Workflow Timeouts - Python SDK

Workflow timeouts

Each Workflow timeout controls the maximum duration of a different aspect of a Workflow Execution.

Before we continue, we want to note that we generally do not recommend setting Workflow Timeouts, because Workflows are designed to be long-running and resilient. Instead, setting a Timeout can limit its ability to handle unexpected delays or long-running processes. If you need to perform an action inside your Workflow after a specific period of time, we recommend using a Timer.

Workflow timeouts are set when starting the Workflow Execution.

• Workflow Execution Timeout - restricts the maximum amount of time that a single Workflow Execution can be executed.
• Workflow Run Timeout: restricts the maximum amount of time that a single Workflow Run can last.
• Workflow Task Timeout: restricts the maximum amount of time that a Worker can execute a Workflow Task.

Set the timeout to either the start_workflow() or execute_workflow() asynchronous methods.

Available timeouts are:

• execution_timeout
• run_timeout
• task_timeout

View the source code

in the context of the rest of the application code.

# ...
    result = await client.execute_workflow(
        YourWorkflow.run,
        "your timeout argument",
        id="your-workflow-id",
        task_queue="your-task-queue",
        # Set Workflow Timeout duration
        execution_timeout=timedelta(seconds=2),
        # run_timeout=timedelta(seconds=2),
        # task_timeout=timedelta(seconds=2),
    )

Workflow retries

How to set a Workflow Retry Policy using the Temporal Python SDK

A Retry Policy can work in cooperation with the timeouts to provide fine controls to optimize the execution experience.

Use a Retry Policy to retry a Workflow Execution in the event of a failure.

Workflow Executions do not retry by default, and Retry Policies should be used with Workflow Executions only in certain situations.

Set the Retry Policy to either the start_workflow() or execute_workflow() asynchronous methods.

View the source code

in the context of the rest of the application code.

# ...
    handle = await client.execute_workflow(
        YourWorkflow.run,
        "your retry policy argument",
        id="your-workflow-id",
        task_queue="your-task-queue",
        retry_policy=RetryPolicy(maximum_interval=timedelta(seconds=2)),
    )