6 Key Steps to Large Scale Web Scraping

Scraping a few pages is easy.

Scaling to millions?

That’s where things break.

You’ll get blocked, rate-limited, run out of memory, and watch your scripts quietly fail halfway through.

Large-scale scraping isn’t about writing better code, it’s about building a system that can survive the chaos.

In this guide, we’ll walk through the 10 key steps that separate small-time scripts from production-ready scraping infrastructure.

Let’s go:

How Large-Scale Web Scraping Is Different

Scraping a few pages? You can get away with a quick Python script and a bit of luck.

But as soon as you scale up — thousands, millions, or even billions of pages — everything changes. What used to be a simple loop becomes an entire system, and the margin for error disappears.

Here’s what makes large-scale scraping a completely different game:

IP Bans and Anti-Bot Systems

At small scale, you might never hit a block. But once you start sending thousands of requests, your IP gets flagged, your User-Agent stands out, and the site knows something’s off.

Sites like Amazon, Chewy, and Fnac use tools like Cloudflare, Akamai, and custom WAF rules to detect and block bots. That means you’re dealing with:

• IP rate limits
• CAPTCHA walls
• Browser fingerprinting
• JavaScript challenges

And they’re getting smarter every month.

Infrastructure Limits

When you’re scraping at scale, you’re not just fighting the website — you’re fighting your own machine.

One script running locally can’t handle millions of requests. You’ll run into:

• Memory limits from loading too much HTML at once
• CPU bottlenecks from parsing or rendering
• Disk I/O issues when writing huge volumes of data
• Bandwidth caps if you’re working from a home connection

This isn’t just code anymore — it’s operations.

Unreliable Pages and Partial Failures

Even with perfect code, the web is messy. Pages time out. Layouts change. Content loads with delay.

At small scale, you might not notice a failure or two. At scale, a 0.1% error rate means thousands of broken records.

You need structured retries, fallback mechanisms, and logging that actually tells you what went wrong.

Keeping the Whole System Running

A simple scraper can run once and be done. A large-scale scraper is never done.

You need:

• Scheduling systems
• Queues
• Retry logic
• Monitoring
• Storage pipelines

Without it, your data breaks silently, and you won’t even notice.

1. Automation and Scheduling

You can’t scale scraping if you’re still hitting “Run” manually.

At large scale, scraping becomes a system — and that system needs to run consistently, reliably, and without you babysitting it.

Whether you’re refreshing product prices every hour or crawling millions of pages across multiple domains, automation is the backbone of a real scraping pipeline.

Start Simple: Cron Jobs or Timers

The easiest way to get started is with a basic scheduler:

• On Linux? Use cron.
• On Windows? Use Task Scheduler.
• Inside your script? Use APScheduler or schedule in Python.

# Run scraper.py every night at 3am
0 3 * * * /usr/bin/python3 /home/user/scraper.py >> /home/user/log.txt 2>&1

This works fine for single scripts with predictable timing. But it won’t scale when your tasks start depending on each other — or when you need error handling and retries.

Upgrade to Workflows and Orchestration

Once you’re running multiple scrapers, or need to chain jobs (e.g., crawl category pages → extract product links → fetch details), you’ll want more control.

That’s where tools like:

• Airflow
• Prefect
• Luigi

…come in. These tools let you define DAGs (Directed Acyclic Graphs) — workflows where tasks depend on each other and are automatically retried if something fails.

With Airflow, for example, you can:

• Trigger scrape_index_pages() every morning
• Only run scrape_detail_pages() if the index scrape succeeds
• Retry failed pages 3 times
• Send alerts if something breaks

And you get a clean UI to monitor everything in real time.

Let Someone Else Run the Infrastructure

If managing servers isn’t your thing, let the cloud do it.

Platforms like:

• Scrape.do
• Scrapy Cloud
• Apify
• AWS Lambda + EventBridge

…let you schedule scraping jobs without worrying about uptime, retries, or logs. You just provide the scraping logic; they handle execution, scaling, and failure recovery.

For example, with Scrape.do, you can trigger headless scraping tasks across multiple endpoints without touching infrastructure — and everything runs with smart retries and session handling out of the box.

