Быстрый веб-скрепинг: Async, потоки и процессы в Python

Web scraping is an invaluable technique for developers, enabling the extraction of data from websites in an automated manner. However, it comes with its own set of challenges, including managing I/O operations effectively, handling rate limits, and bypassing anti-scraping measures. In this blog, we'll explore three powerful methods to enhance your web scraping efficiency: async (asynchronous programming), multithreading, and multiprocessing, and how leveraging these approaches can significantly speed up your data extraction tasks.

Async (Asynchronous Programming)

Asynchronous programming is a paradigm that allows I/O operations to run concurrently without blocking the execution of your program. Unlike synchronous execution, where tasks are completed one after another, async enables your application to handle multiple operations at the same time.

Benefits for Web Scraping

Using async in Python for web scraping has several advantages, mainly due to its non-blocking I/O operations. This means that while one task waits for a response from a server, other tasks can continue running, significantly improving the overall speed of your scraping operations.

Code Example

Here’s a simple example using asyncio and aiohttp to perform asynchronous web scraping:

import asyncio
import aiohttp

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

async def main(urls):
    async with aiohttp.ClientSession() as session:
        tasks = [fetch(url, session) for url in urls]
        return await asyncio.gather(*tasks)

urls = ['http://example.com', 'https://example.org']
loop = asyncio.get_event_loop()
results = loop.run_until_complete(main(urls))

Multithreading

Multithreading is a form of concurrent execution where multiple threads are spawned within the same process to perform tasks simultaneously. It’s particularly useful in I/O-bound tasks where the program spends a significant amount of time waiting for external responses.

Benefits for Web Scraping

The primary benefit of multithreading in web scraping is improved throughput. By running several threads in parallel, you can make multiple HTTP requests simultaneously, reducing the overall time spent waiting for responses.

Code Example

Here’s how you can use the threading module for concurrent web scraping:

import threading
import requests
 
def fetch(url):
    print(requests.get(url).text)
 
threads = []
urls = ['http://example.com', 'https://example.org']
 
for url in urls:
    thread = threading.Thread(target=fetch, args=(url,))
    threads.append(thread)
    thread.start()
 
for thread in threads:
    thread.join()

Multiprocessing

Multiprocessing involves using multiple processes, rather than threads, to execute tasks in parallel. This method is ideal for CPU-bound tasks where the computation itself is the bottleneck.

When to Use Over Multithreading

Choose multiprocessing over multithreading when your web scraping tasks involve heavy data processing that could benefit from spreading across multiple CPU cores.

Benefits for Web Scraping

Multiprocessing can significantly speed up CPU-bound tasks in web scraping by taking advantage of multiple cores for parallel data extraction.

Code Example

Utilizing Python’s multiprocessing module for parallel data extraction looks like this:

from multiprocessing import Pool
import requests
 
def fetch(url):
    return requests.get(url).text
 
with Pool(5) as p:
    print(p.map(fetch, ['http://example.com', 'https://example.org']))

Comparison and Use Cases

Choosing between async, multithreading, and multiprocessing depends on your specific web scraping needs:

• Asynchronous programming is a method where tasks are executed in an overlap manner without waiting for each task to complete before starting the next one. This technique enables a program to handle multiple operations at once, enhancing efficiency particularly in scenarios involving I/O operations.

• Multithreading is effective for I/O-bound tasks, especially when dealing with network requests. It involves creating multiple threads within a single process to handle different tasks almost simultaneously, making it seem like parallel execution. In reality, these threads operate concurrently, not in parallel. In Python, the Global Interpreter Lock (GIL) ensures that only one thread executes Python bytecode at a time, which means that despite multiple threads being active, they do not run simultaneously but rather switch rapidly one after the other.

• Multiprocessing shines in CPU-bound tasks where data processing is intensive. Multiprocessing allows for actual parallelism, utilising multiple processes running across different CPU cores. Each process operates independently without sharing resources with others, allowing each one to execute tasks in separate memory environments. In Python, multiprocessing is facilitated by each process running its own Python interpreter, which manages the execution of its tasks independently of others. This architecture enables tasks to be processed simultaneously across multiple CPUs, maximising computational efficiency.

Заключение

Experimenting with async, multithreading, and multiprocessing can lead to significant improvements in the performance of your web scraping projects. Each method offers unique advantages and limitations, so understanding your project's requirements is key to selecting the most appropriate approach. Remember, incorporating proxies from services like ProxyScrape can further optimise your scraping operations by ensuring reliability and avoiding IP bans. Happy scraping!