from flask import Flask, render_template, request, jsonify, send_from_directory
from cassandra.cluster import Cluster
from cassandra.auth import PlainTextAuthProvider
import json
import os
import logging
import sqlite3
from datetime import datetime, timedelta
import re
import concurrent.futures
import time

app = Flask(__name__)

# 配置日志
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

# 数据库配置
DATABASE_PATH = 'config_groups.db'

# 查询日志收集器
class QueryLogCollector:
    def __init__(self, max_logs=1000, db_path=None):
        self.logs = []  # 内存中的日志缓存
        self.max_logs = max_logs
        self.current_batch_id = None
        self.batch_counter = 0
        self.current_query_type = 'single'
        self.current_history_id = None  # 当前关联的历史记录ID
        self.db_path = db_path or DATABASE_PATH
    
    def start_new_batch(self, query_type='single'):
        """开始新的查询批次"""
        self.batch_counter += 1
        self.current_batch_id = f"batch_{self.batch_counter}_{datetime.now().strftime('%H%M%S')}"
        self.current_query_type = query_type
        self.current_history_id = None  # 重置历史记录ID
        
        # 添加批次开始标记
        self.add_log('INFO', f"=== 开始{query_type}查询批次 (ID: {self.current_batch_id}) ===", force_batch_id=self.current_batch_id)
        return self.current_batch_id
    
    def set_history_id(self, history_id):
        """设置当前批次关联的历史记录ID"""
        self.current_history_id = history_id
        if self.current_batch_id and history_id:
            self.add_log('INFO', f"关联历史记录ID: {history_id}", force_batch_id=self.current_batch_id)
            # 更新当前批次的所有日志记录的history_id
            self._update_batch_history_id(self.current_batch_id, history_id)
    
    def _update_batch_history_id(self, batch_id, history_id):
        """更新批次中所有日志的history_id"""
        try:
            conn = sqlite3.connect(self.db_path, timeout=30)
            cursor = conn.cursor()
            
            cursor.execute('''
                UPDATE query_logs 
                SET history_id = ? 
                WHERE batch_id = ?
            ''', (history_id, batch_id))
            
            conn.commit()
            conn.close()
            logger.info(f"已更新批次 {batch_id} 的历史记录关联到 {history_id}")
        except Exception as e:
            print(f"Warning: Failed to update batch history_id: {e}")
    
    def end_current_batch(self):
        """结束当前查询批次"""
        if self.current_batch_id:
            self.add_log('INFO', f"=== 查询批次完成 (ID: {self.current_batch_id}) ===", force_batch_id=self.current_batch_id)
            self.current_batch_id = None
            self.current_history_id = None
    
    def add_log(self, level, message, force_batch_id=None, force_query_type=None, force_history_id=None):
        """添加日志到内存和数据库"""
        timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f')[:-3]
        batch_id = force_batch_id or self.current_batch_id
        query_type = force_query_type or self.current_query_type
        history_id = force_history_id or self.current_history_id
        
        log_entry = {
            'timestamp': timestamp,
            'level': level,
            'message': message,
            'batch_id': batch_id,
            'query_type': query_type,
            'history_id': history_id
        }
        
        # 添加到内存缓存
        self.logs.append(log_entry)
        if len(self.logs) > self.max_logs:
            self.logs.pop(0)
        
        # 保存到数据库
        self._save_log_to_db(log_entry)
    
    def _save_log_to_db(self, log_entry):
        """将日志保存到数据库"""
        try:
            conn = sqlite3.connect(self.db_path, timeout=30)
            cursor = conn.cursor()
            
            cursor.execute('''
                INSERT INTO query_logs (batch_id, history_id, timestamp, level, message, query_type)
                VALUES (?, ?, ?, ?, ?, ?)
            ''', (
                log_entry['batch_id'],
                log_entry['history_id'],
                log_entry['timestamp'], 
                log_entry['level'],
                log_entry['message'],
                log_entry['query_type']
            ))
            
            conn.commit()
            conn.close()
        except Exception as e:
            # 数据库写入失败时记录到控制台，但不影响程序运行
            print(f"Warning: Failed to save log to database: {e}")
    
    def get_logs(self, limit=None, from_db=True):
        """获取日志，支持从数据库或内存获取"""
        if from_db:
            return self._get_logs_from_db(limit)
        else:
            # 从内存获取
            if limit:
                return self.logs[-limit:]
            return self.logs
    
    def _get_logs_from_db(self, limit=None):
        """从数据库获取日志"""
        try:
            conn = sqlite3.connect(self.db_path, timeout=30)
            conn.row_factory = sqlite3.Row
            cursor = conn.cursor()
            
            query = '''
                SELECT batch_id, history_id, timestamp, level, message, query_type 
                FROM query_logs 
                ORDER BY id DESC
            '''
            
            if limit:
                query += f' LIMIT {limit}'
            
            cursor.execute(query)
            rows = cursor.fetchall()
            
            # 转换为字典格式并反转顺序（最新的在前）
            logs = []
            for row in reversed(rows):
                logs.append({
                    'batch_id': row['batch_id'],
                    'history_id': row['history_id'],
                    'timestamp': row['timestamp'],
                    'level': row['level'], 
                    'message': row['message'],
                    'query_type': row['query_type']
                })
            
            conn.close()
            return logs
        except Exception as e:
            print(f"Warning: Failed to get logs from database: {e}")
            # 如果数据库读取失败，返回内存中的日志
            return self.get_logs(limit, from_db=False)
    
    def _get_total_logs_count(self):
        """获取数据库中的日志总数"""
        try:
            conn = sqlite3.connect(self.db_path, timeout=30)
            cursor = conn.cursor()
            cursor.execute('SELECT COUNT(*) FROM query_logs')
            count = cursor.fetchone()[0]
            conn.close()
            return count
        except Exception as e:
            print(f"Warning: Failed to get logs count from database: {e}")
            return len(self.logs)
    
    def get_logs_by_history_id(self, history_id):
        """根据历史记录ID获取相关日志"""
        try:
            conn = sqlite3.connect(self.db_path, timeout=30)
            conn.row_factory = sqlite3.Row
            cursor = conn.cursor()
            
            cursor.execute('''
                SELECT batch_id, history_id, timestamp, level, message, query_type 
                FROM query_logs 
                WHERE history_id = ?
                ORDER BY id ASC
            ''', (history_id,))
            
            rows = cursor.fetchall()
            logs = []
            for row in rows:
                logs.append({
                    'batch_id': row['batch_id'],
                    'history_id': row['history_id'],
                    'timestamp': row['timestamp'],
                    'level': row['level'], 
                    'message': row['message'],
                    'query_type': row['query_type']
                })
            
            conn.close()
            return logs
        except Exception as e:
            print(f"Warning: Failed to get logs by history_id: {e}")
            return []
    
    def get_logs_grouped_by_batch(self, limit=None, from_db=True):
        """按批次分组获取日志"""
        logs = self.get_logs(limit, from_db)
        grouped_logs = {}
        batch_order = []
        
        for log in logs:
            batch_id = log.get('batch_id', 'unknown')
            if batch_id not in grouped_logs:
                grouped_logs[batch_id] = []
                batch_order.append(batch_id)
            grouped_logs[batch_id].append(log)
        
        # 返回按时间顺序排列的批次
        return [(batch_id, grouped_logs[batch_id]) for batch_id in batch_order]
    
    def clear_logs(self, clear_db=True):
        """清空日志"""
        # 清空内存
        self.logs.clear()
        self.current_batch_id = None
        self.batch_counter = 0
        
        # 清空数据库
        if clear_db:
            try:
                conn = sqlite3.connect(self.db_path, timeout=30)
                cursor = conn.cursor()
                cursor.execute('DELETE FROM query_logs')
                conn.commit()
                conn.close()
            except Exception as e:
                print(f"Warning: Failed to clear logs from database: {e}")
    
    def cleanup_old_logs(self, days_to_keep=30):
        """清理旧日志，保留指定天数的日志"""
        try:
            conn = sqlite3.connect(self.db_path, timeout=30)
            cursor = conn.cursor()
            
            # 删除超过指定天数的日志
            cutoff_date = datetime.now() - timedelta(days=days_to_keep)
            cursor.execute('''
                DELETE FROM query_logs 
                WHERE created_at < ?
            ''', (cutoff_date.strftime('%Y-%m-%d %H:%M:%S'),))
            
            deleted_count = cursor.rowcount
            conn.commit()
            conn.close()
            
            logger.info(f"清理了 {deleted_count} 条超过 {days_to_keep} 天的旧日志")
            return deleted_count
        except Exception as e:
            logger.error(f"清理旧日志失败: {e}")
            return 0

# 全局日志收集器实例
query_log_collector = QueryLogCollector()

# 自定义日志处理器
class CollectorHandler(logging.Handler):
    def __init__(self, collector):
        super().__init__()
        self.collector = collector
    
    def emit(self, record):
        self.collector.add_log(record.levelname, record.getMessage())

# 添加收集器处理器到logger
collector_handler = CollectorHandler(query_log_collector)
logger.addHandler(collector_handler)

class ShardingCalculator:
    """分表计算器，基于TWCS策略"""
    
    def __init__(self, interval_seconds=604800, table_count=14):
        """
        初始化分表计算器
        :param interval_seconds: 时间间隔(秒)，默认604800(7天)
        :param table_count: 分表数量，默认14
        """
        self.interval_seconds = interval_seconds
        self.table_count = table_count
    
    def extract_timestamp_from_key(self, key):
        """
        从Key中提取时间戳
        新规则：优先提取最后一个下划线后的数字，如果没有下划线则提取最后连续的数字部分
        """
        if not key:
            return None
        
        key_str = str(key)
        
