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The goal of semantic optimization is to form query execution plans that use the semantics, or topography, of the database and the relationships and indexes within to form queries that ensure the best practice available for executing a query in the given database. Semantic query optimization uses knowledge of the schema (e.g., integrity constraints) for transforming a query into a form that may be answered more efficiently than the original version. Although not yet implemented in commercial database systems as the primary optimization technique, semantic optimization is currently the focus of considerable research. Semantic optimization operates on the premise that the optimizer has a basic understanding of the actual database schema. When a query is submitted, the optimizer uses its knowledge of system constraints to simplify or to ignore a particular query if it is guaranteed to return an empty result set. This technique holds great promise for providing even more improvements to query processing efficiency in future relational database systems.
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Parametric Optimizers
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Ioannidis, in his work on parametric query optimization, describes a query optimization method that combines the application of heuristic methods with cost-based optimization. The resulting query optimizer provides a means to produce a smaller set of effective query plans from which cost can be estimated, and thus the lowest-cost plan of the set can be executed.7 Query plan generation is created using a random algorithm, called sipR. This permits systems that utilize
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7. Y. E. Ioannidis, R. T. Ng, K. Shim, and T. Sellis. 1997. Parametric Query Optimization. VLDB Journal 6:132 151.
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CHAPTER 11 QUERY OPTIMIZATION
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parametric query optimization to choose query plans that can include the uncertainty of parameter changes (such as buffer sizes) to choose optimal plans either formed on the fly or from storage. It is interesting to note that in his work, Ioannidis suggests that the use of dynamic programming algorithms may not be needed and thus the overhead in using these techniques avoided. Furthermore, he found that database systems that use heuristics to prune or shape the query prior to applying dynamic programming algorithms for query optimization are usually an enhanced version of the original algorithm of System R. Ioannidis showed that for small queries (approximately up to ten joins), dynamic programming is superior to randomized algorithms, whereas for large queries the opposite holds true.
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Heuristic Optimization Revisited
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The heuristic optimization process uses a set of rules that have been defined to guarantee good execution plans. Thus, the effectiveness of a heuristic optimizer to produce good plans is based solely on the effectiveness and completeness of its rules. The following paragraphs describe the rules used to create the DBXP query optimizer. Although these rules are very basic, when applied to typical queries the resulting execution is near optimal with fast performance and accurate results. Some basic strategies were used to construct the query tree initially. Specifically, all executions take place in the query tree node. Restrictions and projections are processed on a branch and do not generate intermediate relations. Joins are always processed as an intersection of two paths. A multiway join would be formed using a series of two-way joins. The following rules represent the best practices for forming a set of heuristics to generate good execution plans. The DBXP optimizer is designed to apply these rules in order to transform the query tree into a form that ensures efficient execution.8 1. Split any nodes that contain a project and join or restrict and join. This step is necessary because some queries specify the join condition in the WHERE clause9 and thus can fool the optimizer into forming join nodes that have portions of the expressions that are not part of the join condition. 2. Push all restrictions down the tree to leaves. Expressions are grouped according to their respective relations into individual query tree nodes. Although there are some complex expressions that cannot be reduced, most can be easily reduced to a single relation. By placing the restrictions at the leaves, the number of resulting tuples that must be passed up the tree is reduced. 3. Place all projections at the lowest point in the tree. Projections should be placed in a node above restrictions and will further reduce the amount of data passed through the tree by eliminating unneeded attributes from the resulting tuples. It should be noted that the projections may be modified to include attributes that are needed for operations such as joins that reside in the parentage of the projection query tree node.
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8. In this case, efficient execution may not be the optimal solution. 9. A common technique practiced by novice database users.
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