Caching in Multidimensional Databases

TL;DR

This paper investigates how caching and data compression affect the performance of OLAP systems in multidimensional databases, comparing different data representations through models and empirical data.

Contribution

It introduces models to evaluate caching effects in OLAP systems and compares multidimensional and table data representations with empirical validation.

Findings

Caching significantly improves OLAP query performance.

Data compression enhances cache efficiency and system speed.

Multidimensional representation outperforms table format in certain scenarios.

Abstract

One utilisation of multidimensional databases is the field of On-line Analytical Processing (OLAP). The applications in this area are designed to make the analysis of shared multidimensional information fast [9]. On one hand, speed can be achieved by specially devised data structures and algorithms. On the other hand, the analytical process is cyclic. In other words, the user of the OLAP application runs his or her queries one after the other. The output of the last query may be there (at least partly) in one of the previous results. Therefore caching also plays an important role in the operation of these systems. However, caching itself may not be enough to ensure acceptable performance. Size does matter: The more memory is available, the more we gain by loading and keeping information in there. Oftentimes, the cache size is fixed. This limits the performance of the multidimensional database, as well, unless we compress the data in order to move a greater proportion of them into the memory. Caching combined with proper compression methods promise further performance improvements. In this paper, we investigate how caching influences the speed of OLAP systems. Different physical representations (multidimensional and table) are evaluated. For the thorough comparison, models are proposed. We draw conclusions based on these models, and the conclusions are verified with empirical data.