FastLanes

Like Parquet, but with 40% better compression and 40× faster decoding.

FastLanes features:

• Fully SIMD-friendly with zero explicit SIMD instructions

• Zero dependencies:

  • no external libraries required

---

Documentation

• Typing System
• File Format Specification (PDF)

---

Getting Started

Python

import pyfastlanes

# Connect to FastLanes
conn = pyfastlanes.connect()

# Convert a CSV directory to FastLanes format
conn.inline_footer().read_csv("path/to/csv_dir").to_fls("data.fls")

# Read back and write to CSV
reader = conn.read_fls("data.fls")
reader.to_csv("decoded.csv")

C++

Add FastLanes as a dependency via CMake:

include(FetchContent)
FetchContent_Declare(
        fastlanes
        GIT_REPOSITORY https://github.com/cwida/FastLanes.git
        GIT_TAG dev
)
FetchContent_MakeAvailable(fastlanes)

add_executable(example example.cpp)
target_link_libraries(example PRIVATE FastLanes)

Example usage:

#include "fastlanes.hpp"

int main() {
    fastlanes::Connection conn;
    conn.read_csv("data/csv_dir").to_fls("data.fls");

    auto reader = fastlanes::Connection().read_fls("data.fls");
    reader->to_csv("decoded.csv");
    return EXIT_SUCCESS;
}

Rust

Add FastLanes Rust bindings to your Cargo.toml:

[dependencies]
fls-rs = { path = "./rust" }

use anyhow::Result;
use fls_rs::connect;

fn main() -> Result {
    let mut conn = connect();
    conn.inline_footer()
        .read_csv("data/csv_dir")
        .to_fls("data.fls");

    conn.read_fls("data.fls")
        .to_csv("decoded.csv");
    Ok(())
}

Coming Soon

• CUDA support for FastLanes CUDA reader

---

Publications

• Azim Afroozeh & Peter Boncz, “The FastLanes Compression Layout: Decoding > 100 Billion Integers per Second with Scalar Code,” PVLDB, 16(9): 2132–2144, May 2023

  • Read the paper
  • Source code

• Azim Afroozeh, Lotte Felius & Peter Boncz, “Accelerating GPU Data Processing Using FastLanes Compression,” DaMoN ’24, Proceedings of the 20th International Workshop on Data Management on New Hardware, Santiago, Chile, June 2024

  • Read the paper
  • Source code

• Azim Afroozeh, Leonardo Kuffó & Peter Boncz, “ALP: Adaptive Lossless Floating-Point Compression,” SIGMOD ’24, ACM SIGMOD, June 2024

  • Read the paper
  • Source code

• Sven Hielke Hepkema, Azim Afroozeh, Charlotte Felius, Peter Boncz & Stefan Manegold, “G‑ALP: Rethinking Light‑weight Encodings for GPUs,” DaMoN ’25, July 2025

  • Read the paper
  • Source code

---

How to Cite

If you use FastLanes in your research or projects, please cite:

…

---

License

This project is released under the MIT License.

---

Contributing

We welcome contributions to FastLanes!
Please see CONTRIBUTING.md for guidelines on how to get started.

---

Join Our Community

Come discuss FastLanes, share feedback, and help shape the future of data formats on Discord: