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June 4, 2012


Nicole Hemsoth

by Dr. William Bain, Ph.D, ScaleOut Software, Inc.

A hallmark of the Information Age is the incredible amount of business data that companies have to store and analyze, so-called “big data.” The ability to efficiently search data for important patterns can provide an essential competitive edge.


Distributed, in-memory data grids (IMDGs) offer two key advantages. First, they store data in memory instead of on disk for fast access, and second, they run seamlessly across a farm of servers to scale performance. But perhaps best of all, they provide a fast, easy to use platform for running near real-time “what if” analyses on the data they store. By breaking the sequential bottleneck, they can take performance to a level that stand-alone database servers and NoSQL stores cannot match.

Software architects and developers often say the following. “OK, I see the advantages, but how do I incorporate a distributed, in-memory data grid into my data storage architecture, and how could it help me to analyze my data?”









Application server farm 

3. Analyze grid-based data using simple analysis codes and the “map/reduce” programming pattern.


ScaleOut SateServer In-Memory Data Grid

Distributed, in-memory data grids offer an ideal platform for analyzing data using this “map/ reduce” programming pattern. Because they store data as memory-based objects, the analy­sis code is very easy to write and debug as a simple “in-memory” code. Programmers do not need to learn parallel programming techniques or understand how the grid works. Also, distributed, in-memory data grids provide the infrastructure needed to automatically run this analysis code on all grid servers in parallel and then combine the results. The net result is that by using a distributed, in-memory data grid, application developers can easily and quickly harness the full scalability of the grid to rapidly discover data patterns and trends that are vital to a company’s success.

ScaleOut StateServer Grid Computing Edition from ScaleOut Software provides an example of built-in map/reduce (called Parallel Method Invocation). This product is an IMDG-based analytics platform that combines the popular map/reduce programming model with property-based object queries, dramatically simplifying the selection and analysis of data stored in its data grid. Fast, automatic, parallel scheduling ensures that memory-based data is analyzed at nearly real-time speeds with minimum data motion. Completely eliminated are batch scheduling and file-I/O overheads, enabling analysis of fast-changing data like network telemetry, market data, or click stream data for fast decision making. In-memory map/reduce dramatically shortens run-times compared to other approaches which analyze disk-based data, such as Hadoop. Built-in high availability ensures that analyses run to completion, even if a server or network connection fails.

As companies become ever more pressed to manage increasing data volumes and quickly respond to changing market conditions, they are turning to distributed, in-memory data grids to obtain the “scalability” boost they need. As clouds become an integral part of en­terprise infrastructures, distributed, in-memory data grids should further prove their value in harnessing the power of scalable computing to provide an essential competitive edge.


To learn more about ScaleOut Software’s in-memory data grids, please visit