# vortex-data/vortex

An extensible, state-of-the-art framework for columnar compression, and the fastest FOSS columnar file format. Formerly at @spiraldb, now an Incubation Stage project at LFAI&Data, part of the Linux Foundation.

Repository: https://github.com/vortex-data/vortex
Canonical: https://ross.abutalabs.com/products/vortex
Homepage: https://vortex.dev
Language: Rust
License: Apache-2.0
License Family: permissive
Topics: array, arrow, compression, python, rust, file, multimodal
Last push: 2026-08-26T22:31:04+00:00

## Health v2 (maintenance only)
Score: 88/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 86, longevity 65
- inputs: {"age_days": 918, "days_push": 7, "days_rel": 12, "gap_med": 4, "n_releases_24m": 96}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3158, forks 211 (observed 2026-08-28T04:07:46.483572+00:00)

## What it is
Vortex is an extensible, high-performance columnar file format and toolkit for compressed data, positioned as a faster alternative to Apache Parquet. It provides zero-copy Apache Arrow compatibility, pluggable encodings, and integrations with query engines like DataFusion, DuckDB, Spark, and Trino.

## Use cases
- compress columnar data faster than parquet
- speed up random access reads on object storage
- convert parquet files to a faster columnar format
- query compressed data with duckdb or datafusion
- build a data system backed by object storage
- read and write columnar files from python or rust
- accelerate analytics scans over wide tables

## When to choose
- you need faster scans or random access than Apache Parquet
- you work in the Rust, Python, Java, or C++ data ecosystem and want zero-copy Arrow interop
- you want pluggable encodings and a stable, extensible file format
- you integrate with DataFusion, DuckDB, Spark, Trino, or Ray

## When to avoid
- you need maximum ecosystem maturity and universal tooling support that Parquet enjoys
- your pipeline requires Iceberg integration today (still coming soon)
- you depend on stable library APIs, which may still change between versions

## Facets
- artifact type: library
- maturity: active
- function: serialization, compression, database, etl
- domain: databases, big-data, analytics, developer-tools
- platform: rust, python, cross-platform, cli
- tags: columnar-format, apache-arrow, parquet-alternative, file-format, data-lake, query-engine-integration, zero-copy, fsst, fastlanes, data-engineering

## Member repositories
- vortex-data/vortex (main) score 88

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:46.483572+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:45:33.612637+00:00, confidence not recorded.
  - readme: https://github.com/vortex-data/vortex (fetched 2026-08-28T04:07:46.483572+00:00, sha 2cb561e0de18)
  - homepage: https://vortex.dev (fetched 2026-08-29T09:40:05.391929+00:00, sha a29f8ae762c4)
  - site_page: https://docs.vortex.dev (fetched 2026-08-29T09:40:05.394651+00:00, sha ce25911494e6)
- Data as of 2026-08-30T08:39:29.467469+00:00.