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。