import logging
from typing import Dict, List, Optional, Any
from datetime import datetime
import json
from app.core.llm.llm_service import llm_client
from app.core.graph.graph_operations import connect_graph, create_or_get_node, get_node, relationship_exists
from app.core.meta_data import translate_and_parse, get_formatted_time
from py2neo import Relationship
from app import db
from sqlalchemy import text

logger = logging.getLogger(__name__)

class DataFlowService:
    """数据流服务类，处理数据流相关的业务逻辑"""
    
    @staticmethod
    def get_dataflows(page: int = 1, page_size: int = 10, search: str = '') -> Dict[str, Any]:
        """
        获取数据流列表
        
        Args:
            page: 页码
            page_size: 每页大小
            search: 搜索关键词
            
        Returns:
            包含数据流列表和分页信息的字典
        """
        try:
            # 从图数据库查询数据流列表
            skip_count = (page - 1) * page_size
            
            # 构建搜索条件
            where_clause = ""
            params = {'skip': skip_count, 'limit': page_size}
            
            if search:
                where_clause = "WHERE n.name CONTAINS $search OR n.description CONTAINS $search"
                params['search'] = search
            
            # 查询数据流列表
            query = f"""
            MATCH (n:DataFlow)
            {where_clause}
            RETURN n, id(n) as node_id
            ORDER BY n.created_at DESC
            SKIP $skip
            LIMIT $limit
            """
            
            with connect_graph().session() as session:
                list_result = session.run(query, **params).data()
                
                # 查询总数
                count_query = f"""
                MATCH (n:DataFlow)
                {where_clause}
                RETURN count(n) as total
                """
                count_params = {'search': search} if search else {}
                count_result = session.run(count_query, **count_params).single()
                total = count_result['total'] if count_result else 0
            
            # 格式化结果
            dataflows = []
            for record in list_result:
                node = record['n']
                dataflow = dict(node)
                dataflow['id'] = record['node_id']  # 使用查询返回的node_id
                dataflows.append(dataflow)
            
            return {
                'list': dataflows,
                'pagination': {
                    'page': page,
                    'page_size': page_size,
                    'total': total,
                    'total_pages': (total + page_size - 1) // page_size
                }
            }
        except Exception as e:
            logger.error(f"获取数据流列表失败: {str(e)}")
            raise e
    
    @staticmethod
    def get_dataflow_by_id(dataflow_id: int) -> Optional[Dict[str, Any]]:
        """
        根据ID获取数据流详情
        
        Args:
            dataflow_id: 数据流ID
            
        Returns:
            数据流详情字典，如果不存在则返回None
        """
        try:
            query = """
            MATCH (n:DataFlow)
            WHERE id(n) = $dataflow_id
            OPTIONAL MATCH (n)-[:label]-(la:data_label)
            OPTIONAL MATCH (n)-[:child]-(child)
            OPTIONAL MATCH (parent)-[:child]-(n)
            RETURN n, id(n) as node_id,
                   collect(DISTINCT {id: id(la), name: la.name}) as tags,
                   collect(DISTINCT {id: id(child), name: child.name}) as children,
                   collect(DISTINCT {id: id(parent), name: parent.name}) as parents
            """
            
            with connect_graph().session() as session:
                result = session.run(query, dataflow_id=dataflow_id).data()
                
                if not result:
                    return None
                
                record = result[0]
                node = record['n']
                dataflow = dict(node)
                dataflow['id'] = record['node_id']  # 使用查询返回的node_id
                dataflow['tags'] = record['tags']
                dataflow['children'] = record['children']
                dataflow['parents'] = record['parents']
                
                return dataflow
        except Exception as e:
            logger.error(f"获取数据流详情失败: {str(e)}")
            raise e
    
    @staticmethod
    def create_dataflow(data: Dict[str, Any]) -> Dict[str, Any]:
        """
        创建新的数据流
        
