Skip to content

System Architecture Overview

This section outlines the high-level architecture of the backend system. The backend is designed to be scalable, maintainable, and secure, supporting a variety of use cases while ensuring flexibility and performance.

Architecture Diagram

Note: Replace with your actual architecture diagram.

Components

1. Client (Frontend)

The client-side (browser, mobile app, or other service) communicates with the backend via a RESTful API or GraphQL (depending on the use case). The client handles the user interface and interaction, sending requests to the backend for data and receiving responses to update the UI.

2. API Gateway

The API Gateway acts as the entry point for all incoming requests from the client. It handles routing, authentication, rate-limiting, and sometimes caching of requests to backend services. It may also handle load balancing if multiple backend services are deployed.

Responsibilities:

  • Authenticate and authorize requests.
  • Route requests to the appropriate backend service.
  • Perform rate-limiting and security checks.

3. Authentication & Authorization Service

This service manages user authentication, session management, and authorization. It verifies the identity of users and generates JWT tokens or OAuth tokens for secure communication between the client and backend.

Responsibilities:

  • Authenticate users (login, logout).
  • Issue and verify tokens (JWT, OAuth).
  • Provide user roles and permissions.

4. Core Backend Services

These are the core services that handle business logic, data processing, and serve API requests. The backend is typically divided into different services, each responsible for a specific set of operations (e.g., user management, product management, payment processing, etc.).

Service Layer:

  • Each backend service can be either monolithic or microservices-based.
  • Microservices may interact with each other via REST or gRPC.
  • The services are stateless and can be scaled independently.

5. Database Layer

The database stores all persistent data, such as user information, transactional data, and application state. The choice of database may vary depending on the project needs.

Relational Database (SQL) Example:

  • PostgreSQL, MySQL, or similar.
  • Stores structured data with relationships between entities (e.g., users, products).

NoSQL Database Example:

  • MongoDB, Redis, etc., for flexible schema or caching purposes.

Responsibilities:

  • Handle CRUD operations for entities.
  • Manage relationships and schema consistency.
  • Ensure data integrity and durability.

6. Caching Layer

Caching is used to store frequently accessed data in-memory to reduce database load and speed up response times. Common tools include Redis or Memcached.

Responsibilities:

  • Cache common query results (e.g., product data, user sessions).
  • Improve read performance by reducing database load.

7. Queue/Message Broker

In a microservices or distributed architecture, a message queue or broker (e.g., RabbitMQ, Kafka) facilitates asynchronous communication between services, decoupling the services and enabling event-driven architecture.

Responsibilities:

  • Process long-running tasks asynchronously (e.g., email notifications, image processing).
  • Ensure services are loosely coupled and resilient.

8. Monitoring and Logging

Monitoring tools (e.g., Prometheus, Grafana) and centralized logging systems (e.g., ELK Stack, Splunk) are essential for tracking the performance, availability, and errors in the backend system.

Responsibilities:

  • Collect metrics on request latency, error rates, and system health.
  • Aggregate logs for debugging and auditing.
  • Send alerts for abnormal system behavior.

9. CI/CD Pipeline

Continuous Integration and Continuous Deployment (CI/CD) automates the build, testing, and deployment process. Tools like Jenkins, GitLab CI, or GitHub Actions ensure that code changes are automatically tested and deployed to production with minimal human intervention.

Responsibilities:

  • Automate code quality checks (linting, unit tests).
  • Deploy to staging and production environments.
  • Rollback deployments if issues occur.

Deployment Architecture

1. Containerization (Docker)

The backend services are containerized using Docker to ensure consistent environments across development, staging, and production. This also simplifies scaling and deployment.

2. Orchestration (Kubernetes)

Kubernetes is used for orchestrating and managing containerized services. It provides features like auto-scaling, self-healing, and load balancing.

3. Cloud Infrastructure (AWS, GCP, Azure)

The backend is deployed on cloud infrastructure to provide scalability, redundancy, and high availability. The cloud provider may offer additional services like managed databases, caching, and message queues.

Responsibilities:

  • Automate scaling based on traffic load.
  • Ensure fault tolerance and disaster recovery.

Summary

The backend architecture is designed for scalability, resilience, and performance. By splitting the system into manageable components and services, we ensure that each part of the system can be developed, tested, and deployed independently. This architecture is adaptable to various backend frameworks and technologies, and it supports a wide range of client applications.

For detailed specifications of each component, see the corresponding documentation sections: