[Merged by Bors] - Airflow cron-DAG demo

demos/airflow-scheduled-job/enable-and-run-dag.yaml

@@ -0,0 +1,17 @@
+---
+apiVersion: batch/v1
+kind: Job
+metadata:
+  name: start-dag-job
+spec:
+  template:
+    spec:
+      containers:
+        - name: start-dag-job
+          image: docker.stackable.tech/stackable/testing-tools:0.1.0-stackable0.1.0
+          command: ["bash", "-c", "
+          curl -s --user airflow:airflow -H 'Content-Type:application/json' -XPATCH http://airflow-webserver-default:8080/api/v1/dags/date_demo -d '{\"is_paused\": false}'
+          && curl -s --user airflow:airflow -H 'Content-Type:application/json' -XPOST http://airflow-webserver-default:8080/api/v1/dags/date_demo/dagRuns -d '{}'
+          "]
+      restartPolicy: OnFailure
+  backoffLimit: 20 # give some time for the Airflow cluster to be available

demos/demos-v1.yaml

@@ -55,3 +55,11 @@ demos:
     manifests:
       - plainYaml: https://raw.githubusercontent.com/stackabletech/stackablectl/main/demos/hbase-hdfs-load-cycling-data/01-distcp-cycling-data.yaml
       - plainYaml: https://raw.githubusercontent.com/stackabletech/stackablectl/main/demos/hbase-hdfs-load-cycling-data/02-create-hfile-and-import-to-hbase.yaml
+  airflow-scheduled-job:
+    description: Activate a simple Airflow DAG to run continously at a set interval
+    stackableStack: airflow
+    labels:
+      - airflow
+      - job-scheduling
+    manifests:
+      - plainYaml: https://raw.githubusercontent.com/stackabletech/stackablectl/main/demos/airflow-scheduled-job/enable-and-run-dag.yaml

docs/modules/ROOT/nav.adoc

@@ -7,8 +7,9 @@
 ** xref:commands/services.adoc[]
 ** xref:commands/stack.adoc[]
 * xref:demos/index.adoc[]
-** xref:demos/trino-taxi-data.adoc[]
-** xref:demos/nifi-kafka-druid-earthquake-data.adoc[]
+** xref:demos/airflow-scheduled-job.adoc[]
 ** xref:demos/kafka-druid-water-level-data.adoc[]
+** xref:demos/nifi-kafka-druid-earthquake-data.adoc[]
+** xref:demos/trino-taxi-data.adoc[]
 * xref:customization.adoc[]
 * xref:troubleshooting.adoc[]

