Headless Web Scraping

Extract data from web pages that resist simple HTTP requests — JS-rendered content, Cloudflare-protected sites, and dynamic SPAs — using scrapling's three-tier fetcher architecture and CSS-based data extraction.

When to Use

• Target page requires JavaScript rendering (SPA, React, Vue)
• Site has anti-bot protections (Cloudflare Turnstile, TLS fingerprinting)
• You need structured extraction of multiple elements via CSS selectors
• Simple

  WebFetch

  or

  requests.get()

  returns empty or blocked responses
• Extracting tabular data, link lists, or repeated DOM structures at scale

Inputs

• Required: Target URL or list of URLs to scrape
• Required: Data to extract (CSS selectors, field names, or description of target elements)
• Optional: Fetcher tier override (default: auto-select based on site behavior)
• Optional: Output format (default: JSON; alternatives: CSV, Python dict)
• Optional: Rate limit delay in seconds (default: 1)

Procedure

Step 1: Select Fetcher Tier

Determine which scrapling fetcher matches the target site's defenses.

# Decision matrix:
# 1. Fetcher        — static HTML, no JS, no anti-bot (fastest)
# 2. StealthyFetcher — Cloudflare/Turnstile, TLS fingerprint checks
# 3. DynamicFetcher  — JS-rendered SPAs, click/scroll interactions

# Quick probe: try Fetcher first, escalate on failure
from scrapling import Fetcher

fetcher = Fetcher()
response = fetcher.get("https://example.com/target-page")

if response.status == 200 and response.get_all_text():
    print("Fetcher tier sufficient")
else:
    print("Escalate to StealthyFetcher or DynamicFetcher")

Fetcher

StealthyFetcher

DynamicFetcher

DynamicFetcher

Expected: One of the three tiers is identified. For most modern sites,

StealthyFetcher

On failure: If all three tiers return blocked responses, check whether the site uses altcha CAPTCHA (proof-of-work challenge that cannot be bypassed). If so, document the limitation and provide manual extraction instructions instead.

Step 2: Configure the Fetcher

Set up the selected fetcher with appropriate options.

from scrapling import Fetcher, StealthyFetcher, DynamicFetcher

# Tier 1: Fast HTTP with TLS fingerprint impersonation
fetcher = Fetcher()
fetcher.configure(
    timeout=30,
    retries=3,
    follow_redirects=True
)

# Tier 2: Headless Chromium with anti-detection
fetcher = StealthyFetcher()
fetcher.configure(
    headless=True,
    timeout=60,
    network_idle=True  # wait for all network requests to settle
)

# Tier 3: Full browser automation
fetcher = DynamicFetcher()
fetcher.configure(
    headless=True,
    timeout=90,
    network_idle=True,
    wait_selector="div.results"  # wait for specific element before extracting
)

Expected: Fetcher instance is configured and ready. No errors on instantiation. For

StealthyFetcher

DynamicFetcher

On failure:

• playwright

  or browser binary not found -- run

  python -m playwright install chromium

• Timeout on

  configure()

  -- increase timeout value or check network connectivity
• Import error -- install scrapling:

  pip install scrapling

Step 3: Fetch and Extract Data

Navigate to the target URL and extract structured data using CSS selectors.

# Fetch the page
response = fetcher.get("https://example.com/target-page")

# Single element extraction
title = response.find("h1.page-title")
if title:
    print(title.get_all_text())

# Multiple elements
items = response.find_all("div.result-item")
for item in items:
    name = item.find("span.name")
    price = item.find("span.price")
    print(f"{name.get_all_text()}: {price.get_all_text()}")

# Get attribute values
links = response.find_all("a.product-link")
urls = [link.get("href") for link in links]

# Get raw HTML content of an element
detail_html = response.find("div.description").html_content

Key API reference:

response.find("selector")

response.find_all("selector")

element.get("attr")

element.get_all_text()

element.html_content

Expected: Extracted data matches the visible page content. Elements are non-None and text content is non-empty for populated pages.

On failure:

• find()

  returns

  None

  -- inspect the actual HTML (

  response.html_content

  ) to verify the selector; the page may use different class names than expected
• Empty text from

  get_all_text()

  -- content may be inside shadow DOM or an iframe; try

  DynamicFetcher

  with a

  wait_selector

• Do NOT use

  .css_first()

  -- this is not part of the scrapling API (common confusion with other libraries)

Step 4: Handle Failures and Edge Cases

Implement fallback logic for CAPTCHA detection, empty responses, and session requirements.

