Web Scraper

Web Scraper

Extracts structured data from any website — handling static HTML, JavaScript-rendered pages, pagination, authentication walls, and rate limiting — and returns clean, structured output ready for downstream processing.

When to Use

• User wants to extract product listings, prices, or reviews from an e-commerce site
• User needs to harvest article text, metadata, or links from a news or blog site
• A dataset must be built by collecting structured records across many pages
• User asks to "scrape", "crawl", "extract data from", or "harvest" a URL
• An automated pipeline needs to pull live data from a website on a schedule
• User needs to monitor a page for changes and capture diffs over time

Process

1. Analyze the target URL and content type:

   • Determine whether the page is static HTML or JavaScript-rendered (check for <script> tags that load content dynamically)
   • Identify the data schema: what fields are needed and where do they appear in the DOM?
   • Check for a robots.txt and note any disallowed paths or crawl delays

2. Select the extraction strategy:

   • Static HTML: Use requests + BeautifulSoup (Python) or cheerio (Node.js)
   • JavaScript-rendered: Use Playwright or Puppeteer to render the page before parsing
   • APIs hiding behind the UI: Inspect network traffic (XHR/fetch calls) — hitting the API directly is faster and more reliable
   • Structured data already present: Check for JSON-LD (<script type="application/ld+json">), Open Graph tags, or schema.org microdata

3. Handle pagination and navigation:

   • Detect pagination pattern: URL query parameter (?page=2), path segment (/page/2), infinite scroll, or "Load More" button
   • For query-parameter pagination: iterate pages until response is empty or a sentinel value is reached
   • For infinite scroll: scroll the viewport incrementally with Playwright, waiting for new content to load

4. Implement rate limiting and politeness:

   • Respect Crawl-delay from robots.txt; default to 1–2 seconds between requests if none specified
   • Randomize delay within ±50% of the target to avoid fingerprinting
   • Use rotating user-agent strings drawn from a realistic pool
   • Back off exponentially on 429 or 503 responses (2s → 4s → 8s → give up after 3 retries)

5. Extract and normalize fields:

   • Use CSS selectors or XPath to target each field precisely
   • Strip HTML entities, extra whitespace, and boilerplate text
   • Normalize dates to ISO 8601, prices to a {amount, currency} object, and URLs to absolute form

6. Handle errors and gaps:

   • Log missing fields per record rather than dropping the entire record
   • Detect CAPTCHAs or login walls and surface them as explicit errors with remediation advice
   • Validate extracted records against the expected schema before returning

7. Return structured output:

   • Default format: JSON array of objects (one per scraped record)
   • Include scrape metadata: source_url, scraped_at (ISO 8601), page_count, record_count

Output Format

{
  "metadata": {
    "source_url": "https://example.com/products",
    "scraped_at": "2025-06-01T14:32:00Z",
    "page_count": 5,
    "record_count": 120
  },
  "records": [
    {
      "title": "Wireless Headphones Pro",
      "price": { "amount": 79.99, "currency": "USD" },
      "rating": 4.5,
      "review_count": 312,
      "url": "https://example.com/products/wireless-headphones-pro"
    }
  ]
}

Examples

Example Input

Scrape all product names and prices from https://books.toscrape.com — there are multiple pages.

Example Output

import requests
from bs4 import BeautifulSoup
import time, random, json
from datetime import datetime, timezone

BASE_URL = "https://books.toscrape.com/catalogue/"
records = []
page = 1

while True:
    url = f"{BASE_URL}page-{page}.html" if page > 1 else "https://books.toscrape.com/catalogue/"
    resp = requests.get(url, headers={"User-Agent": "Mozilla/5.0"})
    if resp.status_code == 404:
        break
    soup = BeautifulSoup(resp.text, "html.parser")
    for article in soup.select("article.product_pod"):
        records.append({
            "title": article.h3.a["title"],
            "price": article.select_one(".price_color").text.strip(),
            "url": "https://books.toscrape.com/catalogue/" + article.h3.a["href"]
        })
    page += 1
    time.sleep(random.uniform(0.8, 1.5))

print(json.dumps({
    "metadata": {"scraped_at": datetime.now(timezone.utc).isoformat(), "record_count": len(records)},
    "records": records
}, indent=2))

Boundaries

• Always check robots.txt before scraping and respect Disallow rules and Crawl-delay.
• Do NOT bypass authentication mechanisms or scrape content behind a login without explicit user confirmation that they are authorized to do so.
• Do NOT extract personally identifiable information (PII) unless the user explicitly needs it and confirms they have legal authority to collect it.
• Do NOT ignore HTTP 429/503 responses — always back off before retrying.
• Do NOT scrape at rates that could constitute a denial-of-service — cap at one request per second by default.
• Flag CAPTCHA walls as an explicit blocker; do NOT attempt to solve CAPTCHAs automatically.
• Remind users that scraping may violate a site's Terms of Service — this is their responsibility to verify.