python - Apache Airflow - 即使关键任务失败,DAG 也会注册为成功
问题描述
我是 Apache Airflow 的新手,我想编写一个 DAG 来将一些数据从源数据库中的一组表移动到目标数据库中的一组表。我正在尝试设计 DAG,以便有人可以简单地为新的源表编写create table
SQLinsert into
脚本 --> 目标表进程并将它们放入文件夹中。然后,在下一次 DAG 运行时,DAG 将从文件夹中提取脚本并运行新任务。我将我的 DAG 设置为:
source_data_check_task_1 (Check Operator or ValueCheckOperator)
source_data_check_task_2 (Check Operator or ValueCheckOperator, Trigger on ALL_SUCCESS)
source_data_check_task_3 (Check Operator or ValueCheckOperator, Trigger on ALL_SUCCESS)
source_data_check_task_1 >> source_data_check_task_2 >> source_data_check_task_3
for tbl_name in tbl_name_list:
tbl_exists_check (Check Operator, trigger on ALL_SUCCESS): check if `new_tbl` exists in database by querying `information_schema`
tbl_create_task (SQL Operator, trigger on ALL_FAILED): run the `create table` SQL script
tbl_insert_task (SQL Operator ,trigger on ONE_SUCCESS): run the `insert into` SQL script
source_data_check_task_3 >> tbl_exists_check
tbl_exists_check >> tbl_create_task
tbl_exists_check >> tbl_insert_task
tbl_create_task >> tbl_insert)task
我在这个设置中遇到了两个问题:(1)如果任何数据质量检查任务失败,tbl_create_task
仍然会启动,因为它会触发;ALL_FAILED
(2)无论哪个任务失败,DAG 都显示运行是SUCCESS
. 如果tbl_exists_check
失败,这很好,因为它应该至少失败一次,但如果某些关键任务失败(如任何数据质量检查任务)则不理想。
有没有办法以不同的方式设置我的 DAG 来解决这些问题?
实际代码如下:
from airflow import DAG
from airflow.operators.postgres_operator import PostgresOperator
from airflow.operators.check_operator import ValueCheckOperator, CheckOperator
from airflow.operators.bash_operator import BashOperator
from airflow.models import Variable
from datetime import datetime, timedelta
from airflow.utils.trigger_rule import TriggerRule
sql_path = Variable.get('sql_path')
default_args = {
'owner': 'enmyj',
'depends_on_past':True,
'email_on_failure': False,
'email_on_retry': False,
'retries': 0
}
dag = DAG(
'test',
default_args=default_args,
schedule_interval=None,
template_searchpath=sql_path
)
# check number of weeks in bill pay (made up example)
check_one = CheckOperator(
task_id='check_one',
conn_id='conn_name',
sql="""select count(distinct field) from dbo.table having count(distinct field) >= 4 """,
dag=dag
)
check_two = CheckOperator(
task_id='check_two',
conn_id='conn_name',
sql="""select count(distinct field) from dbo.table having count(distinct field) <= 100""",
dag=dag
)
check_one >> check_two
ls = ['foo','bar','baz','quz','apple']
for tbl_name in ls:
exists = CheckOperator(
task_id='tbl_exists_{}'.format(tbl_name),
conn_id='conn_name',
sql =""" select count(*) from information_schema.tables where table_schema = 'test' and table_name = '{}' """.format(tbl_name),
trigger_rule=TriggerRule.ALL_SUCCESS,
depends_on_past=True,
dag = dag
)
create = PostgresOperator(
task_id='tbl_create_{}'.format(tbl_name),
postgres_conn_id='conn_name',
database='triforcedb',
sql = 'create table test.{} (like dbo.source)'.format(tbl_name), # will be read from SQL file
trigger_rule=TriggerRule.ONE_FAILED,
depends_on_past=True,
dag = dag
)
insert = PostgresOperator(
task_id='tbl_insert_{}'.format(tbl_name),
postgres_conn_id='conn_name',
database='triforcedb',
sql = 'insert into test.{} (select * from dbo.source limit 10)'.format(tbl_name), # will be read from SQL file
trigger_rule=TriggerRule.ONE_SUCCESS,
depends_on_past=True,
dag = dag
)
check_two >> exists
exists >> create
create >> insert
exists >> insert
解决方案
您有一个利用BranchPythonOperator的完美用例,它允许您执行检查以查看表是否存在,然后在插入该表之前继续创建表,而不必担心 TRIGGER_RULES 并使您的 DAG 逻辑更多从 UI 中清除。
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