

  • Performance improvements. JBoss Data Grid 7.1 features core performance improvements, especially in clustered write operations. Current tests have shown up to 60% increase in write throughput under load. (We have modified various default settings to improve JBoss Data Grid performance.)
  • Elastic scale external state management for JBoss Web Server (Tomcat) and Spring applications (on-premise or cloud/Openshift). JBoss Data Grid 7.1 features the ability to externalize HTTP sessions from a JBoss Web Server node to a remote JBoss Data Grid cluster. This helps make the JBoss Web layer stateless and enables a rolling update of the application layer, while retrieving the session data from the JBoss Data Grid layer. Additionally, JBoss Data Grid 7.1 features Spring session support, which enables you to externalize HTTP session from a Spring (or Spring Boot) deployment to a remote JBoss Data Grid cluster.
  • Real-time analytics, through Apache Spark 2.x integration supporting RDD and DStream interfaces.
  • New string-based querying with Ickle (tech preview). JBoss Data Grid 7.1 introduces a new string-based querying language, Ickle, as technology preview,  which enables you to specify combinations of relational and full-text predicates (based on Apache Lucene). This enhances the querying feature-set available in client-server mode by bringing several additional operations that were previously available only in library mode.
  • Ease of administration. Update and save node-level configurations are now available through the administration console.
  • Feature enhancements to Hot Rod clients, including streaming large-sized objects in chunks from the JBoss Data Grid server to a Java client and adding cross-site failover for C++, C# and Node.js clients.

More Resources

What Are You Getting from (Big) Data?

Gartner has a term for information which is routinely gathered, but not really used: dark data. This is information which is collected for a direct purpose (like processing an online transaction), but then never really used for anything else. By IDC estimates, dark data represent about 90% of the data collected and stored by organizations.

The Internet of Things (specifically) and digital transformation (more generally) are business initiatives that try to harness that dark data by incorporating new or previously untapped data streams into larger business processes.

Big data refers to that new influx of data. The “big” adjective can be a bit misleading — it doesn’t necessarily mean that these are massive amounts of data. Some organizations may be dealing with petabytes of data, but some may only be gigabytes. It’s not a given amount of data, but rather the scale of increase from previous data streams.

Continue reading “What Are You Getting from (Big) Data?”

In-Memory Performance and Elastic Scale Data Management as a Cloud Service

Today we announced three new Red Hat JBoss Middleware services on OpenShift based on JBoss Fuse, JBoss BRMS, and JBoss Data Grid.

Performance and Scalability for Cloud Applications

With cloud computing, businesses expect and demand that their applications deliver higher performance, availability, reliability, flexibility, and scalability than ever before. But the influx of data is creating new obstacles that make it difficult for applications to meet the demands and expectations.

Continue reading “In-Memory Performance and Elastic Scale Data Management as a Cloud Service”