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Learning Complex Predicates for Cardinality Estimation Using Recursive Neural Networks

Information Systems(2024)CCF BSCI 2区SCI 3区

Univ Elect Sci & Technol China

Cited 0|Views26
Abstract
Cardinality estimation is one of the most vital components in the query optimizer, which has been extensively studied recently. On one hand, traditional cardinality estimators, such as histograms and sampling methods, struggle to capture the correlations between multiple tables. On the other hand, current learning-based methods still suffer from the feature extraction of complex predicates and join relations, which will lead to inaccurate cost estimation, eventually a sub-optimal execution plan. To address these challenges, we present a novel end-to-end architecture leveraging deep learning to provide high-quality cardinality estimation. We exploit an effective feature extraction technique, which can fully make use of the structure of tables, join conditions and predicates. Besides, we use sampling-based technique to construct sample bitmaps for the tables and join conditions respectively. We also utilize the characteristics of predicate tree combined with recursive neural network to extract deep-level features of complex predicates. Finally, we embed these feature vectors into the model, which consists of three components: a recursive neural network, a graph convolutional neural network (GCN) and a multi-set convolutional neural network, to obtain the estimated cardinality. Extensive results conducted on real-world workloads demonstrate that our approach can achieve significant improvement in accuracy and be extended to queries with complex semantics.
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Key words
Cardinality estimation,Recursive neural networks,Complex predicates,Sampling
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要点】:论文提出了一种利用递归神经网络学习复杂谓词,以提升查询优化器中基数估计准确性的深度学习方法。

方法】:作者采用递归神经网络结合图卷积神经网络及多集合卷积神经网络,通过有效的特征提取技术,从表格结构、连接条件和谓词中学习特征。

实验】:实验在真实世界的工作负载上进行,使用采样技术构建样本位图,并展示该方法在处理复杂语义查询时,能显著提高基数估计的准确性。数据集名称未在摘要中明确提及。