Zero‑Downtime Deployment with Kubernetes and Spring Boot: Health Checks, Rolling Updates, Graceful Shutdown, Autoscaling, Prometheus Monitoring, and Config Separation

This guide demonstrates how to achieve zero‑downtime releases of a Spring Boot application on Kubernetes by configuring health checks, rolling updates, graceful shutdown, autoscaling, Prometheus monitoring, and externalized configuration.

Health Check Configuration

Define readiness and liveness probes using exec , tcpSocket or httpGet . Add the actuator dependency and expose /actuator/health/readiness and /actuator/health/liveness on a dedicated management port (50000) via application.yaml .

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

management:
  server:
    port: 50000
  endpoint:
    health:
      probes:
        enabled: true
  endpoints:
    web:
      exposure:
        base-path: /actuator
        include: health

Rolling Update

Use a Deployment with strategy.type: RollingUpdate and set maxSurge and maxUnavailable to control the number of pods that can be added or unavailable during the upgrade.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: {APP_NAME}
spec:
  replicas: {REPLICAS}
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 1
      maxUnavailable: 1

Graceful Shutdown

Enable Spring Boot graceful shutdown ( server.shutdown=graceful ) and expose /actuator/shutdown . The Dockerfile must contain curl to invoke the endpoint from a pre‑stop lifecycle hook.

curl -X POST 127.0.0.1:50000/actuator/shutdown

FROM openjdk:8-jdk-alpine
ARG JAR_FILE
ARG WORK_PATH="/app"
ARG EXPOSE_PORT=8080
ENV JAVA_OPTS="" \
    JAR_FILE=${JAR_FILE}
RUN ln -sf /usr/share/zoneinfo/Asia/Shanghai /etc/localtime && echo 'Asia/Shanghai' > /etc/timezone
RUN sed -i 's/dl-cdn.alpinelinux.org/mirrors.ustc.edu.cn/g' /etc/apk/repositories \
    && apk add --no-cache curl
COPY target/${JAR_FILE} ${WORK_PATH}/
WORKDIR ${WORK_PATH}
EXPOSE ${EXPOSE_PORT}
ENTRYPOINT exec java $JAVA_OPTS -jar $JAR_FILE

Autoscaling

Define resource limits and requests, then create a HorizontalPodAutoscaler that scales between {REPLICAS} and 6 based on CPU utilization (target 50%).

resources:
  limits:
    cpu: 0.5
    memory: 1Gi
  requests:
    cpu: 0.15
    memory: 300Mi
---
kind: HorizontalPodAutoscaler
apiVersion: autoscaling/v2beta2
spec:
  minReplicas: {REPLICAS}
  maxReplicas: 6
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 50

Prometheus Integration

Add micrometer-registry-prometheus dependency, enable the metrics and prometheus endpoints, and annotate the pod for Prometheus scraping.

management:
  metrics:
    tags:
      application: ${spring.application.name}
  endpoints:
    web:
      exposure:
        base-path: /actuator
        include: metrics,prometheus

annotations:
  prometheus.io/port: "50000"
  prometheus.io/path: /actuator/prometheus
  prometheus.io/scrape: "true"

Configuration Separation

Create a ConfigMap from an external application-test.yaml and mount it into the container, activating the profile with SPRING_PROFILES_ACTIVE=test .

# Generate ConfigMap yaml (dry‑run)
kubectl create cm -n
--from-file=application-test.yaml --dry-run=1 -o yaml > configmap.yaml
# Apply the ConfigMap
kubectl apply -f configmap.yaml

env:
- name: SPRING_PROFILES_ACTIVE
  value: test
volumeMounts:
- name: conf
  mountPath: "/app/config"
  readOnly: true
volumes:
- name: conf
  configMap:
    name: {APP_NAME}

Summary Configuration

The final deployment combines the actuator dependency, management port 50000, health probes, graceful shutdown, rolling‑update strategy, resource limits, HPA, Prometheus annotations and ConfigMap‑based external configuration, providing a complete zero‑downtime CI/CD pipeline for Spring Boot services on Kubernetes.