AlexOdyn / sqlvisplus

Python package to visualise SQL queries as graphs

Coming soon: SQLVis+, with error messages and error repairs for select syntax errors!

SQLvis

A library to generate a graph-based visualization of SQL queries in Jupyter Notebooks. More information on Jupyter Notebooks is here.

This library works best in Chrome. Please note that this is a research prototype, and therefore may be incomplete. If you find any issues or would like any extensions, feel free to post them under the Issues tab.

If you use SQLvis in your research, please cite this paper:

@inproceedings{SQLVis,
  author = {Miedema, Daphne and Fletcher, George},
  title = {{SQLVis}: Visual Query Representations for Supporting SQL Learners},
  booktitle = {2021 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)},
  publisher = {IEEE},
  year = {2021}
}

Installation

The easiest way is to install via pip:

$ pip install sqlvis

Dependencies

• Pandas
  • $ pip install pandas

Usage

For the minimum working example below, please make sure to download shopping.db from the data folder.

from sqlvis import vis
import sqlite3

conn = sqlite3.connect('shopping.db')

# Retrieve the shema from the db connection
schema = vis.schema_from_conn(conn)

query = '''
SELECT cName FROM customer;
'''

# Generate the visualization.
vis.visualize(query, schema)

Visualization explanation

SQLvis draws graph representations of SQL queries. Below, I show some queries and visualization examples.

Example 1

SELECT c.cName 
FROM customer AS c, purchase AS p 
WHERE p.cID = c.cID;

Visualization	Explanation
	This example shows the basic graph structure we use. Each table that is called within the query is represented as a node. The node also displays its alias, if it has one. Relations between tables, such as JOIN conditions, are shown as edges with the content of the condition on the edge.

Example 2

SELECT * 
FROM customer AS c 
WHERE city = "Amsterdam" OR city = "Utrecht";

Visualization	Explanation
	Each node in the visualization can be expanded to show the schema of the table. The expanded schema is highlighted based on the contents of the query. Selection on columns is highlighted in orange. Here the query contains SELECT *, so all columns are highlighted. Conditions are highlighted in green. This query contains two conditions, both on the city attribute.

Example 3

SELECT c.cName 
FROM customer AS c 
WHERE EXISTS (
    SELECT pr.pID 
    FROM purchase AS p, product AS pr 
    WHERE p.cID = c.cID 
    AND p.pID = pr.pID 
    AND pr.pID < 10);

Visualization	Explanation
	This visualization displays a subquery. The two tables in the subquery are purchase and product. You can see that these are wrapped in a colored rectangle. The visualization can als represent nesting on higher levels. The deeper the nesting, the darker the color.

License

This project is licensed under the MIT License - see the LICENSE file for details.