LAF: A Local Depth Autoregressive Framework for Cardinality Estimation of Multi-attribute Queries

Abstract

Cardinality estimation is significant for database query optimization, which affects the query efficiency. Most existing methods often use a uniform approach to model strongly and weakly correlated attributes and seldom make comprehensively use of data information and query information. Some methods have poor accuracy due to simple structure, while others suffer from low efficiency due to complex structure. The problem of cardinality estimation that strong and weak association coexist among attributes can not be well solved by these methods or their simple combinations. Therefore we propose LAF, a new Local deep Autoregressive Framework, which performs fine-grained modeling for attributes with strong and weak correlation. LAF utilizes mutual information to identify the strong and weak association between attributes, applying the local strategy to construct deep autoregressive models to learn the joint distribution for strongly correlated attributes and outputting corresponding local estimations, using lightweight regression model to capture the complex mapping between local estimations with weak correlation and cardinality, and LAF combines information entropy to sort attributes in descending order. Not only do we enable local deep autoregressive models to learn from data information, but also make lightweight regression model to learn from query information. Extensive experimental evaluations on real datasets show that accurate result is achieved while estimation time is significantly shortened, and model size is controlled within a reasonable range.