Cardinality estimation with local deep learning models

Cardinality estimation is a fundamental task in database query processing and optimization. Unfortunately, the accuracy of traditional estimation techniques is poor resulting in non-optimal query execution plans. With the recent expansion of machine learning into the field of data management, there is the general notion that data analysis, especially neural networks, can lead to better estimation accuracy. Up to now, all proposed neural network approaches for the cardinality estimation follow a global approach considering the whole database schema at once. These global models are prone to sparse data at training leading to misestimates for queries which were not represented in the sample space used for generating training queries. To overcome this issue, we introduce a novel local-oriented approach in this paper, therefore the local context is a specific sub-part of the schema. As we will show, this leads to better representation of data correlation and thus better estimation accuracy. Compared to global approaches, our novel approach achieves an improvement by two orders of magnitude in accuracy and by a factor of four in training time performance for local models.