Scalable Rate Limiting for Real-Time AI Analytics on Kubernetes

Question

Pulumi · Accepted Answer

To implement scalable rate limiting for real-time AI analytics on Kubernetes, we would leverage Kubernetes' API to manage traffic flow to our services. This will ensure that our services are not overwhelmed with too many requests, while also allowing the system to scale effectively when demand increases.

Kubernetes provides resources such as `FlowSchema` and `PriorityLevelConfiguration` which are part of the API Priority and Fairness feature (APF). APF enables cluster operators to define different levels of Quality of Service (QoS) for different classes of API clients by assigning them to different priority levels. It also allows for limiting the amount the server will concurrently serve and the rate at which it accepts requests, thus acting as a form of rate limiting.

Here's a high-level breakdown of the two Kubernetes resources that we'll be using:
- **FlowSchema**: It defines how requests from different sources or types are categorized and maps them to priority levels. Each FlowSchema sets matching criteria based on the request's attributes and assigns a priority level to matching requests.
- **PriorityLevelConfiguration**: This defines a priority level's behavior, determining the queueing and concurrency characteristics for requests in that level.

Below is a Pulumi Python program that sets up a simple configuration for rate limiting. This example demonstrates defining a `FlowSchema` and `PriorityLevelConfiguration` within a Kubernetes cluster to prioritize and limit request rates for real-time AI analytics workloads.

Please note that these configurations need to be carefully tuned based on the actual workload characteristics, cluster capacity, and specific requirements of the real-time AI analytics applications.

```python
import pulumi
import pulumi_kubernetes as kubernetes

# Declare the API Priority and Fairness (APF) features:
# Define a PriorityLevelConfiguration that specifies the priority and concurrency details.
priority_level = kubernetes.flowcontrol.apiserver.v1beta2.PriorityLevelConfiguration(
    "ai-analytics-priority-level",
    metadata=kubernetes.meta.v1.ObjectMetaArgs(
        name="ai-analytics-priority"
    ),
    spec=kubernetes.flowcontrol.apiserver.v1beta2.PriorityLevelConfigurationSpecArgs(
        type="Limited",
        limited=kubernetes.flowcontrol.apiserver.v1beta2.LimitedPriorityLevelConfigurationArgs(
            assure_concurrency_shares=1,
            limit_response=kubernetes.flowcontrol.apiserver.v1beta2.LimitResponseArgs(
                type="Queue",
                queuing=kubernetes.flowcontrol.apiserver.v1beta2.QueuingConfigurationArgs(
                    queues=10,
                    hand_size=5,
                    queue_length_limit=100,
                ),
            ),
        ),
    ),
)

# Define a FlowSchema that matches requests for our AI Analytics service and uses the PriorityLevelConfiguration defined above.
flow_schema = kubernetes.flowcontrol.apiserver.v1beta2.FlowSchema(
    "ai-analytics-flow-schema",
    metadata=kubernetes.meta.v1.ObjectMetaArgs(
        name="ai-analytics-flows"
    ),
    spec=kubernetes.flowcontrol.apiserver.v1beta2.FlowSchemaSpecArgs(
        priority_level_configuration=kubernetes.flowcontrol.apiserver.v1beta2.PriorityLevelConfigurationReferenceArgs(
            name=priority_level.metadata.name
        ),
        matching_precedence=500,
        distinguisher_method=kubernetes.flowcontrol.apiserver.v1beta2.FlowDistinguisherMethodArgs(
            type="ByUser",
        ),
        rules=[kubernetes.flowcontrol.apiserver.v1beta2.PolicyRulesWithSubjectsArgs(
            subjects=[
                kubernetes.flowcontrol.apiserver.v1beta2.SubjectArgs(
                    kind="ServiceAccount",
                    service_account=kubernetes.flowcontrol.apiserver.v1beta2.ServiceAccountSubjectArgs(
                        name="analytics-service",
                        namespace="ai-analytics-ns",
                    ),
                ),
            ],
            resource_rules=[
                kubernetes.flowcontrol.apiserver.v1beta2.ResourcePolicyRuleArgs(
                    verbs=["get", "list", "watch"],
                    api_groups=[""],
                    resources=["pods"],
                    namespaces=["ai-analytics-ns"],
                ),
            ],
        )],
    ),
)

# Make the FlowSchema and PriorityLevelConfiguration visible outside of Pulumi for reference or troubleshooting.
pulumi.export("priority_level_name", priority_level.metadata.name)
pulumi.export("flow_schema_name", flow_schema.metadata.name)
```

In the program above, we declare a `PriorityLevelConfiguration` named "ai-analytics-priority-level" and a `FlowSchema` named "ai-analytics-flow-schema". The `PriorityLevelConfiguration` specifies that requests matching this level have a maximum concurrency of 1 (effectively serializing access) and requests will be queued with the specified parameters.

The `FlowSchema` defines which requests match this priority level based on their characteristics, such as API group, resource, and verbs. In this example, we prioritize requests coming from a particular service account (`analytics-service`) in namespace `ai-analytics-ns` and only for operations like `get`, `list`, and `watch` on Pods. We then apply this `FlowSchema` to our priority level, effectively enforcing these rules for incoming requests.

As with any rate limiting and traffic management infrastructure, monitoring and tuning would be critical. The above settings are starting points and would need to be adjusted based on observations from the system's behavior in a real-time setting.