DB与缓存一致性

一般方案中的设计均有其缺陷:要么会产生脏数据,要么会产生不一致,不同的是代价和概率;

更新数据时,是先删除缓存再更新DB,还是先更新DB再删除缓存?:https://blog.youkuaiyun.com/qq_33999844/article/details/81531461

 

试想:能不能将读取的请求进行区分;

要求强一致性的读取请求,直接从数据库中读取数据,不经过缓存;

不要求强一致性的读取请求,从redis中读取数据,使用更新策略;

 

缓存更新策略可用现在使用场景最多的:

1.先更新数据库数据

2.再删除redis中数据

3.等查询请求来的时候,未击中缓存,查询数据库,再将数据设置到缓存

4.redis要使用过期策略

 

当然缓存也可以使用springboot自带的内存缓存:@cacheable 注解

springboot自带缓存介绍:https://blog.youkuaiyun.com/qq_33999844/article/details/86312014

@cacheable注意事项:https://blog.youkuaiyun.com/qq_33999844/article/details/85601310

在某些场景中会不生效;

 

### MCP in Python Usage and Implementation #### Overview of MCP in Python The Model Context Protocol (MCP) is a protocol designed to facilitate interactions between AI models and external tools, data sources, or APIs[^3]. In the context of Python, MCP can be implemented using the MCP Python SDK, which provides tools for building both servers and clients. This implementation allows developers to interact with MCP bridges or agents effectively. #### Installing MCP Python SDK To integrate MCP into Python projects, the MCP Python SDK can be installed via pip: ```bash pip install mcp ``` This command installs the necessary libraries for interacting with MCP servers or clients[^1]. #### Configuring MCP Server in Python A MCP server can be configured in Python by defining its behavior and endpoints. Below is an example of setting up a basic MCP server using Python: ```python from mcp.server import MCPServer def handle_request(data): # Process incoming request data return {"result": "Processed"} if __name__ == "__main__": server = MCPServer(handle_request, port=8080) server.start() ``` In this example, the `MCPServer` class initializes a server that listens on port 8080 and processes incoming requests by calling the `handle_request` function[^1]. #### Configuring MCP Client in Python For interacting with an existing MCP server, a client can be set up as follows: ```python from mcp.client import MCPClient client = MCPClient(mcp_url="http://localhost:8080", mcp_port=8080) response = client.send_request({"action": "fetch_data"}) print(response) ``` Here, the `MCPClient` sends a request to the MCP server at the specified URL and port, and retrieves the response[^2]. #### Advanced Configuration Options MCP servers and clients can be further customized with additional parameters such as JSON formatting, logging levels, and security settings. For instance: ```python client = MCPClient( mcp_url="http://localhost:8080", mcp_port=8080, hide_json=True, json_width=120 ) ``` This configuration hides JSON results from tool executions and sets the maximum width for JSON output to 120 characters. #### Integration with Databases MCP can also be integrated with databases to enhance data retrieval and model interaction. This approach offers advantages over traditional RAG methods by providing more efficient and precise data access[^4]. An example of integrating MCP with a database might look like this: ```python from mcp.server import MCPServer import sqlite3 def fetch_data_from_db(query): conn = sqlite3.connect("example.db") cursor = conn.cursor() cursor.execute(query) result = cursor.fetchall() conn.close() return result def handle_request(data): query = data.get("query") if query: return {"data": fetch_data_from_db(query)} return {"error": "No query provided"} if __name__ == "__main__": server = MCPServer(handle_request, port=8080) server.start() ``` This script sets up an MCP server that executes SQL queries against a SQLite database[^4]. #### Best Practices for MCP Implementation - Ensure secure communication between MCP clients and servers using authentication mechanisms. - Optimize performance by configuring appropriate logging levels and resource limits. - Test the MCP implementation thoroughly to handle edge cases and errors gracefully.
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