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OData Python Connector
Python Connector Libraries for OData Data Connectivity. Integrate OData with popular Python tools like Pandas, SQLAlchemy, Dash & petl.
How to use SQLAlchemy ORM to access OData Services in Python
The CData Python Connector for OData enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of OData services.
The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for OData and the SQLAlchemy toolkit, you can build OData-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to OData services to query, update, delete, and insert OData services.
With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live OData services in Python. When you issue complex SQL queries from OData, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to OData and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).
Connecting to OData Services
Connecting to OData 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.
The User and Password properties, under the Authentication section, must be set to valid OData user credentials. In addition, you will need to specify a URL to a valid OData server organization root or OData services file.
Follow the procedure below to install SQLAlchemy and start accessing OData 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 OData Services in Python
You can now connect with a connection string. Use the create_engine function to create an Engine for working with OData services.
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("odata:///?URL=http://services.odata.org/V4/Northwind/Northwind.svc&UseIdUrl=True&OData Version=4.0&Data Format=ATOM")
Declare a Mapping Class for OData 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 Orders 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 Orders(base):
__tablename__ = "Orders"
OrderName = Column(String,primary_key=True)
Freight = Column(String)
...
Query OData 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("odata:///?URL=http://services.odata.org/V4/Northwind/Northwind.svc&UseIdUrl=True&OData Version=4.0&Data Format=ATOM")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Orders).filter_by(ShipCity="New York"):
print("OrderName: ", instance.OrderName)
print("Freight: ", instance.Freight)
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
Orders_table = Orders.metadata.tables["Orders"]
for instance in session.execute(Orders_table.select().where(Orders_table.c.ShipCity == "New York")):
print("OrderName: ", instance.OrderName)
print("Freight: ", instance.Freight)
print("---------")
For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.
Insert OData Services
To insert OData 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 OData.
new_rec = Orders(OrderName="placeholder", ShipCity="New York")
session.add(new_rec)
session.commit()
Update OData Services
To update OData 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 OData.
updated_rec = session.query(Orders).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.ShipCity = "New York"
session.commit()
Delete OData Services
To delete OData 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 records (rows).
deleted_rec = session.query(Orders).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
session.delete(deleted_rec)
session.commit()
Free Trial & More Information
Download a free, 30-day trial of the CData Python Connector for OData to start building Python apps and scripts with connectivity to OData services. Reach out to our Support Team if you have any questions.
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