How to integrate IBM Cloud Object Storage with Apache Airflow

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

Configuring the Connection to IBM Cloud Object Storage

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the IBM Cloud Object Storage 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.

Register a New Instance of Cloud Object Storage

If you do not already have Cloud Object Storage in your IBM Cloud account, follow the procedure below to install an instance of SQL Query in your account:

1. Log in to your IBM Cloud account.
2. Navigate to the page, choose a name for your instance and click Create. You will be redirected to the instance of Cloud Object Storage you just created.

Connecting using OAuth Authentication

There are certain connection properties you need to set before you can connect. You can obtain these as follows:

API Key

To connect with IBM Cloud Object Storage, you need an API Key. You can obtain this as follows:

1. Log in to your IBM Cloud account.
2. Navigate to the Platform API Keys page.
3. On the middle-right corner click "Create an IBM Cloud API Key" to create a new API Key.
4. In the pop-up window, specify the API Key name and click "Create". Note the API Key as you can never access it again from the dashboard.

Cloud Object Storage CRN

If you have multiple accounts, you will need to specify the CloudObjectStorageCRN explicitly. To find the appropriate value, you can:

• Query the Services view. This will list your IBM Cloud Object Storage instances along with the CRN for each.
• Locate the CRN directly in IBM Cloud. To do so, navigate to your IBM Cloud Dashboard. In the Resource List, Under Storage, select your Cloud Object Storage resource to get its CRN.

Connecting to Data

You can now set the following to connect to data:

• InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to avoid repeating the OAuth exchange and manually setting the OAuthAccessToken.
• ApiKey: Set this to your API key which was noted during setup.
• CloudObjectStorageCRN (Optional): Set this to the cloud object storage CRN you want to work with. While the connector attempts to retrieve this automatically, specifying this explicitly is recommended if you have more than Cloud Object Storage account.

When you connect, the connector completes the OAuth process.

1. Extracts the access token and authenticates requests.
2. Saves OAuth values in OAuthSettingsLocation to be persisted across connections.

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.: ibmcloudobjectstorage_jdbc
   • Connection Type: JDBC Connection
   • Connection URL: The JDBC connection URL from above, i.e.: jdbc:ibmcloudobjectstorage:RTK=5246...;ApiKey=myApiKey;CloudObjectStorageCRN=MyInstanceCRN;Region=myRegion;OAuthClientId=MyOAuthClientId;OAuthClientSecret=myOAuthClientSecret;)
   • Driver Class: cdata.jdbc.ibmcloudobjectstorage.IBMCloudObjectStorageDriver
   • Driver Path: PATH/TO/cdata.jdbc.ibmcloudobjectstorage.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 IBM Cloud Object Storage 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 ibm cloud object storage_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="ibm cloud object storage_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 "ibm cloud object storage_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 ibm cloud object storage_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 IBM Cloud Object Storage data is now available for use in CSV format thanks to Apache Airflow.

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