Airflow backfill clarification - airflow

I'm just getting started with Airbnb's airflow, and I'm still not clear on how/when backfilling is done.
Specifically, there are 2 use-cases that confuse me:
If I run airflow scheduler for a few minutes, stop it for a minute, then restart it again, my DAG seems to run extra tasks for the first 30 seconds or so, then it continues as normal (runs every 10 sec). Are these extra tasks "backfilled" tasks that weren't able to complete in an earlier run? If so, how would I tell airflow not to backfill those tasks?
If I run airflow scheduler for a few minutes, then run airflow clear MY_tutorial, then restart airflow scheduler, it seems to run a TON of extra tasks. Are these tasks also somehow "backfilled" tasks? Or am I missing something.
Currently, I have a very simple dag:
default_args = {
'owner': 'me',
'depends_on_past': False,
'start_date': datetime(2016, 10, 4),
'email': ['airflow#airflow.com'],
'email_on_failure': False,
'email_on_retry': False,
'retries': 1,
'retry_delay': timedelta(minutes=5),
# 'queue': 'bash_queue',
# 'pool': 'backfill',
# 'priority_weight': 10,
# 'end_date': datetime(2016, 1, 1),
}
dag = DAG(
'MY_tutorial', default_args=default_args, schedule_interval=timedelta(seconds=10))
# t1, t2 and t3 are examples of tasks created by instantiating operators
t1 = BashOperator(
task_id='print_date',
bash_command='date',
dag=dag)
t2 = BashOperator(
task_id='sleep',
bash_command='sleep 5',
retries=3,
dag=dag)
templated_command = """
{% for i in range(5) %}
echo "{{ ds }}"
echo "{{ macros.ds_add(ds, 8)}}"
echo "{{ params.my_param }}"
{% endfor %}
"""
t3 = BashOperator(
task_id='templated',
bash_command=templated_command,
params={'my_param': 'Parameter I passed in'},
dag=dag)
second_template = """
touch ~/airflow/logs/test
echo $(date) >> ~/airflow/logs/test
"""
t4 = BashOperator(
task_id='write_test',
bash_command=second_template,
dag=dag)
t1.set_upstream(t4)
t2.set_upstream(t1)
t3.set_upstream(t1)
The only two things I've changed in my airflow config are
I changed from using a sqlite db to using a postgres db
I'm using a CeleryExecutor instead of a SequentialExecutor
Thanks so much for you help!

When you change the scheduler toggle to "on" for a DAG, the scheduler will trigger a backfill of all dag run instances for which it has no status recorded, starting with the start_date you specify in your "default_args".
For example: If the start date was "2017-01-21" and you turned on the scheduling toggle at "2017-01-22T00:00:00" and your dag was configured to run hourly, then the scheduler will backfill 24 dag runs and then start running on the scheduled interval.
This is essentially what is happening in both of your question. In #1, it is filling in the 3 missing runs from the 30 seconds which you turned off the scheduler. In #2, it is filling in all of the DAG runs from start_date until "now".
There are 2 ways around this:
Set the start_date to a date in the future so that it will only start scheduling dag runs once that date is reached. Note that if you change the start_date of a DAG, you must change the name of the DAG as well due to the way the start date is stored in airflow's DB.
Manually run backfill from the command line with the "-m" (--mark-success) flag which tells airflow not to actually run the DAG, rather just mark it as successful in the DB.
e.g.
airflow backfill MY_tutorial -m -s 2016-10-04 -e 2017-01-22T14:28:30

Please note that since version 1.8, Airflow lets you control this behaviour using catchup. Either set catchup_by_default=False in airflow.cfg or
catchup=False in your DAG definition.
See https://airflow.apache.org/scheduler.html#backfill-and-catchup

The On/Off on Airflow's UI only states "PAUSE" which means, if its ON, it will only pause on the time it was triggered and continue on that date again if it is turned off.

