How to integrate AlloyDB with Apache Airflow

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

Configuring the Connection to AlloyDB

Built-in Connection String Designer

For assistance in constructing the JDBC URL, use the connection string designer built into the AlloyDB 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.

The following connection properties are usually required in order to connect to AlloyDB.

• Server: The host name or IP of the server hosting the AlloyDB database.
• User: The user which will be used to authenticate with the AlloyDB server.
• Password: The password which will be used to authenticate with the AlloyDB server.

You can also optionally set the following:

• Database: The database to connect to when connecting to the AlloyDB Server. If this is not set, the user's default database will be used.
• Port: The port of the server hosting the AlloyDB database. This property is set to 5432 by default.

Authenticating with Standard Authentication

Standard authentication (using the user/password combination supplied earlier) is the default form of authentication.

No further action is required to leverage Standard Authentication to connect.

Authenticating with pg_hba.conf Auth Schemes

There are additional methods of authentication available which must be enabled in the pg_hba.conf file on the AlloyDB server.

Find instructions about authentication setup on the AlloyDB Server here.

Authenticating with MD5 Authentication

This authentication method must be enabled by setting the auth-method in the pg_hba.conf file to md5.

Authenticating with SASL Authentication

This authentication method must be enabled by setting the auth-method in the pg_hba.conf file to scram-sha-256.

Authenticating with Kerberos

The authentication with Kerberos is initiated by AlloyDB Server when the ∏ is trying to connect to it. You should set up Kerberos on the AlloyDB Server to activate this authentication method. Once you have Kerberos authentication set up on the AlloyDB Server, see the Kerberos section of the help documentation for details on how to authenticate with Kerberos.

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.: alloydb_jdbc
   • Connection Type: JDBC Connection
   • Connection URL: The JDBC connection URL from above, i.e.: jdbc:alloydb:RTK=5246...;User=alloydb;Password=admin;Database=alloydb;Server=127.0.0.1;Port=5432)
   • Driver Class: cdata.jdbc.alloydb.AlloyDBDriver
   • Driver Path: PATH/TO/cdata.jdbc.alloydb.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 AlloyDB 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 alloydb_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="alloydb_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 "alloydb_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 alloydb_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 AlloyDB data is now available for use in CSV format thanks to Apache Airflow.

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