I have a requirement that I want to schedule an airflow job every alternate Friday. However, the problem is I am not able to figure out how to write a schedule for this.
I don't want to have multiple jobs for this.
I tried this
'0 0 1-7,15-21 * 5
However it's not working it's running from 1 to 7 and 15 to 21 everyday.
from shubham's answer I realize that we can have a PythonOperator which can skip the task for us. An I tried to implement the solution. However doesn't seem to work.
As testing this on 2 week period would be too difficult. This is what I did.
I schedule the DAG to run every 5 mins
However, I am writing python operator the skip althernate task (pretty similar to what I am trying to do, alternate friday).
DAG:
args = {
'owner': 'Gaurang Shah',
'retries': 0,
'start_date':airflow.utils.dates.days_ago(1),
}
dag = DAG(
dag_id='test_dag',
default_args=args,
catchup=False,
schedule_interval='*/5 * * * *',
max_active_runs=1
)
dummy_op = DummyOperator(task_id='dummy', dag=dag)
def _check_date(execution_date, **context):
min_date = datetime.now() - relativedelta(minutes=10)
print(context)
print(context.get("prev_execution_date"))
print(execution_date)
print(datetime.now())
print(min_date)
if execution_date > min_date:
raise AirflowSkipException(f"No data available on this execution_date ({execution_date}).")
check_date = PythonOperator(
task_id="check_if_min_date",
python_callable=_check_date,
provide_context=True,
dag=dag,
)
I doubt that a single crontab expression can solve this
Using airflow's tricks, solution is much more straightforward:
schedule your DAG every Friday 0 0 * * FRI and
on alternate Fridays (based on your business logic), skip the DAG by raising AirflowSkipException
Here you'll have to let your DAG begin with a dedicated skip_decider task that will let your DAG run / skip every alternate Friday by
conditionally raising AirflowSkipException (to skip the DAG)
not doing anything to let the DAG run
You can also leverage
ShortCircuitOperator
BranchPythonOperator
but IMO, AirflowSkipException is the cleanest solution
Reference: How to define a DAG that scheduler a monthly job together with a daily job?
Depending on your implementation you can use the hash. Worked in my airflow schedules using version 1.10:
Hash (#)
'#' is allowed for the day-of-week field, and must be followed by a number between one and five. It allows specifying constructs such as "the second Friday" of a given month.[19] For example, entering "5#3" in the day-of-week field corresponds to the third Friday of every month. Reference
you can use timedelta as mentioned below, combine it with start_date to schedule your job bi_weekly.
dag = DAG(
dag_id='test_dag',
default_args=args,
catchup=False,
start_date=datetime(2021, 3, 26),
schedule_interval=timedelta(days=14),
max_active_runs=1
)
Related
I have to schedule a DAG that should run on 15th of every month. However, if 15th falls on a Sunday/Saturday then the DAG should skip weekends and run on coming Monday.
For example, May 15 2021 falls on a Saturday. So, instead of running on 15th of May, the DAG should run on 17th, which is Monday.
Can you please help to schedule it in airflow?
Thanks in advance!
The logic of scheduling is limited by what you can do with single cron expression. So if you can't say it in cron expression you can't provide such scheduling in Airflow. For that reason there is an open airflow improvement proposal AIP-39 Richer scheduler_interval to give more scheduling capabilities.
That said, you can still get the desired functionality by writing some code.
You can set your dag to start on the 15th of each month and then place a sensor that verify that the date is Mon-Fri (if not it will wait):
from airflow.sensors.weekday import DayOfWeekSensor
dag = DAG(
dag_id='work',
schedule_interval='0 0 15 * *',
default_args=default_args,
description='Schedule a Job on 15 of each month',
)
weekend_check = DayOfWeekSensor(
task_id='weekday_check_task',
week_day={'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'},
mode='reschedule',
dag=dag)
op_1 = YourOperator(task_id='op1_task',dag=dag)
weekend_check >> op_1
Note: If you are running airflow<2.0.0 you will need to change the import to:
from airflow.contrib.sensors.weekday_sensor import DayOfWeekSensor
The answer posted by Elad works pretty well. I came up with another solution that works as well.
I scheduled the job to run on 15,16 and 17 of the month. However, I added a condition so that the job runs on the 15th if its a weekday. The job runs on 16th and 17th if its a Monday.
