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Scaling Web Scrapers

Once your scraper works on a single page, the next challenge is scaling it to handle thousands or millions of pages efficiently without getting blocked.

Interactive lesson

This topic is covered in the Scrapfly Academy: Scaling lesson.

Concurrency

The biggest bottleneck in scraping is waiting for HTTP responses. Use async/concurrent requests to scrape multiple pages at once.

```python import asyncio import httpx

async def scrape_page(client, url): response = await client.get(url) # parse and return data return {"url": url, "status": response.status_code}

async def main(): urls = [f"https://example.com/page/{i}" for i in range(1, 101)]

async with httpx.AsyncClient() as client:
    # Scrape 10 pages at a time
    semaphore = asyncio.Semaphore(10)

    async def limited_scrape(url):
        async with semaphore:
            return await scrape_page(client, url)

    results = await asyncio.gather(*[limited_scrape(url) for url in urls])

print(f"Scraped {len(results)} pages")

asyncio.run(main()) ```

Key principle: limit concurrency to avoid overwhelming the target. 5-20 concurrent requests is a typical range.

Rate Limiting

Sending requests too fast will get you blocked. Implement delays:

  • Fixed delay - await asyncio.sleep(1) between requests
  • Random delay - await asyncio.sleep(random.uniform(0.5, 2.0)) to look more natural
  • Adaptive delay - increase delay when you get errors, decrease when requests succeed

Proxy Rotation

A single IP address making thousands of requests is easy to detect. Rotate through multiple proxies:

  • Datacenter proxies - cheap and fast, but easily detected
  • Residential proxies - more expensive, but harder to block
  • Mobile proxies - most trusted, highest cost

The Scrapfly Academy: Proxies lesson covers proxy types and rotation strategies in depth.

When to Use a Framework

For simple scrapers (< 100 pages), async code is enough. For larger projects, a framework handles the complexity:

Framework Language Best For
Scrapy Python Production crawlers with pipelines, middleware, and scheduling
Crawlee JavaScript Browser + HTTP scraping with built-in queue management
Colly Go Fast concurrent crawling
Katana Go URL discovery and site mapping
Botasaurus Python Anti-detect scraping with built-in parallelism

See the full Frameworks comparison.

When to Use a Scraping API

Building and maintaining scraping infrastructure (proxies, anti-bot bypass, browser pools, retry logic) is expensive. A scraping API handles all of this:

You Handle Scraping API Handles
What data to extract Proxy rotation
How to parse the response Anti-bot bypass
Where to store results JavaScript rendering
Rate limiting
Retry logic
Infrastructure maintenance

Scrapfly achieves 99% success rate across protected targets. See Scrapeway benchmarks for independent comparisons.

Next Steps

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