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Overview

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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), but it may also be other actions, including arbitrary code. Workflows are written in Python, using dedicated APIs and framework.

Workflows are deployment specific. Every deployment has its own set of workflows (declared in the Blueprint), and executions of a workflow are in the context of that deployment.

Controlling workflows (i.e. executing, cancelling, etc.) is done via REST calls to the management server. In this guide, the examples will be shown using Cloudify CLI commands which in turn call the above REST API calls.

Executing Workflows

Workflows are executed directly. Executing workflows from the CLI is done as follows:

cfy executions start -w my_workflow -d my_deployment

this would execute the my_workflow workflow on the my_deployment deployment.

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. An important dynamic field in the Execution object is the status field, which 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: terminated, failed or cancelled. For more information, refer to the workflow execution statuses section in this page.

Writing a Custom Workflow

Advanced users may wish to write custom workflows. To learn how to write a custom workflow, refer to the Creating your own workflow.