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Booking.com selects Weaviate as its next vector database

Booking.com's ML & DS blog says its tests used 100 million embeddings, filtered searches and concurrent writes.

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TLDR

Booking.com's ML & DS blog says its OpenSearch-backed Embedding Service became harder and costlier to operate as workloads grew. In tests using 100 million embeddings, filtered searches and concurrent reads and writes, the team found dedicated vector databases handled its workloads more efficiently. It selected Weaviate for its consistent performance across the tested scenarios and reported roughly 40% lower usage cost than its OpenSearch baseline at comparable recall and service-level targets.

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Connor Shorten@CShorten30Vector databases are more than just an index! 👀 Booking(dot)com ran their own test with 100M embeddings, queries with filters and concurrent writes. 🔬 They concluded that purpose-built vector databases behave differently from “general-purpose search engines with vector capabilities added on.” 🚀 In Weaviate, the index and storage engine are built together. 🧬 HNSW is an in-memory graph persisted with an append-only log, while objects and filters live in a custom LSM store. Filters come out of the LSM store as bitmaps that steer the HNSW search (ACORN), and writes are cheap appends. HFresh goes further: its vector clusters live directly in the LSM store, so the index rebalances itself in the background instead of needing full rebuilds. 💽 Other systems inherit storage built for something else. Page-based databases turn every update into new row versions, WAL and VACUUM work. Segment-based search engines build a separate graph per segment, so queries search every segment and merges rebuild graphs. 🏗️ The index matters. So does its integration with everything underneath it. http://booking.ai/how-we-selected-the-next-vector-database-at-booking-com-1e738a5e3bb01h
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    Connor Shorten@CShorten30Vector databases are more than just an index! 👀 Booking(dot)com ran their own test with 100M embeddings, queries with filters and concurrent writes. 🔬 They concluded that purpose-built vector databases behave differently from “general-purpose search engines with vector capabilities added on.” 🚀 In Weaviate, the index and storage engine are built together. 🧬 HNSW is an in-memory graph persisted with an append-only log, while objects and filters live in a custom LSM store. Filters come out of the LSM store as bitmaps that steer the HNSW search (ACORN), and writes are cheap appends. HFresh goes further: its vector clusters live directly in the LSM store, so the index rebalances itself in the background instead of needing full rebuilds. 💽 Other systems inherit storage built for something else. Page-based databases turn every update into new row versions, WAL and VACUUM work. Segment-based search engines build a separate graph per segment, so queries search every segment and merges rebuild graphs. 🏗️ The index matters. So does its integration with everything underneath it. http://booking.ai/how-we-selected-the-next-vector-database-at-booking-com-1e738a5e3bb01h
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