在Python 3中,要提高爬虫的速度,可以采取以下措施:
concurrent.futures模块提供了方便的接口来实现多线程和多进程。import concurrent.futures import requests def fetch(url): response = requests.get(url) return response.text urls = ['http://example.com'] * 100 # 使用线程池 with concurrent.futures.ThreadPoolExecutor() as executor: results = list(executor.map(fetch, urls)) # 使用进程池 with concurrent.futures.ProcessPoolExecutor() as executor: results = list(executor.map(fetch, urls)) asyncio库和aiohttp库可以帮助实现异步请求。import aiohttp import asyncio async def fetch(url): async with aiohttp.ClientSession() as session: async with session.get(url) as response: return await response.text() async def main(): urls = ['http://example.com'] * 100 tasks = [fetch(url) for url in urls] results = await asyncio.gather(*tasks) # Python 3.7+ asyncio.run(main()) lxml或BeautifulSoup,并尽量减少不必要的DOM操作。from bs4 import BeautifulSoup def parse(html): soup = BeautifulSoup(html, 'lxml') # 进行高效的DOM操作 return results import time def fetch_with_delay(url, delay=1): response = requests.get(url) time.sleep(delay) # 暂停1秒 return response.text import requests proxies = { 'http': 'http://proxy.example.com:8080', 'https': 'http://proxy.example.com:8080', } response = requests.get('http://example.com', proxies=proxies) import requests import json cache_file = 'cache.json' def fetch(url): if url in cache: return cache[url] response = requests.get(url) data = response.json() cache[url] = data with open(cache_file, 'w') as f: json.dump(cache, f) return data 通过实施这些策略,可以有效地提高Python 3爬虫的速度和效率。