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JBoss Middleware

Red Hat JBoss Data Grid

In-memory data grid

Fast, scalable, always available

Great user experience is ever more dependent on application performance and quality. Even a few seconds delay can mean the difference between success and failure for a new business initiative. Data bottlenecks are becoming more common as organizations need to process larger volumes, greater varieties, and a higher velocity of data to meet customer expectations.

With Red Hat® JBoss® Data Grid—an in-memory, distributed, NoSQL datastore solution—your applications can access, process, and analyze data at in-memory speed to deliver a superior user experience.

What's a data grid?

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

  • Uses memory (RAM) to store information for very fast, low-latency response time and high throughput.
  • Keeps copies of information synchronized across multiple servers for continuous availability, information reliability, and linear scalability.

In-memory data grids are a perfect solution to dramatically boost application performance and scale of new and existing applications. The technical advantages of in-memory data grids provide business benefits in the form of faster decision making, greater productivity, and improved customer experience.

Learn more about what an in-memory data grid does and how it lets you scale your application.

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Highlights of Red Hat JBoss Data Grid

  • Act faster

    Quickly access your data through fast, low-latency data processing using memory (RAM) and distributed parallel execution.

  • Scale quickly

    Achieve linear scalability with data partitioning and distribution across cluster nodes.

  • Always available

    Gain high availability through data replication across cluster nodes.

  • Fault tolerance

    Attain fault tolerance and recover from disaster through cross-datacenter georeplication and clustering.

  • More productivity

    Gain development flexibly and greater productivity with a highly versatile, functionally rich NoSQL data store.

  • Stay secure

    Obtain comprehensive data security with encryption and role-based access.

Use cases

Database and transaction caching; transient data store

Data caching and transient data storage are the most common data grid use cases. Data grids, such as JBoss Data Grid, are deployed as a speedy in-memory data store for the most frequently accessed data. A variation on caching is the use of data grids to store transient data, e.g. web sessions and shopping cart data, common in e-commerce applications.

You gain improved performance and scalability of the data grid-enabled applications and reduced access to expensive database management systems and transactional back ends. This often implies a reduction in the cost of running these systems.

Primary NoSQL data store

Red Hat JBoss Data Grid is an in-memory, distributed, NoSQL key-value data store that offers a simple, flexible way to store a variety of data without the constraints of a fixed data model.

In addition to exploiting fast access to in-memory data, applications can take advantage of advanced functionality to perform parallel distributed workload execution, run rich queries, manage transactions, and elastically scale and recover from network or system faults.

Low-latency compute grid

Data grids bring data physically closer to data processing to reduce latency and increase application performance. JBoss Data Grid enables a scale-out architecture that deploys application logic right next to the data kept in memory in each node. This is preferred over sending large sets of data to the compute nodes over the wire. This approach helps reduce network traffic significantly and increase application performance dramatically. Event-driven computing is supported by triggering application logic as data changes in the cluster.

Examples of real-time compute and analytics include fraud detection and risk management applications.

Big data and the Internet of Things (IoT)

Data grids are well-suited to handle big data's "big 3 Vs." First, they support the velocity needs of big data. That is, data grids support hundreds of thousands of in-memory updates per second. Second, like NoSQL data stores, data grids support big data variability. Finally, data grids can be clustered and scaled to support large volumes of data.

Similarly, the "things" in the IoT all generate great volumes of data, often at more frequent intervals. JBoss Data Grid enables the storage of tens of terabytes of data, faster response times, and almost-instant analytics, so you can process IoT data at nearly the same speed that it's generated.

Certified integration

Use together with Red Hat JBoss Middleware

  • Red Hat JBoss Fuse

    Boost performance, data availability, and elastic scale for integration services. JBoss Data Grid gives you faster data access, storage, and retrieval—working with JBoss Fuse—to implement enterprise integration patterns and service result-set caching.

  • Red Hat JBoss Enterprise Application Platform

    A primary key-value NoSQL data store and database, offering transaction caching and transient data store for Java applications hosted on JBoss Enterprise Application Platform.

