A scalable, production-oriented backend for a LinkedIn-like social media platform, built using Java, Spring Boot, Microservices and distributed-system design principles.
This project is being developed as a hands-on backend engineering and system design project, with a focus on building services that can scale independently, communicate asynchronously, and handle real-world social networking workloads.
The platform provides core capabilities of a professional social networking application, including:
- User registration and authentication
- User profiles
- Connections and social relationships
- Posts and content management
- Likes and interactions
- Personalized feeds
- Notifications
- Asynchronous event processing
- Distributed caching
- Service-to-service communication
- Scalable data access
- Production-ready API design
- Observability and monitoring
- Containerized deployment
- System design for high-scale workloads
The goal is not simply to recreate LinkedIn features, but to understand and implement the backend architecture, scalability patterns and distributed-system concepts required to build a system operating at large scale.
The application follows a microservices-based architecture where business capabilities are separated into independently deployable services.
┌─────────────────────┐
│ Client │
│ Web / Mobile / API │
└──────────┬──────────┘
│
▼
┌─────────────────────┐
│ API Gateway │
└──────────┬──────────┘
│
┌────────────────────┼────────────────────┐
│ │ │
▼ ▼ ▼
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ User Service│ │ Post Service│ │ Feed Service│
└──────┬──────┘ └──────┬──────┘ └──────┬──────┘
│ │ │
│ │ │
└──────────────┬─────┴────────────────────┘
│
▼
┌──────────────────┐
│ Kafka │
│ Event Streaming │
└────────┬─────────┘
│
┌──────────────┼──────────────┐
│ │ │
▼ ▼ ▼
┌────────────┐ ┌────────────┐ ┌──────────────┐
│ Notification│ │ Analytics │ │ Other │
│ Service │ │ / Workers │ │ Consumers │
└────────────┘ └────────────┘ └──────────────┘
┌──────────────────┐
│ Redis │
│ Cache / Sessions │
└──────────────────┘
┌──────────────────┐
│ Databases │
│ SQL + Graph DB │
└──────────────────┘
The system is being developed as a collection of independently deployable services.
| Service | Responsibility |
|---|---|
| User Service | Registration, authentication and user profiles |
| Connection Service | Connections and social relationships |
| Post Service | Create, update, retrieve and delete posts |
| Like Service | Post likes and interaction management |
| Feed Service | Personalized and scalable user feeds |
| Notification Service | Asynchronous user notifications |
| API Gateway | Entry point and routing for client requests |
| Config / Discovery | Centralized configuration and service discovery |
Note: Service boundaries will evolve as the system design develops.
- Java
- Spring Boot
- Spring MVC
- Spring Data JPA
- Spring Security
- Hibernate
- Lombok
- REST APIs
- Spring Cloud
- Service Discovery
- API Gateway
- Centralized Configuration
- Resilience and Fault Tolerance
- PostgreSQL / MySQL
- Neo4j
- JPA / Hibernate
- Apache Kafka
- Event-driven architecture
- Asynchronous processing
- Distributed caching
- Redis
- Database transactions
- Eventual consistency
- Docker
- Kubernetes
- CI/CD
- Application monitoring
- Centralized logging
- JUnit
- Mockito
- Spring Boot Test
- Integration Testing
The backend uses Spring Security to implement secure API access.
- User registration
- Login
- Password hashing
- JWT-based authentication
- Authentication filters
- Role-based authorization
- Method-level security
- Secure service-to-service communication
Apache Kafka is used for asynchronous communication between services.
User creates a post
│
▼
Post Service
│
│ Publish Event
▼
Kafka
│
├──────────────► Feed Service
│
├──────────────► Notification Service
│
└──────────────► Analytics Service
This allows downstream consumers to process events independently without tightly coupling services.
POST_CREATED
POST_LIKED
CONNECTION_CREATED
USER_REGISTERED
COMMENT_CREATED
One of the major system-design challenges is building a scalable social media feed.
The project will explore different feed-generation strategies.
User creates post
│
▼
Generate feed entries
for connected users
---
# Fan-out on Read
User requests feed
│
▼
Fetch posts from
connected users
│
▼
Rank / merge / return
- Read scalability
- Write scalability
- Storage requirements
- Latency
- Hot users
- Celebrity / high-follower scenarios
- Eventual consistency
Redis will be used for performance-sensitive workloads such as:
- API response caching
- Frequently accessed user data
- Feed caching
- Session-related data
- Rate limiting
- Temporary data
- Distributed locks where required
The project will focus on understanding:
- Cache-aside pattern
- TTLs
- Cache invalidation
- Distributed caching challenges
Neo4j is used to model social relationships.
User
│
├── CONNECTED_TO ──► User
│
├── FOLLOWS ───────► User
│
├── LIKES ─────────► Post
│
└── WORKS_WITH ────► User
- Mutual connections
- Connection recommendations
- Second-degree connections
- Social graph traversal
- Relationship discovery
The project focuses heavily on system design, rather than only CRUD functionality.
