A. Swami and B. Iyer. A polynomial time algorithm for optimizing join queries. In Proc. IEEE Conference on Data Engineering, pages 345--354, 1993.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....work. Transformation based optimization is a general and powerful techniques with applications beyond join order selection; see, for example, FMV94] More related to our present work, research on randomized optimization of join queries has been performed by Swami and Gupta [SG88, Swa89b, Swa89a, SI92] Ioannidis and Kang [IK90, IK91, Kan91] and Lanzelotte, Valduriez, and Zait [LVZ93] In contrast to our work, their approach is based mostly on tree transformations. In terms of search space, Swami and Gupta, and Ioannidis and Kang study very large queries (up to 100 relations) Swami and ....

A. N. Swami and B. R. Iyer. A polynomial time algorithm for optimizing join queries. Technical Report RJ 8812, IBM Research Division, Almaden, 1992.

....graphs. Related work. Transformation based optimization is a general and powerful techniques with applications beyond join order selection; see, for example, FMV94] More related to our present work, research on randomized optimization of join queries has been performed by Swami and Gupta [SG88, Swa89b, Swa89a, SI92], Ioannidis and Kang [IK90, IK91, Kan91] and Lanzelotte, Valduriez, and Zait [LVZ93] In contrast to our work, their approach is based mostly on tree transformations. In terms of search space, Swami and Gupta, and Ioannidis and Kang study very large queries (up to 100 relations) Swami and Gupta, ....

A. N. Swami and B. R. Iyer. A polynomial time algorithm for optimizing join queries. Technical Report RJ 8812, IBM Research Division, Almaden, 1992.

....plans with higher probability than linear trees. 5. Related Work The join ordering problem continuously received attention during the past two decades. Besides enumeration techniques for small query sizes (cf. e.g. 16, 22, 14] heuristics have been developed in order to tackle larger instances [12, 20]. However, as Steinbrunn et al. pointed out, heuristics yield only mediocre results as the queries grow in size [17] On the other hand, beginning with [10] randomized techniques have been introduced and attracted particular interest ever since. Swami and Gupta as well as Ioannidis and Kang ....

A. N. Swami and B. R. Iyer. A Polynomial Time Algorithm for Optimizing Join Queries. In Proc. of the IEEE Int'l. Conf. on Data Engineering, pages 345--354, Vienna, Austria, April 1993.

....[KBZ86] As with IK, the applicability of KBZ depends on the cost formulas for joins to be of a specific form. Nested loops and hash join satisfy this requirement but, in general, merge scan does not. The AB algorithm mixes deterministic and randomized techniques and runs in O(N 4 ) time [SI93] It uses KBZ as a subroutine, which needs O(N 2 ) time, and essentially execute it O(N 2 ) times on randomly selected spanning trees of the query graph. Through an interesting separation of the cost of merge scan into a part that affects optimization and a part that does not, AB is ....

A. Swami and B. Iyer. A polynomial time algorithm for optimizing join queries. In Proc. IEEE Int. Conference on Data Engineering, Vienna, Austria, March 1993.

....we obtain several possible join orders with equal cost starting with O, B and W , for instance ( O 1 B) 1 W ) 1 P ) 1 A) 1 C with associated cost C hl = 10:8, that is, as we already have mentioned, the optimum. 4.1. 5 AB Algorithm The AB algorithm has been developed recently by Swami and Iyer [SI93] It is based on the KBZ algorithm with various enhancements. The algorithm permits the use of two different cost models, namely nested loop and sort merge. The sort merge cost model has been simplified by Swami and Iyer such that it confirms to the requirements of the KBZ algorithm (C(R 1 1 R 2 ....

....if only one algorithm can be implemented, the choice is clearly in favour of the RDC optimizer because of its superior overall performance with least variance over the different join graph types and cost models. Recently, another heuristic algorithm that is based on KBZ has been proposed [SI93] the AB algorithm. The working principle comprises not only the computation of a join order, but also the incorporation of different join algorithms. Therefore, within the framework of our benchmarks, we could only implement a simplified version of this algorithm that does not take different ....

A. Swami and B. Iyer. A polynomial time algorithm for optimizing join queries. In Proc. IEEE Conf. on Data Engineering, pages 345-- 354, Vienna, 1993.

....join method assignment invalidates the main advantage of the KBZ algorithm, namely to yield the optimal solution in O(n 2 ) time. In the following section, an algorithm is discussed that tries to remedy this situation. 4.1. 4 AB Algorithm The AB algorithm has been developed by Swami and Iyer [SI93] It is based on the KBZ algorithm with various enhancements, trying to remove the restrictions that are imposed on the join method placement. The algorithm permits the use of two different join methods, namely nested loop and sort merge. The sortmerge cost model has been simplified by Swami and ....

A. Swami and B. Iyer. A polynomial time algorithm for optimizing join queries. In Proc. IEEE Conf. on Data Engineering, pages 345-- 354, Vienna, Austria, April 1993.

....Figure 7: AB Algorithm invalidates the main advantage of the KBZ algorithm, namely to yield the optimal solution in O(n 2 ) time. In the following section, an algorithm is discussed that tries to remedy this situation. 4.1. 5 AB Algorithm The AB algorithm has been developed by Swami and Iyer [SI93] It is based on the KBZ algorithm with various enhancements, trying to remove the restrictions that are imposed on the join method placement. The algorithm permits the use of two different cost models, namely nested loop and sort merge. The sortmerge cost model has been simplified by Swami and ....

A. Swami and B. Iyer. A polynomial time algorithm for optimizing join queries. In Proc. IEEE Conf. on Data Engineering, pages 345-- 354, Vienna, Austria, April 1993.

