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Use SQLAlchemy ORMs to Access JSON Services in Python

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

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

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

Connecting to JSON Services

Connecting to JSON services 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 JSON APIs as bidirectional database tables and 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 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 JSON 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 JSON 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 JSON 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 JSON Services in Python

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

engine = create_engine("json///?URI=C:\people.json&DataModel=Relational")

Declare a Mapping Class for JSON Services

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 JSON Services

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("json///?URI=C:\people.json&DataModel=Relational")
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 JSON Services

To insert JSON services, 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 JSON.

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

Update JSON Services

To update JSON services, 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 JSON.

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

Delete JSON Services

To delete JSON services, 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(people).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()

Free Trial & More Information

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