Who is it for This package makes the assumption that youre using Kubernetes somehow. It assumes the client passes in a path to a yaml file that may have Jinja templated fields. Specify the image, ENTRYPOINT, and CMD in the corresponding parameters of KubernetesPodOperator. Airflow Kubernetes Job Operator What is this An Airflow Operator that manages creation, watching, and deletion of a Kubernetes Job. You could build a Docker image with somescript.py and all its dependencies. class .KubernetesPodOperator(namespace, image, name, cmdsNone, argumentsNone, portsNone, volumemountsNone, volumesNone, envvarsNone, secretsNone, inclusterTrue, clustercontextNone, labelsNone, startuptimeoutseconds120, getlogsTrue, imagepullpolicy'IfNotPresent', annotationsN. This command fails, because of the same reasons as in the first example. ( None is the string representation of the load_users_into_table()'s return value) In the second example, pretty much the same happens as in the first example, but the command that is run in the container (again based on the cmd and arguments parameters) is python somescript.py -c None The executor controls how all tasks get run. Briefly: KubernetesExecutor: You need to specify one of the supported executors when you set up Airflow. That is why the command fails with something like: python: can't open file 'somescrit.py': No such file or directory 1 Answer Sorted by: 14 They serve different purposes. Even if did, it probably would have been not the one that you wrote. Obviously, the official Python image from Docker Hub does not have somescript.py in its working directory. Each task shows an example of what it is possible to do with the KubernetesExecutor such. So, the complete command that is run in the container is python somescript.py -c print('HELLO') Basically, the DAG is composed of four tasks using the PythonOperator. CMD of the python image is replaced by (the arguments parameter).ENTRYPOINT of the python image is replaced by (the cmd parameter) GitHub - apache/airflow-on-k8s-operator: Airflow on Kubernetes Operator This repository has been archived by the owner on Apr 23, 2023.KubernetesPodOperator instructs K8s to lunch a pod and prepare to run a container in it using the python image (the image parameter) from (the default image registry).In the first example, the following happens: The tasks can scale using spark master support made available in spark 2. KubernetesPodOperator launches a Kubernetes pod that runs a container as specified in the operator's arguments. 3-kubernetes-pod-operator-spark: Execute Spark tasks against Kubernetes Cluster using KubernetesPodOperator. Kubernetes offers the use secret to secure sensitive information. įinally: I want to mention something important when working with databases (credentials). If you don't understand where configuration file comes from, look here. In_cluster=False, #False: local, True: clusterĬonfig_file='/usr/local/airflow/include/.kube/config', name name of the pod in which the task will run, will be used (plus a random suffix) to generate a pod id (DNS-1123 subdomain, containing only a-z0-9.-). NB: kubernetes_pod_operator looks for image from public docker repo # build imageĭocker tag my-python-img username/my-python-img image Docker image you wish to launch.Defaults to, but fully qualified URLS will point to custom repositories. Step-2: Build and push the image into public Docker repository. name ( str) name of the pod in which the task will run, will be used to generate a pod id. startuptimeoutseconds ( int) timeout in seconds to startup the pod. labels ( dict) labels to apply to the Pod. Includes ConfigMaps and PersistentVolumes. Some popular operators from core include: BashOperator - executes a bash command PythonOperator - calls an arbitrary Python function EmailOperator - sends an email Use the task decorator to execute an arbitrary Python function. volumes ( .Volume) volumes for launched pod. # copy the python script from local to container Airflow has a very extensive set of operators available, with some built-in to the core or pre-installed providers. # install dependencies into container (geopandas, sqlalchemy) # copy requirement.txt from local to container You can use Apache Airflow DAG operators in any cloud provider, not only GKE.Īirflow-on-kubernetes-part-1-a-different-kind-of-operator as like as Airflow Kubernetes Operator articles provide basic examples how to use DAG's.Īlso Explore Airflow KubernetesExecutor on AWS and kops article provides good explanation, with an example on how to use airflow-dags and airflow-logs volume on AWS.Įxample: from script somescript.py must be in Docker image.
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