System Design Tutorial Summary

Introduction

• Covers system design concepts: scalability, reliability, data handling, high-level architecture
• Focuses on system design interviews
• Emphasizes understanding overall system, not just coding

Understanding Computer Systems

Basic Components

• Binary System: Computers understand only 0s and 1s
  • Bit: Smallest data unit (0 or 1)
  • Byte: 8 bits (Represents a character or number)
• Storage Units: Kilobyte, Megabyte, Gigabyte, Terabyte
• Disk Storage: Primary data holder (HDD or SSD)
  • HDD vs SSD: SSDs are faster (500-3500 MBps) than HDDs (80-160 MBps)
• RAM: Volatile memory, active data holder
  • Size: Few GBs to hundreds of GBs
  • Speed: 5000+ MBps
• Cache: Faster than RAM, smaller size, stores frequently used data
• CPU: Executes instructions, processes operations
• Motherboard: Connects all components

High-Level Architecture of Production-Ready Apps

CICD Pipeline

• Continuous Integration and Continuous Deployment
• Tools: Jenkins, GitHub Actions

Handling User Requests

• Load Balancers: Distribute requests evenly
• External Storage: Separate from production server
• Logging and Monitoring: Tools like PM2 (backend), Sentry (frontend)

Failure Handling

• Alerting Systems: Integrated with platforms like Slack
• Debugging: Use logs, replicate issues in a safe environment, hot fixes

Key Principles in System Design

Scalability, Maintainability, Efficiency

• Plan for failure, build a resilient system

Moving, Storing, Transforming Data

• Moving Data: Optimize speed and security
• Storing Data: SQL vs NoSQL, access patterns
• Transforming Data: Turning raw data into meaningful information

CAP Theorem

• Consistency, Availability, Partition Tolerance: Can only achieve two of the three
  • Example: Banking system needs Consistency and Partition Tolerance

Availability and Reliability

• Availability: Measure of uptime
  • High availability (e.g., 99.999% uptime)
• Reliability, Fault Tolerance, Redundancy: Ensure the system works correctly, handle failures, backups
• Performance: Throughput (requests per second, queries per second), latency

Networking Basics

IP Addressing and Data Packets

• IPV4 vs IPV6: IPV4 (32-bit), IPV6 (128-bit)
• Packets: Contain IP headers
• DNS: Translates domain names to IP addresses
• TCP vs UDP: TCP (reliable), UDP (faster, less reliable)

Application Layer Protocols

• HTTP: Stateless protocol, request-response model
• Websockets: Two-way communication
• SMTP, IMAP, POP3: Email protocols
• FTP, SSH: File transfer protocols
• WebRTC, MQTT, AMQP: Real-time communication
• RPC: Remote procedure calls, execute code on remote servers

API Design

CRUD Operations

• Create, Read, Update, Delete (CRUD)
• RESTful APIs, GraphQL, gRPC

Versioning and Rate Limiting

• Maintain backward compatibility
• Set rate limits to prevent abuse

Caching and CDNs

Types of Caching

• Browser Caching: Local storage on user’s computer
• Server Caching: In-memory or disk-based caching
• Database Caching: Cache query results
• CDNs: Geographically distributed servers for static content

Benefits

• Reduced latency, lower server load, improved user experience

Proxy Servers

Types and Usage

• Forward Proxy: Controls client access to resources
• Reverse Proxy: Manages traffic to servers
• Load Balancing: Distributes traffic to multiple servers

Database Essentials

Types of Databases

• SQL Databases: Relational, ACID compliant
• NoSQL Databases: Flexible, unstructured data
• In-Memory Databases: Fast data retrieval

Scaling Databases

• Vertical Scaling: Enhance individual server capabilities
• Horizontal Scaling: Add more servers
  • Sharding, Data Replication

Performance Techniques

• Caching, Indexing, Query Optimization

Conclusion

• Prioritize requirements, use appropriate design principles
• Importance of getting design right from the start