System Design & Common System Design Components

1. Understand the Basics of System Design

Scalability: How to design systems that can scale horizontally (adding more machines) or vertically (adding more power to existing machines).
Reliability: Ensuring the system is available and fault-tolerant, with redundancy and failover strategies.
Performance: Designing for low latency, high throughput, and efficient resource usage.
Consistency vs. Availability: Understanding trade-offs between consistency, availability, and partition tolerance (CAP theorem).
Security: Implementing authentication, authorization, and encryption.
Cost: Keeping the design cost-effective, balancing between performance and expense.

2. Familiarize Yourself with Common System Design Components

Load Balancers: Distribute incoming traffic across multiple servers.
Caching: Use in-memory storage (e.g., Redis, Memcached) to speed up responses.
Databases: Understand when to use SQL vs. NoSQL, and concepts like sharding, replication, and indexing.
Message Queues: Use queues (e.g., Kafka, RabbitMQ) for decoupling services and handling asynchronous tasks.
Microservices: Design systems using loosely coupled, independently deployable services.
CDNs: Content Delivery Networks for distributing content closer to users.
Rate Limiting: Preventing abuse and ensuring fair usage by limiting the number of requests a user can make.

1. Frontend (ReactJS):

"The frontend is built using ReactJS, where users interact with our application. React provides a fast, responsive user experience and communicates with the backend via APIs."

2. Backend (NodeJS):

"The backend is built with NodeJS. It handles API requests from the frontend, processes business logic, and communicates with the database. It also manages background jobs and integrates with external services."

3. Database (MongoDB):

"MongoDB is our main database. We use it to store user data and application data in a flexible, document-based format. It supports high scalability and replication for fault tolerance."

4. Caching (Redis):

"To improve performance, we use Redis for caching frequently accessed data. This reduces the load on the database and speeds up response times."

5. Background Jobs (BullMQ + Redis):

"For handling background tasks like sending emails or processing data, we use BullMQ. It manages job queues, with Redis storing the job data and providing a fast way to queue and process tasks in the background."

6. Infrastructure (AWS):

"The entire system is hosted on AWS. We use various AWS services like EC2 for servers, S3 for file storage, and CloudFront for CDN. AWS also allows us to auto-scale based on traffic to handle peak loads."

7. Monitoring (Kibana):

"For monitoring and logging, we use Kibana. It helps us track system performance, log errors, and troubleshoot issues by visualizing logs and metrics from our services."

Architecture Overview:

User Interaction: Users interact with the React frontend.
API Requests: React sends API requests to the NodeJS backend.
Database Access: NodeJS retrieves or writes data to MongoDB.
Caching Layer: Frequently accessed data is cached in Redis to improve speed.
Background Jobs: BullMQ queues jobs (like data processing or notifications) with Redis managing the job states.
AWS Infrastructure: The entire system is deployed on AWS with auto-scaling and load balancing.
Monitoring: Kibana tracks logs and performance metrics to ensure the system runs smoothly.

This architecture is scalable, efficient, and well-suited for handling a large number of users and background processes.

Caching: Use Redis to cache frequently accessed data like product catalogs and user sessions.

Database Indexing: Ensure queries (e.g., searching products by name, filtering by category) are fast with appropriate indexes.

Horizontal Scaling: Use load balancers (e.g., Nginx) to distribute traffic across multiple instances.

Microservices: For large applications, consider splitting services into independent microservices (e.g., product service, order service, and payment service).

Vertically scaling in Node.js refers to increasing the resources (such as CPU, memory, or storage) available to a single machine or server running the Node.js application

With vertical scaling, the focus is on making the application faster and able to handle more operations on the same server by increasing its hardware capacity.

Increase CPU cores: A more powerful CPU can handle more simultaneous requests or threads.

Increase RAM: More memory allows the Node.js process to handle more data and larger in-memory caches, leading to better performance.

1. Authentication:

Login process where the user proves their identity (e.g., with a username and password).
After successful authentication, the server generates a JWT token and sends it to the client.

2. Authorization:

After receiving the JWT token, the client includes it in the header of future requests to access protected resources.

The server verifies the token to ensure the user is authorized to access the requested resource.

1. User Login (Authentication)

When a user logs in with their credentials (username and password), the server validates them. If valid, the server generates a JWT and sends it back to the client.

2.Access Protected Routes (Authorization)

After the user receives the JWT, they include it in the Authorization header for any protected routes. The server verifies the token and grants access based on the user's roles or claims in the token.

Indexing:

MongoDB supports a variety of indexes to speed up queries.

Single Field Indexes: Index on a single field to speed up exact match queries.
Compound Indexes: Index on multiple fields to optimize queries with multiple conditions.
Text Indexes: Enable text search on string fields.
TTL Indexes: Automatically delete documents after a specified time, useful for expiring data (e.g., logs).
Geospatial Indexes: Useful for location-based queries.

batching operations to reduce network overhead and improve performance.

MongoDB’s replica sets provide fault tolerance and high availability.

Replica Sets: Consist of a primary and multiple secondaries.

If the primary fails, a failover occurs and a secondary is promoted to primary.

MongoDB is designed for horizontal scaling using sharding. This is essential for handling large-scale systems with massive amounts of data and high traffic.

Handling Query Optimization:

MongoDB provides tools to optimize and analyze queries.

Use the explain() method to analyze query plans.
Identify slow queries and optimize indexes to improve performance.
Consider the impact of sharding and how cross-shard queries could impact performa