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one that has been subjected to advanced transformations Not all query transformation techniques are guaranteed to be bene cial for every query, but by generating a cost estimate for the best plan with and without the transformation applied, Oracle is able to make an intelligent decision Some of the major types of transformations and rewrites supported by Oracle are as follows: View merging A view reference in a query is replaced by the view de nition This transformation is not applicable to all views Complex view merging Oracle offers this feature for certain classes of views that are not subject to regular view merging because they have a group by or select distinct in the view de nition If such a view is joined to other tables, Oracle can commute the joins and the sort operation used for the group by or distinct Subquery attening Oracle has a variety of transformations that convert various classes of subqueries into joins, semijoins, or antijoins Materialized view rewrite Oracle has the ability to rewrite a query automatically to take advantage of materialized views If some part of the query can be matched up with an existing materialized view, Oracle can replace that part of the query with a reference to the table in which the view is materialized If need be, Oracle adds join conditions or group by operations to preserve the semantics of the query If multiple materialized views are applicable, Oracle picks the one that gives the greatest advantage in reducing the amount of data that has to be processed In addition, Oracle subjects both the rewritten query and the original version to the full optimization process producing an execution plan and an associated cost estimate for each Oracle then decides whether to execute the rewritten or the original version of the query on the basis of the cost estimates Star transformation Oracle supports a technique for evaluating queries against star schemas, known as the star transformation When a query contains a join of a fact table with dimension tables, and selections on attributes from the dimension tables, the query is transformed by deleting the join condition between the fact table and the dimension tables, and replacing the selection condition on each dimension table by a subquery of the form: fact tablefki in (select pk from dimension tablei where <conditions on dimension tablei >) One such subquery is generated for each dimension that has some constraining predicate If the dimension has a snow- ake schema (see Section 224), the subquery will contain a join of the applicable tables that make up the dimension
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Oracle uses the values that are returned from each subquery to probe an index on the corresponding fact table column, getting a bitmap as a result The bitmaps generated from different subqueries are combined by a bitmap and operation The resultant bitmap can be used to access matching fact table rows Hence, only those rows in the fact table that simultaneously match the conditions on the constrained dimensions will be accessed Both the decision on whether the use of a subquery for a particular dimension is cost-effective, and the decision on whether the rewritten query is better than the original, are based on the optimizer s cost estimates
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Oracle has a cost-based optimizer that determines join order, join methods, and access paths Each operation that the optimizer considers has an associated cost function, and the optimizer tries to generate the combination of operations that has the lowest overall cost In estimating the cost of an operation, the optimizer relies on statistics that have been computed for schema objects such as tables and indices The statistics contain information about the size of the object, the cardinality, data distribution of table columns, and so forth For column statistics, Oracle supports height-balanced and frequency histograms To facilitate the collection of optimizer statistics, Oracle can monitor modi cation activity on tables and keep track of those tables that have been subject to enough changes that recalculating the statistics may be appropriate Oracle also tracks what columns are used in where clauses of queries, which make them potential candidates for histogram creation With a single command, a user can tell Oracle to refresh the statistics for those tables that were marked as suf ciently changed Oracle uses sampling to speed up the process of gathering the new statistics and automatically chooses the smallest adequate sample percentage It also determines whether the distribution of the marked columns merit the creation of histograms; if the distribution is close to uniform, Oracle uses a simpler representation of the column statistics Oracle uses both CPU cost and disk I/Os in the optimizer cost model To balance the two components, it stores measures about CPU speed and disk I/O performance as part of the optimizer statistics Oracle s package for gathering optimizer statistics computes these measures For queries involving a nontrivial number of joins, the search space is an issue for a query optimizer Oracle addresses this issue in several ways The optimizer generates an initial join order and then decides on the best join methods and access paths for that join order It then changes the order of the tables and determines the best join methods and access paths for the new join order and so forth, while keeping the best plan that has been found so far Oracle cuts the optimization short if the number of different join orders that have been considered becomes so large that the time spent in the optimizer may be noticeable compared to the time it would take to execute the best plan found so far Since this cutoff depends on the cost estimate for the best plan found so far, nding a good plan early is important so that the optimization can be stopped after a smaller number of join orders, resulting in better response time
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