Use SQLAlchemy ORMs to Access Google Directory Data in Python

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Google Directory Python Connector

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

The CData Python Connector for Google Directory enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Google Directory data.

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

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

Connecting to Google Directory Data

Connecting to Google Directory 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.

Google uses the OAuth authentication standard. You can authorize the data provider to access Google Spreadsheets as an individual user or with a Google Apps Domain service account. See the Getting Started section of the data provider help documentation for an authentication guide.

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

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit:

pip install sqlalchemy

Be sure to import the module with the following:

import sqlalchemy

Model Google Directory Data in Python

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

engine = create_engine("googledirectory:///?OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for Google Directory 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 MyTable 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 MyTable(base):
	__tablename__ = "MyTable"
	Id = Column(String,primary_key=True)
	Description = Column(String)

Query Google Directory 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("googledirectory:///?OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(MyTable).filter_by(Status="confirmed"):
	print("Id: ", instance.Id)
	print("Description: ", instance.Description)

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

Using the execute Method

MyTable_table = MyTable.metadata.tables["MyTable"]
for instance in session.execute( == "confirmed")):
	print("Id: ", instance.Id)
	print("Description: ", instance.Description)

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

Insert Google Directory Data

To insert Google Directory 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 Google Directory.

new_rec = MyTable(Id="placeholder", Status="confirmed")

Update Google Directory Data

To update Google Directory 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 Google Directory.

updated_rec = session.query(MyTable).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.Status = "confirmed"

Delete Google Directory Data

To delete Google Directory 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(MyTable).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()

Free Trial & More Information

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