🌪️ Vortex
📚 Documentation | 📊 Performance Benchmarks
Overview
Vortex is a next-generation columnar file format and toolkit designed for high-performance data processing. It is the fastest and most extensible format for building data systems backed by object storage. It provides:
-
⚡️ Blazing Fast Performance
- 200x faster random access reads (vs. modern Apache Parquet)
- 2-10x faster scans
- 2-10x faster writes
- Similar compression ratios
- Efficient support for wide tables with zero-copy/zero-parse metadata
-
🔧 Extensible Architecture
- Modeled after Apache DataFusion's extensible approach
- Pluggable encoding system, type system, compression strategy, & layout strategy
- Zero-copy compatibility with Apache Arrow
-
🗳️ Open Source, Neutral Governance
- A Linux Foundation (LF AI & Data) Project
- Apache-2.0 Licensed
-
↔️ Integrations
- Arrow, DataFusion, DuckDB, Spark, Pandas, Polars, & more
- Apache Iceberg (coming soon)
🟢 Development Status: Library APIs may change from version to version, but we now consider the file format stable. From release 0.36.0, all future releases of Vortex should maintain backwards compatibility of the file format (i.e., be able to read files written by any earlier version >= 0.36.0).
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.
Python Package
Command Line UI (vx)
For browsing the structure of Vortex files, you can use the vx
command-line tool.
# Install latest release
# Or build from source
# Usage
Development Setup
Prerequisites (macOS)
# Optional but recommended dependencies
# Install Rust toolchain
|
# or
# Initialize submodules
# Setup dependencies with uv
Performance Optimization
For optimal performance, we suggest using MiMalloc:
static GLOBAL_ALLOC: MiMalloc = MiMalloc;
Project Information
License
Licensed under the Apache License, Version 2.0.
Governance
Vortex is an independent open-source project and not controlled by any single company. The Vortex Project is a sub-project of the Linux Foundation Projects. The governance model is documented in CONTRIBUTING.md and is subject to the terms of the Technical Charter.
Contributing
See CONTRIBUTING.md for guidelines.
Reporting Vulnerabilities
If you discovery a security vulnerability, please email vuln-report@vortex.dev.
Trademarks
Copyright © Vortex a Series of LF Projects, LLC. For terms of use, trademark policy, and other project policies please see https://lfprojects.org
Acknowledgments 🏆
The Vortex project benefits enormously from groundbreaking work from the academic & open-source communities.
Research in Vortex
- BtrBlocks - Efficient columnar compression
- FastLanes - High-performance integer compression
- FSST - Fast random access string compression
- ALP - Adaptive lossless floating-point compression
- Procella - YouTube's unified data system
- Anyblob - High-performance access to object storage
- ClickHouse - Fast analytics for everyone
Vortex in Research
- Anyblox - A Framework for Self-Decoding Datasets
Open Source Inspiration
- Apache Arrow
- Apache DataFusion
- parquet2 by Jorge Leitao
- DuckDB
- Velox & Nimble