According to a survey of the University of Berkeley [P. Lyman et al., (2003)], about 5 Exabytes of new information has been created in 2002. This information explosion affects also the database volumes of enterprise resource planning (ERP) systems like SAP R/3, the market leader for ERP systems. Just like the overall information explosion, the database volumes of ERP systems are growing at a tremendous rate and some of them have reached a size of several Terabytes. OLTP (online transaction processing) databases of this size are hard to maintain and tend to perform poorly. One way to limit the size of a database is data staging, i.e., to make use of an SAP technique called archiving. That is, data which are not needed for every-day operations are demoted from the database (disks) to tertiary storage (tapes). In cooperation with our research group, SAP is adapting their archiving techniques to accelerate the archiving process by integrating new technologies like XML and advanced database features. However, so far no benchmark existed to evaluate different archiving scenarios and to measure the impact of a change in the archiving technique. We therefore designed and implemented a generic benchmark which is applicable to many different system layouts and allows the users to evaluate various archiving scenarios.

Many applications require that large amounts of data are deleted from a database - typically, such bulk deletes are carried out periodically and involve old or out-of-date data. If the data is not partitioned in such a way that bulk deletes can be carried out by simply deleting whole partitions, then most current database products execute such bulk delete operations very poorly. The reason is that every record is deleted from each index individually. This paper proposes and evaluates a new class of techniques to support bulk delete operations more efficiently. These techniques outperform the "record-at-a-time" approach implemented in many database products by about an order of magnitude.