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Introducing Loon: A New Storage Engine for Vector Data That Never Stops Changing
Loon is a new storage engine for vector data that uses hybrid file formats to handle frequent updates without constant full-dataset rewrites.
What: Developed for Milvus 3.0 beta and Zilliz Vector Lakebase, Loon uses row-ID alignment and versioned manifests. This allows independent updates to scalar data, vectors, and object references, reducing the overhead typically associated with immutable vector storage.
Why it matters: The industry is moving toward 'Vector Lakebases' where high-churn, mixed-workload datasets require the same transactional flexibility previously reserved for traditional tabular data.
Decoder
- Vector Lakebase: A storage architecture that applies lakehouse principles (scalability, separation of storage and compute) to high-dimensional embedding data.
Original article
Vector datasets evolve through backfills, embedding versions, and mixed workloads, not just vector columns. Loon, behind Milvus 3.0 beta and Zilliz Vector Lakebase, uses hybrid file formats, row-ID alignment, and versioned manifests so scalars, vectors, and object references can update independently with less rewriting.
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