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https://github.com/nexsol-technologies/pgassistant
PgAssistant is an open-source tool designed to help developers understand and optimize their PostgreSQL database performance.
https://github.com/nexsol-technologies/pgassistant
Last synced: 3 days ago
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PgAssistant is an open-source tool designed to help developers understand and optimize their PostgreSQL database performance.
- Host: GitHub
- URL: https://github.com/nexsol-technologies/pgassistant
- Owner: nexsol-technologies
- License: mit
- Created: 2024-08-04T06:26:29.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-02-13T09:04:19.000Z (8 days ago)
- Last Synced: 2025-02-13T09:32:27.003Z (8 days ago)
- Language: CSS
- Size: 26.9 MB
- Stars: 593
- Watchers: 6
- Forks: 6
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
- Security: SECURITY.md
Awesome Lists containing this project
README
A PostgreSQL assistant for developers
designed to help understand and optimize PostgreSQL database performance.
PgAssistant is an open-source tool designed to help developers understand and optimize their PostgreSQL database performance. It provides insights into database behavior, identifies schema-related issues, and assists in correcting them. Additionally, PgAssistant makes it easy to manage a library of specific SQL queries useful for your project through a simple JSON file.
## Features
- **Database Behavior Analysis**: Understand how your PostgreSQL database is performing with detailed analysis.
- **Schema Optimization**: Identify and correct [issues](media/issues.png) related to your database schema.
- **Query Library Management**: Easily manage a library of specific SQL queries using a [JSON file](media/myqueries.png).
- **User-Friendly Interface**: Simple and intuitive interface for ease of use.
- **Lint SQL Code**: Pgassistant is using Python **Sqlfluff** to lint your SQL code
- **OpenAI helper**: If you have an OpenAI account, the interface can query OpenAI to help you understand your query plan and optimize your SQL queries
- **Local LLM helper**: If you run a local LLM like [**ollama**](media/llama.png), you can use it also with pgAssistant
- **LLM helper**: If you use a different LLM than OpenAI or ollama, PgAssistant will help you correctly formulate a query to prompt your preferred [LLM engine](media/prompt.png).
- **Get DDL**: get DDL of Top [queries](media/ddl.png) - pgAssistant is using **pg_dump** to achieve this.
- **PGTune**: [PGTune](media/pgtune.png) is on board. Connected to your DB, pgAssistant can help your ALTER SYSTEM parameters !
- **Docker**: based on pgTune recommandations, pgAssistant can help you writing a [docker-compose](media/pgtune-docker.png)
- **On top queries**, pgAssistant help you to provide [values](media/analyze_parameters.png) on parameters and comment the results of EXPLAIN ANALYZE [output](media/analyze_result.png).
- **NEW in version 1.8**:
- **RFC compliance** : For each table, ask the LLM if the table definitions comply with relevant RFC(s). See sample [here](media/table_structure.png). You will find this in a new menu : Table definition.
- pgAssistant can help you to **find a primary key** with LLM : See sample [here](media/issue_missing_pk.png)
## Screen shots
## Mindset
Some existing tools, can be used to optimize your queries. The strength of these tools lies in their deterministic nature. However, they will never tell you if a column is RFC non-compliant, nor will they suggest alternative ways to optimize a query beyond performance metrics.
We believe that LLMs can accomplish tasks that deterministic tools simply cannot, providing deeper insights, compliance checks, and intelligent query optimizations.
Of course, LLMs can make mistakes—and not just small ones. It’s crucial to choose the right model, test extensively, and always keep in mind that the accuracy rate is not 100%. However, by using models like OpenAI, Claude, or Codestral, which we have tested on numerous databases, we believe pgAssistant can effectively help developers refine their schemas and optimize their queries.
Please use pgAssistant in a non-production environment. The purpose of this tool is to help you fix your database before it goes into production.
Finally, pgAssistant was designed to help developers design and understand their PostgreSQL database. The initial idea was clearly to make developers as autonomous as possible, educate them, and also reduce the need for DBA expertise.
## Before you begin
pgAssistant needs the **pg_stat_statements** postgresql module to run.
To activate this module on your database is very simple. Below, you will find an example of how to activate it if you are in a Docker environment, as well as the PostgreSQL documentation.
- Docker environment sample here : https://github.com/nexsol-technologies/pgassistant-postgres-sample/tree/main
- Postgresql documentation here : https://www.postgresql.org/docs/current/pgstatstatements.html
## Special thanks
Not being a front-end developer, I would like to thank the team that developed Volt BootStrap 5, which is available here: https://github.com/themesberg/volt-bootstrap-5-dashboard. Thank you guys, you saved my life !
I've used this project to develop the application' interface.
## Using LLM with pgAssistant
Take a look at this documentation : [here](LLM.md)
## Understanding the myqueries.json file
**myqueries.json** file is used to store your helpfull queries.
Each querie you add to the json file can be searched and executed by pgAssistant.
The JSON format is very simple :
```json
{
"id": "db_version",
"description": "Database version",
"category": "Database",
"sql": "SHOW server_version;",
"type": "select"
"reference": "https://www.postgresql.org/docs/current/sql-show.html"
}
```
- **id** A unique ID of the query
- **description** The description of your SQL query
- **categorie** A SQL category like Database, Issue, Table, Index or whatever you want
- **sql** The SQL query ended with a ";"
- **reference** An URL on the query documentation or your project documentation
- **type** 2 sql types are alowed
- select : performing a select
- param_query : a select query with parameters. Each parameter must be in the format $1, $2, etc.
## ✅ Start in `Docker` and use the Nexsol technologies image
Use this [docker-compse.yml](docker-compose/docker-compose.yml) and run :
```bash
$ docker-compose up
```
Visit `http://localhost:8080/database.html` in your browser. The app should be up & running.
## ✅ Manual Build
> Download the code
### 👉 Set Up for `Unix`, `MacOS`
> Install modules via `VENV`
```bash
$ virtualenv env
$ source env/bin/activate
$ pip3 install -r requirements.txt
```
> Set Up Flask Environment
```bash
$ export FLASK_APP=run.py
```
> Start the app
```bash
$ flask run
// OR
$ flask run --cert=adhoc # For HTTPS server
```
At this point, the app runs at `http://127.0.0.1:5000/database.html`.