Introduction to Apache Airflow
Apache Airflow is an open-source platform designed for orchestrating workflows. It allows developers to create, schedule, and monitor workflows as directed acyclic graphs (DAGs). Airflow is highly extensible and can be used for a variety of automation tasks.
Key Use Cases for Airflow
- Data Engineering: Automating ETL pipelines for data transformation and loading.
- Machine Learning Pipelines: Coordinating training, validation, and deployment of machine learning models.
- DevOps: Managing CI/CD pipelines and system automations.
- Analytics: Scheduling reports and running analytics workflows.
- Integration: Orchestrating tasks across multiple services and APIs.
Prerequisites
- Akash CLI: Ensure the Akash CLI is installed and configured.
- Docker Knowledge: Basic understanding of Docker and images.
- Apache Airflow Docker Image: We’ll use the official
apache/airflow
image. - SDL Template: You can use your pre-built SDL template for deploying applications on Akash.
Steps to Deploy Apache Airflow on Akash
1. Prepare Your SDL File
Create a deploy.yaml
file that describes the resources and configurations for your Airflow deployment. Below is a sample SDL file for deploying Apache Airflow:
version: "2.0"
services: airflow: image: apache/airflow:latest expose: - port: 8080 as: 80 to: - global: true env: - AIRFLOW__CORE__EXECUTOR=LocalExecutor - AIRFLOW__CORE__SQL_ALCHEMY_CONN=sqlite:////usr/local/airflow/airflow.db args: - airflow webserver volumes: - size: 1Gi resources: cpu: units: 500m memory: size: 512Mi
profiles: compute: airflow: resources: cpu: units: 500m memory: size: 512Mi storage: size: 1Gi placement: akash: pricing: airflow: denom: uakt amount: 100
deployment: airflow: airflow: profile: airflow count: 1
2. Customize Airflow Configuration
- Update environment variables under
env
in the SDL file to suit your needs. - For a production setup, consider using a database like PostgreSQL instead of SQLite.
- Adjust resource requirements under the
resources
section.
3. Deploy the SDL File to Akash
Run the following commands to deploy Airflow on Akash:
-
Validate Your SDL File:
akash deployment validate deploy.yaml -
Send the Deployment:
akash deployment create deploy.yaml -
Query the Lease: Find the lease created for your deployment:
akash deployment lease-status --dseq <deployment-sequence> -
Access Airflow: Once the lease is active, you will receive an external IP address and port. Use this to access the Airflow web server in your browser.
4. Set Up and Test DAGs
Once Airflow is running, upload your DAGs to the /dags
directory in the container (use persistent storage or mount a volume). Test workflows to ensure everything is configured properly.
Conclusion
Deploying Apache Airflow on Akash leverages decentralized computing resources, reducing costs while maintaining scalability. By customizing the SDL template, you can deploy Airflow for various use cases, from data engineering to machine learning.