Python crawler library | Scrapy/BeautifulSoup comparison

What's the difference between Scrapy and BeautifulSoup anyway?

First of all, let's talk about the basic positioning of these two tools , Scrapy is like a professional decoration team , from demolition to painting can be done , while BeautifulSoup is more like a Swiss army knife , specializing in handling the web page data has been to hand . For example, if you want to capture 100 pages of product information from an e-commerce site, Scrapy can handle the whole process of turning pages, storing, and handling exceptions by itself. But if you just want to parse the locally saved HTML file, BeautifulSoup minutes to the data out of the key.

How do proxy IPs work in these two tools?

Here's where to draw the line!Scrapy comes with its own middleware mechanismConfiguring a proxy is as simple as adding a spice packet to instant noodles. In settings.py add a few lines of code, the ipipgo API address to fill in, you can automatically rotate the IP. and BeautifulSoup itself does not take the network request function, have to work with the requests library to use, then you have to manually deal with the proxy:

 Example of an exclusive IP with ipipgo
proxies = {
  "http": "http://user:pass@proxy.ipipgo.com:31028",
  "https": "http://user:pass@proxy.ipipgo.com:31028"
}
response = requests.get(url, proxies=proxies)

See here for a performance comparison

If the project needed to grab hundreds of thousands of data per day.Scrapy+ipipgo high stash proxyThe combination of can make you lose less hair. Their dynamic residential IP pool works especially well for anti-climbing strict websites, personally tested to catch a job site for 8 hours in a row without being blocked.

Practical Selection Guide

Look at the size of the project to speak! Small projects such as grabbing a forum post, BeautifulSoup+requests is perfectly adequate. But if commercial-grade data collection, Scrapy's advantages can not be ignored:
1. Automatic retry mechanism (with ipipgo's IP switching)
2. Built-in data export format (JSON/CSV is fine)
3. Support for distributed expansion

There is a pit to be reminded: with free proxies to engage the crawler is like using a papier-mâché umbrella to block rainstorms, minutes to rest. Previously tried an open source proxy pool, 10 IP in 8 failed. Later changed to ipipgo business package, 10,000 IP rotation, collection efficiency directly doubled.

QA time

Q: Will I be found by the website if I use a proxy IP?
A: It depends on the quality of the proxy. ipipgo's hybrid IP pool, which automatically changes the exit IP for each request, together with the random UA header, can basically hide it from the world.

Q: What should I do if the request keeps timing out?
A: First check the availability of the proxy IP, it is recommended to use the connectivity testing interface provided by ipipgo. you can set the DOWNLOAD_TIMEOUT parameter in Scrapy, don't exceed 30 seconds.

Q: Do I need to maintain my own IP pool?
A: No need at all! ipipgo's API can return available proxies in real time, and you can also set up automatic exclusion of failed nodes. Their technical customer service is reliable, the last time I encountered an anti-climbing strategy, half an hour to solve the problem.

Finally said a cold knowledge: Scrapy remember to open CONCURRENT_REQUESTS_PER_IP parameter, with ipipgo's dynamic IP, can pull the collection speed to the limit is not blocking the IP. specific settings you can look at their home documentation, there are ready-made configuration templates can copy homework.