airflow appflow 源码
airflow appflow 代码
文件路径:/airflow/providers/amazon/aws/hooks/appflow.py
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# to you under the Apache License, Version 2.0 (the
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#
# http://www.apache.org/licenses/LICENSE-2.0
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# under the License.
from __future__ import annotations
import json
from datetime import datetime, timezone
from time import sleep
from typing import TYPE_CHECKING
from airflow.compat.functools import cached_property
from airflow.providers.amazon.aws.hooks.base_aws import AwsBaseHook
if TYPE_CHECKING:
from mypy_boto3_appflow.client import AppflowClient
from mypy_boto3_appflow.type_defs import TaskTypeDef
class AppflowHook(AwsBaseHook):
"""
Interact with Amazon Appflow, using the boto3 library.
Hook attribute ``conn`` has all methods that listed in documentation.
.. seealso::
- https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/appflow.html
- https://docs.aws.amazon.com/appflow/1.0/APIReference/Welcome.html
Additional arguments (such as ``aws_conn_id`` or ``region_name``) may be specified and
are passed down to the underlying AwsBaseHook.
.. seealso::
:class:`~airflow.providers.amazon.aws.hooks.base_aws.AwsBaseHook`
"""
EVENTUAL_CONSISTENCY_OFFSET: int = 15 # seconds
EVENTUAL_CONSISTENCY_POLLING: int = 10 # seconds
def __init__(self, *args, **kwargs) -> None:
kwargs["client_type"] = "appflow"
super().__init__(*args, **kwargs)
@cached_property
def conn(self) -> AppflowClient:
"""Get the underlying boto3 Appflow client (cached)"""
return super().conn
def run_flow(self, flow_name: str, poll_interval: int = 20) -> 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
:return: The run execution ID
"""
ts_before: datetime = datetime.now(timezone.utc)
sleep(self.EVENTUAL_CONSISTENCY_OFFSET)
response_start = self.conn.start_flow(flowName=flow_name)
execution_id = response_start["executionId"]
self.log.info("executionId: %s", execution_id)
response_desc = self.conn.describe_flow(flowName=flow_name)
last_exec_details = response_desc["lastRunExecutionDetails"]
# Wait Appflow eventual consistence
self.log.info("Waiting for Appflow eventual consistence...")
while (
response_desc.get("lastRunExecutionDetails", {}).get(
"mostRecentExecutionTime", datetime(1970, 1, 1, tzinfo=timezone.utc)
)
< ts_before
):
sleep(self.EVENTUAL_CONSISTENCY_POLLING)
response_desc = self.conn.describe_flow(flowName=flow_name)
last_exec_details = response_desc["lastRunExecutionDetails"]
# Wait flow stops
self.log.info("Waiting for flow run...")
while (
"mostRecentExecutionStatus" not in last_exec_details
or last_exec_details["mostRecentExecutionStatus"] == "InProgress"
):
sleep(poll_interval)
response_desc = self.conn.describe_flow(flowName=flow_name)
last_exec_details = response_desc["lastRunExecutionDetails"]
self.log.info("lastRunExecutionDetails: %s", last_exec_details)
if last_exec_details["mostRecentExecutionStatus"] == "Error":
raise Exception(f"Flow error:\n{json.dumps(response_desc, default=str)}")
return execution_id
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"
):
self.log.info("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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