Managing Data Pipeline Dependencies with Kubernetes CSI Volumes

Question

Pulumi · Accepted Answer

Container Storage Interface (CSI) is a standard that aims to provide a consistent and portable way to expose block and file storage systems to containerized workloads running in Kubernetes. By using CSI, you can add support for storage systems that Kubernetes does not natively support through a plugin that implements the CSI specification.

When managing data pipelines in Kubernetes, having the ability to attach, mount, and manage the lifecycle of storage volumes is critical. You want your data pipeline tasks to have access to the data they need, when they need it. Kubernetes CSI volumes make it possible to integrate your existing storage system with your Kubernetes cluster, which is particularly beneficial for stateful applications or data processing tasks that require persistent storage.

Here is the main flow for managing data pipeline dependencies with Kubernetes CSI Volumes using Pulumi:

1. Define a `PersistentVolume` that references a CSI driver.
2. Define a `PersistentVolumeClaim` to request storage from the `PersistentVolume`.
3. Attach the `PersistentVolumeClaim` to a Pod that represents a task in your data pipeline.

Below is a Pulumi Python program that defines these resources. The program assumes you already have a CSI driver installed in your Kubernetes cluster.

```python
import pulumi
import pulumi_kubernetes as k8s

# Create a PersistentVolume that uses a CSI volume driver.
# `volume_handle` should be a reference that the CSI driver can understand.
# `driver` is the name of the CSI driver.
csi_pv = k8s.core.v1.PersistentVolume("csi-pv",
    spec=k8s.core.v1.PersistentVolumeSpecArgs(
        access_modes=["ReadWriteOnce"],
        capacity={"storage": "10Gi"},
        csi=k8s.core.v1.CSIPersistentVolumeSourceArgs(
            driver="my-csi-driver",
            volume_handle="unique-volume-handle",
            fs_type="ext4"
        ),
        persistent_volume_reclaim_policy="Retain",
    ))

# Create a PersistentVolumeClaim to request storage from the PersistentVolume.
csi_pvc = k8s.core.v1.PersistentVolumeClaim("csi-pvc",
    spec=k8s.core.v1.PersistentVolumeClaimSpecArgs(
        access_modes=["ReadWriteOnce"],
        resources=k8s.core.v1.ResourceRequirementsArgs(
            requests={"storage": "10Gi"}
        ),
        volume_name=csi_pv.metadata["name"], # Bind to the created PersistentVolume
    ))

# A pod representing a task in the data pipeline, referencing the PersistentVolumeClaim.
pipeline_task_pod = k8s.core.v1.Pod("pipeline-task-pod",
    spec=k8s.core.v1.PodSpecArgs(
        containers=[
            k8s.core.v1.ContainerArgs(
                name="pipeline-task-container",
                image="my-pipeline-task-image",
                volume_mounts=[k8s.core.v1.VolumeMountArgs(
                    mount_path="/data",
                    name="pipeline-task-storage",
                )],
            ),
        ],
        volumes=[
            k8s.core.v1.VolumeArgs(
                name="pipeline-task-storage",
                persistent_volume_claim=k8s.core.v1.PersistentVolumeClaimVolumeSourceArgs(
                    claim_name=csi_pvc.metadata["name"],
                ),
            ),
        ],
    ))

# Export the PersistentVolumeClaim and the name of the pod so it can be accessed outside.
pulumi.export("persistent_volume_claim_name", csi_pvc.metadata["name"])
pulumi.export("pipeline_task_pod_name", pipeline_task_pod.metadata["name"])
```

In this program:

- We define a `PersistentVolume` that uses a CSI driver named `my-csi-driver`. This would be the driver that your storage system requires.
- We create a `PersistentVolumeClaim`, which requests 10 GiB of storage from the PersistentVolume we created. This claim is used by the pipeline tasks that need persistent storage.
- We create a `Pod`, which represents a task in our data pipeline. The Pod references the PersistentVolumeClaim through a `Volume` that is mounted inside the container at `/data`.

With these resources, you can now have a Pod that will attach to a storage volume provided by your CSI driver and retain data across different pipeline runs or tasks, if that's part of your job's requirement. This can be particularly useful for tasks like ETL jobs, where you need to extract data, process it, and then load it elsewhere, and require a consistent and reliable storage layer that persists beyond the lifetime of a single job or pod.