N. Kabra and D. J. DeWitt. "Efficient Mid-Query Re-Optimization of Sub-Optimal Query Execution Plans." In Proc. SIGMOD, 1998.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....plan. This algorithm is not very suitable for a federated database system as the environment may not be completely cooperative and also, the number of messages that are exchanged between the underlying data sources increases exponentially with the size of the query. Mid execution re optimization [31, 30], Query scrambling [47] and Eddies [4] have also been proposed for use in dynamic environments when the required statistics may not be accurately estimated at the optimization time or when the characteristics of the underyling data sources may change dramatically during the query execution. The ....

N. Kabra and D. J. DeWitt. Efficient mid-query reoptimization of sub-optimal query execution plans. In SIGMOD, 1998.

....of decisional queries. 1. 1 Optimization of Decisional Queries Optimization of decisional queries is a complex problem, which has been widely studied, in different contexts [13, 17] Many works have shown the necessity of dynamic load balancing and correction of sub optimal execution plans [10, 12] Dynamic optimization consists in correcting at runtime a query execution plan built at compile time from the database profile and the cost model including environment characteristics. Actually, changing the initial plan is necessary because of two main reasons : data skew : knowledge related ....

....is unlimited. The main costs associated to the basic operations and to the parameters are given in Fig. 3 [15] Disk parameters Disk page size 4 KB Average time to read a 4 KB page 47.5 Average time to write a 4 KB page 195 CPU parameters Pentium III processor 550 Mhz Memory size 512 MB Network parameters Maximum bucket size 4 KB Time to send a 4 KB page 50 ms Latency 20 ms Miscellaneous Average number of tuples in page 32 Agent size 2806 B Fig. 3. Simulation parameters In our simulations, we assume that every site taking part in the execution of a join possesses the ....

N. Kabra, D.- J. DeWitt, Efficient Mid-Query Re-Optimization of Sub-Optimal Query Execution Plans, Proc. of the ACM SIGMOD int'l. Conf. on Management of Data, Vol. 27, ACM Press, NY, 1998, pp. 106-117.

.... is to send subqueries to remote sites for local processing [32, 46] Query scrambling can deal with unexpected delays when processing queries in a wide area net work, a setting similar to a sensor network [3, 49] Kabra and DeWitt proposed to reoptimize parts of queries after blocking operators [24]. There is also a lot of work on adaptive query operators, an area we believe to be relevant to sensor networks. Examples include work on memory adaptive sorting and hashing [13, 28, 30, 34, 53, 54] and online aggregation algorithms [15, 18, 39, 48] Eddies push the idea of feedback on a ....

N. Kabra and D. J. DeWitt. Efficient mid-query re- optimization of sub-optimal query execution plans. In Haas and Tiwary [14], pages 106-117.

....arising from the usage of a P2P paradigm are identified. However there is little work on implementation issues of such systems, especially related to large number of cooperating query processors. Adaptive query processing for single site query processors has been addressed by various works [12, 13, 14], to name a few. A good overview of adaptive query processing can be found in [15] Many of the proposed approaches can be inte grated with the solution proposed here to implement adaptive behavior of each of the mediator peers. This work is different in that we are interested in the adaptivity ....

Kabra, N., DeWitt, D.J.: Efficient mid-query re-optimization of sub-optimal query execution plans. In Haas, L.M., Tiwary, A., eds.: Proceedings of the ACM SIGMOD International Conference on Management of Data, Seattle, Washington, USA, ACM Press (1998) 106-117

....that prioritized inheritance would be similarly beneficial to any application being developed in VenusDB. The VenusDB optimizer uses static optimization techniques. However, significant improvements in performance may be attained through mid query re optimizations such as the ones presented in [58]. Mid query re optimizations allow inefficient queries to be re optimized during query execution. This is particularly important within a multidatabase system such as VenusDB. For example, a useful mid query re optimization includes interrupting execution when a rule is querying a web based data ....

N. Kabra and D. J. DeWitt, "Efficient mid-query re-optimization of sub-optimal query execution plans," in Proceedings of the ACM SIGMOD International Conference on Management of Data. Seattle, WA, June, 1998, pp.

