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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 PingOne and the SQLAlchemy toolkit, you can build PingOne-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to PingOne data to query PingOne data.
With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live PingOne data in Python. When you issue complex SQL queries from PingOne, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to PingOne and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).
Connecting to PingOne 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.
To connect to PingOne, configure these properties:
is the ID of the PingOne environment in which your Worker application resides. This parameter is used only when the environment is using the default PingOne domain (auth.pingone). It is configured after you have created the custom OAuth application you will use to authenticate to PingOne, as described in Creating a Custom OAuth Application in the Help documentation.
First, find the value for this property:
WorkerAppEnvironmentId='11e96fc7-aa4d-4a60-8196-9acf91424eca'
Now set to the value of the Environment ID field.
is the base URL of the PingOne authorization server for the environment where your application is located. This property is only used when you have set up a custom domain for the environment, as described in the PingOne platform API documentation. See Custom Domains.
PingOne supports both OAuth and OAuthClient authentication. In addition to performing the configuration steps described above, there are two more steps to complete to support OAuth or OAuthCliet authentication:
Set to OAuth.
Get and Refresh the OAuth Access Token
After setting the following, you are ready to connect:
When you connect, the driver opens PingOne's OAuth endpoint in your default browser. Log in and grant permissions to the application. The driver then completes the OAuth process:
The driver refreshes the access token automatically when it expires.
For other OAuth methods, including Web Applications, Headless Machines, or Client Credentials Grant, refer to the Help documentation.
Follow the procedure below to install SQLAlchemy and start accessing PingOne 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 PingOne data.
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("pingone:///?AuthScheme=OAuth&WorkerAppEnvironmentId=eebc33a8-xxxx-4f3a-yyyy-d3e5262fd49e&Region=NA&OAuthClientId=client_id&OAuthClientSecret=client_secret&InitiateOAuth=GETANDREFRESH")
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 [CData].[Administrators].Users 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 [CData].[Administrators].Users(base): __tablename__ = "[CData].[Administrators].Users" Id = Column(String,primary_key=True) Username = 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("pingone:///?AuthScheme=OAuth&WorkerAppEnvironmentId=eebc33a8-xxxx-4f3a-yyyy-d3e5262fd49e&Region=NA&OAuthClientId=client_id&OAuthClientSecret=client_secret&InitiateOAuth=GETANDREFRESH")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query([CData].[Administrators].Users).filter_by(EmployeeType="Contractor"):
print("Id: ", instance.Id)
print("Username: ", instance.Username)
print("---------")
Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.
[CData].[Administrators].Users_table = [CData].[Administrators].Users.metadata.tables["[CData].[Administrators].Users"]
for instance in session.execute([CData].[Administrators].Users_table.select().where([CData].[Administrators].Users_table.c.EmployeeType == "Contractor")):
print("Id: ", instance.Id)
print("Username: ", instance.Username)
print("---------")
For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.
Download a free, 30-day trial of the CData Python Connector for PingOne to start building Python apps and scripts with connectivity to PingOne data. Reach out to our Support Team if you have any questions.
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