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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).
CData simplifies access and integration of live OData services data. Our customers leverage CData connectivity to:
Customers use CData's solutions to regularly integrate their OData services with preferred tools, such as Power BI, MicroStrategy, or Tableau, and to replicate data from OData services to their databases or data warehouses.
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, 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.
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
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")
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) ...
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.
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.
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.
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()
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()
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()
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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