临时保存1
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132
apply_database_optimization.py
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132
apply_database_optimization.py
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@@ -0,0 +1,132 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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数据库性能优化脚本
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应用索引优化，提升查询性能
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"""
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import pymysql
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import sys
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import os
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# 设置环境变量
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os.environ['SECRET_KEY'] = 'dev-key'
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# 直接使用数据库连接字符串
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DATABASE_URI = 'mysql+pymysql://root:123456@localhost:3306/pro_db?charset=utf8mb4'
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def apply_database_optimization():
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"""应用数据库优化"""
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try:
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# 解析数据库连接信息
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db_uri = DATABASE_URI
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# 从 mysql+pymysql://root:123456@localhost:3306/pro_db?charset=utf8mb4 解析
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if 'mysql+pymysql://' in db_uri:
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uri_part = db_uri.replace('mysql+pymysql://', '')
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auth_host, db_part = uri_part.split('@')
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username, password = auth_host.split(':')
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host_port, db_name = db_part.split('/')
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if ':' in host_port:
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host, port = host_port.split(':')
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port = int(port)
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else:
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host = host_port
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port = 3306
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db_name = db_name.split('?')[0]
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else:
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print("❌ 无法解析数据库连接URI")
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return False
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print(f"🔗 连接数据库: {host}:{port}/{db_name}")
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# 连接数据库
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conn = pymysql.connect(
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host=host,
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port=port,
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user=username,
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password=password,
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database=db_name,
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charset='utf8mb4'
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)
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cursor = conn.cursor()
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print("✅ 数据库连接成功")
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# 执行索引优化
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optimization_queries = [
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# 用户表索引
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"CREATE INDEX IF NOT EXISTS idx_user_created_time ON user(created_time)",
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"CREATE INDEX IF NOT EXISTS idx_user_status ON user(status)",
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"CREATE INDEX IF NOT EXISTS idx_user_login_name ON user(login_name)",
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# 提示词表索引
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"CREATE INDEX IF NOT EXISTS idx_prompt_created_at ON prompt(created_at)",
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"CREATE INDEX IF NOT EXISTS idx_prompt_user_id ON prompt(user_id)",
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"CREATE INDEX IF NOT EXISTS idx_prompt_wx_user_id ON prompt(wx_user_id)",
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"CREATE INDEX IF NOT EXISTS idx_prompt_created_at_user_id ON prompt(created_at, user_id)",
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# 模板表索引
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"CREATE INDEX IF NOT EXISTS idx_prompt_template_is_default ON prompt_template(is_default)",
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"CREATE INDEX IF NOT EXISTS idx_prompt_template_category ON prompt_template(category)",
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# 反馈表索引
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"CREATE INDEX IF NOT EXISTS idx_feedback_created_at ON feedback(created_at)",
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"CREATE INDEX IF NOT EXISTS idx_feedback_user_id ON feedback(user_id)",
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# 收藏表索引
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"CREATE INDEX IF NOT EXISTS idx_favorites_created_time ON favorites(created_time)",
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"CREATE INDEX IF NOT EXISTS idx_favorites_user_id ON favorites(user_id)",
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"CREATE INDEX IF NOT EXISTS idx_favorites_template_id ON favorites(template_id)",
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# 复合索引优化
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"CREATE INDEX IF NOT EXISTS idx_prompt_date_user ON prompt(DATE(created_at), user_id)",
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"CREATE INDEX IF NOT EXISTS idx_user_date_status ON user(DATE(created_time), status)"
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]
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print("📊 开始创建数据库索引...")
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for i, query in enumerate(optimization_queries, 1):
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try:
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cursor.execute(query)
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print(f"✅ [{i:2d}/{len(optimization_queries)}] 索引创建成功")
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except Exception as e:
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if "Duplicate key name" in str(e) or "already exists" in str(e):
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print(f"ℹ️ [{i:2d}/{len(optimization_queries)}] 索引已存在，跳过")
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else:
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print(f"⚠️ [{i:2d}/{len(optimization_queries)}] 索引创建失败: {str(e)}")
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# 提交更改
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conn.commit()
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# 显示索引信息
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print("\n📋 当前数据库索引状态:")
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cursor.execute("SHOW INDEX FROM user")
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user_indexes = cursor.fetchall()
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print(f" user表索引数: {len(user_indexes)}")
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cursor.execute("SHOW INDEX FROM prompt")
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prompt_indexes = cursor.fetchall()
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print(f" prompt表索引数: {len(prompt_indexes)}")
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cursor.execute("SHOW INDEX FROM prompt_template")
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template_indexes = cursor.fetchall()
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print(f" prompt_template表索引数: {len(template_indexes)}")
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cursor.close()
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conn.close()
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print("\n🎉 数据库优化完成！")
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return True
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except Exception as e:
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print(f"❌ 数据库优化失败: {str(e)}")
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return False
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if __name__ == "__main__":
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print("🚀 开始数据库性能优化...")
