A Performance Evaluation of Online Warehouse Update Algorithms

Abstract

Data warehouse maintenance algorithms usually work off-line, making the warehouse unavailable to users. However, since most organizations require continuous operation, we need be able to perform the updates online, concurrently with user queries. To guarantee that user queries access a consistent view of the warehouse, online update algorithms introduce redundancy in order to store multiple versions of the data objects that are being changed. In this paper, we present an online warehouse update algorithm, that stores multiple versions of data as separate rows (vertical redundancy). We compare our algorithm to another online algorithm that stores multiple versions within each tuple by extending the table schema (horizontal redundancy). We have implemented both algorithms on top of an Informix Dynamic Server and measured their performance under varying workloads, focusing on their impact on query response times. Our experiments show that, except for a limited number of cases, vertical redundancy is a better choice, with respect to storage, implementation overhead, and query performance. (Also cross-referenced as UMIACS TR-98-66)