You are a senior Java + DevOps engineer.

Create a production-style learning project for understanding centralized logging with Spring Boot, Docker, Kubernetes, and ELK Stack.

Project Goal: Build a backend application that generates structured logs, containerize it with Docker, deploy it to Kubernetes, and integrate it with ELK stack for centralized logging and observability.

Tech Stack:

• Java 21
• Spring Boot 3
• Maven
• Logback
• SLF4J
• logstash-logback-encoder
• Docker
• Kubernetes
• Elasticsearch
• Kibana
• Filebeat

Project Requirements:

1. Spring Boot Application Create a REST API project named: order-service

Endpoints:

• POST /orders
• GET /orders/{id}
• DELETE /orders/{id}
• GET /health
• GET /error-test

Requirements:

• Use layered architecture:

  • controller
  • service
  • repository
  • model
  • dto
  • config
  • exception

• Use in-memory storage initially.

• Return JSON responses.

• Add global exception handling.

2. Structured Logging Implement production-style structured JSON logging.

Requirements:

• Use logback-spring.xml
• Use LogstashEncoder
• All logs must go to stdout
• Include:
  • timestamp
  • log level
  • service name
  • traceId
  • request path
  • HTTP method
  • message
  • exception stack trace

Add:

• request logging filter/interceptor
• correlation ID / traceId generation
• MDC usage

Example log: { "timestamp":"2026-01-01T10:00:00", "level":"INFO", "service":"order-service", "traceId":"abc123", "method":"POST", "path":"/orders", "message":"Order created successfully" }

3. Error Simulation Create endpoints that intentionally generate:

• RuntimeException
• validation errors
• 404 errors

Log all exceptions in structured JSON format.

4. Dockerization Create:

• optimized Dockerfile
• .dockerignore

Requirements:

• Use eclipse-temurin:21-jre
• Small image size
• Proper layering
• Expose port 8080

Provide commands:

• mvn clean package
• docker build
• docker run

5. Docker Compose Environment Create docker-compose.yml containing:

• order-service
• Elasticsearch
• Kibana

Requirements:

• Proper networking
• Environment variables
• Volume mappings
• Health checks

Expose:

• Kibana on 5601
• Elasticsearch on 9200
• Application on 8080

6. Kubernetes Deployment Create complete Kubernetes manifests:

• namespace.yaml
• deployment.yaml
• service.yaml
• configmap.yaml

Requirements:

• 2 replicas
• readiness probe
• liveness probe
• resource requests/limits
• environment variables
• labels/selectors

7. Filebeat Integration Create Filebeat configuration for Kubernetes.

Requirements:

• DaemonSet deployment
• Collect container stdout logs
• Parse JSON logs
• Send logs to Elasticsearch
• Add Kubernetes metadata

Provide:

• filebeat.yml
• filebeat-daemonset.yaml

8. Kibana Explain:

• how to create index patterns
• how to search logs
• how to filter by traceId
• how to create dashboards

Create examples for:

• error rate visualization
• request count visualization
• pod-based filtering

9. Observability Concepts Document:

• centralized logging
• stdout logging in containers
• Kubernetes logging architecture
• why JSON logging matters
• correlation IDs
• log aggregation

10. Project Structure Generate a clean professional folder structure.

11. README.md Create a complete README including:

• local development
• Docker usage
• Kubernetes deployment
• ELK setup
• troubleshooting
• useful kubectl commands
• useful docker commands

12. Best Practices Apply:

• production logging practices
• clean architecture
• environment-based configuration
• secure defaults
• container best practices
• Kubernetes best practices

13. Output Format Generate:

• full source code
• all YAML files
• all Docker files
• all configs
• all commands
• explanations for every important step

The project should be educational, production-inspired, and suitable for a DevOps engineer learning ELK and Kubernetes logging architecture.