onesparse

one_sparse : Sparse linear algebra and graph extension for PostgreSQL 18

Overview

Packages

Source

pig build pkg one_sparse;		# build rpm/deb

Install

Make sure PGDG and PIGSTY repo available:

pig repo add pgsql -u   # add both repo and update cache

Install this extension with pig:

pig install one_sparse;		# install via package name, for the active PG version
pig install onesparse;		# install by extension name, for the current active PG version

pig install onesparse -v 18;   # install for PG 18

Create this extension with:

CREATE EXTENSION onesparse;

Usage

Sources: homepage, intro docs, Matrix, Vector, and Algorithms.

OneSparse is a PostgreSQL extension that binds SuiteSparse:GraphBLAS into Postgres and exposes sparse linear algebra and graph algorithms as new types, functions, and operators. The docs treat matrix as the core type, with vector and scalar built on top of the same model.

Core Setup

CREATE EXTENSION onesparse;
SET search_path TO public,onesparse;

SELECT 'int32'::matrix;
SELECT 'int32'::vector;
SELECT 'int32:42'::scalar;

The intro docs note that OneSparse keeps its API in the onesparse schema, and the matrix/vector pages show the same search_path pattern for interactive use.

Matrix and Vector

The matrix page shows common operations such as constructing, printing, drawing, resizing, casting, and aggregating matrices. The vector page shows the matching vector API, including nvals, size, set_element, get_element, eadd, emult, reduce_scalar, choose, and apply.

SELECT print('int32(4:4)'::matrix);
SELECT draw('int32(4:4)[1:2:1 2:3:2 3:1:3]'::matrix);
SELECT eadd('int32[0:1 1:2 2:3]'::vector, 'int32[0:1 1:2 2:3]'::vector, 'plus_int32');
SELECT reduce_scalar('int32[0:1 1:2 2:3]'::vector, 'plus_monoid_int32');

Graph Algorithms

The getting-started docs use graph examples built from Matrix Market files and random graphs. They highlight these algorithms:

• bfs(graph, 1) for level and parent BFS
• sssp(cast_to(graph, 'int32'), 1::bigint, 1) for single-source shortest path
• pagerank(graph) for ranking vertices by link structure
• triangle_centrality(graph) for triangle-based centrality
• betweenness(graph, ARRAY[...]) and square_clustering(graph) for additional graph analysis

Representative example from the docs:

SELECT draw(triu(graph), (SELECT level FROM bfs(graph, 1)), false, false, true, 0.5)
FROM karate;

The same guide also shows graph loading with mmread(...) and graph visualization with draw(...).

Scope

The documentation set is broad. This stub captures the core interface and the main examples that repeat across the intro, matrix, vector, and algorithms pages, without reproducing the full GraphBLAS catalog.