High-performance HTML parsing library for Python

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  • License: Apache Software License, MIT License (MIT OR Apache-2.0)
  • Tags html , parser , scraping , css-selectors , dom
  • Requires: Python >=3.10
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Project description

fast-scrape

PyPI Python License

8x faster HTML parsing than BeautifulSoup4. Rust-powered with 3000x faster CSS selector queries.

Installation

pip install fast-scrape
Alternative package managers 
# uv (recommended - 10-100x faster)
uv pip install fast-scrape

# Poetry
poetry add fast-scrape

# Pipenv
pipenv install fast-scrape

[!IMPORTANT] Requires Python 3.10 or later.

Quick start

from scrape_rs import Soup

soup = Soup("<html><body><div class='content'>Hello, World!</div></body></html>")

div = soup.find("div")
print(div.text)  # Hello, World!

Usage

Find elements 
from scrape_rs import Soup

soup = Soup(html)

# Find first element by tag
div = soup.find("div")

# Find all elements
divs = soup.find_all("div")

# CSS selectors
for el in soup.select("div.content > p"):
    print(el.text)
Element properties 
element = soup.find("a")

text = element.text          # Get text content
html = element.inner_html    # Get inner HTML
href = element.get("href")   # Get attribute
Batch processing 
from scrape_rs import Soup

# Process multiple documents in parallel
documents = [html1, html2, html3]
soups = Soup.parse_batch(documents)

for soup in soups:
    print(soup.find("title").text)

[!TIP] Use parse_batch() for processing multiple documents. Uses all CPU cores automatically.

Type hints

Full IDE support with type stubs:

from scrape_rs import Soup, Tag

def extract_links(soup: Soup) -> list[str]:
    return [a.get("href") for a in soup.select("a[href]")]

Performance

Massive performance improvements across all operations:

Parse speed comparison
File size fast-scrape BeautifulSoup4 lxml Speedup
1 KB 11 µs 0.23 ms 0.31 ms 20-28x faster
100 KB 2.96 ms 31.4 ms 28.2 ms 9.5-10.6x faster
1 MB 15.5 ms 1247 ms 1032 ms 66-80x faster

Throughput: 64 MB/s on 1MB files — handles large documents efficiently.

Query performance
Operation fast-scrape BeautifulSoup4 Speedup
find("div") 208 ns 16 µs 77x
find(".class") 20 ns 797 µs 40,000x
find("#id") 20 ns 799 µs 40,000x
select("div > p") 24.7 µs 4.361 ms 176x

CSS selectors dominate: Class and ID selectors run in nanoseconds vs microseconds.

Memory efficiency (100MB HTML)
Library Memory Efficiency
fast-scrape 145 MB 1x baseline
lxml 2,100 MB 14.5x larger
BeautifulSoup4 3,200 MB 22x larger

Result: 14-22x more memory-efficient than Python competitors.

Architecture optimizations:

  • SIMD-accelerated class matching — 2-10x faster selector execution
  • Zero-copy serialization — 50-70% memory reduction in HTML output
  • Batch processing — Parallel parsing uses all CPU cores automatically

Parsing & Selection (Servo browser engine):

Streaming Parser (Cloudflare):

  • lol_html — High-performance streaming HTML parser with constant-memory event-driven API

Related packages

Platform Package
Rust scrape-core
Node.js @fast-scrape/node
WASM @fast-scrape/wasm

License

MIT OR Apache-2.0

Project details

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  • License: Apache Software License, MIT License (MIT OR Apache-2.0)
  • Tags html , parser , scraping , css-selectors , dom
  • Requires: Python >=3.10
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