603 lines
19 KiB
Markdown
603 lines
19 KiB
Markdown
# DataTools Pro 数据库设计文档
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## 1. 数据库概述
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### 1.1 数据库架构
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DataTools Pro采用多数据库架构，针对不同的数据存储需求选择最适合的数据库技术：
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```
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数据库架构图:
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┌─────────────────────────────────────────────────────────┐
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│ DataTools Pro 数据库架构 │
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├─────────────────────────────────────────────────────────┤
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│ 应用层 (Application Layer) │
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│ ┌─────────────────────────────────────────────────────┐ │
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│ │ Flask应用程序 │ │
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│ └─────────────────────────────────────────────────────┘ │
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├─────────────────────────────────────────────────────────┤
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│ 数据访问层 (Data Access Layer) │
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│ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────┐ │
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│ │ Cassandra │ │ Redis │ │ SQLite │ │
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│ │ Driver │ │ Client │ │ Driver │ │
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│ └─────────────┘ └─────────────┘ └─────────────────────┘ │
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├─────────────────────────────────────────────────────────┤
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│ 数据存储层 (Data Storage Layer) │
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│ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────┐ │
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│ │ Apache │ │ Redis │ │ SQLite │ │
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│ │ Cassandra │ │ Cluster │ │ 本地文件 │ │
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│ │ 集群 │ │ │ │ config_groups.db │ │
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│ │ (外部数据源) │ │ (外部数据源) │ │ (配置和历史数据) │ │
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│ └─────────────┘ └─────────────┘ └─────────────────────┘ │
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└─────────────────────────────────────────────────────────┘
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```
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### 1.2 数据库分类
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#### 1.2.1 外部数据源
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- **Apache Cassandra**: 主要的数据比对源，存储业务数据
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- **Redis Cluster**: 缓存和高性能数据存储，支持多种数据类型
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#### 1.2.2 内部存储
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- **SQLite**: 轻量级关系数据库，存储系统配置、查询历史和日志
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### 1.3 设计原则
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- **数据隔离**: 不同类型数据使用不同的存储方案
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- **性能优化**: 根据访问模式选择合适的数据库
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- **易维护**: 简化数据库管理和备份恢复
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- **扩展性**: 支持数据量增长和功能扩展
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## 2. SQLite数据库设计
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### 2.1 数据库文件
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- **文件名**: `config_groups.db`
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- **位置**: 项目根目录
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- **编码**: UTF-8
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- **版本**: SQLite 3.x
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### 2.2 表结构设计
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#### 2.2.1 config_groups表 - 配置组管理
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```sql
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CREATE TABLE config_groups (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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name TEXT NOT NULL UNIQUE, -- 配置组名称
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description TEXT, -- 描述信息
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pro_config TEXT NOT NULL, -- 生产环境配置(JSON)
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test_config TEXT NOT NULL, -- 测试环境配置(JSON)
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query_config TEXT NOT NULL, -- 查询配置(JSON)
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sharding_config TEXT, -- 分表配置(JSON)
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created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
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updated_at DATETIME DEFAULT CURRENT_TIMESTAMP
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);
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-- 索引
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CREATE INDEX idx_config_groups_name ON config_groups(name);
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CREATE INDEX idx_config_groups_created_at ON config_groups(created_at);
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```
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**字段详解**:
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- `id`: 主键，自增整数
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- `name`: 配置组名称，唯一索引
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- `description`: 配置组描述，可为空
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- `pro_config`: 生产环境Cassandra配置，JSON格式
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- `test_config`: 测试环境Cassandra配置，JSON格式
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- `query_config`: 查询参数配置，JSON格式
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- `sharding_config`: 分表查询配置，JSON格式
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- `created_at`: 创建时间
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- `updated_at`: 更新时间
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**JSON配置示例**:
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```json
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{
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"pro_config": {
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"cluster_name": "production-cluster",
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"datacenter": "datacenter1",
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"hosts": ["10.0.1.100", "10.0.1.101"],
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"port": 9042,
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"username": "cassandra",
