605 lines
28 KiB
Python
605 lines
28 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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CSV数据导入数据库脚本
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功能：将三个CSV文件的数据以author_name为唯一键导入到数据库
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- ai_statistics.csv -> ai_statistics表
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- ai_statistics_day.csv -> ai_statistics_day表
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- ai_statistics_days.csv -> ai_statistics_days表
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"""
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import os
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import sys
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import csv
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from decimal import Decimal
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from typing import Dict, List, Optional
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from datetime import datetime
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from database_config import DatabaseManager, DB_CONFIG
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from log_config import setup_logger
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class CSVImporter:
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"""CSV数据导入器"""
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def __init__(self, db_config: Optional[Dict] = None):
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"""初始化导入器
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Args:
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db_config: 数据库配置，默认使用database_config.DB_CONFIG
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"""
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self.script_dir = os.path.dirname(os.path.abspath(__file__))
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self.db_manager = DatabaseManager(db_config)
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# 初始化日志
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self.logger = setup_logger('import_csv', os.path.join(self.script_dir, 'logs', 'import_csv.log'))
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# CSV文件路径
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self.csv_files = {
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'ai_statistics': os.path.join(self.script_dir, 'ai_statistics.csv'),
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'ai_statistics_day': os.path.join(self.script_dir, 'ai_statistics_day.csv'),
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'ai_statistics_days': os.path.join(self.script_dir, 'ai_statistics_days.csv'),
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}
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def read_csv(self, file_path: str) -> List[Dict]:
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"""读取CSV文件
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Args:
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file_path: CSV文件路径
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Returns:
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数据行列表
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"""
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if not os.path.exists(file_path):
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print(f"[X] CSV文件不存在: {file_path}")
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self.logger.error(f"CSV文件不存在: {file_path}")
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return []
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rows = []
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try:
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with open(file_path, 'r', encoding='utf-8-sig') as f:
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reader = csv.DictReader(f)
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for row in reader:
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rows.append(row)
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print(f"[OK] 读取CSV文件成功: {file_path}, 共 {len(rows)} 行")
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self.logger.info(f"读取CSV文件成功: {file_path}, 共 {len(rows)} 行")
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return rows
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except Exception as e:
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print(f"[X] 读取CSV文件失败: {file_path}, 错误: {e}")
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self.logger.error(f"读取CSV文件失败: {file_path}, 错误: {e}")
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return []
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def convert_value(self, value: str, field_type: str):
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"""转换数据类型
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Args:
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value: 字符串值
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field_type: 字段类型 (int, float, decimal, str)
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Returns:
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转换后的值
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"""
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if value == '' or value is None:
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return None
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try:
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if field_type == 'int':
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return int(float(value))
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elif field_type == 'float':
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return float(value)
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elif field_type == 'decimal':
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return Decimal(str(value))
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else:
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return str(value)
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except Exception as e:
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print(f"[!] 数据类型转换失败: {value} -> {field_type}, 错误: {e}")
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self.logger.warning(f"数据类型转换失败: {value} -> {field_type}, 错误: {e}")
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return None
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def get_author_id(self, author_name: str, channel: int = 1) -> int:
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"""从 ai_authors 表查询 author_id
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Args:
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author_name: 作者名称
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channel: 渠道，默认1=百度
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Returns:
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author_id，如果找不到返回0
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"""
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try:
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sql = "SELECT id FROM ai_authors WHERE author_name = %s AND channel = %s"
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result = self.db_manager.execute_query(sql, (author_name, channel), fetch_one=True)
