The rapid advancement of big data and AI has made web crawlers essential for data collection and analysis. In 2025, efficient, reliable, and secure crawlers dominate the market. This article highlights several leading web crawling tools, enhanced by 98IP proxy services, along with practical code examples to streamline your data acquisition process.
I. Key Considerations When Choosing a Crawler
- Efficiency: Rapid and accurate data extraction from target websites.
- Stability: Uninterrupted operation despite anti-crawler measures.
- Security: Protection of user privacy and avoidance of website overload or legal issues.
- Scalability: Customizable configurations and seamless integration with other data processing systems.
II. Top Web Crawling Tools for 2025
1. Scrapy 98IP Proxy
Scrapy, an open-source, collaborative framework, excels at multi-threaded crawling, ideal for large-scale data collection. 98IP's stable proxy service effectively circumvents website access restrictions.
Code Example:
import scrapy from scrapy.downloadermiddlewares.httpproxy import HttpProxyMiddleware import random # Proxy IP pool PROXY_LIST = [ 'http://proxy1.98ip.com:port', 'http://proxy2.98ip.com:port', # Add more proxy IPs... ] class MySpider(scrapy.Spider): name = 'my_spider' start_urls = ['https://example.com'] custom_settings = { 'DOWNLOADER_MIDDLEWARES': { HttpProxyMiddleware.name: 410, # Proxy Middleware Priority }, 'HTTP_PROXY': random.choice(PROXY_LIST), # Random proxy selection } def parse(self, response): # Page content parsing pass
2. BeautifulSoup Requests 98IP Proxy
For smaller websites with simpler structures, BeautifulSoup and the Requests library provide a quick solution for page parsing and data extraction. 98IP proxies enhance flexibility and success rates.
Code Example:
import requests from bs4 import BeautifulSoup import random # Proxy IP pool PROXY_LIST = [ 'http://proxy1.98ip.com:port', 'http://proxy2.98ip.com:port', # Add more proxy IPs... ] def fetch_page(url): proxy = random.choice(PROXY_LIST) try: response = requests.get(url, proxies={'http': proxy, 'https': proxy}) response.raise_for_status() # Request success check return response.text except requests.RequestException as e: print(f"Error fetching {url}: {e}") return None def parse_page(html): soup = BeautifulSoup(html, 'html.parser') # Data parsing based on page structure pass if __name__ == "__main__": url = 'https://example.com' html = fetch_page(url) if html: parse_page(html)
3. Selenium 98IP Proxy
Selenium, primarily an automated testing tool, is also effective for web crawling. It simulates user browser actions (clicks, input, etc.), handling websites requiring logins or complex interactions. 98IP proxies bypass behavior-based anti-crawler mechanisms.
Code Example:
from selenium import webdriver from selenium.webdriver.chrome.service import Service from selenium.webdriver.chrome.options import Options from selenium.webdriver.common.proxy import Proxy, ProxyType import random # Proxy IP pool PROXY_LIST = [ 'http://proxy1.98ip.com:port', 'http://proxy2.98ip.com:port', # Add more proxy IPs... ] chrome_options = Options() chrome_options.add_argument("--headless") # Headless mode # Proxy configuration proxy = Proxy({ 'proxyType': ProxyType.MANUAL, 'httpProxy': random.choice(PROXY_LIST), 'sslProxy': random.choice(PROXY_LIST), }) chrome_options.add_argument("--proxy-server={}".format(proxy.proxy_str)) service = Service(executable_path='/path/to/chromedriver') # Chromedriver path driver = webdriver.Chrome(service=service, options=chrome_options) driver.get('https://example.com') # Page manipulation and data extraction # ... driver.quit()
4. Pyppeteer 98IP Proxy
Pyppeteer, a Python wrapper for Puppeteer (a Node library for automating Chrome/Chromium), offers Puppeteer's functionality within Python. It's well-suited for scenarios requiring user behavior simulation.
Code Example:
import asyncio from pyppeteer import launch import random async def fetch_page(url, proxy): browser = await launch(headless=True, args=[f'--proxy-server={proxy}']) page = await browser.newPage() await page.goto(url) content = await page.content() await browser.close() return content async def main(): # Proxy IP pool PROXY_LIST = [ 'http://proxy1.98ip.com:port', 'http://proxy2.98ip.com:port', # Add more proxy IPs... ] url = 'https://example.com' proxy = random.choice(PROXY_LIST) html = await fetch_page(url, proxy) # Page content parsing # ... if __name__ == "__main__": asyncio.run(main())
III. Conclusion
Modern web crawling tools (2025) offer significant improvements in efficiency, stability, security, and scalability. Integrating 98IP proxy services further enhances flexibility and success rates. Choose the tool best suited to your target website's characteristics and requirements, and configure proxies effectively for efficient and secure data crawling.
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