Managing Deployment Workflows
Workflows are automation process algorithms. They describe the flow of the automation by determining which tasks will be executed and when. A task may be an operation (implemented by a plugin), or other actions including running arbitrary code. Workflows are written in Python, using a dedicated framework and APIs.
Workflows are deployment-specific. Each deployment has its own set of workflows, which are declared in the Blueprint. Executions of a workflow are in the context of that deployment.
Controlling workflows (i.e. executing, cancelling, etc.) is achieved using REST calls to the management server. In this guide, the examples use Cloudify CLI commands, which in turn call the REST API calls.
Workflows are executed directly. You executing workflows from the CLI as follows:
cfy executions start my_workflow -d my_deployment
This executes the
my_workflow workflow on the
Workflows run on deployment-dedicated workers on the management server, on top of the Cloudify workflow engine.
When a workflow is executed, an execution object is created for the deployment, containing both static and dynamic information about the workflow’s execution run. The
status field in the Execution object is an important dynamic field that conveys the current state of the execution.
An execution is considered to be a running execution until it reaches one of the three final statuses:
cancelled. For more information, see the Workflow Execution Statuses section on this page.
It is recommended that you have only one running execution per deployment at any time. By default, an attempt to execute a workflow while another execution is running for the same deployment triggers an error. To override this behavior and enable multiple executions to run in parallel, use the
force flag for each execute command. To view the syntax reference, see the CLI Commands Reference.
Writing a Custom Workflow
If you are an advanced user, you might want to create custom workflows. For more information, see Creating Custom Workflows.