        Args:
            data: 数据流配置数据
            
        Returns:
            创建的数据流信息
        """
        try:
            # 验证必填字段
            required_fields = ['name', 'describe']
            for field in required_fields:
                if field not in data:
                    raise ValueError(f"缺少必填字段: {field}")
            
            dataflow_name = data['name']
            
            # 使用LLM翻译名称生成英文名
            try:
                result_list = translate_and_parse(dataflow_name)
                name_en = result_list[0] if result_list else dataflow_name.lower().replace(' ', '_')
            except Exception as e:
                logger.warning(f"翻译失败，使用默认英文名: {str(e)}")
                name_en = dataflow_name.lower().replace(' ', '_')
            
            # 准备节点数据
            node_data = {
                'name_zh': dataflow_name,
                'name_en': name_en,
                'category': data.get('category', ''),
                'organization': data.get('organization', ''),
                'leader': data.get('leader', ''),
                'frequency': data.get('frequency', ''),
                'tag': data.get('tag', ''),
                'describe': data.get('describe', ''),
                'status': data.get('status', 'inactive'),
                'created_at': get_formatted_time(),
                'updated_at': get_formatted_time()
            }  
            
            # 创建或获取数据流节点
            dataflow_id = get_node('DataFlow', name=dataflow_name)
            if dataflow_id:
                raise ValueError(f"数据流 '{dataflow_name}' 已存在")
            
            dataflow_id = create_or_get_node('DataFlow', **node_data)
            
            # 处理标签关系
            tag_id = data.get('tag')
            if tag_id is not None:
                try:
                    DataFlowService._handle_tag_relationship(dataflow_id, tag_id)
                except Exception as e:
                    logger.warning(f"处理标签关系时出错: {str(e)}")
            
            # 成功创建图数据库节点后，写入PG数据库
            try:
                DataFlowService._save_to_pg_database(data, dataflow_name, name_en)
                logger.info(f"数据流信息已写入PG数据库: {dataflow_name}")
                
                # PG数据库记录成功写入后，在neo4j图数据库中创建script关系
                try:
                    DataFlowService._handle_script_relationships(data,dataflow_name,name_en)
                    logger.info(f"脚本关系创建成功: {dataflow_name}")
                except Exception as script_error:
                    logger.warning(f"创建脚本关系失败: {str(script_error)}")
                    
            except Exception as pg_error:
                logger.error(f"写入PG数据库失败: {str(pg_error)}")
                # 注意：这里可以选择回滚图数据库操作，但目前保持图数据库数据
                # 在实际应用中，可能需要考虑分布式事务
                
            # 返回创建的数据流信息
            # 查询创建的节点获取完整信息
            query = "MATCH (n:DataFlow {name: $name}) RETURN n, id(n) as node_id"
            with connect_graph().session() as session:
                id_result = session.run(query, name=dataflow_name).single()
                if id_result:
                    dataflow_node = id_result['n']
                    node_id = id_result['node_id']
                    
                    # 将节点属性转换为字典
                    result = dict(dataflow_node)
                    result['id'] = node_id
                else:
                    # 如果查询失败，返回基本信息
                    result = {
                        'id': dataflow_id if isinstance(dataflow_id, int) else None,
                        'name': dataflow_name,
                        'name_en': name_en,
                        'created_at': get_formatted_time()
                    }
            
            logger.info(f"创建数据流成功: {dataflow_name}")
            return result
            
        except Exception as e:
            logger.error(f"创建数据流失败: {str(e)}")
            raise e
    
    @staticmethod
    def _save_to_pg_database(data: Dict[str, Any], script_name: str, name_en: str):
        """
        将脚本信息保存到PG数据库
        
        Args:
            data: 包含脚本信息的数据
            script_name: 脚本名称
            en_name: 英文名称
        """
        try:
            # 提取脚本相关信息
            script_requirement = data.get('script_requirement', '')
            script_content = data.get('script_content', '')
            source_table = data.get('source_table', '')
            target_table = data.get('target_table', name_en)  # 如果没有指定目标表，使用英文名
            script_type = data.get('script_type', 'python')
            user_name = data.get('created_by', 'system')
            target_dt_column = data.get('target_dt_column', '')
            
