Kubernetes Namespace Segregation for Data Science Teams

Question

Pulumi · Accepted Answer

In Kubernetes, a namespace is a way to divide a cluster's resources between multiple users or teams. It's like having multiple virtual clusters within a single physical cluster. When you have data science teams that need to work on different projects or need isolation from one another, using namespaces can be a very effective strategy. This way, you can allocate resources, set permissions, and manage quotas for each project or team individually.

For your goal of setting up namespace segregation for data science teams, we'll use the following resources:

1. **Namespace**: The basic building block for your segregation requirement, it represents the virtual clusters we discussed.
2. **ResourceQuota**: To manage the amount of resources that a namespace can consume, ensuring no single team can utilize all cluster resources.
3. **Role** and **RoleBinding** or **ClusterRole** and **ClusterRoleBinding**: These are for setting up fine-grained access control policies for the namespaces. Teams will only be able to access their own namespaces and resources.
4. **NetworkPolicy**: To configure how groups of pods communicate with each other and with other network endpoints.

Let's create a Pulumi program that sets up a namespace for a data science team, with a resource quota and appropriate role bindings for access control.

```python
import pulumi
import pulumi_kubernetes as k8s

# Create a new Kubernetes namespace for a data science team
data_science_ns = k8s.core.v1.Namespace(
    "data-science-namespace",
    metadata=k8s.meta.v1.ObjectMetaArgs(
        name="data-science",  # Name of the Namespace
        labels={
            "team": "data-science",
        },
    )
)

# Define resource quota specs for the data science namespace
resource_quota = k8s.core.v1.ResourceQuota(
    "data-science-quota",
    metadata=k8s.meta.v1.ObjectMetaArgs(
        name="data-science-quota",
        namespace=data_science_ns.metadata["name"],
    ),
    spec=k8s.core.v1.ResourceQuotaSpecArgs(
        hard={
            "pods": "10",  # Maximum number of pods
            "services": "5",  # Maximum number of services
            # Define other resources like cpu, memory, persistentvolumeclaims, etc.
        }
    )
)

# Create a role that defines permissions within the namespace
# For more fine-grained permissions, define specific API groups and resources
role = k8s.rbac.v1.Role(
    "data-science-team-role",
    metadata=k8s.meta.v1.ObjectMetaArgs(
        namespace=data_science_ns.metadata["name"],
    ),
    rules=[
        k8s.rbac.v1.PolicyRuleArgs(
            api_groups=[""],  # Core API Group
            resources=["pods", "services"],  # Resources they are allowed to manage
            verbs=["create", "get", "list", "watch", "delete"],  # Actions they can perform
        ),
    ]
)

# Bind the role to the data science team members
role_binding = k8s.rbac.v1.RoleBinding(
    "data-science-team-rolebinding",
    metadata=k8s.meta.v1.ObjectMetaArgs(
        namespace=data_science_ns.metadata["name"],
    ),
    subjects=[k8s.rbac.v1.SubjectArgs(
        kind="User",
        name="data-scientist",  # Name of the user or team (you might want to parameterize this)
        api_group="rbac.authorization.k8s.io",
    )],
    role_ref=k8s.rbac.v1.RoleRefArgs(
        kind="Role",
        name=role.metadata["name"],  # Reference to the role above
        api_group="rbac.authorization.k8s.io",
    )
)

# Assuming team members are labeled with `team: data-science` in their pod metadata
network_policy = k8s.networking.v1.NetworkPolicy(
    "data-science-network-policy",
    metadata=k8s.meta.v1.ObjectMetaArgs(
        namespace = data_science_ns.metadata["name"],
    ),
    spec=k8s.networking.v1.NetworkPolicySpecArgs(
        pod_selector=k8s.meta.v1.LabelSelectorArgs(
            match_labels={
                "team": "data-science",
            }
        ),
        policy_types=["Ingress", "Egress"],
        # Define specific ingress and egress rules here
        # e.g., allow communication within the namespace, but restrict access to other namespaces.
        # Note: You might want to configure policies that match your security and communication requirements
    )
)

# Export the namespace name
pulumi.export('data_science_namespace', data_science_ns.metadata["name"])
```

This program will create a namespace called `data-science`, which your data science teams can use to run their workloads. It also sets a resource quota to limit the number of pods and services that can be created within this namespace. This way, you ensure that the data science team has enough resources to work efficiently but does not consume more than their share of the cluster.

The role and role binding ensure that members of the data science team have the necessary permissions to manage their pods and services within the namespace. The NetworkPolicy ensures that the workloads within the namespace can only communicate according to the policy you've defined, adding a layer of network security and isolation. Remember to adjust the roles, quotas, and network policies according to each team's needs.

Keep in mind that for the RoleBinding, you'll want to set the actual usernames of your data science team members, or reference a group if your organization uses groups to manage permissions. Also, you’ll need to apply specific labels to your data science team's pods to match the network policies, as well as adjust egress and ingress rules as necessary for your specific use case.