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"password": "encrypted_password",
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"keyspace": "production_ks",
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"table": "user_data"
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},
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"query_config": {
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"keys": ["user_id"],
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"fields_to_compare": ["name", "email", "status"],
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"exclude_fields": ["created_at", "updated_at"]
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},
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"sharding_config": {
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"use_sharding_for_pro": true,
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"use_sharding_for_test": false,
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"interval_seconds": 604800,
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"table_count": 14
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}
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}
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```
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#### 2.2.2 query_history表 - 查询历史记录
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```sql
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CREATE TABLE query_history (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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name TEXT NOT NULL, -- 历史记录名称
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description TEXT, -- 描述信息
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pro_config TEXT NOT NULL, -- 生产环境配置(JSON)
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test_config TEXT NOT NULL, -- 测试环境配置(JSON)
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query_config TEXT NOT NULL, -- 查询配置(JSON)
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query_keys TEXT NOT NULL, -- 查询的键值(JSON Array)
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results_summary TEXT NOT NULL, -- 结果摘要(JSON)
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execution_time REAL NOT NULL, -- 执行时间(秒)
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total_keys INTEGER NOT NULL, -- 总Key数量
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differences_count INTEGER NOT NULL, -- 差异数量
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identical_count INTEGER NOT NULL, -- 相同数量
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query_type TEXT NOT NULL DEFAULT 'single', -- 查询类型(single/sharding)
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sharding_config TEXT, -- 分表配置(JSON)
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raw_results TEXT, -- 完整查询结果(JSON)
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differences_data TEXT, -- 差异详细数据(JSON)
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identical_data TEXT, -- 相同数据详情(JSON)
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created_at DATETIME DEFAULT CURRENT_TIMESTAMP
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);
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-- 索引
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CREATE INDEX idx_query_history_name ON query_history(name);
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CREATE INDEX idx_query_history_query_type ON query_history(query_type);
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CREATE INDEX idx_query_history_created_at ON query_history(created_at);
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CREATE INDEX idx_query_history_execution_time ON query_history(execution_time);
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```
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**字段详解**:
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- `id`: 主键，自增整数
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- `name`: 历史记录名称
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- `description`: 历史记录描述
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- `pro_config`: 生产环境配置快照
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- `test_config`: 测试环境配置快照
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- `query_config`: 查询配置快照
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- `query_keys`: 查询的Key值列表
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- `results_summary`: 查询结果摘要统计
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- `execution_time`: 查询执行时间
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- `total_keys`: 查询的总Key数量
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- `differences_count`: 发现的差异数量
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- `identical_count`: 相同记录数量
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- `query_type`: 查询类型(single/sharding)
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- `sharding_config`: 分表配置(仅分表查询)
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- `raw_results`: 完整的查询结果数据
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- `differences_data`: 差异数据详情
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- `identical_data`: 相同数据详情
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- `created_at`: 创建时间
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#### 2.2.3 query_logs表 - 查询日志记录
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```sql
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CREATE TABLE query_logs (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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batch_id TEXT NOT NULL, -- 批次ID(UUID)
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history_id INTEGER, -- 关联历史记录ID
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timestamp DATETIME NOT NULL, -- 日志时间戳
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level TEXT NOT NULL, -- 日志级别(INFO/WARNING/ERROR)
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message TEXT NOT NULL, -- 日志消息
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query_type TEXT NOT NULL, -- 查询类型标识
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created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
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FOREIGN KEY (history_id) REFERENCES query_history(id) ON DELETE SET NULL
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);
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-- 索引
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CREATE INDEX idx_query_logs_batch_id ON query_logs(batch_id);
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CREATE INDEX idx_query_logs_history_id ON query_logs(history_id);
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CREATE INDEX idx_query_logs_level ON query_logs(level);
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CREATE INDEX idx_query_logs_timestamp ON query_logs(timestamp);
