@inproceedings{DBLP:conf/sigmod/TsueiPK97, author = {Thin-Fong Tsuei and Allan Packer and Keng-Tai Ko}, editor = {Joan Peckham}, title = {Database Buffer Size Investigation for OLTP Workloads (Experience Paper)}, booktitle = {SIGMOD 1997, Proceedings ACM SIGMOD International Conference on Management of Data, May 13-15, 1997, Tucson, Arizona, USA}, publisher = {ACM Press}, year = {1997}, pages = {112-122}, ee = {http://doi.acm.org/10.1145/253260.253279, db/conf/sigmod/TsueiPK97.html}, crossref = {DBLP:conf/sigmod/97}, bibsource = {DBLP, http://dblp.uni-trier.de} }BibTeX
It is generally accepted that On-Line Transaction Processing (OLTP) systems benefit from large database memory buffers. As enterprise database systems become larger and more complex, hardware vendors are building increasingly large systems capable of supporting huge memory configurations. Database vendors in turn are developing buffer schemes to exploit this physical memory.
How much will these developments benefit OLTP workloads? Through empirical studies on databases sized comparably to those seen in the real-world, this paper presents the characteristics of an industry-standard OLTP benchmark as memory buffer size changes. We design the experiments to investigate how the database size, the buffer size and the number of CPUs impact performance, in particular the throughput and the buffer hit rate on Symmetric Multiprocessor Systems. The relationships of these major database attributes are plotted and key observations are summarized. We discuss how these relationships change as the number of CPUs changes. We further quantify the relationships: 1) between database buffer data hit rate, buffer size and database size, 2) between throughput, buffer data hit rate and database size and 3) between throughput and number of CPUs. Algorithms, rules-of-thumb and examples are presented for predicting performance, sizing memory and making trade-offs between adding more memory and increasing the number of CPUs.
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