MicroRide

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MicroRide is a scalable microservice ride application showcasing key architectural patterns and seamless integration with modern technologies

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<h1 align="center">MicroRide</h1>
MicroRide is a scalable microservice application that showcases a robust implementation of distributed systems and the effective utilization of modern technologies in an event-driven architecture. MicroRide demonstrates key architectural patterns and seamless integration with various tools. Developed as a hands-on project to learn about building microservice applications, MicroRide encompasses essential features while providing insights into future enhancements.

## 🚀 Key Features

- **Microservice Architecture:** MicroRide follows a microservice architecture, ensuring the system's scalability, modularity, and maintainability. Each service is designed with a specific responsibility, making it easy to scale individual components independently.

- **Asynchronous Communication:** The application leverages RabbitMQ, a robust message broker, to enable asynchronous communication between services.
  
- **Efficient Data Storage:** MicroRide uses a combination of relational and NoSQL databases to suit different service requirements. PostgreSQL handles user-account-service,driver-service, payment-service,and ride-service, while MongoDB handles the tracking-service. Redis is utilized as a temporary data store/cache enhancing response times and improving overall user experience.

- **Driver Tracking and ETA Calculation:** The tracking-service incorporates MongoDB geospatial indexes for locating the nearest available driver and employs a [custom algorithm](https://github.com/Stonetech66/MicroRide/blob/main/tracking-service/core/utils.py) to calculate drivers' estimated time of arrival (ETA) based on their recent location data and speed, ensuring accurate and timely ride experiences.

- **Real-time Notifications**: Through the notification-service, MicroRide utilizes WebSockets to provide instantaneous updates to both drivers and users. Whether conveying ride cancellations, ride request, notifying of driver arrivals, or marking ride completions, the system ensures prompt notifications for a seamless user experience.
  
## 📋 Services Overview

1. **👤 User Account Service:** Manages user registration, login, and ensures secure authentication. It functions as the authentication service, validating user credentials, issuing JWT tokens, and maintaining a secure authentication process.

![User account service](https://raw.githubusercontent.com/0x-stone/MicroRide/main/images/user-account-service-swagger-doc.png)

2. **🚗 Driver Service:** Responsible for various driver-related tasks, including driver registration, handling driver responses to ride requests (acceptance and rejection), and retrieving driver information.

![Driver service swagger doc](https://raw.githubusercontent.com/0x-stone/MicroRide/main/images/driver-service-swagger-doc.png)

3. **🚕 Ride Service:** Responsible for managing all aspects of ride-related operations, including ride requests, ride confirmations, ride cancellations, and retrieval of past ride history.

![Ride service swagger doc](https://raw.githubusercontent.com/0x-stone/MicroRide/main/images/ride-service-swagger-doc.png)
   
4. **📍 Tracking Service:** The tracking service is essential for efficient rider-driver matching. It employs [MongoDB geospatial indexing](https://github.com/Stonetech66/MicroRide/blob/main/tracking-service/core/utils.py) to locate the nearest available driver. Additionally, the service calculates the driver's estimated time of arrival (ETA) using a [custom algorithm](https://github.com/Stonetech66/MicroRide/blob/main/tracking-service/core/utils.py), factoring in the driver's recent location history, distance to the pickup location or rider's destinaion, and the driver average speed. To ensure precision, MicroRide provides the client application an endpoint for frequent driver location updates, typically every 30 seconds(configurable), ensuring real-time positioning and enhancing the rider's experience.

![Tracking service swagger doc](https://raw.githubusercontent.com/0x-stone/MicroRide/main/images/tracking-service-swagger-doc.png)
   
5. **💳 Payment Service:** The payment service provides endpoints to simulate payment successes and failures. While it doesn't integrate with an actual payment gateway, it enables testing and validation of payment-related functionalities.
   
     ![Payment service swagger doc](https://raw.githubusercontent.com/0x-stone/MicroRide/main/images/payment-service-swagger-doc.png)
   
6. **📊 Analysis Service:** The analysis service, While not fully functional, its primary function is to gather ride fare price data. This serves as the foundation for future enhancements in data analytics, enabling the service to provide valuable insights into ride fare prices, including trends, patterns, and more accurate pricing information.

