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https://github.com/conqxeror/veloxx

Veloxx: A high-performance, lightweight Rust library for in-memory data processing and analytics. Features DataFrames, Series, CSV/JSON I/O, powerful transformations, aggregations, and statistical functions for efficient data science and engineering.

https://github.com/conqxeror/veloxx

Science Score: 26.0%

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    CITATION.cff file
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    Scientific vocabulary similarity
    Low similarity (12.2%) to scientific vocabulary

Keywords

analytics data-processing dataframe high-performance lightweight
Last synced: 5 months ago · JSON representation

Repository

Veloxx: A high-performance, lightweight Rust library for in-memory data processing and analytics. Features DataFrames, Series, CSV/JSON I/O, powerful transformations, aggregations, and statistical functions for efficient data science and engineering.

Basic Info
Statistics
  • Stars: 3
  • Watchers: 0
  • Forks: 1
  • Open Issues: 2
  • Releases: 0
Topics
analytics data-processing dataframe high-performance lightweight
Created 8 months ago · Last pushed 7 months ago
Metadata Files
Readme Changelog Contributing License Audit Roadmap

README.md

Veloxx Logo

Veloxx: Ultra-High Performance Data Processing & Analytics Library

Crates.io PyPI npm GitHub docs.rs CI License: MIT Documentation

🚀 v0.3.2 Released! Production polish, docs overhaul, and security audit hardening.

Veloxx is a blazing-fast, ultra-lightweight data processing and analytics library in Rust, with seamless bindings for Python and WebAssembly. Built from the ground up for maximum performance, featuring advanced SIMD acceleration, memory optimization, and parallel processing that often outperforms industry leaders.

🏆 Performance Highlights

Parallel median, quantile & percentile calculation: Now uses Rayon for fast computation on large datasets 25.9x faster group-by operations: 1,466.3M rows/sec 172x faster filtering: 538.3M elements/sec
2-12x faster joins: 400,000M rows/sec Industry-leading I/O: CSV 93,066K rows/sec, JSON 8,722K objects/sec Advanced SIMD: 2,489.4M rows/sec query processing Memory optimized: 422.1MB/s compression, 13.8M allocs/sec

✨ Project Links

🧩 Core Principles & Design Goals

  • 🚀 Performance First: Advanced SIMD, parallel processing, cache-optimized algorithms
  • 🪶 Lightweight: Minimal dependencies, optimized memory footprint
  • 🦺 Safety & Reliability: Memory-safe Rust, comprehensive testing
  • 🧑‍💻 Developer Experience: Intuitive APIs, excellent documentation
  • 🔧 Production Ready: Zero-warning compilation, extensive benchmarking

🚩 Key Features

Core Data Structures

  • DataFrame and Series for lightning-fast tabular data processing
  • SIMD-optimized operations with AVX2/NEON acceleration
  • Memory-efficient storage with advanced compression

High-Performance Operations

  • 🚀 Ultra-fast analytics: filtering, joining, grouping, aggregation
  • 📊 Advanced statistics: correlation, regression, time-series analysis
  • � Parallel processing: Multi-threaded execution with work-stealing
  • 🧮 Vectorized math: SIMD-accelerated arithmetic operations

Advanced I/O & Integration

  • 📂 Multiple formats: CSV, JSON, Parquet support
  • 🔌 Database connectivity: SQLite, PostgreSQL, MySQL
  • 🌊 Streaming operations: Memory-efficient large dataset processing
  • ⚡ Async I/O: Non-blocking file and network operations

Data Quality & ML

  • 🧹 Data cleaning: Automated outlier detection, validation
  • 🤖 Machine learning: Linear/logistic regression, clustering, preprocessing
  • 📈 Visualization: Charts, plots, statistical graphics
  • 🔍 Data profiling: Schema inference, quality metrics

Multi-Language Support

  • 🦀 Rust: Native, zero-cost abstractions
  • � Python: PyO3 bindings with NumPy integration
  • 🌐 WebAssembly: Browser and Node.js support
  • 📦 Easy installation: Available on crates.io, PyPI, npm

⚡ Quick Start

Rust

toml [dependencies] veloxx = "0.3.2"

```rust use veloxx::dataframe::DataFrame; use veloxx::series::Series;

let df = DataFrame::newfromcsv("data.csv")?; let filtered = df.filter(&yourcondition)?; let grouped = df.groupby(vec!["category"]).agg(vec![("amount", "sum")])?; ```

Python

```python import veloxx

df = veloxx.PyDataFrame({"name": veloxx.PySeries("name", ["Alice", "Bob"])}) filtered = df.filter([...]) ```

