How to Send Multiple Concurrent Requests in Python   

To send multiple parallel HTTP requests in Python, we can use the requests library.

send multiple HTTP GET requests

Let's start by example of sending multiple HTTP GET requests in Python:

import requests

urls = ['https://example.com/', 'https://httpbin.org/', 'https://example.com/test_page']

for url in urls:
    response = requests.get(url)
    print(response.status_code)

We can find the status of each request below:

200
200
404

concurrent - multiple HTTP requests at once

To send multiple HTTP requests in parallel we can use Python libraries like:

• concurrent
• multiprocessing
• multiprocess
• asyncio

The easiest to use is the concurrent library.

Example of sending multiple GET requests with a concurrent library. We create a pool of worker threads or processes. In this example we have 3 concurrent requests - max_workers=3

import requests
from concurrent.futures import ThreadPoolExecutor

urls = ['https://example.com/', 'https://httpbin.org/', 'https://example.com/test_page']

def get_url(url):
    return requests.get(url)
with ThreadPoolExecutor(max_workers=3) as pool:
    print(list(pool.map(get_url,urls)))

The result of the execution:

[<Response [200]>, <Response [200]>, <Response [404]>]

asyncio & aiohttp - send multiple requests

If we want to send requests concurrently, you can use a library such as:

• asyncio in combination with
• aiohttp

Here's an example of how to use concurrent.futures to send multiple HTTP requests concurrently:

import aiohttp
import asyncio

urls = ['https://httpbin.org/ip', 'https://httpbin.org/get', 'https://httpbin.org/cookies'] * 10

async def get_url_data(url, session):
    r = await session.request('GET', url=f'{url}')
    data = await r.json()
    return data
    
async def main(urls):
    async with aiohttp.ClientSession() as session:
   	 tasks = []
   	 for url in urls:
   		 tasks.append(get_url_data(url=url, session=session))
   	 results = await asyncio.gather(*tasks, return_exceptions=True)
    return results

data = asyncio.run(main(urls))
for item in data:
    print(item)

Sending the multiple requests will result into simultaneous output:

{'origin': '123.123.123.123'}
{'args': {}, 'headers': {'Accept': '*/*', 'Accept-Encoding': 'gzip, deflate', 'Host': 'httpbin.org', 'User-Agent': 'Python/3.8 aiohttp/3.8.4', 'X-Amzn-Trace-Id': 'Root=1-63g7a39b-1ec5e2ed472cf9bf1874g45f'}, 'origin': '123.123.123.123', 'url': 'https://httpbin.org/get'}
{'cookies': {}}

In the example above, we:

• create a list of URLs that we want to send requests to
• then create a two asynchronous method
• we extract JSON data from the result
• finally we print the results

send multiple requests - proxy + file output

Finally let's see an example of sending multiple requests with:

• multiprocess library
• using proxy requests
• parsing JSON output
• saving the results into file:

The tests will run 10 times with 5 parallel processes. You need to use valid proxy and URL address in order to get correct results:

import multi process as mp
import requests
def get_url(arg ):
    proxies = {"http": "http://10.10.10.10:8000",
      		 "https": "https://10.10.10.10:8000"
      		 }
    try:
   	 resp = requests.get('https://example.com/', proxies=proxies)
   	 print(resp.status_code)
   	 if resp.text:
   		 return resp.text # return resp.json()
    except:
   	 print('error', end='')

if __name__ == '__main__':
    f = open("data.csv", "a")
    results = []
    n = 10
    threads = 5
    pool = mp.Pool(processes=threads)
    for i, x in enumerate(range(n)):
   	 x = pool.map( get_url, list(range(threads)))
   	 results = results + x
   	 for xx in x:
   		 f.write(str(xx) + '\n')
    f.close()

result:

200
200
200
200
200

The result is visible on the image below:

summary

In this article we saw how to send multiple HTTP requests in Python with libraries like:

• concurrent
• multiprocessing
• multiprocess
• asyncio

We saw multiple examples with JSON data, proxy usage and file writing. We covered sending requests with libraries - requests and aiohttp

For more parallel examples in Python you can read:

• Parallel Processing Zip Archive CSV Files With Python and Pandas