Two-Level Sampling for Join Size Estimation
Yu Chen, Ke Yi · 2017
Join size estimation is a critical step in query optimization, and has been extensively studied in the literature. Among the many techniques, sampling based approaches are particularly appealing, due to their ability to handle arbitrary selection predicates. In this paper, we propose a new sampling algorithm for join size estimation, called two-level sampling, which combines the advantages of three previous sampling methods while making further improvements. Both analytical and empirical comparisons show that the new algorithm outperforms all the previous algorithms on a variety of joins, including primary key-foreign key joins, many-to-many joins, and multi-table joins. The new sampling algorithm is also very easy to implement, requiring just one pass over the data. It only relies on some basic statistical information about the data, such as the ℓk-norms and the heavy hitters.