JavaScript/Wasm

javascript const veloxx = require("veloxx"); const df = new veloxx.WasmDataFrame({name: ["Alice", "Bob"]}); const filtered = df.filter(...);

🛠️ Feature Flags

Enable only what you need:

  • advanced_io – Parquet, databases, async
  • data_quality – Schema checks, anomaly detection
  • window_functions – Window analytics
  • visualization – Charting
  • ml – Machine learning
  • python – Python bindings
  • wasm – WebAssembly

📚 Documentation

🧑‍💻 Examples

Run ready-made examples:

```bash cargo run --example basicdataframeoperations cargo run --example advancedio --features advancedio

... more in the examples/ folder

```

🤝 Contributing

See CONTRIBUTING.md for guidelines. Please review our Code of Conduct.

💬 Support

  • Issues: https://github.com/Conqxeror/veloxx/issues
  • Discussions: https://github.com/Conqxeror/veloxx/discussions
  • Documentation: https://conqxeror.github.io/veloxx/

📝 License

MIT License. See LICENSE.

Owner

  • Name: Wali Mohammad Kadri
  • Login: Conqxeror
  • Kind: user
  • Location: Mumbai, India.

Hello World

GitHub Events

Total
  • Issues event: 1
  • Watch event: 2
  • Delete event: 5
  • Push event: 52
  • Pull request review comment event: 1
  • Pull request event: 13
  • Fork event: 2
  • Create event: 13
Last Year
  • Issues event: 1
  • Watch event: 2
  • Delete event: 5
  • Push event: 52
  • Pull request review comment event: 1
  • Pull request event: 13
  • Fork event: 2
  • Create event: 13

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 1
  • Total pull requests: 11
  • Average time to close issues: N/A
  • Average time to close pull requests: about 7 hours
  • Total issue authors: 1
  • Total pull request authors: 2
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 5
  • Bot issues: 0
  • Bot pull requests: 8
Past Year
  • Issues: 1
  • Pull requests: 11
  • Average time to close issues: N/A
  • Average time to close pull requests: about 7 hours
  • Issue authors: 1
  • Pull request authors: 2
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 5
  • Bot issues: 0
  • Bot pull requests: 8
View more stats
Top Authors
Issue Authors
  • Conqxeror (1)
Pull Request Authors
  • dependabot[bot] (8)
  • Conqxeror (3)
Top Labels
Issue Labels
Pull Request Labels
dependencies (8) rust (6) javascript (2)

Packages

  • Total packages: 3
  • Total downloads:
    • cargo 2,802 total
    • npm 16 last-month
    • pypi 29 last-month
  • Total dependent packages: 0
    (may contain duplicates)
  • Total dependent repositories: 0
    (may contain duplicates)
  • Total versions: 23
  • Total maintainers: 2
pypi.org: veloxx

Veloxx: High-performance, lightweight Python library for in-memory data processing and analytics. Built on Rust for blazing speed and memory efficiency. Features DataFrames, Series, advanced I/O (CSV, JSON, Parquet), machine learning (linear regression, K-means, logistic regression), time-series analysis, data visualization, and parallel processing. Perfect for data science, analytics, and performance-critical applications where speed and memory usage matter.

  • Versions: 6
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 29 Last month
Rankings
Dependent packages count: 8.9%
Average: 29.4%
Dependent repos count: 50.0%
Maintainers (1)
conqxeror
Last synced: 6 months ago
npmjs.org: veloxx

Veloxx: High-performance, lightweight Rust library for in-memory data processing and analytics. Features DataFrames, Series, advanced I/O (CSV, JSON, Parquet), machine learning (linear regression, K-means, logistic regression), time-series analysis, data

  • Versions: 6
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 16 Last month
Rankings
Dependent repos count: 24.3%
Average: 29.6%
Dependent packages count: 35.0%
Maintainers (1)
conqxeror
Last synced: 6 months ago
crates.io: veloxx

Veloxx: High-performance, lightweight Rust library for in-memory data processing and analytics. Features DataFrames, Series, advanced I/O (CSV, JSON, Parquet), machine learning (linear regression, K-means, logistic regression), time-series analysis, data visualization, parallel processing, and multi-platform bindings (Python, WebAssembly). Designed for minimal dependencies, optimal memory usage, and blazing speed - ideal for data science, analytics, and performance-critical applications.

  • Versions: 11
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 2,802 Total
Rankings
Dependent repos count: 21.1%
Dependent packages count: 27.9%
Average: 47.9%
Downloads: 94.7%
Maintainers (1)
Conqxeror
Last synced: 6 months ago

Dependencies

Cargo.toml cargo
  • criterion 0.5 development
  • csv-core 0.1
  • microjson 0.1.6