PIGSTY

vchord

Vector database plugin for Postgres, written in Rust

Overview

Attributes

YesNoYesYesYesYesNo-

Packages

EL
PIGSTY
vchord_$v0.3.0pgvector_$v
17
16
15
14
13
Debian
PIGSTY
postgresql-$v-vchord0.3.0postgresql-$v-pgvector
17
16
15
14
13

Dependencies

This extension depends on: vector

Comments

pgrx=0.13.1


Availability

PG17PG16PG15PG14PG13
el8.x86_64
0.3.0
0.3.0
0.3.0
0.3.0
el8.aarch64
0.3.0
0.3.0
0.3.0
0.3.0
el9.x86_64
0.3.0
0.3.0
0.3.0
0.3.0
el9.aarch64
0.3.0
0.3.0
0.3.0
0.3.0
d12.x86_64
0.3.0
0.3.0
0.3.0
0.3.0
d12.aarch64
0.3.0
0.3.0
0.3.0
0.3.0
u22.x86_64
0.3.0
0.3.0
0.3.0
0.3.0
u22.aarch64
0.3.0
0.3.0
0.3.0
0.3.0
u24.x86_64
0.3.0
0.3.0
0.3.0
0.3.0
u24.aarch64
0.3.0
0.3.0
0.3.0
0.3.0
CONTRIB
PGDG
PIGSTY

Download

To add the required PGDG / PIGSTY upstream repository, use:

pig repo add pgdg -u    # add PGDG repo and update cache (leave existing repos)
pig repo add pigsty -u  # add PIGSTY repo and update cache (leave existing repos)
pig repo add pgsql -u   # add PGDG + Pigsty repo and update cache (leave existing repos)
pig repo set all -u     # set repo to all = NODE + PGSQL + INFRA  (remove existing repos)
./node.yml -t node_repo -e node_repo_modules=node,pgsql # -l <cluster>

Or download the latest packages directly:


Install

Install this extension with:

pig ext install vchord; # install by extension name, for the current active PG version

pig ext install vchord -v 17;   # install for PG 17
pig ext install vchord -v 16;   # install for PG 16
pig ext install vchord -v 15;   # install for PG 15
pig ext install vchord -v 14;   # install for PG 14
dnf install vchord_17;
dnf install vchord_16;
dnf install vchord_15;
dnf install vchord_14;
apt install postgresql-17-vchord;
apt install postgresql-16-vchord;
apt install postgresql-15-vchord;
apt install postgresql-14-vchord;
./pgsql.yml -t pg_ext -e '{"pg_extensions": ["vchord"]}' # -l <cls>

Preload this extension with:

shared_preload_libraries = 'vchord'; # add to pg cluster config

Create this extension with:

CREATE EXTENSION vchord CASCADE;

Usage

Add this extension to shared_preload_libraries in postgresql.conf

CREATE EXTENSION vchord CASCADE;

Create Index on embedding:

CREATE INDEX ON gist_train USING vchordrq (embedding vector_l2_ops) WITH (options = $$
residual_quantization = true
[build.internal]
lists = [4096]
spherical_centroids = false
$$);

Docs

Query

The query statement is exactly the same as pgvector. VectorChord supports any filter operation and WHERE/JOIN clauses like pgvecto.rs with VBASE.

SELECT * FROM items ORDER BY embedding <-> '[3,1,2]' LIMIT 5;

Supported distance functions are:

  • <-> - L2 distance
  • <#> - (negative) inner product
  • <=> - cosine distance

Due to the limitation of postgresql query planner, we cannot support the range query like SELECT embedding <-> '[3,1,2]' as distance WHERE distance < 0.1 ORDER BY distance directly.

To query vectors within a certain distance range, you can use the following syntax.

