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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.

You based one pivot table on another. You d like to group items in one pivot table, and leave items in that field ungrouped in the other pivot table.

public class RemoteClient { public static void main(String[] args) throws Exception { int retVal = RemoteService.getReply(); System.out.println("Reply is " + retVal); } }

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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.

Now let s write an aspect to handle failures by reattempting the operation three times before giving up and propagating the failure to the caller (listing 3.3).

Because the pivot tables use the same pivot cache, the grouped items will be the same in both tables. If you need different groups in the pivot tables, you can base each pivot table on the source range instead of using another pivot table as the source.

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 execution8 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.

import java.rmi.RemoteException; public aspect FailureHandlingAspect { final int MAX_RETRIES = 3;

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