How we capture real production database traffic at Airbnb and replay it offline to load-test, plan capacity, and de-risk upgrades. By: Zuofei Wang , Erluo Li Introduction At Airbnb, MySQL-compatible databases are a critical backbone of our online database infrastructure: a fleet of hundreds of clusters supporting thousands of use cases at millions of queries per second (QPS). Operating databases a

How Airbnb uses proximity signals to personalize without relying on individual user history. By : Wei Jiang , Bin Xu , Bharathi Thangamani , Weiwei Guo , Sundar Srinivasavaradhan , Tracy Yu , Huiji Gao , Swapnil Ghike , Michael Kinoti Great personalization starts with knowing your user. But what happens when the user is a stranger? A significant share of Airbnb users arrive without a login, withou

How two new Chronon capabilities, Push Mode and NRT Model Transform, allows us to provide more relevant search results instantly as a guest explores, rather than waiting for the next batch run. By : Pengyu Hou , Yuli Han , Daochen Zha , Haozhen Ding , Xin Liu , Sophie Wang , Pallavi Adusumilli , Sherry Li , Henry Saputra , Chun How Tan , Huiji Gao , Yan Zhang , Stephanie Moyerman , Yi Li , and San

How Airbnb’s agent harness transforms unstructured data exploration by encoding scientific methodology into scalable, reproducible, and audit-ready infrastructure. By : Wren Dougherty Ask a coding agent to analyze 100,000 customer support conversations and within minutes you’ll have a polished taxonomy, precise prevalence numbers, and an executive-ready summary. What you can’t see is the investiga

Project Lighthouse — Part 3: Introducing project-lighthouse-anonymize The data in Project Lighthouse is powered by privacy-preserving anonymization code. We’ve put this code into open source, and published two new technical papers detailing the scalable algorithms and data quality frameworks behind it. By: Adam Bloomston Introduction In 2020, we launched Project Lighthouse, which we developed in p

When we retrain, when we rebuild, and when we leave a model alone. By: Harrison Katz A forecast that carries weight The Forecasting Data Science team at Airbnb produces many of the forecasts the rest of the company plans around: demand, bookings, cancellations, and a range of finer cuts by market and segment, refreshed continuously across thousands of markets. The targets differ, and the models di

Rebuilding login and signup surfaced product insights, not just technical challenges. Here’s how we designed Flexible Authentication at the intersection of product intuition and technical architecture. By : Jose Santos , Mike Barry For Airbnb, logins at irregular intervals are normal. A guest books a trip in January and may not open the app again until summer. A host checks back only when a reserv

How Airbnb teams build trustworthy Generative AI products by treating evaluation as a first-class engineering discipline; not an afterthought. Nestled into the lush hillside, this stunning modern retreat features striking natural wood architecture, terraced balconies, and a serene landscape. By: Rohit Girme , Dan Miller , Mia Zhao , Lifan Yang , Clint Kelly Introduction Generative AI breaks a lot

How we built a Transformer-based sequence model that encodes years of guest behavior to surface the right listings at the right time. By: Daochen Zha , Chun How Tan , Xin Liu , Bin Xu , Han Zhao , Xiaowei Liu , Jun Shi , Tracy Yu , Hui Gao , Huiji Gao , Liwei He , Michael Kinoti , Stephanie Moyerman , and Sanjeev Katariya Introduction Planning a trip on Airbnb rarely happens in a single session. A

Training an LLM is the easy part. The hard part is designing experiments and evaluations that you can trust enough to know whether the new model is actually an improvement. By : Baharak Saberidokht Introduction Shipping a production LLM system means iterating fast on improvements to something that is, by construction, non-deterministic. Models drift, judges disagree with themselves, references reg