# dbbert

**Repository Path**: Uncle-Justice/dbbert

## Basic Information

- **Project Name**: dbbert
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: gh-pages
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-05-25
- **Last Updated**: 2025-05-25

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# dbbert
DB-BERT is a database tuning tools that exploits natural language text as additional input. It extracts recommendations for database parameter settings from tuning-related text via natural language analysis. It optimizes parameter settings for a given workload and performance metric using reinforcement learning.

# Quickstart
- DB-BERT was tested on an Amazon EC2 p3.2xlarge instance using Deep Learning AMI (Ubuntu 18.04) Version 43.0.
- Install Postgres 13.2 and the TPC-H benchmark database.
- Install required Python packages, including Huggingface Transformers, stable-baselines-3, psycopg2, Numpy, and Pytorch.
- DB-BERT uses configuration files, an example file can be found under `config/pg_tpch_manydocs.ini`.
- For a first try, update credentials in the `[DBMS]` section. You may adapt tuning time and other parameters in the `[BENCHMARK]` section.
- Execute `src/run/tuning_run.py`, passing the configuration file as first (and only) argument, e.g. (from main directory):
`PYTHONPATH='src' python3 src/run/tuning_run.py config/pg_tpch_manydocs.ini`
- DB-BERT will parse input text (text from 100 Web documents), initialize the RL algorithm, and start tuning Postgres for TPC-H.
- Try `config/pg_tpch_onedoc.ini` for tuning using a single, TPC-H specific text document (forum discussion on TPC-H tuning).

# Ongoing Work
- Switching language models used from BERT to BART (despite the project name).
- Refined text document pre-processing: extract most relevant passage for each parameter.

# How to Cite
A video talk introducing the vision behind this project is [available online](https://youtu.be/Spa5qzKbJ4M).
```
@article{trummer2021case,
  title={The case for NLP-enhanced database tuning: towards tuning tools that" read the manual"},
  author={Trummer, Immanuel},
  journal={Proceedings of the VLDB Endowment},
  volume={14},
  number={7},
  pages={1159--1165},
  year={2021},
  publisher={VLDB Endowment}
}
```