Input credentials and database information: data: Turn off the charts provided PostgreSQL resources: postgresql: Make sure we have some example DAGs to play with: env: Set Airflow to use the KubernetesExecutor: executor: "KubernetesExecutor" GRANT ALL PRIVILEGES ON DATABASE airflow_db TO airflow Pulling the Chart and Value FileĪfter the database is set up, we can move on to preparing the chart and our values file. Using Helm, add the airflow chart repository: helm repo add apache-airflow įor the values file, retrieve the default values from the chart. Next, referring to the Airflow documentation, we can execute the following commands: CREATE DATABASE airflow_db ĬREATE USER airflow WITH PASSWORD 'your-password' First Log into the database server using the psql command: psql "host=************. port=5432 dbname=postgres user=**************** password=********* sslmode=require" I will be using the Azure PostgreSQL Service but any compatible version will do. This most basic of configurations requires a database and we have chosen to use PostgreSQL in this case.Ĭode samples can be found here. This post will focus on getting the Helm chart deployed to our Kubernetes service. Previously, we formulated a plan to provision Airflow in a Kubernetes cluster using Helm and then build up the supporting services and various configurations that we will need to ensure our cluster is production ready. Airflow + Helm: Simple Airflow Deployment.
#Airflow kubernetes executor logs update
I will update these with links as they are published. This is part two of a five-part series addressing Airflow at an enterprise scale.
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