Table of contents
1. Realization of multi-process crawler:
1.1 Divide the crawler task into multiple subtasks:
2. Implementation of the coroutine crawler:
2.1 Define the asynchronous crawler function:
3. Combination of multi-process and coroutine:
3.1 Divide the crawler task into multiple subtasks:
3.2 Using coroutine crawlers in the process:
Python crawler performance optimization is very important to improve crawling efficiency and reduce resource consumption. In practice, using multiprocessing and coroutines is a common way to speed up crawlers. The following is a practical guide on how to use multiprocessing and coroutines for performance optimization.
1. Realization of multi-process crawler:
Multi-process allows crawlers to perform multiple tasks at the same time, making full use of the advantages of multi-core CPUs. In Python, you can use the `multiprocessing` module to implement a multi-process crawler. Proceed as follows:
1.1 Divide the crawler task into multiple subtasks:
Divide the list of URLs to be crawled into multiple subtasks, and each subtask is handled by a process.
1.2 Create a process pool:
Use `multiprocessing.Pool` to create a process pool, set the number of processes and assign the crawler function to each process.
1.3 Executing tasks:
Use the `Pool.map` or `Pool.apply_async` method to assign subtasks to processes in the process pool for execution.
1.4 Processing results:
Wait for all processes to finish crawling tasks, and collect the results.
code example
import multiprocessing
import requests
def crawl(url):
response = requests.get(url)
# 处理返回的数据
if __name__ == '__main__':
urls = [...] # 待爬取的URL列表
pool = multiprocessing.Pool(processes=4) # 创建进程池，设置进程数
# 将任务分配给进程池中的进程执行
results = pool.map(crawl, urls)
# 处理爬取结果
for result in results:
# 处理爬取结果的逻辑2. Implementation of the coroutine crawler:
Coroutines are a lightweight way of concurrent programming that achieve concurrency by switching between tasks rather than by switching between processes or threads. In Python, you can use the `asyncio` module and the `aiohttp` library to implement a coroutine crawler. Proceed as follows:
2.1 Define the asynchronous crawler function:
Use `async def` to define an asynchronous crawler function, where `asyncio.sleep()` can be used to simulate IO blocking during crawling.
2.2 Create an event loop:
Create an event loop using `asyncio.get_event_loop()`.
2.3 Create a task list:
Wrap the asynchronous crawler function into an `asyncio.Task` object and add it to the task list.
2.4 Executing tasks:
Use `asyncio.ensure_future()` to add a list of tasks to the event loop, and `loop.run_until_complete()` to execute the tasks.
2.5 Processing results:
Process and collect the results of asynchronous crawling as needed.
Code example:
import asyncio
import aiohttp
async def crawl(url):
async with aiohttp.ClientSession() as session:
async with session.get(url) as response:
# 处理返回的数据
async def main():
urls = [...] # 待爬取的URL列表
tasks = []
# 创建任务列表
for url in urls:
tasks.append(asyncio.ensure_future(crawl(url)))
# 执行任务
await asyncio.gather(*tasks)
# 处理爬取结果
for task in tasks:
result = task.result()
# 处理爬取结果的逻辑
if __name__ == '__main__':
loop = asyncio.get_event_loop()
loop.run_until_complete(main())3. Combination of multi-process and coroutine:
Multi-process and coroutine can be used in combination to further improve the performance of crawlers. Multiple processes can be applied to different crawling tasks, and each process uses coroutines for concurrent crawling. Proceed as follows:
3.1 Divide the crawler task into multiple subtasks:
Divide the list of URLs to be crawled into multiple subtasks, and each subtask is handled by a process.
3.2 Using coroutine crawlers in the process:
Concurrent crawling is performed in each process using a coroutine crawler, that is, `asyncio` and `aiohttp` are used in each process to implement asynchronous crawling.
3.3 Create a process pool:
Use `multiprocessing.Pool` to create a process pool, set the number of processes and assign crawler tasks to each process.
3.4 Executing tasks:
Use the `Pool.map` or `Pool.apply_async` method to assign subtasks to processes in the process pool for execution.
3.5 Processing results:
Wait for all processes to finish crawling tasks, and process and collect results as needed.
code example
import multiprocessing
import asyncio
import aiohttp
def crawl(url):
async def inner_crawl():
async with aiohttp.ClientSession() as session:
async with session.get(url) as response:
# 处理返回的数据
asyncio.run(inner_crawl())
if __name__ == '__main__':
urls = [...] # 待爬取的URL列表
pool = multiprocessing.Pool(processes=4) # 创建进程池，设置进程数
# 将任务分配给进程池中的进程执行
pool.map(crawl, urls)It should be noted that the use of multi-process and coroutine requires reasonable task division and resource management. When designing crawlers, the appropriateness of concurrent execution should be considered to avoid excessive pressure and frequency restrictions on the target website.
in conclusion
Using multiprocessing and coroutines is a common way to improve the performance of Python crawlers. Multi-process can make full use of the advantages of multi-core CPU and realize parallel crawling tasks. Coroutines can efficiently switch between tasks to improve the execution efficiency of tasks. Combining multi-processing and coroutines can further improve the performance of crawlers. In practical applications, reasonable task division and resource management are carried out according to specific needs and environments to achieve the best performance optimization effect.