No matter what language you use, there are several points that need to be considered clearly before doing high concurrency; for example: data set size, algorithm, whether there are time and performance constraints, whether there is shared state, how to debug (herein refers to Is the log, tracking strategy) and other issues. With these questions in mind, let’s discuss specific cases of python high-concurrency crawlers.
To implement high-concurrency crawlers in Python, we can use asynchronous programming libraries such as asyncio and aiohttp. Here's a simple tutorial:
1. Install the necessary libraries. Run the following command in your command line:
pip install aiohttp
pip install asyncio
2. Create an asynchronous function to send HTTP requests. This function will use theaiohttp library to send the request and return the text content of the response.
import aiohttp
async def fetch(session, url):
async with session.get(url) as response:
return await response.text()
3. Create an asynchronous function to process a URL. This function will create a aiohttpsession and then use the fetch function above to send the request.
async def process_url(session, url):
page_content = await fetch(session, url)
# 在这里处理页面内容，例如解析HTML并提取数据
print(page_content)
4. Create an asynchronous function to process a set of URLs. This function will create a aiohttpsession and then call the process_url function concurrently for each URL.
async def process_urls(urls):
async with aiohttp.ClientSession() as session:
tasks = [process_url(session, url) for url in urls]
await asyncio.gather(*tasks)
5. Finally, you can use the following code to run your crawler:
urls = ['http://example.com/page1', 'http://example.com/page2', 'http://example.com/page3']
asyncio.run(process_urls(urls))
This crawler will process all URLs concurrently, which means it can process multiple pages at the same time, thus greatly increasing the crawling speed.
Crawler IP solution
To use proxy IP in Python's high-concurrency crawler, you need to specify the proxy when sending the request. Here is an example using aiohttp and asyncio:
1. First, you need to install the aiohttp and asyncio libraries. Run the following command in your command line:
pip install aiohttp
pip install asyncio
2. Create an asynchronous function to send HTTP requests. This function will use theaiohttp library to send the request and return the text content of the response. In this function, we added a proxy parameter to specify the proxy.
import aiohttp
async def fetch(session, url, proxy):
async with session.get(url, proxy=proxy) as response:
return await response.text()
3. Create an asynchronous function to process a URL. This function will create a aiohttpsession and then use the fetch function above to send the request.
async def process_url(session, url, proxy):
page_content = await fetch(session, url, proxy)
# 在这里处理页面内容，例如解析HTML并提取数据
# 获取免费IP：http://jshk.com.cn/mb/reg.asp?kefu=xjy&csdn
print(page_content)
4. Create an asynchronous function to process a set of URLs. This function will create a aiohttpsession and then call the process_url function concurrently for each URL.
async def process_urls(urls, proxy):
async with aiohttp.ClientSession() as session:
tasks = [process_url(session, url, proxy) for url in urls]
await asyncio.gather(*tasks)
5. Finally, you can use the following code to run your crawler:
urls = ['http://example.com/page1', 'http://example.com/page2', 'http://example.com/page3']
proxy = 'http://your.proxy.com:port'
asyncio.run(process_urls(urls, proxy))
This crawler will process all URLs concurrently, and each request will be sent through the specified proxy. This can increase the crawling speed and avoid IP being blocked.
It should be noted here that this is just a basic tutorial, the actual crawler may be more complex and many other factors need to be considered, such as error handling, proxy IP, anti-crawler strategy, etc.
The above is my personal understanding of high-concurrency crawlers. After all, personal power is limited. If there are any mistakes, please leave a message in the comment area to correct me.