How Airbnb Rebuilt Search for 150 Million Guests

Episode notes

In this episode, Lucas and Luna dive into Airbnb's multi-year effort to rebuild its search infrastructure to handle 150 million nightly searches. They explore the shift from a monolithic PostgreSQL-backed system to a custom search service built on Elasticsearch, the trade-offs between relevance and latency, and the team's decision to implement a two-phase ranking system with lightweight machine learning at query time. Specific numbers include Airbnb's pre-migration latency of 800 milliseconds for a single search and the post-migration reduction to under 200 milliseconds at peak. The discussion also covers how the engineering team organized around the project, the cultural challenges of migrating a core revenue system without downtime, and the unexpected lesson that algorithmic ranking reduced guest booking friction by 12 percent. Perfect for CTOs, engineering leaders, and anyone architecting large-scale consumer platforms.

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