Dynamic Service Scaling with Spring Microservices on Kubernetes

8 min readOct 21, 2023

Introduction

Microservices architecture is the de-facto standard for building scalable, maintainable, and loosely coupled systems. Coupled with container orchestration platforms like Kubernetes, you get an ecosystem that’s primed for dynamic scaling based on workload and system demands. The Spring ecosystem, primarily Spring Boot and Spring Cloud, offers a set of tools to develop microservices rapidly. This article explores how to achieve dynamic service scaling with Spring Microservices deployed on Kubernetes.

Introduction to Dynamic Scaling with Kubernetes

In the age of cloud-native applications, the ability to adapt and respond to varying workloads dynamically has become a critical aspect of modern software architectures. Enter dynamic scaling — a technique that adjusts computing resources in real-time, based on actual demand rather than just forecasts. Kubernetes, the leading container orchestration platform, serves as the backbone of this paradigm, providing the tools and processes required to achieve this dynamic responsiveness.

Scaling in the context of software deployment refers to the process of adjusting the number of compute resources (like CPU, memory, and instances) available to an application. Traditional approaches to scaling involve a more static perspective: resources are provisioned based on peak load predictions, often resulting in over-provisioning (wasting resources during low traffic) or under-provisioning (leading to potential system outages during unexpected traffic spikes).

Dynamic scaling, in contrast, offers a more agile approach. As the name implies, it shifts resources as and when required, minimizing waste and ensuring optimal performance. Kubernetes, with its innate capabilities, is uniquely equipped to handle this.

Kubernetes provides two primary mechanisms for dynamic scaling:

Horizontal Pod Autoscaling (HPA): As the more common approach, HPA scales the number of pod replicas in a deployment or replica set. It’s driven by specified performance metrics, such as CPU or memory utilization. If, for instance, a set threshold of CPU usage is exceeded, Kubernetes can automatically increase the number of pod replicas to distribute the load. Conversely, if the usage drops below a certain level, it can reduce the replicas to conserve resources.
Vertical Pod Autoscaling (VPA): While HPA adjusts the number of pods, VPA tweaks the resources of individual pods. This means it can dynamically adjust the CPU and memory limits and requests for the containers in a pod, granting them more or fewer resources as needed. This is particularly useful for workloads that have variable resource requirements over time.

To supplement these scaling strategies, Kubernetes also introduces the concept of Cluster Autoscaler. If the cluster runs out of resources due to increased load, the Cluster Autoscaler can provision additional nodes. Similarly, when the demand subsides, it can terminate unneeded nodes, ensuring an efficient use of underlying infrastructure.

Kubernetes’ dynamic scaling capabilities are more than just a response to changing application workloads. They’re a manifestation of a holistic perspective on cloud-native application deployment, where resource optimization, cost-effectiveness, and high availability are intertwined in a harmonious dance. As we delve deeper into deploying Spring Microservices on Kubernetes, it becomes evident how powerful this combination can be for modern software applications.

Building Spring Microservices Ready for Scaling

Building microservices with the Spring ecosystem, primarily Spring Boot and Spring Cloud, is a popular approach due to the tools, conventions, and abstractions provided by these frameworks. However, to ensure that these microservices are scalable, especially when deployed in orchestration platforms like Kubernetes, certain practices and design principles must be adhered to.

Statelessness

One of the most fundamental tenets of scalable microservices is statelessness. A stateless microservice does not retain any session or client-specific data between requests. This ensures that any instance of a microservice can process any request, making horizontal scaling feasible and efficient.

In the context of Spring:

Avoid storing session-related information within the application context.
If session management is required, consider externalized session stores such as Redis.

Health Checks

For Kubernetes to effectively manage the lifecycle and scaling of pods, it needs insights into the health of the applications running within those pods. This is where health checks come into play.

Spring Boot provides Actuator, a set of production-ready features, one of which is health check indicators. By simply adding the Actuator dependency, an endpoint (/actuator/health) becomes available, which Kubernetes can query to determine service health.

Example in pom.xml:

<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-actuator</artifactId>
</dependency>

With this in place, Kubernetes liveness and readiness probes can be configured to point to this health endpoint, ensuring effective pod management.

Externalized Configuration

Scalable microservices should be adaptable to changing environments without the need for code changes or rebuilds. This implies that configuration data should be separated from the application code.

Spring Cloud Config provides centralized configuration management, allowing microservices to fetch their configuration from a centralized source, such as a Git repository. Another alternative within Kubernetes is to use ConfigMaps and Secrets to externalize and manage configuration data.

For instance, database connection details, message broker endpoints, and other environment-specific parameters can be stored in ConfigMaps and referenced by the Spring application using environment variables or property placeholders.

Decoupling and Asynchronous Communication

Tightly coupled services can become bottlenecks in a scaling environment. To ensure services scale independently, consider:

Using asynchronous communication patterns, such as event-driven architectures.
Leveraging tools like Spring Cloud Stream, which abstracts message brokers like Kafka or RabbitMQ, to facilitate decoupled, scalable communication.

Database Scalability Considerations

While the microservice itself might be stateless and scalable, the underlying database might not be. Always:

Design database schemas with scalability in mind, considering practices like sharding.
Use database connection pools, such as HikariCP, integrated into Spring Boot, to manage connections efficiently.

Building scalable Spring microservices involves a combination of design principles and leveraging the rich toolset provided by the Spring ecosystem. By following these guidelines, developers can ensure that their Spring-based applications are primed for the dynamic scaling capabilities of Kubernetes.

Deploying and Scaling Spring Microservices on Kubernetes

Deploying and scaling Spring microservices on Kubernetes combines the power of the Spring ecosystem with the orchestration and scalability features of Kubernetes. The following steps provide an overview of the process, from containerizing the Spring application to setting up dynamic scaling on Kubernetes.

