A Production-Inspired Backend Engineering Project for Ride-Hailing Systems
Built with Spring Boot • Apache Kafka • Redis • MySQL • Docker • WebSockets
RideFlux is a production-inspired backend system that explores how modern ride-hailing platforms solve complex distributed systems and scalability challenges.
Rather than focusing on CRUD operations, RideFlux emphasizes backend engineering concepts such as reliable event processing, asynchronous communication, fault-tolerant ride dispatching, real-time updates, intelligent caching, and scalable driver discovery.
The project combines synchronous REST APIs with asynchronous event-driven communication to simulate the architecture of large-scale ride-hailing platforms.
Building a ride-booking platform is significantly more complex than exposing REST endpoints.
A production backend must be able to:
- Prevent duplicate ride creation
- Find nearby drivers with low latency
- Recover automatically when drivers reject or ignore requests
- Deliver events reliably without losing data
- Process thousands of location updates efficiently
- Notify clients in real time
- Scale independently across multiple backend components
- Remain resilient even during partial failures
RideFlux was built to explore how these engineering problems can be solved using modern backend architecture.
RideFlux focuses on solving real-world backend engineering challenges commonly found in large-scale ride-hailing platforms.
Implements an asynchronous Event Driven Architecture using Apache Kafka's Publish-Subscribe model, enabling loose coupling between business components such as dispatch, notifications, auditing, analytics, and retry processing.
Apache Kafka serves as the asynchronous messaging backbone of RideFlux, enabling reliable communication between independent backend modules.
Implemented Kafka Patterns
- Transactional Outbox Pattern – Ensures database updates and Kafka event publication remain consistent by persisting events in an Outbox table before publishing.
- Dead Letter Topic (DLT) – Failed events are redirected to dedicated dead-letter topics after retry attempts, preventing message loss and simplifying failure analysis.
- Retry Processing – Automatically retries transient failures such as driver timeouts or temporary processing errors before routing events to the DLT.
Key Benefits
- Reliable event delivery
- Asynchronous processing
- Loose coupling between components
- Improved scalability and fault tolerance
- Independent event consumers for dispatch, notifications, analytics, and auditing
Implements the Transactional Outbox Pattern to guarantee reliable event publication between the database and Kafka, preventing event loss and maintaining consistency even during application failures.
Built a resilient ride dispatch engine featuring:
- Automatic retry mechanism
- Driver timeout handling
- Progressive search radius expansion
- Driver reassignment
- Asynchronous dispatch workflow
This improves ride assignment success while avoiding unnecessary driver notifications.
Implements request idempotency to ensure duplicate client requests never create duplicate rides, making APIs safe under retries and unstable network conditions.
Uses Redis GEO for efficient geospatial indexing, enabling millisecond-level nearby driver discovery without expensive database queries.
Leverages Redis for caching frequently accessed data including:
- Driver locations
- Online drivers
- Active rides
- Fare estimates
- Tracking information
This significantly reduces database load and improves response latency.
Uses WebSockets (STOMP) to push live updates without polling.
Supports real-time events such as:
- Driver assignment
- Ride status updates
- Driver requests
- Driver location tracking
- Live trip sharing
Long-running operations are processed asynchronously through Kafka consumers, improving responsiveness and enabling better scalability under concurrent request loads.
Secured using Spring Security with JWT-based authentication and Role-Based Access Control (RBAC) supporting Rider, Driver, and Admin roles.
Applies enterprise software design principles including:
- Strategy Pattern
- Factory Pattern
- Adapter Pattern
- Facade Pattern
- Repository Pattern
- Observer Pattern (Spring Events)
to keep the codebase modular, maintainable, and extensible.
The complete development environment is containerized using Docker Compose, including:
- Spring Boot Application
- MySQL
- Redis
- Apache Kafka
allowing the project to be started locally with a single command.
| Component | Responsibility |
|---|---|
| Ride Management | Ride booking, ride lifecycle management, cancellation, completion, and ride history. |
| Dispatch Engine | Nearby driver discovery, ride assignment, timeout handling, automatic retry mechanism, and progressive radius expansion. |
| Tracking Service | Real-time driver location updates, live ride tracking, route sharing, ETA calculation, and Redis GEO integration. |
| Event Processing | Kafka producers & consumers, Transactional Outbox, event publication, retry processing, and Dead Letter Topic (DLT) handling. |
| Notification Layer | Real-time WebSocket notifications for ride status updates, driver requests, driver locations, and live trip sharing. |
RideFlux incorporates several production-inspired reliability mechanisms that improve consistency, fault tolerance, and scalability under high request volumes.
