Inventory management in LLD - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Architecture

intermediate

Designing a scalable inventory update system

You are tasked with designing an inventory management system that handles thousands of updates per second from multiple warehouses. Which architectural component is essential to ensure updates are processed reliably and in order?

A A distributed message queue to buffer and order inventory update events

B A single monolithic database handling all updates synchronously

C Direct API calls from warehouses to update inventory without buffering

D A local cache in each warehouse that updates the central database once a day

💡 Hint

Think about how to handle high volume and ordering of events reliably.

A distributed message queue ensures that inventory updates are received in order and can be retried if failures occur, which is critical for consistency and scalability.

❓ scaling

intermediate

Estimating capacity for inventory read requests

Your inventory system expects 10 million products and 1 million users. Each user makes on average 5 inventory read requests per minute. What is the approximate number of read requests per second your system must handle?

A About 500,000 read requests per second

B About 5,000 read requests per second

C About 50,000 read requests per second

D About 83,000 read requests per second

💡 Hint

Calculate total requests per minute and convert to per second.

1 million users * 5 requests/minute = 5 million requests per minute. Dividing by 60 seconds gives approximately 83,333 requests per second.

❓ tradeoff

advanced

Choosing between strong and eventual consistency in inventory updates

Your inventory system must update stock levels in real-time for an e-commerce platform. What is the main tradeoff when choosing eventual consistency over strong consistency?

A Strong consistency reduces latency but increases the chance of showing incorrect stock

B Eventual consistency guarantees immediate accuracy but reduces system availability

C Eventual consistency improves availability and performance but may show outdated stock levels temporarily

D Strong consistency allows faster updates but risks data loss during failures

💡 Hint

Consider how consistency models affect user experience and system behavior.

Eventual consistency allows the system to be more available and scalable but may cause users to see stale inventory data briefly. Strong consistency ensures accuracy but can reduce availability and increase latency.

❓ component

advanced

Key components for preventing overselling in inventory systems

Which component is critical to prevent overselling when multiple users try to buy the last items simultaneously?

A A caching layer that stores stock counts without synchronization

B A distributed locking mechanism or atomic decrement operation on stock count

C A batch job that updates stock counts once per hour

D A read-only replica database for inventory queries

💡 Hint

Think about how to handle concurrent updates safely.

Distributed locking or atomic operations ensure that stock decrements happen safely without race conditions, preventing overselling.

🧠 Conceptual

expert

Handling inventory reconciliation after system failures

After a system crash, your inventory counts may be inconsistent with actual stock. What is the best approach to reconcile inventory data accurately?

A Run a periodic full audit comparing system counts with physical stock and adjust discrepancies

B Trust the last known system counts without verification to avoid downtime

C Ignore discrepancies as they will correct themselves over time

D Reset all inventory counts to zero and rebuild from new orders only

💡 Hint

Consider how to ensure data accuracy after failures.

Periodic audits comparing system data with physical stock help detect and fix inconsistencies, ensuring accurate inventory after failures.