python - Apache Airflow scheduler does not trigger DAG at schedule time

Question

When I schedule DAGs to run at a specific time everyday, the DAG execution does not take place at all. However, when I restart Airflow webserver and scheduler, the DAGs execute once on the scheduled time for that particular day and do not execute from the next day onwards. I am using Airflow version v1.7.1.3 with python 2.7.6. Here goes the DAG code:

from airflow import DAG
from airflow.operators.bash_operator import BashOperator
from datetime import datetime, timedelta

import time
n=time.strftime("%Y,%m,%d")
v=datetime.strptime(n,"%Y,%m,%d")
default_args = {
    'owner': 'airflow',
    'depends_on_past': True,
    'start_date': v,
    'email': ['[email protected]'],
    'email_on_failure': False,
    'email_on_retry': False,
    'retries': 1,
    'retry_delay': timedelta(minutes=10),

}

dag = DAG('dag_user_answer_attempts', default_args=default_args, schedule_interval='03 02 * * *')

# t1, t2 and t3 are examples of tasks created by instantiating operators
t1 = BashOperator(
    task_id='user_answer_attempts',
    bash_command='python /home/ubuntu/bigcrons/appengine-flask-skeleton-master/useranswerattemptsgen.py',
    dag=dag)

Am I doing something wrong?

Answer 1

Your issue is the start_date being set for the current time. Airflow runs jobs at the end of an interval, not the beginning. This means that the first run of your job is going to be after the first interval.

Example:

You make a dag and put it live in Airflow at midnight. Today (20XX-01-01 00:00:00) is also the start_date, but it is hard-coded ("start_date":datetime(20XX,1,1)). The schedule interval is daily, like yours (3 2 * * *).

The first time this dag will be queued for execution is 20XX-01-02 02:03:00, because that is when the interval period ends. If you look at your dag being run at that time, it should have a started datetime of roughly one day after the schedule_date.

You can solve this by having your start_date hard-coded to a date or by making sure that the dynamic date is further in the past than the interval between executions (In your case, 2 days would be plenty). Airflow recommends you use static start_dates in case you need to re-run jobs or backfill (or end a dag).

For more information on backfilling (the opposite side of this common stackoverflow question), check the docs or this question: Airflow not scheduling Correctly Python