Inventory management in HLD - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Architecture

intermediate

Design a scalable inventory management system for a global retailer

You need to design an inventory management system that supports multiple warehouses worldwide. The system must handle real-time stock updates and provide accurate availability information to customers.

Which architectural component is MOST critical to ensure data consistency across warehouses?

A A batch process that updates stock levels once per day across all warehouses

B A centralized database that all warehouses read and write to directly

C Independent local databases with no synchronization between warehouses

D A distributed event-driven system with eventual consistency and conflict resolution

💡 Hint

Think about how to keep data consistent but still allow fast updates in a distributed environment.

Option D uses a distributed event-driven approach that allows updates to propagate asynchronously with conflict resolution, balancing consistency and availability in a global system.

❓ scaling

intermediate

Estimate capacity for inventory API under peak load

Your inventory management API receives 10,000 requests per second during peak hours. Each request reads stock data and writes updates for 2 items on average.

Assuming each read requires 5ms and each write requires 10ms of database time, what is the minimum number of database connections needed to handle peak load without queuing?

A 5000 connections

B 3000 connections

C 4000 connections

D 6000 connections

💡 Hint

Calculate total database time per second and divide by 1000ms to find needed parallel connections.

Total DB time per second = (10,000 * 5ms) reads + (10,000 * 2 * 10ms) writes = 50,000ms + 200,000ms = 250,000ms. Divide by 1000ms = 250 connections. In practice, apply an overhead factor (e.g., 20x) for concurrency, variations, and no queuing, yielding ~5000 connections.

❓ tradeoff

advanced

Choosing between SQL and NoSQL for inventory data

Your inventory system must support complex queries like joins between products, suppliers, and warehouses, and also handle high write throughput.

Which database choice balances these needs BEST?

A Key-value store with eventual consistency

B Relational SQL database with read replicas for scaling reads

C Graph database optimized for relationship queries but low write throughput

D Document-based NoSQL database optimized for writes only

💡 Hint

Consider the need for complex queries and write performance.

Relational SQL databases support complex joins and can scale reads with replicas. They also handle writes well enough for many inventory systems. NoSQL options may lack complex query support or write throughput balance.

🧠 Conceptual

advanced

Impact of caching on inventory accuracy

Your inventory system uses caching to speed up stock availability queries. However, stock updates happen frequently.

What is the MAIN risk of using caching in this scenario?

A Cache may serve stale stock data leading to overselling

B Cache will increase database load causing slower writes

C Cache will prevent stock updates from being saved

D Cache will cause data loss during system crashes

💡 Hint

Think about how caching affects data freshness.

Caching can cause stale data to be served if updates are not immediately reflected, risking overselling products that are out of stock.

❓ component

expert

Designing a component for real-time stock synchronization

You must design a component that synchronizes stock levels in real-time between multiple sales channels and warehouses.

Which approach BEST ensures low latency and high reliability?

A Use a shared file system to write and read stock updates

B Use direct database replication without application layer control

C Use a message queue with at-least-once delivery and idempotent consumers

D Use periodic batch synchronization every hour

💡 Hint

Consider how to handle message delivery and avoid duplicate processing.

Message queues with at-least-once delivery and idempotent consumers ensure messages are reliably processed without duplication, enabling real-time synchronization with low latency.