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Transaction Processing and Query Optimization

Transaction Processing and Query Optimization


Transaction processing is dispensation of data which is distributed into separate tasks termed to as transactions. Every transaction has to be successful or fall short of the needs intended; it does not stay in the middle form. It is modeled to maintain database integrity in a known form through making sure that the processes are undertaken on the system which is not reliant and not generally successful. On the other hand, query optimization is a duty that a number of relational database administration model where a number of several query procedures are looked into and a superior query strategy are noted.


Transaction processing advanced as a commercial application came up above the disadvantages of batch ode data processing. In this system, requests are kept to be processed at another point in time. The accrued duties are processed successively hence there lacks intervention (Bernstein, 2009). Batch processing is not viable to applications that need instantaneous reaction updated information.

Prior transaction processing tools is composed of CICS and IMS which are from IBM. Tuxedo came from AT&T which was afterwards acquired by BEA. The two are now dealers in application servers. The current application servers arose from aspects termed to as application servers.

The Query optimization was composed of cardinality approximation for query languages codes, the cost estimation strategies and a flexible programming reliant method to acquire room for processing (Haddara, 2011). The query optimization was quite complicated composing of logical and physical factors. The parallel database made it possible for relational models to manage difficult queries in huge data collections. The SQL tools began to be applied extensively in the current time for the main purpose of data warehousing and resolution maintenance models. Hence database models began undergoing through the desire to manage more complicated queries. This brought about the rewriting of the policies. The operations dwelled on creating a dynamic and efficient structure to make it possible to add new logical and physical tools.

Application with the present programs

Transaction programs are composed of a number of processes which applied variedly. It is applied with a front-end program that interrelates with the end-user. It transmits and acquires lists to accord the user a choice of operations to use and to acquire the user’s option.

Query Optimization with comparison to earlier database models offered better chances for observation of the server (Haddara, 2011). For instance the SQL server offer snapshots of the server and this is acquired in SQL server. The Oracle, IBM and other database enable one to acquire the SQL workload. The transaction processing and query optimization databases include SQL and Oracle.

Transaction processing is useful in that it processes all of the information, there is consistency, it transacts as though it operated solely and it does not lose data when there is a fault (Bernstein, 2009). On the other hand, query optimization is much quicker in processing information, it is cheaper for each and every query, there is high level of efficiency, the stress level of the database is limited, there is appropriate application of the database and the amount of memory applied is limited.


Transaction processing is a strong and well based structure. As the creators, focus on the operations makes it possible to create working software on transaction basis. Concurrency is important for application creators this leads to scalability and efficiency. To acquire concurrency there is need to apply transaction processing abilities created in the database and servers.

Query optimization brings about excellent processing strategies for difficult queries with use of limited time period. It is likely to acquire processing strategies with small skills of information and no addition from the application.


Bernstein, P. et al (2009). Principles of Transaction Processing. Burlington: Morgan Kaufmann, Haddara, M. (2011). Query Optimization: For Data Warehouse Applications. Saarbrücken: LAP Lambert Acad. Publ.


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