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Posted by R.J. Lorimer on Sep 21, 2008
Aster Data Systems recently announced Aster In-Database MapReduce, a component of their nCluster database.Aster nCluster is built on a unique, multi-tiered nCluster architecture which consists of three separate classes of nodes: Queens, Workers, and Loaders. The three-tier design encapsulates a clean separation of roles for analytic processing. Each tier can be independently and incrementally scaled in response to the workload characteristics – adding more capacity (Workers), loading bandwidth (Loaders), or concurrency (Queens) on an as-needed basis.The MapReduce implementation provided in Aster nCluster allows for the execution of MapReduce calculations within the database, using this same architecture:
Just like its massively parallel execution environment for standard SQL queries, Aster nCluster now adds the ability to implement flexible MapReduce functions for parallel data analysis and transformation inside the database. Aster nCluster In-Database MapReduce functions are simple to write and are seamlessly integrated within SQL statements. They rely on SQL queries to manipulate the underlying data and provide input. The functions can procedurally manipulate such input data and provide outputs that can be further consumed by SQL queries or be written into tables within the database.SQL/MR is a special SQL MapReduce function library introduced by Aster that can be used to invoke map-reduce algorithms within the nCluster platform. Aster supports polymorphic functions and dynamic typing, and MapReduce calculations may be developed in languages such as Java, Python, C++ and others.
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