2. Concurrency and Multithreading

If your scraper is still fetching one page at a time, you’re not scraping at scale — you’re crawling.

Modern websites are slow, content-heavy, and often paginated across thousands of URLs. To scrape them efficiently, you need to make many requests at once — without frying your system or getting blocked.

This is where concurrency and multithreading come in.

Why You Can’t Just Loop

Let’s say you want to scrape 1 million pages.

At 1 request per second, it’ll take 11.5 days to finish.

With concurrency? You can cut that down to a few hours.

But going fast creates its own set of problems — memory usage spikes, network errors pile up, and websites start detecting you as a bot.

To avoid that, you need to do this right.

Threads, Async, and Multiprocessing

There are two main ways to speed things up on a single machine:

• Threads or async — great for I/O-bound tasks like downloading HTML
• Multiprocessing — great for CPU-bound work like parsing large pages or rendering JavaScript

Here’s a basic example using threads in Python:

from concurrent.futures import ThreadPoolExecutor
import requests

urls = ["https://example.com/page/{}".format(i) for i in range(100)]

def fetch(url):
    return requests.get(url).status_code

with ThreadPoolExecutor(max_workers=10) as executor:
    results = list(executor.map(fetch, urls))

This sends 10 requests in parallel. But if you bump that number to 500 without control?

You’ll get blocked. Or crash your network stack.

Throttle Everything

Concurrency is useless if it gets you banned.

You need to:

• Limit the number of concurrent requests (10–100 is usually safe depending on the site)
• Sleep between requests (add jitter to mimic human behavior)
• Track error rates and back off if they spike

Async frameworks like aiohttp and Node.js with Promise.allSettled() can help, but they need the same safety checks.

Go Beyond One Machine

Even with threads and async, you’ll hit a ceiling eventually.

That’s when you start going horizontal — split the job across multiple machines or containers.

Each worker scrapes a chunk of the job, then writes to a shared database, message queue, or cloud bucket.

And if you’re using Scrape.do? You don’t have to manage threads, sessions, or rate limits at all — the platform handles concurrency behind the scenes, automatically optimizing for speed without triggering blocks.

3. Bypass Anti-Bot Systems

This is where most scrapers break.

You’ve got your requests flying, your threads humming — then boom: 403 Forbidden. Or worse, a CAPTCHA. Or a blank page that only loads in a real browser.

Welcome to the wall.

At large scale, websites assume you’re a bot — because you are. And they’re armed with advanced defenses to keep you out.

Let’s break down how these systems work, and what you can do to get past them.

What You’re Up Against

Modern anti-bot systems don’t just look for IP spam anymore.

They analyze:

• Request headers (missing browser details? you’re flagged)
• User agents (Python/3.10 is a dead giveaway)
• TLS fingerprints (mismatched encryption patterns)
• Behavior (perfect timing? no scrolling? no mouse movement? you’re a bot)
• Cookies and sessions (stateless scraping sticks out)
• JavaScript execution (if you can’t run it, you won’t see the content)

Solutions like Cloudflare, Akamai, PerimeterX, and custom WAF setups now protect millions of websites — and they’re constantly evolving.

Step One: Rotate Everything

Start by randomizing the obvious:

• IP addresses: rotate with residential or ISP proxies
• User-Agent strings: rotate between real desktop and mobile agents
• Headers: vary Accept-Language, Referer, and connection headers
• Request timing: add jitter, avoid fixed intervals

A simple proxy + header rotation combo is often enough for small jobs.

At scale? You’ll need more.

Step Two: Behave Like a Browser

Some sites won’t show you anything unless your scraper behaves like a real browser.

That means using headless browsers like:

• Selenium or Playwright (Python)
• Puppeteer (Node.js)

These tools render the page, execute JS, manage cookies, and pass basic browser checks. You can even simulate user interactions like scrolling or clicking dropdowns.

But be warned: running 50 headless browsers in parallel eats RAM and CPU like crazy.

You’ll need orchestration (like Docker or Selenium Grid) if you go this route at scale.

Step Three: Solve or Skip CAPTCHAs

Sites like Amazon or Chewy may throw CAPTCHA walls if they suspect automation.

