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how to make python requests faster

Narkw Brahma

Narkw Brahma

• 2 min read

How to Make Python Requests Faster

Python requests is a popular library used for making HTTP requests in Python. It is widely used by developers for web scraping, automation, and testing. However, sometimes the requests can take a long time to complete, especially if you are making many requests or if the server you are connecting to is slow. In this article, we will discuss some ways to make Python requests faster.

1. Use Connection Pooling

Connection pooling is a technique where you reuse existing connections instead of creating a new one for every request. This can significantly reduce the time it takes to establish a connection and improve the overall performance of your requests.

import requests
from requests.adapters import HTTPAdapter
from requests.packages.urllib3.util.retry import Retry

# set up retries and backoff strategy
retry_strategy = Retry(
    total=5,
    backoff_factor=1,
    status_forcelist=[ 500, 502, 503, 504 ]
)

# set up connection pool
adapter = HTTPAdapter(
    max_retries=retry_strategy,
    pool_connections=100,
    pool_maxsize=100
)
session = requests.Session()
session.mount('http://', adapter)
session.mount('https://', adapter)

# make request
response = session.get('https://example.com')

In the code above, we create a connection pool with 100 connections and limit the maximum number of connections to 100. We also set up retries with a backoff factor of 1 and retry on specific status codes (500, 502, 503, 504). We then use this connection pool to make our requests.

2. Use Asyncio

Asyncio is a Python library that provides an event loop and coroutines for writing asynchronous code. It can be used to make multiple requests in parallel, which can significantly improve the performance of your requests.

import asyncio
import aiohttp

async def fetch(session, url):
    async with session.get(url) as response:
        return await response.text()

async def main():
    async with aiohttp.ClientSession() as session:
        tasks = []
        for i in range(10):
            url = f'https://example.com/{i}'
            task = asyncio.ensure_future(fetch(session, url))
            tasks.append(task)
        responses = await asyncio.gather(*tasks)
        print(responses)

if __name__ == '__main__':
    loop = asyncio.get_event_loop()
    loop.run_until_complete(main())

In the code above, we define a coroutine that fetches the content of a URL using aiohttp. We then create a ClientSession and use it to create multiple tasks that fetch different URLs in parallel. We wait for all the tasks to complete using asyncio.gather and print the responses.

3. Use Caching

Caching is a technique where you store the response of a request and reuse it for subsequent requests. This can significantly reduce the amount of time it takes to make a request if the response is the same. There are many caching libraries available for Python, such as requests-cache and cachetools. Here's an example of using requests-cache:

import requests_cache

# enable cache
requests_cache.install_cache('example-cache', expire_after=3600)

# make request
response = requests.get('https://example.com')

# subsequent requests will use cache
response = requests.get('https://example.com')

In the code above, we enable caching using requests-cache and set the expiration time to 1 hour. We then make our request and subsequent requests will use the cached response instead of making a new request.

Conclusion

These are just a few ways to make Python requests faster. There are many other techniques you can try, such as using a faster network connection, optimizing your code, or using a different library or framework. It's important to test and measure the performance of your requests to determine which techniques work best for your use case.

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