How to use SQLAlchemy ORM to access Google Tasks 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 Google Tasks-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Google Tasks data to query Google Tasks data.

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

Connecting to Google Tasks Data

Connecting to Google Tasks 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.

Start by setting the Profile connection property to the location of the Google Tasks Profile on disk (e.g. C:\profiles\GoogleTasks.apip). Next, set the ProfileSettings connection property to the connection string for Google Tasks (see below).

Google Tasks API Profile Settings

In the Google Cloud Console, enable the Google Tasks API and create OAuth 2.0 credentials to obtain your Client ID and Client Secret.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Google Tasks 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\GoogleTasks.apip&Authscheme=OAuth&OAuthClientId=your_client_id&OAuthClientSecret=your_client_secret&CallbackUrl=your_callback_url")

Declare a Mapping Class for Google Tasks 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 TaskLists 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 TaskLists(base):
	__tablename__ = "TaskLists"
	Id = Column(String,primary_key=True)
	Kind = Column(String)
	...

Query Google Tasks 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\GoogleTasks.apip&Authscheme=OAuth&OAuthClientId=your_client_id&OAuthClientSecret=your_client_secret&CallbackUrl=your_callback_url")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(TaskLists).filter_by(Title="My Tasks"):
	print("Id: ", instance.Id)
	print("Kind: ", instance.Kind)
	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

TaskLists_table = TaskLists.metadata.tables["TaskLists"]
for instance in session.execute(TaskLists_table.select().where(TaskLists_table.c.Title == "My Tasks")):
	print("Id: ", instance.Id)
	print("Kind: ", instance.Kind)
	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 Google Tasks data. Reach out to our Support Team if you have any questions.