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if result:
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author_id = result.get('id', 0) if isinstance(result, dict) else result[0]
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self.logger.debug(f"查询author_id: {author_name} -> {author_id}")
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return author_id
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else:
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self.logger.warning(f"未找到author_id: {author_name}, channel={channel}")
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return 0
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except Exception as e:
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self.logger.error(f"查询author_id失败: {author_name}, 错误: {e}")
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return 0
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def import_ai_statistics(self, batch_size: int = 50) -> bool:
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"""导入 ai_statistics 表数据（使用批量提交）
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Args:
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batch_size: 批量提交大小，默认50条
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"""
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print("\n" + "="*70)
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print("开始导入 ai_statistics 表数据")
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print("="*70)
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csv_file = self.csv_files['ai_statistics']
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rows = self.read_csv(csv_file)
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if not rows:
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print("[X] 没有数据可导入")
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self.logger.warning("ai_statistics表没有数据可导入")
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return False
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self.logger.info(f"开始导入ai_statistics表数据，共 {len(rows)} 条记录，批量大小: {batch_size}")
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print(f"\n总计 {len(rows)} 条记录，分批导入（每批 {batch_size} 条）\n")
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success_count = 0
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failed_count = 0
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batch_params = [] # 批量参数列表
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# 构建SQL（所有记录使用相同SQL）
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first_record_keys = None
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sql_template = None
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for idx, row in enumerate(rows, 1):
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author_name = row.get('author_name', '').strip()
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if not author_name:
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continue
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try:
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# 处理slide_ratio值（CSV中已是小数格式）
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slide_ratio_value = float(self.convert_value(row.get('slide_ratio', '0'), 'float') or 0.0)
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slide_ratio_value = min(slide_ratio_value, 9.9999)
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# 获取channel
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channel = self.convert_value(row.get('channel', '1'), 'int') or 1
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# 从数据库查询正确的 author_id（不使用CSV中的0）
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author_id = self.get_author_id(author_name, int(channel))
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if author_id == 0:
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self.logger.warning(f"跳过无效记录: {author_name}, author_id未找到")
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failed_count += 1
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continue
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# 准备数据
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record = {
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'author_id': author_id, # 使用查询到的author_id
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'author_name': author_name,
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'channel': channel,
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'date': row.get('date', '').strip(),
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'submission_count': self.convert_value(row.get('submission_count', '0'), 'int') or 0,
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'read_count': self.convert_value(row.get('read_count', '0'), 'int') or 0,
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'comment_count': self.convert_value(row.get('comment_count', '0'), 'int') or 0,
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'comment_rate': min(float(self.convert_value(row.get('comment_rate', '0'), 'float') or 0.0), 9.9999),
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'like_count': self.convert_value(row.get('like_count', '0'), 'int') or 0,
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'like_rate': min(float(self.convert_value(row.get('like_rate', '0'), 'float') or 0.0), 9.9999),
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'favorite_count': self.convert_value(row.get('favorite_count', '0'), 'int') or 0,
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'favorite_rate': min(float(self.convert_value(row.get('favorite_rate', '0'), 'float') or 0.0), 9.9999),
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'share_count': self.convert_value(row.get('share_count', '0'), 'int') or 0,
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'share_rate': min(float(self.convert_value(row.get('share_rate', '0'), 'float') or 0.0), 9.9999),
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'slide_ratio': slide_ratio_value,
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'baidu_search_volume': self.convert_value(row.get('baidu_search_volume', '0'), 'int') or 0,
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'updated_at': datetime.now().strftime('%Y-%m-%d %H:%M:%S'), # 添加更新时间戳，强制更新
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}
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# 第一条记录时构建SQL
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if sql_template is None:
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first_record_keys = list(record.keys())
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columns = ', '.join(first_record_keys)
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placeholders = ', '.join(['%s'] * len(first_record_keys))
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update_parts = [f"{key} = VALUES({key})" for key in first_record_keys if key not in ['author_name', 'channel', 'date']]
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sql_template = f"""
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INSERT INTO ai_statistics ({columns})
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VALUES ({placeholders})
