Web Data Extraction

Web Data Extraction

Purpose

Get structured data out of web pages reliably. The two questions that determine the entire approach are whether an API exists, and whether the content is rendered by JavaScript — and both are answered in under a minute.

When to Use

• Extracting structured data from a website.
• A scraper that returns empty results or breaks constantly.
• Deciding between static parsing and a headless browser.
• Building a repeatable data-collection pipeline.

Capabilities

• Approach selection: API, static HTML, or browser rendering.
• Resilient selector strategy.
• JavaScript-rendered content.
• Rate limiting, retries, and politeness.
• Detecting and handling structure changes.

Inputs

• The target pages and the data required.
• Whether the site has an API, documented or otherwise.
• Volume and frequency.

Outputs

• Structured, validated data.
• A scraper that fails loudly when the page changes, rather than silently returning nothing.

Workflow

1. Look for an API first — Check the documentation, then check the network tab. Very often the page itself calls a JSON endpoint, and that endpoint is stable, structured, and far easier to use than the HTML.
2. Determine whether the content is in the HTML — curl the URL and search for the data. If it is absent, the page renders it with JavaScript and static parsing will return nothing, forever, with no error.
3. Choose the tool accordingly — Static HTML: an HTTP client and a parser. JavaScript-rendered: a headless browser. Do not use a browser when you do not need one; it is fifty times slower.
4. Write resilient selectors — Anchor on stable attributes (data-*, an id, a semantic element), not on a generated class name that changes with every deploy.
5. Be polite — Rate limit, identify yourself, respect robots.txt, and cache. A scraper that hammers a site is both rude and likely to be blocked.
6. Validate the output — A scraper that silently returns zero rows because the page changed is worse than one that crashes.

Best Practices

• The most common scraping bug is a silent empty result. A page redesign turns your scraper into a machine that confidently produces nothing. Raise on an empty match, always.
• Class names in modern frontends are generated and change on every build. data-testid, id, and semantic elements are the only stable anchors.
• A headless browser is fifty to a hundred times slower and heavier than an HTTP request. Establish that you need one before reaching for it.
• Rate limit and cache. Re-fetching the same page during development, dozens of times, is both wasteful and the fastest way to get your IP blocked.
• Send a real User-Agent that identifies you and provides a contact. Anonymous scraping at volume is what gets scrapers banned as a class.
• Respect robots.txt and the site's terms. Scraping public data is generally lawful in many jurisdictions; ignoring an explicit prohibition is a different matter, and the legal position varies.

Examples

The check that determines everything:

# Is the data in the HTML, or is it rendered by JavaScript?
curl -s "https://example.com/products" | grep -c "product-title"

# 0  -> The content is not in the HTML. It is rendered client-side. A static
#       parser will return nothing and will not tell you why. You need a browser
#       — OR, better, find the API the page itself is calling.
# 24 -> The content is there. Parse it statically; it is 50x faster.

Find the API the page calls, rather than scraping the page:

// In the browser's network tab, filter by Fetch/XHR. Very frequently:
//   GET /api/v2/products?page=1&limit=24
//   -> {"products": [{"id": ..., "title": ..., "price_cents": ...}], "total": 480}
//
// This is structured, paginated, stable, and 100x cheaper to consume than
// parsing the rendered HTML. It is also far less likely to break, because the
// site's own frontend depends on it.

A scraper that fails loudly:

async def extract_products(html: str, url: str) -> list[Product]:
    tree = HTMLParser(html)

    # Anchor on data attributes and semantic structure, not on generated class
    # names like ".css-1x7f9ka" that change on every deploy.
    cards = tree.css("[data-testid='product-card']")

    if not cards:
        # This is the critical branch. A scraper that returns [] when the page
        # structure changes will quietly produce an empty dataset for weeks.
        raise StructureChanged(
            f"No product cards found at {url}. The selector "
            f"[data-testid='product-card'] matched nothing. The page structure "
            f"has probably changed — do not treat this as 'no products'."
        )

    products = []
    for card in cards:
        title = card.css_first("[data-testid='title']")
        price = card.css_first("[data-testid='price']")

        if title is None or price is None:
            logger.warning("card_missing_fields", extra={"url": url, "html": card.html[:200]})
            continue

        products.append(Product(
            title=title.text(strip=True),
            price_cents=parse_price(price.text(strip=True)),
            url=urljoin(url, card.css_first("a").attributes["href"]),
        ))

    return products

Notes

• Finding the JSON API the page already calls is the single highest-value move in web scraping, and it takes thirty seconds in the network tab. Most people go straight to parsing the HTML and never look.
• robots.txt is not legally binding everywhere, but ignoring it is both discourteous and the fastest route to being blocked. Where a site's terms explicitly prohibit automated access, that is a different and more serious question — check before proceeding.
• Cache aggressively during development. Every re-run against a live site is a request someone else pays for.