Conor Cunningham

PIVOT and UNPIVOT: optimization and execution strategies in an RDBMS

PIVOT and UNPIVOT, two operators on tabular data that exchange rows and columns, enable data transformations useful in data modeling, data analysis, and data presentation. They can quite easily be implemented inside a query processor, much like select, project, and join. Such a design provides opportunities for better performance, both during query optimization and query execution. We discuss query optimization and execution implications of this integrated design and evaluate the performance of this approach using a prototype implementation in Microsoft SQL Server.

Distributed/heterogeneous query processing in Microsoft SQL server

This paper presents an architecture overview of the distributed, heterogeneous query processor (DHQP) in the Microsoft SQL server database system to enable queries over a large collection of diverse data sources. The paper highlights three salient aspects of the architecture. First, the system introduces well-defined abstractions such as connections, commands, and rowsets that enable sources to plug into the system. These abstractions are formalized by the OLE DB data access interfaces. The generality of OLE DB and its broad industry adoption enables our system to reach a very large collection of diverse data sources ranging from personal productivity tools, to database management systems, to file system data. Second, the DHQP is built-in to the relational optimizer and execution engine of the system. This enables DH queries and updates to benefit from the cost-based algebraic transformations and execution strategies available in the system. Finally, the architecture is inherently extensible to support new data sources as they emerge as well as serves as a key extensibility point for the relational engine to add new features such as full-text search and distributed partitioned views.