Turbopuffer plans a new primary index for its v3 storage engine

In a September 30, 2026 post, Turbopuffer introduced a storage redesign for an engine informally called turbopuffer v3. The company says the work will change how documents and indexes are organized, written, compacted and queried, with the aim of supporting more query plans at greater scale. A central change is to replace the current primary index and make approximate nearest neighbor search, or ANN, a secondary index. The existing ANN index has been the main structure around which other indexes and query plans are built.

The post traces that arrangement to the database’s early focus on vector search. In its first version, documents contained an identifier and a vector, and a hierarchical clustering index grouped vectors beneath layers of centroids. Turbopuffer says that structure suited object storage, which served as the source of truth, alongside NVMe SSD and memory caches. The company began with SPANN and later adopted SPFresh to support incremental indexing. Each cluster received a ClusterId, while its vectors used dense LocalIds; together, those identifiers formed an ANN address.

Attribute filtering and full-text search marked the informal transition from v1 to v2, according to the post. For filtering, the system added an inverted index that maps an attribute value to the addresses of documents containing it. It also stores document attributes alongside identifiers and vectors for queries that request attributes in their results. Full-text search uses postings to identify documents containing query terms, with term-count and document-length information included for BM25 scoring. The post also lists aggregations, regex search, fuzzy matching, sparse vector search and attribute ordering as capabilities built around the same vector-primary layout.

Turbopuffer says the current architecture has performed well for vector search on object storage, citing single indexes with 100B+ vectors, 200 ms p99 reads and 1k+ QPS. It identifies storage amplification, write amplification and limited vectorization as constraints on non-vector query patterns. For storage amplification, the post notes that the system currently stores each document’s full contents under its ANN address. For AI builders, the announcement describes a shift in the database’s indexing foundation as its query plans extend beyond vector search; the company’s stated goal is to serve more of those plans at greater scale.