How to use SQLAlchemy ORM to access Paddle Data in Python

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData API Driver for Python and the SQLAlchemy toolkit, you can build Paddle-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Paddle data to query Paddle data.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Paddle data in Python. When you issue complex SQL queries from Paddle, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to Paddle and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Paddle Data

Connecting to Paddle data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

Using API Key Authentication

Paddle uses API key authentication. To obtain an API key:

1. Sign in to your Paddle account at https://vendors.paddle.com
2. Navigate to Developer Tools > Authentication
3. Click "Generate API Key"
4. Assign the appropriate permissions for the data you wish to access
5. Copy the generated key (sandbox keys begin with pdl_sdbx_apikey_; production keys begin with pdl_live_apikey_)

After obtaining your API key, set the following connection properties:

• AuthScheme: Set this to APIKey.

Set the following in the ProfileSettings connection property:

• APIKey: Set this to your Paddle API key.

Example Connection String

Profile=C:\profiles\Paddle.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_api_key";

Connecting to Paddle

Once the authentication is configured, you can connect to Paddle and query data from any of the available tables such as Products, Customers, Subscriptions, and Transactions.

Follow the procedure below to install SQLAlchemy and start accessing Paddle through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit and SQLAlchemy ORM package:

pip install sqlalchemy
pip install sqlalchemy.orm

Be sure to import the appropriate modules:

from sqlalchemy import create_engine, String, Column
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

Model Paddle Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Paddle data.

NOTE: Users should URL encode the any connection string properties that include special characters. For more information, refer to the SQL Alchemy documentation.

engine = create_engine("api:///?Profile=C:\profiles\Paddle.apip&AuthScheme=APIKey&ProfileSettings="APIKey=your_api_key"")

Declare a Mapping Class for Paddle Data

After establishing the connection, declare a mapping class for the table you wish to model in the ORM (in this article, we will model the Products table). Use the sqlalchemy.ext.declarative.declarative_base function and create a new class with some or all of the fields (columns) defined.

base = declarative_base()
class Products(base):
	__tablename__ = "Products"
	 = Column(String,primary_key=True)
	 = Column(String)
	...

Query Paddle Data

With the mapping class prepared, you can use a session object to query the data source. After binding the Engine to the session, provide the mapping class to the session query method.

Using the query Method

engine = create_engine("api:///?Profile=C:\profiles\Paddle.apip&AuthScheme=APIKey&ProfileSettings="APIKey=your_api_key"")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Products).filter_by(=""):
	print(": ", instance.)
	print(": ", instance.)
	print("---------")

Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.

Using the execute Method

Products_table = Products.metadata.tables["Products"]
for instance in session.execute(Products_table.select().where(Products_table.c. == "")):
	print(": ", instance.)
	print(": ", instance.)
	print("---------")

For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps and scripts with connectivity to Paddle data. Reach out to our Support Team if you have any questions.