Abstract: In recent years, deep learning-based approaches have been increasingly adopted for cardinality estimation to enhance the accuracy of query optimization. However, there are still some ...

Today we're looking at HyperLogLog, an algorithm that leverages random chance to count the number of distinct items are in a ...

We study two classes of summary-based cardinality estima tors that use statistics about input relations and joins of a small number of input relations: (i) optimistic estimators, which were defined in ...

Query engines are really good at choosing an efficient query plan. Users do not need to worry about how they write their query, since the optimizer makes all the right choices for executing the query ...

Database optimization has long relied on traditional methods that struggle with the complexities of modern data environments. These methods often fail to efficiently handle large-scale data, complex ...

Nearly two dozen private colleges are offering an online tool that factors in need-based grants and scholarships to estimate students’ actual costs. By Ann Carrns High school seniors applying to ...

Currently if running GROUP BY, the cardinality estimation is using this method EstimateColsNDVWithMatchedLen, and it will look into the backed index, if no index found, it will fall back to max(NDV(a) ...

therefore a real puzzle to explain why research on this popular and fundamental problem has been unusually slow. This talk presents a complete history of the Cardinality Estimation problem from ...

Currently, when StarRocks performs CE (Cardinality Estimation), the Cost-Based Optimizer (CBO) mostly assumes that multiple columns are independent, meaning there is ...