Auto-generated REST API for MS SQL database


January 16, 2021

The Concept

Suppose you have a database with tables and views that you want to expose as JSON through a REST API but you don’t want to couple the database tables with object models in your API but rather have it simply expose the data for querying (read-only).

How could this be done easily and with minimal code?

The Approach

Flask has to be the simplest web app microframework I’ve ever dealt with (though Node’s Express and Ruby’s Sinatra certainly come close). It has a great function jsonify that makes it dead simple to serve a JSON response to a client request.

from flask import Flask, jsonify

app = Flask(__name__)

def index():
    data = {
        "name": "dchess",
        "text": "Hello, World",
    return jsonify(data)

Spin it up on localhost:5000/api/ and voila, data!

Here comes the Sorcery

That’s simple enough but how do we substitute the hard coded dictionary for a database query? This is where SQLAlchemy and Pandas come in handy.

I almost always want to use these two packages together and so a while back I created a pypi package to provide a simple facade with some syntactic sugar to make that even simpler called SQLSorcery. It’s built on top of both of them and has a simple way to optionally install database adapter packages like pyodbc.

That allows me to easily pass in my database connection credentials with environment variables and make sql queries using the Pandas .read_sql_query() method.


I’m a fan of Pipenv. It’s so simple to install python package dependencies into a local virtual environment and also handle environment variables from a .env file. Effectively combining all the functionality of pip, venv, and python-dotenv.

$ pipenv install Flask, sqlsorcery[mssql]

And just like that we’re ready to develop.

Let’s add some environment variables to a .env file to start:


Querying our Data

Then we can return data from a table as simple as:

from sqlsorcery import MSSQL
import pandas as pd

db = MSSQL()

data = pd.read_sql_table("your_table_name", con=db.engine, schema=db.schema)

It’s that simple!

To quickly convert that dataframe into a list of dictionaries (to return as JSON), we can use the Pandas to_dict() method.

data = data.to_dict(orient="records")

What if we want to list all the tables in our database schema? Simple!

tables = db.engine.table_names(schema=db.schema)

With those two approaches and Flask’s jsonify we have everything we need to make a quick, easy, and minimal API on top of any tables in our database schema.

A User Interface

While we can create an API for machines to read from, it’d be nice to have at least an index of tables that a human can read, navigate, and learn what data exists before pointing tools like curl, postman, or Requests at it.

No worries! A tiny jinja HTML template should suffice for a quick list of table names linked to their api endpoint will be just minimal enough to work!

# templates/index.html
    {% for table in tables %}
    <li><a href="/api/{{ table}}">{{ table }}</a></li>
    {% endfor %}

Putting it all together

At this point we should have a file directory that looks like this:

├── Pipfile
├── .env
└── templates
    └── index.html

Let’s finish off our and give it a test run:


from flask import Flask, jsonify, render_template
from sqlsorcery import MSSQL
import pandas as pd

app = Flask(__name__)
db = MSSQL()

def index():
    tables = db.engine.table_names(schema=db.schema)
    return render_template("index.html", tables=tables)

@app.route("/api/<table>", methods=["GET"])
def endpoint(table):
    data = pd.read_sql_table(table, con=db.engine, schema=db.schema)
    data = data.to_dict(orient="records")
    return jsonify(data)

Spin it up on localhost:5000/api/ and explore your data!

$ pipenv run flask run


I wouldn’t take this and deploy it to production anywhere without some serious security decisions, but it makes for a nice proof-of-concept. And certainly could be expanded to include user authentication, a proper production server like gunicorn and a nicer user interface. But at that point you might be better off with Flask-API or Django REST Framework, both of which I’ve used with success.

But still, not bad for less than 20 lines of code.

All the code for this blog can be found on my github. Feel free to fork and use it however you like.

SQLSorcery is also MIT licensed and free to use. It’s in active development and contributors are welcome.