Author:
Lan Hai,Bao Zhifeng,Peng Yuwei
Abstract
AbstractQuery optimizer is at the heart of the database systems. Cost-based optimizer studied in this paper is adopted in almost all current database systems. A cost-based optimizer introduces a plan enumeration algorithm to find a (sub)plan, and then uses a cost model to obtain the cost of that plan, and selects the plan with the lowest cost. In the cost model, cardinality, the number of tuples through an operator, plays a crucial role. Due to the inaccuracy in cardinality estimation, errors in cost model, and the huge plan space, the optimizer cannot find the optimal execution plan for a complex query in a reasonable time. In this paper, we first deeply study the causes behind the limitations above. Next, we review the techniques used to improve the quality of the three key components in the cost-based optimizer, cardinality estimation, cost model, and plan enumeration. We also provide our insights on the future directions for each of the above aspects.
Funder
Australia Research Council
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Computational Mechanics
Reference108 articles.
1. Acharya J, Diakonikolas I, Hegde C, Li JZ, Schmidt L (2015) Fast and near-optimal algorithms for approximating distributions by histograms. In: PODS, pp 249–263
2. Akdere M, Çetintemel U, Riondato M, Upfal E, Zdonik SB (2012) Learning-based query performance modeling and prediction. In: ICDE, pp 390–401
3. Boulos J, Ono K (1999) Cost estimation of user-defined methods in object-relational database systems. SIGMOD Rec 28(3):22–28
4. Boulos J, Viemont Y, Ono K (1997) A neural networks approach for query cost evaluation. Trans Inf Process Soc Jpn 38(12):2566–2575
5. Bruno N, Galindo-Legaria C, Joshi M (2010) Polynomial heuristics for query optimization. In: ICDE, pp 589–600
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