Failure detection - Python SDK feature guide

This page shows how to do the following:

• Set Workflow timeouts
• set Workflow retries
• Set Activity timeouts
• Set an Activity Retry Policy
• Heartbeat an Activity

Workflow timeouts

How to set Workflow timeouts using the Temporal Python SDK.

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

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)),
    )

Set Activity timeouts

How to set an Activity Execution Timeout using the Temporal Python SDK.

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

The following timeouts are available in the Activity Options.

• Schedule-To-Close Timeout: is the maximum amount of time allowed for the overall Activity Execution.
• Start-To-Close Timeout: is the maximum time allowed for a single Activity Task Execution.
• Schedule-To-Start Timeout: is the maximum amount of time that is allowed from when an Activity Task is scheduled to when a Worker starts that Activity Task.

An Activity Execution must have either the Start-To-Close or the Schedule-To-Close Timeout set.

Activity options are set as keyword arguments after the Activity arguments.

Available timeouts are:

• schedule_to_close_timeout
• schedule_to_start_timeout
• start_to_close_timeout

View the source code

in the context of the rest of the application code.

# ...
        activity_timeout_result = await workflow.execute_activity(
            your_activity,
            YourParams(greeting, "Activity Timeout option"),
            # Activity Execution Timeout
            start_to_close_timeout=timedelta(seconds=10),
            # schedule_to_start_timeout=timedelta(seconds=10),
            # schedule_to_close_timeout=timedelta(seconds=10),
        )

Set an Activity Retry Policy

How to set an Activity Retry Policy using the Temporal Python SDK.

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

Activity Executions are automatically associated with a default Retry Policy if a custom one is not provided.

To create an Activity Retry Policy in Python, set the RetryPolicy class within the start_activity() or execute_activity() function.

View the source code

in the context of the rest of the application code.

from temporalio.common import RetryPolicy
# ...
        activity_result = await workflow.execute_activity(
            your_activity,
            YourParams(greeting, "Retry Policy options"),
            start_to_close_timeout=timedelta(seconds=10),
            # Retry Policy
            retry_policy=RetryPolicy(
                backoff_coefficient=2.0,
                maximum_attempts=5,
                initial_interval=timedelta(seconds=1),
                maximum_interval=timedelta(seconds=2),
                # non_retryable_error_types=["ValueError"],
            ),
        )

Heartbeat an Activity

How to Heartbeat an Activity using the Temporal Python SDK.

An Activity Heartbeat is a ping from the Worker Process that is executing the Activity to the Temporal Cluster. Each Heartbeat informs the Temporal Cluster that the Activity Execution is making progress and the Worker has not crashed. If the Cluster does not receive a Heartbeat within a Heartbeat Timeout time period, the Activity will be considered failed and another Activity Task Execution may be scheduled according to the Retry Policy.

Heartbeats may not always be sent to the Cluster—they may be throttled by the Worker.

Activity Cancellations are delivered to Activities from the Cluster when they Heartbeat. Activities that don't Heartbeat can't receive a Cancellation. Heartbeat throttling may lead to Cancellation getting delivered later than expected.

Heartbeats can contain a details field describing the Activity's current progress. If an Activity gets retried, the Activity can access the details from the last Heartbeat that was sent to the Cluster.

To Heartbeat an Activity Execution in Python, use the heartbeat() API.

@activity.defn
async def your_activity_definition() -> str:
    activity.heartbeat("heartbeat details!")

In addition to obtaining cancellation information, Heartbeats also support detail data that persists on the server for retrieval during Activity retry. If an Activity calls heartbeat(123, 456) and then fails and is retried, heartbeat_details returns an iterable containing 123 and 456 on the next Run.

Set a Heartbeat Timeout

How to set a Heartbeat Timeout using the Temporal Python SDK.

A Heartbeat Timeout works in conjunction with Activity Heartbeats.

heartbeat_timeout is a class variable for the start_activity() function used to set the maximum time between Activity Heartbeats.

workflow.start_activity(
    activity="your-activity",
    schedule_to_close_timeout=timedelta(seconds=5),
    heartbeat_timeout=timedelta(seconds=1),
)

execute_activity() is a shortcut for start_activity() that waits on its result.

To get just the handle to wait and cancel separately, use start_activity(). execute_activity() should be used in most cases unless advanced task capabilities are needed.

workflow.execute_activity(
    activity="your-activity",
    name,
    schedule_to_close_timeout=timedelta(seconds=5),
    heartbeat_timeout=timedelta(seconds=1),
)