Query estimation techniques in database systems

The effectiveness of query optimization in database systems critically depends on the system’s ability to assess the execution costs of different query execution plans. For this purpose, the sizes and data distributions of the intermediate results generated during plan execution need to be estimated as accurately as possible. This estimation requires the maintenance of statistics on the data stored in the database, which are referred to as data synopses. While the problem of query cost estimation has received significant attention for over a decade, it has remained an open issue in practice, because most previous techniques have focused on singular aspects of the problem such as minimizing the estimation error of a single type of query and a single data distribution, whereas database management systems generally need to support a wide range of queries over a number of datasets. In this thesis I introduce a new technique for query result estimation, which extends existing techniques in that it offers estimation for all combinations of the three major database operators selection, projection, and join. The approach is based on separate and independent approximations of the attribute values contained in a dataset and their frequencies. Through the use of space-filling curves, the approach extends to multi-dimensional data, while maintaining its accuracy and computational properties. The resulting estimation accuracy is competitive with specialized techniques and superior to the histogram techniques currently implemented in commercial database management systems. Because data synopses reside in main memory, they compete for available space with the database cache and query execution buffers. Consequently, the memory available to data synopses needs to be used efficiently. This results in a physical design problem for data synopses, which is to determine the best set of synopses for a given combination of datasets, queries, and available memory. This thesis introduces a formalization of the problem, and efficient algorithmic solutions. All discussed techniques are evaluated with regard to their overhead and resulting estimation accuracy on a variety of synthetic and real-life datasets.