bfast-py 1.2.1

⚡ B-FAST (Binary Fast Adaptive Serialization Transfer) - Protocolo de serialização binária de ultra-alta performance, desenvolvido em Rust para o ecossistema Python e TypeScript

⚡ B-FAST (Binary Fast Adaptive Serialization Transfer)

B-FAST is an ultra-high performance binary serialization protocol, developed in Rust for Python and TypeScript ecosystems. It's designed to replace JSON in critical routes where latency, CPU usage, and bandwidth are bottlenecks.

"Performance is not just about speed—it's about efficiency where it matters most"

B-FAST was born from the recognition that modern applications need more than just fast serialization—they need smart serialization that adapts to real-world constraints. After extensive optimization, B-FAST has found its perfect niche in bandwidth-constrained environments, achieving 1.7x faster than orjson for simple objects and 5.7x faster on slow networks.

Philosophy: We believe that the future of data transfer lies not in raw CPU speed alone, but in intelligent protocols that minimize network overhead while maintaining excellent performance. B-FAST represents our contribution to a more efficient, bandwidth-conscious web.

📚 Documentation

Full documentation available at: https://marcelomarkus.github.io/b-fast/

🚀 Why B-FAST?

• Rust Engine: Native serialization without Python interpreter overhead.
• Pydantic Native: Reads Pydantic model attributes directly from memory, skipping the slow .model_dump() process.
• Zero-Copy NumPy: Serializes tensors and numeric arrays directly, achieving 14-96x speedup vs JSON/orjson.
• Parallel Compression: LZ4 with multi-thread processing for large payloads (>1MB).
• Cache Optimized: Aligned allocation and batch processing for maximum efficiency.

📊 Benchmarks (Updated Results)

🚀 Simple Objects (10,000)

Format	Time (ms)	Speedup
JSON	12.0ms	1.0x
orjson	8.19ms	1.5x
B-FAST	4.83ms	🚀 2.5x

B-FAST is 1.7x faster than orjson!

🔄 Round-Trip (Encode + Network + Decode)

Complete test including network transfer and deserialization (10,000 objects):

📡 100 Mbps (Slow Network)

Format	Total Time	Speedup vs orjson
JSON	114.5ms	0.8x
orjson	91.7ms	1.0x
B-FAST + LZ4	16.1ms	🚀 5.7x

📡 1 Gbps (Fast Network)

Format	Total Time	Speedup vs orjson
JSON	29.4ms	0.5x
orjson	15.3ms	1.0x
B-FAST + LZ4	7.2ms	🚀 2.1x

📡 10 Gbps (Ultra-Fast Network)

Format	Total Time	Speedup vs orjson
JSON	20.9ms	0.4x
orjson	7.7ms	1.0x
B-FAST + LZ4	6.3ms	🚀 1.2x

🎯 Ideal Use Cases

• 📱 Mobile/IoT: 89% data savings + 5.7x performance on slow networks
• 🌐 APIs with slow networks: Up to 5.7x faster than orjson
• 📊 Data pipelines: 14-96x speedup for NumPy arrays
• 🗜️ Storage/Cache: Superior integrated compression
• 🚀 Simple objects: 1.7x faster than orjson

📦 Installation

Backend (Python)

# Basic installation
pip install bfast-py

# With FastAPI support
pip install "bfast-py[fastapi]"

or with uv:

uv add bfast-py
# or
uv add "bfast-py[fastapi]"

Frontend (TypeScript)

npm install bfast-client

🛠️ How to Use

Backend (Python)

1. FastAPI (Direct Integration) ⭐ Recommended

B-FAST includes a built-in BFastResponse for seamless integration.

from fastapi import FastAPI
from pydantic import BaseModel
from b_fast import BFastResponse

app = FastAPI()

class User(BaseModel):
    id: int
    name: str

@app.get("/users", response_class=BFastResponse)
async def get_users():
    # Returns binary B-FAST data with automatic LZ4 compression
    return [User(id=i, name=f"User {i}") for i in range(1000)]

2. Flask

from flask import Flask, Response
import b_fast

app = Flask(__name__)
encoder = b_fast.BFast()

@app.route('/users')
def get_users():
    users = [{"id": i, "name": f"User {i}"} for i in range(1000)]
    data = encoder.encode_packed(users, compress=True)
    return Response(data, mimetype='application/octet-stream')

3. Django

from django.http import HttpResponse
import b_fast

encoder = b_fast.BFast()

def get_users(request):
    users = [{"id": i, "name": f"User {i}"} for i in range(1000)]
    data = encoder.encode_packed(users, compress=True)
    return HttpResponse(data, content_type='application/octet-stream')

4. Any Python Framework

import b_fast

encoder = b_fast.BFast()

# Encode your data
data = encoder.encode_packed(your_data, compress=True)

# Return as bytes (binary response)

Frontend (TypeScript)

import { BFastDecoder } from 'bfast-client';

async function loadData() {
    const response = await fetch('/users');
    const buffer = await response.arrayBuffer();
    
    // Decodes and decompresses LZ4 automatically
    const users = BFastDecoder.decode(buffer);
    console.log(users);
}

About B-FAST

Key Achievements:

• 🚀 1.7x faster than orjson for simple objects
• 🚀 5.7x faster than orjson on 100 Mbps networks (round-trip)
• 📦 89% smaller payloads with built-in LZ4 compression
• ⚡ 14-96x speedup for NumPy arrays
• 🎯 Competitive even on ultra-fast 10 Gbps networks

B-FAST performance comparison across different scenarios: simple objects, large objects on 100 Mbps network, NumPy arrays, and payload size. B-FAST demonstrates superiority in speed (1.7-14x faster) and bandwidth efficiency (90% reduction with LZ4).

Developed by: marcelomarkus

📄 License

Distributed under the MIT License. See LICENSE for more information.