Horizontal vs Vertical Scaling
This document explains the key differences, trade-offs, and recommendations for horizontal and vertical scaling, specifically within a Spring Boot + Kubernetes microservice architecture.
Vertical Scaling (Scale-Up)
Definition: Increase the capacity (CPU, RAM, IOPS) of a single machine or container.
Advantages
- Easier to implement (especially for legacy systems).
- Useful for single-node databases or monoliths.
- Requires fewer nodes = lower overhead in networking.
Disadvantages
- Hardware limits: can't scale indefinitely.
- Downtime required for upgrades.
- Cost grows non-linearly with power.
Spring Boot Context:
Vertical scaling might be useful for legacy Spring Boot monoliths or stateful components like a PostgreSQL master node.
Horizontal Scaling (Scale-Out)
Definition: Add more instances (pods, containers, servers) to handle more load.
Advantages
- Near-infinite scalability in Kubernetes.
- Redundancy & fault tolerance.
- Easier to autoscale and load balance.
Disadvantages
- More complex orchestration (requires service discovery, coordination).
- Stateful workloads require externalizing state.
Spring Boot Context:
Stateless Spring Boot microservices are ideal for horizontal scaling. Use:
- Kubernetes Deployments + HorizontalPodAutoscaler
- Spring Cloud LoadBalancer / Eureka if needed
Practical Recommendations
| Component | Scaling Strategy | Notes |
|---|---|---|
| Spring Boot App | Horizontal | Stateless, autoscale with metrics |
| PostgreSQL | Vertical + Read Replicas | Master node scales vertically |
| Redis | Horizontal | Use clustering, partitioning |
| Kafka | Horizontal | Partition-based scaling |
| REST APIs | Horizontal | Behind ingress with load balancing |
- Document Version: 1.0
- Date: 2025-06-24
- Author: ArturChernets