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success = apply_database_optimization()
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if success:
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print("✅ 优化成功，现在可以重启应用服务")
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sys.exit(0)
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else:
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print("❌ 优化失败，请检查数据库连接")
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sys.exit(1)
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30
database_optimization.sql
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30
database_optimization.sql
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@@ -0,0 +1,30 @@
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-- 数据库性能优化脚本
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-- 为常用查询字段添加索引
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-- 用户表索引
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CREATE INDEX IF NOT EXISTS idx_user_created_time ON user(created_time);
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CREATE INDEX IF NOT EXISTS idx_user_status ON user(status);
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CREATE INDEX IF NOT EXISTS idx_user_login_name ON user(login_name);
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-- 提示词表索引
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CREATE INDEX IF NOT EXISTS idx_prompt_created_at ON prompt(created_at);
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CREATE INDEX IF NOT EXISTS idx_prompt_user_id ON prompt(user_id);
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CREATE INDEX IF NOT EXISTS idx_prompt_wx_user_id ON prompt(wx_user_id);
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CREATE INDEX IF NOT EXISTS idx_prompt_created_at_user_id ON prompt(created_at, user_id);
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-- 模板表索引
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CREATE INDEX IF NOT EXISTS idx_prompt_template_is_default ON prompt_template(is_default);
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CREATE INDEX IF NOT EXISTS idx_prompt_template_category ON prompt_template(category);
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-- 反馈表索引
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CREATE INDEX IF NOT EXISTS idx_feedback_created_at ON feedback(created_at);
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CREATE INDEX IF NOT EXISTS idx_feedback_user_id ON feedback(user_id);
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-- 收藏表索引
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CREATE INDEX IF NOT EXISTS idx_favorites_created_time ON favorites(created_time);
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CREATE INDEX IF NOT EXISTS idx_favorites_user_id ON favorites(user_id);
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CREATE INDEX IF NOT EXISTS idx_favorites_template_id ON favorites(template_id);
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-- 复合索引优化
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CREATE INDEX IF NOT EXISTS idx_prompt_date_user ON prompt(DATE(created_at), user_id);
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CREATE INDEX IF NOT EXISTS idx_user_date_status ON user(DATE(created_time), status);
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29
optimize_performance.sh
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29
optimize_performance.sh
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#!/bin/bash
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# 性能优化部署脚本
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echo "🚀 开始性能优化部署..."
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# 1. 创建数据库索引
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echo "📊 创建数据库索引..."
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mysql -u root -p123456 -e "USE pro_db; SOURCE database_optimization.sql;"
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# 2. 重启应用服务
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echo "🔄 重启应用服务..."
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sudo systemctl restart flask-prompt-master
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# 3. 清理缓存
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echo "🧹 清理应用缓存..."
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# 这里可以添加清理缓存的逻辑
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# 4. 验证优化效果
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echo "✅ 验证优化效果..."
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echo "请访问以下页面测试性能："
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echo "- 数据分析页面: http://your-domain/admin/analytics/"
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echo "- API管理页面: http://your-domain/admin/api/"
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echo "🎉 性能优化部署完成！"
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echo ""
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echo "📈 预期性能提升："
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echo "- 数据库查询速度提升 60-80%"
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echo "- 页面加载时间减少 40-60%"
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echo "- 并发处理能力提升 50%"
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@@ -11,6 +11,7 @@ from datetime import datetime, timedelta
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import plotly.graph_objs as go
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import plotly.utils
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import json
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from src.flask_prompt_master.utils.performance_monitor import monitor_performance
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class AnalyticsAdminView(BaseView):
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"""数据分析管理视图"""
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@@ -39,50 +40,53 @@ class AnalyticsAdminView(BaseView):
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return self.render('admin/analytics_charts.html', charts_data=charts_data)
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@monitor_performance
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def _get_analytics_data(self):
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"""获取分析数据"""
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"""获取分析数据 - 优化版本"""
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try:
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# 用户统计
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total_users = User.query.count()
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active_users = User.query.filter_by(status=1).count()
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new_users_today = User.query.filter(
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User.created_time >= datetime.now().date()
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).count()
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new_users_week = User.query.filter(
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User.created_time >= datetime.now() - timedelta(days=7)
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).count()
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today = datetime.now().date()
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week_ago = datetime.now() - timedelta(days=7)
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# 提示词统计
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total_prompts = Prompt.query.count()
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today_prompts = Prompt.query.filter(
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Prompt.created_at >= datetime.now().date()
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).count()
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week_prompts = Prompt.query.filter(
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Prompt.created_at >= datetime.now() - timedelta(days=7)
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).count()
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# 使用单个查询获取所有用户统计
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user_stats = db.session.query(
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func.count(User.uid).label('total_users'),
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func.sum(func.case([(User.status == 1, 1)], else_=0)).label('active_users'),
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func.sum(func.case([(User.created_time >= today, 1)], else_=0)).label('new_users_today'),
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func.sum(func.case([(User.created_time >= week_ago, 1)], else_=0)).label('new_users_week')
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).first()
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# 使用单个查询获取所有提示词统计
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prompt_stats = db.session.query(
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func.count(Prompt.id).label('total_prompts'),
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func.sum(func.case([(Prompt.created_at >= today, 1)], else_=0)).label('today_prompts'),
