Autoscaling
11 articles from official documentation
Scaling StarRocks on EKS: Harnessing KEDA and Karpenter for OLAP Efficiency
In the world of enterprise OLAP workloads, scaling efficiently is crucial. By leveraging KEDA for autoscaling and Karpenter for node provisioning on Amazon EKS, you can dynamically adjust your StarRocks cluster to meet fluctuating query demands without data movement.
- →Leverage KEDA for near-instant scaling of compute resources based on Prometheus metrics.
- →Utilize the StarRocks Kubernetes Operator for automated cluster lifecycle management.
Scaling StarRocks on EKS: Harnessing KEDA and Karpenter for OLAP Power
Unlock the full potential of your OLAP workloads with StarRocks on Amazon EKS. Learn how KEDA and Karpenter enable near-instant scaling of compute resources while maintaining a cost-effective shared-data architecture.
- →Utilize KEDA for autoscaling BE and CN nodes based on Prometheus metrics.
- →Deploy BE nodes as StatefulSets for durable storage using Amazon EBS volumes.
GPU Autoscaling in Kubernetes: Mastering KEDA with External Scalers
Unlock the power of GPU autoscaling in Kubernetes with KEDA. Learn how to build a custom external scaler that reads GPU metrics via NVML and drives Horizontal Pod Autoscaler (HPA) decisions. This is essential for optimizing resource usage in GPU-heavy workloads.
- →Build a custom DaemonSet to read GPU metrics using NVML.
- →Serve GPU metrics over gRPC with KEDA's ExternalScaler interface.
Unlocking Efficiency with Amazon EKS Auto Mode: Strategies for Control and Optimization
Amazon EKS Auto Mode is a game changer for Kubernetes management, automating everything from provisioning to patching. With just-in-time scaling, it dynamically adjusts resources based on workload demands, minimizing operational overhead.
- →Automate cluster management to reduce operational overhead.
- →Leverage just-in-time scaling to provision capacity based on workload demands.
Kubernetes v1.36: Mastering In-Place Vertical Scaling for Pods
Kubernetes v1.36 introduces a game-changing feature: in-place vertical scaling for pod-level resources. This allows you to adjust resource budgets without container restarts, streamlining your operations. Dive into how this works and what you need to know to leverage it effectively.
- →Utilize the InPlacePodLevelResourcesVerticalScaling feature to adjust resource budgets without restarts.
- →Set the restartPolicy to NotRequired for minimal disruption during resource updates.
KEDA in Action: Dynamic Autoscaling for Kubernetes
KEDA transforms how you scale applications in Kubernetes by responding to real-world events. With components like ScaledObjects and TriggerAuthentication, it offers a robust solution for dynamic resource management.
- →Utilize ScaledObjects to link your app to external event sources for dynamic scaling.
- →Configure TriggerAuthentication to securely access event sources with environment variables or cloud-specific credentials.
Mastering In-Place Resizing of Kubernetes Container Resources
Need to adjust CPU and memory for your Kubernetes containers? Learn how to resize resources in place without downtime. Discover the critical role of the resizePolicy in managing container behavior during updates.
- →Request a resize by updating the desired resources in the Pod's spec.
- →Use `kubectl patch` with the `--subresource=resize` flag for in-place resizing.
HPA in Production: What the Docs Don't Tell You
Scaling workloads in Kubernetes is crucial for performance and cost efficiency. The Horizontal Pod Autoscaler (HPA) automatically adjusts the number of pods based on CPU utilization, but there are nuances to consider. Dive into the specifics of HPA and how to avoid common pitfalls.
- →Configure HPA with a target average CPU utilization of 50% for optimal performance.
- →Set minimum and maximum replicas to control scaling behavior effectively.
HPA in Production: What the Docs Don't Tell You
Horizontal Pod Autoscaling (HPA) is a game-changer for managing workloads in Kubernetes. It automatically scales your Pods to match demand, but there are critical nuances you need to grasp for effective implementation. Dive in to learn how to configure it properly and avoid common pitfalls.
- →Configure the sync period with --horizontal-pod-autoscaler-sync-period to optimize responsiveness.
- →Set resource requests for all containers to ensure accurate CPU utilization metrics.
Mastering Autoscaling in Kubernetes: HPA, VPA, and Beyond
Autoscaling is crucial for maintaining application performance and resource efficiency in Kubernetes. With tools like HorizontalPodAutoscaler and VerticalPodAutoscaler, you can dynamically adjust your workloads based on real-time metrics. This article dives into how these components work and what you need to watch out for in production.
- →Implement HorizontalPodAutoscaler to adjust replicas based on CPU or memory usage.
- →Install the Metrics Server for VerticalPodAutoscaler to function correctly.
AWS EKS Innovations: Powering Kubernetes at KubeCon EU 2026
AWS is pushing the boundaries of Kubernetes with innovations like the EKS Provisioned Control Plane and Seekable OCI Parallel Pull mode. These advancements promise to enhance performance and scalability, making it easier to manage large-scale workloads.
- →Utilize Amazon EKS to manage up to 100K worker nodes in a single cluster for large-scale applications.
- →Implement the EKS Provisioned Control Plane for predictable performance during peak demand.
Unified observability — logs, uptime monitoring, and on-call in one place. Used by 50,000+ engineering teams to ship faster and sleep better.
Try Better Stack free →Get the daily digest
One email. 5 articles. Every morning.
No spam. Unsubscribe anytime.