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ON DUPLICATE KEY UPDATE {', '.join(update_parts)}
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"""
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# 添加到批量参数（确保first_record_keys已初始化）
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if first_record_keys is not None:
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batch_params.append(tuple(record[key] for key in first_record_keys))
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# 当达到批量大小或最后一条记录时，执行批量插入
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if len(batch_params) >= batch_size or idx == len(rows):
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try:
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# 使用 execute_many 批量提交
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result_count = self.db_manager.execute_many(sql_template, batch_params, autocommit=True)
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success_count += result_count
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print(f"[批次提交] 已导入 {success_count} 条记录（本批: {result_count}/{len(batch_params)}）")
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self.logger.info(f"ai_statistics表批量提交: {result_count}/{len(batch_params)} 条记录")
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batch_params = [] # 清空批次
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except Exception as batch_error:
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failed_count += len(batch_params)
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print(f" [X] 批次提交失败: {batch_error}")
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self.logger.error(f"ai_statistics表批量提交失败: {batch_error}")
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batch_params = []
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except Exception as e:
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failed_count += 1
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print(f" [X] 处理失败 ({author_name}): {e}")
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self.logger.error(f"ai_statistics表处理失败: {author_name}, 错误: {e}")
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continue
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print("\n" + "="*70)
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print(f"[OK] ai_statistics 表数据导入完成")
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print(f" 成功: {success_count} 条记录")
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if failed_count > 0:
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print(f" 失败: {failed_count} 条记录")
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print("="*70)
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self.logger.info(f"ai_statistics表数据导入完成: 成功 {success_count} 条，失败 {failed_count} 条")
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return success_count > 0
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def import_ai_statistics_day(self, batch_size: int = 50) -> bool:
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"""导入 ai_statistics_day 表数据（使用批量提交）
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Args:
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batch_size: 批量提交大小，默认50条
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"""
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print("\n" + "="*70)
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print("开始导入 ai_statistics_day 表数据")
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print("="*70)
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csv_file = self.csv_files['ai_statistics_day']
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rows = self.read_csv(csv_file)
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if not rows:
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print("[X] 没有数据可导入")
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self.logger.warning("ai_statistics_day表没有数据可导入")
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return False
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self.logger.info(f"开始导入ai_statistics_day表数据，共 {len(rows)} 条记录，批量大小: {batch_size}")
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print(f"\n总计 {len(rows)} 条记录，分批导入（每批 {batch_size} 条）\n")
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success_count = 0
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failed_count = 0
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batch_params = []
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first_record_keys = None
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sql_template = None
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for idx, row in enumerate(rows, 1):
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author_name = row.get('author_name', '').strip()
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if not author_name:
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continue
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try:
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# 处理avg_slide_ratio值（CSV中已是小数格式）
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avg_slide_ratio_value = float(self.convert_value(row.get('avg_slide_ratio', '0'), 'float') or 0.0)
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avg_slide_ratio_value = min(avg_slide_ratio_value, 9.9999)
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# 获取channel并查询author_id
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channel = self.convert_value(row.get('channel', '1'), 'int') or 1
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author_id = self.get_author_id(author_name, int(channel))
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if author_id == 0:
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self.logger.warning(f"跳过无效记录: {author_name}, author_id未找到")
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failed_count += 1
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continue
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record = {
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'author_id': author_id,
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'author_name': author_name,
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'channel': channel,
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'stat_date': row.get('stat_date', '').strip(),
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'total_submission_count': self.convert_value(row.get('total_submission_count', '0'), 'int') or 0,
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'total_read_count': self.convert_value(row.get('total_read_count', '0'), 'int') or 0,
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'total_comment_count': self.convert_value(row.get('total_comment_count', '0'), 'int') or 0,
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'total_like_count': self.convert_value(row.get('total_like_count', '0'), 'int') or 0,
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'total_favorite_count': self.convert_value(row.get('total_favorite_count', '0'), 'int') or 0,
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'total_share_count': self.convert_value(row.get('total_share_count', '0'), 'int') or 0,
