P. Seshadri, J. Hellerstein, H. Pirahesh, T. Leung, R. Ramakrishnan, D. Srivastava, P. Stuckey, and S. Sudarshan. Cost-based optimization for magic: algebra and implementation. In Proceedings of the ACM SIGMOD Int. Conf. on Management of Data, pages 435--446, Montreal, QC, Canada, June 1996.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....whose output is available as bound attributes for processing the query Qid as a result of a previous subquery execution. In other words, Context represent the set of bindings propagated from the left dependent join operand to the right dependent join operand by Sideways Information Passing (SIP) [17]. Until we get to join queries in Section 5.5, the context will be the empty set and we will ignore it by writing implemented by(Qid, WS) instead of implemented by(Qid, WS, ffl query output(Qids, Attrs) This predicate is true, if the set of attributes Attrs is the union of the set of output ....

P. Seshadri et al. Cost-based optimization for magic: Algebra and implementation. Proc. of the ACM Sigmod Conference, 1996.

....Alon Levy, Ioana Manolescu, Dan Suciu of adding annotations on the size of the resulting search space, and describe an eOEcient algorithm for searching the space. The idea of adding binding patterns as annotations to subqueries is not new. Such annotations were used in magic set transformations [8, 9] and for exploring sideways information passing strategies. The focus of this paper is on incorporating such annotations into cost based optimization and studying the eoeects of such annotations on the size of the space of query execution plans. Furthermore, System R also annotates query execution ....

Praveen Seshadri, Joseph M. Hellerstein, Hamid Pirahesh, T. Y. Clioe Leung, Raghu Ramakrishnan, Divesh Srivastava, Peter J. Stuckey and S. Sudarshan. Cost-Based Optimization for Magic: Algebra and Implementation. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 435-446, 1996.

....to the subplan. We study the effect of adding annotations on the size of the resulting search space, and describe an efficient algorithm for searching the space. The idea of adding binding patterns as annotations to subqueries is not new. Such annotations were used in magicset transformations [16, 14] and for exploring sideways information passing strategies. The focus of this paper is on incorporating such annotations into a cost based optimizer. Specifically, we make the following contributions. ffl We show how the presence of binding pattern limitations affects several fundamental ....

....also handles the placement of selections in a way that is tailored to this new context. A natural question to ask is whether one of the other query optimization paradigms such as the transformational or randomized approach would be more appropriate. For example, in a transformation based approach [16] the optimizer would start with some initial complete plan, and apply transformations to it in order to find an optimal plan. However, this approach requires a set of transformation rules that take one valid plan into another. In our context, the classical transformation rules such as ....

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P. Seshadri, J. M. Hellerstein, H. Pirahesh, T. Y. C. Leung, R. Ramakrishnan, D. Srivastava, P. J. Stuckey, and S. Sudarshan. Cost-based optimization for magic: Algebra and implementation. In Proc. of ACM SIGMOD, 1996.

....[11, 31] sizes of query results are estimated by sampling procedures; better estimates are obtained by spending more time on sampling. Several researchers in the past have exploited filtering as a tool improve specific processes such as spatial joins [18] magic rewriting for OLAP style queries [22], image retrieval [9] and in approximating Datalog [3] However the techniques we discuss in this paper are on a meta level; we discuss how to compose a set of several approximate filters, such as the above, to optimize a user query. 1 This is the current estimated number of pages in popular ....

P. Seshadri and et al. Cost-based optimization for Magic: Algebra and implementation. In Proceedings of ACM SIGMOD International Conference on Management of Data (SIGMOD'96), May 1996.

....not an issue. Rather, the information received from A is used to reduce the computation needed in B as well as to ensure that the results produced by B are relevant to A as well. This technique requires introducing new table expressions and views. For example, consider the following query from [56]: CREATE VIEW DepAvgSal As ( SELECT E.did, Avg(E.Sal) AS avgsal FROM Emp E GROUP BY E.did) SELECT E.eid, E.sal FROM Emp E, Dept D, DepAvgSal V WHERE E.did = D.did AND E.did = V.did AND E.age 30 AND D.budget 100k AND E.sal V.avgsal The technique recognizes that we can create the set of ....

....the relationship to semijoin more intuitive and less magical. SELECT P.eid, P.sal FROM PartialResult P, LimitedDepAvgSal V WHERE P.did = V.did AND P.sal V. avgsal The above technique can be used in a multi block query containing view (including recursive view) definitions or nested subqueries [42,43,56,57]. In each case, the goal is to avoid redundant computation in the views or the nested subqueries. It is also important to recognize the tradeoff between the cost of computing the views (the view PartialResult in the example above) and use of such views to reduce the cost of computation. The formal ....

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Seshadri P., et al. Cost Based Optimization for Magic: Algebra and Implementation. In Proc. of ACM SIGMOD, Montreal, 1996.

....Recent research has addressed some of the issues involved in optimizing aggregate queries and queries with expensive (possibly user defined) functions. Magic sets, and their cost based extensions, have proven valuable in optimizing complex relational queries, including queries over views [35]. Algebraic and cost based optimization of queries over heterogeneous DBMS has also been addressed, though much work remains to be done (see [39] for a summary) Work on optimizing queries over semi structured data has just begun. Multimedia Database Management Systems Recent advances in ....

P. Seshadri, J. Hellerstein, H. Pirahesh, T.Y. C. Leung, R. Ramakrishnan, D. Srivastava, P. J. Stuckey, and S. Sudarshan. "Cost-Based Optimization for Magic: Algebra and Implementation". In Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, pages 435--446, Montreal, Canada, June 1996.

