Class design (Book, Member, Librarian, Loan) in LLD - Scalability & System Analysis

Scalability Analysis - Class design (Book, Member, Librarian, Loan)

Growth Table: Class Design for Library System

Scale	Users (Members)	Books	Loans Active	System Changes
100 users	100	1,000	50	Simple in-memory data structures, single server, no caching needed
10,000 users	10,000	100,000	5,000	Use database for persistence, add indexing on books and members, basic caching for frequent queries
1,000,000 users	1,000,000	10,000,000	500,000	Database sharding by member ID or book ID, read replicas, caching layer (Redis), horizontal scaling of application servers
100,000,000 users	100,000,000	1,000,000,000	50,000,000	Advanced sharding and partitioning, distributed caching, microservices for different domains (Book, Member, Loan), CDN for static content, asynchronous processing for loan updates

First Bottleneck

At small scale, the database is the first bottleneck because it handles all queries for books, members, and loans. As users grow, the database query load and storage needs increase rapidly. Without indexing and caching, response times degrade.

At medium scale, the application server CPU and memory become bottlenecks due to processing many concurrent requests and managing business logic.

At large scale, network bandwidth and data partitioning challenges arise, especially for loan transactions and book availability updates.

Scaling Solutions

• Database: Add indexes on frequently queried fields (book ID, member ID). Use read replicas to distribute read load. Implement sharding by member or book ID to split data across servers.
• Caching: Use Redis or Memcached to cache frequent queries like book availability and member info.
• Application Servers: Horizontally scale by adding more servers behind a load balancer to handle more concurrent users.
• Data Partitioning: Partition loans and books by region or category to reduce cross-server queries.
• Asynchronous Processing: Use message queues for loan updates and notifications to reduce synchronous load.
• Microservices: Separate Book, Member, and Loan services to isolate load and scale independently.

Back-of-Envelope Cost Analysis

• Requests per second (QPS): For 1M users, assume 10% active daily, each making 5 requests/hour -> ~140 QPS.
• Storage: 10M books at 1 KB metadata each -> ~10 GB; 1M members at 1 KB each -> ~1 GB; 500K active loans at 1 KB each -> ~0.5 GB.
• Bandwidth: Assuming 1 KB per request/response, 140 QPS -> ~0.14 MB/s (~1.1 Mbps), manageable with standard network.

Interview Tip

Structure your scalability discussion by first describing the system components and their interactions. Then, analyze how load grows with users and data. Identify the first bottleneck clearly. Propose targeted solutions for each bottleneck, explaining why they fit. Use concrete numbers to justify your choices. Finally, mention trade-offs and future scaling steps.

Self Check

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

Answer: Add read replicas to distribute read queries and reduce load on the primary database. Also, implement caching for frequent reads to reduce database hits. Consider query optimization and indexing if needed.

Key Result

The database is the first bottleneck as users and data grow; scaling requires sharding, caching, and horizontal scaling of application servers.