Server monitoring is usually reactive in nature. Some predefined threshold is exceeded, an alert is sent, and by the time you receive the alert, something bad has already happened. Wouldn’t it be nice to be able to foresee trouble before it rears its ugly head? We present our initial investigation into using analytical tools available within the R statistical environment to easily monitor server activity, predict potential performance problems, and possibly prevent faults on PostgreSQL database servers.
Joe Conway has been involved with PostgreSQL as a contributor since 2001. He is also the author and maintainer of a PostgreSQL procedural language handler for the R language, PL/R. Joe is President/CEO of credativ USA, which specializes in open source software with its "Open Source Support Center" and a comprehensive range of services, including consulting, architectural and technical advice, software development, training, and personalized support.
Jeff Hamann has developed open source analysis and optimization tools for forestry people for over 20 years. He co-authored Forest Analytics with R for the Springer Use-R series, and is a Wiley Science Advisor. He has a Bachelors of Science in Forestry from Humboldt State University, and an MS and Ph.D. in Forest Biometrics and Forest Engineering from Oregon State University. As president of Forest Informatics, Jeff is obsessed with analyzing data from, developing tools for, and presenting collaborative, geek-friendly stories and solutions for forests and people.
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BibTeX
@conference {255722,
author = {Joe Conway and Jeff Hamann},
title = {Database Server Safety Nets: Options for Predictive Server Analytics},
year = {2012},
address = {San Diego, CA},
publisher = {{USENIX} Association},
month = dec,
}
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