In this paper we address cardinality estimation problem which is an important subproblem in query optimization. Query optimization is a part of every relational DBMS responsible for finding the best way of the execution for the given query. These ways are called plans. The execution time of different plans may differ by several orders, so query optimizer has a great influence on the whole DBMS performance. We consider cost-based query optimization approach as the most popular one. It was observed that cost-based optimization quality depends much on cardinality estimation quality. Cardinality of the plan node is the number of tuples returned by it. In the paper we propose a novel cardinality estimation approach with the use of machine learning methods. The main point of the approach is using query execution statistics of the previously executed queries to improve cardinality estimations. We called this approach adaptive cardinality estimation to reflect this point. The approach is general, flexible, and easy to implement. The experimental evaluation shows that this approach significantly increases the quality of cardinality estimation, and therefore increases the DBMS performance for some queries by several times or even by several dozens of times.
Database query optimizers benefit greatly from accurate cardinality estimation; however, this is hard to achieve on tables with correlated and or skewed columns. We present a novel approach using neural networks to learn and approximate selectivity functions that take a bounded range on each column as input, effectively estimating selectivities for all relational operators. Experimental results with a simplified prototype show a significant improvement over state-of-the-art cardinality estimators on constructed datasets in terms of accuracy, efficiency, and amount of user input required.