Requirements

Kubernetes Versions

Drycc Workflow requires Kubernetes v1.16.15 or later.

Components Requirements

Drycc uses gateway as a routing implementation, so you have to choose an gateway. We recommend using istio or kong.

Workflow supports the use of ACME to manage automatic certificates, cert-manager is also one of the necessary components, if you use cert-manager EAB, you need to set the clusterResourceNamespace to the namespace of drycc.

Workflow supports stateful apps. You can create and use them through the ‘drycc volumes’ command. If you want to use this feature, you must have a StorageClass that supports ReadWriteMany.

Workflow also supports the OSB API through the ‘drycc resources’ command. If you want to use this function, you need to install service-catalog.

Storage Requirements

A variety of Drycc Workflow components rely on an object storage system to do their work, including storing application slugs, Container images and database logs.

Drycc Workflow ships with drycc storage by default, which provides in-cluster.

Workflow supports Amazon Simple Storage Service (S3), Google Cloud Storage (GCS), OpenShift Swift, and Azure Blob Storage. See configuring object storage for setup instructions.

Resource Requirements

When deploying Drycc Workflow, it’s important to provision machines with adequate resources. Drycc is a highly-available distributed system, which means that Drycc components and your deployed applications will move around the cluster onto healthy hosts as hosts leave the cluster for various reasons (failures, reboots, autoscalers, etc.). Because of this, you should have ample spare resources on any machine in your cluster to withstand the additional load of running services for failed machines.

Drycc Workflow components use about 2.5GB of memory across the cluster, and require approximately 30GB of hard disk space. Because it may need to handle additional load if another one fails, each machine has minimum requirements of:

• At least 4GB of RAM (more is better)
• At least 40GB of hard disk space

Note that these estimates are for Drycc Workflow and Kubernetes only. Be sure to leave enough spare capacity for your application footprint as well.

Running smaller machines will likely result in increased system load and has been known to result in component failures and instability.