- 1 year ago
multiprocessing chance proxy all 300 task
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To use proxies with the multiprocessing
module in Python to distribute tasks across multiple processes, you can follow these steps:
- Import the required modules:
pythonimport multiprocessing
import requests
- Define your task function that will be executed by each process. This function should include the code to make the HTTP request using the proxy. Here's an example:
pythondef task(proxy):
# Set the proxy
proxies = {
'http': proxy,
'https': proxy
}
# Make the HTTP request using the proxy
response = requests.get('https://www.example.com', proxies=proxies)
# Process the response or perform other tasks
print(response.text)
- Create a list of proxies to be used by the processes. This list can be obtained from a file, a database, or any other source. For demonstration purposes, let's assume you have a list of proxies in a variable called
proxy_list
:
pythonproxy_list = ['proxy1', 'proxy2', 'proxy3', ...]
- Create a
Pool
of processes using themultiprocessing
module, and map thetask
function to the proxy list:
pythonwith multiprocessing.Pool(processes=len(proxy_list)) as pool:
pool.map(task, proxy_list)
This will distribute the task across multiple processes, with each process using a different proxy from the proxy_list
for making HTTP requests.
Note that this is a basic example, and you may need to modify it based on your specific requirements and the structure of your proxy list. Additionally, keep in mind that using proxies can introduce additional complexity and potential issues, such as proxy availability and reliability. Make sure to handle error conditions and implement appropriate error handling and retry mechanisms as needed.