- Author:
- Bennet Vance,
- Supervisor:
- David Maier
Join-order optimization plays a central role in the processing of relational database queries. This dissertation presents two new algorithms for join-order optimization: a deterministic, exhaustive-search algorithm, and a stochastic algorithm that is based on the deterministic one. The deterministic algorithm achieves new complexity bounds for exhaustive search in join-order optimization; and in timing tests, both algorithms are shown to run many times faster than their predecessors. In addition, these new, fast algorithms search a larger space of join orders than is customary in join-order optimization. Not only do they consider all the so-called bushy join orders, rather than just the left-deep ones, but--what is more unusual--they also consider all join orders that contain Cartesian products. The novel construction of these algorithms enables them to search a space including Cartesian products without paying the performance penalty that is conventionally associated with such a search.
Cited By
Chen Y, Cole R, McKenna W, Perfilov S, Sinha A and Szedenits E Partial join order optimization in the paraccel analytic database Proceedings of the 2009 ACM SIGMOD International Conference on Management of data, (905-908)- Moerkotte G and Neumann T Analysis of two existing and one new dynamic programming algorithm for the generation of optimal bushy join trees without cross products Proceedings of the 32nd international conference on Very large data bases, (930-941)
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