1. Containerized Data Preprocessing on Kubernetes


    To perform containerized data preprocessing on Kubernetes, you'll need to create a few different resources:

    1. Docker Image: This contains your application and any dependencies it requires. It should include the data processing application you want to run.

    2. Pods/Containers: These are instances of your Docker image running on your Kubernetes cluster. Pods are the smallest deployable units in Kubernetes and contain one or more containers.

    3. CronJob: This is a Kubernetes resource that runs Pods at scheduled times. This is particularly useful for running data processing tasks that need to happen regularly, such as every hour or once a day.

    The code below demonstrates the process:

    • It defines a CronJob resource that periodically runs a data processing task.
    • The task is defined in the form of a container that gets its specification from a user-provided image.
    • The schedule is defined using the standard cron format.

    For demonstration, let's say you have a Python script for data preprocessing. You first need to containerize this script (build a Docker image with this script and all its dependencies). Then, you push this image to a container registry like Docker Hub, Google Container Registry, or AWS Elastic Container Registry.

    Assuming you have the image ready and available in a registry, here's how you set up a CronJob in Pulumi to run this containerized task.

    import pulumi import pulumi_kubernetes as k8s # Define the cron job schedule, e.g., every day at 5am (UTC) schedule = "0 5 * * *" # Containerized data preprocessing job definition data_preprocessing_job = k8s.batch.v1.CronJob( "data-preprocessing-job", metadata=k8s.meta.v1.ObjectMetaArgs( name="data-preprocessing" ), spec=k8s.batch.v1.CronJobSpecArgs( schedule=schedule, job_template=k8s.batch.v1.JobTemplateSpecArgs( spec=k8s.batch.v1.JobSpecArgs( template=k8s.core.v1.PodTemplateSpecArgs( spec=k8s.core.v1.PodSpecArgs( containers=[ k8s.core.v1.ContainerArgs( name="data-preprocessor", image="your-registry/your-image:latest", # Replace with your actual image args=["python", "your-script.py"], # Replace with the command to run your script ), ], restart_policy="OnFailure", # Policy for what happens if the job fails ), ), ), ), ), ) # Export the name of the cron job pulumi.export('cron_job_name', data_preprocessing_job.metadata["name"])

    In this code snippet:

    • We use the CronJob class from the pulumi_kubernetes package to define a Kubernetes CronJob resource.
    • The metadata part assigns a name to the CronJob.
    • The schedule parameter is set to run the job every day at 5 am.
    • The jobTemplate specifies the job to be executed, which runs a single container. Replace "your-registry/your-image:latest" with the Docker image containing your data preprocessing application. You should also replace the args list with the command that should be executed within the container.
    • The restart_policy is set to OnFailure to restart the job if it fails.

    Once you have this code in place, you can use the Pulumi CLI to deploy your workload to the desired Kubernetes cluster, assuming you have the proper context and authorization configured for kubectl.

    Remember to replace the placeholders with real values suitable for your use case. With this setup, your data preprocessing task will run as defined by the CronJob on your Kubernetes cluster.