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xeniaperazvan
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fix: Fix broken link to Airflow docs (#397)
Co-authored-by: Razvan-Daniel Mihai <84674+razvan@users.noreply.github.com>
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docs/modules/demos/pages/airflow-scheduled-job.adoc

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@@ -24,7 +24,7 @@ This demo should not be run alongside other demos.
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To run this demo, your system needs at least:
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* 2.5 https://kubernetes.io/docs/tasks/debug/debug-cluster/resource-metrics-pipeline/#cpu[cpu units] (core/hyperthread)
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* 2.5 https://kubernetes.io/docs/tasks/debug/debug-cluster/resource-metrics-pipeline/#cpu[cpu units{external-link-icon}^] (core/hyperthread)
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* 10GiB memory
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* 24GiB disk storage
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@@ -135,7 +135,7 @@ Click on the `run_every_minute` box in the centre of the page to select the logs
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In this demo, the KubernetesExecutor is deployed which means that logs are only preserved (and available in the UI) if either remote logging or the SDP logging framework is configured.
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In this demo we set up remote logging using S3/Minio.
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Since Minio in this case is set up with TLS, the Airflow connection requires that the webserver has access to a relevant certificate and that every pod has environment variables containing the access and secret keys.
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See the https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/executor/kubernetes.html#managing-dags-and-logs[Airflow Documentation] for more details.
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See the https://airflow.apache.org/docs/apache-airflow-providers-cncf-kubernetes/stable/kubernetes_executor.html#managing-dags-and-logs[Airflow Documentation{external-link-icon}^] for more details.
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If you are interested in persisting the logs using the SDP logging framework, take a look at the xref:logging.adoc[] demo.
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====
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Go back to DAG overview screen.
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The `sparkapp_dag` job has a scheduled entry of `None` and a last-execution time.
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This allows a DAG to be executed exactly once, with neither schedule-based runs nor any
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https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/dag-run.html#backfill[backfill].
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https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/dag-run.html#backfill[backfill{external-link-icon}^].
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The DAG can always be triggered manually again via REST or from within the Webserver UI.
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image::airflow-scheduled-job/airflow_10.png[]
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The task checks the configuration, runs a task that inserts some dummy data into a table, and then runs some tests to verify the result.
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The details of the simple DBT project can be found https://github.com/stackabletech/demos/tree/main/demos/airflow-scheduled-job/dbt/dbt_test[in the demos repository].
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The details of the simple DBT project can be found https://github.com/stackabletech/demos/tree/main/demos/airflow-scheduled-job/dbt/dbt_test[in the demos repository{external-link-icon}^].
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== Patching Airflow to stress-test DAG parsing using relevant environment variables
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Make sure you are still logged in 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 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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The patch also sets some environment variables that can be used to change the frequency of certain operations. The descriptions can be found in the https://airflow.apache.org/docs/apache-airflow/stable/configurations-ref.html[Airflow configuration reference{external-link-icon}^].
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[source,yaml]
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