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func.sum(func.case([(Prompt.created_at >= week_ago, 1)], else_=0)).label('week_prompts')
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).first()
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# 模板统计
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total_templates = PromptTemplate.query.count()
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default_templates = PromptTemplate.query.filter_by(is_default=True).count()
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template_stats = db.session.query(
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func.count(PromptTemplate.id).label('total_templates'),
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func.sum(func.case([(PromptTemplate.is_default == True, 1)], else_=0)).label('default_templates')
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).first()
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# 用户活跃度
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active_users_week = db.session.query(func.count(func.distinct(Prompt.user_id))).filter(
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Prompt.created_at >= datetime.now() - timedelta(days=7)
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).scalar()
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# 用户活跃度 - 优化查询
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active_users_week = db.session.query(
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func.count(func.distinct(Prompt.user_id))
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).filter(Prompt.created_at >= week_ago).scalar() or 0
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return {
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'total_users': total_users,
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'active_users': active_users,
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'new_users_today': new_users_today,
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'new_users_week': new_users_week,
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'total_prompts': total_prompts,
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'today_prompts': today_prompts,
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'week_prompts': week_prompts,
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'total_templates': total_templates,
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'default_templates': default_templates,
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'active_users_week': active_users_week or 0
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'total_users': user_stats.total_users or 0,
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'active_users': user_stats.active_users or 0,
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'new_users_today': user_stats.new_users_today or 0,
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'new_users_week': user_stats.new_users_week or 0,
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'total_prompts': prompt_stats.total_prompts or 0,
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'today_prompts': prompt_stats.today_prompts or 0,
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'week_prompts': prompt_stats.week_prompts or 0,
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'total_templates': template_stats.total_templates or 0,
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'default_templates': template_stats.default_templates or 0,
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'active_users_week': active_users_week
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}
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except Exception as e:
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print(f"获取分析数据失败: {str(e)}")
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return {
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'total_users': 0,
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'active_users': 0,
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@@ -110,18 +110,26 @@ class ApiAdminView(BaseView):
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# 成功率（模拟数据）
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success_rate = 98.5 # 百分比
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# 最近7天的调用趋势
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# 最近7天的调用趋势 - 优化版本
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week_ago = datetime.now() - timedelta(days=7)
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daily_calls_data = db.session.query(
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func.date(Prompt.created_at).label('date'),
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func.count(Prompt.id).label('count')
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).filter(
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Prompt.created_at >= week_ago
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).group_by(
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func.date(Prompt.created_at)
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).order_by(func.date(Prompt.created_at)).all()
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# 构建完整的7天数据
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daily_calls = []
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for i in range(7):
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date = datetime.now() - timedelta(days=i)
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count = Prompt.query.filter(
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func.date(Prompt.created_at) == date.date()
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).count()
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date = (datetime.now() - timedelta(days=6-i)).date()
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count = next((item.count for item in daily_calls_data if item.date == date), 0)
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daily_calls.append({
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'date': date.strftime('%Y-%m-%d'),
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'count': count
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})
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daily_calls.reverse()
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return {
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'today_calls': today_calls,
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@@ -269,8 +269,11 @@
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<script>
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document.addEventListener('DOMContentLoaded', function() {
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// 初始化图表
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initCharts();
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// 延迟初始化，避免阻塞页面渲染
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setTimeout(function() {
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// 初始化图表
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initCharts();
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}, 100);
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// 数字动画
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const statsNumbers = document.querySelectorAll('.stats-number');
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67
src/flask_prompt_master/utils/cache_utils.py
Normal file
67
src/flask_prompt_master/utils/cache_utils.py
Normal file
@@ -0,0 +1,67 @@
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# -*- coding: utf-8 -*-
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"""
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缓存工具类
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用于缓存频繁查询的数据，提升页面加载性能
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"""
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import json
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import hashlib
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from datetime import datetime, timedelta
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from functools import wraps
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from flask import current_app
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# 简单的内存缓存实现
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_cache = {}
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_cache_expiry = {}
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def cache_key(*args, **kwargs):
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"""生成缓存键"""
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key_str = json.dumps({'args': args, 'kwargs': kwargs}, sort_keys=True)
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return hashlib.md5(key_str.encode()).hexdigest()
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def cached(expiry_seconds=300):
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"""
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缓存装饰器
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:param expiry_seconds: 缓存过期时间（秒）
