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Posted: April 26th, 2022
There are queries that can benefit from parallel processing
Essay
There are queries that can benefit from parallel processing. Decision support system (DSS) queries that access big tables and return summary information are the ones that can benefit most from parallel processing. Online transaction processing (OLTP) queries can also benefit from parallel processing. However, it is important to note that only queries that can access twenty pages of data or more are the ones that are considered for parallel processing.
Parallel databases have several benefits. One such benefit is speed. The server normally splits a user database request into parts and transmits every part to a separate computer (Singh, 2011). As such, they work on the parts concurrently and combine the outcomes, and pass them back to the user. This expedites a majority of data requests, permitting faster access to very big databases. Another benefit pertains to reliability. A parallel database that is configured properly can continue to work even when a computer in the cluster fails. The server of the database senses that a certain computer is not responding and reroutes its work to the rest of the computer (Singh, 2011). Another benefit relates to capacity. As more users make requests to access the database, the computer administrators are able to add more computers to the parallel server, increasing its overall capacity (Singh, 2011). For instance, a parallel database permits a big online retailer to have thousands of users that can access information concurrently.
Oracle utilizes memory to speed up processes. It achieves this through its In-Memory Database. This database boosts performance via an architectural twist to the relational database replica that has long been the norm in corporate data centers. As such, this In-memory database adds a column store to the conventional row store, an addition that substantially speeds up the analytic queries and intricate transaction processing (Banerjee, 2017). Its dual-format strategy concurrently arranges data in both rows, for maximum transaction performance, and columns for high speed analytics.
References
Banerjee, J. (2017). Oracle database 12c release 2 in-memory: Tips and techniques for maximum performance. McGraw Hill Professional.
Singh, S. K. (2011). Database systems: Concepts, design and applications. Pearson
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