This is what I mean by dynamic is this in your code:
if table["branch_flag"] == BranchFlags.yes:
task_1 >> task_2
This will be run at DAG creation time not a DAG run time, and therefore your
“branch” will not work as you expect.
To get conditional branches you have to use the Branch Operators (either the
BranchPythonOperator or sub-class the BaseBranchOperator):
https://airflow.apache.org/docs/stable/concepts.html?highlight=branch#branching
https://www.astronomer.io/guides/airflow-branch-operator/
Damian
From: Reed Villanueva <[email protected]>
Sent: Tuesday, January 28, 2020 14:54
To: [email protected]
Subject: Re: Can airflow dags have branches that both run, but do not converge?
I am not trying to dynamically branch while the dag is running if that's what
you're asking.
The dag is intended to be static.
In the code given, the list_of_tables variable is a list of dicts of configs
for different datasets, some of which use tasks that could be done concurrently
(eg. write to local drive and write to remote DB) hence the branching.
On Tue, Jan 28, 2020 at 6:51 AM Shaw, Damian P.
<[email protected]<mailto:[email protected]>> wrote:
Hi Reed,
Your DAG creation shouldn’t be dynamically changing frequently. The DAG itself
in Airflow is fairly static, if in your DAG definition file you dynamically
change your DAG based on conditions Airflow is simply going to think the tasks
you are no longer defining don’t exist anymore. That’s what Airflow is telling
you, that you are no longer defining that task so it can’t find it anymore.
Damian
From: Reed Villanueva <[email protected]<mailto:[email protected]>>
Sent: Monday, January 27, 2020 17:50
To: [email protected]<mailto:[email protected]>
Subject: Can airflow dags have branches that both run, but do not converge?
...because these seems to not be that case.
I have an airflow graph with a conditional branch defined like
class BranchFlags(Enum):
yes = "yes"
no = "no"
...
for table in list_of_tables # type list(dict)
task_1 = BashOperator(
task_id='task_1_%s' % table["conf1"],
bash_command='bash script1.sh %s' % table["conf1"],
dag=dag)
if table["branch_flag"] == BranchFlags.yes:
consolidate = BashOperator(
task_id='task_3_%s' % table["conf2"],
bash_command='python %s/consolidate_parquet.py %s' % table["conf2"],
dag=dag)
task_3 = BashOperator(
task_id='task_3_%s' % table["conf3"],
bash_command='bash script3.sh %s' % table["conf3"],
dag=dag)
task_1 >> task_3
if table["branch_flag"] == BranchFlags.yes:
task_1 >> task_2
Even though the longer parts of the graph run fine, the lone branch is not
being run for the one sequence / pipeline that was supposed to branch. When
viewing the logs for the task, I see
*** Task instance did not exist in the DB
This is weird to me, since ostensibly the scheduler DB sees the task since it
does appear in the web UI graph. Not sure what is going on here and adding
other changes to the dag .py file do show up in the graph and are executed by
the scheduler when running the graph. And attempting to view the tasks Task
Instance Details throws error
Task [dagname.task_3_qwerty] doesn't seem to exist at the moment
Running airflow resetdb (as I've seen in other posts) does nothing for the
problem.
Note that the intention is that the short branch runs concurrently with the
longer branch (not as an either or choice).
Anyone know why this would be happening or have some debugging tips?
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