Kubernetes Stateful Workloads for AI Data Pipelines

Question

Pulumi · Accepted Answer

When creating stateful workloads for AI data pipelines on Kubernetes, you'll want to use StatefulSets. StatefulSets are ideal for applications that require stable, unique network identifiers, stable, persistent storage, and ordered, graceful deployment and scaling. They maintain the order and uniqueness guarantee of the pod deployment.

StatefulSets are used for applications like databases (e.g., MySQL, PostgreSQL), distributed storage systems (e.g., Cassandra, ElasticSearch), and other applications where the pods need to be aware of each other and access each other’s data.

Let’s write a simplified Pulumi Python program to create a StatefulSet.

1. **Pulumi Kubernetes SDK**: The program uses the Pulumi Kubernetes SDK to create resources on a Kubernetes cluster.

2. **StatefulSet Resource**: The StatefulSet resource will contain specifications for creating stable and unique pods.

3. **PersistentVolumeClaim (PVC)**: To create persistent storage for each pod, the program includes VolumeClaimTemplates that allow each Pod in the StatefulSet to have its own persistent storage.

4. **Service**: Since we want to access the pods through a stable network identity, we create a headless Service that provides network identity to each pod.

### Detailed Program:

```python
import pulumi
import pulumi_kubernetes as kubernetes

# This is the name of the namespace where the StatefulSet will be deployed.
# Replace 'your-namespace' with the target namespace where you want to deploy your StatefulSet.
namespace_name = 'your-namespace'

# Here we define a headless service to provide network identity for the pods.
# For stateful workloads it's important to have a stable network identity.
headless_service = kubernetes.core.v1.Service("headless-service",
    metadata=kubernetes.meta.v1.ObjectMetaArgs(
        name="headless-service",
        namespace=namespace_name,
        labels={"app": "my-stateful-app"},
    ),
    spec=kubernetes.core.v1.ServiceSpecArgs(
        selector={"app": "my-stateful-app"},
        cluster_ip="None", # This makes it a headless service.
        ports=[kubernetes.core.v1.ServicePortArgs(
            port=80, # The port that the service will serve on.
            target_port=80, # The target port of the container.
        )],
    )
)

# Define the StatefulSet.
statefulset = kubernetes.apps.v1.StatefulSet("statefulset",
    metadata=kubernetes.meta.v1.ObjectMetaArgs(
        name="my-stateful-app",
        namespace=namespace_name,
    ),
    spec=kubernetes.apps.v1.StatefulSetSpecArgs(
        selector=kubernetes.meta.v1.LabelSelectorArgs(
            match_labels={"app": "my-stateful-app"},
        ),
        service_name="headless-service",
        replicas=3, # Number of desired pods.
        template=kubernetes.core.v1.PodTemplateSpecArgs(
            metadata=kubernetes.meta.v1.ObjectMetaArgs(
                labels={"app": "my-stateful-app"},
            ),
            spec=kubernetes.core.v1.PodSpecArgs(
                containers=[kubernetes.core.v1.ContainerArgs(
                    name="nginx",
                    image="nginx:1.14.2", # Docker image to use for the pods.
                    ports=[kubernetes.core.v1.ContainerPortArgs(
                        container_port=80, # Port the container exposes.
                    )],
                )],
            ),
        ),
        volume_claim_templates=[kubernetes.core.v1.PersistentVolumeClaimArgs(
            metadata=kubernetes.meta.v1.ObjectMetaArgs(
                name="my-volume-claim",
            ),
            spec=kubernetes.core.v1.PersistentVolumeClaimSpecArgs(
                access_modes=["ReadWriteOnce"], # This PVC will be accessible for read/write by a single node.
                resources=kubernetes.core.v1.ResourceRequirementsArgs(
                    requests={
                        "storage": "1Gi", # Define the size of the storage.
                    },
                ),
            ),
        )],
    )
)

# Export the StatefulSet name
pulumi.export('statefulset_name', statefulset.metadata['name'])
```

Let's go through each section of the program:

1. **Service Definition**: We define a `Service` object with a label selector that matches pods with the label `app=my-stateful-app`. The service is marked as a headless service (`cluster_ip="None"`) as it's used for creating a stable network ID for each pod.

2. **StatefulSet Definition**: The `StatefulSet` object is created with specifics about the pod deployment. It includes label selectors, the number of replicas, pod template specifications (including container image and ports), and the volume claim template for persistent storage. Each pod will have a volume associated with it as defined by the PVC template.

3. **Volume Claim Templates**: A `PersistentVolumeClaim` template is provided which ensures that each pod in the StatefulSet has its own persistent volume with the specified storage size and access mode.

4. **Exporting StatefulSet Name**: The name of the StatefulSet is exported using Pulumi's export feature so that it can be easily identified in the output.

This program sets up the fundamental resources required for stateful workloads in Kubernetes suitable for AI data pipelines. Depending on the specific use case, more customizations might be necessary, such as setting up appropriate resource limits, environment variables, and volume mounts for actual data pipeline applications (e.g., TensorFlow, PyTorch, Jupyter Notebooks).