Options:

• Integrate CAPTCHA solving services like 2Captcha or AntiCaptcha
• Try to avoid triggering them in the first place
• Use full browser simulation to sneak past

But CAPTCHA solving adds cost, latency, and can break frequently — so use it only when you must.

Or Skip the Headaches Entirely

Here’s where platforms like Scrape.do change the game.

Behind the scenes, Scrape.do:

• Rotates IPs (residential, mobile, datacenter)
• Spoofs TLS fingerprints
• Handles cookies and sessions
• Renders JavaScript if needed
• Bypasses WAFs and CAPTCHAs

You just get back clean HTML — no blocks, no hassle.

For example: scraping Amazon or Fnac with Scrape.do is as simple as plugging in the URL. No proxy setup. No headless browser. No trial-and-error.

4. Error Handling at Scale

At some point, your scraper will break.

Pages will time out. Proxies will fail. Servers will throw 503s, or worse — return empty HTML that looks totally fine unless you check it closely. Maybe a CAPTCHA appears mid-run and silently redirects every response. And if you’re not watching, you’ll just end up with a nice clean log and 100,000 rows of garbage.

That’s why error handling isn’t optional — it’s a core part of scraping at scale.

Catch the Fall

First things first: your scraper should never crash because of a bad request.

Wrap your fetch logic in a try/except block. When something goes wrong — a timeout, a connection error, even a parsing failure — log the problem and move on. Don’t let one broken page take down your entire run.

Good error handling means bad data doesn’t poison good progress.

Retry, But Be Smart About It

Some errors are temporary. A server might reject a request with a 503 just because it’s overloaded for a second. That’s not a dead-end — it’s a sign to wait and try again.

This is where retry logic with backoff makes all the difference. Instead of hammering the server three times in a row, space the retries out — one second, then two, then four. Most transient issues resolve themselves if you give them a little breathing room.

But not everything should be retried. A 404 is permanent. A parsing error means your selector is broken. Repeating those requests just wastes time — and in some cases, can get you banned faster.

Know What Broke — and Why

At scale, you won’t be watching your terminal. You need visibility after the fact.

That means every failure should be logged with useful context: the URL, what failed, what the response looked like, what you expected. If you’re getting 500s on one domain and 403s on another, those are very different problems — and you can’t treat them the same.

Group your errors. Spot patterns. Build dashboards if you’re running jobs regularly.

If 5% of your scrape failed, you should know exactly which 5% — and decide whether to retry, ignore, or dig deeper.

Build for Recovery

Even with good logging and retries, some pages will always fail the first time. That’s fine — as long as you can recover.

Failed URLs should go into a queue, a database, a CSV — something you can pick up and reprocess later. You don’t want to rescrape everything just because 2% of it glitched.

This doesn’t have to be fancy. Even writing failed URLs to a file can be enough — as long as you use it.

And when you do re-run those URLs? Use a different proxy, a longer timeout, or even a different scraper logic. Don’t just repeat the same failed attempt and hope for a different outcome.

Let the Platform Handle It (If You Want)

Scrape.do users don’t have to build all this from scratch.

Retries, error detection, proxy failures, and timeout recovery are baked in. If a page returns an error, Scrape.do automatically handles it behind the scenes — and surfaces the details in your dashboard.

You don’t need to engineer a backoff system or log every timeout. You can just send requests and trust the system to be resilient.

But whether you build your own or rely on a platform — error handling is the difference between scraping 10,000 pages and getting 10,000 rows of usable data.

5. Logging and Monitoring

When your scraper grows from a script to a system, visibility becomes everything. You can’t rely on terminal output or gut feeling to know if things are working. At scale, issues don’t crash your code — they quietly corrupt your data.

Why Logging Matters

Imagine scraping 1 million pages and discovering the next day that 30% of them were login redirects. Or worse — blank responses that got saved anyway. This happens when you don’t log what each request did.

Every request should write to a log: what URL was fetched, how long it took, what status code came back, and whether parsing succeeded. Not after the fact — during the run.

This gives you a trail to follow when something breaks. If success rates drop, if request times spike, if data volume suddenly shrinks — you’ll know when and where it started.

