Benchmarking database systems has a long and successful history in making industrial database systems comparable and is also a cornerstone of quantifiable experimental data systems research. Defining a benchmark involves identifying a dataset, a query- and update-workload, and performance metrics, as well as creating infrastructure to generate/load data, drive queries/updates into the system-under-test and record performance metrics.
Creating good benchmarks has been described as an art. One can inspire dataset and workload design from "representative" use cases queries, typically informed by knowledge from domain experts; but also exploit technical insights from database architects in what features, operations, and data distributions should come together in order to invoke a particularly challenging task.a
No entries found
Log in to Read the Full Article
Sign in using your ACM Web Account username and password to access premium content if you are an ACM member, Communications subscriber or Digital Library subscriber.
Please select one of the options below for access to premium content and features.
Create a Web Account
If you are already an ACM member, Communications subscriber, or Digital Library subscriber, please set up a web account to access premium content on this site.
Join the ACM
Become a member to take full advantage of ACM's outstanding computing information resources, networking opportunities, and other benefits.
Subscribe to Communications of the ACM Magazine
Get full access to 50+ years of CACM content and receive the print version of the magazine monthly.
Purchase the Article
Non-members can purchase this article or a copy of the magazine in which it appears.