Booking conflict resolution in LLD - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the primary cause of booking conflicts in a shared resource system?

Consider a system where multiple users can book the same resource (e.g., a meeting room). What is the main reason booking conflicts occur?

A The resource is physically unavailable due to maintenance.

B Users forget to confirm their bookings after selecting a time slot.

C The system does not allow cancellations or modifications of bookings.

D Multiple users attempt to book overlapping time slots for the same resource simultaneously.

💡 Hint

Think about what happens when two people try to reserve the same room at the same time.

Booking conflicts happen mainly when two or more users try to reserve the same resource for overlapping times. This causes the system to have to decide which booking to accept.

❓ Architecture

intermediate

Which component is essential to prevent booking conflicts in a distributed booking system?

In a distributed booking system, which component helps ensure that two users cannot book the same resource at the same time?

A A load balancer to distribute booking requests evenly.

B A caching layer to store booking data temporarily.

C A centralized locking mechanism that locks the resource during booking.

D A notification service to alert users of booking status.

💡 Hint

Think about how to make sure only one booking can happen at a time for the same resource.

A centralized locking mechanism ensures that when one user is booking a resource, others cannot book it simultaneously, preventing conflicts.

❓ scaling

advanced

How to scale a booking system to handle thousands of simultaneous booking requests without conflicts?

You need to design a booking system that supports thousands of users booking resources at the same time. Which approach best helps to scale while preventing booking conflicts?

A Cache all bookings on the client side to reduce server load.

B Partition resources by time slots and use distributed locks per partition to allow parallel bookings.

C Allow bookings without locks and resolve conflicts manually later.

D Use a single global lock for all bookings to ensure consistency.

💡 Hint

Think about dividing the problem to handle many requests at once without blocking all bookings.

Partitioning resources by time slots and using distributed locks per partition allows multiple bookings to proceed in parallel without conflicts, improving scalability.

❓ tradeoff

advanced

What is a tradeoff when using optimistic concurrency control for booking conflict resolution?

Optimistic concurrency control lets users book without locking but checks for conflicts before finalizing. What is a key tradeoff of this approach?

A Users may experience booking failures and need to retry, causing possible frustration.

B It guarantees zero booking conflicts with no retries needed.

C It requires a centralized lock, reducing system availability.

D It prevents any concurrent booking attempts from happening.

💡 Hint

Think about what happens if two users book the same slot without locking first.

Optimistic concurrency control allows conflicts to happen but detects them before finalizing. This means users might have to retry their booking if a conflict is found.

❓ estimation

expert

Estimate the maximum booking requests per second a system can handle with a single resource and a locking mechanism that takes 50ms per booking.

Assuming each booking request requires a lock on the resource that takes 50 milliseconds to process, what is the maximum number of booking requests per second the system can handle for that single resource?

A 20 requests per second

B 50 requests per second

C 200 requests per second

D 500 requests per second

💡 Hint

Calculate how many 50ms intervals fit into one second.

One second has 1000 milliseconds. Each booking takes 50 milliseconds, so 1000 / 50 = 20 bookings per second.