            # 验证必需字段
            if not target_table:
                target_table = name_en
            if not script_name:
                raise ValueError("script_name不能为空")
            
            # 构建插入SQL
            insert_sql = text("""
                INSERT INTO dags.data_transform_scripts 
                (source_table, target_table, script_name, script_type, script_requirement, 
                 script_content, user_name, create_time, update_time, target_dt_column)
                VALUES 
                (:source_table, :target_table, :script_name, :script_type, :script_requirement,
                 :script_content, :user_name, :create_time, :update_time, :target_dt_column)
                ON CONFLICT (target_table, script_name) 
                DO UPDATE SET 
                    source_table = EXCLUDED.source_table,
                    script_type = EXCLUDED.script_type,
                    script_requirement = EXCLUDED.script_requirement,
                    script_content = EXCLUDED.script_content,
                    user_name = EXCLUDED.user_name,
                    update_time = EXCLUDED.update_time,
                    target_dt_column = EXCLUDED.target_dt_column
            """)
            
            # 准备参数
            current_time = datetime.now()
            params = {
                'source_table': source_table,
                'target_table': target_table,
                'script_name': script_name,
                'script_type': script_type,
                'script_requirement': script_requirement,
                'script_content': script_content,
                'user_name': user_name,
                'create_time': current_time,
                'update_time': current_time,
                'target_dt_column': target_dt_column
            }
            
            # 执行插入操作
            db.session.execute(insert_sql, params)
            db.session.commit()
            
            logger.info(f"成功将脚本信息写入PG数据库: target_table={target_table}, script_name={script_name}")
            
        except Exception as e:
            db.session.rollback()
            logger.error(f"写入PG数据库失败: {str(e)}")
            raise e
    
    @staticmethod
    def _handle_children_relationships(dataflow_node, children_ids):
        """处理子节点关系"""
        logger.debug(f"处理子节点关系，原始children_ids: {children_ids}, 类型: {type(children_ids)}")
        
        # 确保children_ids是列表格式
        if not isinstance(children_ids, (list, tuple)):
            if children_ids is not None:
                children_ids = [children_ids]  # 如果是单个值，转换为列表
                logger.debug(f"将单个值转换为列表: {children_ids}")
            else:
                children_ids = []  # 如果是None，转换为空列表
                logger.debug("将None转换为空列表")
        
        for child_id in children_ids:
            try:
                # 查找子节点
                query = "MATCH (n) WHERE id(n) = $child_id RETURN n"
                with connect_graph().session() as session:
                    result = session.run(query, child_id=child_id).data()
                    
                    if result:
                        child_node = result[0]['n']
                        
                        # 获取dataflow_node的ID
                        dataflow_id = getattr(dataflow_node, 'identity', None)
                        if dataflow_id is None:
                            # 如果没有identity属性，从名称查询ID
                            query_id = "MATCH (n:DataFlow) WHERE n.name = $name RETURN id(n) as node_id"
                            id_result = session.run(query_id, name=dataflow_node.get('name')).single()
                            dataflow_id = id_result['node_id'] if id_result else None
                        
                        # 创建关系 - 使用ID调用relationship_exists
                        if dataflow_id and not relationship_exists(dataflow_id, 'child', child_id):
                            session.run("MATCH (a), (b) WHERE id(a) = $dataflow_id AND id(b) = $child_id CREATE (a)-[:child]->(b)", 
                                      dataflow_id=dataflow_id, child_id=child_id)
                            logger.info(f"创建子节点关系: {dataflow_id} -> {child_id}")
            except Exception as e:
                logger.warning(f"创建子节点关系失败 {child_id}: {str(e)}")
    
    @staticmethod
    def _handle_tag_relationship(dataflow_id, tag_id):
        """处理标签关系"""
        try:
            # 查找标签节点
            query = "MATCH (n:data_label) WHERE id(n) = $tag_id RETURN n"
            with connect_graph().session() as session:
                result = session.run(query, tag_id=tag_id).data()
                
                if result:
                    tag_node = result[0]['n']
                    