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CREATE INDEX idx_query_logs_query_type ON query_logs(query_type);
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```
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**字段详解**:
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- `id`: 主键，自增整数
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- `batch_id`: 批次标识，同一次查询操作的日志共享同一个batch_id
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- `history_id`: 关联的历史记录ID，外键关联
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- `timestamp`: 日志产生的精确时间戳
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- `level`: 日志级别(INFO/WARNING/ERROR)
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- `message`: 日志消息内容
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- `query_type`: 查询类型标识(cassandra_single/cassandra_sharding/redis_compare)
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- `created_at`: 记录创建时间
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### 2.3 数据库触发器
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#### 2.3.1 自动更新updated_at触发器
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```sql
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-- 配置组更新时间触发器
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CREATE TRIGGER update_config_groups_updated_at
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AFTER UPDATE ON config_groups
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FOR EACH ROW
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BEGIN
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UPDATE config_groups
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SET updated_at = CURRENT_TIMESTAMP
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WHERE id = NEW.id;
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END;
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```
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### 2.4 数据库视图
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#### 2.4.1 查询历史统计视图
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```sql
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CREATE VIEW query_history_stats AS
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SELECT
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query_type,
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COUNT(*) as total_queries,
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AVG(execution_time) as avg_execution_time,
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AVG(total_keys) as avg_keys_per_query,
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AVG(differences_count) as avg_differences,
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AVG(CAST(differences_count AS REAL) / total_keys * 100) as avg_diff_rate,
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MIN(created_at) as first_query,
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MAX(created_at) as last_query
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FROM query_history
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GROUP BY query_type;
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```
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#### 2.4.2 日志统计视图
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```sql
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CREATE VIEW query_logs_stats AS
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SELECT
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DATE(created_at) as log_date,
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level,
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query_type,
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COUNT(*) as log_count
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FROM query_logs
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GROUP BY DATE(created_at), level, query_type
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ORDER BY log_date DESC, level;
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```
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## 3. Cassandra数据库设计
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### 3.1 数据模型理解
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DataTools Pro作为数据比对工具，**不直接管理**Cassandra的数据模型，而是连接到现有的Cassandra集群进行数据查询和比对。
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### 3.2 支持的Cassandra特性
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#### 3.2.1 表结构支持
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- **单表查询**: 支持任意Cassandra表结构
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- **分表查询**: 支持TWCS(Time Window Compaction Strategy)分表模式
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- **复合主键**: 支持多字段组合主键
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- **所有数据类型**: text、int、timestamp、uuid、blob等
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#### 3.2.2 查询模式
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```cql
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-- 单主键查询示例
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SELECT * FROM keyspace.table_name
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WHERE primary_key IN (?, ?, ?);
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-- 复合主键查询示例
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SELECT * FROM keyspace.table_name
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WHERE (key1='value1' AND key2='value2')
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OR (key1='value3' AND key2='value4');
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-- 分表查询示例
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SELECT * FROM keyspace.table_name_0
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WHERE primary_key IN (?, ?);
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SELECT * FROM keyspace.table_name_1
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WHERE primary_key IN (?);
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```
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#### 3.2.3 分表命名规范
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```
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基础表名: user_data
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分表命名: user_data_0, user_data_1, user_data_2, ..., user_data_13
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分表计算: shard_index = timestamp // interval_seconds % table_count
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```
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### 3.3 连接管理
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```python
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# Cassandra连接配置
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cassandra_config = {
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"cluster_name": "production-cluster",
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"datacenter": "datacenter1",
