Data Mining on an OLTP System (Nearly) for Free
Appears in the Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, Dallas, TX, May 14-19, 2000. Supercedes Carnegie Mellon University SCS Technical Report CMU-CS-99-151.
Erik Riedel*, Christos Faloutsos, Gregory R. Ganger, David F. Nagle
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
*now with Seagate Technology, Pgh, PA.
This paper proposes a scheme for scheduling disk requests that takes advantage of the ability of high-level functions to operate directly at individual disk drives. We show that such a scheme makes it possible to support a Data Mining workload on an OLTP system almost for free: there is only a small impact on the throughput and response time of the existing workload. Specifically, we show that an OLTP system has the disk resources to consistently provide one third of its sequential bandwidth to a background Data Mining task with close to zero impact on OLTP throughput and response time at high transaction loads. At low transaction loads, we show much lower impact than observed in previous work. This means that a production OLTP system can be used for Data Mining tasks without the expense of a second dedicated system. Our scheme takes advantage of close interaction with the on-disk scheduler by reading blocks for the Data Mining workload as the disk head "passes over" them while satisfying demand blocks from the OLTP request stream. We show that this scheme provides a consistent level of throughput for the background workload even at very high foreground loads. Such a scheme is of most benefit in combination with an Active Disk environment that allows the background Data Mining application to also take advantage of the processing power and memory available directly on the disk drives.