                    # 创建关系 - 使用ID调用relationship_exists
                    if dataflow_id and not relationship_exists(dataflow_id, 'label', tag_id):
                        session.run("MATCH (a), (b) WHERE id(a) = $dataflow_id AND id(b) = $tag_id CREATE (a)-[:label]->(b)", 
                                  dataflow_id=dataflow_id, tag_id=tag_id)
                        logger.info(f"创建标签关系: {dataflow_id} -> {tag_id}")
        except Exception as e:
            logger.warning(f"创建标签关系失败 {tag_id}: {str(e)}")
    
    @staticmethod
    def update_dataflow(dataflow_id: int, data: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        """
        更新数据流
        
        Args:
            dataflow_id: 数据流ID
            data: 更新的数据
            
        Returns:
            更新后的数据流信息，如果不存在则返回None
        """
        try:
            # 查找节点
            query = "MATCH (n:DataFlow) WHERE id(n) = $dataflow_id RETURN n"
            with connect_graph().session() as session:
                result = session.run(query, dataflow_id=dataflow_id).data()
                
                if not result:
                    return None
                
                # 更新节点属性
                update_fields = []
                params = {'dataflow_id': dataflow_id}
                
                for key, value in data.items():
                    if key not in ['id', 'created_at']:  # 保护字段
                        if key == 'config' and isinstance(value, dict):
                            value = json.dumps(value, ensure_ascii=False)
                        update_fields.append(f"n.{key} = ${key}")
                        params[key] = value
                
                if update_fields:
                    params['updated_at'] = get_formatted_time()
                    update_fields.append("n.updated_at = $updated_at")
                    
                    update_query = f"""
                    MATCH (n:DataFlow) WHERE id(n) = $dataflow_id
                    SET {', '.join(update_fields)}
                    RETURN n, id(n) as node_id
                    """
                    
                    result = session.run(update_query, **params).data()
                    
                    if result:
                        node = result[0]['n']
                        updated_dataflow = dict(node)
                        updated_dataflow['id'] = result[0]['node_id']  # 使用查询返回的node_id
                        
                        logger.info(f"更新数据流成功: ID={dataflow_id}")
                        return updated_dataflow
                
                return None
            
        except Exception as e:
            logger.error(f"更新数据流失败: {str(e)}")
            raise e
    
    @staticmethod
    def delete_dataflow(dataflow_id: int) -> bool:
        """
        删除数据流
        
        Args:
            dataflow_id: 数据流ID
            
        Returns:
            删除是否成功
        """
        try:
            # 删除节点及其关系
            query = """
            MATCH (n:DataFlow) WHERE id(n) = $dataflow_id
            DETACH DELETE n
            RETURN count(n) as deleted_count
            """
            
            with connect_graph().session() as session:
                delete_result = session.run(query, dataflow_id=dataflow_id).single()
                result = delete_result['deleted_count'] if delete_result else 0
                
                if result and result > 0:
                    logger.info(f"删除数据流成功: ID={dataflow_id}")
                    return True
                
                return False
            
        except Exception as e:
            logger.error(f"删除数据流失败: {str(e)}")
            raise e
    
    @staticmethod
    def execute_dataflow(dataflow_id: int, params: Dict[str, Any] = None) -> Dict[str, Any]:
        """
        执行数据流
        
        Args:
            dataflow_id: 数据流ID
            params: 执行参数
            
        Returns:
            执行结果信息
        """
        try:
            # 检查数据流是否存在
            query = "MATCH (n:DataFlow) WHERE id(n) = $dataflow_id RETURN n"
            with connect_graph().session() as session:
                result = session.run(query, dataflow_id=dataflow_id).data()
                
                if not result:
                    raise ValueError(f"数据流不存在: ID={dataflow_id}")
            
            execution_id = f"exec_{dataflow_id}_{int(datetime.now().timestamp())}"
            
