How to integrate PostgreSQL with Apache Airflow

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

Configuring the Connection to PostgreSQL

Built-in Connection String Designer

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

java -jar cdata.jdbc.postgresql.jar

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

To connect to PostgreSQL, set the Server, Port (the default port is 5432), and Database connection properties and set the User and Password you wish to use to authenticate to the server. If the Database property is not specified, the data provider connects to the user's default database.

SSH Connectivity for PostgreSQL

You can use SSH (Secure Shell) to authenticate with PostgreSQL, whether the instance is hosted on-premises or in supported cloud environments. SSH authentication ensures that access is encrypted (as compared to direct network connections).

SSH Connections to PostgreSQL in Password Auth Mode

To connect to PostgreSQL via SSH in Password Auth mode, set the following connection properties:

• User: PostgreSQL User name
• Password: PostgreSQL Password
• Database: PostgreSQL database name
• Server: PostgreSQL Server name
• Port: PostgreSQL port number like 3306
• UserSSH: "true"
• SSHAuthMode: "Password"
• SSHPort: SSH Port number
• SSHServer: SSH Server name
• SSHUser: SSH User name
• SSHPassword: SSH Password

SSH Connections to PostgreSQL in Public Key Auth Mode

To connect to PostgreSQL via SSH in Password Auth mode, set the following connection properties:

• User: PostgreSQL User name
• Password: PostgreSQL Password
• Database: PostgreSQL database name
• Server: PostgreSQL Server name
• Port: PostgreSQL port number like 3306
• UserSSH: "true"
• SSHAuthMode: "Public_Key"
• SSHPort: SSH Port number
• SSHServer: SSH Server name
• SSHUser: SSH User name
• SSHClientCret: the path for the public key certificate file

👁 Using the built-in connection string designer to generate a JDBC URL (postgresql is shown.)

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. 👁 Clicking 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.: postgresql_jdbc
   • Connection Type: JDBC Connection
   • Connection URL: The JDBC connection URL from above, i.e.: jdbc:postgresql:RTK=5246...;User=postgres;Password=admin;Database=postgres;Server=127.0.0.1;Port=5432;)
   • Driver Class: cdata.jdbc.postgresql.PostgreSQLDriver
   • Driver Path: PATH/TO/cdata.jdbc.postgresql.jar
   👁 Add JDBC connection form
   
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: 👁 New connection added
   

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 PostgreSQL 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 postgresql_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="postgresql_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 "postgresql_hook". 👁 New DAG added
   
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 postgresql_hook.py file and export the results as a CSV to whichever file path we designated in our code. 👁 Run the DAG
   
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. 👁 CSV created
   
6. Open the CSV file to see that your PostgreSQL data is now available for use in CSV format thanks to Apache Airflow. 👁 CSV file with PostgreSQL data.
   

More Information & Free Trial

Download a free, 30-day trial of the CData JDBC Driver for PostgreSQL and start working with your live PostgreSQL data in Apache Airflow. Reach out to our Support Team if you have any questions.