I need to build the DAG which can trigger on the basis of file arrival , schedule like(weekdays,daily,TTS) and predecessor DAG success.
Please help me to achieve to trigger job based on the success of all above condition
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I want to extract statistics on our Airflow processes using its database. One of these statistics is how many DAG runs are finished smoothly, without any failures and reruns. Doing that using the try_number column of the dag_run table doesn't help, since it also counts automatic retries. I want to count only the cases in which an engineer had to rerun or resume the DAG run.
Thank you.
If I understand correctly you want to get all Dagruns that never had a failed task in them? You can do this by excluding all DAG run_id s that have an entry in the task_failed table:
SELECT *
FROM dag_run
WHERE run_id NOT IN (
SELECT run_id
FROM dag_run
JOIN task_fail USING(run_id)
)
;
This of course would not catch other task states that an engineer might intervene with like marking a task as successful that is stuck in running or a deferred state etc.
One note as of Airflow 2.5.0 you can add notes to DAG and task runs in the UI when manually intervening. Those notes are stored in the tables dag_run_note and task_instance_note.
I am completely new to Airflow and am trying to grasp the concepts of scheduling and default args.
I have a scenario where I would like to schedule my DAG hourly to do some data transfer task between a source and a database. What I am trying to understand is, lets say one of my DAG runs has triggered at 00:00 AM. Now if it takes more than an hour for this run to successfully complete all of its tasks (say 1 hour 30 min), does it mean that the next DAG run that was supposed to be triggered at 01:00 AM will NOT get triggered but the DAG run from 02:00 AM will get triggered?
Yes.
In order to avoid, you need catchup=True for the DAG object.
Reference : https://airflow.apache.org/docs/apache-airflow/stable/dag-run.html
(Search for Catchup)
Airflow Scheduler used to monitor all DAGs and tasks in Airflow.Default Arguments can be used to create tasks with default parameters in DAG.
The first DAG runs based on start_date and runs based on scheduled_interval sequentially. Scheduler doesn’t trigger tasks until the period has ended.For your requirement you can set dag.catchup to true as to run the DAG for each completed interval and scheduler will execute them sequentially.Catchup is used to start the DAG run since the last data interval which has not started for any data interval.
I have a DAG that has run tasks for over a decade of execution dates. Now I needed to add another year to the beginning. I googled a little bit and the recommendation was to do this under a new dag_id. Because the old DAG has run already for that named execution date range, I want to mark those in the new DAG as a success. How can I archive this in a convenient way?
Thanks in advance. Have a nice start to this week.
Airflow's backfill feature is designed to do exactly what you're trying to do.
That said, not everyone likes using the feature. For example, if your dag is ordinarily an hourly job, backfilling several years of data in hourly batches might be really inefficient.
So for various reasons, creating a temporary "backfill" dag is not a bad way to go.
And to be clear, with "backfill" dag I refer to a dag you are using for purpose of backfill, while not using airflow backfill feature.
For your "backfill" dag, use the DAG parameters start_date and end_date to control the range of execution_date a dag will create dag runs for.
Then after your "backfill" dag is done with all its runs, you can delete it. Airflow won't know the old task instances are now backfilled, but you may not care about that. If you do, you can update the dag_id manually in the metastore database. And otherwise, your "old" dag has correct metadata for more recent periods.
I am in a situation where I have started getting some data scheduled daily at a certain time and I have to create ETL for that data.
Meanwhile, when I am still creating the DAGs for scheduling the tasks in Airflow. The data keeps on arriving daily. So when I will start running my DAGs from today I want to schedule it daily and also wants to backfill all the data from past days which I missed while I was creating DAGs.
I know that if I put start_date as the date from which the data started arriving airflow will start backfilling from that date, but wouldn't in that case, my DAGs will always be behind of current day? How can I achieve backfilling and scheduling at the same time? Do I need to create separate DAGs/tasks for backfill and scheduling?
There are several things you need to consider.
1. Is your daily data independent or the next run is dependent on the previous run?
If the data is dependent on previous state you can run backfill in Airflow.
How backfilling works in Airflow ?
Airflow gives you the facility to run past DAGs. The process of running past DAGs is called Backfill. The process of Backfill actually let Airflow forset some status of all DAGs since it’s inception.
I know that if I put start_date as the date from which the data
started arriving airflow will start backfilling from that date, but
wouldn't in that case, my DAGs will always be behind of current day?
Yes setting a past start_date is the correct way of backfilling in airflow.
No, If you use celery executer, the jobs will be running in parallel and it will eventually catch up to the current day , obviously depending upon your execution duration.
How can I achieve backfilling and scheduling at the same time? Do I
need to create separate DAGs/tasks for backfill and scheduling?
You do not need to do anything extra to achieve scheduling and backfilling at the same time, Airflow will take care of both depending on your start_date
Finally , If this activity is going to be one time task I recommend , you process your data(manually) offline to airflow , this will give you more control over the execution.
and then either mark the backfilled tasks as succeed or below
Run an airflow backfill command like this: airflow backfill -m -s "2016-12-10 12:00" -e "2016-12-10 14:00" users_etl.
This command will create task instances for all schedule from 12:00 PM to 02:00 PM and mark it as success without executing the task at all. Ensure that you set your depends_on_past config to False, it will make this process a lot faster. When you’re done with it, set it back to True.
Or
Even simpler set the start_date to current date
I have a DAG that runs on a schedule, and the tasks inside it find a file in a path, process it, and move that file to an archive folder.
Instead of waiting for the schedule, I manually triggered the DAG. The manually triggered DAG executed it's first task and "found a new file to process", but before starting the second task to load that file, the DAG schedule, automatically picked up and started to process the same file.
When the scheduled dag started, it paused executing the manually triggered DAG.
After the scheduled DAG finished, it went back to running the tasks from the manually triggered DAG which caused a failed state since the DAG moves files to an archive from a source directory, and the manually scheduled DAG started processing a file that "it believed was there" due to the success and information from the first task.
So:
DAG manually triggered
DAG manually Triggered Task 1 Executed
DAG scheduled invoked
DAG scheduled task 1 executed
DAG scheduled task 2 executed
DAG scheduled task 3 executed
DAG scheduled completed as Success
DAG manually Triggered Task 2 Failed (because of scheduled task 2 moving file detected in task 1)
DAG manually triggered skips other tasks due to failed task 2.
DAG manually triggered complete as Failed
So, my question is:
How do I configure Airflow such that invocations of the same DAG are executed FIFO, regardless if the DAG was invoked by a schedule, manual, or trigger?
Regarding the question:
Why do Apache Airflow scheduled DAGs prioritize over manually
triggered DAGs?
I can not say something. But probably is a good idea take a look on airflow code.
Regarding the task you want to accomplish:
working with files in mounted folder (or similar), I can suggest you that the first thing to do is copy them in another folder lets say "process folder" in order to be sure that these files are untouchable by "others".
In my project we work with group of videos and we generate trigger related folder using the {{ ts_nodash }} default variable.