Official account of the Airbnb Technology Team, sharing Airbnb's tech innovations and real-world implementations, building a world where home is everywhere through technology.
Airbnb completely rewrote its internal key‑value store Mussel, moving from a legacy V1 system to a cloud‑native, NewSQL‑backed V2 that reduces operational complexity, improves scalability, adds flexible consistency, and supports massive batch imports, all while achieving zero‑downtime migration of over 1 PB of data.
Airbnb’s Sitar platform demonstrates how a modern dynamic configuration system can provide safe, reliable, and flexible runtime changes through a Git‑centric workflow, multi‑tenant control and data planes, staged rollouts, rapid rollback, and local caching, balancing developer agility with operational stability.
This article explains how Airbnb’s internal Impulse load‑testing‑as‑a‑service framework combines a load generator, dependency simulator, traffic collector, and test‑API generator to provide decentralized, containerized performance testing that integrates seamlessly with CI/CD pipelines and mimics production traffic.
Airbnb's media team engineered a scalable postcard generation pipeline that matches destinations, handles localized text layout, and delivers high‑performance images across iOS, Android, Web, and external messaging platforms using flexible templates, async processing, and pre‑generation strategies.
Chronon is an open‑source ML feature platform that lets engineers declaratively define, compute, and serve both batch and real‑time features with built‑in observability, data‑quality checks, and a low‑latency retrieval API, ensuring online‑offline consistency while simplifying pipeline management and enabling future automation.
Chronon is an open‑source framework that centralizes feature definitions to guarantee training‑inference consistency, eliminates complex ETL pipelines, and supports real‑time and batch processing across diverse data sources, cutting feature‑development cycles from months to under a week, as demonstrated by Airbnb’s 40,000‑feature deployment.
Airbnb’s Automation Platform v2 replaces the rigid, workflow‑driven architecture of v1 with an LLM‑centric design that orchestrates context gathering, chain‑of‑thought reasoning, tool execution, and guardrails, enabling more natural, scalable, and safe conversational AI while preserving the reliability of traditional workflows.
This article explains Airbnb’s Android Trio framework, focusing on Props—a Kotlin data‑class mechanism that lets parent pages safely pass mutable data and callbacks to child Compose pages, illustrated with inbox and Todo screen‑flow examples and complete code snippets.
The article shows how Trio stores navigation state in a ViewModel‑backed stack, uses type‑safe routers for screen and activity transitions, supports async save‑and‑exit, result‑returning navigation, and interoperates with legacy fragments, enabling modular, testable navigation in Jetpack Compose.
Airbnb’s Android team introduced Trio, a Compose‑only framework built atop the Mavericks state‑management library that replaces Fragment‑based navigation with type‑safe, modular page routing, ViewModel‑driven state, compile‑time validation, and dedicated developer tools, now powering the majority of the app’s screens.
