1. Security Patching for Databricks Clusters in AI Platforms


    To implement security patching for Databricks clusters in AI platforms using Pulumi, you would typically follow a strategy that involves periodically updating the clusters with the latest security patches. Pulumi doesn't directly manage the security patching process but allows you to manage infrastructure in a way that supports these activities.

    For Databricks clusters, security patching generally involves updating the underlying runtime versions, which include updates and security patches from the Databricks team, or by applying library updates that address specific security issues.

    To do this, you'd set up a Pulumi program that defines a Databricks cluster with a specific runtime version. When a new patch is available, you can update the Pulumi program to reflect the new runtime version and redeploy the cluster. Additionally, a continuous integration and deployment (CI/CD) system could be set up to automatically redeploy clusters when updates are made to the Pulumi program.

    Here, I'll demonstrate how to define a Databricks cluster with Pulumi in Python. Let's begin by setting up a Databricks cluster with a specified runtime version that includes language support for PySpark, Scala, and R. This example assumes that you have the necessary Databricks provider set up in your Pulumi project.

    import pulumi import pulumi_databricks as databricks # Create a Databricks cluster cluster = databricks.Cluster("my-databricks-cluster", # Specify the unique cluster name cluster_name="my-security-patched-cluster", # Define the runtime version with the latest security patches # Please refer to the Databricks documentation for the latest runtime version spark_version="8.3.x-scala2.12", # Specify the node type. This determines the size of the virtual machines that run the cluster workload node_type_id="Standard_D3_v2", # Define the number of worker nodes in the cluster. 0 means no worker nodes and cluster is in a "Quiescent" state. num_workers=2, # Define the autoscale properties of the cluster autoscale=databricks.ClusterAutoscaleArgs( min_workers=1, # Minimum number of nodes to which cluster can scale-down. max_workers=8, # Maximum number of nodes to which cluster can scale-up. ), # Other necessary configuration for the cluster # such as enabling certain features, attaching libraries etc. # For example, enable logging (this example assumes an existing S3 bucket for logs) cluster_log_conf=databricks.ClusterLogConfArgs( dbfs=databricks.ClusterLogConfArgsDbfsArgs( destination="dbfs:/cluster-logs" ) ) ) # Export the Cluster ID pulumi.export('cluster_id', cluster.id)

    In the above program:

    • We import the Pulumi library and the Databricks provider.
    • We define a Databricks cluster with the name my-databricks-cluster.
    • We specify the cluster_name as my-security-patched-cluster which makes it easy to recognize.
    • We set the spark_version to a specific version that includes recent security patches.
    • We use the node_type_id to specify the type of nodes that the cluster should use. This property should be set based on the performance requirements of your workload.
    • We define an autoscale policy to automatically scale the number of worker nodes in the cluster based on the workload, between 1 and 8 nodes.
    • Optional configurations such as logging can be included. This example assumes that logs are stored in Databricks File System (DBFS) under dbfs:/cluster-logs.

    After defining the cluster, we use pulumi.export to export the Cluster ID for easy retrieval.

    For applying the actual patch when a security update is released, you would:

    1. Update the spark_version to the newer version that includes the security patch.
    2. Run pulumi up to apply the changes to your Databricks environment. The Pulumi CLI will display the planned changes and prompt for confirmation before applying them.

    If you have enabled the CI/CD pipeline, the update to the runtime version can be committed to your version control system, which would automatically trigger the redeployment.

    Do note that for real applications, you will also have to manage state, enable logging, attach necessary libraries, handle networking, and IAM roles, which are not covered in detail in this example. However, this script serves as an outline for how to define and manage Databricks clusters for security patching with Pulumi.