-- Query vectors within a certain distance range
-- sphere(center, radius) means the vectors within the sphere with the center and radius, aka range query
-- <<->> is L2 distance, <<#>> is inner product, <<=>> is cosine distance
SELECT vec FROM t WHERE vec <<->> sphere('[0.24, 0.24, 0.24]'::vector, 0.012) 

Query Performance Tuning

You can fine-tune the search performance by adjusting the probes and epsilon parameters:

-- Set probes to control the number of lists scanned. 
-- Recommended range: 3%–10% of the total `lists` value.
SET vchordrq.probes = 100;

-- Set epsilon to control the reranking precision.
-- Larger value means more rerank for higher recall rate.
-- Don't change it unless you only have limited memory.
-- Recommended range: 1.0–1.9. Default value is 1.9.
SET vchordrq.epsilon = 1.9;

-- vchordrq relies on a projection matrix to optimize performance.
-- Add your vector dimensions to the `prewarm_dim` list to reduce latency.
-- If this is not configured, the first query will have higher latency as the matrix is generated on demand.
-- Default value: '64,128,256,384,512,768,1024,1536'
-- Note: This setting requires a database restart to take effect.
ALTER SYSTEM SET vchordrq.prewarm_dim = '64,128,256,384,512,768,1024,1536';

And for postgres's setting

-- If using SSDs, set `effective_io_concurrency` to 200 for faster disk I/O.
SET effective_io_concurrency = 200;

-- Disable JIT (Just-In-Time Compilation) as it offers minimal benefit (1–2%) 
-- and adds overhead for single-query workloads.
SET jit = off;

-- Allocate at least 25% of total memory to `shared_buffers`. 
-- For disk-heavy workloads, you can increase this to up to 90% of total memory. You may also want to disable swap with network storage to avoid io hang.
-- Note: A restart is required for this setting to take effect.
ALTER SYSTEM SET shared_buffers = '8GB';

Indexing prewarm

To prewarm the index, you can use the following SQL. It will significantly improve performance when using limited memory.

-- vchordrq_prewarm(index_name::regclass) to prewarm the index into the shared buffer
SELECT vchordrq_prewarm('gist_train_embedding_idx'::regclass)"

Index Build Time

Index building can parallelized, and with external centroid precomputation, the total time is primarily limited by disk speed. Optimize parallelism using the following settings:

-- Set this to the number of CPU cores available for parallel operations.
SET max_parallel_maintenance_workers = 8;
SET max_parallel_workers = 8;

-- Adjust the total number of worker processes. 
-- Note: A restart is required for this setting to take effect.
ALTER SYSTEM SET max_worker_processes = 8;

Indexing Progress

You can check the indexing progress by querying the pg_stat_progress_create_index view.

SELECT phase, round(100.0 * blocks_done / nullif(blocks_total, 0), 1) AS "%" FROM pg_stat_progress_create_index;

External Index Precomputation

Unlike pure SQL, an external index precomputation will first do clustering outside and insert centroids to a PostgreSQL table. Although it might be more complicated, external build is definitely much faster on larger dataset (>5M).

To get started, you need to do a clustering of vectors using faiss, scikit-learn or any other clustering library.

The centroids should be preset in a table of any name with 3 columns:

  • id(integer): id of each centroid, should be unique
  • parent(integer, nullable): parent id of each centroid, should be NULL for normal clustering
  • vector(vector): representation of each centroid, pgvector vector type

And example could be like this:

-- Create table of centroids
CREATE TABLE public.centroids (id integer NOT NULL UNIQUE, parent integer, vector vector(768));
-- Insert centroids into it
INSERT INTO public.centroids (id, parent, vector) VALUES (1, NULL, '{0.1, 0.2, 0.3, ..., 0.768}');
INSERT INTO public.centroids (id, parent, vector) VALUES (2, NULL, '{0.4, 0.5, 0.6, ..., 0.768}');
INSERT INTO public.centroids (id, parent, vector) VALUES (3, NULL, '{0.7, 0.8, 0.9, ..., 0.768}');
-- ...

-- Create index using the centroid table
CREATE INDEX ON gist_train USING vchordrq (embedding vector_l2_ops) WITH (options = $$
[build.external]
table = 'public.centroids'
$$);

To simplify the workflow, we provide end-to-end scripts for external index pre-computation, see scripts.


Limitations

  • Data Type Support: Currently, only the f32 data type is supported for vectors.
  • Architecture Compatibility: The fast-scan kernel is optimized for x86_64 architectures. While it runs on aarch64, performance may be lower.
  • KMeans Clustering: The built-in KMeans clustering is not yet fully optimized and may require substantial memory. We strongly recommend using external centroid precomputation for efficient index construction.