Supporting Streaming Updates in an Active Data Warehouse

Neoklis Polyzotis, Spiros Skiadopoulos, Panos Vassiliadis, Alkis Simitsis, Nils-Erik Frantzell
2007 2007 IEEE 23rd International Conference on Data Engineering  
Active Data Warehousing has emerged as an alternative to conventional warehousing practices in order to meet the high demand of applications for up-to-date information. In a nutshell, an active warehouse is refreshed on-line and thus achieves a higher consistency between the stored information and the latest data updates. The need for on-line warehouse refreshment introduces several challenges in the implementation of data warehouse transformations, with respect to their execution time and
more » ... overhead to the warehouse processes. In this paper, we focus on a frequently encountered operation in this context, namely, the join of a fast stream S of source updates with a disk-based relation R, under the constraint of limited memory. This operation lies at the core of several common transformations, such as, surrogate key assignment, duplicate detection or identification of newly inserted tuples. We propose a specialized join algorithm, termed mesh join (MESHJOIN), that compensates for the difference in the access cost of the two join inputs by (a) relying entirely on fast sequential scans of R, and (b) sharing the I/O cost of accessing R across multiple tuples of S. We detail the MESHJOIN algorithm and develop a systematic cost model that enables the tuning of MESHJOIN for two objectives: maximizing throughput under a specific memory budget or minimizing memory consumption for a specific throughput. We present an experimental study that validates the performance of MESHJOIN on synthetic and real-life data. Our results verify the scalability of MESH-JOIN to fast streams and large relations, and demonstrate its numerous advantages over existing join algorithms.
doi:10.1109/icde.2007.367893 dblp:conf/icde/PolyzotisSVSF07 fatcat:5wywvrab4zcanaj4tslbioqyc4