![]() I want to know how can I avoid that every DAG triggers again the next time we change timezones. In my example case the DAG triggered normally on tuesday "" and friday "", but on Sunday "" we did the timezone change and it triggered a new DAG execution with run execution "" (same value as the friday execution). This frees up a worker slot while it is waiting. To make an Airflow DAG understand daylight savings, we have to use the pendulum library, create a time zone object and add it to the starttime parameter of the DAG object as below. Waits until the specified time of the day. When I changed the variable value from "+3" to "+4" it changed the timezone in every single DAG configured this way which is correct, but I also triggered again the last execution of every single DAG Function defined by the sensors while deriving this class should override. The problem occurs when we change timezones for the daylight saving, the idea is that I don't have to change every DAG schedule interval, I only need to change the timezoneVariable from "+3" to "+4" and we are ready (I know that we can put Chile/Continental timezone for it to change automatically but we want to have the control in order to plan when we'll do the timezone change). When I do this the schedule_interval gets hooked to the chilean timezone and not to UTC. How to configure the Airflow dag to execute at specified time on daily basis no matter what. Start_date = datetime.datetime(2022,1,26, tzinfo=chile_tz), Additional timetables may be available in plugins. Then the DAG is configurated as it follows: with DAG( Airflow comes with several common timetables built in to cover the most common use cases. The Variable timezoneVariable is set to "+3" in order to simulate our timezone. Returns the last dag run for a dag, None if there was none. The same is the scenario for Sequential, Local and Celery Executors. (dagid, session, includeexternallytriggeredFalse)source ΒΆ. When I try to run a DAG in Airflow 1.8.0 I find that it takes a lot of time between the time of completion predecessor task and the time at which the successor task is picked up for execution (usually greater the execution times of individual tasks). In order to manage this I changed the timezone of my DAG like this: timezon = "Etc/GMT" + Variable.get("timezoneVariable") Create a Timetable instance from a scheduleinterval argument. For example, we can change Airflows default timezone ( core. I have an issue managing the Timezones, I know that Airflow runs with UTC Timezone, I live in Santiago/Chile and I need the Dag's to work with the local time zone. Apache Airflow is a popular open-source platform designed to schedule and monitor workflows.
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