        # 方法1：如果包含下划线，尝试提取最后一个下划线后的部分
        if '_' in key_str:
            parts = key_str.split('_')
            last_part = parts[-1]
            # 检查最后一部分是否为纯数字
            if last_part.isdigit():
                timestamp = int(last_part)
                logger.info(f"Key '{key}' 通过下划线分割提取到时间戳: {timestamp}")
                return timestamp
        
        # 方法2：使用正则表达式找到所有数字序列，取最后一个较长的
        number_sequences = re.findall(r'\d+', key_str)
        
        if not number_sequences:
            logger.warning(f"Key '{key}' 中没有找到数字字符")
            return None
        
        # 如果有多个数字序列，优先选择最长的，如果长度相同则选择最后一个
        longest_sequence = max(number_sequences, key=len)
        
        # 如果最长的有多个，选择最后一个最长的
        max_length = len(longest_sequence)
        last_longest = None
        for seq in number_sequences:
            if len(seq) == max_length:
                last_longest = seq
        
        try:
            timestamp = int(last_longest)
            logger.info(f"Key '{key}' 通过数字序列提取到时间戳: {timestamp} (从序列 {number_sequences} 中选择)")
            return timestamp
        except ValueError:
            logger.error(f"Key '{key}' 时间戳转换失败: {last_longest}")
            return None
    
    def calculate_shard_index(self, timestamp):
        """
        计算分表索引
        公式：timestamp // interval_seconds % table_count
        """
        if timestamp is None:
            return None
        return int(timestamp) // self.interval_seconds % self.table_count
    
    def get_shard_table_name(self, base_table_name, key):
        """
        根据Key获取对应的分表名称
        """
        timestamp = self.extract_timestamp_from_key(key)
        if timestamp is None:
            return None
        
        shard_index = self.calculate_shard_index(timestamp)
        return f"{base_table_name}_{shard_index}"
    
    def get_all_shard_tables_for_keys(self, base_table_name, keys):
        """
        为一批Keys计算所有需要查询的分表
        返回: {shard_table_name: [keys_for_this_shard], ...}
        """
        shard_mapping = {}
        failed_keys = []
        calculation_stats = {
            'total_keys': len(keys),
            'successful_extractions': 0,
            'failed_extractions': 0,
            'unique_shards': 0
        }
        
        for key in keys:
            shard_table = self.get_shard_table_name(base_table_name, key)
            if shard_table:
                if shard_table not in shard_mapping:
                    shard_mapping[shard_table] = []
                shard_mapping[shard_table].append(key)
                calculation_stats['successful_extractions'] += 1
            else:
                failed_keys.append(key)
                calculation_stats['failed_extractions'] += 1
        
        calculation_stats['unique_shards'] = len(shard_mapping)
        
        return shard_mapping, failed_keys, calculation_stats

def init_database():
    """初始化数据库"""
    try:
        conn = sqlite3.connect(DATABASE_PATH)
        cursor = conn.cursor()
        
        # 创建配置组表
        cursor.execute('''
            CREATE TABLE IF NOT EXISTS config_groups (
                id INTEGER PRIMARY KEY AUTOINCREMENT,
                name TEXT NOT NULL UNIQUE,
                description TEXT,
                pro_config TEXT NOT NULL,
                test_config TEXT NOT NULL,
                query_config TEXT NOT NULL,
                sharding_config TEXT,
                created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
                updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
            )
        ''')
        
        # 创建查询历史表，包含分表配置字段
        cursor.execute('''
            CREATE TABLE IF NOT EXISTS query_history (
                id INTEGER PRIMARY KEY AUTOINCREMENT,
                name TEXT NOT NULL,
                description TEXT,
                pro_config TEXT NOT NULL,
                test_config TEXT NOT NULL,
                query_config TEXT NOT NULL,
                query_keys TEXT NOT NULL,
                results_summary TEXT NOT NULL,
                execution_time REAL NOT NULL,
                total_keys INTEGER NOT NULL,
                differences_count INTEGER NOT NULL,
                identical_count INTEGER NOT NULL,
                sharding_config TEXT,
                query_type TEXT DEFAULT 'single',
                created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
            )
        ''')
        
        # 创建分表配置组表
        cursor.execute('''
            CREATE TABLE IF NOT EXISTS sharding_config_groups (
                id INTEGER PRIMARY KEY AUTOINCREMENT,
                name TEXT NOT NULL UNIQUE,
                description TEXT,
                pro_config TEXT NOT NULL,
                test_config TEXT NOT NULL,
                query_config TEXT NOT NULL,
                sharding_config TEXT NOT NULL,
                created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
                updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
            )
        ''')
        
        # 创建查询日志表
        cursor.execute('''
            CREATE TABLE IF NOT EXISTS query_logs (
                id INTEGER PRIMARY KEY AUTOINCREMENT,
                batch_id TEXT NOT NULL,
                history_id INTEGER,
                timestamp TEXT NOT NULL,
                level TEXT NOT NULL,
                message TEXT NOT NULL,
                query_type TEXT DEFAULT 'single',
                created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
                FOREIGN KEY (history_id) REFERENCES query_history (id) ON DELETE CASCADE
            )
        ''')
        
        # 创建索引
        cursor.execute('CREATE INDEX IF NOT EXISTS idx_query_logs_batch_id ON query_logs(batch_id)')
        cursor.execute('CREATE INDEX IF NOT EXISTS idx_query_logs_history_id ON query_logs(history_id)')
        cursor.execute('CREATE INDEX IF NOT EXISTS idx_query_logs_timestamp ON query_logs(timestamp)')
        cursor.execute('CREATE INDEX IF NOT EXISTS idx_query_logs_level ON query_logs(level)')
        
        conn.commit()
        conn.close()
        logger.info("数据库初始化完成")
        return True
    except Exception as e:
        logger.error(f"数据库初始化失败: {e}")
        return False

def ensure_database():
    """确保数据库和表存在"""
    if not os.path.exists(DATABASE_PATH):
        logger.info("数据库文件不存在，正在创建...")
        return init_database()
    
    # 检查表是否存在
    try:
        conn = sqlite3.connect(DATABASE_PATH)
        cursor = conn.cursor()
        cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name IN ('config_groups', 'query_history', 'sharding_config_groups', 'query_logs')")
        results = cursor.fetchall()
        existing_tables = [row[0] for row in results]
        
        required_tables = ['config_groups', 'query_history', 'sharding_config_groups', 'query_logs']
        missing_tables = [table for table in required_tables if table not in existing_tables]
        
        if missing_tables:
            logger.info(f"数据库表不完整，缺少表：{missing_tables}，正在重新创建...")
            return init_database()
        
        # 检查config_groups表是否有sharding_config字段
        cursor.execute("PRAGMA table_info(config_groups)")
        columns = cursor.fetchall()
        column_names = [column[1] for column in columns]
        
        if 'sharding_config' not in column_names:
            logger.info("添加sharding_config字段到config_groups表...")
            cursor.execute("ALTER TABLE config_groups ADD COLUMN sharding_config TEXT")
            conn.commit()
            logger.info("sharding_config字段添加成功")
        
        # 检查query_history表是否有分表相关字段
        cursor.execute("PRAGMA table_info(query_history)")
        history_columns = cursor.fetchall()
        history_column_names = [column[1] for column in history_columns]
        
        if 'sharding_config' not in history_column_names:
            logger.info("添加sharding_config字段到query_history表...")
            cursor.execute("ALTER TABLE query_history ADD COLUMN sharding_config TEXT")
            conn.commit()
            logger.info("query_history表sharding_config字段添加成功")
            
        if 'query_type' not in history_column_names:
            logger.info("添加query_type字段到query_history表...")
            cursor.execute("ALTER TABLE query_history ADD COLUMN query_type TEXT DEFAULT 'single'")
            conn.commit()
            logger.info("query_history表query_type字段添加成功")
        
        # 添加查询结果数据存储字段
        if 'raw_results' not in history_column_names:
            logger.info("添加raw_results字段到query_history表...")
            cursor.execute("ALTER TABLE query_history ADD COLUMN raw_results TEXT")
            conn.commit()
            logger.info("query_history表raw_results字段添加成功")
            
        if 'differences_data' not in history_column_names:
            logger.info("添加differences_data字段到query_history表...")
            cursor.execute("ALTER TABLE query_history ADD COLUMN differences_data TEXT")
            conn.commit()
            logger.info("query_history表differences_data字段添加成功")
            
        if 'identical_data' not in history_column_names:
            logger.info("添加identical_data字段到query_history表...")
            cursor.execute("ALTER TABLE query_history ADD COLUMN identical_data TEXT")
            conn.commit()
            logger.info("query_history表identical_data字段添加成功")
        
        # 检查query_logs表是否存在history_id字段
        cursor.execute("PRAGMA table_info(query_logs)")
        logs_columns = cursor.fetchall()
        logs_column_names = [column[1] for column in logs_columns]
        
        if 'history_id' not in logs_column_names:
            logger.info("添加history_id字段到query_logs表...")
            cursor.execute("ALTER TABLE query_logs ADD COLUMN history_id INTEGER")
            # 创建外键索引
            cursor.execute('CREATE INDEX IF NOT EXISTS idx_query_logs_history_id ON query_logs(history_id)')
            conn.commit()
            logger.info("query_logs表history_id字段添加成功")
        
        conn.close()
        return True
    except Exception as e:
        logger.error(f"检查数据库表失败: {e}")
        return init_database()

def get_db_connection():
    """获取数据库连接"""
    conn = sqlite3.connect(DATABASE_PATH)
    conn.row_factory = sqlite3.Row
    return conn

def normalize_json_string(value):
    """标准化JSON字符串，用于比较"""
    if not isinstance(value, str):
        return value
    
    try:
        # 尝试解析JSON
        json_obj = json.loads(value)
        
