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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# specific language governing permissions and limitations
# 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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