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"hosts": ["10.0.1.100", "10.0.1.101", "10.0.1.102"],
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"port": 9042,
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"username": "app_user",
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"password": "secure_password",
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"keyspace": "application_data",
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"table": "user_profiles"
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}
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# 连接池配置
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connection_settings = {
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"connect_timeout": 10,
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"request_timeout": 30,
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"load_balancing_policy": "DCAwareRoundRobinPolicy",
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"retry_policy": "RetryPolicy",
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"compression": "lz4"
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}
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```
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## 4. Redis数据库设计
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### 4.1 数据模型理解
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DataTools Pro支持Redis集群的数据比对，**不直接管理**Redis的数据结构，而是连接到现有的Redis集群进行数据查询和比对。
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### 4.2 支持的Redis数据类型
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#### 4.2.1 基本数据类型
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- **String**: 字符串类型，最常用的数据类型
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- **Hash**: 哈希表，存储字段-值对
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- **List**: 列表，有序的字符串集合
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- **Set**: 集合，无序的字符串集合
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- **Sorted Set**: 有序集合，带分数的有序字符串集合
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#### 4.2.2 查询操作
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```python
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# 不同数据类型的查询操作
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redis_operations = {
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"string": "GET key",
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"hash": "HGETALL key",
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"list": "LRANGE key 0 -1",
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"set": "SMEMBERS key",
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"zset": "ZRANGE key 0 -1 WITHSCORES"
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}
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```
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### 4.3 Redis集群配置
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```python
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# Redis集群连接配置
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redis_config = {
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"cluster_name": "production-redis",
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"nodes": [
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{"host": "10.0.1.100", "port": 6379},
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{"host": "10.0.1.101", "port": 6379},
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{"host": "10.0.1.102", "port": 6379}
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],
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"password": "redis_password",
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"socket_timeout": 3,
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"socket_connect_timeout": 3,
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"max_connections_per_node": 16,
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"skip_full_coverage_check": True,
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"decode_responses": True
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}
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```
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## 5. 数据迁移和版本管理
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### 5.1 数据库版本控制
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#### 5.1.1 版本信息表
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```sql
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CREATE TABLE database_version (
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version TEXT PRIMARY KEY,
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applied_at DATETIME DEFAULT CURRENT_TIMESTAMP,
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description TEXT
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);
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INSERT INTO database_version (version, description)
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VALUES ('1.0.0', '初始数据库结构');
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```
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#### 5.1.2 迁移脚本示例
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```sql
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-- 版本1.0.0 -> 1.1.0 迁移脚本
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-- 添加Redis配置支持
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ALTER TABLE config_groups
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ADD COLUMN redis_config TEXT;
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INSERT INTO database_version (version, description)
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VALUES ('1.1.0', '添加Redis配置支持');
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```
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### 5.2 数据备份策略
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#### 5.2.1 SQLite备份
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```bash
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# 完整备份
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cp config_groups.db config_groups_backup_$(date +%Y%m%d_%H%M%S).db
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# 增量备份(基于时间戳)
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sqlite3 config_groups.db "
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SELECT * FROM config_groups
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WHERE updated_at > '2024-08-05 00:00:00';" > incremental_backup.sql
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```
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#### 5.2.2 备份恢复
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```bash
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# 恢复完整备份
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cp config_groups_backup_20240805_100000.db config_groups.db
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# 恢复增量数据
