Overprovision Deployments on Astronomer Software

By default, Deployments specify CPU and memory requests and limits for Kubernetes in terms of Astronomer Units (AU). For example, if an Airflow scheduler uses 1AU, it has both a request and a limit of 0.1 CPU and 385 MB of memory on its given Kubernetes node. Because you can't normally have a request lower than a limit, some Airflow components might reserve more resources on a node than they actually require.

To change this behavior, you can change the amount of CPU and memory that an AU requests, allowing you to more efficiently provision resources based on the requirements for your Deployments. This is known as overprovisioning, because it allows more Airflow component instances to exist on a single Kubernetes node.

1. Add the following configuration to your values.yaml file. Replace the values for overProvisioningFactorMem and overProvisioningFactorCPU with the factor by which you want to set your resource requests as a percentage of your resource limits.

   astronomer:
     houston:
       config:
         deployments:
           # This is a multiplying factor as a percentage of the limits. Defaults to 1
           overProvisioningFactorMem: 1
           overProvisioningFactorCPU: 1
   
           ## (Optional) Remove components that the overprovisioning factor should not apply to
           overProvisioningComponents:
   
             - scheduler
             - webserver
             - workers
             - triggerer
             - flower
             - pgbouncer
             - statsd
             - triggerer-log-groomer
   

   For example, if you set overProvisioningFactorMem: 0.75 and overProvisioningFactorCPU: 0.5, a scheduler using 1 AU will only request 0.075 CPU and 192.5 MB of memory on a node, allowing you to run more components on that node than before.

   By default, this configuration applies to all Airflow components. Any components that you remove from overProvisioningComponents will not have overprovisioning applied.

2. Save the values.yaml file and push the configuration change to your platform. See Apply a config change. After the change is applied, new Deployments automatically use the updated AU definition.

3. Redeploy code to your existing Deployments to have them start using your updated AU definition. See Deploy code.