As enterprises adopt cloud-native architectures, the ability to scale applications efficiently is crucial for meeting dynamic business demands. SAP Kyma, built on Kubernetes, offers a powerful platform to develop and run microservices and serverless functions integrated with SAP landscapes. Leveraging Kubernetes' native scaling capabilities ensures that Kyma applications can handle varying workloads while maintaining performance and cost-efficiency.
This article explores how Kubernetes enables efficient scaling of SAP Kyma applications, best practices, and how SAP developers can optimize resource usage to deliver resilient and responsive business solutions.
SAP Kyma applications often handle critical business processes, including extensions to SAP S/4HANA, real-time data processing, and third-party integrations. Workloads may vary significantly due to:
Efficient scaling allows these applications to respond dynamically without manual intervention, ensuring high availability and optimal resource utilization.
Kubernetes provides multiple mechanisms for scaling containerized workloads:
Kyma uses Kubernetes as its foundation, so all native scaling features apply. Additionally:
Each container in your microservice should specify CPU and memory requests and limits in its deployment YAML to enable Kubernetes to schedule and scale effectively.
resources:
requests:
cpu: "100m"
memory: "128Mi"
limits:
cpu: "500m"
memory: "512Mi"
Create an HPA resource based on CPU usage or custom metrics:
apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
metadata:
name: my-app-hpa
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: my-app
minReplicas: 2
maxReplicas: 10
metrics:
- type: Resource
resource:
name: cpu
target:
type: Utilization
averageUtilization: 60
For business-specific scaling (e.g., queue length, event backlog), integrate Kubernetes with tools like Prometheus Adapter to use custom metrics for HPA.
Use Kyma’s Knative-based functions to handle bursty workloads that can scale down to zero when idle, optimizing costs.
Use Kyma’s monitoring tools (Grafana, Prometheus) to observe scaling behavior and tune thresholds.
Start with realistic resource requests
Avoid over-provisioning to reduce waste.
Test scaling under load
Use stress-testing tools to validate scaling policies.
Use readiness and liveness probes
Prevent traffic routing to unhealthy pods during scaling.
Plan for cold starts in serverless
Design applications to handle latency spikes during function scaling.
Combine HPA and Cluster Autoscaler
Ensure node availability matches pod demand.
Secure and control scaling endpoints
Protect your services against traffic surges that might cause scaling storms.
Consider a microservice extending SAP S/4HANA to process incoming sales orders:
This ensures smooth, cost-effective handling of business-critical workloads.
Kubernetes’ robust scaling capabilities empower SAP Kyma applications to be responsive, resilient, and cost-efficient. By properly configuring autoscaling policies, resource limits, and leveraging serverless paradigms within Kyma, SAP developers can build cloud-native extensions that meet dynamic enterprise demands.
Efficient scaling is not just about technology but also about continuous monitoring, tuning, and aligning with business goals — making Kyma a strategic platform for SAP cloud innovations.