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  Inspector joins (2005) [2 citations — 2 self]

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by Shimin Chen, Anastassia Ailamaki, Phillip B. Gibbons, Todd C. Mowry
In VLDB
http://www.vldb2005.org/program/paper/thu/p817-chen.pdf
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Abstract:

The key idea behind Inspector Joins is that during the I/O partitioning phase of a hash-based join, we have the opportunity to look at the actual data itself and then use this knowledge in two ways: (1) to create specialized indexes, specific to the given query on the given data, for optimizing the CPU cache performance of the subsequent join phase of the algorithm, and (2) to decide which join phase algorithm best suits this specific query. We show how inspector joins, employing novel statistics and specialized indexes, match or exceed the performance of state-of-the-art cache-friendly hash join algorithms. For example, when run on eight or more processors, our experiments show that inspector joins offer 1.1–1.4X speedups over these previous algorithms, with the speedup increasing as the number of processors increases. 1

Citations

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521 Query evaluation techniques for large databases – Graefe - 1993
521 Combining branch predictors – McFarling - 1993
174 Join indices – Valduriez - 1987
170 Wm.; Efficiently Updating Materialized Views – Blakeley, Larson, et al. - 1986
150 Join processing in database systems with large main memory – Shapiro - 1986
123 Multiple-query optimization – Sellis - 1988
89 Efficient Mid-Query ReOptimization of Sub-Optimal Query Execution Plans – Kabra, DeWitt - 1998
70 Database Architecture Optimized for the New Bottleneck: Memory Access – Boncz, Manegold, et al. - 1999
63 Cache conscious algorithms for relational query processing – Shatdal - 1994
50 Application of hash to data base machine and its architecture – Kitsuregawa, Tanaka, et al. - 1983
25 db2’s learning optimizer – Leo
19 Improving hash join performance through prefetching – Chen, Ailamaki, et al. - 2004
19 Robust Query Processing Through Progressive Optimization – Markl, Raman, et al. - 2004
18 What happens during a join? Dissecting cpu and memory optimization effects – Manegold, Boncz, et al.