[MCP学习笔记]MCP电商大促方案：峰值流量削峰策略

# MCP模型基础代码框架
import numpy as np

class MCPModel:
    def __init__(self):
        self.frontend = FrontendService()  # 负责接收和处理用户请求
        self.middleware = MiddlewareService()  # 执行核心业务逻辑
        self.datastore = DataStore()  # 保存和检索数据
        self.trainer = ModelTrainer()  # 利用历史数据训练预测模型
        
    def handle_request(self, request):
        # 前端处理：初步解析和验证请求
        processed_req = self.frontend.process(request)
        # 中间层计算：执行核心业务逻辑
        result = self.middleware.compute(processed_req)
        # 数据存储：保存结果或更新状态
        self.datastore.save(result)
        return result

# LSTM流量预测模型代码
from keras.models import Sequential
from keras.layers import LSTM, Dense

def build_model(look_back=3, feature_count=10):
    """构建LSTM预测模型"""
    model = Sequential()
    model.add(LSTM(128, input_shape=(look_back, feature_count)))  # 输入层
    model.add(Dense(1))  # 输出层
    model.compile(loss='mean_squared_error', optimizer='adam')  # 编译模型
    return model

# 训练模型：用过去的数据预测未来
model = build_model()
model.fit(trainX, trainY, epochs=20, batch_size=64, verbose=2)

# 论文参考：《Deep Learning for Time Series Forecasting: A Review》
# 这篇论文系统地分析了深度学习在时间序列预测中的应用，为我们选择LSTM提供了理论依据。

# 动态资源分配代码
from kubernetes import client, config

class ResourceAllocator:
    def __init__(self):
        config.load_kube_config()  # 加载Kubernetes配置
        self.api = client.AppsV1Api()  # 获取API客户端
        
    def scale_deployment(self, deployment_name, replicas):
        """调整服务实例数量"""
        deployment = self.api.read_namespaced_deployment(
            name=deployment_name,
            namespace="default"
        )
        deployment.spec.replicas = replicas  # 设置新副本数量
        self.api.replace_namespaced_deployment(
            name=deployment_name,
            namespace="default",
            body=deployment
        )
        print(f"Deployment {deployment_name} scaled to {replicas} replicas")

# Redis缓存实现代码
import redis
import json

class CacheService:
    def __init__(self):
        self.cache = redis.Redis(host='cache-server', port=6379, db=0)  # 连接缓存服务器
        
    def get(self, key):
        """从缓存获取数据"""
        value = self.cache.get(key)
        if value is not None:
            return json.loads(value)  # 反序列化返回
        return None
        
    def set(self, key, value, expiration=900):
        """设置缓存数据"""
        self.cache.set(key, json.dumps(value), ex=expiration)  # 序列化存储
        print(f"Cache set: {key} expires in {expiration}s")

# 数据库优化示例：分表分库策略
class ShardingStrategy:
    def __init__(self, shard_count=8):
        self.shard_count = shard_count  # 分片数量
        
    def get_shard_id(self, item_id):
        """根据商品ID计算分片ID"""
        return item_id % self.shard_count  # 简单取模分片
    
    def get_shard_connection(self, shard_id):
        """获取对应分片的数据库连接"""
        connections = [
            f"db_{i}.example.com" for i in range(self.shard_count)
        ]
        return connections[shard_id]

# 性能监控代码
import time
from prometheus_client import start_http_server, Histogram

# 设置性能指标监控
REQUEST_LATENCY = Histogram('request_latency_seconds', 'Request latency')

class PerformanceMonitor:
    def __init__(self):
        start_http_server(8000)  # 启动监控服务器
        print("Performance monitoring started on port 8000")
        
    @REQUEST_LATENCY.time()  # 装饰器自动记录耗时
    def timed_request_handler(self, func):
        """带性能监控的请求处理函数"""
        def wrapper(request):
            start_time = time.time()
            result = func(request)
            duration = time.time() - start_time
            print(f"Request handled in {duration:.2f}s")
            return result
        return wrapper

# 未来规划：边缘计算集成示例
class EdgeComputingIntegration:
    def __init__(self):
        self.edge_nodes = ["node1.edge.example.com", "node2.edge.example.com"]
        
    def distribute_to_edge(self, request):
        """将请求分发到边缘节点处理"""
        edge_node = self.select_optimal_node(request)  # 选择最佳边缘节点
        response = self.forward_request(edge_node, request)  # 转发请求
        return response
        
    def select_optimal_node(self, request):
        """基于用户位置和节点负载选择最佳边缘节点"""
        user_location = self.get_user_location(request)
        node_latencies = self.measure_node_latencies(user_location)
        return min(node_latencies, key=node_latencies.get)  # 选择延迟最低的节点