Why Web Scrapers Break (And How I Learned to Fix Them)

I still remember the panic when our production scraper stopped working at 3 AM on a Monday. The logs showed successful requests, HTTP 200s across the board, but zero data extracted. After two hours of debugging with increasingly desperate coffee consumption, I found the culprit: the target site had changed a single CSS class name from product-title to product-name. That's when I learned that web scraping isn't just about writing code that works today—it's about writing code that survives tomorrow.

The Illusion of Stability

Here's what nobody tells you when you're starting with web scraping: websites are living, breathing organisms that change constantly. That beautifully structured HTML you scraped yesterday? It might be completely different today. And unlike APIs where breaking changes come with deprecation notices and migration guides, websites just... change. No warning, no changelog, no apology.
I've been scraping data professionally for about six years now, and I've seen scrapers break in ways that would make you laugh if they hadn't cost real time and money. A site redesign. An A/B test that randomly shows different HTML structures. A lazy-loaded section that didn't exist before. Even something as simple as adding a cookie consent banner can throw your entire scraper off balance.
The real challenge isn't writing a scraper that works once. It's writing one that keeps working.
The Three Ways Scrapers Actually Break
Structure changes are the obvious killer. Someone on the web team decides that <div class="price"> should actually be <span class="product-price"> and boom—your scraper returns empty strings. The HTML is still valid, the page still loads, but your selectors are now pointing at nothing.
I've learned to use multiple fallback selectors for critical data. Instead of just .price, I'll try .price, [data-price], .product-price, and even parse the JSON-LD structured data if it exists. It's more code upfront, but it's saved me countless times when sites do partial rollouts or A/B tests.
Dynamic content is the sneaky one. A site that used to render everything server-side suddenly switches to a React SPA. Now that simple HTTP request returns just a loading spinner and a bunch of empty divs. The data is still there—it's just being fetched by JavaScript after the page loads.
This is where browser automation becomes non-negotiable. I used to fight this with increasingly complex timing hacks and network sniffing. Now I just launch a headless browser, let the JavaScript do its thing, and extract data from the fully rendered page. It's slower and heavier, but it actually works reliably.
Anti-bot measures are the adversarial ones. Rate limiting, IP blocking, browser fingerprinting, CAPTCHAs—sites are getting smarter about detecting scrapers. And they should be. Not everyone scraping their site has good intentions.
The key here is to scrape respectfully. Add delays between requests. Rotate user agents. Use residential proxies if you're doing serious volume. And most importantly, check the robots.txt and terms of service. I've walked away from scraping projects when the target site explicitly prohibited it. It's not worth the legal headache.

My Actual Debugging Process

When a scraper breaks, I follow the same checklist every time:
First, I verify the site is actually accessible. Sounds obvious, but I've wasted time debugging code when the site was just down for maintenance.
Next, I check if the page structure changed. I'll load the page in a browser, inspect the HTML, and compare it to what my scraper expects. Usually the problem reveals itself in about 30 seconds.
If the structure looks fine, I check for dynamic content. Open DevTools, look at the Network tab, and see if the data I need is being loaded via AJAX calls. If it is, I can either intercept those API calls directly or switch to browser automation.
Finally, I check if I'm being blocked. Look for CAPTCHA pages, rate limit errors, or suspicious redirects. If I'm blocked, I back off, add delays, and revisit my approach.
Here's what a resilient scraper structure looks like in practice:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37

async function extractProductData(page) {
  // Try multiple selectors with fallbacks
  const price = await page.evaluate(() => {
    const selectors = [
      '.price',
      '[data-price]',
      '.product-price',
      'span[itemprop="price"]'
    ];
    
    for (const selector of selectors) {
      const element = document.querySelector(selector);
      if (element?.textContent) {
        return element.textContent.trim();
      }
    }
    
    // Last resort: try JSON-LD structured data
    const jsonLd = document.querySelector('script[type="application/ld+json"]');
    if (jsonLd) {
      try {
        const data = JSON.parse(jsonLd.textContent);
        return data.offers?.price;
      } catch (e) {
        console.error('Failed to parse JSON-LD:', e);
      }
    }
    
    return null;
  });
  
  if (!price) {
    throw new Error('Could not extract price with any method');
  }
  
  return price;
}

The Maintenance Reality

The uncomfortable truth about web scraping is that it requires ongoing maintenance. I've tried to automate scraper health checks with monitoring scripts that run daily and alert me when data extraction fails or returns suspicious patterns. It's not perfect, but it catches most breaks before they become emergencies.
I also version my scrapers and keep detailed logs of what selectors work for which sites. When something breaks, I can see exactly what changed and how to fix it quickly. Documentation feels like busywork until you're debugging at 3 AM and can't remember why you used that weird XPath selector six months ago.

When to Just Give Up

Sometimes scraping isn't the answer. If a site has a public API, use it. Seriously. It'll be more stable, faster, and you won't be fighting anti-bot measures. Even if the API costs money, calculate the engineering time you'd spend building and maintaining a scraper. Often the API is cheaper.
If a site is actively hostile to scraping with aggressive anti-bot measures and no public API, ask yourself if the data is really worth it. There's usually an alternative source, a partnership opportunity, or a different approach entirely.

The Long Game

Web scraping isn't a "set it and forget it" task. It's more like gardening—you plant the scraper, but then you need to tend it, watch for problems, and adapt as the environment changes. The scrapers I'm proudest of aren't the clever ones that used some brilliant technique. They're the boring, well-tested ones with multiple fallbacks that have been running reliably for years with minimal maintenance.
Build for resilience, not cleverness. Use multiple selectors. Handle errors gracefully. Log everything. Monitor constantly. And most importantly, be ready for things to break—because they will.
That 3 AM debugging session taught me that web scraping is as much about defensive coding as it is about data extraction. The best scraper is the one that's still working six months from now when everyone's forgotten it exists.

Posted on: Monday, 8 December 2025 Zain Aftab
Debugging Javascript web scrapping