To achieve that, I added a BranchPythonOperator:
from airflow.operators.python_operator import BranchPythonOperator
def _conditinal_task_initiator(**kwargs):
execution_date=kwargs['execution_date']
if int(datetime.strftime(execution_date,'%d'))==15 and (execution_date.weekday()<5):
return 'dummy_task_run_cmo_longit'
elif int(datetime.strftime(execution_date,'%d'))==16 and (execution_date.weekday()==0):
return 'dummy_task_run_cmo_longit'
elif int(datetime.strftime(execution_date,'%d'))==17 and (execution_date.weekday()==0):
return 'dummy_task_run_cmo_longit'
else:
return 'dummy_task_skip_cmo_longit'
with DAG(dag_id='NXS_FM_LOAD_CMO_CHOICE_LONGIT',default_args = default_args, schedule_interval = "0 8 15-17 * *", catchup=False) as dag:
conditinal_task_initiator=BranchPythonOperator(
task_id='cond_task_check_day',
provide_context=True,
python_callable=_conditinal_task_initiator,
do_xcom_push=False)
dummy_task_run_cmo_longit=DummyOperator(
task_id='dummy_task_run_cmo_longit')
dummy_task_skip_cmo_longit=DummyOperator(
task_id='dummy_task_skip_cmo_longit')
conditinal_task_initiator >> [dummy_task_run_cmo_longit,dummy_task_skip_cmo_longit]
dummy_task_run_cmo_longit >> <main tasks for execution>
Using this, the job'll run on 15,16 and 17 of every month. However, it'll run the functional tasks only once every month.
I am trying to use Cronitier to schedule job a monthly job to run every 2 Tuesday. I tried below
This is my partial script.
from croniter import croniter
cron = croniter("00 21 * * 2#2")
dag = DAG('recurring_job', catchup=False, default_args=default_args, schedule_interval=cron)
But it didn't work. Would you please help me or give some reference.
You can just specify the cron string directly:
dag = DAG('recurring_job', schedule_interval="00 21 * * 2#2")
Airflow will translate that using croniter internally.
Airflow supports also datetime.timedelta object for schedule_interval, see Scheduling & Triggers:
Each DAG may or may not have a schedule, which informs how DAG Runs are created. schedule_interval is defined as a DAG arguments, and receives preferably a cron expression as a str, or a datetime.timedelta object.
Together with start_date, it should work out without the usage of croniter:
dag = DAG('recurring_job', catchup=False, default_args=default_args, schedule_interval=datetime.timedelta(weeks=2), start_date = datetime(2020,11,3,21))
I take input from the user about the schedule of the tasks. I want to run the task multiple times a day. How is it possible using Airflow?
You can schedule a DAG to run multiple times a day by using the schedule_interval arg in your DAG like so:
dag = DAG(
dag_id='fake_dag',
schedule_interval="* * * * *"
)
schedule_interval uses CRON format. This may help you understand CRON format.
Example: if you want your DAG to run every 4 hours during a day, your DAG would look like below:
dag = DAG(
dag_id='fake_dag',
schedule_interval="* 0-23/4 * * *"
)
I have scheduled the execution of a DAG to run daily.
It works perfectly for one day.
However each day I would like to re-execute not only for the current day {{ ds }} but also for the previous n days (let's say n = 7).
For example, in the next execution scheduled to run on "2018-01-30" I would like Airflow not only to run the DAG using as execution date "2018-01-30", but also to re-run the DAGs for all the previous days from "2018-01-23" to "2018-01-30".
Is there an easy way to "invalidate" the previous execution so that a backfill is run automatically?
You can generate dynamically tasks in a loop and pass the offset to your operator.
Here is an example with the Python one.
import airflow
from airflow.operators.python_operator import PythonOperator
from airflow.models import DAG
from datetime import timedelta
args = {
'owner': 'airflow',
'start_date': airflow.utils.dates.days_ago(2),
'schedule_interval': '0 10 * * *'
}
def check_trigger(execution_date, day_offset, **kwargs):
target_date = execution_date - timedelta(days=day_offset)
# use target_date
for day_offset in xrange(1, 8):
PythonOperator(
task_id='task_offset_' + i,
python_callable=check_trigger,
provide_context=True,
dag=dag,
op_kwargs={'day_offset' : day_offset}
)
Have you considered having the dag that runs once a day just run your task for the last 7 days? I imagine you’ll just have 7 tasks that each spawn a SubDAG with a different day offset from your execution date.
I think that will make debugging easier and history cleaner. I believe trying to backfill already executed tasks will involve deleting task instances or setting their states all to NONE. Then you’ll still have to trigger a backfill on those dag runs. It’ll be harder to track when things fail and just seems a bit messier.
When building an Airflow dag, I typically specify a simple schedule to run periodically - I expect this is the most common use.
dag = DAG('my_dag',
description='this is what it does',
schedule_interval='0 12 * * *',
start_date=datetime(2017, 10, 1),
catchup=False)
I then need to use the 'date' as a parameter in my actual process, so I just check the current date.
date = datetime.date.today()
# do some date-sensitive stuff
operator = MyOperator(..., params=[date, ...])
My understanding is that setting catchup=True will have Airflow schedule my dag for every schedule interval between start_date and now (or end_date); e.g. every day.
How do I get the scheduled_date for use within my dag instance?
I think you mean execution date here, You can use Macros inside your operators, more detail can be found here: https://airflow.apache.org/code.html#macros. So airflow will respect it so you don't need to have your date been generated dynamically
Inside of Operator, you can call {{ ds }} in a str directly
Outside of Operator, for example PythonOperator, you will need provide_context=True first then to pass **kwargs as last arguments to your function then you can call kwargs['ds']