        # 如果是数组，需要进行特殊处理
        if isinstance(json_obj, list):
            # 尝试对数组元素进行标准化排序
            normalized_array = normalize_json_array(json_obj)
            return json.dumps(normalized_array, sort_keys=True, separators=(',', ':'))
        else:
            # 普通对象，直接序列化
            return json.dumps(json_obj, sort_keys=True, separators=(',', ':'))
    except (json.JSONDecodeError, TypeError):
        # 如果不是JSON，返回原值
        return value

def normalize_json_array(json_array):
    """标准化JSON数组，处理元素顺序问题"""
    try:
        normalized_elements = []
        
        for element in json_array:
            if isinstance(element, dict):
                # 对字典元素进行标准化
                normalized_elements.append(json.dumps(element, sort_keys=True, separators=(',', ':')))
            elif isinstance(element, str):
                # 如果是字符串，尝试解析为JSON
                try:
                    parsed_element = json.loads(element)
                    normalized_elements.append(json.dumps(parsed_element, sort_keys=True, separators=(',', ':')))
                except:
                    normalized_elements.append(element)
            else:
                normalized_elements.append(element)
        
        # 对标准化后的元素进行排序，确保顺序一致
        normalized_elements.sort()
        
        # 重新解析为对象数组
        result_array = []
        for element in normalized_elements:
            if isinstance(element, str):
                try:
                    result_array.append(json.loads(element))
                except:
                    result_array.append(element)
            else:
                result_array.append(element)
        
        return result_array
        
    except Exception as e:
        logger.warning(f"数组标准化失败: {e}")
        return json_array

def is_json_array_field(value):
    """检查字段是否为JSON数组格式"""
    if not isinstance(value, (str, list)):
        return False
    
    try:
        if isinstance(value, str):
            parsed = json.loads(value)
            return isinstance(parsed, list)
        elif isinstance(value, list):
            # 检查是否为JSON字符串数组
            if len(value) > 0 and isinstance(value[0], str):
                try:
                    json.loads(value[0])
                    return True
                except:
                    return False
            return True
    except:
        return False

def compare_array_values(value1, value2):
    """专门用于比较数组类型的值"""
    try:
        # 处理字符串表示的数组
        if isinstance(value1, str) and isinstance(value2, str):
            try:
                array1 = json.loads(value1)
                array2 = json.loads(value2)
                if isinstance(array1, list) and isinstance(array2, list):
                    return compare_json_arrays(array1, array2)
            except:
                pass
        
        # 处理Python列表类型
        elif isinstance(value1, list) and isinstance(value2, list):
            return compare_json_arrays(value1, value2)
        
        # 处理混合情况：一个是字符串数组，一个是列表
        elif isinstance(value1, list) and isinstance(value2, str):
            try:
                array2 = json.loads(value2)
                if isinstance(array2, list):
                    return compare_json_arrays(value1, array2)
            except:
                pass
        elif isinstance(value1, str) and isinstance(value2, list):
            try:
                array1 = json.loads(value1)
                if isinstance(array1, list):
                    return compare_json_arrays(array1, value2)
            except:
                pass
        
        return False
    except Exception as e:
        logger.warning(f"数组比较失败: {e}")
        return False

def compare_json_arrays(array1, array2):
    """比较两个JSON数组，忽略元素顺序"""
    try:
        if len(array1) != len(array2):
            return False
        
        # 标准化两个数组
        normalized_array1 = normalize_json_array(array1.copy())
        normalized_array2 = normalize_json_array(array2.copy())
        
        # 将标准化后的数组转换为可比较的格式
        comparable1 = json.dumps(normalized_array1, sort_keys=True)
        comparable2 = json.dumps(normalized_array2, sort_keys=True)
        
        return comparable1 == comparable2
        
    except Exception as e:
        logger.warning(f"JSON数组比较失败: {e}")
        return False

def format_json_for_display(value):
    """格式化JSON用于显示"""
    if not isinstance(value, str):
        return str(value)
    
    try:
        # 尝试解析JSON
        json_obj = json.loads(value)
        # 格式化显示（带缩进）
        return json.dumps(json_obj, sort_keys=True, indent=2, ensure_ascii=False)
    except (json.JSONDecodeError, TypeError):
        # 如果不是JSON，返回原值
        return str(value)

def is_json_field(value):
    """检查字段是否为JSON格式"""
    if not isinstance(value, str):
        return False
    
    try:
        json.loads(value)
        return True
    except (json.JSONDecodeError, TypeError):
        return False

def compare_values(value1, value2):
    """智能比较两个值，支持JSON标准化和数组比较"""
    # 首先检查是否为数组类型
    if is_json_array_field(value1) or is_json_array_field(value2):
        return compare_array_values(value1, value2)
    
    # 如果两个值都是字符串，尝试JSON标准化比较
    if isinstance(value1, str) and isinstance(value2, str):
        normalized_value1 = normalize_json_string(value1)
        normalized_value2 = normalize_json_string(value2)
        return normalized_value1 == normalized_value2
    
    # 其他情况直接比较
    return value1 == value2

# 默认配置（不显示敏感信息）
DEFAULT_CONFIG = {
    'pro_config': {
        'cluster_name': '',
        'hosts': [],
        'port': 9042,
        'datacenter': '',
        'username': '',
        'password': '',
        'keyspace': '',
        'table': ''
    },
    'test_config': {
        'cluster_name': '',
        'hosts': [],
        'port': 9042,
        'datacenter': '',
        'username': '',
        'password': '',
        'keyspace': '',
        'table': ''
    },
    'keys': [],
    'fields_to_compare': [],
    'exclude_fields': []
}

def save_config_group(name, description, pro_config, test_config, query_config, sharding_config=None):
    """保存配置组"""
    if not ensure_database():
        logger.error("数据库初始化失败")
        return False
        
    conn = get_db_connection()
    cursor = conn.cursor()
    
    try:
        cursor.execute('''
            INSERT OR REPLACE INTO config_groups 
            (name, description, pro_config, test_config, query_config, sharding_config, updated_at)
            VALUES (?, ?, ?, ?, ?, ?, ?)
        ''', (
            name, description, 
            json.dumps(pro_config), 
            json.dumps(test_config), 
            json.dumps(query_config),
            json.dumps(sharding_config) if sharding_config else None,
            datetime.now().isoformat()
        ))
        conn.commit()
        logger.info(f"配置组 '{name}' 保存成功，包含分表配置: {sharding_config is not None}")
        return True
    except Exception as e:
        logger.error(f"保存配置组失败: {e}")
        return False
    finally:
        conn.close()

def get_config_groups():
    """获取所有配置组"""
    if not ensure_database():
        logger.error("数据库初始化失败")
        return []
        
    conn = get_db_connection()
    cursor = conn.cursor()
    
    try:
        cursor.execute('''
            SELECT id, name, description, created_at, updated_at 
            FROM config_groups 
            ORDER BY updated_at DESC
        ''')
        rows = cursor.fetchall()
        
        config_groups = []
        for row in rows:
            config_groups.append({
                'id': row['id'],
                'name': row['name'],
                'description': row['description'],
                'created_at': row['created_at'],
                'updated_at': row['updated_at']
            })
        
        return config_groups
    except Exception as e:
        logger.error(f"获取配置组失败: {e}")
        return []
    finally:
        conn.close()

def get_config_group_by_id(group_id):
    """根据ID获取配置组详情"""
    if not ensure_database():
        logger.error("数据库初始化失败")
        return None
        
    conn = get_db_connection()
    cursor = conn.cursor()
    
    try:
        cursor.execute('''
            SELECT id, name, description, pro_config, test_config, query_config, 
                   sharding_config, created_at, updated_at 
            FROM config_groups WHERE id = ?
        ''', (group_id,))
        row = cursor.fetchone()
        
        if row:
            config = {
                'id': row['id'],
                'name': row['name'],
                'description': row['description'],
                'pro_config': json.loads(row['pro_config']),
                'test_config': json.loads(row['test_config']),
                'query_config': json.loads(row['query_config']),
                'created_at': row['created_at'],
                'updated_at': row['updated_at']
            }
            
            # 添加分表配置
            if row['sharding_config']:
                try:
                    config['sharding_config'] = json.loads(row['sharding_config'])
                except (json.JSONDecodeError, TypeError):
                    config['sharding_config'] = None
            else:
                config['sharding_config'] = None
                
            return config
        return None
    except Exception as e:
        logger.error(f"获取配置组详情失败: {e}")
        return None
    finally:
        conn.close()

def delete_config_group(group_id):
    """删除配置组"""
    if not ensure_database():
        logger.error("数据库初始化失败")
        return False
        
    conn = get_db_connection()
    cursor = conn.cursor()
    
    try:
        cursor.execute('DELETE FROM config_groups WHERE id = ?', (group_id,))
        conn.commit()
        success = cursor.rowcount > 0
        if success:
            logger.info(f"配置组ID {group_id} 删除成功")
        return success
    except Exception as e:
        logger.error(f"删除配置组失败: {e}")
        return False
    finally:
        conn.close()

def save_query_history(name, description, pro_config, test_config, query_config, query_keys, 
                      results_summary, execution_time, total_keys, differences_count, identical_count,
                      sharding_config=None, query_type='single', raw_results=None, differences_data=None, identical_data=None):
    """保存查询历史记录，支持分表查询和查询结果数据，返回历史记录ID"""
    if not ensure_database():
        logger.error("数据库初始化失败")
        return None
        
    conn = get_db_connection()
    cursor = conn.cursor()
    
    try:
        cursor.execute('''
            INSERT INTO query_history 
            (name, description, pro_config, test_config, query_config, query_keys, 
             results_summary, execution_time, total_keys, differences_count, identical_count,
             sharding_config, query_type, raw_results, differences_data, identical_data)
            VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
        ''', (
            name, description,
            json.dumps(pro_config), 
            json.dumps(test_config), 
            json.dumps(query_config),
            json.dumps(query_keys),
            json.dumps(results_summary),
            execution_time,
            total_keys,
            differences_count,
            identical_count,
            json.dumps(sharding_config) if sharding_config else None,
            query_type,
            json.dumps(raw_results) if raw_results else None,
            json.dumps(differences_data) if differences_data else None,
            json.dumps(identical_data) if identical_data else None
        ))
        
