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from __future__ import annotations

from typing import TYPE_CHECKING

from import AwsGenericHook
from import wait

    from mypy_boto3_appflow.client import AppflowClient  # noqa: F401

[docs]class AppflowHook(AwsGenericHook["AppflowClient"]): """ Interact with Amazon AppFlow. Provide thin wrapper around :external+boto3:py:class:`boto3.client("appflow") <Appflow.Client>`. Additional arguments (such as ``aws_conn_id``) may be specified and are passed down to the underlying AwsBaseHook. .. seealso:: - :class:`` - `Amazon Appflow API Reference <>`__ """ def __init__(self, *args, **kwargs) -> None: kwargs["client_type"] = "appflow" super().__init__(*args, **kwargs)
[docs] def run_flow( self, flow_name: str, poll_interval: int = 20, wait_for_completion: bool = True, max_attempts: int = 60, ) -> str: """ Execute an AppFlow run. :param flow_name: The flow name :param poll_interval: Time (seconds) to wait between two consecutive calls to check the run status :param wait_for_completion: whether to wait for the run to end to return :param max_attempts: the number of polls to do before timing out/returning a failure. :return: The run execution ID """ response_start = self.conn.start_flow(flowName=flow_name) execution_id = response_start["executionId"]"executionId: %s", execution_id) if wait_for_completion: wait( waiter=self.get_waiter("run_complete", {"EXECUTION_ID": execution_id}), waiter_delay=poll_interval, waiter_max_attempts=max_attempts, args={"flowName": flow_name}, failure_message="error while waiting for flow to complete", status_message="waiting for flow completion, status", status_args=[ f"flowExecutions[?executionId=='{execution_id}'].executionStatus", f"flowExecutions[?executionId=='{execution_id}'].executionResult.errorInfo", ], ) self._log_execution_description(flow_name, execution_id) return execution_id
def _log_execution_description(self, flow_name: str, execution_id: str): response_desc = self.conn.describe_flow_execution_records(flowName=flow_name) last_execs = {fe["executionId"]: fe for fe in response_desc["flowExecutions"]} exec_details = last_execs[execution_id]"Run complete, execution details: %s", exec_details)
[docs] def update_flow_filter(self, flow_name: str, filter_tasks, set_trigger_ondemand: bool = False) -> None: """ Update the flow task filter; all filters will be removed if an empty array is passed to filter_tasks. :param flow_name: The flow name :param filter_tasks: List flow tasks to be added :param set_trigger_ondemand: If True, set the trigger to on-demand; otherwise, keep the trigger as is :return: None """ response = self.conn.describe_flow(flowName=flow_name) connector_type = response["sourceFlowConfig"]["connectorType"] tasks = [] # cleanup old filter tasks for task in response["tasks"]: if ( task["taskType"] == "Filter" and task.get("connectorOperator", {}).get(connector_type) != "PROJECTION" ):"Removing task: %s", task) else: tasks.append(task) # List of non-filter tasks tasks += filter_tasks # Add the new filter tasks if set_trigger_ondemand: # Clean up attribute to force on-demand trigger del response["triggerConfig"]["triggerProperties"] self.conn.update_flow( flowName=response["flowName"], destinationFlowConfigList=response["destinationFlowConfigList"], sourceFlowConfig=response["sourceFlowConfig"], triggerConfig=response["triggerConfig"], description=response.get("description", "Flow description."), tasks=tasks, )

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