# -*- coding: utf-8 -*-
# Author : Charley
# Python : 3.10.8
# Date   : 2025/8/6 16:53
import pandas as pd
from sqlalchemy import create_engine
from loguru import logger
import os
import json
from typing import TextIO

# 配置 loguru
logger.add("data_migration.log", rotation="500 MB", level="INFO")

# 断点续传文件路径
CHECKPOINT_FILE = "migration_checkpoint.json"


def save_checkpoint(last_id: int) -> None:
    """保存断点信息"""
    checkpoint_data = {
        "last_processed_id": last_id,
        "table_name": "whatnot_shop_record"
    }
    with open(CHECKPOINT_FILE, 'w', encoding='utf-8') as f:  # type: TextIO
        json.dump(checkpoint_data, f)
    logger.info(f"保存断点信息: last_processed_id={last_id}")


def load_checkpoint() -> int:
    """加载断点信息"""
    if os.path.exists(CHECKPOINT_FILE):
        with open(CHECKPOINT_FILE, 'r', encoding='utf-8') as f:  # type: TextIO
            checkpoint_data = json.load(f)
        logger.info(f"加载断点信息: last_processed_id={checkpoint_data['last_processed_id']}")
        return checkpoint_data['last_processed_id']
    return 0


def clear_checkpoint() -> None:
    """清除断点信息"""
    if os.path.exists(CHECKPOINT_FILE):
        os.remove(CHECKPOINT_FILE)
        logger.info("清除断点信息")


def migrate_with_resume() -> None:
    current_id: int = 0  # 初始化变量，解决未定义问题
    try:
        # 创建数据库连接
        source_engine = create_engine('mysql+pymysql://crawler:Pass2022@100.64.0.23/crawler')
        target_engine = create_engine('mysql+pymysql://crawler:Pass2022@100.64.0.21/crawler')

        # 加载断点信息
        start_id = load_checkpoint()

        # 设置批次大小
        batch_size = 10000
        current_id = start_id
        total_migrated = 0

        logger.info(f"开始从ID {start_id} 处继续迁移数据...")

        while True:
            # 分批读取数据，使用ID作为游标而不是OFFSET，提高性能
            query = """
                SELECT * FROM whatnot_shop_record 
                WHERE id > %s 
                ORDER BY id 
                LIMIT %s
            """
            logger.info(f"正在读取ID大于 {current_id} 的数据，批次大小: {batch_size}")
            df = pd.read_sql(query, source_engine, params=(current_id, batch_size))

            # 如果没有更多数据，退出循环
            if df.empty:
                logger.info("已读取完所有数据")
                break

            logger.info(f"读取到 {len(df)} 条记录")

            # 记录当前批次的最大ID，用于下次查询的起点
            max_id_in_batch = int(df['id'].max())

            # 删除id列
            if 'id' in df.columns:
                df = df.drop('id', axis=1)
                logger.info("已删除id列")

            # 写入目标数据库
            logger.info("开始写入目标数据库...")
            df.to_sql('whatnot_shop_record', target_engine, if_exists='append', index=False)

            # 更新计数器
            total_migrated += len(df)
            logger.success(f"成功迁移 {len(df)} 条记录")

            # 更新当前ID为本批次最大ID
            current_id = max_id_in_batch

            # 保存断点信息
            save_checkpoint(current_id)

        logger.success(f"数据迁移完成，总共迁移 {total_migrated} 条记录")
        # 清除断点文件
        clear_checkpoint()

    except Exception as e:
        logger.error(f"数据迁移过程中发生错误: {e}")
        logger.info(f"迁移中断，下次运行将从ID {current_id} 继续")
        raise


# 执行迁移
if __name__ == "__main__":
    migrate_with_resume()