We will branch 1.10-test as soon as we get the Kubernetes executor in. Or if it
takes too long. Whatever comes first ;-).
B.
> On 18 Apr 2018, at 18:38, Arthur Wiedmer wrote:
>
> Hi,
>
> We are currently looking to release 1.10 soon-ish. I do not think we plan
> on releasing 1.9.1 unless ther
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 *next
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 date for Airflow 1.9.1?
>
>
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 t
