Order tracking state machine in LLD - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Identify the correct next state in an order tracking state machine

An order tracking system has the following states: Placed, Confirmed, Shipped, Delivered, and Cancelled. Which state should the order transition to immediately after Placed?

A Confirmed

B Shipped

C Delivered

D Cancelled

💡 Hint

Think about the logical flow of order processing after a customer places an order.

After an order is placed, it must be confirmed by the system or seller before shipping. So, the next state after Placed is Confirmed.

❓ Architecture

intermediate

Choose the best component to handle order state transitions

In designing an order tracking system, which component is best suited to manage the state transitions of orders reliably?

A A stateless REST API endpoint

B A state machine service with persistent storage

C A client-side JavaScript function

D A simple database table without logic

💡 Hint

Consider where state and logic should be maintained to ensure consistency and reliability.

A state machine service with persistent storage ensures that order states and transitions are managed reliably and consistently, even across failures.

❓ scaling

advanced

Scaling order state updates under high load

Your order tracking system must handle thousands of state updates per second during peak sales. Which approach best supports scaling these state transitions?

A Partition orders by region and use distributed state machines per partition

B Process all state transitions synchronously in a single service instance

C Use a single centralized database with strong locking for state updates

D Store states only in cache without persistence

💡 Hint

Think about how to distribute load and avoid bottlenecks.

Partitioning orders by region and using distributed state machines allows parallel processing and reduces contention, enabling better scalability.

❓ tradeoff

advanced

Tradeoff between consistency and availability in order state updates

In a distributed order tracking system, choosing between strong consistency and high availability for state updates is a key tradeoff. Which statement best describes this tradeoff?

A Choosing availability means the system never loses data even if nodes disagree on state

B Consistency and availability can always be achieved together without tradeoffs

C High availability means all nodes always have the latest state with zero delay

D Strong consistency guarantees all nodes see the same state immediately but may reduce availability during network issues

💡 Hint

Recall the CAP theorem and its implications for distributed systems.

Strong consistency ensures all nodes agree on the state immediately but can reduce availability if network partitions occur. This is a classic CAP theorem tradeoff.

❓ estimation

expert

Estimate storage requirements for order state history

Your system tracks the full state history of each order. Each state change record is 200 bytes. You expect 1 million orders per day, each with an average of 5 state changes. Estimate the storage needed for one month (30 days) of state history.

A 3 trillion bytes (3 TB)

B 300 billion bytes (300 GB)

C 30 billion bytes (30 GB)

D 30 trillion bytes (30 TB)

💡 Hint

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

1 million orders/day * 5 changes = 5 million records/day. Over 30 days = 150 million records. 150 million * 200 bytes = 30 billion bytes = 30 GB (A).