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docs/modules/demos/pages/airflow-scheduled-job.adoc

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@@ -164,7 +164,15 @@ kubectl exec -n airflow-demo kafka-broker-default-0 -c kafka -- bash -c \
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--producer.config /stackable/config/client.properties'
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----
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The triggerer logs will show that this DAG was fired (logging out the message that we wrote to the topic above):
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The triggerer logs will show that this DAG was fired (logging out the message that we wrote to the topic above).
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You can do this by either displaying the pod logs directly (e.g. if you are using k9s) or by issuing:
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[source,bash]
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----
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kubectl logs -n airflow-demo airflow-triggerer-default-0 --tail=30
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----
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The logs show that our message was detected, triggering the job:
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image::airflow-scheduled-job/airflow_13.png[]
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@@ -217,7 +225,7 @@ image::airflow-scheduled-job/opa_01.png[]
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Log back into the UI as `admin`.
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The demo also created a third DAG in the ConfigMap, called `dag_factory.py`, which was not mounted to the cluster and therefore does not appear in the UI.
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This DAG can be used to create a number of individual DAGs on-the-fly, thus allowing a certain degree of stress-testing of the DAG scan/register steps (the generated DAGs themselves are trivial and so this approach will not really increase the burden of DAG _parsing_).
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To include this in the list of DAGs (without removing the existing ones), an extra volumeMount is needed, as shown below.
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To show these individual DAGs in the overall list (and to remove the existing ones), adjust the volumeMounts as shown below.
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The patch also sets some environment variables that can be used to change the frequency of certain operations. The descriptions can be found here: https://airflow.apache.org/docs/apache-airflow/stable/configurations-ref.html[window=_blank].
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[source,yaml]

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