High-performance parallel database processing and grid by David Taniar

By David Taniar

The most up-to-date innovations and rules of parallel and grid database processing

the expansion in grid databases, coupled with the application of parallel question processing, offers a big chance to appreciate and make the most of high-performance parallel database processing inside of a tremendous database administration method (DBMS). this crucial new ebook presents readers with a basic figuring out of parallelism in data-intensive purposes, and demonstrates tips to improve speedier features to help them. It provides a balanced therapy of the theoretical and useful elements of high-performance databases to illustrate how parallel question is carried out in a DBMS, together with techniques, algorithms, analytical types, and grid transactions.

High-Performance Parallel Database Processing and Grid Databases serves as a necessary source for researchers operating in parallel databases and for practitioners drawn 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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By David Taniar

The most up-to-date innovations and rules of parallel and grid database processing

the expansion in grid databases, coupled with the application of parallel question processing, offers a big chance to appreciate and make the most of high-performance parallel database processing inside of a tremendous database administration method (DBMS). this crucial new ebook presents readers with a basic figuring out of parallelism in data-intensive purposes, and demonstrates tips to improve speedier features to help them. It provides a balanced therapy of the theoretical and useful elements of high-performance databases to illustrate how parallel question is carried out in a DBMS, together with techniques, algorithms, analytical types, and grid transactions.

High-Performance Parallel Database Processing and Grid Databases serves as a necessary source for researchers operating in parallel databases and for practitioners drawn 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.

Show description

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The drawback of this architecture is that it suffers from memory and bus contention, since many processors may compete for access to the shared data. Shared-memory architectures normally use a bus interconnection network. Since there is a limit to the capacity that a bus connection can handle, data/message transfer along the bus can be limited, and consequently it can serve only a limited number of processors in the system. 5 Parallel Database Architectures 21 shared-memory machine to be equipped with no more than 64 processors in a computer system box.

Theoretically, the degree of dimension in a hypercube can be arbitrary. To visualize a higher degree of dimension, a 4-dimensional hypercube, it looks like a cube inside a bigger cube (possibly like a room), in which the corner of the inner cube is connected with the corner of the outer cube. In a 5-dimensional hypercube, the 4-dimensional hypercube is inside another bigger cube, and so on. In a hypercube connection, a message from a processing element can reach any other processing element by going via at most log(n) links, where n is the number of processing elements connected in a hypercube interconnection network.

In other words, the final query results are the amalgamation of all subquery results. 4 Forms of Parallelism 15 Execution of a single query can be parallelized in two ways: ž ž Intraoperation parallelism. We can speed up the processing of a query by parallelizing the execution of each individual operation, such as parallel sort, parallel search, etc. Interoperation parallelism. We can speed up the processing of a query by executing in parallel the different operations in a query expression, such as simultaneously sorting and searching.

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