mattinbits commented on issue #21171:
URL: https://github.com/apache/airflow/issues/21171#issuecomment-1025238837
More details on reproducibility. Start a SQL Server instance with timezone
set to one hour ahead of UTC:
```
docker container run \
--rm \
--name mssql \
--env 'ACCEPT_EULA=Y' \
--env 'MSSQL_SA_PASSWORD=Mssqlpwd123' \
--env 'TZ=Europe/Copenhagen' \
--publish 1433:1433 \
--detach mcr.microsoft.com/mssql/server:2019-CU5-ubuntu-18.04
```
Create the Airflow DB:
```
docker exec mssql /opt/mssql-tools/bin/sqlcmd -U sa -P Mssqlpwd123 -Q
"CREATE DATABASE AIRFLOW;"
docker exec mssql /opt/mssql-tools/bin/sqlcmd -U sa -P Mssqlpwd123 -Q "ALTER
DATABASE Airflow SET READ_COMMITTED_SNAPSHOT ON;"
```
Conn string:
```
sql_alchemy_conn =
mssql+pyodbc://sa:Mssqlpwd123@localhost/airflow?driver=ODBC+Driver+17+for+SQL+Server
```
And a DAG. Change the cron to be less than one hour ahead of the current
time in UTC:
```
from airflow.models.dag import DAG
from airflow.operators.dummy import DummyOperator
from datetime import datetime
dag = DAG(
dag_id="example",
start_date=datetime(2021, 1, 1),
schedule_interval="00 22 * * *",
catchup=False
)
with dag:
task = DummyOperator(task_id="dummy")
```
Start Airflow, start the DAG, observe the premature DAG execution.
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