This timestamp should closely match the latest timestamp for Under Last Run, check the timestamp for the latest DAG run. On the DAGs page, locate your new target DAG in the list of DAGs. To verify that your DAG was successfully invoked, navigate to the Apache Airflow UI for your new environment, then do the following: DAG A is successful only if DAG B is successful i.e. Now DAG A performs some tasks and then Triggers DAG B. INFO - 0 downstream tasks scheduled from follow-on schedule check on I am using Airflow 2.0.0, MySQL 5.7 on Python 3.8 for my workflow. If the DAG runs successfully, you'll see output similar to the following in the task logs for invoke_dag_task. Airflow recommends you use static start_dates in case you need to re-run jobs or backfill (or end a dag).$ aws s3 cp your-dag.py s3:// your-environment-bucket/dags/ You can solve this by having your start_date hard-coded to a date or by making sure that the dynamic date is further in the past than the interval between executions (In your case, 2 days would be plenty). If you look at your dag being run at that time, it should have a started datetime of roughly one day after the schedule_date. The standard way to trigger a DAG in response to.

The first time this dag will be queued for execution is 20XX-01-02 02:03:00, because that is when the interval period ends. You can trigger a DAG manually from the Airflow UI, or by running an Airflow CLI command from gcloud. The schedule interval is daily, like yours ( 3 2 * * *). You make a dag and put it live in Airflow at midnight. Listing 6.4 Triggering other DAGs using the TriggerDagRunOperator import from airflow import DAG from import. This means that the first run of your job is going to be after the first interval. Airflow runs jobs at the end of an interval, not the beginning. Your issue is the start_date being set for the current time. This left-bound labelling makes a lot of sense when thinking in terms of ETL and differential loads, but gets confusing when thinking in terms of a simple, cron-like scheduler. Similarly, if you run a daily job, the run an with execution_date of would trigger soon after midnight on. The task that fires at 2pm will have an execution_date of 1pm because it assumes that you are processing the 1pm to 2pm time window at 2pm. When running an task for instance, a task will fire every hour. Airflow sets execution_date based on the left bound of the schedule period it is covering, not based on when it fires (which would be the right bound of the period). Now, about execution_date and when it is triggered, this is a common gotcha for people onboarding on Airflow. dagprime: Scans through a directory and intends to call dagtertiary on each one. More information about start_date here in an FAQ entry that I wrote and sort all this out: In airflow, is there a good way to call another dag's task Ask Question Asked 5 years, 10 months ago Modified 6 months ago Viewed 28k times 12 I've got dagprime and dagtertiary. If a DAG fails its task, Airflow will automatically perform retry. Simply clear the old job or use a different datetime.įirst, I recommend you use constants for start_date, because dynamic ones would act unpredictably based on with your airflow pipeline is evaluated by the scheduler. The user can define the frequency for it to run and trigger rule for each task for DAG.

I can't think of a reason you would ever want to run two identical dags with the same execution_date in production anyway, but it is something I ran into while testing and you should not be alarmed by it. This is because the execution_date and dag_id are used to create the row index and rows with identical indexes cannot be inserted. There is an issue you may run into when using this and not setting execution_date=now(): your operator will throw a mysql error if you try to start a dag with an identical execution_date twice. Run_id_dt = datetime.strptime(self.execution_date, '%Y-%m-%d %H:%M:%S')ĭro = DagRunOrder(run_id='trig_' + run_id_dt.isoformat()) Trigger_dag_id=trigger_dag_id, python_callable=python_callable, Super(MMTTriggerDagRunOperator, self)._init_( Self, trigger_dag_id, python_callable, execution_date, Python_callable=lambda: random.getrandbits(1), My_dag_trigger_operator = MMTTriggerDagRunOperator( Use when you want to explicity set the execution date on the target DAGĪdapted from Paul Elliot's solution on airflow-dev mailing list str (otherwise it's hard to hook the datetime.now() date). MMT-patched for passing explicit execution date from datetime import datetimeįrom _operator import TriggerDagRunOperator, DagRunOrderĬlass MMTTriggerDagRunOperator(TriggerDagRunOperator): It's a bit hacky but it is the only way I found to get the job done. The following class expands on TriggerDagRunOperator to allow passing the execution date as a string that then gets converted back into a datetime.

Airflow dag trigger another dag