        # 获取插入记录的ID
        history_id = cursor.lastrowid
        conn.commit()
        logger.info(f"查询历史记录 '{name}' 保存成功，查询类型：{query_type}，ID：{history_id}")
        return history_id
    except Exception as e:
        logger.error(f"保存查询历史记录失败: {e}")
        return None
    finally:
        conn.close()

def get_query_history():
    """获取所有查询历史记录"""
    if not ensure_database():
        logger.error("数据库初始化失败")
        return []
        
    conn = get_db_connection()
    cursor = conn.cursor()
    
    try:
        cursor.execute('''
            SELECT id, name, description, execution_time, total_keys, 
                   differences_count, identical_count, created_at, query_type
            FROM query_history 
            ORDER BY created_at DESC
        ''')
        rows = cursor.fetchall()
        
        history_list = []
        for row in rows:
            # 获取列名列表以检查字段是否存在
            column_names = [desc[0] for desc in cursor.description]
            history_list.append({
                'id': row['id'],
                'name': row['name'],
                'description': row['description'],
                'execution_time': row['execution_time'],
                'total_keys': row['total_keys'],
                'differences_count': row['differences_count'],
                'identical_count': row['identical_count'],
                'created_at': row['created_at'],
                'query_type': row['query_type'] if 'query_type' in column_names else 'single'
            })
        
        return history_list
    except Exception as e:
        logger.error(f"获取查询历史记录失败: {e}")
        return []
    finally:
        conn.close()

def get_query_history_by_id(history_id):
    """根据ID获取查询历史记录详情"""
    if not ensure_database():
        logger.error("数据库初始化失败")
        return None
        
    conn = get_db_connection()
    cursor = conn.cursor()
    
    try:
        cursor.execute('''
            SELECT * FROM query_history WHERE id = ?
        ''', (history_id,))
        row = cursor.fetchone()
        
        if row:
            # 获取列名列表以检查字段是否存在
            column_names = [desc[0] for desc in cursor.description]
            return {
                'id': row['id'],
                'name': row['name'],
                'description': row['description'],
                'pro_config': json.loads(row['pro_config']),
                'test_config': json.loads(row['test_config']),
                'query_config': json.loads(row['query_config']),
                'query_keys': json.loads(row['query_keys']),
                'results_summary': json.loads(row['results_summary']),
                'execution_time': row['execution_time'],
                'total_keys': row['total_keys'],
                'differences_count': row['differences_count'],
                'identical_count': row['identical_count'],
                'created_at': row['created_at'],
                # 处理新字段，保持向后兼容
                'sharding_config': json.loads(row['sharding_config']) if 'sharding_config' in column_names and row['sharding_config'] else None,
                'query_type': row['query_type'] if 'query_type' in column_names else 'single',
                # 添加查询结果数据支持
                'raw_results': json.loads(row['raw_results']) if 'raw_results' in column_names and row['raw_results'] else None,
                'differences_data': json.loads(row['differences_data']) if 'differences_data' in column_names and row['differences_data'] else None,
                'identical_data': json.loads(row['identical_data']) if 'identical_data' in column_names and row['identical_data'] else None
            }
        return None
    except Exception as e:
        logger.error(f"获取查询历史记录详情失败: {e}")
        return None
    finally:
        conn.close()

def delete_query_history(history_id):
    """删除查询历史记录"""
    if not ensure_database():
        logger.error("数据库初始化失败")
        return False
        
    conn = get_db_connection()
    cursor = conn.cursor()
    
    try:
        cursor.execute('DELETE FROM query_history WHERE id = ?', (history_id,))
        conn.commit()
        success = cursor.rowcount > 0
        if success:
            logger.info(f"查询历史记录ID {history_id} 删除成功")
        return success
    except Exception as e:
        logger.error(f"删除查询历史记录失败: {e}")
        return False
    finally:
        conn.close()

def create_connection(config):
    """创建Cassandra连接，带有增强的错误诊断和容错机制"""
    start_time = time.time()
    
    logger.info(f"=== 开始创建Cassandra连接 ===")
    logger.info(f"主机列表: {config.get('hosts', [])}")
    logger.info(f"端口: {config.get('port', 9042)}")
    logger.info(f"用户名: {config.get('username', 'N/A')}")
    logger.info(f"Keyspace: {config.get('keyspace', 'N/A')}")
    
    try:
        logger.info("正在创建认证提供者...")
        auth_provider = PlainTextAuthProvider(username=config['username'], password=config['password'])
        
        logger.info("正在创建集群连接...")
        # 设置连接池配置，提高容错性
        from cassandra.policies import DCAwareRoundRobinPolicy
        
        # 设置负载均衡策略，避免单点故障
        load_balancing_policy = DCAwareRoundRobinPolicy(local_dc=config.get('datacenter', 'dc1'))
        
        # 创建连接配置，增加容错参数
        cluster = Cluster(
            config['hosts'], 
            port=config['port'], 
            auth_provider=auth_provider,
            load_balancing_policy=load_balancing_policy,
            # 增加容错配置
            protocol_version=4,  # 使用稳定的协议版本
            connect_timeout=15,   # 连接超时
            control_connection_timeout=15,  # 控制连接超时
            max_schema_agreement_wait=30   # schema同步等待时间
        )
        
        logger.info("正在连接到Keyspace...")
        session = cluster.connect(config['keyspace'])
        
        # 设置session级别的容错参数
        session.default_timeout = 30  # 查询超时时间
        
        connection_time = time.time() - start_time
        logger.info(f"✅ Cassandra连接成功: 连接时间={connection_time:.3f}秒")
        
        # 记录集群状态
        try:
            cluster_name = cluster.metadata.cluster_name or "Unknown"
            logger.info(f"   集群名称: {cluster_name}")
            
            # 记录可用主机状态
            live_hosts = [str(host.address) for host in cluster.metadata.all_hosts() if host.is_up]
            down_hosts = [str(host.address) for host in cluster.metadata.all_hosts() if not host.is_up]
            
            logger.info(f"   可用节点: {live_hosts} ({len(live_hosts)}个)")
            if down_hosts:
                logger.warning(f"   故障节点: {down_hosts} ({len(down_hosts)}个)")
                
        except Exception as meta_error:
            logger.warning(f"无法获取集群元数据: {meta_error}")
        
        return cluster, session
        
    except Exception as e:
        connection_time = time.time() - start_time
        error_msg = str(e)
        
        logger.error(f"❌ Cassandra连接失败: 连接时间={connection_time:.3f}秒")
        logger.error(f"错误类型: {type(e).__name__}")
        logger.error(f"错误详情: {error_msg}")
        
        # 提供详细的诊断信息
        if "connection refused" in error_msg.lower() or "unable to connect" in error_msg.lower():
            logger.error("❌ 诊断：无法连接到Cassandra服务器")
            logger.error("🔧 建议检查：")
            logger.error("   1. Cassandra服务是否启动")
            logger.error("   2. 主机地址和端口是否正确")
            logger.error("   3. 网络防火墙是否阻挡连接")
            
        elif "timeout" in error_msg.lower():
            logger.error("❌ 诊断：连接超时")
            logger.error("🔧 建议检查：")
            logger.error("   1. 网络延迟是否过高")
            logger.error("   2. Cassandra服务器负载是否过高")
            logger.error("   3. 增加连接超时时间")
            
        elif "authentication" in error_msg.lower() or "unauthorized" in error_msg.lower():
            logger.error("❌ 诊断：认证失败")
            logger.error("🔧 建议检查：")
            logger.error("   1. 用户名和密码是否正确")
            logger.error("   2. 用户是否有访问该keyspace的权限")
            
        elif "keyspace" in error_msg.lower():
            logger.error("❌ 诊断：Keyspace不存在")
            logger.error("🔧 建议检查：")
            logger.error("   1. Keyspace名称是否正确")
            logger.error("   2. Keyspace是否已创建")
            
        else:
            logger.error("❌ 诊断：未知连接错误")
            logger.error("🔧 建议：")
            logger.error("   1. 检查所有连接参数")
            logger.error("   2. 查看Cassandra服务器日志")
            logger.error("   3. 测试网络连通性")
        
        return None, None

def execute_query(session, table, keys, fields, values, exclude_fields=None):
    """执行查询，支持单主键和复合主键"""
    try:
        # 参数验证
        if not keys or len(keys) == 0:
            logger.error("Keys参数为空，无法构建查询")
            return []
        
        if not values or len(values) == 0:
            logger.error("Values参数为空，无法构建查询")
            return []
        
