How to use SQLAlchemy ORM to access Cosmos DB Data in Python

Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Cosmos DB data.

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

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

Connecting to Cosmos DB Data

Connecting to Cosmos DB 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 obtain the connection string needed to connect to a Cosmos DB account using the SQL API, log in to the Azure Portal, select Azure Cosmos DB, and select your account. In the Settings section, click Connection String and set the following values:

AccountEndpoint: The Cosmos DB account URL from the Keys blade of the Cosmos DB account
AccountKey: In the Azure portal, navigate to the Cosmos DB service and select your Azure Cosmos DB account. From the resource menu, go to the Keys page. Find the PRIMARY KEY value and set AccountKey to this value.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Cosmos DB 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("cosmosdb:///?AccountEndpoint=myAccountEndpoint&AccountKey=myAccountKey")

Declare a Mapping Class for Cosmos DB 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 Customers 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 Customers(base):
	__tablename__ = "Customers"
	City = Column(String,primary_key=True)
	CompanyName = Column(String)
	...

Query Cosmos DB 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("cosmosdb:///?AccountEndpoint=myAccountEndpoint&AccountKey=myAccountKey")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Customers).filter_by(Name="Morris Park Bake Shop"):
	print("City: ", instance.City)
	print("CompanyName: ", instance.CompanyName)
	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


Customers_table = Customers.metadata.tables["Customers"]
for instance in session.execute(Customers_table.select().where(Customers_table.c.Name == "Morris Park Bake Shop")):
	print("City: ", instance.City)
	print("CompanyName: ", instance.CompanyName)
	print("---------")

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

Insert Cosmos DB Data

To insert Cosmos DB 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 Cosmos DB.


new_rec = Customers(City="placeholder", Name="Morris Park Bake Shop")
session.add(new_rec)
session.commit()

Update Cosmos DB Data

To update Cosmos DB 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 Cosmos DB.


updated_rec = session.query(Customers).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.Name = "Morris Park Bake Shop"
session.commit()

Delete Cosmos DB Data

To delete Cosmos DB 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(Customers).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 Cosmos DB to start building Python apps and scripts with connectivity to Cosmos DB data. Reach out to our Support Team if you have any questions.

Ready to get started?

Download a free trial of the Cosmos DB Connector to get started:

Download Now

Learn more:

Cosmos DB Python Connector

Python Connector Libraries for Cosmos DB Data Connectivity. Integrate Cosmos DB with popular Python tools like Pandas, SQLAlchemy, Dash & petl.