How to integrate PingOne with Apache Airflow

Apache Airflow supports the creation, scheduling, and monitoring of data engineering workflows. When paired with the CData JDBC Driver for PingOne, Airflow can work with live PingOne data. This article describes how to connect to and query PingOne data from an Apache Airflow instance and store the results in a CSV file.

With built-in optimized data processing, the CData JDBC driver offers unmatched performance for interacting with live PingOne data. When you issue complex SQL queries to PingOne, the driver 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). Its built-in dynamic metadata querying allows you to work with and analyze PingOne data using native data types.

Configuring the Connection to PingOne

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the PingOne JDBC Driver. Either double-click the JAR file or execute the jar file from the command-line.

java -jar cdata.jdbc.pingone.jar

Fill in the connection properties and copy the connection string to the clipboard.

To connect to PingOne, configure these properties:

• Region: The region where the data for your PingOne organization is being hosted.
• AuthScheme: The type of authentication to use when connecting to PingOne.
• Either WorkerAppEnvironmentId (required when using the default PingOne domain) or AuthorizationServerURL, configured as described below.

Configuring WorkerAppEnvironmentId

WorkerAppEnvironmentId 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:

1. From the home page of your PingOne organization, move to the navigation sidebar and click Environments.
2. Find the environment in which you have created your custom OAuth/Worker application (usually Administrators), and click Manage Environment. The environment's home page displays.
3. In the environment's home page navigation sidebar, click Applications.
4. Find your OAuth or Worker application details in the list.
5. Copy the value in the Environment ID field. It should look similar to:
   

   WorkerAppEnvironmentId='11e96fc7-aa4d-4a60-8196-9acf91424eca'

Now set WorkerAppEnvironmentId to the value of the Environment ID field.

Configuring AuthorizationServerURL

AuthorizationServerURL 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.

Authenticating to PingOne with OAuth

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:

• Create and configure a custom OAuth application, as described in Creating a Custom OAuth Application in the Help documentation.
• To ensure that the driver can access the entities in Data Model, confirm that you have configured the correct roles for the admin user/worker application you will be using, as described in Administrator Roles in the Help documentation.
• Set the appropriate properties for the authscheme and authflow of your choice, as described in the following subsections.

OAuth (Authorization Code grant)

Set AuthScheme to OAuth.

Desktop Applications

Get and Refresh the OAuth Access Token

After setting the following, you are ready to connect:

• InitiateOAuth: GETANDREFRESH. To avoid the need to repeat the OAuth exchange and manually setting the OAuthAccessToken each time you connect, use InitiateOAuth.
• OAuthClientId: The Client ID you obtained when you created your custom OAuth application.
• OAuthClientSecret: The Client Secret you obtained when you created your custom OAuth application.
• CallbackURL: The redirect URI you defined when you registered your custom OAuth application. For example: https://localhost:3333

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:

1. The driver obtains an access token from PingOne and uses it to request data.
2. The OAuth values are saved in the location specified in OAuthSettingsLocation, to be persisted across connections.

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.

To host the JDBC driver in clustered environments or in the cloud, you will need a license (full or trial) and a Runtime Key (RTK). For more information on obtaining this license (or a trial), contact our sales team.

The following are essential properties needed for our JDBC connection.

Establishing a JDBC Connection within Airflow

1. Log into your Apache Airflow instance.
2. On the navbar of your Airflow instance, hover over Admin and then click Connections.
3. Next, click the + sign on the following screen to create a new connection.
4. In the Add Connection form, fill out the required connection properties:
   • Connection Id: Name the connection, i.e.: pingone_jdbc
   • Connection Type: JDBC Connection
   • Connection URL: The JDBC connection URL from above, i.e.: jdbc:pingone:RTK=5246...;AuthScheme=OAuth;WorkerAppEnvironmentId=eebc33a8-xxxx-4f3a-yyyy-d3e5262fd49e;Region=NA;OAuthClientId=client_id;OAuthClientSecret=client_secret;InitiateOAuth=GETANDREFRESH)
   • Driver Class: cdata.jdbc.pingone.PingOneDriver
   • Driver Path: PATH/TO/cdata.jdbc.pingone.jar
   
5. Test your new connection by clicking the Test button at the bottom of the form.
6. After saving the new connection, on a new screen, you should see a green banner saying that a new row was added to the list of connections:

Creating a DAG

A DAG in Airflow is an entity that stores the processes for a workflow and can be triggered to run this workflow. Our workflow is to simply run a SQL query against PingOne data and store the results in a CSV file.

1. To get started, in the Home directory, there should be an "airflow" folder. Within there, we can create a new directory and title it "dags". In here, we store Python files that convert into Airflow DAGs shown on the UI.
2. Next, create a new Python file and title it pingone_hook.py. Insert the following code inside of this new file:

   	import time
   	from datetime import datetime
   	from airflow.decorators import dag, task
   	from airflow.providers.jdbc.hooks.jdbc import JdbcHook
   	import pandas as pd
   
   	# Declare Dag
   	@dag(dag_id="pingone_hook", schedule_interval="0 10 * * *", start_date=datetime(2022,2,15), catchup=False, tags=['load_csv'])
   	
   	# Define Dag Function
   	def extract_and_load():
   	# Define tasks
   		@task()
   		def jdbc_extract():
   			try:
   				hook = JdbcHook(jdbc_conn_id="jdbc")
   				sql = """ select * from Account """
   				df = hook.get_pandas_df(sql)
   				df.to_csv("/{some_file_path}/{name_of_csv}.csv",header=False, index=False, quoting=1)
   				# print(df.head())
   				print(df)
   				tbl_dict = df.to_dict('dict')
   				return tbl_dict
   			except Exception as e:
   				print("Data extract error: " + str(e))
               
   		jdbc_extract()
       
   	sf_extract_and_load = extract_and_load()
   

3. Save this file and refresh your Airflow instance. Within the list of DAGs, you should see a new DAG titled "pingone_hook".
4. Click on this DAG and, on the new screen, click on the unpause switch to make it turn blue, and then click the trigger (i.e. play) button to run the DAG. This executes the SQL query in our pingone_hook.py file and export the results as a CSV to whichever file path we designated in our code.
5. After triggering our new DAG, we check the Downloads folder (or wherever you chose within your Python script), and see that the CSV file has been created - in this case, account.csv.
6. Open the CSV file to see that your PingOne data is now available for use in CSV format thanks to Apache Airflow.

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