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DataRush Technology
DataRush Technology, introduced in 2009 by Austin-based technology company Pervasive Software, uses multicore technology to process data sets for analytics and other business applications. The technology enables performance on a single server or small cluster and allows high-throughput analytics on massive datasets. A parallel data flow engine, it is used to power batch processing jobs, and runs data preparation tasks (e.g. sorting, deduplicating, aggregating, cleansing, joining, loading, validating) and machine learning programs like fuzzy matching algorithms.
==Architecture==
DataRush dataflow architecture executes as a graph of computation nodes interconnected by dataflow queues. The nodes use the queues to share data. In this sense, dataflow is a shared nothing architecture. The lack of share state simplifies node implementation, since threads do not have to synchronize share state. The in-memory, blocking queues implement the synchronization required to safely hand off data from node to node.
In DataRush, the computation nodes of a dataflow graph are known as operators. DataRush provides a library of ready-to-use operator components. Developers can also write custom operators to extend the standard library. For example, several of the sample applications have their own implementations of operators.
To support the creation of a dataflow graph for execution, DataRush provides a composition phase for constructing operators and linking them in an execution graph. Operator properties can be set to determine both operator composition and runtime behavior. At runtime, a composed graph is realized by creating threads for each computation node, creating dataflow queues, and linking nodes. The execution engine also supports monitoring using Java Management Extensions (JMXs). During the execution phase, statistics objects may be created and MBeans instantiated to export profile and debug information. DataRush provides a VisualVM plug-in that can be used within VisualVM to display the exported run-time information.
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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