Scaling and Charact rizing Database Workloads: Bridging the Gap between Research and Practice

On-ine Transaction Processing (OLTP) workloads arecrucial benchmarks for the design and analysis of serverprocessors. Typical cached configurations used byresearchers to simulate OLTP workloads are orders ofmagnitude smaller than the fully scaled configurationsused by OEM vendors to achieve world-record transactionprocessing throughput. The objective of this study is todiscover the underlying relationships that characterizeOLTP performance over a wide range of configurations.To this end, we have derived the "iron law" of databaseperformance. Using our iron law, we show that both theaverage instructions executed per transaction (IPX) andthe average cycles per instruction (CPI) are critical to thetransaction-throughput performance. We use an extensive,empirical examination of an Oracle® based commercialOLTP workload on an Intel® XeonTM multiprocessorsystem to characterize the scaling behavior of both theIPX and the CPI. We demonstrate that across a widerange of configurations the IPX and CPI behavior followspredictable trends, which can be accurately characterizedby simple linear or piece-wise linear approximations.Based on our data,we propose a method for selecting aminimal, representative workload configuration fromwhich behaviors of much larger OLTP configurations canbe accurately extrapolated.