Dealing with Rate Limiting and Throttling

When scraping websites, you will often encounter rate limiting and throttling mechanisms put in place by the site owners to prevent excessive or abusive requests. Rate limiting caps the number of requests you can make in a given time period, while throttling slows down your requests if you go over a threshold. Hitting these limits can cause your scraper to receive 429 Too Many Requests errors or even get your IP address blocked. However, there are a few strategies you can employ to avoid triggering rate limits and keep your web scraping running smoothly.

The simplest approach is to add delays between your requests using Python's time.sleep() function. For example:

import requests

import time

def make_request(url):

response = requests.get(url)

# Sleep for 5 seconds before making the next request

time.sleep(5)

return response

By pausing for a few seconds between each request, you avoid bombarding the server too quickly. The optimal delay depends on the specific site - some can handle faster rates than others.

Another technique is to spread out your requests across multiple IP addresses using proxies. Each website tracks incoming requests by IP, so alternating addresses prevents any single one from exceeding the rate limit:

import requests

from itertools import cycle

proxies = [

{'http':'http://ip1:port1'},

{'http':'http://ip2:port2'},

{'http':'http://ip3:port3'}

]

proxy_pool = cycle(proxies)

def make_request(url):

proxy = next(proxy_pool)

response = requests.get(url, proxies=proxy)

return response

The itertools cycle lets you round-robin through the list of proxies so each request uses the next one in the sequence.

Some sites use more sophisticated tracking like cookies and browser fingerprinting to identify unique visitors. In those cases, you may need to rotate user agent strings and clear cookies periodically to reset your session and avoid triggering rate limits.

Finally, be respectful and limit concurrent requests. Even if you avoid rate limits, slamming a server with tons of simultaneous requests is abusive and may get you blocked. Use a task queue system like Celery to throttle your scraping speed:

from celery import Celery

app = Celery('scraper', broker='redis://localhost:6379')

app.conf.task_routes = {'scraper.tasks.make_request': {'rate_limit': '10/m'}}

@app.task

def make_request(url):

response = requests.get(url)

return response.text

This example Celery setup routes all make_request tasks through a queue that allows only 10 requests per minute, throttling your overall scraping rate.

In summary, dealing with rate limits when web scraping requires adding delays, rotating IP addresses with proxies, avoiding cookies and fingerprinting, and limiting concurrent requests. By employing these techniques, you can scrape data respectfully and reliably without triggering blocking or bans from your target websites.