What to Monitor While It’s Running

Logging tells you what happened. Monitoring tells you what’s happening now.

You need a way to answer questions like:

• Is the scraper still making progress?
• Are errors increasing?
• Is throughput slowing down?
• Are we getting the data we expect?

Even lightweight monitoring — a periodic console print, a small Grafana panel, or a status report every few minutes — can reveal when something drifts off course. Don’t wait until the job finishes to find out something went wrong halfway through.

Catch Problems Before They Compound

Most large-scale failures start small: a single selector breaking, a proxy dying, a site layout changing. If you notice the signs early — fewer extracted items, longer response times, abnormal status codes — you can pause, investigate, and recover.

Without monitoring, you’ll keep scraping junk for hours and only notice when you open the results.

Minimal Setup, Maximum Payoff

You don’t need a full observability stack. A rotating logfile, a CSV of failed URLs, a few counters, and a timestamp can go a long way.

But the system should always answer this: Is it working right now, and how do I know?

If you’re using Scrape.do, you still need to monitor your own extraction layer. Scrape.do handles retries, proxy issues, and HTTP response handling — but once the HTML is returned, it’s up to you to validate what gets parsed and saved.

6. Data Storage

Scraping isn’t just about fetching data — it’s about keeping it.

At scale, saving what you scrape becomes its own challenge. You’re no longer writing a few rows to a CSV file; you’re handling gigabytes of structured data, maybe even terabytes if you’re archiving full HTML. And if you don’t think about storage early, it’s going to slow everything down — or worse, lose data silently.

Why Small-Scale Tactics Don’t Work

When you’re scraping 500 pages, appending rows to a CSV or keeping everything in memory might be fine. But once you’re pulling thousands of pages per minute, writing one row at a time becomes a bottleneck. Your memory usage creeps up. Your disk I/O chokes. And if something crashes mid-run, you’re left with partial data and no way to pick up where you left off.

This is where having a real storage strategy matters.

Choose the Right Format for the Job

There’s no one-size-fits-all storage method — it depends on how you want to use the data.

If you’re doing analysis later, raw dumps in JSONL or CSV might be enough. If you need to run queries, aggregate results, or feed dashboards, a proper database is a better fit.

Relational databases like PostgreSQL or MySQL are great when your data has a consistent structure. If it varies — for example, scraping multiple sites with different fields — something like MongoDB or another document store gives you more flexibility.

For massive pipelines, consider separating storage into two layers: one for raw HTML or JSON, and one for cleaned, structured data. That way, you can reprocess pages without re-downloading them — and recover quickly if parsing logic changes.

Stream, Don’t Accumulate

One of the biggest mistakes at scale is holding everything in memory.

You want to process and write incrementally — fetch a page, extract the data, save the result, move on. That keeps memory usage flat and lets you resume scraping easily if something crashes halfway.

Writing in batches helps too. Instead of inserting one row at a time, buffer 100 or 1000 and write them together. It’s faster, more stable, and reduces I/O pressure.

Think About the Long Run

At scale, storage isn’t just about writing data — it’s about managing it later.

You might want to track changes over time, compare results from different runs, or clean up duplicates. That means keeping timestamps, job IDs, or hashes in your data so it can be audited later.

And if your scraper runs daily or continuously, you’ll need a way to archive or expire old records — before they pile up and make your database unusable.

Conclusion

Scaling a scraper isn’t about writing more code; it’s about building a system that can survive the chaos of the web.

You need automation that runs without supervision, concurrency that doesn’t crash your machine, anti-bot defenses that actually work, and logging that catches problems before they snowball. It’s not easy — but it’s possible.

If you’re building a high-scale scraper, take the time to get these ten steps right. They’re what turn a fragile script into real infrastructure.

And when you’re ready to move fast without worrying about proxies, CAPTCHAs, or rate limits, Scrape.do can handle the heavy lifting:

• Built-in anti-bot bypass
• Geo-targeted IP rotation
• JavaScript rendering with headless browser support
• Scalable infrastructure with 99.98% success rate

Start scraping for free with 1000 credits and focus on the part that matters: the data.