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Dana Van Aken e56309dc28
Update requirements.txt 
Increased required numpy version from 1.14.0 to 1.15.4.
 		2020-07-31 12:29:10 -04:00
.travis travis-ci: trigger a build in the other repository instead of cloning and building it here 2020-01-23 16:15:49 -05:00
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.dockerignore Changes: when we deploy our docker images we now also build/deploy the internal driver image; fixed .dockerignore; moved integration test data into the driver; fixed cast in oracle db_time target objective. 2019-12-04 17:27:05 -05:00
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.gitlint.yaml The Oracle collector now prepends the view name to each metric to avoid overwriting metrics from different views with the same names 2019-11-18 13:04:14 -05:00
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README.md Initial commit with BSL 2019-08-23 11:47:19 -04:00

README.md

OtterTune

Build Status codecov.io

OtterTune is a new tool developed by students and researchers in the Carnegie Mellon Database Group that can automatically find good settings for a database management system's configuration knobs. The goal is to make it easier for anyone to deploy a DBMS without any expertise in database administration. To tune new DBMS deployments, OtterTune reuses training data gathered from previous tuning sessions. Because OtterTune does not need to generate an initial dataset for training its ML models, tuning time is drastically reduced.

For more information, see our paper.

@inproceedings{vanaken17,
  author = {Van Aken, Dana and Pavlo, Andrew and Gordon, Geoffrey J. and Zhang, Bohan},
  title = {Automatic Database Management System Tuning Through Large-scale Machine Learning},
  booktitle = {Proceedings of the 2017 ACM International Conference on Management of Data},
  series = {SIGMOD '17},
  year = {2017},
  pages = {1009--1024},
  numpages = {16},
 }

Contributors

See the people page for the full list of contributors.