  • Red Hat JBoss BRMS

    Store or cache fact data. JBoss Data Grid stores and caches facts data and passes these to JBoss BRMS for rules execution. Applications achieve near real-time response due to in-memory cache lookups instead of disk I/O-based database lookups.

  • Red Hat JBoss Data Virtualization

    Use JBoss Data Grid as a read/write data source and materialization target. With JBoss Data Virtualization, you can deliver high-performance, unified data services fabric through right-time data integration from disparate sources. Support business transactions, analytics, workloads, and patterns with coherent data services.

Product details

The solution for big data and in-memory computing

Data Interoperability support for multiple polyglot client

JBoss Data Grid enables applications written in multiple programming languages to easily access and share (read/write) data in the grid. Applications can access the data grid remotely using REST, memcached, Hot Rod (Java, C++, .Net).

A simple Java API also supports local access. Support for JSR-107, CDI, and Spring Cache APIs are provided for Java applications. All other application languages, e.g. Python, Ruby, PHP, etc., are supported via popular REST and memcached protocols.

Search with a rich querying interface

Querying lets you easily search and find objects using values and ranges, instead of relying on key-based lookups or needing to know the object's exact location in the grid. Continuous queries ensure the latest results in real time, without the need for polling.

Protect your sensitive data

To support strict security requirements, JBoss Data Grid provides secured communications between client and server—and between server nodes in a secure cluster. Authentication, role-based authorization, and access control is integrated with existing security and identity structures.

With JBoss Data Grid, only trusted users, services, and applications get access.

An event-driven, distributed architecture

Event-driven processing enables real-time response to data changes throughout the data grid. Distributed parallel executions, such as map/reduce, can also be performed. Map/reduce uses the data grid’s parallel processing capabilities to move batch workloads away from the client application and to divide them across the distributed system.

Open hybrid cloud deployment

JBoss Data Grid can be deployed to support diverse IT environments with applications on-premise and in the cloud—legacy or contemporary. JBoss Data Grid can act as a data abstraction layer, decoupling the application, cache, and database.

As a result, you gain control over life cycle, maintenance, and costs of each component independently. JBoss Data Grid for xPaaS is also available as a middleware service on OpenShift by Red Hat. JBoss Data Grid for xPaaS enables cloud applications running on OpenShift with in-memory speed and elastic data management.

Continuous operation always available and fault tolerant

With Red Hat JBoss Data Grid, applications can replicate across datacenters and achieve high availability to meet SLA requirements within and across datacenters. It allows you to implement load balancing and resource efficiency through a "follow the sun" approach.

Rolling upgrades allow JBoss Data Grid cluster upgrades with no downtime.

Services and support

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Invest in your or your team's skills

Our training courses are hands-on and role-based. This means students stay at their keyboards for up to 80% of a course, boosting the retention of skills they'll use every day. We offer several convenient ways to train and save, and training facilities around the world. Visit our student center to learn more or read some training successes.

Red Hat JBoss Data Grid Development (JB453)
Learn how to install a data grid, develop a cache-based application, cluster and secure caches, and more.
Learn more about Red Hat Training

Rely on the experts

Let Red Hat Consulting help you successfully deploy Red Hat JBoss Data Grid. We offer flexible engagement models to help meet your IT goals. Have unallocated end-of-year budget? Consider using Consulting Units to secure resources you'll need in the upcoming year—without committing to a specific topic up front.

Enterprise Data and Storage
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Together, we can do more

Collaboration is a pillar of the open source community. And it’s how Red Hat approaches support. Connect to Red Hat Support to access industry-leading technical resources available in our award-winning Red Hat Customer Portal anywhere, any time.

Red Hat JBoss Data Grid

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Related resources

Use cases

Use Red Hat JBoss Data Grid in any type of application that requires heavy interaction with the data tier. It has broad industry appeal and performs especially well in high-transaction apps. Typical use cases include:

  • Retail and e-commerce: B2B partner-procurement data and consumer catalogs
  • Financial services: options and stock-trading performance optimization
  • Media and entertainment: on-demand video and data management
  • Telecommunications: real-time billing and management of data services
  • Transportation and logistics: tracking, routing, and delivery operations
  • Travel: bookings, reservations, and order processing

Learn how organizations are using data grids to meet their business goals:

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