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

@@ -0,0 +1,93 @@
+= airflow-scheduled-job
+
+[NOTE]
+====
+This guide assumes you already have the demo `airflow-cron-dag` installed.
+If you don't have it installed please follow the xref:commands/demo.adoc#_install_demo[documentation on how to install a demo].
+To put it simply you have to run `stackablectl demo install airflow-cron-dag`.
+====
+
+This demo will
+
+* Install the required Stackable operators
+* Spin up the follow data products
+** *Postgresql*: An open-source database used for Airflow cluster- and job-metadata.
+** *Redis*: An in-memory data structure store used for queuing Airflow jobs
+** *Airflow*: An open-source workflow management platform for data engineering pipelines.
+* Mount an Airflow job (referred to as a Directed Acyclic Graph, or DAG) for the cluster to use
+* Enable and schedule the job
+* Verify the job status with the Airflow Webserver UI
+
+You can see the deployed products as well as their relationship in the following diagram:
+
+image::demo-airflow-scheduled-job/overview.png[]
+
+== List deployed Stackable services
+To list the installed Stackable services run the following command:
+
+[source,console]
+----
+$ stackablectl services list --all-namespaces
+ PRODUCT  NAME     NAMESPACE  ENDPOINTS                                  EXTRA INFOS
+
+ airflow  airflow  default    webserver-airflow http://172.18.0.4:32754  Admin user: airflow, password: airflow
+----
+
+[NOTE]
+====
+When a product instance has not finished starting yet, the service will have no endpoint.
+Starting all the product instances might take a considerable amount of time depending on your internet connectivity.
+In case the product is not ready yet a warning might be shown.
+====
+
+== Airflow Webserver UI
+Superset gives the ability to execute SQL queries and build dashboards.
+Open the `airflow` endpoint `webserver-airflow` in your browser (`http://172.18.0.4:32754` in this case).
+
+image::demo-airflow-scheduled-job/airflow_1.png[]
+
+Log in with the username `airflow` and password `airflow`. The overview screen shows the DAG that has been mounted during the demo set-up (`date_demo`).
+
+image::demo-airflow-scheduled-job/airflow_2.png[]
+
+There are two things to notice here. The DAG has been enabled, as shown by the slider to the left of the DAG name (DAGs are initially all `paused` and can be activated either manually in the UI or via a REST call, as done in the setup for this demo):
+
+image::demo-airflow-scheduled-job/airflow_3.png[]
+
+Secondly, the job has been busy, with several runs already logged!
+
+image::demo-airflow-scheduled-job/airflow_4.png[]
+
+Clicking on this number will display the individual job runs:
+
+image::demo-airflow-scheduled-job/airflow_5.png[]
+
+The job is running every minute. With Airflow, DAGs can be started manually or scheduled to run when certain conditions are fulfilled: in this case the DAG has been set up to run using a cron table, which is part of the DAG definition.
+
+=== View DAG details
+
+Let's drill down a bit deeper into this DAG. Click on one of the individual job runs shown in the previous step to display the job details. The DAG is displayed in the form of a graph (this job is so simple that it only has one step, called `run_every_minute`).
+
+image::demo-airflow-scheduled-job/airflow_6.png[]
+
+In the top right-hand corner there is some scheduling information, which tells us that this job will run every minute continuously:
+
+image::demo-airflow-scheduled-job/airflow_7.png[]
+
+Click on the `run_every_minute` box in the centre of the page and then select `Log`:
+
+image::demo-airflow-scheduled-job/airflow_8.png[]
+
+This will navigate to the worker where this job was run (with multiple workers the jobs will be queued and distributed to the next free worker) and display the log. In this case the output is a simple printout of the timestamp:
+
+image::demo-airflow-scheduled-job/airflow_9.png[]
+
+To look at the actual DAG code click on `Code`. Here we can see the crontab information used to schedule the job as well the `bash` command that provides the output:
+
+image::demo-airflow-scheduled-job/airflow_10.png[]
+
+== Summary
+
+This demo showed how a DAG can be made available for Airflow, scheduled, run and then inspected with the Webserver UI.
+
+

stacks/airflow/airflow.yaml

@@ -7,23 +7,64 @@ spec:
   version: 2.2.5-python39-stackable0.3.0
   statsdExporterVersion: v0.22.4
   executor: CeleryExecutor
-  loadExamples: true
+  loadExamples: false
   exposeConfig: false
   credentialsSecret: airflow-credentials
+  volumes:
+    - name: airflow-dags
+      configMap:
+        name: airflow-dags
+  volumeMounts:
+    - name: airflow-dags
+      mountPath: /dags/date_demo.py
+      subPath: date_demo.py
   webservers:
     roleGroups:
       default:
+        envOverrides:
+          AIRFLOW__CORE__DAGS_FOLDER: "/dags"
         replicas: 1
   workers:
     roleGroups:
       default:
+        envOverrides:
+          AIRFLOW__CORE__DAGS_FOLDER: "/dags"
         replicas: 2
   schedulers:
     roleGroups:
       default:
+        envOverrides:
+          AIRFLOW__CORE__DAGS_FOLDER: "/dags"
         replicas: 1
 ---
 apiVersion: v1
+kind: ConfigMap
+metadata:
+  name: airflow-dags
+data:
+  date_demo.py: |
+    """Example DAG returning the current date"""
+    from datetime import datetime, timedelta
+
+    from airflow import DAG
+    from airflow.operators.bash import BashOperator
+
+    with DAG(
+        dag_id='date_demo',
+        schedule_interval='0-59 * * * *',
+        start_date=datetime(2021, 1, 1),
+        catchup=False,
+        dagrun_timeout=timedelta(minutes=5),
+        tags=['example'],
+        params={},
+    ) as dag:
+
+        run_this = BashOperator(
+            task_id='run_every_minute',
+            bash_command='date',
+        )
+---
+apiVersion: v1
 kind: Secret
 metadata:
   name: airflow-credentials