Mastering How to Rate Limit Asynchronous Python Requests: A Comprehensive Guide

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Table of Contents

In the intricate dance of web scraping, where efficiency and respect for the target server’s bandwidth are paramount, mastering the art of rate limiting asynchronous requests becomes a critical skill. This is particularly true when working with Selenium web scrapers, designed to mimic real-world browsing behavior. While Selenium excels in tasks requiring interaction with JavaScript-heavy websites, its approach can sometimes be like using a sledgehammer to crack a nut, especially in terms of network resource consumption. To mitigate this, developers often employ strategies such as media blocking or request reduction. However, these methods only scratch the surface of optimization. Integrating an advanced web scraping API can provide a more nuanced control over request frequency, ensuring your scraping activities remain efficient and server-friendly. By leveraging such APIs, you can easily intercept and manage outgoing requests, reducing unnecessary load on both the scraper and the website, and streamlining your data collection process.

A proxy such as mitmproxy, renowned for its flexibility, can be configured to block requests by either resource type or name. Installation is straightforward with the pip package manager. Following installation, a script can be created to implement custom blocking logic, targeting common third-party resources or specific resource extensions:

from mitmproxy import http

BLOCK_RESOURCE_NAMES = ['adzerk', 'analytics', ...]
BLOCK_RESOURCE_EXTENSIONS = ['.gif', '.jpg', ...]

def request(flow: http.HTTPFlow) -> None:
    url = flow.request.pretty_url
    if any(url.endswith(ext) for ext in BLOCK_RESOURCE_EXTENSIONS) or 
       any(block in url for block in BLOCK_RESOURCE_NAMES):
        flow.response = http.Response.make(404, b"Blocked", {"Content-Type": "text/html"})

Running the proxy with mitmproxy -s initiates it on localhost:8080, filtering requests as configured. This setup can then be integrated with a Selenium instance to ensure the blocking of non-essential requests:

from selenium import webdriver

PROXY = "localhost:8080"

chrome_options = webdriver.ChromeOptions()
chrome_options.add_argument('--proxy-server=%s' % PROXY)

chrome = webdriver.Chrome(options=chrome_options)

This method, by limiting unnecessary resource loading, can reduce bandwidth consumption by 2 to 10 times, significantly enhancing the scraping process’s speed.

For comprehensive web scraping projects encountering sophisticated anti-scraping measures, the Scrape Network API offers an integrated solution with Scrapy through its SDK. This API facilitates scraping operations and bypasses anti-scraping protections, backed by features like anti-scraping protection bypass, JavaScript rendering, and access to a vast pool of residential or mobile proxies.

To incorporate Scrape Network into Python projects, the Scrapfly-sdk package can be installed with pip. This enables scraping without restrictions, utilizing options like proxy country selection and anti-scraping protection bypass for efficient data collection:

from scrapfly import ScrapflyClient, ScrapeConfig

client = ScrapflyClient(key="YOUR_SCRAPE_NETWORK_KEY")
result = client.scrape(ScrapeConfig(url="", asp=True))

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