# High-Level Architecture Overview

**Bragabot's** architecture is strategically designed to be modular, scalable, and secure, leveraging modern technologies and **AWS** infrastructure. The system is composed of several key layers, each serving a critical role within the platform's overall operation. This layered approach ensures that **Bragabot** can scale, deploy, and evolve without disrupting other parts of the system.&#x20;

The primary layers include:

### **1. Infrastructure Layer**

* **Virtual Private Cloud (VPC):** The _VPC_ serves as the foundational network layer, providing isolated networking for all **Bragabot** services. It includes both _Public and Private Subnets_, ensuring secure communication between components.
* **Internet Gateway and Public Subnet:** The _Internet Gateway_ allows communication between instances in the Public Subnet and the internet. The Public Subnet hosts the _Elastic Load Balancer (ELB)_, which distributes incoming traffic to the backend services.
* **Elastic Load Balancer (ELB):** The _ELB_ distributes traffic across multiple _EC2_ instances in the _Private Subnet_, ensuring high availability and fault tolerance.
* **EC2 Instances:** These instances host the _Docker_ containers running **Bragabot’s** _microservices_. They are deployed within the _Private Subnet_ to protect them from direct internet exposure, enhancing security.

### **2. Container Orchestration Layer**

* **Deployment and Scaling Management**: _Docker_ containers encapsulate **Bragabot’s** _microservices_, which are orchestrated by _Kubernetes_. These containers are deployed across _EC2_ instances within the _Private Subnet_ of the _VPC_. _Jenkins_ is integrated into the CI/CD pipeline, automating the build, testing, and deployment processes of these containers. _Kubernetes_ then automates the deployment, scaling, and management of these containers, utilizing the _Horizontal Pod Autoscaler (HPA)_ to adjust the number of pods based on demand.
* **NGINX Ingress Controller**: Manages incoming HTTP/HTTPS traffic, providing SSL termination, load balancing, and routing to the appropriate services within the _Kubernetes_ _cluster_.

### 3. **Data Layer**

* **Persistent Data Management**: _MongoDB_ is used for managing and storing unstructured data, such as user profiles and activity logs. _Firebase_ is utilized for handling media files and other assets. The data layer is hosted within a _Private Subnet_ in the _AWS Virtual Private Cloud_ (VPC) to ensure security and isolation from the public internet.

### 4. **Messaging Layer**

* **Asynchronous Communication:** The messaging layer facilitates asynchronous, event-driven communication between _microservices_. _Job queues_ provided by _Telegram_ are used for scheduling tasks such as notifications, raid management etc.. This layer ensures that the system remains responsive and can handle background tasks without impacting user experience.

### **5. Microservices Layer**

* **Business Logic Management:** The backend, powered by _Django_, is organized into _microservices_, each responsible for specific business logic such as user management, tweet forwarding, raid orchestration etc.. These microservices run within containers managed by _Docker_ and are deployed across _EC2_ instances in a _Kubernetes_ cluster, allowing for independent scaling and deployment.

### **6. API Gateway**

* **Unified Entry Point:** The API Gateway serves as the centralized entry point for all external requests, managing routing, security, and load balancing. It integrates with the _Django_ backend to serve various functionalities, ensuring that only authenticated and authorized requests reach the backend services. The API Gateway also handles rate limiting and request aggregation to maintain system performance.

### **7. Frontend Layer**

* **User and Administrator Interfaces**: The frontend layer is developed using _HTML, CSS, and JavaScript_, providing responsive and interactive interfaces for both users and administrators. This layer includes the web interface and Telegram bot interactions, facilitating seamless user engagement with **Bragabot's** core functionalities.

### **8. Monitoring and Logging Layer**

* **System Visibility and Analysis**: The monitoring and logging layer uses tools like _Prometheus_ for collecting metrics, _Grafana_ for visualizing system performance, and the _ELK stack (Elasticsearch, Logstash, Kibana)_ for centralized logging and analysis. This layer ensures that **Bragabot's** health, performance, and security are constantly monitored, and any anomalies are promptly addressed.
