sqltool 0.4.1

use crate::models::{TableSchema, Field, FieldMapping};
use anyhow::Result;
use serde::{Deserialize, Serialize};


/// 表结构比较结果
#[derive(Debug, Serialize, Deserialize)]
pub struct SchemaComparison {
    pub source_table: String,
    pub target_table: String,
    pub missing_fields: Vec<Field>,
    pub extra_fields: Vec<Field>,
    pub different_fields: Vec<(Field, Field)>,
    pub suggested_mappings: Vec<FieldMapping>,
}

/// 表结构比较工具
pub struct SchemaComparator {
    // 可以添加配置参数
}

impl SchemaComparator {
    pub fn new() -> Self {
        Self {}
    }

    /// 比较两个表的结构
    pub fn compare(&self, source_schema: &TableSchema, target_schema: &TableSchema) -> Result<SchemaComparison> {
        let mut missing_fields = vec![];
        let mut extra_fields = vec![];
        let mut different_fields = vec![];
        let mut suggested_mappings = vec![];

        // 构建源表字段映射
        let mut source_fields = std::collections::HashMap::new();
        for field in &source_schema.fields {
            source_fields.insert(field.name.clone(), field);
        }

        // 构建目标表字段映射
        let mut target_fields = std::collections::HashMap::new();
        for field in &target_schema.fields {
            target_fields.insert(field.name.clone(), field);
        }

        // 检查源表中存在但目标表中不存在的字段
        for (name, field) in &source_fields {
            if !target_fields.contains_key(name) {
                missing_fields.push((*field).clone());
            }
        }

        // 检查目标表中存在但源表中不存在的字段
        for (name, field) in &target_fields {
            if !source_fields.contains_key(name) {
                extra_fields.push((*field).clone());
            }
        }

        // 检查字段类型差异
        for (name, source_field) in &source_fields {
            if let Some(target_field) = target_fields.get(name) {
                if source_field.data_type != target_field.data_type ||
                   source_field.length != target_field.length ||
                   source_field.nullable != target_field.nullable {
                    different_fields.push(((*source_field).clone(), (*target_field).clone()));
                }
            }
        }

        // 生成建议的字段映射
        suggested_mappings = self.generate_suggested_mappings(source_schema, target_schema)?;

        Ok(SchemaComparison {
            source_table: source_schema.name.clone(),
            target_table: target_schema.name.clone(),
            missing_fields,
            extra_fields,
            different_fields,
            suggested_mappings,
        })
    }

    /// 生成建议的字段映射
    fn generate_suggested_mappings(&self, source_schema: &TableSchema, target_schema: &TableSchema) -> Result<Vec<FieldMapping>> {
        let mut mappings = vec![];

        // 首先尝试完全匹配字段名
        for source_field in &source_schema.fields {
            for target_field in &target_schema.fields {
                if source_field.name == target_field.name {
                    mappings.push(FieldMapping {
                        source_table: source_schema.name.clone(),
                        source_field: source_field.name.clone(),
                        target_table: target_schema.name.clone(),
                        target_field: target_field.name.clone(),
                    });
                    break;
                }
            }
        }

        // 然后尝试智能匹配（基于字段类型和名称相似性）
        // 这里可以实现更复杂的匹配算法

        Ok(mappings)
    }
}

/// 字段映射工具
pub struct FieldMapper {
    // 可以添加配置参数
}

impl FieldMapper {
    pub fn new() -> Self {
        Self {}
    }

    /// 手动设置字段映射
    pub fn set_mapping(&self, _mappings: Vec<FieldMapping>) -> Result<()> {
        // 验证映射的有效性
        // 可以添加更多验证逻辑
        Ok(())
    }

    /// 从文件加载映射
    pub fn load_mappings_from_file(&self, _file_path: &str) -> Result<Vec<FieldMapping>> {
        // 实现从文件加载映射的逻辑
        Ok(vec![])
    }

    /// 保存映射到文件
    pub fn save_mappings_to_file(&self, _mappings: &[FieldMapping], _file_path: &str) -> Result<()> {
        // 实现保存映射到文件的逻辑
        Ok(())
    }
}