7. **🔔 Notification Service:** Enhances the overall user experience and engagement by delivering real-time notifications to users and drivers for various events, including ride updates, payment status, and more.

## ⚒️ Technology Stack

- **Backend:** The backend of all services is powered by FastAPI, a modern and high-performance web framework for building APIs with Python. FastAPI's asynchronous capabilities complement the microservices architecture, ensuring exceptional performance and scalability.
  
- **Databases:** PostgreSQL is used for services handling structured data, while MongoDB is utilized for the tracking service's location data storage. Redis acts as a cache/temporary data storage, optimizing performance.

- **Message Broker:** RabbitMQ enables efficient asynchronous communication between services, promoting scalability and flexibility.

- **Websockets:** Websockets are employed in the notification service to provide real-time updates to users and drivers, ensuring timely notifications.
  
- **Docker:** Each microservice is containerized using Docker, enabling seamless deployment across various environments.

- **Helm Charts and Helmfile**: Helmfile and Helm charts are utilized to manage the Kubernetes deployments of the microservices,simplifying service management and scalability.
  
- **Kubernetes:** The services are deployed and orchestrated using Kubernetes, facilitating easy scaling and management.

## 💫 Running MicroRide

To run the MicroRide project follow these simple steps:

### Prerequisites

1. [Helm](https://helm.sh/docs/intro/install/ ) and [Helmfile](https://github.com/roboll/helmfile#installation): Make sure you have Helm and Helmfile installed on your local machine.

2. [Kubernetes Cluster](https://kubernetes.io/docs/setup/): Set up a Kubernetes cluster locally or use any Kubernetes cluster that you can access.

3. [Nginx Ingress Controller](https://kubernetes.github.io/ingress-nginx/deploy/): Ensure that you have an Nginx Ingress Controller deployed in your Kubernetes cluster.

### Step 1: Clone the Repository

Clone the MicroRide repository to your local machine:

```
git clone https://github.com/your-username/MicroRide.git
cd MicroRide
```

### Step 2: Start MicroRide Services

In the project directory, run the start script to deploy all services:

```
./start.sh
```

### Step 3: Port Forwarding (Local Setup Only)

If you are running MicroRide locally, you need to port forward to the Ingress Controller to access the services. Execute the following command:

```
kubectl port-forward -n ingress-nginx svc/ingress-nginx-controller 8080:80
```

### Step 3: Get External IP(Online Setup Only)
If you are running MicroRide on an online Kubernetes cluster, get the external IP of the Ingress Controller by running:
```
kubectl get service ingress-nginx-controller --namespace=ingress-nginx
```

### Step 4: Accessing the API Docs

Documentation for each service can be accessed at the following URLs:

**Locally (using localhost):**
 - User Account Service API Docs: `http://localhost:8080/user-account-service/docs`
- Driver Service API Docs: `http://localhost:8080/driver-service/docs`
- Ride Service API Docs: `http://localhost:8080/ride-service/docs`
- Payment Service API Docs: `http://localhost:8080/payment-service/docs`
- Tracking Service API Docs: `http://localhost:8080/tracking-service/docs`

**Online (using external IP):**
  - User Account Service Docs: `http://EXTERNAL_IP/user-account-service/docs`
  - Driver Service API Docs: `http://EXTERNAL_IP/driver-service/docs`
  - Ride Service API Docs: `http://EXTERNAL_IP/ride-service/docs`
  - Payment Service API Docs: `http://EXTERNAL_IP/payment-service/docs`
  - Tracking Service API Docs: `http://EXTERNAL_IP/tracking-service/docs`

Replace `EXTERNAL_IP` with the actual external IP of the Nginx Ingress Controller.

### Step 4: Stop MicroRide Services

To stop all services and clean up the deployment, run the end script:

```
./end.sh
```

## Future Ehancements

**Implement Saga Pattern for Distributed Transactions**: As part of upcoming ehancments MicroRide will undergo enhancements to its microservice architecture by implementing the Saga pattern(choreography). This will help in improving fault tolerance and maintain data consistency across the microservices.

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