Data Warehouse Performance: Selected Techniques and Data Structures

Data stored in a data warehouse (DW) are retrieved and analyzed by complex analytical applications, often expressed by means of star queries. Such queries often scan huge volumes of data and are computationally complex. For this reason, an acceptable (or good) DW performance is one of the important features that must be guaranteed for DW users. Good DW performance can be achieved in multiple components of a DW architecture, starting from hardware (e.g., parallel processing on multiple nodes, fast disks, huge main memory, fast multi-core processor), through physical storage schemes (e.g., row storage, column storage, multidimensional store, data and index compression algorithms), state of the art techniques of query optimization (e.g., cost models and size estimation techniques, parallel query optimization and execution, join algorithms), and additional data structures improving data searching efficiency (e.g., indexes, materialized views, clusters, partitions). In this chapter we aim at presenting only a narrow aspect of the aforementioned technologies. We discuss three types of data structures, namely indexes (bitmap, join, and bitmap join), materialized views, and partitioned tables. We show how they are being applied in the process of executing star queries in three commercial database/data warehouse management systems, i.e., Oracle, DB2, and SQL Server.