        # 构建查询条件
        if len(keys) == 1:
            # 单主键查询（保持原有逻辑）
            quoted_values = [f"'{value}'" for value in values]
            query_conditions = f"{keys[0]} IN ({', '.join(quoted_values)})"
        else:
            # 复合主键查询
            conditions = []
            for value in values:
                # 检查value是否包含复合主键分隔符
                if isinstance(value, str) and ',' in value:
                    # 解析复合主键值
                    key_values = [v.strip() for v in value.split(',')]
                    if len(key_values) == len(keys):
                        # 构建单个复合主键条件: (key1='val1' AND key2='val2')
                        key_conditions = []
                        for i, (key, val) in enumerate(zip(keys, key_values)):
                            key_conditions.append(f"{key} = '{val}'")
                        conditions.append(f"({' AND '.join(key_conditions)})")
                    else:
                        logger.warning(f"复合主键值 '{value}' 的字段数量({len(key_values)})与主键字段数量({len(keys)})不匹配")
                        # 将其作为第一个主键的值处理
                        conditions.append(f"{keys[0]} = '{value}'")
                else:
                    # 单值，作为第一个主键的值处理
                    conditions.append(f"{keys[0]} = '{value}'")
            
            if conditions:
                query_conditions = ' OR '.join(conditions)
            else:
                logger.error("无法构建有效的查询条件")
                return []
        
        # 确定要查询的字段
        if fields:
            fields_str = ", ".join(fields)
        else:
            fields_str = "*"
        
        query_sql = f"SELECT {fields_str} FROM {table} WHERE {query_conditions};"
        
        # 记录查询SQL日志
        logger.info(f"执行查询SQL: {query_sql}")
        if len(keys) > 1:
            logger.info(f"复合主键查询参数: 表={table}, 主键字段={keys}, 字段={fields_str}, Key数量={len(values)}")
        else:
            logger.info(f"单主键查询参数: 表={table}, 主键字段={keys[0]}, 字段={fields_str}, Key数量={len(values)}")
        
        # 执行查询
        start_time = time.time()
        result = session.execute(query_sql)
        execution_time = time.time() - start_time
        
        result_list = list(result) if result else []
        logger.info(f"查询完成: 执行时间={execution_time:.3f}秒, 返回记录数={len(result_list)}")
        
        return result_list
    except Exception as e:
        logger.error(f"查询执行失败: SQL={query_sql if 'query_sql' in locals() else 'N/A'}, 错误={str(e)}")
        return []

def execute_sharding_query(session, shard_mapping, keys, fields, exclude_fields=None):
    """
    执行分表查询
    :param session: Cassandra会话
    :param shard_mapping: 分表映射 {table_name: [keys]}
    :param keys: 主键字段名列表
    :param fields: 要查询的字段列表
    :param exclude_fields: 要排除的字段列表
    :return: (查询结果列表, 查询到的表列表, 查询失败的表列表)
    """
    all_results = []
    queried_tables = []
    error_tables = []
    
    logger.info(f"开始执行分表查询，涉及 {len(shard_mapping)} 张分表")
    total_start_time = time.time()
    
    for table_name, table_keys in shard_mapping.items():
        try:
            logger.info(f"查询分表 {table_name}，包含 {len(table_keys)} 个key: {table_keys}")
            # 为每个分表执行查询
            table_results = execute_query(session, table_name, keys, fields, table_keys, exclude_fields)
            all_results.extend(table_results)
            queried_tables.append(table_name)
            logger.info(f"分表 {table_name} 查询成功，返回 {len(table_results)} 条记录")
        except Exception as e:
            logger.error(f"分表 {table_name} 查询失败: {e}")
            error_tables.append(table_name)
    
    total_execution_time = time.time() - total_start_time
    logger.info(f"分表查询总计完成: 执行时间={total_execution_time:.3f}秒, 成功表数={len(queried_tables)}, 失败表数={len(error_tables)}, 总记录数={len(all_results)}")
    
    return all_results, queried_tables, error_tables

def execute_mixed_query(pro_session, test_session, pro_config, test_config, keys, fields_to_compare, values, exclude_fields, sharding_config):
    """
    执行混合查询（生产环境分表，测试环境可能单表或分表）
    """
    results = {
        'pro_data': [],
        'test_data': [],
        'sharding_info': {
            'calculation_stats': {}
        }
    }
    
    # 处理生产环境查询
    if sharding_config.get('use_sharding_for_pro', False):
        # 获取生产环境分表配置参数，优先使用专用参数，否则使用通用参数
        pro_interval = sharding_config.get('pro_interval_seconds') or sharding_config.get('interval_seconds', 604800)
        pro_table_count = sharding_config.get('pro_table_count') or sharding_config.get('table_count', 14)
        
        # 记录生产环境分表配置信息
        logger.info(f"=== 生产环境分表配置 ===")
        logger.info(f"启用分表查询: True")
        logger.info(f"时间间隔: {pro_interval}秒 ({pro_interval//86400}天)")
        logger.info(f"分表数量: {pro_table_count}张")
        logger.info(f"基础表名: {pro_config['table']}")
        
        pro_calculator = ShardingCalculator(
            interval_seconds=pro_interval,
            table_count=pro_table_count
        )
        pro_shard_mapping, pro_failed_keys, pro_calc_stats = pro_calculator.get_all_shard_tables_for_keys(
            pro_config['table'], values
        )
        
        logger.info(f"生产环境分表映射结果: 涉及{len(pro_shard_mapping)}张分表, 失败Key数量: {len(pro_failed_keys)}")
        
        pro_data, pro_queried_tables, pro_error_tables = execute_sharding_query(
            pro_session, pro_shard_mapping, keys, fields_to_compare, exclude_fields
        )
        
        results['pro_data'] = pro_data
        results['sharding_info']['pro_shards'] = {
            'enabled': True,
            'interval_seconds': sharding_config.get('pro_interval_seconds', 604800),
            'table_count': sharding_config.get('pro_table_count', 14),
            'queried_tables': pro_queried_tables,
            'error_tables': pro_error_tables,
            'failed_keys': pro_failed_keys
        }
        results['sharding_info']['calculation_stats'].update(pro_calc_stats)
    else:
        # 生产环境单表查询
        logger.info(f"=== 生产环境单表配置 ===")
        logger.info(f"启用分表查询: False")
        logger.info(f"表名: {pro_config['table']}")
        
        pro_data = execute_query(pro_session, pro_config['table'], keys, fields_to_compare, values, exclude_fields)
        results['pro_data'] = pro_data
        results['sharding_info']['pro_shards'] = {
            'enabled': False,
            'queried_tables': [pro_config['table']]
        }
    
    # 处理测试环境查询
    if sharding_config.get('use_sharding_for_test', False):
        # 获取测试环境分表配置参数，优先使用专用参数，否则使用通用参数
        test_interval = sharding_config.get('test_interval_seconds') or sharding_config.get('interval_seconds', 604800)
        test_table_count = sharding_config.get('test_table_count') or sharding_config.get('table_count', 14)
        
        # 记录测试环境分表配置信息
        logger.info(f"=== 测试环境分表配置 ===")
        logger.info(f"启用分表查询: True")
        logger.info(f"时间间隔: {test_interval}秒 ({test_interval//86400}天)")
        logger.info(f"分表数量: {test_table_count}张")
        logger.info(f"基础表名: {test_config['table']}")
        
        test_calculator = ShardingCalculator(
            interval_seconds=test_interval,
            table_count=test_table_count
        )
        test_shard_mapping, test_failed_keys, test_calc_stats = test_calculator.get_all_shard_tables_for_keys(
            test_config['table'], values
        )
        
        logger.info(f"测试环境分表映射结果: 涉及{len(test_shard_mapping)}张分表, 失败Key数量: {len(test_failed_keys)}")
        
        test_data, test_queried_tables, test_error_tables = execute_sharding_query(
            test_session, test_shard_mapping, keys, fields_to_compare, exclude_fields
        )
        
        results['test_data'] = test_data
        results['sharding_info']['test_shards'] = {
            'enabled': True,
            'interval_seconds': test_interval,
            'table_count': test_table_count,
            'queried_tables': test_queried_tables,
            'error_tables': test_error_tables,
            'failed_keys': test_failed_keys
        }
        
        # 合并计算统计信息
        if not results['sharding_info']['calculation_stats']:
            results['sharding_info']['calculation_stats'] = test_calc_stats
    else:
        # 测试环境单表查询
        logger.info(f"=== 测试环境单表配置 ===")
        logger.info(f"启用分表查询: False")
        logger.info(f"表名: {test_config['table']}")
        
        test_data = execute_query(test_session, test_config['table'], keys, fields_to_compare, values, exclude_fields)
        results['test_data'] = test_data
        results['sharding_info']['test_shards'] = {
            'enabled': False,
            'queried_tables': [test_config['table']]
        }
    
    return results

def compare_results(pro_data, test_data, keys, fields_to_compare, exclude_fields, values):
    """比较查询结果，支持复合主键"""
    differences = []
    field_diff_count = {}
    identical_results = []  # 存储相同的结果
    
    def match_composite_key(row, composite_value, keys):
        """检查数据行是否匹配复合主键值"""
        if len(keys) == 1:
            # 单主键匹配
            return getattr(row, keys[0]) == composite_value
        else:
            # 复合主键匹配
            if isinstance(composite_value, str) and ',' in composite_value:
                key_values = [v.strip() for v in composite_value.split(',')]
                if len(key_values) == len(keys):
                    return all(str(getattr(row, key)) == key_val for key, key_val in zip(keys, key_values))
            # 如果不是复合值，只匹配第一个主键
            return getattr(row, keys[0]) == composite_value
    
    for value in values:
        # 查找生产表和测试表中该主键值的相关数据
        rows_pro = [row for row in pro_data if match_composite_key(row, value, keys)]
        rows_test = [row for row in test_data if match_composite_key(row, value, keys)]
        
        for row_pro in rows_pro:
            # 在测试表中查找相同主键的行
            row_test = next(
                (row for row in rows_test if all(getattr(row, key) == getattr(row_pro, key) for key in keys)),
                None
            )
            
            if row_test:
                # 确定要比较的列
                columns = fields_to_compare if fields_to_compare else row_pro._fields
                columns = [col for col in columns if col not in exclude_fields]
                
                has_difference = False
                row_differences = []
                identical_fields = {}
                
                for column in columns:
                    value_pro = getattr(row_pro, column)
                    value_test = getattr(row_test, column)
                    
