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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 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.|
Additional information and downloads: