vortex-rdf

Python bindings for Vortex-RDF, a modern, high-performance columnar RDF serialization format.

Stores can be opened lazily from .vortex files and queried in place, without loading the dataset into memory.

The vortex-rdflib package, which implements an rdflib Store (with SPARQL capabilities) on top of these bindings.

Install

pip install vortex-rdf

Development build (managed with uv; maturin runs under the hood as the build backend):

cd python
uv sync                      # creates .venv, builds + installs the extension
uv run pytest tests          # run the test suite
uv run maturin develop --uv  # fast rebuild while iterating on Rust code

Rust source changes are picked up by uv sync automatically (see [tool.uv] cache-keys in pyproject.toml). Without uv, the classic flow works too: python -m venv .venv && pip install maturin && maturin develop.

Building from source (the sdist or a development build) additionally requires libclang: a transitive build dependency of the Vortex file engine (custom-labels, via vortex-io) generates C bindings with bindgen at compile time. It is preinstalled on most dev setups (Xcode, LLVM on Windows); on Linux install e.g. clang-devel (dnf) or libclang-dev (apt). Installing a published wheel needs none of this.

Usage

from vortex_rdf import VortexRdfStore, serialize_rdf

# RDF file -> .vortex store file
serialize_rdf("data.nt", "data.vortex", layout="dictionary")

store = VortexRdfStore("data.vortex")   # lazy open; file layout is auto-detected
len(store)                              # number of quads
store.match_triples(p="<http://xmlns.com/foaf/0.1/name>")
store.match_compact(p="<http://xmlns.com/foaf/0.1/name>")  # (term_table, rows)

Terms cross the boundary as N-Triples strings (<iri>, _:b0, "lit"@en, "3"^^<http://www.w3.org/2001/XMLSchema#integer>). match_compact returns a de-duplicated term table plus index triples so callers parse each distinct term once.

Code columns (Dictionary layout)

For Dictionary-layout stores, match_codes returns the matched rows as four zero-copy u32 term-code columns — memoryview(col).cast("I") views the Rust memory directly — decodable through a term_dict() handle:

cols = store.match_codes(p="<http://xmlns.com/foaf/0.1/name>")  # (s, p, o, g)
dictionary = store.term_dict()
subjects = memoryview(cols[0]).cast("I")
dictionary.decode(subjects[0])           # N-Triples string for that code

Consumers can join, count, and de-duplicate entirely in code space and decode each distinct term once — this is what powers vortex-rdflib's SPARQL BGP pushdown.

Layouts

serialize_rdf(..., layout=...) accepts "default", "typed-object" and "dictionary" (with dictionary_placement="padded"|"sidecar"). Opening auto-detects the layout — VortexRdfStore takes no layout argument.

For Dictionary-layout files, the term dictionary is held in memory when it fits the residency budget; pass VortexRdfStore(path, max_resident_terms=...) to raise the budget (recommended for benchmarking large stores).

File-backed vs in-memory

The default open is lazy and file-backed. VortexRdfStore(path, in_memory=True) loads the store into memory once: each subsequent match skips the per-call file-scan pipeline (~1 ms → ~0.15 ms per call).

Tests

pip install -e .[test]   # or: maturin develop && pip install pytest
pytest tests

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vortex-rdf 0.5.0

vortex-rdf

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