Practice/C3 AI/Design Car Rental Service System

Design Car Rental Service System

System DesignMust

Problem Statement

Design a car rental system that allows users to search for available vehicles by location and dates, compare options by vehicle class and features, make reservations with instant confirmation, and manage bookings including modifications, extensions, and cancellations. The platform serves both individual travelers and business accounts across a network of rental locations at airports, city centers, and hotels.

The core difficulty lies in managing time-based inventory: a single vehicle might be reserved for Monday through Wednesday, available Thursday, and reserved again Friday through Sunday. Your system must efficiently query availability across overlapping date ranges, prevent double-bookings when multiple users target the same vehicle class at a popular airport location, and orchestrate a multi-step booking flow that coordinates inventory holds, payment authorization, driver verification, and confirmation. Consider peak travel periods like holidays when airport locations see 10x normal demand and inventory at a single location may be fewer than 50 vehicles per class.

Key Requirements

Functional

Vehicle search -- users search by pickup location, pickup and return dates, and optional filters such as vehicle class, price range, and features like GPS or child seats
Reservation booking -- users select a vehicle class, authorize payment, and receive instant confirmation with a reservation number, pricing breakdown, and pickup instructions
Reservation management -- users view, modify (change dates, upgrade vehicle class), extend, or cancel reservations, with policy-appropriate fees or refunds applied automatically
Account and profile management -- users manage driver profiles with license verification, saved payment methods, loyalty points, and rental history

Non-Functional

Scalability -- support thousands of rental locations, millions of vehicles in the fleet, and tens of thousands of concurrent search and booking requests during peak travel seasons
Reliability -- zero double-bookings through strong consistency on inventory writes; 99.9% uptime for the reservation path
Latency -- search results returned within 500ms at p95, booking confirmation within 3 seconds end-to-end including payment gateway round-trip
Consistency -- eventual consistency acceptable for search index freshness, but strong consistency required for inventory reservations and payment records

What Interviewers Focus On

Based on real interview experiences, these are the areas interviewers probe most deeply:

1. Time-Window Inventory and Overlap Prevention

Unlike simple stock counts, car rental inventory is date-range-based. A vehicle available today may be reserved tomorrow through Friday. Querying "what is available from Wednesday to Sunday" must consider partial overlaps, and booking must atomically claim the date range without conflicting with existing reservations.

Hints to consider:

Model inventory availability using calendar-slot records or date-range intervals per vehicle, enabling overlap detection via range intersection queries
Use Postgres exclusion constraints or application-level checks with SELECT FOR UPDATE to prevent overlapping reservations on the same vehicle
Consider maintaining per-location, per-class availability counters for each date to enable fast search without scanning individual vehicle records
Handle edge cases like early returns that free up inventory mid-reservation and extensions that consume additional days

2. Contention at Popular Locations During Peak Periods

Airport locations during holidays have limited inventory per vehicle class, and dozens of users may try to book the same class simultaneously. Without proper concurrency control, the system will oversell.

Hints to consider:

Implement a short-lived inventory hold with TTL when a user begins checkout, preventing other users from claiming the same slot during the payment flow
Use optimistic locking with version columns on inventory records, or atomic decrement operations on availability counters, to serialize competing writes
Shard inventory data by location to isolate contention to the specific hot location rather than impacting the entire system
Consider overbooking buffers for high-demand classes, similar to airline practices, with clear policies for handling overflow

3. Multi-Step Booking Workflow

A reservation involves inventory hold, payment authorization, driver verification check, confirmation generation, and notification dispatch. Failure at any step requires coordinated rollback or retry.

Hints to consider:

Model the booking flow as a saga with compensating actions: if payment fails after inventory is held, release the hold; if confirmation fails after payment, retry asynchronously rather than refunding immediately
Use idempotency keys on payment gateway calls to prevent duplicate charges when retrying after timeouts
Implement a durable workflow engine or state machine that persists the booking's current step, enabling recovery after process crashes
Queue non-critical steps like email notifications and loyalty point accrual to avoid blocking the critical confirmation path

4. Search Performance Across Locations and Dates

Users expect fast search results filtered by location, date range, vehicle class, and features. Computing real-time availability from raw reservation data on every search request would be too slow.

