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Use SQLAlchemy ORMs to Access DigitalOcean Data in Python

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

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

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

Connecting to DigitalOcean Data

Connecting to DigitalOcean 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.

DigitalOcean uses OAuth 2.0 authentication. To authenticate using OAuth, you can use the embedded credentials or register an app with DigitalOcean.

See the Getting Started guide in the CData driver documentation for more information.

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

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

engine = create_engine("digitalocean///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for DigitalOcean 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 Droplets 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 Droplets(base):
	__tablename__ = "Droplets"
	Id = Column(String,primary_key=True)
	Name = Column(String)

Query DigitalOcean 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("digitalocean///?InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Droplets).filter_by(Id="1"):
	print("Id: ", instance.Id)
	print("Name: ", instance.Name)

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

Using the execute Method

Droplets_table = Droplets.metadata.tables["Droplets"]
for instance in session.execute( == "1")):
	print("Id: ", instance.Id)
	print("Name: ", instance.Name)

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

Insert DigitalOcean Data

To insert DigitalOcean 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 DigitalOcean.

new_rec = Droplets(Id="placeholder", Id="1")

Update DigitalOcean Data

To update DigitalOcean 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 DigitalOcean.

updated_rec = session.query(Droplets).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.Id = "1"

Delete DigitalOcean Data

To delete DigitalOcean 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 recoreds (rows).

deleted_rec = session.query(Droplets).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()

Free Trial & More Information

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