                    # 使用智能比较函数
                    if not compare_values(value_pro, value_test):
                        has_difference = True
                        # 格式化显示值
                        formatted_pro_value = format_json_for_display(value_pro)
                        formatted_test_value = format_json_for_display(value_test)
                        
                        row_differences.append({
                            'key': {key: getattr(row_pro, key) for key in keys},
                            'field': column,
                            'pro_value': formatted_pro_value,
                            'test_value': formatted_test_value,
                            'is_json': is_json_field(value_pro) or is_json_field(value_test),
                            'is_array': is_json_array_field(value_pro) or is_json_array_field(value_test)
                        })
                        
                        # 统计字段差异次数
                        field_diff_count[column] = field_diff_count.get(column, 0) + 1
                    else:
                        # 存储相同的字段值
                        identical_fields[column] = format_json_for_display(value_pro)
                
                if has_difference:
                    differences.extend(row_differences)
                else:
                    # 如果没有差异，存储到相同结果中
                    identical_results.append({
                        'key': {key: getattr(row_pro, key) for key in keys},
                        'pro_fields': identical_fields,
                        'test_fields': {col: format_json_for_display(getattr(row_test, col)) for col in columns}
                    })
            else:
                # 在测试表中未找到对应行
                differences.append({
                    'key': {key: getattr(row_pro, key) for key in keys},
                    'message': '在测试表中未找到该行'
                })
        
        # 检查测试表中是否有生产表中不存在的行
        for row_test in rows_test:
            row_pro = next(
                (row for row in rows_pro if all(getattr(row, key) == getattr(row_test, key) for key in keys)),
                None
            )
            if not row_pro:
                differences.append({
                    'key': {key: getattr(row_test, key) for key in keys},
                    'message': '在生产表中未找到该行'
                })
    
    return differences, field_diff_count, identical_results

def generate_comparison_summary(total_keys, pro_count, test_count, differences, identical_results, field_diff_count):
    """生成比较总结报告"""
    # 计算基本统计
    different_records = len(set([list(diff['key'].values())[0] for diff in differences if 'field' in diff]))
    identical_records = len(identical_results)
    missing_in_test = len([diff for diff in differences if diff.get('message') == '在测试表中未找到该行'])
    missing_in_pro = len([diff for diff in differences if diff.get('message') == '在生产表中未找到该行'])
    
    # 计算百分比
    def safe_percentage(part, total):
        return round((part / total * 100), 2) if total > 0 else 0
    
    identical_percentage = safe_percentage(identical_records, total_keys)
    different_percentage = safe_percentage(different_records, total_keys)
    
    # 生成总结
    summary = {
        'overview': {
            'total_keys_queried': total_keys,
            'pro_records_found': pro_count,
            'test_records_found': test_count,
            'identical_records': identical_records,
            'different_records': different_records,
            'missing_in_test': missing_in_test,
            'missing_in_pro': missing_in_pro
        },
        'percentages': {
            'data_consistency': identical_percentage,
            'data_differences': different_percentage,
            'missing_rate': safe_percentage(missing_in_test + missing_in_pro, total_keys)
        },
        'field_analysis': {
            'total_fields_compared': len(field_diff_count) if field_diff_count else 0,
            'most_different_fields': sorted(field_diff_count.items(), key=lambda x: x[1], reverse=True)[:5] if field_diff_count else []
        },
        'data_quality': {
            'completeness': safe_percentage(pro_count + test_count, total_keys * 2),
            'consistency_score': identical_percentage,
            'quality_level': get_quality_level(identical_percentage)
        },
        'recommendations': generate_recommendations(identical_percentage, missing_in_test, missing_in_pro, field_diff_count)
    }
    
    return summary

def get_quality_level(consistency_percentage):
    """根据一致性百分比获取数据质量等级"""
    if consistency_percentage >= 95:
        return {'level': '优秀', 'color': 'success', 'description': '数据一致性非常高'}
    elif consistency_percentage >= 90:
        return {'level': '良好', 'color': 'info', 'description': '数据一致性较高'}
    elif consistency_percentage >= 80:
        return {'level': '一般', 'color': 'warning', 'description': '数据一致性中等，需要关注'}
    else:
        return {'level': '较差', 'color': 'danger', 'description': '数据一致性较低，需要重点处理'}

def generate_recommendations(consistency_percentage, missing_in_test, missing_in_pro, field_diff_count):
    """生成改进建议"""
    recommendations = []
    
    if consistency_percentage < 90:
        recommendations.append('建议重点关注数据一致性问题，检查数据同步机制')
    
    if missing_in_test > 0:
        recommendations.append(f'测试环境缺失 {missing_in_test} 条记录，建议检查数据迁移过程')
    
    if missing_in_pro > 0:
        recommendations.append(f'生产环境缺失 {missing_in_pro} 条记录，建议检查数据完整性')
    
    if field_diff_count:
        top_diff_field = max(field_diff_count.items(), key=lambda x: x[1])
        recommendations.append(f'字段 "{top_diff_field[0]}" 差异最多({top_diff_field[1]}次)，建议优先处理')
    
    if not recommendations:
        recommendations.append('数据质量良好，建议继续保持当前的数据管理流程')
    
    return recommendations

@app.route('/')
def index():
    return render_template('index.html')

@app.route('/test-config-load')
def test_config_load():
    """配置加载测试页面"""
    return send_from_directory('.', 'test_config_load.html')

@app.route('/db-compare')
def db_compare():
    return render_template('db_compare.html')

@app.route('/api/sharding-query', methods=['POST'])
def sharding_query_compare():
    """分表查询比对API"""
    try:
        data = request.json
        
        # 开始新的查询批次
        batch_id = query_log_collector.start_new_batch('分表')
        
        logger.info("开始执行分表数据库比对查询")
        
        # 解析配置
        pro_config = data.get('pro_config', DEFAULT_CONFIG['pro_config'])
        test_config = data.get('test_config', DEFAULT_CONFIG['test_config'])
        
        # 从query_config中获取keys等参数
        query_config = data.get('query_config', {})
        keys = query_config.get('keys', DEFAULT_CONFIG['keys'])
        fields_to_compare = query_config.get('fields_to_compare', DEFAULT_CONFIG['fields_to_compare'])
        exclude_fields = query_config.get('exclude_fields', DEFAULT_CONFIG['exclude_fields'])
        
        values = data.get('values', [])
        sharding_config = data.get('sharding_config', {})
        
        # 参数验证
        if not values:
            logger.warning("分表查询失败：未提供查询key值")
            return jsonify({'error': '请提供查询key值'}), 400
            
        if not keys:
            logger.warning("分表查询失败：未提供主键字段")
            return jsonify({'error': '请提供主键字段'}), 400
        
        # 添加详细的参数日志
        logger.info(f"分表查询参数解析结果:")
        logger.info(f"  keys: {keys}")
        logger.info(f"  values数量: {len(values)}")
        logger.info(f"  fields_to_compare: {fields_to_compare}")
        logger.info(f"  exclude_fields: {exclude_fields}")
        logger.info(f"  sharding_config原始数据: {sharding_config}")
        logger.info(f"  sharding_config具体参数:")
        logger.info(f"    use_sharding_for_pro: {sharding_config.get('use_sharding_for_pro')}")
        logger.info(f"    use_sharding_for_test: {sharding_config.get('use_sharding_for_test')}")
        logger.info(f"    pro_interval_seconds: {sharding_config.get('pro_interval_seconds')}")
        logger.info(f"    pro_table_count: {sharding_config.get('pro_table_count')}")
        logger.info(f"    test_interval_seconds: {sharding_config.get('test_interval_seconds')}")
        logger.info(f"    test_table_count: {sharding_config.get('test_table_count')}")
        logger.info(f"    interval_seconds: {sharding_config.get('interval_seconds')}")
        logger.info(f"    table_count: {sharding_config.get('table_count')}")
        
        logger.info(f"分表查询配置：{len(values)}个key值，生产表：{pro_config['table']}，测试表：{test_config['table']}")
        
        # 创建数据库连接
        pro_cluster, pro_session = create_connection(pro_config)
        test_cluster, test_session = create_connection(test_config)
        
        if not pro_session or not test_session:
            logger.error("数据库连接失败")
            return jsonify({'error': '数据库连接失败，请检查配置信息'}), 500
        
        try:
            # 执行混合查询（支持生产环境分表、测试环境单表/分表的组合）
            logger.info("执行分表混合查询")
            query_results = execute_mixed_query(
                pro_session, test_session, pro_config, test_config,
                keys, fields_to_compare, values, exclude_fields, sharding_config
            )
            
            pro_data = query_results['pro_data']
            test_data = query_results['test_data']
            sharding_info = query_results['sharding_info']
            
            logger.info(f"分表查询结果：生产表 {len(pro_data)} 条记录，测试表 {len(test_data)} 条记录")
            
            # 比较结果
            differences, field_diff_count, identical_results = compare_results(
                pro_data, test_data, keys, fields_to_compare, exclude_fields, values
            )
            
            # 统计信息
            different_ids = set()
            for diff in differences:
                if 'field' in diff:
                    different_ids.add(list(diff['key'].values())[0])
            
            non_different_ids = set(values) - different_ids
            
            # 生成比较总结
            summary = generate_comparison_summary(
                len(values), len(pro_data), len(test_data), 
                differences, identical_results, field_diff_count
            )
            
            result = {
                'total_keys': len(values),
                'pro_count': len(pro_data),
                'test_count': len(test_data),
                'differences': differences,
                'identical_results': identical_results,
                'field_diff_count': field_diff_count,
                'different_ids': list(different_ids),
                'non_different_ids': list(non_different_ids),
                'summary': summary,
                'sharding_info': sharding_info,  # 包含分表查询信息
                'raw_pro_data': [dict(row._asdict()) for row in pro_data] if pro_data else [],
                'raw_test_data': [dict(row._asdict()) for row in test_data] if test_data else []
            }
            
            logger.info(f"分表比对完成：发现 {len(differences)} 处差异")
            
