Restaurant, Menu, Order classes in LLD - Scalability & System Analysis

Scalability Analysis - Restaurant, Menu, Order classes

Growth Table: Restaurant, Menu, Order Classes

Scale	Users	Orders per Second	Menu Items	Data Size	System Changes
Small	100 users	10 orders/sec	100 items	MBs	Single server, simple DB, no caching
Medium	10,000 users	1,000 orders/sec	1,000 items	GBs	DB read replicas, caching menu, load balancer
Large	1,000,000 users	50,000 orders/sec	10,000 items	TBs	Sharded DB, distributed cache, multiple app servers
Very Large	100,000,000 users	5,000,000 orders/sec	100,000+ items	Petabytes	Microservices, global CDN, event-driven architecture, data partitioning

First Bottleneck

At small scale, the database is the first bottleneck because it handles all order writes and menu reads. As users grow, the DB CPU and disk I/O limit throughput. The application server can handle more connections, but DB queries slow down.

Scaling Solutions

• Database Read Replicas: Use replicas to serve menu reads and reduce load on primary DB.
• Caching: Cache menu data in memory (e.g., Redis) to reduce DB hits.
• Horizontal Scaling: Add more app servers behind a load balancer to handle more users.
• Sharding: Partition orders by restaurant or region to distribute DB load.
• Event-Driven Architecture: Use message queues to process orders asynchronously at large scale.
• CDN: Use CDN to serve static menu images and reduce bandwidth.

Cost Analysis

• At 10,000 orders/sec, DB needs to handle ~10,000 writes/sec plus reads.
• Menu data size grows with items; caching reduces DB storage cost.
• Network bandwidth must support order data and menu images; estimate 1 Gbps for medium scale.
• Storage for orders grows linearly; archiving old orders reduces DB size.

Interview Tip

Start by describing the system components and their roles. Then discuss expected traffic and data growth. Identify the first bottleneck logically (usually DB). Propose scaling solutions step-by-step, explaining why each helps. Mention trade-offs and cost considerations. Keep answers structured and clear.

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 for menu data to reduce DB load. Also, consider connection pooling and optimize queries before scaling vertically.

Key Result

The database is the first bottleneck as user and order volume grows; scaling requires read replicas, caching, and sharding to maintain performance.