How to handle large-scale data scraping efficiently?

Elora Shani · 2024-12-17T10:48:26+00:00

Large-scale data scraping requires careful planning and the right tools to avoid bottlenecks. How do you handle thousands of pages efficiently without overloading the website? One approach is to use parallel scraping with libraries like asyncio in Python or multithreading in Java. Parallelism allows multiple pages to be scraped simultaneously, significantly reducing overall scraping time. Another method is to use a framework like Scrapy, which manages concurrency and retries automatically.Here’s an example of parallel scraping using aiohttp in Python:import aiohttp import asyncioasync def fetch(session, url): async with session.get(url) as response: return await response.text()async def scrape_pages(urls): async with aiohttp.ClientSession() as session: tasks responses await asyncio.gather(*tasks) for idx, content in enumerate(responses, 1): print(f"Page {idx}: {len(content)} characters fetched.")urls asyncio.run(scrape_pages(urls))Efficient storage is equally important. Instead of saving data to files, consider using databases like MongoDB or PostgreSQL to handle large datasets. How do you manage storage and processing when scraping at scale?

General Web Scraping

How to handle large-scale data scraping efficiently?

Posted by Elora Shani on 12/17/2024 at 10:48 am
Large-scale data scraping requires careful planning and the right tools to avoid bottlenecks. How do you handle thousands of pages efficiently without overloading the website? One approach is to use parallel scraping with libraries like asyncio in Python or multithreading in Java. Parallelism allows multiple pages to be scraped simultaneously, significantly reducing overall scraping time. Another method is to use a framework like Scrapy, which manages concurrency and retries automatically.
Here’s an example of parallel scraping using aiohttp in Python:
```
import aiohttp
import asyncio
async def fetch(session, url):
    async with session.get(url) as response:
        return await response.text()
async def scrape_pages(urls):
    async with aiohttp.ClientSession() as session:
        tasks = [fetch(session, url) for url in urls]
        responses = await asyncio.gather(*tasks)
        for idx, content in enumerate(responses, 1):
            print(f"Page {idx}: {len(content)} characters fetched.")
urls = [f"https://example.com/page/{i}" for i in range(1, 101)]
asyncio.run(scrape_pages(urls))
```
Efficient storage is equally important. Instead of saving data to files, consider using databases like MongoDB or PostgreSQL to handle large datasets. How do you manage storage and processing when scraping at scale?
Elora Shani replied 5 days, 14 hours ago 1 Member · 0 Replies
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