airflow step_function 源码

airflow step_function 代码

文件路径：/airflow/providers/amazon/aws/operators/step_function.py

# 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
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.
from __future__ import annotations

import json
from typing import TYPE_CHECKING, Sequence

from airflow.exceptions import AirflowException
from airflow.models import BaseOperator
from airflow.providers.amazon.aws.hooks.step_function import StepFunctionHook

if TYPE_CHECKING:
    from airflow.utils.context import Context


class StepFunctionStartExecutionOperator(BaseOperator):
    """
    An Operator that begins execution of an AWS Step Function State Machine.

    Additional arguments may be specified and are passed down to the underlying BaseOperator.

    .. seealso::
        For more information on how to use this operator, take a look at the guide:
        :ref:`howto/operator:StepFunctionStartExecutionOperator`

    :param state_machine_arn: ARN of the Step Function State Machine
    :param name: The name of the execution.
    :param state_machine_input: JSON data input to pass to the State Machine
    :param aws_conn_id: aws connection to uses
    :param do_xcom_push: if True, execution_arn is pushed to XCom with key execution_arn.
    """

    template_fields: Sequence[str] = ('state_machine_arn', 'name', 'input')
    template_ext: Sequence[str] = ()
    ui_color = '#f9c915'

    def __init__(
        self,
        *,
        state_machine_arn: str,
        name: str | None = None,
        state_machine_input: dict | str | None = None,
        aws_conn_id: str = 'aws_default',
        region_name: str | None = None,
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.state_machine_arn = state_machine_arn
        self.name = name
        self.input = state_machine_input
        self.aws_conn_id = aws_conn_id
        self.region_name = region_name

    def execute(self, context: Context):
        hook = StepFunctionHook(aws_conn_id=self.aws_conn_id, region_name=self.region_name)

        execution_arn = hook.start_execution(self.state_machine_arn, self.name, self.input)

        if execution_arn is None:
            raise AirflowException(f'Failed to start State Machine execution for: {self.state_machine_arn}')

        self.log.info('Started State Machine execution for %s: %s', self.state_machine_arn, execution_arn)

        return execution_arn


class StepFunctionGetExecutionOutputOperator(BaseOperator):
    """
    An Operator that returns the output of an AWS Step Function State Machine execution.

    Additional arguments may be specified and are passed down to the underlying BaseOperator.

    .. seealso::
        For more information on how to use this operator, take a look at the guide:
        :ref:`howto/operator:StepFunctionGetExecutionOutputOperator`

    :param execution_arn: ARN of the Step Function State Machine Execution
    :param aws_conn_id: aws connection to use, defaults to 'aws_default'
    """

    template_fields: Sequence[str] = ('execution_arn',)
    template_ext: Sequence[str] = ()
    ui_color = '#f9c915'

    def __init__(
        self,
        *,
        execution_arn: str,
        aws_conn_id: str = 'aws_default',
        region_name: str | None = None,
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.execution_arn = execution_arn
        self.aws_conn_id = aws_conn_id
        self.region_name = region_name

    def execute(self, context: Context):
        hook = StepFunctionHook(aws_conn_id=self.aws_conn_id, region_name=self.region_name)

        execution_status = hook.describe_execution(self.execution_arn)
        execution_output = json.loads(execution_status['output']) if 'output' in execution_status else None

        self.log.info('Got State Machine Execution output for %s', self.execution_arn)

        return execution_output

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