BrunoGaoandClaude
3248bb0a2a
## 主要更改 ### 1. 数据库表结构增强 - **t_device_message_v2**: 新增7个关键字段 - priority_level: 优先级数值(1-5) - channels: 分发渠道JSON - require_ack: 是否需要确认 - metadata: 扩展元数据JSON - target_count: 目标用户总数 - version: 乐观锁版本号 - **t_device_message_detail_v2**: 新增8个关键字段 - distribution_id: 分发唯一ID - device_sn: 设备序列号 - user_id: 用户ID(核心查询字段) - response_duration: 响应耗时 - delivery_details: 分发详情JSON - client_info: 客户端信息JSON - location_info: 位置信息JSON - last_retry_time: 最后重试时间 ### 2. 索引优化重构 (基于user_id) - 删除原有device_sn相关索引 - 新建5个user_id高性能复合索引: - idx_customer_user_time: 客户+用户+时间 - idx_customer_user_status: 客户+用户+状态 - idx_customer_user_type: 客户+用户+类型 - idx_user_org_priority: 用户+组织+优先级 - idx_user_status_time: 用户+状态+时间 - detail表新建4个user_id相关索引: - uk_message_user_target: 唯一约束 - idx_customer_user_status: 客户+用户+状态 - idx_user_channel_time: 用户+渠道+时间 - idx_user_delivery_status: 用户+分发状态 ### 3. 数据迁移和初始化 - 自动生成distribution_id - 根据urgency设置priority_level - 初始化默认channels配置 - 保持现有数据完整性 ### 4. 性能提升预期 - 用户消息查询: 预计10-20倍性能提升 - 确认状态查询: 支持快速人员清单检索 - 存储空间: JSON字段替代冗余VARCHAR - 查询覆盖: 所有user_id查询都能命中索引 ### 5. 文档和脚本 - 完整的V2优化SQL脚本 - 消息机制设计文档 - 数据库备份(22MB) - 性能验证查询 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]>
58 lines
2.0 KiB
Python
58 lines
2.0 KiB
Python
#!/usr/bin/env python3
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"""
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Test script to check if daily data exists for the test user
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"""
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import requests
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import json
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# Test the API with the same parameters
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api_url = "http://192.168.1.83:3333/proxy-default/t_user_health_data/page"
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params = {
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"page": 1,
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"pageSize": 20,
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"customerId": 0,
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"orgId": "1939964806110937090",
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"userId": "1940034533382479873",
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"startDate": 1751299200000,
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"endDate": 1757865599999
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}
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try:
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response = requests.get(api_url, params=params)
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response.raise_for_status()
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data = response.json()
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print("API Response Status:", data.get("code"))
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print("API Response Message:", data.get("message"))
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if "data" in data:
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print("Total Records:", data["data"].get("total", 0))
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print("Columns Count:", len(data["data"].get("columns", [])))
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records = data["data"].get("records", [])
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if records:
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first_record = records[0]
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print("\nFirst Record Fields:")
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for key in sorted(first_record.keys()):
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print(f" {key}: {first_record[key]}")
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# Check specifically for slow fields
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slow_fields = ["sleep", "sleepData", "work_out", "workoutData", "exercise_daily", "exercise_week"]
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found_slow_fields = [field for field in slow_fields if field in first_record]
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print(f"\nFound slow fields: {found_slow_fields}")
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# Print columns info
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columns = data["data"].get("columns", [])
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if columns:
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print(f"\nColumns ({len(columns)} total):")
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for i, col in enumerate(columns[:10]): # Show first 10 columns
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title = col.get("title", "No title")
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dataIndex = col.get("dataIndex", "No dataIndex")
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print(f" {i+1}. {title} ({dataIndex})")
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if len(columns) > 10:
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print(f" ... and {len(columns) - 10} more columns")
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except Exception as e:
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print(f"Error testing API: {e}") |