            # 自动保存分表查询历史记录
            try:
                # 生成历史记录名称
                timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
                history_name = f"分表查询_{timestamp}"
                history_description = f"自动保存 - 分表查询{len(values)}个Key，发现{len(differences)}处差异"
                
                # 保存历史记录
                history_id = save_query_history(
                    name=history_name,
                    description=history_description,
                    pro_config=pro_config,
                    test_config=test_config,
                    query_config={
                        'keys': keys,
                        'fields_to_compare': fields_to_compare,
                        'exclude_fields': exclude_fields
                    },
                    query_keys=values,
                    results_summary=summary,
                    execution_time=0.0,  # 可以后续优化计算实际执行时间
                    total_keys=len(values),
                    differences_count=len(differences),
                    identical_count=len(identical_results),
                    # 新增分表相关参数
                    sharding_config=sharding_config,
                    query_type='sharding',
                    # 添加查询结果数据
                    raw_results={
                        'raw_pro_data': [dict(row._asdict()) for row in pro_data] if pro_data else [],
                        'raw_test_data': [dict(row._asdict()) for row in test_data] if test_data else [],
                        'sharding_info': sharding_info  # 包含分表信息
                    },
                    differences_data=differences,
                    identical_data=identical_results
                )
                
                # 关联查询日志与历史记录
                if history_id:
                    query_log_collector.set_history_id(history_id)
                    logger.info(f"分表查询历史记录保存成功: {history_name}, ID: {history_id}")
                else:
                    logger.warning("分表查询历史记录保存失败，无法获取history_id")
            except Exception as e:
                logger.warning(f"保存分表查询历史记录失败: {e}")
            
            # 结束查询批次
            query_log_collector.end_current_batch()
            return jsonify(result)
            
        except Exception as e:
            logger.error(f"分表查询执行失败：{str(e)}")
            # 结束查询批次（出错情况）
            query_log_collector.end_current_batch()
            return jsonify({'error': f'分表查询执行失败：{str(e)}'}), 500
        finally:
            # 关闭连接
            if pro_cluster:
                pro_cluster.shutdown()
            if test_cluster:
                test_cluster.shutdown()
                
    except Exception as e:
        logger.error(f"分表查询请求处理失败：{str(e)}")
        # 结束查询批次（请求处理出错）
        query_log_collector.end_current_batch()
        return jsonify({'error': f'分表查询请求处理失败：{str(e)}'}), 500

@app.route('/api/query', methods=['POST'])
def query_compare():
    try:
        data = request.json
        
        # 开始新的查询批次
        batch_id = query_log_collector.start_new_batch('单表')
        
        logger.info("开始执行数据库比对查询")
        
        # 解析配置
        pro_config = data.get('pro_config', DEFAULT_CONFIG['pro_config'])
        test_config = data.get('test_config', DEFAULT_CONFIG['test_config'])
        
        # 从query_config中获取keys等参数
        query_config = data.get('query_config', {})
        keys = query_config.get('keys', DEFAULT_CONFIG['keys'])
        fields_to_compare = query_config.get('fields_to_compare', DEFAULT_CONFIG['fields_to_compare'])
        exclude_fields = query_config.get('exclude_fields', DEFAULT_CONFIG['exclude_fields'])
        
        values = data.get('values', [])
        
        # 参数验证
        if not values:
            logger.warning("查询失败：未提供查询key值")
            return jsonify({'error': '请提供查询key值'}), 400
            
        if not keys:
            logger.warning("查询失败：未提供主键字段")
            return jsonify({'error': '请提供主键字段'}), 400
        
        # 添加详细的参数日志
        logger.info(f"单表查询参数解析结果:")
        logger.info(f"  keys: {keys}")
        logger.info(f"  values数量: {len(values)}")
        logger.info(f"  fields_to_compare: {fields_to_compare}")
        logger.info(f"  exclude_fields: {exclude_fields}")
        
        logger.info(f"查询配置：{len(values)}个key值，生产表：{pro_config['table']}，测试表：{test_config['table']}")
        
        # 创建数据库连接
        pro_cluster, pro_session = create_connection(pro_config)
        test_cluster, test_session = create_connection(test_config)
        
        if not pro_session or not test_session:
            logger.error("数据库连接失败")
            return jsonify({'error': '数据库连接失败，请检查配置信息'}), 500
        
        try:
            # 执行查询
            logger.info("执行生产环境查询")
            pro_data = execute_query(pro_session, pro_config['table'], keys, fields_to_compare, values, exclude_fields)
            logger.info("执行测试环境查询")
            test_data = execute_query(test_session, test_config['table'], keys, fields_to_compare, values, exclude_fields)
            
            logger.info(f"查询结果：生产表 {len(pro_data)} 条记录，测试表 {len(test_data)} 条记录")
            
            # 比较结果
            differences, field_diff_count, identical_results = compare_results(pro_data, test_data, keys, fields_to_compare, exclude_fields, values)
            
            # 统计信息
            different_ids = set()
            for diff in differences:
                if 'field' in diff:
                    different_ids.add(list(diff['key'].values())[0])
            
            non_different_ids = set(values) - different_ids
            
            # 生成比较总结
            summary = generate_comparison_summary(
                len(values), len(pro_data), len(test_data), 
                differences, identical_results, field_diff_count
            )
            
            result = {
                'total_keys': len(values),
                'pro_count': len(pro_data),
                'test_count': len(test_data),
                'differences': differences,
                'identical_results': identical_results,
                'field_diff_count': field_diff_count,
                'different_ids': list(different_ids),
                'non_different_ids': list(non_different_ids),
                'summary': summary,
                'raw_pro_data': [dict(row._asdict()) for row in pro_data] if pro_data else [],
                'raw_test_data': [dict(row._asdict()) for row in test_data] if test_data else []
            }
            
            logger.info(f"比对完成：发现 {len(differences)} 处差异")
            
            # 自动保存查询历史记录（可选，基于执行结果）
            try:
                # 生成历史记录名称
                timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
                history_name = f"查询_{timestamp}"
                history_description = f"自动保存 - 查询{len(values)}个Key，发现{len(differences)}处差异"
                
                # 保存历史记录
                history_id = save_query_history(
                    name=history_name,
                    description=history_description,
                    pro_config=pro_config,
                    test_config=test_config,
                    query_config={
                        'keys': keys,
                        'fields_to_compare': fields_to_compare,
                        'exclude_fields': exclude_fields
                    },
                    query_keys=values,
                    results_summary=summary,
                    execution_time=0.0,  # 可以后续优化计算实际执行时间
                    total_keys=len(values),
                    differences_count=len(differences),
                    identical_count=len(identical_results),
                    # 添加查询结果数据
                    raw_results={
                        'raw_pro_data': [dict(row._asdict()) for row in pro_data] if pro_data else [],
                        'raw_test_data': [dict(row._asdict()) for row in test_data] if test_data else []
                    },
                    differences_data=differences,
                    identical_data=identical_results
                )
                
                # 关联查询日志与历史记录
                if history_id:
                    query_log_collector.set_history_id(history_id)
                    logger.info(f"查询历史记录保存成功: {history_name}, ID: {history_id}")
                else:
                    logger.warning("查询历史记录保存失败，无法获取history_id")
            except Exception as e:
                logger.warning(f"保存查询历史记录失败: {e}")
            
            # 结束查询批次
            query_log_collector.end_current_batch()
            return jsonify(result)
            
        except Exception as e:
            logger.error(f"查询执行失败：{str(e)}")
            # 结束查询批次（出错情况）
            query_log_collector.end_current_batch()
            return jsonify({'error': f'查询执行失败：{str(e)}'}), 500
        finally:
            # 关闭连接
            if pro_cluster:
                pro_cluster.shutdown()
            if test_cluster:
                test_cluster.shutdown()
                
    except Exception as e:
        logger.error(f"请求处理失败：{str(e)}")
        # 结束查询批次（请求处理出错）
        query_log_collector.end_current_batch()
        return jsonify({'error': f'请求处理失败：{str(e)}'}), 500

@app.route('/api/default-config')
def get_default_config():
    return jsonify(DEFAULT_CONFIG)

# 配置组管理API
@app.route('/api/config-groups', methods=['GET'])
def api_get_config_groups():
    """获取所有配置组"""
    config_groups = get_config_groups()
    return jsonify({'success': True, 'data': config_groups})

@app.route('/api/config-groups', methods=['POST'])
def api_save_config_group():
    """保存配置组"""
    try:
        data = request.json
        name = data.get('name', '').strip()
        description = data.get('description', '').strip()
        pro_config = data.get('pro_config', {})
        test_config = data.get('test_config', {})
        
        # 获取查询配置，支持两种格式
        if 'query_config' in data:
            # 嵌套格式
            query_config = data.get('query_config', {})
        else:
            # 平铺格式
            query_config = {
                'keys': data.get('keys', []),
                'fields_to_compare': data.get('fields_to_compare', []),
                'exclude_fields': data.get('exclude_fields', [])
            }
        
