I have a DAG configured like this:
AIRFLOW_DEFAULT_ARGS = {
'owner': 'airflow',
'depends_on_past': False,
'email_on_failure': False,
'email_on_retry': False,
'retries': 1,
'retry_delay': timedelta(minutes=5),
'dagrun_timeout': timedelta(hours=5)
}
DAILY_RUNNER = DAG(
'daily_runner',
max_active_runs=1,
start_date=datetime(2019, 1, 1),
schedule_interval="0 17 * * *",
default_args=AIRFLOW_DEFAULT_ARGS)
My current understanding is that retries says that a task will be retried once before failing for good. Is there a way to set a similar limit for the number of times a DAG gets retried? If I have a dag in the running state, I want to be able to set it to failed from within the UI once and have it stop rerunning.
Currently, there is no way to set retry at dag level.
Please refer the below answer for retrying a set of tasks/whole-dag in case of failures.
Can a failed Airflow DAG Task Retry with changed parameter
Related
I did a DAG's with the following configuration:
default_args = {
'owner': 'airflow',
'depends_on_past': False,
'start_date': airflow.utils.dates.days_ago(0, 0, minute=1),
'email': ['francisco.salazar.12#sansano.usm.cl'],
'email_on_failure': False,
'email_on_retry': False,
'max_active_runs': 1,
'retries': 1,
'retry_delay': timedelta(minutes=1),
'provide_context': True
}
dag = DAG(
'terralink_environmetal_darksky',
default_args=default_args,
description='Extract Data from Darksky API',
catchup=False,
schedule_interval='31 * * * *',
)
The issue is that scheduler works correctly and execute DAG run at every hour that I defined in schedule_inverval (in minute 31 of every hour) BUT in midnight or the last execution of the day (scheduled at 00:31:00 for the next day) the DAG execution is not triggered.
I think that is a problem based on start_date but I don't know yet how to define this parameter in order to avoid the problem.
Airflow recommends to state a fixed startstart_date for your DAG. start_date is mainly for the purpose to specify when do you want your DAG to start running for the very first time. schedule_interval will be the most relevant one after the start_date did its purpose or (if you do not need to backfill or reset your dag).
In its 2 out of 10 runs, the DAG status automatically sets to succes even when no tasks inside of it ran. Following is the DAG args which was passed and its tree view.
args = {
'owner': 'xyz',
'depends_on_past': False,
'catchup': False,
'start_date': datetime(2019, 7, 8),
'email': ['a#b.c'],
'email_on_failure': False,
'email_on_retry': False,
'retries': 0,
'provide_context': True,
'retry_delay': timedelta(minutes=2)
}
And I am passing DAG as a context like this:
with DAG(PARENT_DAG_NAME, default_args=args, schedule_interval='30 * * * *') as main_dag:
task1 = DummyOperator(
task_id='Raw_Data_Ingestion_Started',
)
task2 = DummyOperator(
task_id='Raw_Data_Ingestion_Completed',
)
task1 >> task2
Any idea what could be the issue? Is it something I need to change in the config file? And this behaviour is not periodic.
According to the official airflow documentation on DummyOperator:
Operator that does literally nothing. It can be used to group tasks in a DAG.
I'm using airflow.operators.sensors.ExternalTaskSensor to make one Dag wait for another.
dag = DAG(
'dag2',
default_args={
'owner': 'Me',
'depends_on_past': False,
'start_date': start_datetime,
'email': ['me#example.com'],
'email_on_failure': True,
'email_on_retry': False,
'retries': 2,
'retry_delay': timedelta(minutes=10),
},
template_searchpath="%s/me/resources/" % DAGS_FOLDER,
schedule_interval="{} {} * * *".format(minute, hour),
max_active_runs=1
)
wait_for_dag1 = ExternalTaskSensor(
task_id='wait_for_dag1',
external_dag_id='dag1',
external_task_id='dag1_task1',
dag=dag
)
If something seriously wrong happens with upstream Dag and it fails to complete during the given time period, I want upstream Dag (ExternalTaskSensor operator) crash as well, instead of hanging forever.
How can I add a timeout to ExternalTaskSensor?
I'm looking into documentation, but it does not seem to have a timeout parameter or something similar. What should I do?
https://airflow.readthedocs.io/en/stable/_modules/airflow/sensors/external_task_sensor.html
The ExternalTaskSensor does take a timeout argument in seconds. It inherits the argument from BaseSensorOperator (https://airflow.apache.org/docs/apache-airflow/stable/_api/airflow/sensors/base/index.html). If you pass it timeout=60 on instantiation, it will fail after 60 seconds.
I created Apache Airflow DAG with following default args. I want this DAG to run every day at 10PM UTC but it's always running at 12AM UTC and ignoring the date time I had set in start_date. Is this not the right way? Thanks.
default_args = {
'owner': config.OWNER,
'depends_on_past': False,
'start_date': datetime(2018, 10, 14, 22, 0, 0),
'email': [config.ALERT_EMAIL],
'email_on_failure': True,
'email_on_retry': False,
'retry_delay': timedelta(minutes=1),
'retries': 2,
}
# DAG
dag = DAG('Test',
default_args=default_args,
description='Initial setup',
schedule_interval='#daily')
You can also use cron format in your schedule interval argument like this:
# DAG
dag = DAG('Test',
default_args=default_args,
description='Initial setup',
schedule_interval='0 22 * * *')
Regarding the schedule_interval you have at least three options:
datetime.timedelta
dateutil.relativedelta
cron style string
The schedule_interval defines how often that DAG runs. This timedelta object is added to your latest task instance’s execution_date to figure out the next schedule. And keep in mind that: start_date for the task, determines the execution_date for the first task instance.
All of the above is correct.
I have encountered an issue where, in Airflow 2.0, schedule_interval is ignored when put in the default_args. When I removed it and put it in the DAG declaration, all worked. I could test this by looking at the DAG details in the UI.
Example:
default_args = {
'owner': 'Hector Hoffman',
'depends_on_past': False,
'start_date':start_date,
'schedule_interval': '0 5 * * *',
'email': ['hector#email.com'],
'email_on_failure': True,
'email_on_retry': False,
'retries': 0,
'on_failure_callback': task_fail_slack_alert
}
Results in:
Whereas, when I put it in the DAG:
with models.DAG(
"dealstampede_workflow",
default_args=default_args,
catchup=False,
schedule_interval='0 5 * * *'
) as dag:
Results in:
If anyone has any insight as to why the schedule_interval doesn't work in the default_args I'd appreciate feedback. Thanks.
I configured my DAG like this:
default_args = {
'owner': 'Aviv',
'depends_on_past': False,
'start_date': datetime(2017, 1, 1),
'email': ['aviv#oron.com'],
'email_on_failure': False,
'email_on_retry': False,
'retries': 0,
'retry_delay': timedelta(minutes=1)
}
dag = DAG(
'MyDAG'
, schedule_interval=timedelta(minutes=3)
, default_args=default_args
, catchup=False
)
and for some reason, when i un-pause the DAG, its being executed twice immediatly.
Any idea why? And is there any rule i can apply to tell this DAG to never run more than once in the same time?
You can specify max_active_runs like this:
dag = airflow.DAG(
'customer_staging',
schedule_interval="#daily",
dagrun_timeout=timedelta(minutes=60),
template_searchpath=tmpl_search_path,
default_args=args,
max_active_runs=1)
I've never seen it happening, are you sure that those runs are not backfills, see: https://stackoverflow.com/a/47953439/9132848
I think its because you have missed the scheduled time and airflow is backfilling it automatically when you ON it again. You can disable this by
catchup_by_default = False in the airflow.cfg.