Related

Airflow DAG scheduled monthly not queued

I have an Airflow DAG set up to run monthly (with the #monthly time_interal). The next dag runs seem to be scheduled but they don't appear as "queued" in the Airflow UI. I don't understant because everythink seems good otherwise. Here is how my DAG is configured :
with DAG(
"dag_name",
start_date=datetime(2023, 1, 1),
schedule_interval="#monthly",
catchup=True,
default_args={"retries": 5, "retry_delay": timedelta(minutes=1)},
) as dag:
Do you get no runs at all when you unpause your DAG or one that is being backfilled and it says Last run 2023-01-01, 00:00:00?
In the latter case Airflow is behaving as intended, the run that just happened was the one that would have actually been queued and ran at midnight on 2023-02-01. :)
I used your configuration on a new simple DAG and it gave me one backfilled successful run with the run ID scheduled__2023-01-01T00:00:00+00:00 so running for the data interval 2023-01-01 (logical_date) to 2023-02-01, which means the Run that would have actually been queued at midnight on 2023-02-01.
The next run is scheduled for the logical date 2023-02-01 which means for the data from 2023-02-01 to 2023-03-01. This run will only actually be queued and happen at midnight 2023-03-01 as the Run After date shows:
This guide might help with terminology Airflow uses around schedules.
I'm assuming you wanted the DAG to backfill two runs, one that would have happened on 2023-01-01 and one that would have happened on 2023-02-01. This DAG should do that:
from airflow import DAG
from datetime import datetime, timedelta
from airflow.operators.empty import EmptyOperator
with DAG(
"dag_name_3",
start_date=datetime(2022, 12, 1),
schedule_interval="#monthly",
catchup=True,
default_args={"retries": 5, "retry_delay": timedelta(minutes=1)},
) as dag:
t1 = EmptyOperator(task_id="t1")

How to force a Airflow Task to restart at the new scheduling date?

I have this simple Airflow DAG:
from airflow import DAG
from datetime import datetime, timedelta
from airflow.operators.bash import BashOperator
with DAG("Second Dag",
start_date=datetime(2022,1,1),
schedule_interval="0 5 * * *",
catchup=False,
max_active_runs=1
) as dag:
task_a = BashOperator(
task_id="ToRepeat",
bash_command="cd /home/xdf/local/ && (env/bin/python workflow/test1.py)",
retries =1,
)
The task takes a variable amount of time between one run and the other, and I don't have any guarantee that it will be finished within the 5 A.M of the next day.
If the task is still running when a new task is scheduled to start, I need to kill the old one before it starts running.
How can I design Airflow DAG to automatically kill the old task if it's still running when a new task is scheduled to start?
More details:
I am looking for something dynamic. The old DAG should be killed only when the new DAG is starting. If, for any reason, the new DAG does not start for one week, then old DAG should be able to run for an entire week. That's why using a timeout is sub-optimal
You should set dagrun_timeout for your DAG.
dagrun_timeout: specify how long a DagRun should be up before
timing out / failing, so that new DagRuns can be created. The timeout
is only enforced for scheduled DagRuns.
Since your DAG runs daily you can set 24 hours for timeout.
with DAG("Second Dag",
start_date=datetime(2022,1,1),
schedule_interval="0 5 * * *",
catchup=False,
max_active_runs=1
dagrun_timeout=timedelta(hours=24)
) as dag:
If you want to set timeout on a specific task in your DAG you should use execution_timeout on your operator.
execution_timeout: max time allowed for the execution of this task instance, if it goes beyond it will raise and fail
Example:
MyOperator(task_id='task', execution_timeout=timedelta(hours=24))
If you really are looking for a dynamic solution; you can take help of Airflow DAGRun APIs and Xcoms; you can push your current dag run_id to Xcom and for subsequent runs you can pull this Xcom to consume with airflow API to check and kill the dag run with that run_id.
check_previous_dag_run_id >> kill_previous_dag_run >> push_current_run_id >> your_main_task
and your API call task should be something like
...
kill_previous_dag_run = BashOperator(
task_id="kill_previous_dag_run",
bash_command="curl -X 'DELETE' \
'http://<<your_webserver_dns>>/api/v1/dags/<<your_dag_name>>/dagRuns/<<url_encoded_run_id>>' \
-H 'accept: */*' --user <<api_username>>:<<api_user_password>>",
dag=dag
)
...

