"name": "torrent_parser", "selectors": "torrent_name": "css:h1.torrent-name", "hash": "regex:[a-fA-F0-9]40", "seeders": "css:.seeds", "file_list": "css:ul.file-list li"
[ "name": "Ubuntu 22.04", "infohash": "2A3B4C5D...", "seeders": 120, "leechers": 40, "filelist": ["ubuntu.iso", "readme.txt"], "magnet": "magnet:?xt=urn:btih:..." ] 5.1 Incremental Parsing (Avoid Re-crawling) Maintain a Redis or SQLite DB of seen infohashes. Only process new ones. 5.2 Tracker Scraping via UDP/TCP Instead of scraping HTML, some advanced parsers scrape trackers directly using the BitTorrent protocol. DataCol can be extended to call scrape commands: DataCol can be extended to call scrape commands:
Begin with the configuration examples above, test on a single page, then scale with proxies and async workers. Keywords used: parser datacol torrent, DataCol parser configuration, torrent metadata extraction, infohash parsing, BitTorrent scraping, torrent site crawler. Our focus is on metadata extraction , not file downloading
Parsing torrent sites does not mean you distribute copyrighted content. Our focus is on metadata extraction , not file downloading. Chapter 3: Understanding Torrent Site Structure (For Effective Parsing) Torrent sites share a common HTML/DOM structure. Here is what a typical torrent detail page contains, and how DataCol should target them: Our focus is on metadata extraction
pattern = r'urn:btih:([a-fA-F0-9]40)' infohash = parser.extract_regex(page_html, pattern) Once parsed, save results as JSON, CSV, or directly into a database:
<div class="torrent-detail"> <h1 class="torrent-name">Ubuntu 22.04 LTS ISO</h1> <div class="meta"> <span>Hash: 2A3B4C5D6E7F...</span> <span>Seeds: 120</span> <span>Leeches: 40</span> </div> <ul class="file-list"> <li>ubuntu.iso (2.3 GB)</li> <li>readme.txt (1 KB)</li> </ul> <a href="magnet:?xt=urn:btih:...">Magnet Link</a> </div> Using DataCol, you define :
pip install datacol-parser # or clone custom build git clone https://github.com/example/datacol-torrent.git Create torrent_config.yaml :