Hi Kyle,
The execution_date of the DAG run will always be lagged one day for your
daily DAG and one week for your weekly DAG. Under the hood, airflow will
calculate the execution_date and next execution_date of the task, and only
schedule the task when the current timestamp is bigger than the
I'm a bit confused with how the scheduler catches up in relation to
start_date and schedule_interval. I have one dag that runs hourly:
dag = DAG(
dag_id='hourly_dag',
start_date=days_ago(1),
schedule_interval='@hourly',
default_args=ARGS)
When I start this DAG fresh it will catch
Hi,
We are currently looking to release 1.10 soon-ish. I do not think we plan
on releasing 1.9.1 unless there is a vulnerability or critical bug.
Best,
Arthur
On Wed, Apr 18, 2018 at 9:13 AM, Zsolt Tóth
wrote:
> Hey all,
>
> is there a planned (approximate) release
Hey all,
is there a planned (approximate) release date for Airflow 1.9.1?
Best,
Zsolt
We are exploring following approach for DAG cancellation. Please let us know if
you see any issue with this
1) Set/create the xcom variable "cancel":"true". It would be set out of the
band by updating the xcom Table in metadata store.
2) Operators would have the code to periodically check for
On 2018/04/13 17:00:36, Maxime Beauchemin wrote:
> If you're concerned about scheduler scalability I'd go with a bigger box.
> The scheduler uses multiprocessing so more CPU power means more throughput.
>
> Also you may want to provision a beefy MySQL box to make
On 2018/04/16 19:09:12, Kyle Hamlin wrote:
> This morning I tried to upgrade to the newer version of the logging config
> file but I keep getting the following a TypeError for my database session.
> I know my credentials are correct so I'm confused why this is happening