![]() ![]() This necessitated modifying some configurations to initiate a restart of all Apache Airflow components. To resolve such situations, we had to manually reboot Apache Airflow since it doesn't provide an option to restart within the application. You have to build it into a Kubernetes container, which is not easy to maintain, and I find it to be very clunky." "The graphical user interface can be improved." "We have faced scenarios where Apache Airflow becomes non-responsive, leading to job failures. Else, we have to put a senior engineer to operate it." "Adding more automated components in Apache Airflow for basic things like exporting the data would be helpful." "The problem with Apache Airflow is that it is an open-source tool. ![]() ![]() They should make it simple for newcomers. "I would like to see some no-code capabilities and drag and drop abilities in Airflow." "For admins, there should be improved logging capabilities because Apache Airflow does have logging, but it's limited to some database data." "There is an area for improvement in onboarding new people. The number of operators and features I've used are mainly related to connectivity services and integrated services because I primarily work with GCP." "I found the following features very useful: DAG - Workload management and orchestration of tasks using." They also have a graphical view, so if you are not a programmer and you are just an administrator, you can easily track everything and see if everything is working or not." "Every feature in Apache Airflow is valuable. Everything is in Python, so it's not hard to understand. Additionally, the reattempt at failed jobs is useful." "Since the solution is programmatic, it allows users to define pipelines in code rather than drag and drop." "Development on Apache Airflow is really fast, and it's easy to use with the newer updates. Manually run backfill from the command line with the "-m" (-mark-success) flag which tells airflow not to actually run the DAG, rather just mark it as successful in the DB.Į.g."Designing processes and workflows is easier, and it assists in coordinating all of the different processes." "The best feature is the customization." "Since Apache works very well on Python, we can manage everything and create pipelines there." "The most valuable feature of Apache Airflow is creating and scheduling jobs.Note that if you change the start_date of a DAG, you must change the name of the DAG as well due to the way the start date is stored in airflow's DB. Set the start_date to a date in the future so that it will only start scheduling dag runs once that date is reached.In #2, it is filling in all of the DAG runs from start_date until "now". In #1, it is filling in the 3 missing runs from the 30 seconds which you turned off the scheduler. This is essentially what is happening in both of your question. When you change the scheduler toggle to "on" for a DAG, the scheduler will trigger a backfill of all dag run instances for which it has no status recorded, starting with the start_date you specify in your "default_args".įor example: If the start date was "" and you turned on the scheduling toggle at "T00:00:00" and your dag was configured to run hourly, then the scheduler will backfill 24 dag runs and then start running on the scheduled interval. I'm using a CeleryExecutor instead of a SequentialExecutor.I changed from using a sqlite db to using a postgres db.The only two things I've changed in my airflow config are Are these tasks also somehow "backfilled" tasks? Or am I missing something.Ĭurrently, I have a very simple dag: default_args = , If I run airflow scheduler for a few minutes, then run airflow clear MY_tutorial, then restart airflow scheduler, it seems to run a TON of extra tasks. Are these extra tasks "backfilled" tasks that weren't able to complete in an earlier run? If so, how would I tell airflow not to backfill those tasks? If I run airflow scheduler for a few minutes, stop it for a minute, then restart it again, my DAG seems to run extra tasks for the first 30 seconds or so, then it continues as normal (runs every 10 sec). Specifically, there are 2 use-cases that confuse me: I'm just getting started with Airbnb's airflow, and I'm still not clear on how/when backfilling is done. ![]()
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