            # TODO: 这里应该实际执行数据流
            # 目前返回模拟结果
            result = {
                'execution_id': execution_id,
                'dataflow_id': dataflow_id,
                'status': 'running',
                'started_at': datetime.now().isoformat(),
                'params': params or {},
                'progress': 0
            }
            
            logger.info(f"开始执行数据流: ID={dataflow_id}, execution_id={execution_id}")
            return result
        except Exception as e:
            logger.error(f"执行数据流失败: {str(e)}")
            raise e
    
    @staticmethod
    def get_dataflow_status(dataflow_id: int) -> Dict[str, Any]:
        """
        获取数据流执行状态
        
        Args:
            dataflow_id: 数据流ID
            
        Returns:
            执行状态信息
        """
        try:
            # TODO: 这里应该查询实际的执行状态
            # 目前返回模拟状态
            query = "MATCH (n:DataFlow) WHERE id(n) = $dataflow_id RETURN n"
            with connect_graph().session() as session:
                result = session.run(query, dataflow_id=dataflow_id).data()
                
                if not result:
                    raise ValueError(f"数据流不存在: ID={dataflow_id}")
            
            status = ['running', 'completed', 'failed', 'pending'][dataflow_id % 4]
            
            return {
                'dataflow_id': dataflow_id,
                'status': status,
                'progress': 100 if status == 'completed' else (dataflow_id * 10) % 100,
                'started_at': datetime.now().isoformat(),
                'completed_at': datetime.now().isoformat() if status == 'completed' else None,
                'error_message': '执行过程中发生错误' if status == 'failed' else None
            }
        except Exception as e:
            logger.error(f"获取数据流状态失败: {str(e)}")
            raise e
    
    @staticmethod
    def get_dataflow_logs(dataflow_id: int, page: int = 1, page_size: int = 50) -> Dict[str, Any]:
        """
        获取数据流执行日志
        
        Args:
            dataflow_id: 数据流ID
            page: 页码
            page_size: 每页大小
            
        Returns:
            执行日志列表和分页信息
        """
        try:
            # TODO: 这里应该查询实际的执行日志
            # 目前返回模拟日志
            query = "MATCH (n:DataFlow) WHERE id(n) = $dataflow_id RETURN n"
            with connect_graph().session() as session:
                result = session.run(query, dataflow_id=dataflow_id).data()
                
                if not result:
                    raise ValueError(f"数据流不存在: ID={dataflow_id}")
            
            mock_logs = [
                {
                    'id': i,
                    'timestamp': datetime.now().isoformat(),
                    'level': ['INFO', 'WARNING', 'ERROR'][i % 3],
                    'message': f'数据流执行日志消息 {i}',
                    'component': ['source', 'transform', 'target'][i % 3]
                }
                for i in range(1, 101)
            ]
            
            # 分页处理
            total = len(mock_logs)
            start = (page - 1) * page_size
            end = start + page_size
            logs = mock_logs[start:end]
            
            return {
                'logs': logs,
                'pagination': {
                    'page': page,
                    'page_size': page_size,
                    'total': total,
                    'total_pages': (total + page_size - 1) // page_size
                }
            }
        except Exception as e:
            logger.error(f"获取数据流日志失败: {str(e)}")
            raise e

    @staticmethod
    def create_script(request_data: str) -> str:
        """
        使用Deepseek模型生成脚本
        
        Args:
            request_data: 请求数据，用户需求的文本描述
            
        Returns:
            生成的脚本内容（TXT格式）
        """
        try:
            # 构建prompt
            prompt_parts = []
            
            # 添加系统提示
            prompt_parts.append("请根据以下需求生成相应的数据处理脚本：")
            
            # 直接将request_data作为文本描述添加到prompt中
            prompt_parts.append(request_data)
            
            # 添加格式要求
            prompt_parts.append("\n请生成完整可执行的脚本代码，包含必要的注释和错误处理。")
            
            # 组合prompt
            full_prompt = "\n\n".join(prompt_parts)
            
            logger.info(f"开始调用Deepseek模型生成脚本，prompt长度: {len(full_prompt)}")
            
