Order state machine in LLD - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Identify the correct next state for an order

An order is currently in the "Processing" state. Which state should it transition to next if the payment is successful?

A Returned

B Cancelled

C Delivered

D Shipped

💡 Hint

Think about the typical flow of an order after payment is confirmed.

After payment is successful, the order moves from Processing to Shipped before it can be Delivered.

❓ Architecture

intermediate

Choose the best component to handle order state transitions

In designing an order state machine, which component is best suited to manage state transitions and enforce valid state changes?

A A dedicated State Machine Service that validates and updates order states

B A UI component that changes states based on user clicks

C A database trigger that automatically changes states

D A logging service that records state changes

💡 Hint

Consider where the logic for valid state changes should reside for consistency and control.

A dedicated State Machine Service centralizes state transition logic, ensuring valid and consistent changes.

❓ scaling

advanced

Scaling order state machine for high traffic

Your order system handles thousands of orders per second. Which approach best ensures the order state machine scales without losing consistency?

A Handle state transitions only on the client side to reduce server load

B Use a distributed state machine with event sourcing and a message queue

C Use local in-memory state machines on each server without synchronization

D Store all states in a single database table with frequent polling

💡 Hint

Think about how to keep state consistent across multiple servers and handle high throughput.

Event sourcing with message queues allows distributed systems to process state changes reliably and scalably.

❓ tradeoff

advanced

Tradeoff between strict and eventual consistency in order states

Choosing between strict consistency and eventual consistency for order state updates impacts system design. Which statement best describes a tradeoff?

A Strict consistency allows faster processing but risks stale data

B Eventual consistency guarantees immediate state accuracy but increases latency

C Strict consistency ensures immediate state accuracy but may reduce system availability under load

D Eventual consistency prevents any state conflicts by locking all orders

💡 Hint

Consider how consistency models affect availability and latency.

Strict consistency provides accurate state immediately but can reduce availability during high load or failures.

❓ estimation

expert

Estimate storage needed for order state history

Your system processes 1 million orders daily. Each order has on average 5 state transitions. Each state transition record requires 200 bytes. Estimate the storage needed to keep 30 days of order state history.

A 30 GB

B 3 GB

C 300 GB

D 60 GB

💡 Hint

Calculate total records and multiply by size per record, then convert bytes to GB.

1,000,000 orders/day × 5 transitions = 5,000,000 records/day. 5,000,000 × 200 bytes = 1,000,000,000 bytes/day ≈ 1 GB/day. For 30 days: ≈ 30 GB (D).