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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Extra info for High-performance parallel database processing and grid databases
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.