Exploring System Design in Depth
๐๏ธ Exploring System Design in Depth
From Fundamentals to Scalable Architectures (with Real-World Examples) ๐โ๏ธ
โGreat systems arenโt built by chance โ theyโre designed with clarity, trade-offs, and foresight.โ
System Design is one of the most critical skills for backend, full-stack, and senior engineers. Itโs not about coding โ itโs about thinking at scale ๐ง .
In this blog, weโll cover: โ Core concepts โ Key terminologies โ Architecture patterns โ Tools & technologies โ Real-world examples โ Advanced interview Q&A
Letโs dive in ๐
๐น What is System Design?
System Design is the process of defining:
- Architecture ๐๏ธ
- Components ๐งฉ
- Data flow ๐
- Communication ๐ธ๏ธ
- Scalability & reliability โก
๐ฏ Goal:
Build a system that is:
- Scalable
- Reliable
- Maintainable
- Cost-efficient
๐ Example: Designing Instagram, Uber, YouTube, or a Payment Gateway.
๐น Functional vs Non-Functional Requirements
๐งฉ Functional Requirements
๐ What the system should do
Examples:
- User can upload photos ๐ธ
- User can like and comment โค๏ธ
- User can follow other users ๐ฅ
โ๏ธ Non-Functional Requirements
๐ How well the system performs
| Requirement | Meaning |
|---|---|
| Scalability ๐ | Handle growing users |
| Availability ๐ข | System uptime |
| Latency โก | Response time |
| Consistency ๐ | Data correctness |
| Security ๐ | Data protection |
๐ Interview Tip: Always clarify these first!
๐น High-Level vs Low-Level Design
๐บ๏ธ High-Level Design (HLD)
- Services
- Databases
- APIs
- Communication flow
๐ Example:
Client โ API Gateway โ Auth Service โ Media Service โ DB
๐ง Low-Level Design (LLD)
- Classes
- Methods
- Data models
- Algorithms
๐ Example:
class User
has_many :posts
has_many :followers
end
๐น Scalability: Vertical vs Horizontal
โฌ๏ธ Vertical Scaling
- Increase CPU / RAM
- Easy but limited
โก๏ธ Horizontal Scaling
- Add more servers
- Complex but scalable ๐
๐ Modern systems prefer Horizontal Scaling
๐น Load Balancer โ๏ธ
Distributes traffic across servers.
Popular Algorithms:
- Round Robin ๐
- Least Connections ๐
- IP Hashing ๐งฎ
Tools:
- Nginx
- HAProxy
- AWS ALB / ELB
๐ Example:
User โ Load Balancer โ App Server 1 / 2 / 3
๐น Databases: SQL vs NoSQL ๐๏ธ
๐งฑ SQL (Relational)
- MySQL
- PostgreSQL
โ Strong consistency โ Structured schema
๐ NoSQL
- MongoDB
- DynamoDB
- Cassandra
โ Flexible schema โ Massive scale
๐ Rule of Thumb:
Transactions โ SQL Scale & Speed โ NoSQL
๐น Database Sharding ๐งฉ
Splitting data across databases.
Types:
- Range-based
- Hash-based
- Geo-based ๐
๐ Example:
Users AโM โ DB1
Users NโZ โ DB2
๐น Caching โก (Speed Booster)
Avoid hitting DB every time.
Types:
- In-memory cache
- Distributed cache
Tools:
- Redis
- Memcached
๐ Example:
Client โ Cache โ DB (only on cache miss)
๐น CAP Theorem ๐ง
You can only guarantee 2 out of 3:
| Term | Meaning |
|---|---|
| Consistency ๐ | Same data everywhere |
| Availability ๐ข | Always responds |
| Partition Tolerance ๐ | Survives network failure |
๐ Examples:
- CP: HBase
- AP: Cassandra
- CA: Traditional RDBMS (single node)
๐น Message Queues & Async Processing ๐ฉ
Decouple services & handle spikes.
Tools:
- Kafka
- RabbitMQ
- AWS SQS
๐ Example:
Order Service โ Queue โ Email Service
โ Improves reliability โ Handles high traffic
๐น Microservices Architecture ๐งฉ
Each service:
- Independent
- Own database
- Own deployment
๐ Example Services:
- Auth Service ๐
- Payment Service ๐ณ
- Notification Service ๐
Tools:
- Docker ๐ณ
- Kubernetes โธ๏ธ
- Service Mesh (Istio)
๐น API Design ๐
REST Principles:
- Stateless
- Resource-based
- HTTP verbs
๐ Example:
GET /users/1
POST /orders
GraphQL (Advanced):
- Fetch exact data
- Single endpoint
๐น Observability & Monitoring ๐
What to Monitor:
- Logs ๐
- Metrics ๐
- Traces ๐
Tools:
- Prometheus
- Grafana
- ELK Stack
- New Relic
๐น Security in System Design ๐
- HTTPS
- OAuth / JWT
- Rate Limiting ๐ฆ
- Encryption (at rest & in transit)
๐ Example:
User โ Auth Token โ Protected API
๐ฏ Advanced System Design Interview Questions (With Answers)
โ 1. How would you design a URL Shortener like Bitly?
Answer:
- API Gateway
- Hashing (Base62)
- DB for mapping
- Cache for hot URLs
- Load Balancer
- Analytics Service
๐ Key Challenge: Collision handling
โ 2. How do you handle millions of concurrent users?
Answer:
- Horizontal scaling
- Load balancers
- Caching
- Async queues
- CDN for static content ๐
โ 3. How do you ensure data consistency in microservices?
Answer:
- Saga Pattern
- Eventual consistency
- Message queues
- Idempotent APIs
โ 4. Difference between Kafka and RabbitMQ?
| Kafka | RabbitMQ |
|---|---|
| Streaming ๐ | Messaging ๐ฉ |
| High throughput | Low latency |
| Event replay | No replay |
โ 5. How would you design a payment system?
Answer:
- Strong consistency
- Idempotency keys
- Transaction logs
- Retry mechanism
- Audit trail
๐ Final Thoughts
System Design is a mindset, not a framework.
๐ง Think in trade-offs, not perfection.
Mastering System Design will:
- Crack senior interviews ๐ผ
- Improve architectural thinking ๐๏ธ
- Make you a 10x engineer ๐
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