Airbnb System Design Interview Questions

Yes. They’re challenging and comparable to those at Google or Meta, focusing heavily on distributed systems, user experience, and operational robustness.

Start with System Design fundamentals, especially for marketplace and consumer platforms. Practice common scenarios and review Airbnb’s engineering blog for real-world insights.

Common mistakes include neglecting user experience, skipping performance trade-offs, overlooking availability and trust, and jumping into solutions without clarifying requirements first.

It is very important. Airbnb places a strong emphasis on trust, safety, and transparency. Interviewers will look for designs that include fraud detection, review authenticity, and secure user data handling.

You need a system that guarantees atomic availability and booking confirmation in real time. When a user selects dates, the backend must check for overlapping reservations and lock the listing before processing payment. A distributed lock service or reservation hold mechanism (e.g., Redis-based) can prevent double bookings. The system also needs to send confirmations, sync calendars with third-party tools, and support flexible cancellation workflows—all while maintaining uptime during peak travel seasons.

Search needs to be fast, personalized, and filterable by dozens of parameters—from price to amenities to cancellation policies. Start with a reverse index (Elasticsearch or similar) for keyword and filter queries. For recommendations, use collaborative filtering (similar travelers/bookings), contextual signals (season, group size), and geo-boosting. Caching common queries and using an async ML pipeline for recommendations helps reduce search latency and personalize results.

Each transaction involves a guest, a host, a payout schedule, and possibly a third-party service fee. Your system needs to split payments, delay host payouts until check-in, and comply with local regulations (e.g., VAT). Use an external payment processor (like Stripe or Adyen) and manage payment state internally. Every step—authorization, capture, settlement—must be idempotent. Also support refunds, chargebacks, and international currencies.

This messaging system must be reliable, asynchronous, and privacy-respecting. Use a persistent message queue, store conversation threads in a database (relational for structure, maybe NoSQL for speed), and allow push/email notifications. Messages should support rich media (photos, emojis), and include anti-spam filters. Add in read receipts, blocking/reporting, and temporary masking of real contact info until booking is confirmed.

Each booking can result in mutual reviews from guest and host. Store these in a relational DB with foreign keys to listings and users. Delay publishing until both parties submit or a time limit passes (to reduce retaliation). Aggregate ratings by category (cleanliness, communication, etc.) and display scores using pre-computed rollups. Fraud detection is key—prevent fake reviews and enforce one-review-per-stay logic.

Use cloud object storage (e.g., S3) for image blobs, with CDN integration for fast delivery. Store image metadata (dimensions, format, tags) in a fast key-value store or DB. Generate thumbnails and device-optimized versions using a background job system (e.g., Lambda or worker queues). Use lazy loading and WebP/AVIF formats to reduce frontend payload. Apply moderation or ML to detect NSFW content.

Dynamic pricing must reflect seasonality, demand, local events, and user preferences. Each listing can opt-in to smart pricing, which runs periodic ML predictions to recommend a price range. Factors include lead time, host flexibility, search trends, and neighborhood occupancy. Pricing changes should trigger calendar updates and be visible in real time. Add override options for hosts and explainability to build trust in automation.

Geospatial search must support bounding boxes, proximity filters, and custom map zoom levels. Store listing coordinates in a geohash or R-tree index for efficient querying. When users move the map, fetch listings within the new bounds using the spatial index. Boost results based on popularity or recency. For dense cities, cluster listings to improve usability. Always pair this with caching and pagination to manage load.

Fraud on Airbnb can include fake listings, identity theft, payment scams, or bad actors. Build real-time rules for basic checks (IP mismatch, name mismatch, multiple account patterns) and use ML for behavioral analysis (e.g., host reply times, cancellation rates). Flag suspicious users or transactions for manual review. Integrate this system with booking, payment, and messaging flows so that fraud checks are real-time and friction-minimizing.

In a microservices setup, you need a centralized log aggregation pipeline (e.g., Fluentd + Elasticsearch). Metrics and traces flow into a TSDB (e.g., Prometheus) and distributed tracing tools like OpenTelemetry. Use alerting rules for error spikes, slow response times, and anomalies in business metrics (e.g., sudden drop in bookings). Logs must be tagged by service and region to support fast incident triage.

Notifications span email, SMS, push, and in-app alerts. Use an event-driven system where services emit events (e.g., “booking confirmed”), which trigger workflows in a central notification service. Maintain user preferences (opt-in/out per channel), templates per locale, and retry logic for failed messages. Prioritize urgent alerts (e.g., guest check-in instructions) and send via the fastest available channel. All outbound traffic must be logged for compliance.

Dashboards must support real-time and historical metrics—e.g., bookings, revenue, response times. Use stream processing (e.g., Kafka + Flink) to ingest live events and update materialized views for fast access. Long-term data lands in a warehouse (like Snowflake or BigQuery) for exploratory queries. Use an API or embedded BI layer (e.g., Looker) for front-end display. Permissioning is key—hosts should only access their own data, while internal teams need broader access with audit logs.

Users can save listings for future trips by creating wishlists. Store wishlists as a mapping between user ID and listing IDs, with support for titles, notes, and sharing. Use a document store (e.g., MongoDB) or relational DB with indexing on user ID. Optimize for reads (e.g., to load wishlists on home screen), and allow duplicate prevention. Sync across devices, and track wishlist popularity for ranking.

This wallet supports guest payments, host payouts, refunds, and credits. Store balances per user in a secure ledger service with ACID properties. Log every transaction with audit trails and timestamps. Wallets must support holding funds (e.g., pending payout), currency conversion, and incentive credit (e.g., gift cards, referral bonuses). Integrate with payment and fraud systems. All access must be secure, permissioned, and reversible in case of disputes.