How to use SQLAlchemy ORM to access Lakebase Data in Python

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

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

Connecting to Lakebase Data

Connecting to Lakebase 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.

To connect to Databricks Lakebase, start by setting the following properties:

• DatabricksInstance: The Databricks instance or server hostname, provided in the format instance-abcdef12-3456-7890-abcd-abcdef123456.database.cloud.databricks.com.
• Server: The host name or IP address of the server hosting the Lakebase database.
• Port (optional): The port of the server hosting the Lakebase database, set to 5432 by default.
• Database (optional): The database to connect to after authenticating to the Lakebase Server, set to the authenticating user's default database by default.

OAuth Client Authentication

To authenicate using OAuth client credentials, you need to configure an OAuth client in your service principal. In short, you need to do the following:

1. Create and configure a new service principal
2. Assign permissions to the service principal
3. Create an OAuth secret for the service principal

For more information, refer to the Setting Up OAuthClient Authentication section in the Help documentation.

OAuth PKCE Authentication

To authenticate using the OAuth code type with PKCE (Proof Key for Code Exchange), set the following properties:

• AuthScheme: OAuthPKCE.
• User: The authenticating user's user ID.

For more information, refer to the Help documentation.

Follow the procedure below to install SQLAlchemy and start accessing Lakebase 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 Lakebase Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Lakebase 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("lakebase:///?DatabricksInstance=lakebase&Server=127.0.0.1&Port=5432&Database=my_database&InitiateOAuth=GETANDREFRESH")

Declare a Mapping Class for Lakebase 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 Orders 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 Orders(base):
	__tablename__ = "Orders"
	ShipName = Column(String,primary_key=True)
	ShipCity = Column(String)
	...

Query Lakebase 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("lakebase:///?DatabricksInstance=lakebase&Server=127.0.0.1&Port=5432&Database=my_database&InitiateOAuth=GETANDREFRESH")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Orders).filter_by(ShipCountry="USA"):
	print("ShipName: ", instance.ShipName)
	print("ShipCity: ", instance.ShipCity)
	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

Orders_table = Orders.metadata.tables["Orders"]
for instance in session.execute(Orders_table.select().where(Orders_table.c.ShipCountry == "USA")):
	print("ShipName: ", instance.ShipName)
	print("ShipCity: ", instance.ShipCity)
	print("---------")

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

Insert Lakebase Data

To insert Lakebase data, define an instance of the mapped class and add it to the active session. Call the commit function on the session to push all added instances to Lakebase.

new_rec = Orders(ShipName="placeholder", ShipCountry="USA")
session.add(new_rec)
session.commit()

Update Lakebase Data

To update Lakebase data, fetch the desired record(s) with a filter query. Then, modify the values of the fields and call the commit function on the session to push the modified record to Lakebase.

updated_rec = session.query(Orders).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.ShipCountry = "USA"
session.commit()

Delete Lakebase Data

To delete Lakebase data, fetch the desired record(s) with a filter query. Then delete the record with the active session and call the commit function on the session to perform the delete operation on the provided records (rows).

deleted_rec = session.query(Orders).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
session.delete(deleted_rec)
session.commit()

Free Trial & More Information

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