        # 提取分表配置
        sharding_config = data.get('sharding_config')
        
        if not name:
            return jsonify({'success': False, 'error': '配置组名称不能为空'}), 400
        
        success = save_config_group(name, description, pro_config, test_config, query_config, sharding_config)
        
        if success:
            return jsonify({'success': True, 'message': '配置组保存成功'})
        else:
            return jsonify({'success': False, 'error': '配置组保存失败'}), 500
            
    except Exception as e:
        logger.error(f"保存配置组API失败: {e}")
        return jsonify({'success': False, 'error': str(e)}), 500

@app.route('/api/config-groups/<int:group_id>', methods=['GET'])
def api_get_config_group(group_id):
    """获取指定配置组详情"""
    config_group = get_config_group_by_id(group_id)
    
    if config_group:
        return jsonify({'success': True, 'data': config_group})
    else:
        return jsonify({'success': False, 'error': '配置组不存在'}), 404

@app.route('/api/config-groups/<int:group_id>', methods=['DELETE'])
def api_delete_config_group(group_id):
    """删除配置组"""
    success = delete_config_group(group_id)
    
    if success:
        return jsonify({'success': True, 'message': '配置组删除成功'})
    else:
        return jsonify({'success': False, 'error': '配置组删除失败'}), 500

@app.route('/api/init-db', methods=['POST'])
def api_init_database():
    """手动初始化数据库（用于测试）"""
    success = init_database()
    if success:
        return jsonify({'success': True, 'message': '数据库初始化成功'})
    else:
        return jsonify({'success': False, 'error': '数据库初始化失败'}), 500

# 查询历史管理API
@app.route('/api/query-history', methods=['GET'])
def api_get_query_history():
    """获取所有查询历史记录"""
    history_list = get_query_history()
    return jsonify({'success': True, 'data': history_list})

@app.route('/api/query-history', methods=['POST'])
def api_save_query_history():
    """保存查询历史记录，支持分表查询"""
    try:
        data = request.json
        name = data.get('name', '').strip()
        description = data.get('description', '').strip()
        pro_config = data.get('pro_config', {})
        test_config = data.get('test_config', {})
        query_config = data.get('query_config', {})
        query_keys = data.get('query_keys', [])
        results_summary = data.get('results_summary', {})
        execution_time = data.get('execution_time', 0.0)
        total_keys = data.get('total_keys', 0)
        differences_count = data.get('differences_count', 0)
        identical_count = data.get('identical_count', 0)
        # 新增分表相关字段
        sharding_config = data.get('sharding_config')
        query_type = data.get('query_type', 'single')
        
        if not name:
            return jsonify({'success': False, 'error': '历史记录名称不能为空'}), 400
        
        success = save_query_history(
            name, description, pro_config, test_config, query_config,
            query_keys, results_summary, execution_time, total_keys,
            differences_count, identical_count, sharding_config, query_type
        )
        
        if success:
            query_type_desc = '分表查询' if query_type == 'sharding' else '单表查询'
            return jsonify({'success': True, 'message': f'{query_type_desc}历史记录保存成功'})
        else:
            return jsonify({'success': False, 'error': '查询历史记录保存失败'}), 500
            
    except Exception as e:
        logger.error(f"保存查询历史记录API失败: {e}")
        return jsonify({'success': False, 'error': str(e)}), 500

@app.route('/api/query-history/<int:history_id>', methods=['GET'])
def api_get_query_history_detail(history_id):
    """获取指定查询历史记录详情"""
    history_record = get_query_history_by_id(history_id)
    
    if history_record:
        return jsonify({'success': True, 'data': history_record})
    else:
        return jsonify({'success': False, 'error': '查询历史记录不存在'}), 404

@app.route('/api/query-history/<int:history_id>/results', methods=['GET'])
def api_get_query_history_results(history_id):
    """获取查询历史记录的完整结果数据"""
    try:
        history_record = get_query_history_by_id(history_id)
        if not history_record:
            return jsonify({'success': False, 'error': '历史记录不存在'}), 404
        
        # 安全获取raw_results数据
        raw_results = history_record.get('raw_results')
        if raw_results and isinstance(raw_results, dict):
            raw_pro_data = raw_results.get('raw_pro_data', []) or []
            raw_test_data = raw_results.get('raw_test_data', []) or []
            sharding_info = raw_results.get('sharding_info') if history_record.get('query_type') == 'sharding' else None
        else:
            raw_pro_data = []
            raw_test_data = []
            sharding_info = None
            
        # 安全获取差异和相同结果数据
        differences_data = history_record.get('differences_data') or []
        identical_data = history_record.get('identical_data') or []
        
        # 构建完整的查询结果格式，与API查询结果保持一致
        result = {
            'total_keys': history_record['total_keys'],
            'pro_count': len(raw_pro_data),
            'test_count': len(raw_test_data),
            'differences': differences_data,
            'identical_results': identical_data,
            'field_diff_count': {},  # 可以从differences_data中重新计算
            'summary': history_record.get('results_summary', {}),
            'raw_pro_data': raw_pro_data,
            'raw_test_data': raw_test_data,
            # 如果是分表查询，添加分表信息
            'sharding_info': sharding_info,
            # 添加历史记录元信息
            'history_info': {
                'id': history_record['id'],
                'name': history_record['name'],
                'description': history_record['description'],
                'created_at': history_record['created_at'],
                'query_type': history_record.get('query_type', 'single')
            }
        }
        
        # 重新计算field_diff_count
        if differences_data:
            field_diff_count = {}
            for diff in differences_data:
                if isinstance(diff, dict) and 'field' in diff:
                    field_name = diff['field']
                    field_diff_count[field_name] = field_diff_count.get(field_name, 0) + 1
            result['field_diff_count'] = field_diff_count
        
        return jsonify({
            'success': True,
            'data': result,
            'message': f'历史记录 "{history_record["name"]}" 结果加载成功'
        })
        
    except Exception as e:
        logger.error(f"获取查询历史记录结果失败: {e}")
        return jsonify({'success': False, 'error': f'获取历史记录结果失败: {str(e)}'}), 500

@app.route('/api/query-history/<int:history_id>', methods=['DELETE'])
def api_delete_query_history(history_id):
    """删除查询历史记录"""
    success = delete_query_history(history_id)
    
    if success:
        return jsonify({'success': True, 'message': '查询历史记录删除成功'})
    else:
        return jsonify({'success': False, 'error': '查询历史记录删除失败'}), 500

@app.route('/api/query-logs', methods=['GET'])
def api_get_query_logs():
    """获取查询日志，支持分组显示和数据库存储"""
    try:
        limit = request.args.get('limit', type=int)
        grouped = request.args.get('grouped', 'true').lower() == 'true'  # 默认分组显示
        from_db = request.args.get('from_db', 'true').lower() == 'true'  # 默认从数据库获取
        
        if grouped:
            # 返回分组日志
            grouped_logs = query_log_collector.get_logs_grouped_by_batch(limit, from_db)
            # 获取总数（用于统计）
            total_logs = query_log_collector._get_total_logs_count() if from_db else len(query_log_collector.logs)
            
            return jsonify({
                'success': True,
                'data': grouped_logs,
                'total': total_logs,
                'grouped': True,
                'from_db': from_db
            })
        else:
            # 返回原始日志列表
            logs = query_log_collector.get_logs(limit, from_db)
            total_logs = query_log_collector._get_total_logs_count() if from_db else len(query_log_collector.logs)
            
            return jsonify({
                'success': True,
                'data': logs,
                'total': total_logs,
                'grouped': False,
                'from_db': from_db
            })
    except Exception as e:
        logger.error(f"获取查询日志失败: {e}")
        return jsonify({'success': False, 'error': str(e)}), 500

@app.route('/api/query-logs', methods=['DELETE'])
def api_clear_query_logs():
    """清空查询日志，支持清空数据库日志"""
    try:
        clear_db = request.args.get('clear_db', 'true').lower() == 'true'  # 默认清空数据库
        query_log_collector.clear_logs(clear_db)
        
        message = '查询日志已清空（包括数据库）' if clear_db else '查询日志已清空（仅内存）'
        return jsonify({'success': True, 'message': message})
    except Exception as e:
        logger.error(f"清空查询日志失败: {e}")
        return jsonify({'success': False, 'error': str(e)}), 500

@app.route('/api/query-logs/cleanup', methods=['POST'])
def api_cleanup_old_logs():
    """清理旧的查询日志"""
    try:
        days_to_keep = request.json.get('days_to_keep', 30) if request.json else 30
        deleted_count = query_log_collector.cleanup_old_logs(days_to_keep)
        
        return jsonify({
            'success': True,
            'message': f'成功清理 {deleted_count} 条超过 {days_to_keep} 天的旧日志',
            'deleted_count': deleted_count
        })
    except Exception as e:
        logger.error(f"清理旧日志失败: {e}")
        return jsonify({'success': False, 'error': str(e)}), 500

@app.route('/api/query-logs/history/<int:history_id>', methods=['GET'])
def api_get_query_logs_by_history(history_id):
    """根据历史记录ID获取相关查询日志"""
    try:
        logs = query_log_collector.get_logs_by_history_id(history_id)
        
        # 按批次分组显示
        grouped_logs = {}
        batch_order = []
        
        for log in logs:
            batch_id = log.get('batch_id', 'unknown')
            if batch_id not in grouped_logs:
                grouped_logs[batch_id] = []
                batch_order.append(batch_id)
            grouped_logs[batch_id].append(log)
        
        # 返回按时间顺序排列的批次
        grouped_result = [(batch_id, grouped_logs[batch_id]) for batch_id in batch_order]
        
        return jsonify({
            'success': True,
            'data': grouped_result,
            'total': len(logs),
            'history_id': history_id,
            'grouped': True
        })
    except Exception as e:
        logger.error(f"获取历史记录相关查询日志失败: {e}")
        return jsonify({'success': False, 'error': str(e)}), 500

if __name__ == '__main__':
    app.run(debug=True, port=5001)