:) I did read this before posting. The question I have is: Say I have 3 DAGS. Lets say I set 'start_date' : datetime(2015, 6, 1)
Now, in my pipeline.py, if I add a dynamically query some database table and create DAGS. Lets say tomorrow if I add a new DAG. That new DAG will get the same start_date = datetime(2015, 6, 1). Which means, the pipeline for this new dag will start from datetime(2015, 6 , 1) and not from datetime.now(). I am trying to understand what is a correct approach for setitng this param so that it becomes flexible and extensible for future dags? On Sun, Jun 12, 2016 at 4:42 PM, Maxime Beauchemin < [email protected]> wrote: > From: http://pythonhosted.org/airflow/faq.html > > *What’s the deal with ``start_date``?* > > start_date is partly legacy from the pre-DagRun era, but it is still > relevant in many ways. When creating a new DAG, you probably want to set a > global start_date for your tasks usingdefault_args. The first DagRun to be > created will be based on the min(start_date) for all your task. From that > point on, the scheduler creates new DagRuns based on your > schedule_interval and > the corresponding task instances run as your dependencies are met. When > introducing new tasks to your DAG, you need to pay special attention to > start_date, and may want to reactivate inactive DagRuns to get the new task > to get onboarded properly. > > We recommend against using dynamic values as start_date, especially > datetime.now() as it can be quite confusing. The task is triggered once the > period closes, and in theory an @hourly DAG would never get to an hour > after now as now() moves along. > > We also recommend using rounded start_date in relation to your > schedule_interval. This means an @hourly would be at 00:00 minutes:seconds, > a @daily job at midnight, a @monthly job on the first of the month. You can > use any sensor or a TimeDeltaSensor to delay the execution of tasks within > that period. While schedule_interval does allow specifying a > datetime.timedelta object, we recommend using the macros or cron > expressions instead, as it enforces this idea of rounded schedules. > > When using depends_on_past=True it’s important to pay special attention to > start_date as the past dependency is not enforced only on the specific > schedule of the start_date specified for the task. It’ also important to > watch DagRun activity status in time when introducing new > depends_on_past=True, unless you are planning on running a backfill for the > new task(s). > > Also important to note is that the tasks start_date, in the context of a > backfill CLI command, get overridden by the backfill’s command start_date. > This allows for a backfill on tasks that havedepends_on_past=True to > actually start, if it wasn’t the case, the backfill just wouldn’t start. > > On Sun, Jun 12, 2016 at 3:17 PM, harish singh <[email protected]> > wrote: > > > These are the default args to my DAG. > > I am trying to run a standard hourly job (basically, at the end of > > this hour, process last hours data) > > I noticed that my pipeline is 1 hour late. > > > > For some reason, I am messing up with my start_date I guess. > > What is the best practice for setting up start_date? > > > > > > scheduling_start_date = (datetime.utcnow()).replace(minute=0, > > second=0, microsecond=0) + > > datetime.timedelta(minutes=15)default_schedule_interval = > > datetime.timedelta(minutes=60)default_args = { > > > > 'owner': 'airflow', > > 'depends_on_past': False, > > 'start_date': scheduling_start_date, > > 'email': ['[email protected]'], > > 'email_on_failure': False, > > 'email_on_retry': False, > > 'retries': 2, > > 'retry_delay': default_retries_delay, 'schedule_interval'= > > default_schedule_interval > > > > # 'queue': 'bash_queue', > > # 'pool': 'backfill', > > # 'priority_weight': 10, > > # 'end_date': datetime(2016, 1, 1), > > } > > >
