I have a following scenario/DAG;
|----->Task1----| |---->Task3---|
start task-->| |-->Merge Task --->| | ----->End Task
|----->Task2----| |---->Task4---|
Currently the Task, Task2, Task3 and Task4 are ShortCircuitOperators, When one of the Task1 and Task2 are ShortCircuted all the downstream tasks are skipped.
But my requirement is to break the skipped state being propagated to Task3 and Task4 at Merge Task.
Cause I want the Task 3 and Task 4 to be run no matter what happens upstream.
Is there a way I can achieve this.? I want to have the dependencies in place as depicted/showed in the DAG.
Yes it can be achieved
Instead of using ShortCircuitOperator, use AirflowSkipException (inside a PythonOperator) to skip a task (that is conditionally executing tasks / branches)
You might be able to achieve the same thing using a BranchPythonOperator
but ShortCircuitOperator definitely doesn't behave as per most people's expectations. Citing this line closely resembling your problem from this link
... When one of the upstreams gets skipped by ShortCircuitOperator
this task gets skipped as well. I don't want final task to get skipped
as it has to report on DAG success.
To avoid it getting skipped I used trigger_rule='all_done', but it
still gets skipped.
If I use BranchPythonOperator instead of ShortCircuitOperator final
task doesn't get skipped. ...
Furthermore the docs do warn us about it (this is really the expected behaviour of ShortCircuitOperator)
It evaluates a condition and short-circuits the workflow if the condition is False. Any downstream tasks are marked with a state
of “skipped”.
And for tasks downstream of your (possibly) skipped tasks, use different trigger_rules
So instead of default all_success, use something like none_failed or all_done (depending on your requirements)
Related
When I trigger a DAG manually, prev_execution_date and execution_date are the same.
echo_exec_date = BashOperator(
task_id='bash_script',
bash_command='echo "prev_exec_date={{ prev_execution_date }} execution_date={{ execution_date }}"',
dag=dag)
results in:
prev_exec_date=2022-06-29T08:50:37.506898+00:00 execution_date=2022-06-29T08:50:37.506898+00:00
They are different if the DAG is triggered automatically by the scheduler.
I would like to have prev_execution_date regardless of triggering it manually or automatically.
When manually triggering DAG, the schedule will be ignored, and prev_execution_date == next_execution_date == execution_date
This is explained in the Airflow docs
This is because previous / next of manual run is not something that is well defined. Consider you have a daily schedule (say at 00:00) and you invoke a manual run on 13:00. What is the expected next schedule? should it be daily from 00:00 or daily from 13:00? a DagRun can have only 1 prev and only 1 next. In your senario it seems like you are interested in a case where there can be more than 1 or that the manual run "comes between" the two scheduled runs. This is not something that Airflow supports - It really over complicate things.
If you want to workaround it you can create custom macro that checks the run_type, searches the specific DagRun that you consider as previous and return it's execution_date. Be noted that it might create some side effects (overlapping data interval process etc..) you need to really verify that the logic you implement make sense for your specific use case.
I made dag with branchpythonperator task, and it calls two task. One task will be skipped, one will be executed. After executing this task, dag must execute another task.
For this task I used trigger rule all_done, and try none_failed. Both this rule behave pretty similar. What is the difference?
ALL_DONE : All the tasks are done i.e. in one of the following state : succeeded, failed, skipped or upstream_failed
NONE_FAILED : None of the tasks have failed i.e would be in one of the following state : succeeded, skipped
For your scenario, try failing one of the tasks in-between and see the difference in behavior. The task with trigger rule all_done will be kicked off while the task with trigger rule 'none_failed' will not.
For further reference, take a look at the source code for Trigger_Rule (line 187 to line 198 would answer your question) : https://github.com/apache/airflow/blob/master/airflow/ti_deps/deps/trigger_rule_dep.py#L187
I am running 5 PythonOperator tasks in my airflow DAG and one of them is performing an ETL job which is taking a long time, due to which all my resources are blocked. Is there a way I can set a max execution time per task, after which the task either fails or is marked successful (so that the DAG doesnt fail) with a message?
In every operator we have an execution_timeout variable where you have to pass a datetime.timedelta object.
As per the base operator code comments:
:param execution_timeout: max time allowed for the execution of
this task instance, if it goes beyond it will raise and fail.
:type execution_timeout: datetime.timedelta
Also bear in mind that this will fail a single run of the DAG and will trigger re-runs and will only be declared to be a failed DAG when all re-runs have failed.
So depending on what number of auto retries you have assigned, you could have a potential maximum time of ( number of retries ) x ( timeout ) in case the code keeps taking too long.
Check out this previous answer.
In short, using airflow's built in pools or even specifying a start_date for a task (instead of an entire DAG) seem to be potential solutions.
From this documentation, you'd want to set the execution_timeout task parameter, which would look something like this
from datetime import timedelta
sensor = SFTPSensor(
task_id="sensor",
path="/root/test",
execution_timeout=timedelta(hours=2),
timeout=3600,
retries=2,
mode="reschedule",
)
I worked my way through an example script on BranchPythonOperator and I noticed the following:
The final task gets Queued before the the follow_branch_x task is done. This I found strange, because before queueing the final task, it should know whether its upstream task is a succes (TriggerRule is ONE_SUCCESS).
To test this, I replaced the 3 of the 4 follow_branch_ tasks with tasks that would fail, and noticed that regardless of the follow_x branch task state, the downstream task gets done. See the image:
Could anyone explain this behaviour to me, as it does not feel intuitive as normally failed tasks prevent downstream tasks from being executed.
Code defining the join task:
join = DummyOperator(
task_id = "join",
trigger_rule = TriggerRule.ONE_SUCCESS,
dag=dag
)
Try setting the trigger_rule to all_done on your branching operator like this:
branch_task = BranchPythonOperator(
task_id='branching',
python_callable=decide_which_path(),
trigger_rule="all_done",
dag=dag)
No idea if this will work but it seems to have helped some people out before:
How does Airflow's BranchPythonOperator work?
Definition from Airflow API
one_success: fires as soon as at least one parent succeeds, it does not wait for all parents to be done
If you continue to use one_success, you need all of your four follow_branch-x tasks to fail for join not to be run.
In airflow.models.BaseOperator. You have two default parameter:
depends_on_past=False and trigger_rule=u'all_success'
According to doc
depends_on_past (bool) – when set to true, task instances will run sequentially while relying on the previous task’s schedule to succeed.
trigger_rule (str) – defines the rule by which dependencies are applied for the task to get triggered.
Isn't both the same thing ? I don't get why there are redundant parameters.
No, both are entirely different. depends_on_past(boolean) is for to check whether to run a task or not depending on its previous DAG run(last run). trigger_rule is used to trigger a task depends on its parent task(s) state.
refer offical document