Pg_vectorsearch: An index that is faster than pgvector's IVFFlat and HNSW

pg_vectorsearch A PostgreSQL extension for high-performance approximate nearest neighbor (ANN) search. Status: alpha. Meant for testing, not production. Upgrading between prereleases may not be possible; plan to drop and recreate the extension and its indexes. See the changelog for known issues. Status: alpha. Meant for testing, not production. Upgrading between prereleases may not be possible; plan to drop and recreate the extension and its indexes. See the changelog for known issues. PRISM index (100M+ vectors) — IVF-style, with hierarchical routing RaBitQ quantization with theoretical error bounds for two-stage search SIMD-optimized distance computation (AVX2, AVX512, NEON) Dynamic updates — inserts, updates and deletes, with posting-list splits via prism_rebalance (see Maintenance) pgvector compatible — index existing vector and halfvec columns as-is (see Existing pgvector tables) PRISM Index PRISM (Partitioned Routing Index for Similarity Matching) is a PostgreSQL-native index access method that is conceptually similar to pgvector's IVFFlat, but it is built around state-of-the-art quantization, routing, and partitioning techniques. It is implemented in C for high performance and seamless integration with PostgreSQL. The result: an index that is significantly faster than pgvector's IVFFlat and HNSW, for both querying and index building. In fact, compared to graph-based HNSW, PRISM is faster at equivalent recall on a 100-million-vector dataset, on an instance with a quarter of the memory. To achieve this performance, PRISM uses a hierarchical centroid tree to partition vectors into clusters, and relies on the PostgreSQL buffer cache to keep that tree warm in memory. Vectors are RaBitQ-quantized and stored in posting lists with a page layout optimized for SIMD distance computation. The RaBitQ encoding carries error bounds that let a search rule out vectors that cannot possibly be nearest neighbors, so far fewer of them need reranking against the full-precision vectors stored in the table. Query Flow: 1. Traverse centroid tree to find nearest clusters (buffer cache) 2. Scan posting lists for candidates (buffer cache, async I/O) 3. RaBitQ two-stage filtering: estimate → error bound → rerank 4. Full-precision reranking from heap 5. Return k nearest neighbors For detailed architecture, see docs/architecture.md. Usage Install the extension first; see Building. There are two ways to use a PRISM index. Pick the one that matches where your vectors live: Native types. Starting fresh, or not using pgvector? Store vectors in pg_vectorsearch's own vec32 (float32) or vec16 (float16) columns. No other extension is needed. Existing pgvector tables. Already have vector or halfvec columns? Install pg_vectorsearch next to pgvector and build a PRISM index on those columns as they are. Your table and queries stay unchanged. Both paths build the same index and use the same operators (<->, <#>, <=>) and tuning knobs. Native types CREATE EXTENSION pg_vectorsearch; -- PLAIN keeps the vector inline. EXTERNAL (the default) toasts a value -- over ~2 kB, and rerank then pays an extra fetch the planner does not cost. CREATE TABLE items ( id serial PRIMARY KEY, embedding vec32(3) STORAGE PLAIN ); -- Load first; the index is sized from the row count at CREATE INDEX. -- 500 rows is enough for the planner to use the index. INSERT INTO items (embedding) SELECT ARRAY[i::real, (i % 7)::real, (i % 5)::real]::vec32 FROM generate_series(1, 1000) i; -- For inner product or cosine, see Operator classes below. CREATE INDEX ON items USING prism (embedding vec32_l2_ops); -- 0 = auto (~0.95 recall). Raise for recall, lower for speed. SET prism.nprobe = 40; SELECT * FROM items ORDER BY embedding <-> '[3,1,2]' LIMIT 5; Use vec16 instead of vec32 to halve the space vectors take in the table, with the matching vec16_* operator classes. Existing pgvector tables If you already have a table with pgvector's vector or halfvec columns, you can build a PRISM index on it directly. Nothing about the table or your queries needs to change. vec32 and vec16 are binary-compatible with pgvector's vector and halfvec but have different names, so the two extensions can be installed side by side, even in the same schema. -- The table you already have, included so the example runs on its own. CREATE EXTENSION vector; CREATE TABLE documents ( id bigserial PRIMARY KEY, body text, embedding vector(384) ); CREATE INDEX documents_embedding_hnsw ON documents USING hnsw (embedding vector_l2_ops); -- Install pg_vectorsearch next to pgvector and build the index on the -- existing column. The column is not rewritten or copied. CREATE EXTENSION pg_vectorsearch; CREATE INDEX documents_embedding_prism ON documents USING prism (embedding vec32_l2_ops); -- Your queries keep working as before. PRISM is normally faster, so -- the planner should prefer it over the HNSW index while both exist. -- Verify with an EXPLAIN: EXPLAIN (COSTS OFF) SELECT id, body FROM documents ORDER BY embedding <-> (SELECT embedding FROM documents WHERE id = 42) LIMIT 10; -- Limit -- InitPlan 1 -- -> Index Scan using documents_pkey on documents documents_1 -- Index Cond: (id = 42) -- -> Index Scan using documents_embedding_prism on documents -- Order By: (embedding <-> (InitPlan 1).col1) -- Once you are happy with it, drop the HNSW index. DROP INDEX documents_embedding_hnsw; Use the PRISM operator class for the same distance metric as your HNSW index. Operator classes Pick the operator class for your distance metric and column type. L2 is the default. When replacing an HNSW index, pgvector's vector_ _ops maps to vec32_ _ops and halfvec_ _ops to vec16_ _ops. Tuning Defaults are tuned from large-scale benchmarks; most deployments only ever adjust prism.nprobe. See the tuning guide for every index parameter and GUC, their tradeoffs, and when changing them makes sense. Maintenance An insert appends to whichever posting list its vector routes to, so lists grow as rows arrive and never split on their own. Besides REINDEX, the prism_rebalance procedure splits oversized lists in place. See the maintenance guide for how it works and its current limitations. Building Requirements: PostgreSQL 18+ (with development headers) GNU C11 compiler (GCC 11+ or Clang 14+) Meson build system Optional: CBLAS, for faster RaBitQ encoding. See Development. # Release build meson setup builddir --buildtype=release # Build and install to PostgreSQL meson install -C builddir See the development guide for build options. Documentation Tuning - Index parameters, GUCs, tradeoffs, defaults Maintenance - Rebalancing and splitting posting lists Architecture - High-level design and data structures SIMD - SIMD build options and distance computation Development - Build options, testing, CI scripts References Research RaBitQ - Binary quantization with theoretical error bounds (SIGMOD 2024) SPFresh - LIRE protocol for incremental updates (SOSP 2023) SPANN - Billion-scale ANN with boundary replication (NeurIPS 2021) ScaNN for AlloyDB - Hierarchical clustering (Google) Related Projects pgvector - Vector type and operators for PostgreSQL pgvectorscale - DiskANN-based vector index for PostgreSQL License The PostgreSQL License