....re scheduling of a query, so it can adjust a query plan as it acquires better knowledge about the data sources. These desiderata are design goals of adaptive query processing, and considerable work has recently been done in this area, including query scrambling [UFA98] mid query re optimization [KD98] ripple [HH99] and pipelined hash [WA91, IFF 99, UF00] joins, eddies [AH00] dynamic scheduling of pipelines [UF01] and streaming of partial results through blocking operators [STD 00] For more details, see the June 2000 issue of the IEEE Data Engineering Bulletin. In the context of ....

Navin Kabra and David J. DeWitt. Efficient mid-query re-optimization of sub-optimal query execution plans. In SIGMOD '98, pages 106--117, 1998.

....of the IEEE Data Engineering bulletin highlights several projects. Reactive query evaluation techniques at the plan level are described in [1, 4, 21, 26] Alternately, adaptive evaluation techniques at the plan and operator implementation level are described in [3, 13, 14, 16, 25] Research in [2, 7, 19, 15, 17, 32] have addressed various aspects of the task of modifying an optimizer to handle distributions for various parameters, e.g. available memory, or intermediate join cardinality. The Tukwila project [16] provides an adaptive framework composed of rules, and adaptive operators that are sensitive to ....

N. Kabra and D. DeWitt. Efficient mid-query reoptimization of sub-optimal query execution plans. Proceedings of the ACM Sigmod Conference, pages 103--114, 1998.

....query optimization. Therefore, static optimization techniques, which create a complete query plan before beginning the evaluation, are inappropriated for queries containing variable substitutions. Better approaches should support runtime re optimization at certain points of query processing [GW89,KD98] FRAQL is implemented as part of a federated query system. This system consists of the following main components: the query parser, the decomposer and the global optimizer, the query evaluator, the Java VM for evaluating user defined functions, and the catalog. The adapter layer contains the ....

N. Kabra and D.J. DeWitt. Efficient Mid-Query Re-Optimization of Sub-Optimal Query Execution Plans. In L.M. Haas and A. Tiwary, editors, SIGMOD'98, Seattle, Washington, USA, pages 106--117, 1998.

....other hand, even with good metadata, it may be best to abandon the plan that the optimizer originally chose, if actual data transmission rates and cardinalities differ from the optimizer s expectations. While run time adaptivity has been shown to speed up performance even in traditional systems [26, 19], it becomes critical to performance in the data integration context (e.g. 39] 1.2 Adaptive Query Processing in Tukwila This paper describes the Tukwila 1 data integration system. The main principle underlying the design of Tukwila is that adaptivity should be built into the core of the ....

....and execution and specialized operators. Furthermore, Tukwila provides a platform for incorporating hybrid optimization [34, p181] and important query optimization techniques that have been developed previously in isolation (e.g. query scrambling [39] choose nodes [19] runtime re optimization [26], optimization of fusion queries [47] 2. We describe the design and implementation of query operators that are especially suited for adaptive behavior, including the double pipelined join and the collector operator. In particular, we describe how Tukwila adapts the execution of a ....

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Navin Kabra and David J. DeWitt. Efficient mid-query re-optimization of sub-optimal query execution plans. In Proc. of ACM SIGMOD Conf. on Management of Data, pages 106--117, Seattle, WA, 1998.

....is dynamic query optimization [14, 30] Dynamic query evaluation plans include several alternative subplans which are chosen for execution depending on run time conditions. From the planning perspective, this is a simple form of contingency planning [16,55] A second type is query scrambling [36,38, 65]. As subqueries are answered during the execution of a query plan, the system can refine the cost estimates based on the actual results returned. This opens the opportunity to rewrite the remainder of the plan if the difference between expected and actual costs warrants it. A rewriting based ....

N. Kabra, D.J. DeWitt, Efficient mid-query re-optimization of sub-optimal query execution plans, in: Proc. ACM SIGMOD International Conference on Management of Data (SIGMOD-98), SIGMOD Record 27 (2) (1998) 106--117.

....implemented to test the system. Section 5 covers the experiments we conducted to test how well the CRQP optimizes the flow of data through a query. Section 6 presents future work and Section 7 concludes this paper. 1 2 2 RELATED WORK Recent work on mid query re optimization by Kabra and DeWitt [4] collects query specific statistics as the query runs, waits until enough of the query has completed for the statistics to be valid, and uses them to reallocate resources or modify the query plan if warranted. The current query plan performance must be significantly poor in order to justify the ....

Navin Kabra and David J. DeWitt. Efficient mid-query re-optimization of sub-optimal query execution plans. In ACM SIGMOD, pages 106--117, Seatttle, Washington, June 1998.

