Why library management tests CRUD design in LLD - Scalability Evidence

Scalability Analysis - Why library management tests CRUD design

Growth Table: Library Management CRUD Design

Users	Operations per Second	Data Size	System Changes
100 users	~50 CRUD ops/sec	Small (few hundred books)	Single server, simple DB
10,000 users	~5,000 CRUD ops/sec	Medium (tens of thousands books)	DB indexing, caching, load balancing
1,000,000 users	~500,000 CRUD ops/sec	Large (millions of books and records)	Sharding DB, horizontal scaling, CDN for static content
100,000,000 users	~50,000,000 CRUD ops/sec	Very large (global scale)	Multi-region clusters, advanced sharding, event-driven architecture

First Bottleneck

At small to medium scale, the database is the first bottleneck because CRUD operations require consistent reads and writes. As users and data grow, the database struggles to handle concurrent requests and data volume.

Scaling Solutions

• Read Replicas: Offload read operations to replicas to reduce DB load.
• Caching: Use in-memory caches (e.g., Redis) for frequent reads like book details.
• Horizontal Scaling: Add more application servers behind load balancers to handle more users.
• Database Sharding: Split data by library branches or book categories to distribute load.
• CDN: Serve static content like book images or PDFs closer to users.

Back-of-Envelope Cost Analysis

• At 10,000 users: ~5,000 CRUD ops/sec, requiring a DB that can handle 5k QPS.
• Storage: Tens of GBs for book data and user records.
• Bandwidth: Moderate, mostly for user requests and book metadata.
• At 1M users: ~500k ops/sec, needing sharded DB and multiple app servers.
• Bandwidth grows significantly, requiring network upgrades.

Interview Tip

Start by explaining CRUD basics and their importance in library management. Discuss expected load and data growth. Identify the first bottleneck (usually DB). Propose scaling solutions step-by-step, focusing on read/write separation, caching, and sharding. Use real numbers to show understanding.

Self Check

Your database handles 1000 QPS. Traffic grows 10x to 10,000 QPS. What do you do first?

Answer: Add read replicas and implement caching to reduce DB load before considering sharding or adding more app servers.

Key Result

CRUD operations in library management systems first hit database limits as users grow; scaling starts with read replicas and caching before moving to sharding and horizontal scaling.