Dockerize Your Service

Before deploying to Kubernetes, you need to containerize your Spring microservice. This involves creating a Docker image of your application.

FROM openjdk:11-jre-slim
COPY target/my-service.jar /app.jar
ENTRYPOINT ["java", "-jar", "/app.jar"]

Build the Docker image:

docker build -t my-repo/my-service:latest .

Kubernetes Deployment

With the Docker image ready, create a deployment configuration for Kubernetes. This configuration describes how the microservice should run.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-service
spec:
  replicas: 3
  selector:
    matchLabels:
      app: my-service
  template:
    metadata:
      labels:
        app: my-service
    spec:
      containers:
      - name: my-service
        image: my-repo/my-service:latest
        ports:
        - containerPort: 8080
      livenessProbe:
        httpGet:
          path: /actuator/health
          port: 8080
      readinessProbe:
        httpGet:
          path: /actuator/health
          port: 8080

Apply the configuration:

kubectl apply -f deployment.yaml

Expose the Service

Once the microservice is running, it needs to be accessible. In Kubernetes, this is achieved using a Service. For external access, consider using a LoadBalancer or an Ingress.

apiVersion: v1
kind: Service
metadata:
  name: my-service
spec:
  selector:
    app: my-service
  ports:
    - protocol: TCP
      port: 80
      targetPort: 8080
  type: LoadBalancer

Apply the service configuration:

kubectl apply -f service.yaml

Implement Horizontal Pod Autoscaling (HPA)

For dynamic scaling based on specific metrics, set up the HPA.

apiVersion: autoscaling/v1
kind: HorizontalPodAutoscaler
metadata:
  name: my-service-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: my-service
  minReplicas: 3
  maxReplicas: 10
  targetCPUUtilizationPercentage: 80

This configuration will increase the pod replicas when the average CPU utilization across all pods exceeds 80%. If it drops below this percentage, it’ll scale down, maintaining a minimum of 3 replicas.

Apply the HPA:

kubectl apply -f hpa.yaml

Monitoring and Feedback

While Kubernetes handles scaling, it’s essential to keep an eye on metrics and logs. Tools like Prometheus can be integrated to collect metrics, while Grafana can provide visualization.

Deploying and scaling Spring microservices on Kubernetes is a process that leverages best practices from both worlds. It’s not just about making the application available but ensuring it’s resilient, scalable, and optimized for the dynamic cloud environment. Properly done, you get a system that can efficiently handle variable workloads, providing consistent performance and resource optimization.

Monitoring and Adjusting the Scaling Policies

Effective scaling is a balance between proactive decision-making and reactive adjustments. While Kubernetes provides robust tools for dynamic scaling, the real-world is often unpredictable. Monitoring system performance, understanding the feedback, and fine-tuning scaling policies become essential steps to maintain optimal performance. Here’s how you can monitor and make adjustments:

Set Up Monitoring Tools

Prometheus: A widely used monitoring tool in the Kubernetes ecosystem. Deploy Prometheus to your cluster to scrape and store metrics from your applications and Kubernetes itself.

Installation is often done using Helm, a Kubernetes package manager:

helm install prometheus stable/prometheus --namespace monitoring

Grafana: Complements Prometheus by offering a visualization platform. With Grafana, you can create insightful dashboards showing your application and infrastructure metrics.

Similarly, install using Helm:

helm install grafana stable/grafana --namespace monitoring

Create Relevant Dashboards

With Grafana and Prometheus set up, you can now create dashboards tailored to your application’s metrics. These may include:

CPU and Memory usage per pod
Request rates and latencies
Error rates and counts
Custom application metrics if you’ve instrumented your Spring applications using Micrometer or similar libraries

Analyze the Metrics

Continuously monitor these dashboards to understand:

Normal Baselines: What does a typical load look like?
Anomalies: Any unexpected spikes or drops in traffic or performance?
Resource Utilization: Are resources underutilized or overextended?

Adjust Scaling Policies

Based on the insights:

Refine HPA and VPA thresholds: If you find your pods are scaling out too late, leading to performance drops, or too early, leading to resource wastage, adjust the thresholds in your HPA or VPA configurations.
Review Minimum and Maximum Pod Counts: Based on average loads, you might want to adjust the minimum and maximum replica counts for better resource utilization.
Evaluate Custom Metrics: Default metrics like CPU and memory are effective for many use cases, but your application might benefit from scaling based on custom metrics, like queue length or user sessions.

Feedback Loop

The cycle of monitoring, analyzing, and adjusting is ongoing. As traffic patterns change, as the application evolves, or as infrastructure undergoes modifications, you’ll need to revisit and refine your scaling strategies.

Alarming and Notifications

To be proactive:

Set up Alerts: Use Grafana or Prometheus to set up alerts for critical metrics. For instance, alert if CPU usage is consistently high or if error rates surge.
Integrate Notification Channels: Link alerts to communication platforms like Slack, Email, or PagerDuty. Immediate notifications can help in quickly addressing and resolving potential issues.

While Kubernetes automates much of the scaling process, maintaining optimal performance requires continuous oversight. By effectively monitoring your Spring microservices and the underlying infrastructure, and by making informed adjustments, you can ensure that your applications remain robust, efficient, and responsive to changing demands.

Conclusion

Dynamic service scaling with Spring Microservices on Kubernetes offers a potent combination of rapid development, deployment, and dynamic resource allocation. By leveraging Kubernetes’ built-in scaling mechanisms and designing your Spring microservices correctly, you can ensure that your applications remain responsive, efficient, and cost-effective even under varying loads.