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sqlite3 config_groups.db < incremental_backup.sql
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```
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## 6. 性能优化
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### 6.1 SQLite优化
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#### 6.1.1 配置优化
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```sql
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-- SQLite性能优化设置
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PRAGMA journal_mode = WAL; -- 写前日志模式
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PRAGMA synchronous = NORMAL; -- 平衡性能和安全
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PRAGMA cache_size = 10000; -- 缓存页面数量
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PRAGMA temp_store = memory; -- 临时表存储在内存
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PRAGMA mmap_size = 268435456; -- 内存映射大小(256MB)
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```
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#### 6.1.2 查询优化
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```sql
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-- 分页查询优化
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SELECT * FROM query_history
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WHERE created_at >= ?
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ORDER BY created_at DESC
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LIMIT ? OFFSET ?;
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-- 统计查询优化
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SELECT query_type, COUNT(*), AVG(execution_time)
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FROM query_history
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WHERE created_at >= DATE('now', '-30 days')
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GROUP BY query_type;
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```
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### 6.2 连接池优化
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#### 6.2.1 Cassandra连接池
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```python
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# 连接池配置
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cluster = Cluster(
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hosts=['10.0.1.100', '10.0.1.101'],
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port=9042,
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load_balancing_policy=DCAwareRoundRobinPolicy(local_dc='datacenter1'),
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default_retry_policy=RetryPolicy(),
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compression=True,
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protocol_version=4
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)
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# 会话池管理
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session_pool = cluster.connect()
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session_pool.default_timeout = 30
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```
|
||
|
||
#### 6.2.2 Redis连接池
|
||
```python
|
||
# Redis集群连接池
|
||
from rediscluster import RedisCluster
|
||
|
||
redis_cluster = RedisCluster(
|
||
startup_nodes=[
|
||
{"host": "10.0.1.100", "port": 6379},
|
||
{"host": "10.0.1.101", "port": 6379}
|
||
],
|
||
password="redis_password",
|
||
socket_timeout=3,
|
||
socket_connect_timeout=3,
|
||
max_connections_per_node=16,
|
||
skip_full_coverage_check=True
|
||
)
|
||
```
|
||
|
||
## 7. 数据安全
|
||
|
||
### 7.1 敏感数据处理
|
||
|
||
#### 7.1.1 密码加密
|
||
```python
|
||
# 配置中的密码加密存储
|
||
import base64
|
||
from cryptography.fernet import Fernet
|
||
|
||
def encrypt_password(password, key):
|
||
"""加密密码"""
|
||
f = Fernet(key)
|
||
encrypted_password = f.encrypt(password.encode())
|
||
return base64.b64encode(encrypted_password).decode()
|
||
|
||
def decrypt_password(encrypted_password, key):
|
||
"""解密密码"""
|
||
f = Fernet(key)
|
||
decoded_password = base64.b64decode(encrypted_password.encode())
|
||
return f.decrypt(decoded_password).decode()
|
||
```
|
||
|
||
#### 7.1.2 数据脱敏
|
||
```python
|
||
def mask_sensitive_data(config):
|
||
"""配置数据脱敏显示"""
|
||
masked_config = config.copy()
|
||
if 'password' in masked_config:
|
||
masked_config['password'] = '***masked***'
|
||
return masked_config
|
||
```
|
||
|
||
### 7.2 访问控制
|
||
|
||
#### 7.2.1 数据库权限
|
||
```sql
|
||
-- SQLite访问控制(文件系统级别)
|
||
chmod 600 config_groups.db -- 仅所有者可读写
|
||
chown app:app config_groups.db -- 设置正确的所有者
|
||
```
|
||
|
||
#### 7.2.2 网络安全
|
||
```python
|
||
# Cassandra SSL连接
|
||
from cassandra.auth import PlainTextAuthProvider
|
||
from ssl import SSLContext, PROTOCOL_TLS
|
||
|
||
ssl_context = SSLContext(PROTOCOL_TLS)
|
||
auth_provider = PlainTextAuthProvider(username='app_user', password='password')
|
||
|
||
cluster = Cluster(
|
||
['10.0.1.100'],
|
||
ssl_context=ssl_context,
|
||
auth_provider=auth_provider
|
||
)
|
||
```
|
||
|
||
## 8. 监控和维护
|
||
|
||
### 8.1 数据库监控
|
||
|
||
#### 8.1.1 SQLite监控
|
||
```sql
|
||
-- 数据库大小监控
|
||
SELECT
|
||
(page_count * page_size) / 1024 / 1024 as db_size_mb
|
||
FROM pragma_page_count(), pragma_page_size();
|
||
|
||
-- 表大小统计
|
||
SELECT
|
||
name,
|
||
COUNT(*) as row_count
|
||
FROM sqlite_master sm
|
||
JOIN (
|
||
SELECT 'config_groups' as name, COUNT(*) as cnt FROM config_groups
|
||
UNION ALL
|
||
SELECT 'query_history' as name, COUNT(*) as cnt FROM query_history
|
||
UNION ALL
|
||
SELECT 'query_logs' as name, COUNT(*) as cnt FROM query_logs
|
||
) t ON sm.name = t.name;
|
||
```
|
||
|
||
#### 8.1.2 连接监控
|
||
```python
|
||
# 连接健康检查
|
||
def check_database_health():
|
||
"""检查数据库连接健康状态"""
|
||
health_status = {
|
||
"sqlite": check_sqlite_health(),
|
||
"cassandra": check_cassandra_health(),
|
||
"redis": check_redis_health()
|
||
}
|
||
return health_status
|
||
|
||
def check_sqlite_health():
|
||
"""检查SQLite健康状态"""
|
||
try:
|
||
conn = sqlite3.connect('config_groups.db')
|
||
cursor = conn.cursor()
|
||
cursor.execute("SELECT 1")
|
||
conn.close()
|
||
return {"status": "healthy", "timestamp": datetime.now()}
|
||
except Exception as e:
|
||
return {"status": "unhealthy", "error": str(e)}
|
||
```
|
||
|
||
### 8.2 数据维护
|
||
|
||
#### 8.2.1 数据清理
|
||
```sql
|
||
-- 清理30天前的查询日志
|
||
DELETE FROM query_logs
|
||
WHERE created_at < DATE('now', '-30 days');
|
||
|
||
-- 清理大型历史记录的详细数据
|
||
UPDATE query_history
|
||
SET raw_results = NULL,
|
||
differences_data = NULL,
|
||
identical_data = NULL
|
||
WHERE created_at < DATE('now', '-90 days');
|
||
```
|
||
|
||
#### 8.2.2 数据库维护
|
||
```sql
|
||
-- SQLite数据库优化
|
||
VACUUM; -- 重建数据库文件，回收空间
|
||
ANALYZE; -- 更新查询计划统计信息
|
||
PRAGMA optimize; -- 优化数据库性能
|
||
REINDEX; -- 重建所有索引
|
||
```
|
||
|
||
---
|
||
|
||
**版本**: v1.0
|
||
**更新日期**: 2024-08-05
|
||
**维护者**: DataTools Pro Team |