Apache Airflow does not enforce dagrun_timeout

I am using Apache Airflow version 1.10.3 with the sequential executor, and I would like the DAG to fail after a certain amount of time if it has not finished. I tried setting dagrun_timeout in the example code
from airflow import DAG
from airflow.operators.bash_operator import BashOperator
from datetime import datetime, timedelta
default_args = {
'owner': 'me',
'depends_on_past': False,
'start_date': datetime(2019, 6, 1),
'retries': 0,
}
dag = DAG('min_timeout', default_args=default_args, schedule_interval=timedelta(minutes=5), dagrun_timeout = timedelta(seconds=30), max_active_runs=1)
t1 = BashOperator(
task_id='fast_task',
bash_command='date',
dag=dag)
t2 = BashOperator(
task_id='slow_task',
bash_command='sleep 45',
dag=dag)
t2.set_upstream(t1)
slow_task alone takes more than the time limit set by dagrun_timeout, so my understanding is that airflow should stop DAG execution. However, this does not happen, and slow_task is allowed to run for its entire duration. After this occurs, the run is marked as failed, but this does not kill the task or DAG as desired. Using execution_timeout for slow_task does cause the task to be killed at the specified time limit, but I would prefer to use an overall time limit for the DAG rather than specifying execution_timeout for each task.
Is there anything else I should try to achieve this behavior, or any mistakes I can fix?
The Airflow scheduler runs a loop at least every SCHEDULER_HEARTBEAT_SEC (the default is 5 seconds).
Bear in mind at least here, because the scheduler performs some actions that may delay the next cycle of its loop.
These actions include:
parsing the dags
filling up the DagBag
checking the DagRun and updating their state
scheduling next DagRun
In your example, the delayed task isn't terminated at the dagrun_timeout because the scheduler performs its next cycle after the task completes.
According to Airflow documentation:
dagrun_timeout (datetime.timedelta) – specify how long a DagRun should be up before timing out / failing, so that new DagRuns can be created. The timeout is only enforced for scheduled DagRuns, and only once the # of active DagRuns == max_active_runs.
So dagrun_timeout wouldn't work for non-scheduled DagRuns (e.g. manually triggered) and if the number of active DagRuns < max_active_runs parameter.

Airflow scheduler not scheduling simple DAG task immediately

I have scheduled a DAG with a simple bash task to run every 5th minute:
# bash_dag.py
from airflow import DAG
from airflow.operators.bash_operator import BashOperator
from datetime import datetime, timedelta
default_args = {
'owner': 'airflow',
'start_date' : datetime(2019, 5, 30)
}
dag = DAG(
'bash_count',
default_args=default_args,
schedule_interval='*/5 * * * *',
catchup = False
)
t1 = BashOperator(
task_id='print_date',
bash_command='date',
dag=dag
)
Scheduling works fine, DAG is executing every 5th minute threshold. However, I have noticed that there is a significant delay between the 5th minute threshold and task queueing time. For the examples shown in the image, task queueing takes in between 3 to 50 seconds. For example, last DAG execution in the image was supposed to be triggered after 20:05:00 but task instance was queued 28 seconds later (20:05:28).
I'm surprised this is the case, since the DAG being scheduled has a single very simple task. Is this a normal airflow delay? Should I expect further delays when dealing with more complex DAGs?
I'm running a local airflow server with Postgres as db on a 16 GB Mac with OS Mojave. Machine is not resource constrained.

Airflow scheduler fails to pickup scheduled DAG's but runs when triggered manually

I have Airflow 1.10.2 installation with python 3.5.6.
Metadata is lying into Mysql database with LocalExecutor for execution.
I have created sample helloworld.py dag with below schedule.
default_args = {
'owner': 'Ashish',
'depends_on_past': False,
'start_date': datetime(2019, 2, 15),
'email_on_failure': False,
'email_on_retry': False,
'retries': 1,
'retry_delay': timedelta(minutes=1),
}
dag = DAG('Helloworld',schedule_interval='56 6 * * *', default_args=default_args)
But scheduler didn't pickup this dag as per scheduled time whereas when i run it manually from UI it runs perfectly fine.
Concern here is why does scheduler fails to pickup dag run as per the scheduled time.
I think you are confused on start_date:. Your current schedule is set to run at 6:56 AM UTC on 2/15/2019. With this schedule, the DAG will run tomorrow with no problem. This is because Airflow runs jobs at the end of an interval, not the beginning.
start_date: is not when you want the DAG to be triggered, but when you want the scheduling interval to start. If you wanted your job to run today, start date should be: 'start_date': datetime(2019, 2, 14). Then your current daily scheduling interval would have ended at 6:56 AM today as intended and your DAG would have ran.
Taken from this answer.

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