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from __future__ import annotations

import json

from import AwsBaseHook

[docs]class StepFunctionHook(AwsBaseHook): """ Interact with an AWS Step Functions State Machine. Provide thin wrapper around :external+boto3:py:class:`boto3.client("stepfunctions") <SFN.Client>`. Additional arguments (such as ``aws_conn_id``) may be specified and are passed down to the underlying AwsBaseHook. .. seealso:: - :class:`` """ def __init__(self, *args, **kwargs) -> None: kwargs["client_type"] = "stepfunctions" super().__init__(*args, **kwargs)
[docs] def start_execution( self, state_machine_arn: str, name: str | None = None, state_machine_input: dict | str | None = None, ) -> str: """ Start Execution of the State Machine. .. seealso:: - :external+boto3:py:meth:`SFN.Client.start_execution` :param state_machine_arn: AWS Step Function State Machine ARN. :param name: The name of the execution. :param state_machine_input: JSON data input to pass to the State Machine. :return: Execution ARN. """ execution_args = {"stateMachineArn": state_machine_arn} if name is not None: execution_args["name"] = name if state_machine_input is not None: if isinstance(state_machine_input, str): execution_args["input"] = state_machine_input elif isinstance(state_machine_input, dict): execution_args["input"] = json.dumps(state_machine_input)"Executing Step Function State Machine: %s", state_machine_arn) response = self.conn.start_execution(**execution_args) return response.get("executionArn")
[docs] def describe_execution(self, execution_arn: str) -> dict: """ Describe a State Machine Execution. .. seealso:: - :external+boto3:py:meth:`SFN.Client.describe_execution` :param execution_arn: ARN of the State Machine Execution. :return: Dict with execution details. """ return self.get_conn().describe_execution(executionArn=execution_arn)

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