High Performance Parallel Database Processing and Grid by David Taniar, Clement H. C. Leung, Wenny Rahayu, Sushant

By David Taniar, Clement H. C. Leung, Wenny Rahayu, Sushant Goel

The most modern concepts and rules of parallel and grid database processing

the expansion in grid databases, coupled with the software of parallel question processing, provides a major chance to appreciate and make the most of high-performance parallel database processing inside of a huge database administration process (DBMS). this crucial new ebook offers readers with a primary knowing of parallelism in data-intensive purposes, and demonstrates the right way to boost quicker functions to help them. It offers a balanced therapy of the theoretical and functional facets of high-performance databases to illustrate how parallel question is completed in a DBMS, together with recommendations, algorithms, analytical versions, and grid transactions.

High-Performance Parallel Database Processing and Grid Databases serves as a priceless source for researchers operating in parallel databases and for practitioners attracted to development a high-performance database. it's also a much-needed, self-contained textbook for database classes on the complicated undergraduate and graduate degrees.

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Extra resources for High Performance Parallel Database Processing and Grid Databases (Wiley Series on Parallel and Distributed Computing) 1st Edition by Taniar, David; Leung, Clement H. C.; Rahayu, Wenny; Goel, Su published by Wiley

Example text

For example, the number of records of table R is indicated by jRj. Again, if table S is used in the query, jSj denotes number of records of this table. In calculating the cost of an equation, if there are 1 million records in table R, variable jRj will have a value of 1,000,000. 1 Cost notations Symbol Description Data parameters R Size of table in bytes Ri Size of table fragment in bytes on processor i |R | Number of records in table R |Ri | Number of records in table R on processor i Systems parameters N Number of processors P Page size H Hash table size Query parameters π Projectivity ratio σ Selectivity ratio Time unit cost IO Effective time to read a page from disk tr Time to read a record in the main memory tw Time to write a record to the main memory td Time to compute destination Communication cost mp Message protocol cost per page ml Message latency for one page In a multiprocessor environment, the table is fragmented into multiple processors.

The time taken to perform a computation in the main memory varies from one computation type to another, but basically, the notation is t followed by a subscript that denotes the type of computation. Computation time in this case is the time taken to compute a single process in the CPU. For example, the time taken to hash a record to a hash table is shown as th , and the time taken to add a record to current aggregate value in a group by operation is denoted as ta . Finally, the time taken to compute the destination of a record is denoted by td .

This will make the integration of data sources convenient with the Grid. Most of the work done in Data Grid infrastructure assumes the existence of file systems like Network File System (NFS) for data storage. Considering the global vision of Grids, it is believed that Grids must also integrate database systems into the infrastructure to support a wide range of applications. Hence, databases offer a much richer set of operations such as queries and transactions. Oracle has launched its Grid-enabled database systems, denoted with a suffix g in its versions.

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