Source code for

# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.
from __future__ import annotations

from typing import TYPE_CHECKING

from airflow.compat.functools import cached_property
from import AwsBaseHook

    from mypy_boto3_appflow.client import AppflowClient
    from mypy_boto3_appflow.type_defs import TaskTypeDef

[docs]class AppflowHook(AwsBaseHook): """ 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) @cached_property
[docs] def conn(self) -> AppflowClient: """Get the underlying boto3 Appflow client (cached)""" return super().conn
[docs] def run_flow(self, flow_name: str, poll_interval: int = 20, wait_for_completion: bool = True) -> 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 :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: self.get_waiter("run_complete", {"EXECUTION_ID": execution_id}).wait( flowName=flow_name, WaiterConfig={"Delay": poll_interval}, ) 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: list[TaskTypeDef], 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: list[TaskTypeDef] = [] # 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,

Was this entry helpful?