Scaling and characterizing database workloads: bridging the gap between research and practice

On-line transaction processing (OLTP) workloads are crucial benchmarks for the design and analysis of server processors. Typical cached configurations used by researchers to simulate OLTP workloads are orders of magnitude smaller than the fully scaled configurations used by OEM vendors to achieve world-record transaction processing throughput. The objective of this study is to discover the underlying relationships that characterize OLTP performance over a wide range of configurations. To this end, we have derived the "iron law" of database performance. Using our iron law, we show that both the average instructions executed per transaction (IPX) and the average cycles per instruction (CPI) are critical to the transaction-throughput performance. We use an extensive, empirical examination of an Oracle based commercial OLTP workload on an Intel Xeon multiprocessor system to characterize the scaling behaviour of both the IPX and the CPI. We demonstrate that across a wide range of configurations the IPX and CPI behaviour follows predictable trends, which can be accurately characterized by simple linear or piece-wise linear approximations. Based on our data, we propose a method for selecting a minimal, representative workload configuration from which behaviours of much larger OLTP configurations can be accurately extrapolated.