....allows us to separate query mediation from query optimization and execution. As we will illustrate later in this paper, query mediation is driven by logical inferences which do not bond well with (predominantly cost based) optimization techniques that have been developed [Mumick and Pirahesh 1994; Seshadri et al. 1996]. The advantage of keeping the two tasks apart is thus not merely a conceptual convenience, but allows us to take advantage of mature techniques for query optimization in determining how best a query can be evaluated. To the best of our knowledge, the application of abductive reasoning to ....

Seshadri, P., Hellerstein, J. M., Pirahesh, H., Leung, T. C., Ramakrishnan, R., Srivastava, D., Stuckey, P. J., and Sudarshan, S. 1996. Cost-based optimization for magic: algebra and implementation. In Proc. ACM SIGMOD (Montreal, Quebec, Canada, June 1996), pp. 435--446.

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Praveen Seshadri, Joseph M. Hellerstein, Hamid Pirahesh, T.Y. Cliff Leung, Raghu Ramakrishnan, Divesh Srivastava, Peter J. Stuckey, and S. Sudarshan. Cost-Based Optimization for Magic: Algebra and Implementation. In Proc. ACMSIGMOD International Conference on Management of Data, pages 435--446, Montreal, June 1996.

....operator appears to be substitutable for regular sort operators at other places in query plans. For instance, it can replace a sort operator that is designed to reuse memoized values of a correlated subquery or expensive user defined function [Selinger et al. 1979; Hellerstein and Naughton 1996; Seshadri et al. 1996]. Here, online reordering amounts to computing the set of variable bindings on the fly, possibly with some duplication. Acknowledgments We would like to thank all the members of the CONTROL project at Berkeley for many useful discussions. Mehul Shah helped clarifying the performance goals for ....

Seshadri, P., Hellerstein, J. M., Pirahesh, H., Leung, T. Y. C., Ramakrishnan, R., Srivastava, D., Stuckey, P. J., and Sudarshan, S. Cost based optimization for Magic: Algebra and implementation. In Proc. ACM SIGMOD Intl. Conf. on Management of Data, 1996.

....depending on the complexity and size of the subquery. While some subquery predicates can be converted into joins (thereby becoming subject to traditional join based optimization and execution strategies) even sophisticated SQL rewrite systems such as that of DB2 CS [Pirahesh et al. 1992; Seshadri et al. 1996; Seshadri et al. 1996] cannot convert all subqueries to joins. When one is forced to compute a subquery in order to evaluate a predicate, then the predicate should be treated as an expensive method. Thus the work presented in this paper is applicable to the majority of today s production RDBMSs, ....

....the complexity and size of the subquery. While some subquery predicates can be converted into joins (thereby becoming subject to traditional join based optimization and execution strategies) even sophisticated SQL rewrite systems such as that of DB2 CS [Pirahesh et al. 1992; Seshadri et al. 1996; Seshadri et al. 1996] cannot convert all subqueries to joins. When one is forced to compute a subquery in order to evaluate a predicate, then the predicate should be treated as an expensive method. Thus the work presented in this paper is applicable to the majority of today s production RDBMSs, which support SQL ....

Seshadri, P., Hellerstein, J. M., Pirahesh, H., Leung, T. C., Ramakrishnan, R., Srivastava, D., Stuckey, P. J., and Sudarshan, S. 1996. Cost-Based Optimization for Magic: Algebra and Implementation. In Proc. ACM-SIGMOD International Conference on Management of Data (Montreal, June 1996), pp. 435--446.

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P. Seshadri, J. M. Hellerstein, H. Pirahesh, T. C. Leung, R. Ramakrishnan, D. Srivastava, P. J. Stuckey, and S. Sudarshan. Cost-based optimization for magic: Algebra and implementation. In Proc. ACM-SIGMOD Intl. Conf. Management of Data, Montreal, June 1996, pages 435--446.

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P. Seshadri, J. Hellerstein, H. Pirahesh, T. Leung, R. Ramakrishnan, D. Srivastava, P. Stuckey, and S. Sudarshan. Cost-based optimization for magic: algebra and implementation. In Proceedings of the ACM SIGMOD Int. Conf. on Management of Data, pages 435--446, Montreal, QC, Canada, June 1996.

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Praveen Seshadri, Joseph M. Hellerstein, Hamid Pirahesh, T.Y. Cliff Leung, Raghu Ramakrishnan, Divesh Srivastava, Peter J. Stuckey, and S. Sudarshan. Cost-based optimization for magic: Algebra and implementation. In Proc. ACM SIGMOD Int'l Conference on Management of Data, Montr'eal, Qu'ebec, Canada, June 1996.

No context found.

Praveen Seshadri, Joseph M. Hellerstein, Hamid Pirahesh, T.Y. Cliff Leung, Raghu Ramakrishnan, Divesh Srivastava, Peter J. Stuckey, and S. Sudarshan. Cost-based optimization for magic: Algebra and implementation. In Proc. ACM SIGMOD Int'l Conference on Management of Data, Montr'eal, Qu'ebec, Canada, June 1996.

No context found.

P. Seshadri, J.M. Hellerstein, H. Pirahesh, T.Y.C. Leung, R. Ramakrishnan, D. Srivastava, P.J. Stuckey, and S. Sudarshan. Cost-based optimization for magic: Algebra and implementation. Proceedings of the ACM SIGMOD Conf on Management of Data, Montreal, pages 435--446, 1996.

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