import time

def scrape_with_fallback(url, selector):
    """Try each fetcher tier in order, with CAPTCHA detection."""
    tiers = [
        ("Fetcher", Fetcher),
        ("StealthyFetcher", StealthyFetcher),
        ("DynamicFetcher", DynamicFetcher),
    ]

    for tier_name, tier_class in tiers:
        fetcher = tier_class()
        fetcher.configure(headless=True, timeout=60)

        try:
            response = fetcher.get(url)
        except Exception as error:
            print(f"{tier_name} failed: {error}")
            continue

        # Detect CAPTCHA / challenge pages
        page_text = response.get_all_text().lower()
        if "altcha" in page_text or "proof of work" in page_text:
            print(f"altcha CAPTCHA detected -- cannot automate")
            return None

        if response.status == 403 or response.status == 503:
            print(f"{tier_name} blocked (HTTP {response.status}), escalating")
            continue

        result = response.find(selector)
        if result and result.get_all_text().strip():
            return result.get_all_text()

        print(f"{tier_name} returned empty content, escalating")

    print("All tiers exhausted. Manual extraction required.")
    return None

Expected: Function returns extracted text on success, or

None

On failure:

• All tiers return 403 -- the site blocks all automated access (common with WIPO, TMview, some government databases); document the URL as requiring manual access
• Timeout errors -- the page may be behind a slow CDN; increase timeout to 120s
• Session/cookie errors -- the site may require login; add cookie handling or authenticate first

Step 5: Rate Limiting and Ethical Scraping

Implement delays and respect site policies before running at scale.

import time
import urllib.robotparser

def check_robots_txt(base_url, target_path):
    """Check if scraping is allowed by robots.txt."""
    rp = urllib.robotparser.RobotFileParser()
    rp.set_url(f"{base_url}/robots.txt")
    rp.read()
    return rp.can_fetch("*", f"{base_url}{target_path}")

def scrape_urls(urls, selector, delay=1.0):
    """Scrape multiple URLs with rate limiting."""
    results = []
    fetcher = StealthyFetcher()
    fetcher.configure(headless=True, timeout=60)

    for url in urls:
        response = fetcher.get(url)
        data = response.find(selector)
        if data:
            results.append(data.get_all_text())

        time.sleep(delay)  # respect the server

    return results

Ethical scraping checklist:

1. Check

   robots.txt

   before scraping -- respect

   Disallow

   directives
2. Use a minimum 1-second delay between requests
3. Identify your scraper with a descriptive User-Agent when possible
4. Do not scrape personal data without legal basis
5. Cache responses locally to avoid redundant requests
6. Stop immediately if you receive a 429 (Too Many Requests)

Expected: Scraping runs at a controlled rate.

robots.txt

On failure:

• 429 Too Many Requests -- increase delay to 3-5 seconds, or stop and retry later

• robots.txt

  disallows the path -- respect the directive; do not override it
• IP ban -- stop scraping immediately; the rate limiting was insufficient. If access is legitimate (public data, ToS-permitted, robots.txt-respected) and you must continue, see rotate-scraping-proxies for network-layer escalation

Validation

• Correct fetcher tier is selected (not over- or under-powered for the target)

• configure()

  method is used (not deprecated constructor kwargs)
• CSS selectors match actual page structure (verified against page source)

• .find()

  /

  .find_all()

  API is used (not

  .css_first()

  or other library methods)
• CAPTCHA detection is in place (altcha pages are reported, not retried)
• Rate limiting is implemented for multi-URL scraping

• robots.txt

  is checked before bulk operations
• Extracted data is non-empty and structurally correct

Common Pitfalls

• Using

  .css_first()

  instead of

  .find()

  : scrapling uses

  .find()

  and

  .find_all()

  for element selection --

  .css_first()

  belongs to a different library and will raise

  AttributeError

• Starting with DynamicFetcher: Always try

  Fetcher

  first, then escalate --

  DynamicFetcher

  is 10-50x slower due to full browser startup
• Constructor kwargs instead of

  configure()

  : scrapling v0.4.x deprecated passing options to the constructor; always use the

  configure()

  method
• Ignoring altcha CAPTCHA: No fetcher tier can solve altcha proof-of-work challenges -- detect them early and fall back to manual instructions
• No rate limiting: Even if the site does not return 429, aggressive scraping can get your IP banned or cause service degradation
• Assuming stable selectors: Website CSS classes change frequently -- validate selectors against current page source before each scraping campaign

Related Skills

• rotate-scraping-proxies -- network-layer escalation when client-side stealth is exhausted and IP bans block legitimate, ToS-permitted access
• use-graphql-api -- structured API queries when the site offers a GraphQL endpoint (preferred over scraping)
• serialize-data-formats -- converting extracted data to JSON, CSV, or other formats
• deploy-searxng -- self-hosted search engine that aggregates results from multiple sources
• forage-solutions -- broader pattern for gathering information from diverse sources