Use SQLAlchemy ORMs to Access REST Data in Python

Ready to get started?

Download for a free trial:

Download Now

Learn more:

REST Python Connector

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

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

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

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

Connecting to REST Data

Connecting to REST 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.

See the Getting Started chapter in the data provider documentation to authenticate to your data source: The data provider models REST APIs as bidirectional database tables and XML/JSON files as read-only views (local files, files stored on popular cloud services, and FTP servers). The major authentication schemes are supported, including HTTP Basic, Digest, NTLM, OAuth, and FTP. See the Getting Started chapter in the data provider documentation for authentication guides.

After setting the URI and providing any authentication values, set Format to "XML" or "JSON" and set DataModel to more closely match the data representation to the structure of your data.

The DataModel property is the controlling property over how your data is represented into tables and toggles the following basic configurations.

  • Document (default): Model a top-level, document view of your REST data. The data provider returns nested elements as aggregates of data.
  • FlattenedDocuments: Implicitly join nested documents and their parents into a single table.
  • Relational: Return individual, related tables from hierarchical data. The tables contain a primary key and a foreign key that links to the parent document.

See the Modeling REST Data chapter for more information on configuring the relational representation. You will also find the sample data used in the following examples. The data includes entries for people, the cars they own, and various maintenance services performed on those cars.

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

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

engine = create_engine("rest:///?DataModel=Relational&URI=C:/people.xml&Format=XML")

Declare a Mapping Class for REST 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 people 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 people(base):
	__tablename__ = "people"
	[ ] = Column(String,primary_key=True)
	[ ] = Column(String)

Query REST 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("rest:///?DataModel=Relational&URI=C:/people.xml&Format=XML")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(people).filter_by([ ]="Roberts"):
	print("[ ]: ", instance.[ ])
	print("[ ]: ", instance.[ ])

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

Using the execute Method

people_table = people.metadata.tables["people"]
for instance in session.execute([ ] == "Roberts")):
	print("[ ]: ", instance.[ ])
	print("[ ]: ", instance.[ ])

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

Insert REST Data

To insert REST 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 REST.

new_rec = people([ ]="placeholder", [ ]="Roberts")

Update REST Data

To update REST 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 REST.

updated_rec = session.query(people).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.[ ] = "Roberts"

Delete REST Data

To delete REST 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(people).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()

Free Trial & More Information

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