Chapter 1. Introduction

1.1. Red Hat JBoss Data Grid

Red Hat JBoss Data Grid is a distributed in-memory data grid, which provides the following capabilities:

  • Schemaless key-value store – JBoss Data Grid is a NoSQL database that provides the flexibility to store different objects without a fixed data model.
  • Grid-based data storage – JBoss Data Grid is designed to easily replicate data across multiple nodes.
  • Elastic scaling – Adding and removing nodes is simple and non-disruptive.
  • Multiple access protocols – It is easy to access the data grid using REST, Memcached, Hot Rod, or simple map-like API.

1.2. Supported Configurations

The set of supported features, configurations, and integrations for Red Hat JBoss Data Grid (current and past versions) are available at the Supported Configurations page at https://access.redhat.com/articles/115883.

1.3. Components and Versions

Red Hat JBoss Data Grid includes many components for Library and Remote Client-Server modes. A comprehensive (and up to date) list of components included in each of these usage modes and their versions is available in the Red Hat JBoss Data Grid Component Details page at https://access.redhat.com/articles/488833

1.4. About Performance Tuning in Red Hat JBoss Data Grid

The Red Hat JBoss Data Grid Performance Tuning Guide provides information about optimizing and configuring specific elements within the product in an attempt to improve the JBoss Data Grid implementation performance.

Due to each business case being different it is not possible to provide a "one size fits all" approach to tuning. Instead, this guide attempts to present various elements that have proven to be effective in increasing performance, along with potential values to begin testing for a user’s specific case. It is imperative that after each individual change performance be measured once again to isolate any improvements or negative effects; this approach allows a methodical approach to establishing a baseline of parameters.

When testing it is strongly recommended to use a workload that closely mirrors the expected production load. Using any other workload may result in performance differences between the testing and production environments.