Haider Rizvi

Developing a characterization of business intelligence workloads for sizing new database systems

Computer system sizing involves estimating the amount of hardware resources needed to support a new workload not yet deployed in a production environment. In order to determine the type and quantity of resources required, a methodology is required for describing the new workload. In this paper, we discuss the sizing process for database management systems and describe an analysis for characterizing business intelligence (BI) workloads, using the TPC-H benchmark as our workload basis. The characterization yields four general classes of queries, each with different characteristics. Our approach for sizing a BI applications database tier quantifies a new BI workload in terms of the response time goals and mix of the different query classes obtained from the characterization analysis.

Performance evaluation of linux file systems for data warehousing workloads

Many database users store data on raw or block devices for performance reasons, since file caching and file locking by the file system can be bypassed. However, many database users would prefer to use file systems for the ease of long-term maintenance. To our knowledge, there have not been any major efforts to systematically assess the performance of Linux file systems for database workloads. In this paper, we present our initial performance study on data warehousing systems. We first provide a brief introduction to various Linux file systems, namely Ext2, Ext3, ReiserFS, XFS and JFS. We examine the performance impact of asynchronous I/O, direct I/O, file caching, I/O schedulers, file fragmentation, and database storage methods. We then quantify the performance of these Linux file systems utilizing a well-known data warehousing workload. Finally, system configurations are recommended and future work is suggested.