Hints to consider:

Maintain a denormalized search index in Elasticsearch with pre-computed availability snapshots per location, class, and date range
Update the search index asynchronously via events published from the booking path, accepting seconds of staleness in exchange for fast reads
Cache popular search queries (e.g., major airports for the upcoming weekend) at the application layer with short TTLs
Re-validate availability against the authoritative inventory store at checkout time to catch stale search results before confirming a booking

5. Reservation Modifications and Extensions

Changing dates, upgrading a vehicle class, or extending a rental are complex operations that must secure new inventory before releasing the old allocation, handle price adjustments, and coordinate with payment.

Hints to consider:

Treat a modification as a "hold new, release old" atomic operation rather than an in-place update, ensuring the user never loses their original reservation if the new request cannot be fulfilled
Calculate price differences and apply additional charges or refunds as part of the modification saga
For extensions, check availability for the additional days and process incremental payment before confirming
Maintain an audit trail of all modifications with before and after states for customer support and dispute resolution

Practice/C3 AI/Design Car Rental Service System

Design Car Rental Service System

System DesignMust

Problem Statement

Key Requirements

Functional

Vehicle search -- users search by pickup location, pickup and return dates, and optional filters such as vehicle class, price range, and features like GPS or child seats
Reservation booking -- users select a vehicle class, authorize payment, and receive instant confirmation with a reservation number, pricing breakdown, and pickup instructions
Reservation management -- users view, modify (change dates, upgrade vehicle class), extend, or cancel reservations, with policy-appropriate fees or refunds applied automatically
Account and profile management -- users manage driver profiles with license verification, saved payment methods, loyalty points, and rental history

Non-Functional

Scalability -- support thousands of rental locations, millions of vehicles in the fleet, and tens of thousands of concurrent search and booking requests during peak travel seasons
Reliability -- zero double-bookings through strong consistency on inventory writes; 99.9% uptime for the reservation path
Latency -- search results returned within 500ms at p95, booking confirmation within 3 seconds end-to-end including payment gateway round-trip
Consistency -- eventual consistency acceptable for search index freshness, but strong consistency required for inventory reservations and payment records

What Interviewers Focus On

Based on real interview experiences, these are the areas interviewers probe most deeply:

1. Time-Window Inventory and Overlap Prevention

Hints to consider:

Model inventory availability using calendar-slot records or date-range intervals per vehicle, enabling overlap detection via range intersection queries
Use Postgres exclusion constraints or application-level checks with SELECT FOR UPDATE to prevent overlapping reservations on the same vehicle
Consider maintaining per-location, per-class availability counters for each date to enable fast search without scanning individual vehicle records
Handle edge cases like early returns that free up inventory mid-reservation and extensions that consume additional days

2. Contention at Popular Locations During Peak Periods

Hints to consider:

Implement a short-lived inventory hold with TTL when a user begins checkout, preventing other users from claiming the same slot during the payment flow
Use optimistic locking with version columns on inventory records, or atomic decrement operations on availability counters, to serialize competing writes
Shard inventory data by location to isolate contention to the specific hot location rather than impacting the entire system
Consider overbooking buffers for high-demand classes, similar to airline practices, with clear policies for handling overflow

3. Multi-Step Booking Workflow

Hints to consider:

Model the booking flow as a saga with compensating actions: if payment fails after inventory is held, release the hold; if confirmation fails after payment, retry asynchronously rather than refunding immediately
Use idempotency keys on payment gateway calls to prevent duplicate charges when retrying after timeouts
Implement a durable workflow engine or state machine that persists the booking's current step, enabling recovery after process crashes
Queue non-critical steps like email notifications and loyalty point accrual to avoid blocking the critical confirmation path

4. Search Performance Across Locations and Dates

Users expect fast search results filtered by location, date range, vehicle class, and features. Computing real-time availability from raw reservation data on every search request would be too slow.

Hints to consider:

Maintain a denormalized search index in Elasticsearch with pre-computed availability snapshots per location, class, and date range
Update the search index asynchronously via events published from the booking path, accepting seconds of staleness in exchange for fast reads
Cache popular search queries (e.g., major airports for the upcoming weekend) at the application layer with short TTLs
Re-validate availability against the authoritative inventory store at checkout time to catch stale search results before confirming a booking

5. Reservation Modifications and Extensions

Hints to consider:

Treat a modification as a "hold new, release old" atomic operation rather than an in-place update, ensuring the user never loses their original reservation if the new request cannot be fulfilled
Calculate price differences and apply additional charges or refunds as part of the modification saga
For extensions, check availability for the additional days and process incremental payment before confirming
Maintain an audit trail of all modifications with before and after states for customer support and dispute resolution