Managing Deployment Workflows

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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), 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.

Executing Workflows

Workflows can be executed directly. You can execute workflows from the CLI as follows:
cfy executions start my_workflow -d my_deployment

This executes 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. 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: terminated, failed or 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.

Queing Executions

In general, executions run in parallel. There are a few exceptions: * When a system-wide execution is running (e.g snapshots create), no other execution will be allowed to start. * Two executions under the same deployment cannot run parallely. * System-wide executions (e.g snapshots create) cannot start while an execution (e.g install workflow) is running.

If you start an execution and receive one of the following errors: “You cannot start an execution if there is a running system-wide execution” / “The following executions are currently running for this deployment…” / “You cannot start a system-wide execution if there are other executions running.”, you can add the execution to the executions queue:

cfy executions start -d deployment1 install --queue
cfy snapshots create --queue

Queued executions will begin automatically when possible.

Note: * If an execution can start immidiatly it will, even when the queue flag is passed. * If the queue contains a system-wide execution waiting to start (e.g snapshot create), Cloudify will not accept any other execution request unless the queue flag is passed. This behavior ensures there is no starvation of blocking system operations. If the queue flag isn’t provided, an error will be returned.

Queing Executions

Writing a Custom Workflow

If you are an advanced user, you might want to create custom workflows. For more information, see Creating Custom Workflows.