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), > } >
