# System Design: Design Booking.com — Hotel Search, Availability, Dynamic Pricing, Overbooking, Cancellations, Reviews

Source: https://www.techinterview.org/post/3233474469/system-design-design-booking-com-hotel-booking-search-availability-pricing-overbooking-cancellation-review-system/
Updated: 2026-04-21 · techinterview.org

Booking.com connects 28+ million accommodation listings with hundreds of millions of travelers. Designing an online travel agency (OTA) tests your understanding of: geospatial search with complex filtering, real-time availability management across multiple distribution channels, dynamic pricing, and the overbooking strategies that maximize occupancy. This guide covers the hotel booking architecture for system design interviews.

## Hotel Search

The core search query: "find available hotels in Paris for Aug 10-15, 2 adults, sorted by best value." This combines: (1) Geospatial search -- hotels in Paris (city boundary or radius from city center). Elasticsearch geo_bounding_box or geo_polygon. (2) Availability filtering -- hotels with at least one room available for ALL nights of the stay. This is the hardest part (see Availability section below). (3) Guest capacity -- rooms that accommodate 2 adults. (4) Complex filters -- star rating, price range, guest rating (8+), amenities (pool, wifi, parking), property type (hotel, apartment, hostel), meal plan (breakfast included), cancellation policy (free cancellation). All indexed in Elasticsearch. (5) Sorting -- by price (lowest first), by rating (highest first), by distance, or by "our recommendations" (ML-ranked balancing value, popularity, and commission). Autocomplete: as the user types a destination, suggest: cities, regions, specific properties, landmarks, and airports. Pre-computed suggestion lists with popularity weighting. "Par" -> "Paris, France (12,345 properties)" as the top suggestion. Map view: search results displayed on an interactive map. Each result is a pin with price. Panning the map triggers a new search for the visible viewport. Clustering: when zoomed out, cluster nearby hotels into a single icon showing the count and price range.

## Availability and Inventory Management

Availability is the most complex component. Each property has multiple room types. Each room type has: a finite inventory (e.g., 10 Standard Double rooms), per-night pricing, and restrictions (minimum stay, closed-to-arrival on certain days, max occupancy). Availability check for a 5-night stay: the room must have available inventory on ALL 5 nights. If 4 of 5 nights have availability but one is sold out: the room is not bookable for this stay. Data model: room_night: property_id, room_type_id, date, total_inventory, booked_count, available_count (= total - booked), price, restrictions. For 28M listings * average 3 room types * 365 days = ~30 billion room-night records. Storage: partitioned by property_id for efficient per-property queries. Availability for search: checking 30 billion records for every search is too slow. Optimization: (1) Pre-compute availability summaries per property per date range (rolled up from room-night data). Index in Elasticsearch: available_from, available_to, min_price per date range. (2) Two-phase search: broad search in Elasticsearch (geospatial + filters + approximate availability), then detailed availability verification for the top results against the authoritative room-night database. Overbooking: properties sometimes sell more rooms than they have (expecting cancellations). The system allows booking beyond 100% availability up to a configurable overbooking threshold (e.g., 105%). If a guest arrives and no room is available: the hotel must relocate them (walk the guest to a comparable or better property). Revenue management: overbooking maximizes occupancy and revenue but carries the cost of relocation.

## Booking Transaction

When a guest clicks "Reserve": (1) Availability re-check -- verify the room is still available for all selected dates (real-time check against the authoritative inventory). Between the search results and the booking click: another guest may have booked the last room. (2) Price confirmation -- recalculate the total price (nightly rates * nights + taxes + fees). Display to the guest for confirmation. Prices may change between search and booking. (3) Guest details -- collect: name, email, phone, special requests, and payment information. (4) Payment -- two strategies: (a) Pay now: charge the full amount immediately. Lower cancellation rates but some guests prefer flexibility. (b) Pay at property: guarantee with a credit card (authorization hold for first night). The property charges at check-in/check-out. Higher cancellation rates but more bookings. (5) Inventory decrement -- atomically decrement available_count for each night. If any night fails (sold out during the transaction): roll back all nights and inform the guest. Use: UPDATE room_nights SET booked_count = booked_count + 1 WHERE property_id = X AND room_type_id = Y AND date IN (...) AND available_count > 0. (6) Confirmation -- generate a booking reference, send confirmation email to guest and notification to property, update availability in the search index. Multi-channel inventory: the same room is sold on Booking.com, Expedia, and the hotel direct website. A channel manager (SiteMinder, RateGain) syncs availability across all channels in real-time. When a room is booked on Booking.com: the channel manager decrements inventory on all other channels within seconds.

## Dynamic Pricing

Hotels adjust prices daily based on: (1) Demand -- high demand (events, holidays, weekends) = higher prices. Low demand (midweek off-season) = lower prices to attract bookings. (2) Occupancy -- as rooms fill up: remaining rooms are priced higher (scarcity premium). When occupancy is low: prices drop to fill rooms. (3) Competition -- monitor competitor prices (other hotels in the area on the same dates). Price relative to the market. (4) Lead time -- bookings far in advance are cheaper (the hotel wants to fill a base level of occupancy). Last-minute bookings: either very cheap (desperate to fill) or very expensive (scarcity). Revenue management system (RMS): an ML model predicts optimal prices per room type per date. Features: historical demand patterns, local events (concerts, conferences, sports), day of week, season, current occupancy, competitor prices, and time until check-in. The model optimizes: RevPAR (Revenue Per Available Room) = occupancy rate * ADR (Average Daily Rate). A hotel with 100 rooms can earn more with 80% occupancy at $200/night ($16,000) than 100% at $150 ($15,000). The RMS finds the price that maximizes total revenue considering demand elasticity. Booking.com perspective: the OTA does not set prices (the hotel does), but it influences through: visibility (better-priced listings rank higher in search), promotions (Genius discounts for loyal guests), and dynamic commission adjustments.

## Cancellations and Modifications

Cancellation policies: (1) Free cancellation until X days before check-in (most common on Booking.com). After the deadline: a penalty applies (typically first night or a percentage). (2) Non-refundable -- cheaper rate, no cancellation without penalty. Attracts price-sensitive guests. (3) Flexible -- full refund until check-in day. Premium rate. Policy enforcement: when a guest cancels, the system checks: is it within the free cancellation window? If yes: full refund, release inventory. If no: calculate the penalty, charge the guest card, and release the remaining nights inventory. The property keeps the penalty amount. Modification: change dates or room type. Treated as: cancel the old booking + create a new booking. If the new booking is more expensive: charge the difference. If cheaper: refund the difference. Availability must be checked for the new dates. No-shows: the guest does not arrive and does not cancel. The property charges a no-show penalty (typically first night). The room is released for the remaining nights (may be sold to walk-in guests). No-show detection: the property marks the booking as no-show via their extranet after check-in time passes. Impact on future search: high cancellation rate properties may be deprioritized in search (unreliable availability frustrates guests who find their "confirmed" hotel is actually full).