flowml-core 0.1.2

FlowML: High-performance data pipeline framework with FastAPI, Rust, and real-time WebSocket execution

🚀 FlowML

FlowML is a high-performance, real-time data pipeline framework designed to simplify how developers and data practitioners process, transform, and monitor datasets at scale.

In modern data workflows, developers often struggle with fragmented tooling: scripts for preprocessing, separate systems for monitoring, and limited visibility into execution. Traditional pipelines are either slow, difficult to debug, or lack real-time feedback. FlowML addresses these gaps by providing a unified system that combines fast computation, structured pipelines, and live observability.

At its core, FlowML brings together a FastAPI-based backend, a Rust-powered execution layer, and WebSocket-driven real-time updates to create a seamless pipeline experience. Instead of writing isolated scripts or waiting for batch jobs to complete, users can define structured workflows, execute them asynchronously, and observe progress instantly.

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🎯 Problem It Solves

Working with data pipelines often involves:

• Writing repetitive preprocessing scripts
• Handling large datasets with slow performance (pandas bottlenecks)
• Lack of real-time visibility into execution
• Poor orchestration of multiple steps
• Difficulty tracking job status and debugging failures

These challenges lead to inefficient workflows, delayed feedback, and increased development overhead.

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💡 Solution

FlowML introduces a structured and scalable approach to data processing:

• A pipeline engine that allows step-by-step transformations
• A background job system for asynchronous execution
• A real-time monitoring layer using WebSockets
• A Rust-powered processing core for high-performance operations
• A CLI + API interface for flexible usage

This enables users to move from manual scripting to a reusable, observable, and high-performance pipeline system.

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✨ Key Features

• ⚡ FastAPI backend for scalable API-driven workflows
• 🦀 Rust acceleration using Rayon for parallel data processing
• 🔄 Real-time updates via WebSockets (live job tracking)
• 🧠 Modular pipeline execution engine
• 🛠 CLI interface for automation and scripting
• 📂 Support for CSV, Excel, and database sources
• 🧹 Built-in data cleaning operations
• 📊 Data preview and summary statistics
• 🔄 Background job execution with progress tracking

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🏗 System Architecture

FlowML is designed with a clear separation of concerns:

CLI / Frontend
      ↓
FastAPI Backend (API + WebSockets)
      ↓
Pipeline Engine (orchestration)
      ↓
Rust Core (parallel computation via Rayon)

This architecture ensures:

• Clean orchestration in Python
• High-performance execution in Rust
• Real-time communication via WebSockets

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⚡ Performance Advantage

Unlike traditional pipelines that rely entirely on Python:

• FlowML delegates heavy computation to Rust
• Uses Rayon for parallel execution
• Avoids Python’s GIL limitations
• Handles large datasets efficiently

This results in significantly faster processing compared to pandas-only workflows.

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🚀 Installation

pip install flowml-core

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🧪 Local Development Setup

git clone https://github.com/Frosty-8/flowml
cd flowml
pip install -e .

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▶️ Run Server

uvicorn flowml.api.server:create_app --factory --reload

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🛠 CLI Usage

flowml run pipeline.json
flowml jobs
flowml status <job_id>
flowml preview <dataset_id>

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📄 Example Pipeline

{
  "dataset_id": "your-dataset-id",
  "steps": [
    { "step": "fill_nulls" },
    { "step": "drop_nulls" },
    { "step": "summary" }
  ]
}

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🔄 CI/CD

FlowML includes automated workflows using GitHub Actions:

• ✅ Code linting and testing
• 🦀 Rust build integration
• 📦 Package build and PyPI deployment

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📌 Roadmap

• ⚛️ Full React dashboard integration
• 🧩 Drag-and-drop pipeline builder
• ☁️ Cloud deployment support
• 🔐 Authentication and multi-user support
• 📊 Advanced visualization and analytics

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👨‍💻 Author

Sarthak Dongare

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⭐ Why FlowML Stands Out

FlowML is not just a script or utility—it is a framework that bridges the gap between data engineering and real-time systems, combining:

• Backend engineering
• Systems design
• Performance optimization
• Developer experience

It reflects a shift from simple data processing toward scalable, observable, and high-performance data workflows.

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