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Continue-As-New

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This page covers the following for Python developers:

What is Continue-As-New?​

Continue-As-New lets a Workflow Execution close successfully and creates a new Workflow Execution. You can think of it as a checkpoint when your Workflow gets too long or approaches certain scaling limits.

The new Workflow Execution is in the same chain; it keeps the same Workflow Id but gets a new Run Id and a fresh Event History. It also receives your Workflow's usual parameters.

Use Continue-As-New with the Python SDK​

First, design your Workflow parameters so that you can pass in the "current state" when you Continue-As-New into the next Workflow run. This state is typically set to None for the original caller of the Workflow.

python-sandbox/workflows/continue_as_new/workflow.py

@dataclass
class ClusterManagerInput:
    state: Optional[ClusterManagerState] = None
    test_continue_as_new: bool = False


@workflow.defn
class ClusterManagerWorkflow:
    @workflow.init
    def __init__(self, input: ClusterManagerInput) -> None:
        # The original caller passes state=None. Later runs receive the state from the previous run.
        self.state = input.state or ClusterManagerState()
        # Test hook: use a small history limit so tests reach Continue-As-New quickly.
        self.max_history_length: Optional[int] = (
            120 if input.test_continue_as_new else None
        )

    @workflow.run
    async def run(self, input: ClusterManagerInput) -> ClusterManagerResult:

The test hook in the above snippet is covered below.

Inside your Workflow, call the continue_as_new() function with the same type. This stops the Workflow right away and starts a new one.

python-sandbox/workflows/continue_as_new/workflow.py

workflow.continue_as_new(
    ClusterManagerInput(
        state=self.state,
        test_continue_as_new=input.test_continue_as_new,
    )
)

Considerations for Workflows with message handlers​

If you use Updates or Signals, don't call Continue-as-New from the handlers. Instead, wait for your handlers to finish in your main Workflow before you run continue_as_new. See the all_handlers_finished example for guidance.

When is it right to Continue-As-New with the Python SDK?​

Use Continue-as-New when your Workflow might encounter degraded performance or Event History Limits.

Temporal tracks your Workflow's progress against these limits to let you know when you should Continue-as-New. Call workflow.info().is_continue_as_new_suggested() to check if it's time.

Test Continue-As-New with the Python SDK​

Testing Workflows that naturally Continue-as-New may be time-consuming and resource-intensive. Instead, add a test hook to check your Workflow's Continue-as-New behavior faster in automated tests.

For example, when test_continue_as_new == True, this sample creates a test-only variable called self.max_history_length and sets it to a small value. A helper method in the Workflow checks it each time it considers using Continue-as-New:

python-sandbox/workflows/continue_as_new/workflow.py

def should_continue_as_new(self) -> bool:
    if workflow.info().is_continue_as_new_suggested():
        return True
    # For testing
    if (
        self.max_history_length
        and workflow.info().get_current_history_length() > self.max_history_length
    ):
        return True
    return False