NDLI: Optimizing Iceberg Queries with Complex Joins

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Optimizing Iceberg Queries with Complex Joins

Content Provider	ACM Digital Library
Author	Yang, Jun Walenz, Brett Roy, Sudeepa
Abstract	Iceberg queries, commonly used for decision support, find groups whose aggregate values are above or below a threshold. In practice, iceberg queries are often posed over complex joins that are expensive to evaluate. This paper proposes a framework for combining a number of techniques---a-priori, memoization, and pruning---to optimize iceberg queries with complex joins. A-priori pushes partial GROUP BY and HAVING condition before a join to reduce its input size. Memoization caches and reuses join computation results. Pruning uses cached results to infer that certain tuples cannot contribute to the final query result, and short-circuits join computation. We formally derive conditions for correctly applying these techniques. Our practical rewrite algorithm produces highly efficient SQL that can exploit combinations of optimization opportunities in ways previously not possible. We evaluate our PostgreSQL-based implementation experimentally and show that it outperforms both baseline PostgreSQL and a commercial database system.
Starting Page	1243
Ending Page	1258
Page Count	16
File Format	PDF
ISBN	9781450341974
DOI	10.1145/3035918.3064053
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-05-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Databases Postgresql Iceberg Iceberg queries Query optimization
Content Type	Text
Resource Type	Article

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