In Python crawler development, requests and aiohttp are two commonly used libraries. The requests library provides a clean and powerful HTTP request interface, while aiohttp is an asyncio-based HTTP client/server framework. This article will introduce the usage of these two libraries in detail and demonstrate their application through actual project cases.
1. Requests library
Installation and basic usage
Use the pip command to easily install the requests library:
pip install requests
After the installation is complete, you can use the following code to send a GET request:
import requests
response = ('')
print()Request parameters and header information
Requests can be customized by passing parameters and header information:
import requests
params = {'key1': 'value1', 'key2': 'value2'}
headers = {'User-Agent': 'Mozilla/5.0'}
response = ('', params=params, headers=headers)
print()Response processing
The requests library provides rich response processing methods, such as obtaining response status codes, response header information, response content, etc.:
import requests
response = ('')
print(response.status_code)
print()
print()Actual project cases
Here is a simple example of crawling web content using the requests library:
import requests
response = ('')
if response.status_code == 200:
print()
else:
print('Request failed')2. aiohttp library
Installation and basic usage
Use the pip command to install the aiohttp library:
pip install aiohttp
After the installation is complete, you can use the following code to send a GET request:
import aiohttp
async def main():
async with () as session:
async with ('') as response:
print(await ())
(main())Request parameters and header information
Requests can be customized by passing parameters and header information:
import aiohttp
async def main():
async with () as session:
params = {'key1': 'value1', 'key2': 'value2'}
headers = {'User-Agent': 'Mozilla/5.0'}
async with ('', params=params, headers=headers) as response:
print(await ())
(main())Response processing
The aiohttp library provides asynchronous response processing methods, such as obtaining response status code, response header information, response content, etc.:
import aiohttp
async def main():
async with () as session:
async with ('') as response:
print()
print()
print(await ())
(main())Actual project cases
Here is a simple example of crawling web content using the aiohttp library:
import aiohttp
async def main():
async with () as session:
async with ('') as response:
if == 200:
print(await ())
else:
print('Request failed')
(main())3. Comparison between requests and aiohttp
- performance
The requests library is based on synchronization, while the aiohttp library is based on asynchronous. The aiohttp library generally performs better than the requests library when handling a large number of concurrent requests.
- Complexity
The use of aiohttp library is relatively complicated and requires a certain understanding of asyncio. The use of the requests library is relatively simple.
- Applicable scenarios
The requests library is suitable for simple crawler scenarios, while the aiohttp library is suitable for complex crawler scenarios that need to handle a large number of concurrent requests.
4. The role of requests and aiohttp
- requests
requests is a simple and powerful Python HTTP library. It can easily send various HTTP requests (such as GET, POST, etc.) and process the response.
For example, in a simple news website data collection project, if we only need to get a small amount of web content in order, requests can be easily competent.
import requests
# Send a GET request to a page on the news websiteresponse = ('/article1')
if response.status_code == 200:
# Process the obtained news content news_content =
print(news_content)
else:
print('Request failed')- aiohttp
aiohttp is an asyncio-based HTTP client/server framework. It is designed for asynchronous programming and can efficiently handle large numbers of concurrent HTTP requests.
For example, in a large-scale web crawler project, when data is needed from multiple different web pages simultaneously, aiohttp's asynchronous features can significantly improve efficiency.
import aiohttp
import asyncio
async def fetch(session, url):
async with (url) as response:
return await ()
async def main():
async with () as session:
tasks = []
urls = ['', '', '']
for url in urls:
task = asyncio.ensure_future(fetch(session, url))
(task)
responses = await (*tasks)
for response in responses:
print(response)
(main())5. Selection factors in actual projects
1. Concurrent requirements
requests: If there are fewer HTTP requests in the project and do not require concurrent execution, such as a simple script that querys a single API to get data, requests are a good choice. Its synchronous execution is simple and intuitive, and the code is easy to understand and maintain.
aiohttp: When a large number of HTTP requests are needed to be processed simultaneously, such as large-scale web crawlers, batch data acquisition of multiple APIs, aiohttp's asynchronous features can give full play to their advantages. For example, when crawling 100 different web pages, aiohttp can send requests concurrently, greatly reducing the total execution time.
2. Project complexity and maintenance cost
requests: For beginners or small projects, the use of requests is very simple. There is no need to deeply understand the concept of asynchronous programming, and the code structure is clear. For example, a small personal blog data collection project involves data acquisition on only a few pages. Requests can quickly implement functions and subsequent maintenance is also easier.
aiohttp: Due to the involvement of asynchronous programming, the code of aiohttp is relatively complicated. You need to have a certain understanding of the asyncio library, including event loops, coroutines and other concepts. In large projects, team members are not familiar with asynchronous programming, which may increase the difficulty of development and maintenance. But its performance improvements may be worth the additional development cost when dealing with complex, high concurrency scenarios.
3. Performance requirements
requests: The performance of requests is sufficient to meet the needs when handling single or small number of HTTP requests executed sequentially. However, when the number of concurrent requests increases, due to its synchronous execution, each request needs to wait for the previous request to complete, which may result in a longer wait time.
aiohttp: In high concurrency scenarios, aiohttp can take advantage of the advantages of asynchronous I/O and can process other requests while waiting for the response of a request, thereby significantly improving overall performance. For example, in a project that requires a large amount of web page data to be obtained in a short time, aiohttp can complete tasks faster.
6. Summary
Choosing requests or aiohttp in an actual project depends on multiple factors, including concurrency requirements, project complexity, maintenance costs, and performance requirements. If it is a simple, non-concurrent small project, requests are a simple and efficient choice; aiohttp is more suitable for projects with high concurrency requirements, high performance requirements and development teams with the ability to handle asynchronous programming complexity.
This is the article about the selection strategies of requests and aiohttp in Python in actual projects. For more related Python requests and aiohttp content, please search for my previous articles or continue browsing the related articles below. I hope everyone will support me in the future!