Cardinality Estimation of Federated Complex Queries Based on Query Feature Representation Learning
Accurate cardinality estimation is the key factor to realize the best query plan.Most of the existing methods are based on deep learning to solve the base estimation problem.However,this method based on RDF graph pattern focuses on simple queries with specific topological structure,which is limited in application scope,and lacks support for complex queries frequently used in real scenes.In order to solve the above problems,we propose a federated complex query cardinality estimation model based on query feature representation learning.This model mainly deals with complex queries with FILTER or DISTINCT keywords.The SPARQL query is expressed as a feature vector by using the newly proposed FILTER query characterization method,and the query cardinality is predicted by the model.Also the model is used to predict the ratio of unique rows in DISITINCT queries.Experiments on LUBM data sets show that compared with the most advanced cardinality estimation methods,this model performs better in cardinality estimation,with an average median estimation error of 1.16,and shows potential and scalability for the estimation of multi-join queries.
federal systemquery optimizationcomplex querydeep learningcardinality estimation
万方数据
维普