.... throughout the entire execution of the query, it is possible to efficiently generate an optimal plan over the desired execution space [CS96] Another line of research refines query optimization by focusing on join reordering where an important working assumption is that predicates are zero cost [IK84, KBZ86, SI92]. A general formulation of query optimization for various buffer sizes can be found in [INS 92] This runtime parameter is typically unknown before the actual query execution. By constructing various plans in advance, the most appropriate one can be chosen at run time just before the query is ....

A. Swami and B. Iyer. A Polynomial Time Algorithm for Optimizing Join Queries. Research Report RJ8812, IBM Almaden Research Center, June 1992.

....data, to support global change researchers. It is expected that these researchers will be writing queries with expensive functions to analyze this data. A benchmark of such queries is presented in [SFGM93] Ibaraki and Kameda [IK84] Krishnamurthy, Boral and Zaniolo [KBZ86] and Swami and Iyer [SI92] have developed and refined a query optimization scheme that is built on the notion of rank that we will use below. However, their scheme uses rank to reorder joins rather than restrictions. Their techniques do not consider the possibility of expensive restriction predicates, and only reorder ....

....paths in an arbitrary tree. Furthermore, their schemes are a proposal for a completely new method for query optimization, while ours is an extension that can be applied to the plans of any query optimizer. It is possible to fuse the technique we develop in this paper with those of [IK84, KBZ86, SI92] but we do not focus on that issue here since their schemes are not widely in use. The notion of expensive restrictions was considered in the context of the LDL logic programming system [CGK89] Their solution was to model a restriction on relation R as a join between R and a virtual relation ....

Arun Swami and Balakrishna R. Iyer. A Polynomial Time Algorithm for Optimizing Join Queries. Research Report RJ 8812, IBM Almaden Research Center, June 1992.

....graphs. Related work. Transformation based optimization is a general and powerful techniques with applications beyond join order selection; see, for example, FMV94] More related to our present work, research on randomized optimization of join queries has been performed by Swami and Gupta [SG88, Swa89a, Swa89b, SI92], Ioannidis and Kang [IK90, IK91, Kan91] and Lanzelotte, Valduriez, and Zait [LVZ93] In contrast to our work, their approach is based mostly on tree transformations. In terms of search space, Swami and Gupta, and Ioannidis and Kang study very large queries (up to 100 relations) Swami and Gupta, ....

A. N. Swami and B. R. Iyer. A polynomial time algorithm for optimizing join queries. Technical Report RJ 8812, IBM Research Division, Almaden, 1992.

....data, to support global change researchers. It is expected that these researchers will be writing queries with expensive functions to analyze this data. A benchmark of such queries is presented in [SFG92] Ibaraki and Kameda [IK84] Krishnamurthy, Boral and Zaniolo [KBZ86] and Swami and Iyer [SI92] have developed and refined a query optimization scheme that is built on the notion of rank that we will use below. However, their scheme uses rank to reorder joins rather than restrictions. Their techniques do not consider the possibility of expensive restriction predicates, and only reorder ....

....paths in an arbitrary tree. Furthermore, their schemes are a proposal for a completely new method for query optimization, while ours is an extension that can be applied to the plans of any query optimizer. It is possible to fuse the technique we develop in this paper with those of [IK84, KBZ86, SI92] but we do not focus on that issue here since their schemes are not widely in use. The notion of expensive restrictions was considered in the context of the LDL logic programming system [CGK89] Their solution was to model a restriction on relation R as a join between R and a virtual relation of ....

Arun Swami and Balakrishna R. Iyer. A Polynomial Time Algorithm for Optimizing Join Queries. Research Report RJ 8812, IBM Almaden Research Center, June 1992.

....and selectivity estimates can be inaccurate. 7 As a result, it is a truism in the database community that a query optimizer is optimal enough if it avoids the worst query plans and generally picks good query plans [Krishnamurthy et al. 1986; Mackert and Lohman 1986a; Mackert and Lohman 1986b; Swami and Iyer 1992]. What remains open to debate are the definitions of generally and good in the previous statement. In any situation where an optimizer chooses a suboptimal plan, a database and query can be constructed to make that error look arbitrarily detrimental. Database queries are by definition 7 In ....

....LDL approach with an IK KBZ optimizer, but they use an exhaustive mechanism that requires time exponential in the number of expensive selections. 4. 2 Other Related Work Ibaraki and Kameda [Ibaraki and Kameda 1984] Krishnamurthy, Boral and Zaniolo [Krishnamurthy et al. 1986] and Swami and Iyer [Swami and Iyer 1992] have 30 Delta J.M. Hellerstein developed and refined a query optimization scheme that is built on the the notion of rank. However, their scheme uses rank to reorder joins rather than selections. Their techniques do not consider the possibility of expensive selection predicates, and only ....

Swami, A. and Iyer, B. R. 1992. A Polynomial Time Algorithm for Optimizing Join Queries. Research Report RJ 8812 (June), IBM Almaden Research Center.

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A. Swami and B. Iyer. A polynomial time algorithm for optimizing join queries. In Proc. IEEE Conference on Data Engineering, pages 345--354, 1993.

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Arun N. Swami and Balakrishna R. Iyer. A polynomial time algorithm for optimizing join queries. In Proceedings of the Ninth International Conference on Data Engineering, April 19-23, 1993.

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A. Swami and B. Iyer. A polynomial time algorithm for optimizing join queries. In Proc. IEEE Conference on Data Engineering, pages 345--354, 1993.

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