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Using Airflow with Astra

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melienherrera edited this page Feb 10, 2022 · 5 revisions

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  • Last Update 2/10/2022
  • This article was written by Aaron Ploetz

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A - Overview

Apache Airflow is an open source workflow management system. It provides components which allow engineers to build data pipelines between different systems. These instructions will step through tasks/adjustments to be done in each product (Astra DB, cql-proxy, Apache Airflow), ultimately resulting in Airflow being able to work with AstraDB in its directed acyclic graphs (DAG).

B - Prerequisites

This article was written for Apache Airflow version 2.2.3 on MacOS with Python 3.9.

C - Installation

✅ Step 0 Download and install

Following the Apache Airflow reference documentation download and install the software.

✅ Step 1 Create the keyspace airflow

From the Astra DB dashboard, click on your database name. Scroll down to where the keyspaces are listed, and click the Add Keyspace button to create a new keyspace. Name this keyspace airflow.

✅ Step 2 Start Cql Proxy

DataStax’s cql-proxy is designed to function as an intermediate connection point to allow legacy Apache Cassandra applications to connect to DataStax Astra DB using its new Secure Connect Bundle. There are a few ways to install and run DataStax’s cql-proxy, as outlined in CQL-PROXY.

Be sure to start cql-proxy with the following settings:

  • Using the Secure Connect Bundle downloaded in the previous section
  • Binding it to the listen IP of the server instance
  • Specifying the username of “token”
  • Specifying the Astra Token created for the user in Astra DB as the password

You can run cql-proxy (in the foreground) from the command line in this way, like this:

./cql-proxy --bundle ~/local/astraCreds/secure-connect.zip \
    --bind 127.0.0.1 \
    --username token \
    --password AstraCS:rtFckUZblahblahblahblahblahblaha3953d799a525

Important to note that the command shown above binds cql-proxy to localhost (127.0.0.1), meaning it is not reachable (by Airflow) from outside the server instance.

✅ Step 3 Create a new connection in Apache Airflow

Inside Apache Airflow, click Connections from underneath the Admin drop-down menu. Then click on the blue button labeled with the plus sign (+) to add a new connection. Fill out the form as shown in Figure 2:

  • Connection Id: A unique identifier for the connection in Apache Airflow, which will be referenced inside the DAG code. We will use “cassandra_cqlproxy.”
  • Connection Type: Select “Cassandra” from the drop-down. If it is not present, you will have to install Airflow’s Cassandra provider.
  • Host: The listen address that cql-proxy is bound to. In this case, that is “127.0.0.1.”
  • Schema: The Cassandra keyspace which we created in Astra DB. We’ll set that to “airflow” in this case.
  • Login: Your Astra DB client id.
  • Password: Your Astra DB client secret.
  • Port: The port that cql-proxy is listening on for the CQL native binary protocol, most likely 9042.

Figure 2 - Create a new Cassandra connection for Apache Airflow.

Click the blue Save button to persist the new connection.

✅ Step 4 Create a new DAG in Apache Airflow

A directed acyclic graph (DAG) is essentially a Python script which imports one or more libraries specific to Airflow. To create a new DAG, first locate your DAG directory. By default, Airflow looks for custom DAGs in the ~/airflow/dags/ directory.

For testing, there is a sample DAG out in the following GitHub repository: https://github.com/aar0np/DS_Python_stuff/blob/main/cassandra_test_dag.py

This DAG uses the following line to reference the Cassandra connection we created in the above step:

hook = CassandraHook('cassandra_cqlproxy')

The other important aspect is that this DAG sets its unique identifier as cass_hooks_tutorial:

with DAG(
    'cass_hooks_tutorial',

It also specifically creates two unique tasks:

  • check_table_exists
  • query_system_local

✅ Step 5 Final Test

To test the connection, copy the DAG mentioned above into the /dags/ directory. Then we will invoke Airflow’s task testing functionality, by running airflow tasks test and specifying:

  • The DAG’s unique identifier
  • The name of the task to be run
  • The execution date

If today’s date is 2022-02-08, the command looks like this:

airflow tasks test cass_hooks_tutorial check_table_exists 2022-02-08

Many messages will go by quickly. If it worked, the final messages should look something like this:

INFO - Done. Returned value was: True
INFO - Marking task as SUCCESS. dag_id=cass_hooks_tutorial, task_id=check_table_exists, execution_date=20220208T000000, start_date=20220208T195333, end_date=20220208T195334

D - Acknowledgements

Special thanks goes out to Obioma Anomnachi of Anant. Obi’s video and GitHub repo proved quite helpful in building out this tutorial.

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