Terracotta, a provider of enterprise scalability solutions, says it is shipping a new release of its distributed caching solution that enables customers to store over a terabyte of data and hundreds of millions of entries in a single cache. The new product, Ehcache 2.2, also features a new management console intended to simplify application scale and enhance visibility and control for developers and operators. Terracotta claims these new capabilities can be turned on with two lines of configuration.
Ehcache is most prevalent in the financial services and telco sector, which require extremely high levels of rapid scalability. However, many companies from a variety of industry sectors are also attempting to ramp up their infrastructures for the new high-volume and large data cache realities of online business, Amit Pandey, CEO of Terracotta, tells 5 Minute Briefing. "Ehcache's usage is not just restricted to large enterprises but it is also used by a large number of small and medium sized businesses."
Ehcache is designed to enable companies to achieve terabyte scale in a non-disruptive manner, as well as scale from a single computer to large virtualized data center environments and private clouds. There are many types of situations are best suited to Ehcache's capabilities, Pandey says. "Any Java application that has performance issues is a good candidate. Ehcache is often used to offload the database and can reduce database performance bottlenecks significantly."
Ehcache 2.2 enhancements include a new storage option that supports terabyte scale, decreases memory usage and increases the addressable cache size tenfold. In addition, the new tool adds Java Authentication and Authorization Service (JAAS) support that provides LDAP authentication support for integration to corporate user management systems.
A new common runtime library is designed to reduce memory usage and network connections and provides new common developer constructs; it also exposes an API that programmers can use alongside the Ehcache API to perform complex inter-process coordination tasks across multiple machines with just a few lines of code.
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