https://github.com/spiraldb/vortex
An extensible, state of the art columnar file format
https://github.com/spiraldb/vortex
array arrow compression python rust
Last synced: 3 months ago
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An extensible, state of the art columnar file format
- Host: GitHub
- URL: https://github.com/spiraldb/vortex
- Owner: spiraldb
- License: apache-2.0
- Created: 2024-02-27T10:40:00.000Z (over 1 year ago)
- Default Branch: develop
- Last Pushed: 2025-04-28T11:39:18.000Z (3 months ago)
- Last Synced: 2025-04-28T12:13:36.868Z (3 months ago)
- Topics: array, arrow, compression, python, rust
- Language: Rust
- Homepage: https://vortex.dev
- Size: 13.6 MB
- Stars: 1,214
- Watchers: 13
- Forks: 39
- Open Issues: 133
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
🌪️ Vortex
=========
[](https://github.com/spiraldb/vortex/actions)
[](https://crates.io/crates/vortex-array)
[](https://docs.rs/vortex-array)
[](https://pypi.org/project/vortex-array/)
📚 [Documentation](https://docs.vortex.dev/) | 📊 [Performance Benchmarks](https://bench.vortex.dev)
## Overview
Vortex is a next-generation columnar file format and toolkit designed for high-performance data analytics. It provides:
- **⚡️ Blazing Fast Performance**
- 100-200x faster random access reads than Apache Parquet
- 2-10x faster scans with similar compression ratios and write throughput
- Efficient support for wide tables with zero-copy/zero-parse metadata
- **🔧 Extensible Architecture**
- Modeled after Apache DataFusion's extensible approach
- Pluggable encoding system
- Zero-copy compatibility with Apache Arrow
> 🚧 **Development Status**: This project is under active development. APIs and file formats may change, and some
> features are still being implemented.
## Key Features
### Core Capabilities
- ✨ **Logical Types** - Clean separation between logical schema and physical layout
- 🔄 **Zero-Copy Arrow Integration** - Seamless conversion to/from Apache Arrow arrays
- 🧩 **Extensible Encodings** - Pluggable physical layouts with built-in optimizations
- 📦 **Cascading Compression** - Support for nested encoding schemes
- 🚀 **High-Performance Computing** - Optimized compute kernels for encoded data
- 📊 **Rich Statistics** - Lazy-loaded summary statistics for optimization
### Technical Architecture
#### Logical vs Physical Design
Vortex strictly separates logical and physical concerns:
- **Logical Layer**: Defines data types and schema
- **Physical Layer**: Handles encoding and storage implementation
- **Built-in Encodings**: Compatible with Apache Arrow's memory format
- **Extension Encodings**: Optimized compression schemes (RLE, dictionary, etc.)
## Quick Start
### Installation
#### Rust Crate
All features are exported through the main `vortex` crate.
```bash
cargo add vortex
```
#### Python Package
```bash
uv add vortex-array
```
#### Command Line UI (vx)
For browsing the structure of Vortex files, you can use the `vx` command-line tool.
```bash
# Install latest release
cargo install vortex-tui --locked
# Or build from source
cargo install --path vortex-tui --locked
# Usage
vx browse
```
### Development Setup
#### Prerequisites (macOS)
```bash
# Optional but recommended dependencies
brew install flatbuffers protobuf # For .fbs and .proto files
brew install duckdb # For benchmarks
# Install Rust toolchain
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
# or
brew install rustup
# Initialize submodules
git submodule update --init --recursive
# Setup dependencies with uv
uv sync --all-packages
```
### Performance Optimization
For optimal performance, use [MiMalloc](https://github.com/microsoft/mimalloc):
```rust
#[global_allocator]
static GLOBAL_ALLOC: MiMalloc = MiMalloc;
```
## Project Information
### License
Licensed under the Apache License, Version 2.0
### Governance
Vortex is committed to remaining open-source, following governance models inspired by
the [Substrait project](https://substrait.io/governance/) and Apache Software Foundation.
### Contributing
See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.
## Acknowledgments 🏆
This project builds upon groundbreaking work from the academic and open-source communities:
### Key Research Papers
- [BtrBlocks](https://www.cs.cit.tum.de/fileadmin/w00cfj/dis/papers/btrblocks.pdf) - Efficient columnar compression
- [FastLanes](https://www.vldb.org/pvldb/vol16/p2132-afroozeh.pdf) - High-performance integer compression
- [FSST](https://www.vldb.org/pvldb/vol13/p2649-boncz.pdf) - Fast random access string compression
- [ALP](https://ir.cwi.nl/pub/33334/33334.pdf) - Adaptive lossless floating-point compression
- [Procella](https://dl.acm.org/citation.cfm?id=3360438) - YouTube's unified data system
- [Cloud Object Storage Analytics](https://www.durner.dev/app/media/papers/anyblob-vldb23.pdf) - High-performance
analytics
- [ClickHouse](https://www.vldb.org/pvldb/vol17/p3731-schulze.pdf) - Fast analytics for everyone
### Open Source Inspiration
- [Apache Arrow](https://arrow.apache.org) & [Apache DataFusion](https://github.com/apache/datafusion)
- [parquet2](https://github.com/jorgecarleitao/parquet2) by Jorge Leitao
- [DuckDB](https://github.com/duckdb/duckdb)
- [Velox](https://github.com/facebookincubator/velox) & [Nimble](https://github.com/facebookincubator/nimble)
---
*Thanks to all contributors who have shared their knowledge and code with the community! 🚀*