1. Kubernetes-Stateful Workload Autoscaling with Redis Metrics


    When dealing with stateful workloads on Kubernetes, you often need to handle scaling differently than you might with stateless services. Stateful workloads can include databases, queues, search indices, and more. In this case, we'll look at scaling a Kubernetes workload based on metrics from a Redis instance.

    To implement autoscaling for a Kubernetes StatefulSet workload based on Redis metrics, we will need the following components:

    1. StatefulSet - This will define our stateful workload that we want to scale.
    2. Horizontal Pod Autoscaler (HPA) - Kubernetes component that automatically scales the number of pod replicas based on observed CPU utilization (or, with custom metrics support, on some other application-provided metrics).
    3. Custom Metrics - Metrics provided by our Redis instance. We will use these metrics to inform the HPA when to scale up or down. Custom metrics in Kubernetes typically require a metrics server that can provide such custom metrics. Prometheus is a common choice for this.

    Pulumi provides resources for managing Kubernetes objects that we can use to define each component:

    Below is a Pulumi program in Python that sets up autoscaling for a Kubernetes StatefulSet based on Redis metrics:

    import pulumi import pulumi_kubernetes as k8s # Creating a StatefulSet for a hypothetical application that uses Redis for caching or as a data store. stateful_set = k8s.apps.v1.StatefulSet( "app-statefulset", spec=k8s.apps.v1.StatefulSetSpecArgs( selector=k8s.meta.v1.LabelSelectorArgs(match_labels={"app": "myapp"}), serviceName="myapp", replicas=2, # Starting with 2 replicas template=k8s.core.v1.PodTemplateSpecArgs( metadata=k8s.meta.v1.ObjectMetaArgs(labels={"app": "myapp"}), spec=k8s.core.v1.PodSpecArgs( containers=[ k8s.core.v1.ContainerArgs( name="myapp-container", image="myapp-image:v1", ) ] ) ) ) ) # Creating a HorizontalPodAutoscaler to scale the workload # This is a simplified example and assumes that the custom Redis metrics are collected and exposed to Kubernetes hpa = k8s.autoscaling.v2beta2.HorizontalPodAutoscaler( "app-hpa", spec=k8s.autoscaling.v2beta2.HorizontalPodAutoscalerSpecArgs( scale_target_ref=k8s.autoscaling.v2beta2.CrossVersionObjectReferenceArgs( api_version="apps/v1", kind="StatefulSet", name=stateful_set.metadata.name, ), min_replicas=1, max_replicas=5, metrics=[ k8s.autoscaling.v2beta2.MetricSpecArgs( type="Object", object=k8s.autoscaling.v2beta2.ObjectMetricSourceArgs( described_object=k8s.autoscaling.v2beta2.CrossVersionObjectReferenceArgs( kind="Redis", name="my-redis", api_version="v1" # This should match the API version of your Redis metric provider ), target=k8s.autoscaling.v2beta2.MetricTargetArgs( type="Value", value="1000" # The target number of requests per second per replica ), metric=k8s.autoscaling.v2beta2.MetricIdentifierArgs( name="requests-per-second" # The name of the custom metric reported by Redis or a metrics server like Prometheus ) ) ) ] ) ) # Export the StatefulSet name pulumi.export('stateful_set_name', stateful_set.metadata.name) # Export the HPA name pulumi.export('hpa_name', hpa.metadata.name)

    In this Pulumi program, a StatefulSet called app-statefulset is defined with an initial replica count of two. This could be any stateful application that uses Redis as its backend. We use it as a placeholder for your actual application container image and configuration.

    An HorizontalPodAutoscaler is then defined, targeting the StatefulSet. It specifies a minimum number of one replica and a maximum of five replicas. The scaling behavior is driven by a custom Redis metric named requests-per-second, which we assume is already exposed and collected through a metric server like Prometheus.

    This program assumes that you have a mechanics in place to expose Redis metrics to your Kubernetes clusters, such as a Prometheus Redis exporter. The HorizontalPodAutoscaler will scale the StatefulSet based on the requests-per-second custom metric exported by Redis.

    It is important to adjust the metric name and target values (value) according to the actual metrics and the scaling requirements of your application. The value field represents the target value for the metric. When the current value exceeds the target, the autoscaler increases the replica count, and when it's lower, the autoscaler reduces the replica count.

    Remember that horizontal pod autoscaling for stateful applications has nuances, such as ensuring that the storage layer can handle the dynamic scaling and that the stateful nature of workloads is respected (e.g., avoiding data inconsistency or split-brain scenarios). Always test autoscaling policies in a controlled environment before applying them to production.