Implementation of Query Optimization for Reducing Run Time

Query optimization is the process of selecting the most efficient query-evaluation plan from many strategies so, In this paper  we have developed a technique that performs query optimization at compile-time to reduce the burden of optimization at run-time to improve the performance of the code execution. using histograms that are computed from the data and these histograms are used to get the estimate of selectivity for query joins and predicates in a query at compile-time. With these estimates, a query plan is constructed at compile-time and executed it at run-time Keywords: runtime, query optimization ,compile time histogra

Query Optimization to Improve Performance of the Code Execution

Object-Oriented Programming (OOP) is one of the most successful techniques for abstraction. Bundling together objects into collections of objects, and then operating on these collections, is a fundamental part of main stream object-oriented programming languages. Object querying is an abstraction of operations over collections, whereas manual implementations are performed at low level which forces the developers to specify how a task must be done. Some object-oriented languages allow the programmers to express queries explicitly in the code, which are optimized using the query optimization techniques from the database domain. In this regard, we have developed a technique that performs query optimization at compile-time to reduce the burden of optimization at run-time to improve the performance of the code execution. Keywords- Querying; joins; compile time; run-time; histograms; query optimizatio

Query Optimization in OODBMS using Query Decomposition & Query Caching

Query optimization is of great importance for the performance of databases, especially for the execution of complex query statements. A query optimizer determines the best strategy for performing each query. These decisions have a tremendous effect on quer y performance, and query optimization is a key technology for every application, from operational systems to data warehouse and analysis systems to content - management systems. For example, query optimizers transform query statements, so that these complex statements can be transformed into semantically equivalent, but better performing, query statements. The query optimizer chooses, for example, whether or not to use indexes for a given query, and which join techniques to use when joining multiple tables. Query optimizers are typically cost - based. In a cost - based optimization strategy, multiple execution plans are generated for a given query, and then an estimated cost is computed for each plan. The query optimizer chooses the plan with the lowest estimate d cost. This report is based on relatively newer approach for query optimization in object databases, which uses query decomposition and cached query results to improve execution times for a query. Multiple experiments were performed to prove the productivity of this newer way of optimizing a query . The limitation of this technique is that its useful especially in scenarios where data manipulation rate is very low as compared to data retrieval rate