python - 尝试根据文件名数组从父 dag 创建动态 subdag

Question

我正在尝试使用 Airflow 将 s3 文件从“非删除”存储桶(意味着我无法删除文件)移动到 GCS。我不能保证每天都会有新文件，但我必须每天检查新文件。

我的问题是 subdag 的动态创建。如果有文件，我需要 subdags。如果没有文件，我不需要子标签。我的问题是上游/下游设置。在我的代码中，它确实会检测文件，但不会像预期的那样启动 subdag。我错过了一些东西。

这是我的代码:

from airflow import models
from  airflow.utils.helpers import chain
from airflow.providers.amazon.aws.hooks.s3 import S3Hook
from airflow.operators.python_operator import PythonOperator, BranchPythonOperator
from airflow.operators.dummy_operator import DummyOperator
from airflow.operators.subdag_operator import SubDagOperator
from airflow.contrib.operators.s3_to_gcs_operator import S3ToGoogleCloudStorageOperator
from airflow.utils import dates
from airflow.models import Variable
import logging

args = {
    'owner': 'Airflow',
    'start_date': dates.days_ago(1),
    'email': ['sinistersparrow1701@gmail.com'],
    'email_on_failure': True,
    'email_on_success': True,
}

bucket = 'mybucket'
prefix = 'myprefix/'
LastBDEXDate = int(Variable.get("last_publish_date"))
maxdate = LastBDEXDate
files = []

parent_dag = models.DAG(
    dag_id='My_Ingestion',
    default_args=args,
    schedule_interval='@daily',
    catchup=False
)

def Check_For_Files(**kwargs):
    s3 = S3Hook(aws_conn_id='S3_BOX')
    s3.get_conn()
    bucket = bucket
    LastBDEXDate = int(Variable.get("last_publish_date"))
    maxdate = LastBDEXDate
    files = s3.list_keys(bucket_name=bucket, prefix='myprefix/file')
    for file in files:
        print(file)
        print(file.split("_")[-2])
        print(file.split("_")[-2][-8:])  ##proves I can see a date in the file name is ok.
        maxdate = maxdate if maxdate > int(file.split("_")[-2][-8:]) else int(file.split("_")[-2][-8:])
    if maxdate > LastBDEXDate:
        return 'Start_Process'
    return 'finished'

def create_subdag(dag_parent, dag_id_child_prefix, file_name):
    # dag params
    dag_id_child = '%s.%s' % (dag_parent.dag_id, dag_id_child_prefix)

    # dag
    subdag = models.DAG(dag_id=dag_id_child,
              default_args=args,
              schedule_interval=None)

    # operators
    s3_to_gcs_op = S3ToGoogleCloudStorageOperator(
        task_id=dag_id_child,
        bucket=bucket,
        prefix=file_name,
        dest_gcs_conn_id='GCP_Account',
        dest_gcs='gs://my_files/To_Process/',
        replace=False,
        gzip=True,
        dag=subdag)


    return subdag

def create_subdag_operator(dag_parent, filename, index):
    tid_subdag = 'file_{}'.format(index)
    subdag = create_subdag(dag_parent, tid_subdag, filename)
    sd_op = SubDagOperator(task_id=tid_subdag, dag=dag_parent, subdag=subdag)
    return sd_op

def create_subdag_operators(dag_parent, file_list):
    subdags = [create_subdag_operator(dag_parent, file, file_list.index(file)) for file in file_list]
    # chain subdag-operators together
    chain(*subdags)
    return subdags

check_for_files = BranchPythonOperator(
    task_id='Check_for_s3_Files',
    provide_context=True,
    python_callable=Check_For_Files,
    dag=parent_dag
)

finished = DummyOperator(
    task_id='finished',
    dag=parent_dag
)

decision_to_continue = DummyOperator(
    task_id='Start_Process',
    dag=parent_dag
)

if len(files) > 0:
    subdag_ops = create_subdag_operators(parent_dag, files)
    check_for_files >> decision_to_continue >> subdag_ops[0] >> subdag_ops[-1] >> finished


check_for_files >> finished

Answer 1

下面是在 Airflow 中创建动态 DAG 或子 DAG 的推荐方法，尽管还有其他方法，但我想这在很大程度上适用于您的问题。

首先，创建一个文件(yaml/csv)其中包括所有 s3 的列表文件和位置，在您的情况下，您编写了一个函数将它们存储在列表中，我会说将它们存储在单独的 yaml 中。文件并在运行时在 Airflow 环境中加载它，然后创建 DAG。

