![]() For Saturday and Sunday, the downstream tasks should be skipped. So if you have for example scheduleinterval set to 0 3, Airflow will start the DAG at 3:00 EAT, but it in the UI you will see it as 0:00. Although UI uses UTC, Airflow uses local time to launch DAGs. I changed the schedule interval to '0 6 * * *' to run everyday at 6:00 AM and at the start of my dag, filter for dates that fall within ‘0 6 * * 1-5’, so effectively Monday to Friday. According to Airflow docs: Please note that the Web UI currently only runs in UTC. DAGs scheduled with a cron expression or timedelta object are internally converted to always use a. The timetable also determines the data interval and the logical date of each run created for the DAG. ![]() Web UI By default the Web UI will show times in UTC. Pendulum is installed when you install Airflow. It is dependent on pendulum, which is more accurate than pytz. I noticed using a schedule interval of '0 6 * * 1-5' didn't work because Fridays execution didn't occur until the following Monday. For DAGs with time-based schedules (as opposed to event-driven), the scheduling decisions are driven by its internal timetable. By default it is set to UTC, but you change it to use the system’s settings or an arbitrary IANA time zone, e.g. Even when running the same DAGs over and over again, it's still possible for a Composer environment to suffer from performance bottlenecks like this, because work can be distributed differently each time, or there may be different processes running in the. ![]() The DAG has to run the Python script on Monday with Mondays date as an argument, same for Tuesday all the way to Friday with Fridays date as an argument. High inter-task latency is usually an indicator that there is a scheduler-related bottleneck (as opposed to something worker-related). I'm scheduling the DAG to run at 6:00 AM Monday through Friday i.e weekdays Eastern Standard Time. I have a DAG executing a Python script which takes a date argument (the current date). With the following plugin, you can override the executors timezone, which modifies the timezone in which Apache Airflow writes logs.
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