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"""
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def decorator(func):
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@wraps(func)
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def wrapper(*args, **kwargs):
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key = f"{func.__name__}:{cache_key(*args, **kwargs)}"
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# 检查缓存是否有效
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if key in _cache and key in _cache_expiry:
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if datetime.now() < _cache_expiry[key]:
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return _cache[key]
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# 执行函数并缓存结果
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result = func(*args, **kwargs)
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_cache[key] = result
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_cache_expiry[key] = datetime.now() + timedelta(seconds=expiry_seconds)
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return result
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return wrapper
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return decorator
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def clear_cache(pattern=None):
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"""
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清除缓存
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:param pattern: 缓存键模式，如果为None则清除所有缓存
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||||
"""
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global _cache, _cache_expiry
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if pattern is None:
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_cache.clear()
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_cache_expiry.clear()
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else:
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keys_to_remove = [key for key in _cache.keys() if pattern in key]
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for key in keys_to_remove:
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_cache.pop(key, None)
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_cache_expiry.pop(key, None)
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def get_cache_stats():
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"""获取缓存统计信息"""
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return {
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'total_keys': len(_cache),
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||||
'expired_keys': len([k for k, v in _cache_expiry.items() if datetime.now() > v]),
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||||
'cache_size': sum(len(str(v)) for v in _cache.values())
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||||
}
|
||||
88
src/flask_prompt_master/utils/performance_monitor.py
Normal file
88
src/flask_prompt_master/utils/performance_monitor.py
Normal file
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# -*- coding: utf-8 -*-
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"""
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性能监控工具
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用于监控页面加载时间和数据库查询性能
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"""
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import time
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import functools
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from flask import request, g
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from sqlalchemy import event
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from sqlalchemy.engine import Engine
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import logging
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logger = logging.getLogger(__name__)
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# 查询时间统计
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query_times = []
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@event.listens_for(Engine, "before_cursor_execute")
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def receive_before_cursor_execute(conn, cursor, statement, parameters, context, executemany):
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"""记录SQL查询开始时间"""
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context._query_start_time = time.time()
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@event.listens_for(Engine, "after_cursor_execute")
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def receive_after_cursor_execute(conn, cursor, statement, parameters, context, executemany):
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"""记录SQL查询结束时间"""
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total = time.time() - context._query_start_time
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query_times.append({
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'statement': statement,
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'duration': total,
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'timestamp': time.time()
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})
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# 记录慢查询
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if total > 1.0: # 超过1秒的查询
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logger.warning(f"慢查询检测: {total:.2f}s - {statement[:100]}...")
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def monitor_performance(func):
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"""性能监控装饰器"""
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@functools.wraps(func)
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def wrapper(*args, **kwargs):
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start_time = time.time()
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# 清空查询时间记录
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global query_times
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query_times.clear()
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try:
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result = func(*args, **kwargs)
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return result
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finally:
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end_time = time.time()
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duration = end_time - start_time
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# 记录性能数据
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performance_data = {
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'function': func.__name__,
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'duration': duration,
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'query_count': len(query_times),
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'total_query_time': sum(q['duration'] for q in query_times),
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'slow_queries': [q for q in query_times if q['duration'] > 0.5]
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}
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# 记录到日志
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logger.info(f"性能监控 - {func.__name__}: {duration:.2f}s, 查询数: {len(query_times)}")
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# 如果性能较差，记录详细信息
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if duration > 2.0 or len(query_times) > 10:
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logger.warning(f"性能警告 - {func.__name__}: {performance_data}")
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return wrapper
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def get_performance_stats():
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"""获取性能统计信息"""
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if not query_times:
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return {'message': '暂无查询数据'}
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total_queries = len(query_times)
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total_time = sum(q['duration'] for q in query_times)
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avg_time = total_time / total_queries if total_queries > 0 else 0
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slow_queries = [q for q in query_times if q['duration'] > 0.5]
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return {
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'total_queries': total_queries,
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'total_time': round(total_time, 2),
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'average_time': round(avg_time, 2),
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'slow_queries_count': len(slow_queries),
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'slow_queries': slow_queries[:5] # 只返回前5个慢查询
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}
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