How to use SQLAlchemy ORM to access Linear 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 Linear and the SQLAlchemy toolkit, you can build Linear-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Linear data to query Linear data.

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

Connecting to Linear Data

Connecting to Linear 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.

You can authenticate to Linear with a personal API key or with OAuth 2.0. The API key is the simplest option for connecting with your own Linear account.

Authenticating with an API Key

Set the following connection properties:

• AuthScheme: Set this to APIKey.
• APIKey: A Linear personal API key.

To create a personal API key, log in to Linear, open Settings > Security & access > Personal API keys, select New API key, and create it. Copy the key immediately, because Linear shows it only once.

Authenticating with OAuth

OAuth requires a custom OAuth application registered in Linear (Settings > API > OAuth applications), which provides the OAuthClientId and OAuthClientSecret. Two flows are supported:

• Authorization code: Set AuthScheme to OAuth, InitiateOAuth to GETANDREFRESH, and provide OAuthClientId, OAuthClientSecret, and the CallbackURL defined in your application (e.g., http://localhost:33333). The driver opens Linear in your browser so you can grant access.
• Client credentials: Set AuthScheme to OAuthClient and provide OAuthClientId and OAuthClientSecret. This authenticates the application itself, with no browser interaction, and suits machine-to-machine integrations.

By default, the driver requests the read,write scopes. The driver refreshes the access token automatically when it expires.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Linear 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("linear:///?AuthScheme=APIKey&APIKey=myAPIKey")

Declare a Mapping Class for Linear 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 Team 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 Team(base):
	__tablename__ = "Team"
	id = Column(String,primary_key=True)
	name = Column(String)
	...

Query Linear 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("linear:///?AuthScheme=APIKey&APIKey=myAPIKey")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Team).filter_by(key="ENG"):
	print("id: ", instance.id)
	print("name: ", instance.name)
	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

Team_table = Team.metadata.tables["Team"]
for instance in session.execute(Team_table.select().where(Team_table.c.key == "ENG")):
	print("id: ", instance.id)
	print("name: ", instance.name)
	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 Python Connector for Linear to start building Python apps and scripts with connectivity to Linear data. Reach out to our Support Team if you have any questions.