Design Airbnb
Variants:
Airbnb System Design Requirements
Functional Requirements
Search for Listings
Users should be able to search for listings by location, dates, and number of guests.
Book Listings
Users should be able to book a listing for a specific date range.
Manage Listings
Hosts should be able to create, update, and manage their property listings.
Reviews and Ratings
Guests and hosts should be able to leave reviews for each other after a completed stay.
Non-Functional Requirements
High Availability
The platform must be highly available to both guests and hosts.
Consistency
The booking system must be strongly consistent to prevent double-bookings.
Scalability
The system must be able to handle a large inventory of listings and a high volume of searches and bookings.
CAP Theorem Trade-offs
Trade-off Explanation:
For a booking platform like Airbnb, we prioritize Consistency and Partition Tolerance (CP). It is absolutely critical that a property is not double-booked, so we cannot compromise on consistency. If there is a network partition, we would rather have the booking service be temporarily unavailable than to allow a double-booking.
Scale Estimates
API Design
The API for a platform like Airbnb needs to support a variety of operations, including searching for listings, viewing listing details, managing bookings, and submitting reviews. Your interviewer will expect a well-defined API that separates these concerns.
The core endpoints will revolve around searching for listings, creating a booking, and managing user profiles and reviews. We'll need robust filtering and pagination for the search endpoint and a secure process for handling bookings.
Search for listings based on location, dates, and number of guests.
Create a new reservation for a listing.
Submit a review for a completed stay.
Database Schema
The database schema for Airbnb must support a complex set of entities: users (both hosts and guests), listings, bookings, reviews, and availability. Your interviewer will be particularly interested in how you handle the availability data to prevent double-bookings.
We'll have tables for users, listings, bookings, and reviews. A critical table will be listing_availability, which will store the available dates for each listing. This allows for efficient querying of available properties for a given date range.
users
listings
reservations
listing_availability
High-Level Architecture
The architecture for a platform like Airbnb will be a distributed system composed of several microservices. This allows for scalability and independent development of different features. Your interviewer will expect you to identify the key services and their responsibilities.
Core Services
- Search Service: This service is responsible for handling all search queries. It will use a specialized search index (like Elasticsearch) to provide fast, relevant results with complex filtering and ranking.
- Booking Service: This service manages the entire booking workflow, including checking availability, handling payments, and creating new bookings. This is a critical service that requires strong consistency guarantees.
- Listing Service: This service is responsible for managing property listings. Hosts will interact with this service to create, update, and manage their properties.
- User Service: This service handles user accounts, profiles, and reviews.
Deep Dive: Reservations and Inventory Management
A core challenge in any booking system is managing inventory to prevent double-booking. It's important to distinguish between a reservation (a temporary hold) and a booking (a confirmed stay). In an interview, you'll need to explain how you would handle concurrent requests to reserve the same dates for the same property.
Pessimistic Locking with Database Transactions
Why: This approach provides the strongest consistency guarantees by locking the availability records in the database during a transaction.
How it works: When a user initiates a booking, we start a database transaction. We then acquire a lock on the relevant rows in the listing_availability table for the requested dates. If the lock is successful, we proceed with the payment and create the booking. If another user tries to book the same dates, their transaction will be blocked until the first one completes.
Trade-offs:
- Pros: Guarantees that a property can't be double-booked. Relatively simple to reason about.
- Cons: Can lead to poor performance and low concurrency, as it holds database locks for the duration of the booking process. A long-running transaction can block other users for an extended period.
Optimistic Locking with a Distributed Lock Manager
Why: This is a more scalable approach that avoids long-held database locks. It's better suited for a high-concurrency environment.
How it works: When a user wants to book a property, we first acquire a short-lived distributed lock (e.g., using Redis or Zookeeper) for that property's availability. Once we have the lock, we verify that the dates are still available in the database. If they are, we proceed with the booking and update the availability. If not, we release the lock and inform the user that the dates are no longer available.
Trade-offs:
- Pros: More scalable and performant than pessimistic locking.
- Cons: More complex to implement. Requires a separate distributed lock manager, which adds another component to the system.
Deep Dive: Search and Discovery
A great search experience is crucial for Airbnb's success. Your interviewer will want to know how you would design a search service that can handle millions of listings and provide fast, relevant results.
Geospatial Indexing
To efficiently search for listings in a specific geographic area, we need to use a geospatial index. Standard database indexes are not designed for this type of query. A common approach is to use a geohashing algorithm, which converts latitude and longitude coordinates into a short string. We can then use a standard B-tree index on the geohash strings to perform efficient location-based searches. Many databases and search engines, like Elasticsearch and PostgreSQL (with PostGIS), have built-in support for geospatial indexing.
Elasticsearch for Search
Why: Elasticsearch is a powerful, open-source search engine that is purpose-built for complex search queries. It provides advanced features like full-text search, filtering, ranking, and geospatial search out of the box.
How it works: We would create a separate Elasticsearch cluster and index all of our listing data in it. The Listing Service would be responsible for keeping the Elasticsearch index in sync with the primary database. When a user performs a search, the request would go directly to the Search Service, which would then query the Elasticsearch cluster.
Trade-offs:
- Pros: Highly scalable and performant. Provides a rich set of search features.
- Cons: Adds another system to manage and maintain. Keeping the search index in sync with the primary database can be challenging.
Apache Solr for Search
Why: Apache Solr is another powerful, open-source search platform that is a great alternative to Elasticsearch. It is known for its stability and has a strong ecosystem of plugins and integrations.
How it works: Similar to Elasticsearch, we would set up a separate Solr cluster and index our listing data. Solr provides a rich set of features for full-text search, faceting, and geospatial search. The Listing Service would be responsible for keeping the Solr index up-to-date.
Trade-offs:
- Pros: Highly scalable and performant. Strong community support and a mature ecosystem.
- Cons: Can be more complex to set up and manage than Elasticsearch. The learning curve can be steeper for developers who are not familiar with it.
Search Ranking
In addition to simply returning a list of results, a good search engine will also rank the results based on their relevance to the user. Your interviewer may ask how you would rank the search results. It's important to note that you are not expected to have a deep understanding of machine learning for a standard system design interview. A high-level discussion of the signals you would use to rank the results is usually sufficient.
Some common ranking signals include:
- Relevance: How well does the listing match the user's search query?
- Popularity: How many times has the listing been booked or viewed?
- Rating: What is the average rating of the listing?
- Price: How does the price of the listing compare to other similar listings?
- Personalization: Does the user have a history of booking similar listings?
Complete Design
Now that we've covered all the major components individually, let's look at how everything fits together in our complete Airbnb system design. This diagram shows the end-to-end flow from a user searching for a listing to booking a stay.
The complete architecture demonstrates how the Search Service, Booking Service, Listing Service, and User Service work together to provide a scalable and reliable home-sharing platform. The use of a dedicated search index and a distributed lock manager ensures that the system can handle a large inventory of listings and a high volume of bookings.