- Horizontal scaling
- Stateless services
- Load balancing
- Caching
- Database indexing
- Database partitioning
- Read replicas
- Asynchronous processing
- Message queues
- Event-driven architecture
- Eventual consistency
- Distributed transactions
- Fault tolerance
- Service isolation
- Backpressure
- Rate limiting
This project is also used as a practical system-design laboratory.
- RESTful API design
- Pagination
- Sorting
- Filtering
- Idempotency
- API versioning
- Error handling
- Relational modeling
- Indexing
- Query optimization
- Transactions
- Connection pooling
- Read/write separation
- CAP theorem
- Consistency models
- Eventual consistency
- Distributed transactions
- Saga pattern
- Retry mechanisms
- Circuit breakers
- Cache-aside
- Write-through
- TTL
- Cache invalidation
- Distributed caching
- Kafka producers
- Kafka consumers
- Consumer groups
- Partitioning
- Ordering
- Delivery semantics
- Retry and dead-letter strategies
- Horizontal scaling
- Failure isolation
- Graceful degradation
- Health checks
- Fault tolerance
Testing will be implemented at multiple levels.
Unit Tests
│
▼
Service Tests
│
▼
Integration Tests
│
▼
API Tests
│
▼
End-to-End Tests
- JUnit
- Mockito
- Spring Boot Test
- Integration Testing
The project will progressively add production-oriented observability.
- Application logs
- Structured logging
- Metrics
- Health checks
- Distributed tracing
- API latency
- Error rates
- Kafka consumer lag
- Cache hit/miss rates
- Database performance
Each microservice is designed to be independently containerized.
Docker
│
┌───────────────┼────────────────┐
│ │ │
▼ ▼ ▼
User Service Post Service Feed Service
│ │ │
└───────────────┼────────────────┘
│
▼
Kafka / Redis
The deployment architecture will progressively incorporate Kubernetes concepts including:
- Deployments
- Services
- ConfigMaps
- Secrets
- Ingress
- Horizontal scaling
- Health probes
- Resource limits
The repository follows a multi-service structure:
linkedin-clone-backend/ │ ├── user-service/ │ ├── src/ │ └── pom.xml │ ├── post-service/ │ ├── src/ │ └── pom.xml │ ├── connection-service/ │ ├── src/ │ └── pom.xml │ ├── feed-service/ │ ├── src/ │ └── pom.xml │ ├── notification-service/ │ ├── src/ │ └── pom.xml │ ├── like-service/ │ ├── src/ │ └── pom.xml │ ├── docker/ ├── kubernetes/ ├── docs/ ├── .gitignore └── README.md
Note: Service names and repository structure may evolve as the architecture is refined.
- Project setup
- REST APIs
- DTO pattern
- Validation
- Exception handling
- JPA
- Database integration
- User registration
- Login
- Password hashing
- JWT authentication
- Spring Security
- Role-based authorization
- User profiles
- Connections
- Posts
- Likes
- Comments
- Follow / connection relationships
- Service decomposition
- API Gateway
- Service discovery
- Centralized configuration
- Inter-service communication
- Resilience patterns
- Kafka producer
- Kafka consumers
- User events
- Post events
- Like events
- Notification events
- Feed events
- Retry strategy
- Dead-letter topics
- Redis integration
- Cache-aside pattern
- Feed caching
- Cache invalidation
- Rate limiting
- Performance testing
- Feed generation
- Fan-out on write
- Fan-out on read
- Feed ranking
- Pagination
- Hot-user handling
- Docker
- Kubernetes
- CI/CD
- Monitoring
- Logging
- Distributed tracing
- Load testing
This project is being built with the following goals:
- Build strong Spring Boot fundamentals
- Design and implement production-grade REST APIs
- Understand Spring Security deeply
- Build and operate microservices
- Learn event-driven architecture using Kafka
- Understand distributed caching using Redis
- Work with relational and graph databases
- Design scalable social-media feeds
- Apply system-design concepts to a real application
- Understand the trade-offs behind architectural decisions
The project emphasizes:
- Clean architecture
- SOLID principles
- Separation of concerns
- Domain-driven service boundaries
- Stateless services
- API-first design
- Database-per-service where appropriate
- Asynchronous communication
- Observability
- Fault tolerance
- Scalability
- Maintainability
- Testability
This project is being developed as part of a hands-on learning journey focused on advanced Java backend engineering, Spring Boot, microservices, Kafka, Redis, Docker, Kubernetes and system design.
The LinkedIn-like platform is one of the major projects used to apply these concepts to a realistic, large-scale social networking system.
🚧 Work in Progress
The system is being developed incrementally, starting with core Spring Boot services and gradually evolving toward a distributed, event-driven microservices architecture.
Ashim Roy
Backend Engineer @ LinkedIn| Java | Spring Boot | Microservices | Distributed Systems