Airflow UI comes very handy to check the status of Airflow DAG progress.
Airflow Rest API is another way to check the status to Airflow DAGs but it requires authentication token.
We can get authentication token from Airflow UI, but if Airflow UI is down it would be difficult to get authentication token and Airflow DAG status.
Is there any other way to check/monitor and clear task instance from backend (apart from Rest API and Airflow UI) ?
We have setup status checks on airflow health check endpoint based on this Airflow doc page Checking Airflow Health Status. We have serverless functions running every 5 minutes to check that the status for metabase and scheduler are healthy.
When Airflow is down, you can get alerts routed directly to Slack channel / Email / Opsgenie Alerts through another code block defined in the serverless function.
from airflow.models.dagrun import DagRun
from airflow.utils.state import DagRunState
dag_runs = DagRun.find(dag_id='the_dag_id_you_want_to_check')
last_run = dag_runs[-1]
print('the dag state is -->: ', last_run.state)
Related
I had created a SCHEDULED DAG in AIRFLOW which used to give email alert when whenever it exceeds a particular time threshold.But now I deleted the DAG py file and also deleted it from the AIRFLOW UI as I don't want it now.Still I am getting email alerts everyday.Can anyone help with this.
I have two DAGs in my airflow scheduler, which were working in the past. After needing to rebuild the docker containers running airflow, they are now stuck in queued. DAGs in my case are triggered via the REST API, so no actual scheduling is involved.
Since there are quite a few similar posts, I ran through the checklist of this answer from a similar question:
Do you have the airflow scheduler running?
Yes!
Do you have the airflow webserver running?
Yes!
Have you checked that all DAGs you want to run are set to On in the web ui?
Yes, both DAGS are shown in the WebUI and no errors are displayed.
Do all the DAGs you want to run have a start date which is in the past?
Yes, the constructor of both DAGs looks as follows:
dag = DAG(
dag_id='image_object_detection_dag',
default_args=args,
schedule_interval=None,
start_date=days_ago(2),
tags=['helloworld'],
)
Do all the DAGs you want to run have a proper schedule which is shown in the web ui?
No, I trigger my DAGs manually via the REST API.
If nothing else works, you can use the web ui to click on the dag, then on Graph View. Now select the first task and click on Task Instance. In the paragraph Task Instance Details you will see why a DAG is waiting or not running.
Here is the output of what this paragraph is showing me:
What is the best way to find the reason, why the tasks won't exit the queued state and run?
EDIT:
Out of curiousity I tried to trigger the DAG from within the WebUI and now both Runs executed (the one triggered from the WebUI failed, but that was expected, since there was no config set)
I have two independent DAGs let's say DAG_A and DAG_B, each has multiple tasks
The two DAGs are in different GCP projects let's say projct-1 and project-2 respectively.
What I want to do is to create a 3rd DAG let's call it DAG_C
DAG_C will be part of project-1 , and will be used to orchestrate DAG_A and DAG_B.
DAG_C should start by triggering DAG_A and on task_2 success it should trigger DAG_B
Please take a look at this picture that simplifies the problem:
Overview of the architecture
The question is: would this be possible using the TriggerDagRunOperator , as I can't see any option to change the GCP project id on that operator ?
Also what would be the best approach to go towards that "assuming that TriggerDagRunOperator will not work in that case" ?
There is no option to do that with TriggerDagRunOperator as the operator see only the scope of the Airflow instance that it's in.
Your only option is to use the Airflow Rest API.
In DAG_C the trigger_B task will need to be a PythonOperator that authenticate with the Rest API of project_2 and then use the Trigger new DagRun endpoint to trigger DAG_B. Note that Airflow provides official Python client for the API so you can use it for this task.
We have encountered a scenario recently where someone mistakenly turned off a production dag, and we want to get alert whenever a dag is paused using datadog.
I have checked https://docs.datadoghq.com/integrations/airflow/?tab=host
But have not got any metric for dag to check if it is paused or not.
I can run a custom script in datadog as well.
One of the method is that I exec into postgres pod and get the list of active dags:
select * from dag where is_paused=true;
Or is there any other way I can get the unpaused dag list and also when new dag is added what is the best way to handle it.
I want the alert whenever a unpaused dag is paused.
If you are on Airflow 2 you can use the REST API to query for state of the DAG.
https://airflow.apache.org/docs/apache-airflow/stable/stable-rest-api-ref.html#operation/get_dag
There is "is_paused" field.
And of you are not Airflow 2, you should be. Airflow 1.10 is end-of-life and will not receive any fixes (including critical security fixes) so you should upgrade as soon as you can.
I am trying to manage airflow dags (create, execute etc.) via java backend. Currently after creating a dag and placing it in dags folder of airflow my backend is constantly trying to run the dag. But it can't run it until its picked up by airflow scheduler, which can take quite some time if the number of dags are more. I am wondering if there any events that airflow emits which I can tap to check for new dags processed by scheduler, and then trigger, execute command from my backend. Or is there a way or configuration where airflow will automatically start a dag once it processes it rather than we triggering it ?
is there a way or configuration where airflow will automatically start a dag once it processes it rather than we triggering it ?
Yes, one of the parameters that you can define is is_paused_upon_creation.
If you set your DAG as:
DAG(
dag_id='tutorial',
default_args=default_args,
description='A simple tutorial DAG',
schedule_interval="#daily",
start_date=datetime(2020, 12, 28),
is_paused_upon_creation=False
)
The DAG will start as soon as picked up by the scheduler (assuming conditions to run it are met)
I am wondering if there any events that airflow emits which I can tap to check for new dags processed by scheduler
In Airflow >=2.0.0 you can use the API - list dags endpoint to get all dags that are in the dagbag
In any Airflow version you can use this code to list the dag_ids:
from airflow.models import DagBag
print(DagBag().dag_ids())