....too, using techniques for incremental sampling [10, 8] the other forms of adaptivity, to our knowledge, have so far been restricted to parametric techniques. 3 Architectural Assumptions and Notation 3. 1 Feedback driven Architecture Following the earlier proposals by [4] and, especially, [18] for adaptive selectivity estimation and dynamic re optimization of query execution plans, we assume that the database system monitors the sizes (i.e. cardinalities in the sense of bags) of intermediate results of the executed queries. Like [18] we do not assume that we can observe all data ....

....the earlier proposals by [4] and, especially, 18] for adaptive selectivity estimation and dynamic re optimization of query execution plans, we assume that the database system monitors the sizes (i.e. cardinalities in the sense of bags) of intermediate results of the executed queries. Like [18], we do not assume that we can observe all data values in an intermediate result, nor their frequency or density distribution. The rationale for this limitation is that such more insightful observations incur additional run time overhead that could slow down the underlying database engine. For ....

N. Kabra and D.J. DeWitt. Efficient mid-query reoptimization of sub-optimal query execution plans. In Proceedings of the ACM SIGMOD Conference, 1998.

....following the cheaper order is close to 50 . This observation is intuitive, but quite significant. The lotterybased eddy approaches the cost of an optimal ordering, but does not concern itself about strictly observing the optimal ordering. Contrast this to earlier work on runtime reoptimization [KD98, UFA98, IFF 99] where a traditional query optimizer runs during processing to determine the optimal plan remnant. By focusing on overall cost rather than on finding the optimal plan, the lottery scheme probabilistically provides nearly optimal performance with much less effort, allowing ....

....though [NWMN99] considers the special case of unary operators. Our characterization of barriers and moments of symmetry also appears to be new, arising as it does from our interest in reoptimizing general pipelines. Recent papers consider reoptimizing queries at the ends of pipelines [UFA98, KD98, IFF 99] reordering operators only after temporary results are materialized. IFF 99] observantly notes that this approach dates back to the original INGRES query decomposition scheme [SWK76] These inter pipeline techniques are not adaptive in the sense used in traditional control ....

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N. Kabra and D. J. DeWitt. Efficient Mid-Query Reoptimization of Sub-Optimal Query Execution Plans. In Proc. ACM-SIGMOD International Conference on Management of Data, pages 106-- 117, Seattle, 1998.

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N. Kabra and D. J. DeWitt. "Efficient Mid-Query Re-Optimization of Sub-Optimal Query Execution Plans." In Proc. SIGMOD, 1998.

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N. Kabra and D. DeWitt. Efficient mid-query reoptimization of sub-optimal query execution plans. In Proc. of ACM SIGMOD 1998.

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N. Kabra and D. J. DeWitt. Efficient Mid-Query ReOptimization of Sub-Optimal Query Execution Plans. In Proceedings of the 1998.

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N. Kabra and D. J. DeWitt. "Efficient Mid-Query Re-Optimization of Sub-Optimal Query Execution Plans." In Proc. SIGMOD, 1998.

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Navin Kabra and David J. DeWitt. Efficient midquery re-optimization of sub-optimal query execution plans. In SIGMOD, 1998.

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N. Kabra and D. J. DeWitt. Efficient mid-query re-optimization of sub-optimal query execution plans. In SIGMOD '98.

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Navin Kabra and David J. DeWitt. Efficient mid-query re-optimization of sub-optimal query execution plans. In SIGMOD, 1998.

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N. Kabra and D. DeWitt. Efficient mid-query reoptimization of sub-optimal query execution plans. In Proc. of ACM SIGMOD 1998.

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N. Kabra and D. DeWitt. Efficient mid-query re-optimization of sub-optimal query execution plans. In Proc. of ACM SIGMOD 1998.

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Navin Kabra and David J. DeWitt. Efficient mid-query re-optimization of sub-optimal query execution plans. In SIGMOD 1998, Proceedings ACM SIGMOD International Conference on Management of Data, June 2-4, 1998, Seattle, Washington, USA, pages 106--117, 1998.

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N. Kabra et al. Efficient mid-query reoptimization of suboptimal query execution plans. In SIGMOD, 1998.

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N. Kabra and D. J. DeWitt. Efficient mid-query reoptimization of sub-optimal query execution plans. In Proc. of ACM SIGMOD Conf. on Management of Data, pages 106-- 117, Seattle, WA, 1998.

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