GigaSpaces In-Memory Data-Grid Solution Handles Massive Data Sets

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"In recent years, software architects have begun to employ distributed in-memory data grids to turbocharge data access. The early versions of these grids were somewhat raw, but their capabilities have grown, and new traits have been added in response to user needs. They are poised now to play an important role in some significant shifts in software architecture.

Many of the new traits of in-memory data grids (IMDGs) target apps that fall under the ambiguous umbrella called "big data." According to industry analyst firm Gartner Inc., IMDGs are suited to handle big data's big-three Vs. First, they support the velocity needs of big data. That is, IMDGs support hundreds of thousands of in-memory data updates per second. Second, like NoSQL data stores, they can support big data variability. Finally, they can be clustered and scaled in ways that support large volumes of data...

...Advances in 64-bit and multi-core systems have made it fairly easy to store tens of gigabytes and even terabytes of data completely in memory, said Nati Shalom, chief technology officer at GigaSpaces. This has helped data caches and in-memory data grids move ahead. The early APIs for the in-memory caches could be described as "raw," but Shalom uses the term "simple."

 

"During the early stages of the technology evolution, memory-based caches exposed a simple key-value API and enabled fairly simple query with no transaction or advanced query semantics," Shalom told SearchSOA.com. "Programming to this interface was fairly simple and intuitive; however, it fit the simple use cache of read-mostly side-cache," he said. There was, however, complexity in mapping complex queries, and synchronizing with an external database.

"As memory-based caches evolved into in-memory data grids, this complexity challenge was addressed by introducing better computability [via] the data grid API with standard SQL APIs such as [Java Persistence API]JPA, [Java Database Connectivity] JDBC and SQL, as well as new APIs designed for the new generation of Web and social applications, [to] expose 'schemaless APIs' and object graph APIs," Shalom said. Serious enhancements continue for the purposes of big data apps, as shown in the fact that GigaSpaces' most recent release allows object properties to be handled in native, binary and compressed modes."...

 

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