r/scrapingtheweb

Hey everyone,

I’ve been running a large-scale data collection operation for the past 3 years (currently hitting around 15M requests/month), and I recently had to do a hard pivot in our infrastructure. I wanted to share the numbers and the "why" behind it, as it might help anyone hitting the same wall.

The Old Setup (The DIY Era):

•Stack: Custom Python/Playwright + Scrapy.

•Proxy: A mix of residential and mobile IPs from 3 different providers.

•Maintenance: 1 full-time dev dedicated to patching TLS fingerprints and rotating User-Agents to bypass JA4+ detection.

•Success Rate: Averaged 65-70% on high-security targets (Cloudflare/Akamai).

The Problem:
In 2026, the "cat-and-mouse" game has become an operational tax. We were spending more on developer hours fixing broken scrapers than we were on the actual data infrastructure. The "stealth" libraries just can't keep up with the server-side behavioral analysis and protocol-level fingerprinting anymore.

The Pivot:
Last quarter, we moved the entire extraction layer to a managed "Smart Scraping" setup. Instead of managing the browser instances and proxy rotation ourselves, we shifted to an API-first approach that handles the TLS handshakes and anti-bot challenges at the edge.

The Results:

•Success Rate: Jumped to 96%+.

•Cost: While the per-request cost is slightly higher than raw proxies, our Total Cost of Ownership (TCO) dropped by 40% because we reclaimed that full-time dev's bandwidth.

•Latency: Actually improved because we're no longer running heavy headless browsers for 80% of our tasks.

My takeaway: If you're doing <100k requests/month, DIY is fine. But at scale, managing the "anti-bot ops" yourself is becoming a liability rather than an asset.

I’m curious to hear from others at scale: At what point did you decide to stop building your own "stealth" stack and move to a managed layer? Or are you still finding success with custom-patched SSL libraries?

Heyo, story time: Spent the last year running Walmart scrapers in production. Headless browsers (Playwright specifically) are almost always recommended over plain "requests" + BeautifulSoup for JS-heavy sites like Walmart, and that's true, but "use a headless browser" isn't the whole story. Here's what I learned that actually works in practice You may ask why depend on headless at all? Walmart's product pages are JavaScript-rendered. A raw HTTP request returns an HTML shell, prices, titles, and availability are injected by JS after load. BeautifulSoup never sees that data. Now for the headless browser part, it runs a Chromium engine, executes the JS, and lets you query the fully-rendered DOM. That part works well. Even with a headless browser, you'll hit blocks, it's not a holy grail as some of people have praised it over the reddit. Walmart fingerprints more than just your IP, browser canvas signatures, WebGL data, timing patterns, and TLS handshake characteristics are all signals. Vanilla Playwright out of the box is detectable. You need "playwright-stealth" or equivalent patches to mask the most obvious headless tells.

Walmart A/B tests constantly. The "<h1>" for the product title and "<span itemprop="price">`" for pricing, the selectors everyone uses, can and do shift. A scraper that worked Monday can silently return empty strings by Wednesday. You need selector fallbacks and output validation, not just "element.inner_text()". As for the resources, well, each Chromium instance eats ~150–300MB of RAM. If you're running concurrent scrapers, this adds up fast. For small datasets it's fine at scale, you either need careful concurrency limits or a distributed setup. Rotating proxies help with IP bans but don't solve fingerprinting. Worse, misconfigured proxies inside a browser context can cause silent failures, the request goes through but returns a CAPTCHA page that your parser doesn't catch. Always validate that your response actually contains product data before storing it.

Honest suggestions, people:

- ALWAYS USE "playwright-stealth" to patch headless fingerprints

- Add "wait_for_selector()" with a timeout before extracting, don't assume the element is there

- Build in retry logic with exponential backoff on failures

- VALIDATE YOUR OUTPUT: if price is empty string, treat it as a failed scrape and retry

- Rotate User-Agents per session, not per request

- Use residential proxies, not datacenter, Walmart's filters are tuned to spot datacenter ranges, (however, I was running datacenter at first with the help of residential proxies, ditched datacenter after some time).

Headless browsers are the right tool for Walmart, but they're not a reliability silver bullet as some of you praise it. For me particularly, ~85–90% success rate with a well-tuned setup was what I got at most, dropping toward 60–70% if you skip stealth patches and output validation. The remaining failures are mostly CAPTCHAs and transient blocks that retries will catch. For anything production-scale, budget time for maintenance. Walmart's defenses update, and your selectors will break. That's just the reality of scraping a site this sophisticated.

I'm looking for rotating residential proxies for webscraping, i was looking at proxly.cc or plainproxies.com but i'm not sure if they're good, does anyone else have suggestions?

I’m trying to collect past data from CoinDCX’s Expert Picks feature for analysis, but I’ve hit a wall after trying a few different approaches.

Here’s what I’ve already tried:

• Using mitmproxy to capture the app’s network traffic, but it looks like CoinDCX is using certificate pinning, so the traffic never really showed up properly
• Decompiling the APK with JADX, but the code seemed heavily obfuscated and I couldn’t find any useful API endpoints
• Searching for keywords like expert, picks, and signals, but nothing useful came out
• Looking on the website too, but this feature appears to be app-only and I couldn’t find any direct access there

It seems like CoinDCX has intentionally hidden or secured this feature, probably through an internal API or obfuscation.

I’m not very experienced with scraping or reverse engineering, so I’m posting here to ask:
does anyone know a reliable way to extract past data from a mobile-only feature like this?

My goal is simple: get the historical Expert Picks data into a usable format like CSV for research and analysis.

If anyone knows how to do it, tell me its helps me a lot, DM

https://preview.redd.it/5bdzlap8f3xg1.jpg?width=738&format=pjpg&auto=webp&s=9de596e8aaa1b4d4f303c761cf00f71cefb7eeaf

Mobile/lte proxies with the option of tcp fingerprint alternation?