以下是样本 yaml文件:dynamicDagConfigFile.yaml

job: dynamic-dag
bucket_name: 'bucket-name'
prefix: 'bucket-prefix'
S3Files:
    - File1: 'S3Loc1'
    - File2: 'S3Loc2'
    - File3: 'S3Loc3'

您可以修改您的 Check_For_Files函数将它们存储在 yaml 中文件。

现在我们可以继续动态创建 dag:

首先使用虚拟运算符定义两个任务，即开始和结束任务。这些任务是我们将在 DAG 上构建的任务。通过在它们之间动态创建任务:

start = DummyOperator(
    task_id='start',
    dag=dag
)

end = DummyOperator(
    task_id='end',
    dag=dag)

动态 DAG:
我们将使用 PythonOperators在 Airflow 中。该函数应接收任务 id 作为参数；要执行的python函数，即python运算符的python_callable；和一组要在执行过程中使用的参数。

包括参数 task id .因此，我们可以在以动态方式生成的任务之间交换数据，例如，通过 XCOM .

您可以在此动态 dag 中指定您的操作功能，例如 s3_to_gcs_op .

def createDynamicDAG(task_id, callableFunction, args):
    task = PythonOperator(
        task_id = task_id,
        provide_context=True,
        #Eval is used since the callableFunction var is of type string
        #while the python_callable argument for PythonOperators only receives objects of type callable not strings.
        python_callable = eval(callableFunction),
        op_kwargs = args,
        xcom_push = True,
        dag = dag,
    )
    return task

最后，根据 yaml 文件中存在的位置，您可以创建动态 dag，首先阅读 yaml文件如下并创建动态 dag:

with open('/usr/local/airflow/dags/config_files/dynamicDagConfigFile.yaml') as f:
    # use safe_load instead to load the YAML file
    configFile = yaml.safe_load(f)

    #Extract file list
    S3Files = configFile['S3Files']

    #In this loop tasks are created for each table defined in the YAML file
    for S3File in S3Files:
        for S3File, fieldName in S3File.items():

            #Remember task id is provided in order to exchange data among tasks generated in dynamic way.
            get_s3_files = createDynamicDAG('{}-getS3Data'.format(S3File), 
                                            'getS3Data', 
                                            {}) #your configs here.

            #Second step is upload S3 to GCS
            upload_s3_toGCS = createDynamicDAG('{}-uploadDataS3ToGCS'.format(S3File), 'uploadDataS3ToGCS', {'previous_task_id':'{}-'})

#write your configs again here like S3 bucket name prefix extra or read from yaml file, and other GCS config.

最终 DAG 定义:

这个想法是

#once tasks are generated they should linked with the
#dummy operators generated in the start and end tasks. 
start >> get_s3_files
get_s3_files >> upload_s3_toGCS
upload_s3_toGCS >> end

完整的 Airflow 代码按顺序:

import yaml
import airflow
from airflow import DAG
from datetime import datetime, timedelta, time
from airflow.operators.python_operator import PythonOperator
from airflow.operators.dummy_operator import DummyOperator

start = DummyOperator(
    task_id='start',
    dag=dag
)


def createDynamicDAG(task_id, callableFunction, args):
    task = PythonOperator(
        task_id = task_id,
        provide_context=True,
        #Eval is used since the callableFunction var is of type string
        #while the python_callable argument for PythonOperators only receives objects of type callable not strings.
        python_callable = eval(callableFunction),
        op_kwargs = args,
        xcom_push = True,
        dag = dag,
    )
    return task


end = DummyOperator(
    task_id='end',
    dag=dag)



with open('/usr/local/airflow/dags/config_files/dynamicDagConfigFile.yaml') as f:
    configFile = yaml.safe_load(f)

    #Extract file list
    S3Files = configFile['S3Files']

    #In this loop tasks are created for each table defined in the YAML file
    for S3File in S3Files:
        for S3File, fieldName in S3File.items():

            #Remember task id is provided in order to exchange data among tasks generated in dynamic way.
            get_s3_files = createDynamicDAG('{}-getS3Data'.format(S3File), 
                                            'getS3Data', 
                                            {}) #your configs here.

            #Second step is upload S3 to GCS
            upload_s3_toGCS = createDynamicDAG('{}-uploadDataS3ToGCS'.format(S3File), 'uploadDataS3ToGCS', {'previous_task_id':'{}-'})

#write your configs again here like S3 bucket name prefix extra or read from yaml file, and other GCS config.


start >> get_s3_files
get_s3_files >> upload_s3_toGCS
upload_s3_toGCS >> end