Optimizing performance is critical when developing Python agents. This article will introduce you to a set of ultimate solutions, which can greatly improve the efficiency and stability of Python agents through technologies such as caching, connection pooling and concurrent processing.
Change the virtual database area containing ip address in the game mainland
1. Cache technology
cache is
.0-*-+69+6ES2 0 An important performance optimization technology, by storing the obtained response data, avoiding repeated requests for the same resource, thereby improving the response speed. In the proxy program, we can use caching to reduce the number of requests to the backend server.
In order to implement the cache function, you can use the LRU (Least Recently Used) cache algorithm, which automatically deletes the least recently used cache items to maintain the cache size. Python provides a decorator functoolsin the module lru_cache, which can conveniently implement the caching function.
Sample code:
```python
import requests
from functools import lru_cache
Set cache size and expiration time
lru_cache(maxsize=128)
def fetch_data(url):
response=requests.get(url)
return response.text
Example: Fetching Data Using a Cache
data=fetch_data(“http://example.com/resource”)
```
2. Connection pool technology
Frequently creating and closing network connections incurs a performance overhead. The connection pool technology can maintain a set of pre-created network connections, avoid frequently creating and closing connections, and improve connection reuse and efficiency.
In order to implement the connection pool function, we can use the objects requestsprovided by the library Sessionand set the appropriate connection pool size and retry strategy. By multiplexing connections, we can significantly reduce connection establishment time and resource consumption.
Sample code:
```python
import requests
from requests.adapters import HTTPAdapter
from requests.packages.urllib3.util.retry import Retry
Create a connection pool
session=requests.Session()
retries=Retry(total=5,backoff_factor=0.1,status_forcelist=[500,502,503,504])
adapter=HTTPAdapter(max_retries=retries,pool_connections=10,pool_maxsize=10)
session.mount(‘http://’,adapter)
session.mount(‘https://’,adapter)
Example: Send a request using a connection pool
response=session.get(“http://example.com/resource”)
```
3. Concurrent processing
Concurrent processing is one of the key techniques to improve agent performance. By sending multiple requests concurrently, we can make full use of system resources and improve request processing speed.
In Python, you can use concurrent.futuresthe thread pool or process pool provided by the module to achieve concurrent processing. Thread pools are suitable for I/O intensive tasks, while process pools are suitable for CPU intensive tasks. Select the appropriate concurrent processing method according to specific needs.
Sample code:
```python
import requests
from concurrent.futures import ThreadPoolExecutor
Create a thread pool
executor=ThreadPoolExecutor(max_workers=10)
Example: send requests concurrently
urls=[“http://example.com/resource1”,“http://example.com/resource2”,“http://example.com/resource3”]
def fetch_data(url):
response=requests.get(url)
return response.text
Use the thread pool to send requests concurrently
results=executor.map(fetch_data,urls)
```
By properly using techniques such as caching, connection pooling, and concurrent processing, we can greatly improve the performance of Python agents. Caching can reduce repeated requests, connection pooling can improve connection reuse rate, and concurrent processing can make full use of system resources.
Together, they allow the agent to handle high volumes of requests more efficiently and stably.
I hope the solutions provided in this article will help you when developing Python agents. If you have any questions or need further understanding, welcome to leave a message in the comment area for discussion!