            # 调用LLM服务
            script_content = llm_client(full_prompt)
            
            if not script_content:
                raise ValueError("Deepseek模型返回空内容")
            
            # 确保返回的是文本格式
            if not isinstance(script_content, str):
                script_content = str(script_content)
            
            logger.info(f"脚本生成成功，内容长度: {len(script_content)}")
            
            return script_content
            
        except Exception as e:
            logger.error(f"生成脚本失败: {str(e)}")
            raise e

    @staticmethod
    def _handle_script_relationships(data: Dict[str, Any],dataflow_name:str,name_en:str):
        """
        处理脚本关系，在Neo4j图数据库中创建从source_table到target_table之间的DERIVED_FROM关系
        
        Args:
            data: 包含脚本信息的数据字典，应包含script_name, script_type, schedule_status, source_table, target_table, update_mode
        """
        try:
            # 从data中读取键值对
            script_name = dataflow_name,
            script_type = data.get('script_type', 'sql')
            schedule_status = data.get('status', 'inactive')
            source_table = data.get('source_table', '')
            target_table = data.get('target_table', '')
            update_mode = data.get('update_mode', 'full')
            
            # 验证必要字段
            if not source_table or not target_table:
                logger.warning(f"source_table或target_table为空，跳过关系创建: source_table={source_table}, target_table={target_table}")
                return
            
            logger.info(f"开始创建脚本关系: {source_table} -> {target_table}")
            
            with connect_graph().session() as session:
                # 创建或获取source_table节点
                source_node_query = """
                MERGE (source:table {name: $source_table})
                ON CREATE SET source.created_at = $created_at,
                             source.type = 'source'
                RETURN source, id(source) as source_id
                """
                
                # 创建或获取target_table节点
                target_node_query = """
                MERGE (target:table {name: $target_table})
                ON CREATE SET target.created_at = $created_at,
                             target.type = 'target'
                RETURN target, id(target) as target_id
                """
                
                current_time = get_formatted_time()
                
                # 执行创建节点的查询
                source_result = session.run(source_node_query, 
                                          source_table=source_table, 
                                          created_at=current_time).single()
                target_result = session.run(target_node_query, 
                                          target_table=target_table, 
                                          created_at=current_time).single()
                
                if source_result and target_result:
                    source_id = source_result['source_id']
                    target_id = target_result['target_id']
                    
                    # 检查关系是否已存在
                    relationship_check_query = """
                    MATCH (source:table)-[r:DERIVED_FROM]-(target:table)
                    WHERE id(source) = $source_id AND id(target) = $target_id
                    RETURN r
                    """
                    
                    existing_relationship = session.run(relationship_check_query, 
                                                      source_id=source_id, 
                                                      target_id=target_id).single()
                    
                    if not existing_relationship:
                        # 创建DERIVED_FROM关系，从source_table到target_table
                        create_relationship_query = """
                        MATCH (source:table), (target:table)
                        WHERE id(source) = $source_id AND id(target) = $target_id
                        CREATE (target)-[r:DERIVED_FROM]->(source)
                        SET r.script_name = $script_name,
                            r.script_type = $script_type,
                            r.schedule_status = $schedule_status,
                            r.update_mode = $update_mode,
                            r.created_at = $created_at,
                            r.updated_at = $created_at
                        RETURN r
                        """
                        
                        session.run(create_relationship_query,
                                  source_id=source_id,
                                  target_id=target_id,
                                  script_name=script_name,
                                  script_type=script_type,
                                  schedule_status=schedule_status,
                                  update_mode=update_mode,
                                  created_at=current_time)
                        
                        logger.info(f"成功创建DERIVED_FROM关系: {target_table} -> {source_table} (script: {script_name})")
                    else:
                        logger.info(f"DERIVED_FROM关系已存在: {target_table} -> {source_table}")
                else:
                    logger.error(f"创建表节点失败: source_table={source_table}, target_table={target_table}")
                    
        except Exception as e:
            logger.error(f"处理脚本关系失败: {str(e)}")
            raise e