AI feasts on data. rs-trafilatura with spider-rs delivers.
Picture this: the web’s a sprawling jungle, thick with ads, nav bars, footers — all choking the good stuff. Enter rs-trafilatura, Rust’s slick extractor, paired with spider-rs, the async crawling beast. Together? They slice through the mess, handing you pure text, markdown, even images, scored for quality. It’s not just scraping; it’s intelligent harvesting.
And here’s the thrill — this combo feels like the early days of wget on steroids, but with ML smarts predicting page types before you blink. Back in the ’90s, we’d hack Perl scripts for this; now Rust makes it async, safe, blazing.
Why rs-trafilatura Crushes spider’s Built-in Tools?
spider-rs? Killer at discovering and queuing URLs, fetching in a blink. But extraction? Left to you — or its basic spider_transformations crate. That one’s readability-style: strips junk, spits markdown or text. Fine for articles. Useless for forums, products, JSON-LD hidden gems.
rs-trafilatura changes everything. ML classifies page types — article, forum, product — tailoring extraction. Merges sections smartly, falls back to structured data. Quality score from 0-1 flags duds.
On the WCEB benchmark, about 8% of pages score below 0.80. These are typically product pages with content in JSON-LD, forums with unusual markup, or service pages with highly distributed content.
That’s gold for real crawls. Low scores? Route to manual review or fallback. No more sifting trash.
Quick setup — toss these in Cargo.toml:
[dependencies] rs-trafilatura = { version = “0.2”, features = [“spider”] } spider = “2” tokio = { version = “1”, features = [“full”] }
Boom. spider feature unlocks extract_page(&page). Feed it spider’s Page, get ExtractResult packed with text, markdown (opt-in), HTML, metadata, images.
Crawl-and-Extract: The Simple Blast-Off
Simplest? New Website, crawl, loop pages, extract.
use spider::website::Website; use rs_trafilatura::spider_integration::extract_page;
[tokio::main]
async fn main() { let mut website = Website::new(“https://example.com”); website.crawl().await; for page in website.get_pages().into_iter().flatten() { match extract_page(&page) { Ok(result) => { println!(“[{}] {} (confidence: {:.2})”, result.metadata.page_type.unwrap_or_default(), result.metadata.title.unwrap_or_default(), result.extraction_quality, ); } Err(e) => eprintln!(“Extraction failed: {e}”), } } }
Waits for full crawl — perfect for small sites. But scale up? Nah.
Streaming Extraction: Real-Time Magic
Big sites scream for streaming. spider’s subscribe channel pipes pages hot off the press.
Spawn a task, recv pages as-they-come, extract on-the-fly. ~44ms per page — won’t bottleneck your crawl. Count ‘em as you go.
Each page is extracted in the spawned task as soon as spider fetches it. Extraction takes ~44ms per page, so it easily keeps up with typical crawl rates.
That’s the pace. Wonder hits when you realize: this powers live data pipelines for AI fine-tunes, scraping news as it drops.
Tweak with extract_page_with_options. Markdown output? Check. Images with hero flags? Yup. Favor precision for strict filters? Done. Force page_type like Product? Precision strike.
let options = Options { output_markdown: true, include_images: true, favor_precision: true, page_type: Some(PageType::Product), ..Options::default() };
URL in options overrides page’s — handy for redirects.
Filter by score: if under 0.80, warn and skip. 8% lowballs on benchmarks? That’s your cue for hybrid extractors.
ExtractResult spoils you:
| Field | Type | Description |
|---|---|---|
| content_text | String | Main content as plain text |
| content_markdown | Option | GFM Markdown (when enabled) |
(And more — titles, authors, dates, page_type, images vec.)
The Killer Edge: Beyond Hype
spider_transformations? Basic. No ML, no types, no scores, no metadata from Open Graph or JSON-LD. rs-trafilatura? Full stack.
My bold call — and it’s fresh here: this pair previews Rust’s takeover in AI data factories. Python’s trafilatura birthed it; Rust ports it for speed. Prediction? By 2025, indie devs feed local LLMs with these crawls, sidestepping Big Tech’s paywalls. Democratized data deluge.
Corporate spin? None here — pure open source. crates.io/rs-trafilatura, GitHub ready. Python fans: pypi.org/project/rs-trafilatura.
Look, if you’re building scrapers, indexes, or training sets, ditch the half-measures. This is the shift.
Can rs-trafilatura Handle Massive Crawls?
Yes — streaming keeps memory low, extraction async-friendly. Tokio powers it; scale to thousands of pages/hour on modest iron. Bench it yourself.
But watch queues. spider respects robots.txt, rate-limits — add politeness delays for ethics.
Is This Better Than Scrapy or BeautifulSoup?
Rust speed laps Python for high-volume. No GIL, zero-cost abstractions. trafilatura’s lineage means SOTA accuracy; spider’s Rust purity means crashes? Never.
Tradeoff: steeper curve if you’re JS-head. Worth it for perf obsessives.
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Frequently Asked Questions
What is rs-trafilatura and how does it work with spider-rs?
rs-trafilatura extracts clean content from web pages with ML page-type detection and quality scoring. With spider-rs, use extract_page on crawled Page objects for smoothly integration.
How fast is extraction with rs-trafilatura?
About 44ms per page, keeping pace with spider’s async fetches even on large crawls.
Does rs-trafilatura beat spider’s default extractor?
Absolutely — adds ML classification, metadata, images, scores that basics lack.
