# cmu-db/ottertune

The automatic DBMS configuration tool

Repository: https://github.com/cmu-db/ottertune
Canonical: https://ross.abutalabs.com/products/ottertune
Language: Python
License: NOASSERTION
License Family: other
Archived: true
Last push: 2020-11-13T17:19:31+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3429, "days_push": 2119, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1233, forks 312 (observed 2026-08-28T04:04:04.611795+00:00)

## What it is
OtterTune is a tool from the CMU Database Group that automatically finds good settings for a DBMS's configuration knobs using machine learning. It reuses training data from previous tuning sessions to drastically reduce tuning time for new database deployments.

## Use cases
- automatically tune postgresql configuration knobs
- optimize mysql settings without a DBA
- find best database configuration with machine learning
- reduce database tuning time using prior tuning data
- tune dbms performance parameters automatically

## When to choose
- you want automated DBMS knob tuning without database administration expertise
- you can run a self-hosted tuning service against your database
- you want to leverage ML models trained on prior tuning sessions

## When to avoid
- you need actively maintained software - the last release was 2020 and the project is research-oriented
- you want a managed cloud tuning service (the commercial OtterTune product is separate)
- you need support for DBMSs beyond those the tool targets

## Facets
- artifact type: service
- maturity: maintenance
- function: machine-learning, database, configuration-management, benchmarking
- domain: databases, machine-learning, performance
- platform: python, self-hosted
- tags: database-tuning, dbms-configuration, auto-tuning, knob-optimization, research-project, devops, linux, docker

## Member repositories
- cmu-db/ottertune (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:04.611795+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T08:22:15.284605+00:00, confidence not recorded.
  - readme: https://github.com/cmu-db/ottertune (fetched 2026-08-28T04:04:04.611795+00:00, sha b76bbce06996)
- Data as of 2026-08-30T08:39:29.467469+00:00.