Current businesses rely heavily on efficient access to their databases. Manual tuning of these database systems by performance experts is increasingly infeasible: For small companies, hiring an expert may be too expensive; for large enterprises, even an expert may not fully understand the interaction between a large system and its multiple changing workloads. This trend has led major vendors to offer tools that automatically and dynamically tune a database system. Many database tuning knobs concern the buffer pool for caching data and disk pages. Specifically, these knobs control the buffer allocation and thus the cache miss probability, which has direct impact on performance. Previous methods for automatic buffer tuning are based on simulation, black-box control, gradient descent, and empirical equations. This article presents a new approach, using calculations with an analytically-derived equation that relates miss probability to buffer allocation; this equation fits four buffer replacement policies, as well as twelve datasets from mainframes running commercial databases in large corporations. The equation identifies a buffer-size limit that is useful for buffer tuning and powering down idle buffers. It can also replace simulation in predicting I/O costs. Experiments with PostgreSQL

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.