How to integrate HubDB with Apache Airflow

Apache Airflow supports the creation, scheduling, and monitoring of data engineering workflows. When paired with the CData JDBC Driver for HubDB, Airflow can work with live HubDB data. This article describes how to connect to and query HubDB 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 HubDB data. When you issue complex SQL queries to HubDB, the driver pushes supported SQL operations, like filters and aggregations, directly to HubDB 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 HubDB data using native data types.

Configuring the Connection to HubDB

Built-in Connection String Designer

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

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

There are two authentication methods available for connecting to HubDB data source: OAuth Authentication with a public HubSpot application and authentication with a Private application token.

Using a Custom OAuth App

AuthScheme must be set to "OAuth" in all OAuth flows. Be sure to review the Help documentation for the required connection properties for you specific authentication needs (desktop applications, web applications, and headless machines).

Follow the steps below to register an application and obtain the OAuth client credentials:

1. Log into your HubSpot app developer account.
   • Note that it must be an app developer account. Standard HubSpot accounts cannot create public apps.
2. On the developer account home page, click the Apps tab.
3. Click Create app.
4. On the App info tab, enter and optionally modify values that are displayed to users when they connect. These values include the public application name, application logo, and a description of the application.
5. On the Auth tab, supply a callback URL in the "Redirect URLs" box.
   • If you're creating a desktop application, set this to a locally accessible URL like http://localhost:33333.
   • If you are creating a Web application, set this to a trusted URL where you want users to be redirected to when they authorize your application.
6. Click Create App. HubSpot then generates the application, along with its associated credentials.
7. On the Auth tab, note the Client ID and Client secret. You will use these later to configure the driver.

8. Under Scopes, select any scopes you need for your application's intended functionality.

   A minimum of the following scopes is required to access tables:

   • hubdb
   • oauth
   • crm.objects.owners.read
9. Click Save changes.
10. Install the application into a production portal with access to the features that are required by the integration.
    • Under "Install URL (OAuth)", click Copy full URL to copy the installation URL for your application.
    • Navigate to the copied link in your browser. Select a standard account in which to install the application.
    • Click Connect app. You can close the resulting tab.

Using a Private App

To connect using a HubSpot private application token, set the AuthScheme property to "PrivateApp."

You can generate a private application token by following the steps below:

1. In your HubDB account, click the settings icon (the gear) in the main navigation bar.
2. In the left sidebar menu, navigate to Integrations > Private Apps.
3. Click Create private app.
4. On the Basic Info tab, configure the details of your application (name, logo, and description).
5. On the Scopes tab, select Read or Write for each scope you want your private application to be able to access.
6. A minimum of hubdb and crm.objects.owners.read is required to access tables.
7. After you are done configuring your application, click Create app in the top right.
8. Review the info about your application's access token, click Continue creating, and then Show token.
9. Click Copy to copy the private application token.

To connect, set PrivateAppToken to the private application token you retrieved.

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.: hubdb_jdbc
   • Connection Type: JDBC Connection
   • Connection URL: The JDBC connection URL from above, i.e.: jdbc:hubdb:RTK=5246...;AuthScheme=OAuth;OAuthClientID=MyOAuthClientID;OAuthClientSecret=MyOAuthClientSecret;CallbackURL=http://localhost:33333;InitiateOAuth=GETANDREFRESH)
   • Driver Class: cdata.jdbc.hubdb.HubDBDriver
   • Driver Path: PATH/TO/cdata.jdbc.hubdb.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 HubDB 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 hubdb_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="hubdb_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 "hubdb_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 hubdb_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 HubDB data is now available for use in CSV format thanks to Apache Airflow.

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