FastFeedParser

A high-performance feed parser for Python that handles RSS, Atom, and RDF. Built for speed, efficiency, and ease of use while delivering complete parsing capabilities.

Why FastFeedParser?

It's about 25x faster (check included benchmark.py) than popular feedparser library while keeping a familiar API. This speed comes from:

• lxml for efficient XML parsing
• Smart memory management
• Minimal dependencies
• Focused, streamlined code

Powers feed processing for Kagi Small Web, handling processing of thousands of feeds at scale.

Features

• Fast parsing of RSS 2.0, Atom 1.0, and RDF/RSS 1.0 feeds
• Robust error handling and encoding detection
• Support for media content and enclosures
• Automatic date parsing and standardization to UTC ISO 8601 format
• Clean, Pythonic API similar to feedparser
• Comprehensive handling of feed metadata
• Support for various feed extensions (Media RSS, Dublin Core, etc.)

Installation

pip install fastfeedparser

Quick Start

import fastfeedparser

# Parse from URL
myfeed = fastfeedparser.parse('https://example.com/feed.xml')

# Parse from string
xml_content = '''<?xml version="1.0"?>
<rss version="2.0">
    <channel>
        <title>Example Feed</title>
        ...
    </channel>
</rss>'''
myfeed = fastfeedparser.parse(xml_content)

# Access feed global information
print(myfeed.feed.title)
print(myfeed.feed.link)

# Access feed entries
for entry in myfeed.entries:
    print(entry.title)
    print(entry.link)
    print(entry.published)

Run Benchmark

python benchmark.py

This will run benchmark on a number of feeds with output looking like this

Testing https://gessfred.xyz/rss.xml
FastFeedParser: 17 entries in 0.004s
Feedparser: 17 entries in 0.098s
Speedup: 26.3x

Testing https://fanf.dreamwidth.org/data/rss
FastFeedParser: 25 entries in 0.005s
Feedparser: 25 entries in 0.087s
Speedup: 17.9x

Testing https://jacobwsmith.xyz/feed.xml
FastFeedParser: 121 entries in 0.030s
Feedparser: 121 entries in 0.166s
Speedup: 5.5x

Testing https://bernsteinbear.com/feed.xml
FastFeedParser: 11 entries in 0.007s
Feedparser: 11 entries in 0.339s
Speedup: 50.1x

And publish a full report looking like this

Summary:
--------------------------------------------------
Total wall-clock time: 38.70s
Successfully tested 200/200 feeds

FastFeedParser:
  Total entries: 6600
  Total parsing time: 0.46s
  Average per feed: 0.002s
  Feeds/sec: 439.0

Feedparser:
  Total entries: 6555
  Total parsing time: 12.31s
  Average per feed: 0.062s
  Feeds/sec: 16.2

Speedup: FastFeedParser is 27.0x faster

OUTLIERS: Entry Count Mismatches (2 feeds)
--------------------------------------------------
  https://dylanharris.org/feed-me.rss
    FastFeedParser: 35 entries
    Feedparser: 0 entries
    Difference: +35
  https://humanwhocodes.com/feeds/all.json
    FastFeedParser: 10 entries
    Feedparser: 0 entries
    Difference: +10

Key Features

Feed Types Support

• RSS 2.0
• Atom 1.0
• RDF/RSS 1.0

Content Handling

• Automatic encoding detection
• HTML content parsing
• Media content extraction
• Enclosure handling

Metadata Support

• Feed title, link, and description
• Publication dates
• Author information
• Categories and tags
• Media content and thumbnails

API Reference

Main Functions

• parse(source, *, include_content=True, include_tags=True, include_media=True, include_enclosures=True): Parse feed from a URL/XML/JSON source, with optional field extraction toggles for faster parsing.

Feed Object Structure

The parser returns a FastFeedParserDict object with two main sections:

• feed: Contains feed-level metadata
• entries: List of feed entries

Each entry contains:

• title: Entry title
• link: Entry URL
• description: Entry description/summary
• published: Publication date
• author: Author information
• content: Full content
• media_content: Media attachments
• enclosures: Attached files

Requirements

• Python 3.7+
• lxml
• python-dateutil

Optional extras:

• brotli (pip install fastfeedparser[brotli]) for Content-Encoding: br
• dateparser (pip install fastfeedparser[dateparser]) for the slowest date parsing fallback
• pip install fastfeedparser[full] for both

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

Inspired by the feedparser project, FastFeedParser aims to provide a modern, high-performance alternative while maintaining a familiar API.