Abstract
This technical note shows how to combine some well-known techniques to create a method that will efficiently execute common multi-table joins. We concentrate on a commonly occurring type of join known as a
star-join,
although the method presented will generalize to any type of multi-table join. A star-join consists of a central
detail
table with large cardinality, such as an orders table (where an order row contains a single purchase) with foreign keys that join to
descriptive
tables, such as customers, products, and (sales) agents. The method presented in this note uses
join indices
with
compressed bitmap
representations, which allow predicates restricting columns of descriptive tables to determine an answer set (or
foundset
) in the central detail table; the method uses different predicates on different descriptive tables in combination to restrict the detail table through compressed bitmap representations of join indices, and easily completes the join of the fully restricted detail table rows back to the descriptive tables. We outline realistic examples where the combination of these techniques yields substantial performance improvements over alternative, more traditional query evaluation plans.
Publisher
Association for Computing Machinery (ACM)
Subject
Information Systems,Software
Reference4 articles.
1. [KOOI80] Robert Kooi The Optimization of Queries in Relational Databases Ph.D. thesis Case Western Reserve University Cleveland OH 1980. [KOOI80] Robert Kooi The Optimization of Queries in Relational Databases Ph.D. thesis Case Western Reserve University Cleveland OH 1980.
2. [O'NEI91] Patrick O'Neil The Set Query Benchmark The Benchmark Handbook for Database and Transaction Processing Systems Jim Gray (Editor) 2nd Edition 1993. [O'NEI91] Patrick O'Neil The Set Query Benchmark The Benchmark Handbook for Database and Transaction Processing Systems Jim Gray (Editor) 2nd Edition 1993.
3. Join indices
Cited by
121 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献