
Scaling Applications in ROSA (Red Hat OpenShift Service on AWS)
How Red Hat OpenShift Service on AWS (ROSA) facilitates dynamic scaling through its integrated capabilities. It covers various scaling strategies available in ROSA, such as Horizontal Pod Autoscaling and Cluster Autoscaler, along with best practices, example configurations, and advanced techniques like progressive delivery with GitOps.
Published Nov 1, 2024
Scaling is the lifeblood of modern applications, allowing them to adapt to fluctuating demand. Red Hat OpenShift Service on AWS (ROSA) leverages OpenShift's native scaling capabilities alongside the power of AWS to seamlessly scale your applications dynamically, at both the application and infrastructure level. This blog dives into scaling within ROSA, exploring how it works, the different scaling options available, best practices, and example configurations to get you started.
In today's digital landscape, applications need to handle unpredictable traffic surges without compromising performance or racking up unnecessary costs. Scaling in ROSA empowers you to:
- Handle Traffic Spikes: As user activity intensifies during peak hours, ROSA can automatically add more application instances (pods) or worker nodes to manage the increased load. Imagine a popular e-commerce platform experiencing a surge in traffic during Black Friday sales. Scaling ensures a smooth user experience despite the temporary spike.
- Optimize Costs: During periods of low traffic, ROSA can intelligently scale down resources by reducing the number of pods and worker nodes. This translates to significant cost savings on your AWS bill.
- Ensure Reliability: Scaling in ROSA allows for automatic workload redistribution in response to failures or maintenance activities. This ensures continued application availability and minimizes downtime, crucial for mission-critical applications like a hospital patient management system.
ROSA provides versatile scaling options tailored to diverse workload demands, including Horizontal Pod Autoscaling (HPA) for scaling applications and Cluster Autoscaler for managing node capacity.
Horizontal scaling increases or decreases the number of pod replicas (copies of your application running within the cluster) to match workload demands. HPAs enable automatic application scaling based on key metrics like CPU or memory usage.
Example: An e-commerce application experiences a dramatic increase in user traffic during a flash sale. The HPA, configured to monitor CPU usage, automatically provisions additional application pods to handle the surge in requests. This ensures a seamless user experience without compromising performance.
This sample configuration enables the HorizontalPodAutoscaler, sets a minimum of 2 replicas, and allows scaling up to 10 replicas if CPU usage exceeds 70%.
The Cluster Autoscaler automatically adjusts the number of nodes (worker machines) in your ROSA cluster based on workload requirements. If additional pod capacity is needed, Cluster Autoscaler provisions more nodes, and it can also scale down to save costs during low-demand periods.
Example: Let's consider a data processing pipeline that involves multiple stages, such as data ingestion, transformation, and analysis. During peak periods, the pipeline may experience increased data volume, leading to longer processing times. To address this, we can use the Cluster Autoscaler to automatically scale the underlying infrastructure. The Cluster Autoscaler can monitor the resource utilization of the pods running the pipeline stages and provision additional nodes when necessary. This ensures that the pipeline can handle the increased workload without compromising performance.
This configuration enables the ClusterAutoscaler on AWS ROSA, allowing the cluster to automatically scale nodes based on workload demands. It prioritizes scaling down unneeded nodes after a short delay and thereby optimizes costs.
- Use Metrics Wisely: Configure HPAs with meaningful thresholds that balance responsiveness with resource costs. Avoid setting overly aggressive thresholds that might lead to unnecessary scaling events.
- Combine HPA and Cluster Autoscaler: For optimal performance, leverage both HPA and Cluster Autoscaler. HPA manages application-level scaling, while the Cluster Autoscaler handles infrastructure scaling.
- Monitor Resource Usage: Utilize OpenShift's built-in monitoring with Prometheus and Grafana or your own tools to monitor scaling behaviors and make adjustments as needed.
- Leverage AWS Spot Instances: For non-critical, cost-sensitive workloads, consider using AWS Spot Instances with the Cluster Autoscaler. Spot Instances offer significant cost savings, but come with the possibility of interruption.
Progressive delivery is an advanced deployment strategy that enables controlled, incremental rollouts of application updates, ensuring stability and minimizing risks associated with introducing new versions. With AWS ROSA (Red Hat OpenShift Service on AWS), progressive delivery becomes even more efficient when combined with GitOps principles through Argo CD. GitOps and Argo CD allow you to manage and monitor deployment rollouts declaratively, syncing application configurations in Git with ROSA clusters automatically. By integrating GitOps with scaling methods like Horizontal Pod Autoscaling (HPA), and Cluster Autoscaler, you can safely deploy, monitor, and scale updates, all through a single source of truth.
When managing application updates in a dynamic environment like AWS ROSA, GitOps and progressive delivery allow for updates with minimal risk, rollback capabilities, and high reliability. Combining GitOps principles via Argo CD with HPA and Cluster Autoscaler, ROSA enables optimized scaling and controlled adaptation to updates.Here’s how progressive delivery integrates with scaling and GitOps strategies on AWS ROSA:
- Canary Releases: In a canary release, a new application version is deployed to a small subset of users for testing. Using HPA, ROSA scales canary pods based on real-time traffic data, ensuring only a limited user base experiences the update initially. Argo CD syncs and monitors the canary configuration directly from Git, automating updates based on Git-based triggers and allowing for easy rollback if issues arise. As the canary version proves successful, it can scale out gradually to reach more users.
- Blue/Green Deployments: Blue/green deployments create parallel environments where one environment (blue) runs the current version while the other (green) hosts the update. With ROSA’s Cluster Autoscaler, resources are dynamically allocated to each environment based on demand, allowing for a smooth, low-risk switch from blue to green without downtime. Through Argo CD, changes are synced directly from Git to ROSA, enabling rapid deployment of blue/green environments and making rollback seamless if issues occur during transition.
- A/B Testing: In A/B testing, a portion of traffic is routed to the updated version to compare its performance against the original version. HPA within ROSA scales each version based on its resource metrics, enabling data-driven comparison. Argo CD, by syncing A/B testing configurations from Git, ensures that both environments are correctly configured and monitored, providing clear visibility into each version’s performance. This controlled approach allows impact assessment of changes without full-scale rollout.
Scaling can introduce potential security challenges, especially when using cost-saving methods like spot instances. Here are a few best practices:
- Use Proper IAM Roles: Ensure that nodes and pods have the appropriate IAM roles to access AWS services, preventing unauthorized access.
- Limit Network Exposure: Utilize AWS security groups and OpenShift’s network policies to restrict pod-to-pod communication to only essential services.
- Use Trusted Spot Instances: Spot instances can be used for non-critical workloads, but avoid using them for sensitive workloads unless you have proper IAM restrictions in place.
AWS ROSA’s scaling capabilities empower applications to handle varying demand efficiently and securely. By leveraging Horizontal Pod Autoscaling and Cluster Autoscaler you can optimize application performance and cost-effectiveness, whether handling sudden traffic surges for a microservices architecture or ensuring stable operation for a stateful application. With additional strategies like blue/green deployments and robust security practices, ROSA provides a resilient foundation for scalable cloud-native applications.