| Engineering Problem | Solution |
|---|---|
| Duplicate Ride Requests | Idempotency Layer |
| Lost Events | Transactional Outbox Pattern |
| Failed Event Processing | Dead Letter Topic (DLT) |
| Driver Doesn't Respond | Automatic Retry Mechanism |
| No Nearby Driver | Progressive Radius Expansion |
| Slow Database Queries | Redis Caching |
| Driver Discovery | Redis GEO Indexing |
| Real-Time Communication | WebSockets |
| Loose Coupling | Event Driven Architecture |
| Asynchronous Processing | Apache Kafka |
These mechanisms work together to build a backend capable of handling failures without affecting the user experience.
| Category | Technologies |
|---|---|
| Backend | Java 21, Spring Boot, Spring MVC, Spring Security, Spring Data JPA, Hibernate |
| Database & Caching | MySQL, Redis (Caching & GEO) |
| Messaging | Apache Kafka |
| Real-Time Communication | WebSockets (STOMP) |
| Authentication & Authorization | JWT, Spring Security, Role-Based Access Control (RBAC) |
| Build & Dependency Management | Maven |
| Containerization | Docker, Docker Compose |
RideFlux is fully containerized using Docker Compose, making local setup straightforward.
Install the following tools before running the project:
- Java 21
- Maven
- Docker
- Docker Compose
- Git
git clone https://github.com/<your-username>/RideFlux.git
cd RideFluxmvn clean packagedocker compose up --buildThis command starts:
- RideFlux Application
- MySQL
- Redis
- Apache Kafka
After all containers have started successfully, load the provided sample dataset.
Open the MySQL container:
docker exec -it <mysql-container-name> mysql -u root -pEnter your MySQL password.
Select the database:
USE RideFlux;Execute the SQL script available in:
src/main/resources/data.sql
or directly from GitHub:
https://github.com/<your-username>/RideFlux/blob/main/src/main/resources/data.sql
The sample dataset contains:
- Demo Users
- Riders
- Drivers
- Vehicles
- Driver Locations
- Vehicle Categories
- Ride Types
- Pricing Rules
- Test Data for Dispatch Engine
Loading this dataset allows immediate testing of ride booking, dispatch, tracking and WebSocket functionality.
Several architectural decisions were made to simulate production backend systems rather than simple CRUD applications.
Ride dispatch, analytics, notifications and auditing should not block REST requests.
Using asynchronous events allows these components to evolve independently while improving scalability.
Kafka provides:
- Reliable event delivery
- Asynchronous communication
- Loose coupling
- Independent consumers
- High throughput
This makes it suitable for backend systems processing large numbers of business events.
Publishing events directly after committing a database transaction introduces the risk of losing events if the application crashes.
The Transactional Outbox Pattern guarantees that committed business operations are eventually published to Kafka.
Searching nearby drivers using SQL queries becomes inefficient as the number of drivers increases.
Redis GEO enables efficient geospatial searches with low latency, making it ideal for driver discovery.
Polling REST endpoints for ride updates increases server load and introduces unnecessary latency.
WebSockets provide instant communication between the backend and connected clients.
Ride-hailing applications continuously access rapidly changing information such as:
- Driver locations
- Active rides
- Online drivers
- Fare estimates
Caching this data significantly reduces database load and improves response times.
Network instability often causes mobile applications to resend requests.
Without idempotency, duplicate requests may create duplicate rides.
RideFlux guarantees that identical requests are processed exactly once.
RideFlux has been designed with scalability in mind.
Current architecture enables:
- Asynchronous event processing
- Independent Kafka consumers
- Fast Redis-based lookups
- Cached frequently accessed data
- Real-time communication
- Decoupled backend components
Although currently implemented as a modular monolith, the architecture intentionally follows patterns commonly adopted by distributed microservice systems.
- Payment Gateway Integration
- Api Gateway
- Kubernetes Deployment
- Convert to Microservices Architecture
This project was built to explore backend engineering principles beyond standard CRUD applications.
Key learning areas include:
- Designing scalable backend architectures
- Building fault-tolerant systems
- Reliable event processing
- Distributed messaging using Kafka
- High-performance caching with Redis
- Geospatial search using Redis GEO
- Real-time communication using WebSockets
- Secure authentication and authorization
- Enterprise software design patterns
- Docker-based local deployment
Contributions, suggestions and improvements are welcome.
If you discover bugs or have ideas for new features, feel free to open an issue or submit a pull request.
This project was built for educational purposes and backend engineering practice.
Feel free to fork, learn from, and extend the project.
If you find the project useful, consider giving it a ⭐ on GitHub.
RideFlux
Exploring scalable backend architecture through modern distributed systems patterns.
Built with ❤️ using Spring Boot, Apache Kafka, Redis and Docker.