BigMemory Overview

BigMemory gives Java applications instant, effortless access to a large memory footprint with in-memory data management that lets you store large amounts of data closer to your application, improving memory utilization and application performance with both standalone and distributed caching. BigMemory's in-process, off-heap cache is not subject to Java garbage collection, is 100x faster than DiskStore, and allows you to create very large caches. In fact, the size of the off-heap cache is limited only by address space and the amount of RAM on your hardware. In performance tests, we’ve achieved fast, predictable response times with terabyte caches on a single machine.

Rather than stack lots of 1-4 GB JVMs on a single machine in an effort to minimize the GC problem, with BigMemory you can increase application density, running a smaller number of larger-memory JVMs. This simpler deployment model eases application scale out and provides a more sustainable, efficient solution as your dataset inevitably grows.

The following sections provide a documentation Table of Contents and additional information sources for BigMemory.

BigMemory Table of Contents

Topic Description
BigMemory Configuration Introduction to BigMemory, how to configure Ehcache with BigMemory, performance comparisons, FAQs, and more.
Further Performance Analysis Further performance results for off-heap store for a range of scenarios.
Pooling Resources Versus Sizing Individual Caches Additional information for configuring Ehcache to use local off-heap memory.
Storage Options Discussion of BigMemory in the context of storage options for Ehcache.
Terracotta Clustering Configuration Elements The role of BigMemory in data consistency for the distributed cache.

BigMemory Resources

Additional information and downloads: