multi-persistence-backend

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multi-persistence-backend

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Academic project implementing and comparing relational and NoSQL databases in a backend system, focusing on data modeling, query patterns, and architectural trade-offs.

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# Multi Persistence Backend

This project was developed as part of a Bachelor's thesis in Computer Engineering.

The goal is to compare **relational (SQL)** and **NoSQL** database paradigms by implementing the same backend application using:

- PostgreSQL (relational model via JPA/Hibernate)
- MongoDB (document model via Spring Data MongoDB)

The application is designed to start against **one datasource at a time** (`APP_DATASOURCE=postgres` or `APP_DATASOURCE=mongo`), enabling a direct comparison of:

- data modeling approaches
- query strategies
- performance characteristics
- consistency and constraint handling

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## 🎯 Objectives

- Provide a unified API layer independent from the underlying database
- Compare relational and document-based data modeling
- Analyze trade-offs between SQL and NoSQL
- Demonstrate real-world backend patterns using both approaches

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## 🧩 Key Features

- Dual database support (PostgreSQL / MongoDB)
- Datasource selection at startup via configuration property (`app.datasource`)
- Shared business logic across implementations
- REST API generated from OpenAPI specification
- Versioned relational schema migrations with Flyway (PostgreSQL)
- Versioned MongoDB migrations with Mongock
- Swagger UI for API exploration
- Validation with Jakarta Bean Validation
- Caching with Caffeine
- Health checks and metrics via Spring Actuator
- Containerized environment (Docker & Docker Compose)
- Integration testing with Testcontainers
- Code formatting with Spotless
- Code coverage with JaCoCo
- Cross-cutting logging and HTTP tracing via Spring AOP aspects

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## 📚 Documentation

- 👉 [Quick Start](docs/quick-start.md) → Step-by-step guide to run the application locally with Docker
- 👉 [Architecture](docs/architecture.md) → Overview of the system design, components, and the comparison between SQL and NoSQL approaches
- 👉 [Services](docs/services.md) → List of all available services, APIs, UIs, and monitoring endpoints
- 👉 [Benchmark](docs/benchmark.md) → Lightweight k6-based protocol and template for PostgreSQL vs MongoDB comparative results

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## 📦 Tech Stack

### Backend
- Java 21
- Spring Boot 3.x
- Spring Web (REST APIs)
- Spring Data JPA (Hibernate)
- Spring Data MongoDB
- Spring Validation (Jakarta Bean Validation)
- Spring Cache + Caffeine
- Spring Boot Actuator

### API & Code Generation
- OpenAPI 3
- springdoc-openapi (Swagger UI)
- OpenAPI Generator (interface-first approach)

### Mapping & Boilerplate
- MapStruct
- Lombok

### Database
- PostgreSQL
- MongoDB

### Testing
- JUnit 5
- Testcontainers (PostgreSQL + MongoDB)
- Instancio (test data generation)

### DevOps & Tooling
- Docker & Docker Compose
- Maven
- Flyway (PostgreSQL schema migrations)
- Mongock (MongoDB migrations)
- Spotless (code formatting)
- JaCoCo (code coverage)

### Logs
- Spring AOP for centralized logging

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## 🤖 AI Usage

Part of this project's documentation and JavaDoc comments were generated with the support of Artificial Intelligence tools.

All generated content has been reviewed and refined to ensure correctness, clarity, and alignment with the intended design and implementation.

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## 📄 License

This project is licensed under the [MIT License](LICENSE).

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