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'avg_comment_rate': min(float(self.convert_value(row.get('avg_comment_rate', '0'), 'float') or 0.0), 9.9999),
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'avg_like_rate': min(float(self.convert_value(row.get('avg_like_rate', '0'), 'float') or 0.0), 9.9999),
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'avg_favorite_rate': min(float(self.convert_value(row.get('avg_favorite_rate', '0'), 'float') or 0.0), 9.9999),
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'avg_share_rate': min(float(self.convert_value(row.get('avg_share_rate', '0'), 'float') or 0.0), 9.9999),
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'avg_slide_ratio': avg_slide_ratio_value,
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'total_baidu_search_volume': self.convert_value(row.get('total_baidu_search_volume', '0'), 'int') or 0,
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'updated_at': datetime.now().strftime('%Y-%m-%d %H:%M:%S'), # 添加更新时间戳，强制更新
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}
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if sql_template is None:
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first_record_keys = list(record.keys())
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columns = ', '.join(first_record_keys)
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placeholders = ', '.join(['%s'] * len(first_record_keys))
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update_parts = [f"{key} = VALUES({key})" for key in first_record_keys if key not in ['author_name', 'channel', 'stat_date']]
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sql_template = f"""
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INSERT INTO ai_statistics_day ({columns})
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VALUES ({placeholders})
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ON DUPLICATE KEY UPDATE {', '.join(update_parts)}
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"""
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if first_record_keys is not None:
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batch_params.append(tuple(record[key] for key in first_record_keys))
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if len(batch_params) >= batch_size or idx == len(rows):
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try:
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result_count = self.db_manager.execute_many(sql_template, batch_params, autocommit=True)
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success_count += result_count
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print(f"[批次提交] 已导入 {success_count} 条记录（本批: {result_count}/{len(batch_params)}）")
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self.logger.info(f"ai_statistics_day表批量提交: {result_count}/{len(batch_params)} 条记录")
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batch_params = []
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except Exception as batch_error:
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failed_count += len(batch_params)
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print(f" [X] 批次提交失败: {batch_error}")
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self.logger.error(f"ai_statistics_day表批量提交失败: {batch_error}")
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batch_params = []
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except Exception as e:
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failed_count += 1
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print(f" [X] 处理失败 ({author_name}): {e}")
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self.logger.error(f"ai_statistics_day表处理失败: {author_name}, 错误: {e}")
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continue
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print("\n" + "="*70)
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print(f"[OK] ai_statistics_day 表数据导入完成")
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print(f" 成功: {success_count} 条记录")
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if failed_count > 0:
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print(f" 失败: {failed_count} 条记录")
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print("="*70)
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self.logger.info(f"ai_statistics_day表数据导入完成: 成功 {success_count} 条，失败 {failed_count} 条")
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return success_count > 0
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def import_ai_statistics_days(self, batch_size: int = 50) -> bool:
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"""导入 ai_statistics_days 表数据（仅当日数据：day_revenue）
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同时自动拆分数据到 ai_statistics_weekly 和 ai_statistics_monthly 表
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Args:
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batch_size: 批量提交大小，默认50条
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"""
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print("\n" + "="*70)
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print("开始导入 ai_statistics_days 表数据（拆分到3个表）")
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print("="*70)
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csv_file = self.csv_files['ai_statistics_days']
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rows = self.read_csv(csv_file)
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if not rows:
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print("[X] 没有数据可导入")
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self.logger.warning("ai_statistics_days表没有数据可导入")
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return False
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self.logger.info(f"开始导入数据，共 {len(rows)} 条记录，批量大小: {batch_size}")
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print(f"\n总计 {len(rows)} 条记录，将拆分到3个表\n")
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# 三个表的统计
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days_success = 0
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weekly_success = 0
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monthly_success = 0
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failed_count = 0
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# 批量参数
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days_batch = []
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weekly_batch = []
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monthly_batch = []
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# SQL模板
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days_sql = None
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weekly_sql = None
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monthly_sql = None
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days_keys = None
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weekly_keys = None
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monthly_keys = None
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for idx, row in enumerate(rows, 1):
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author_name = row.get('author_name', '').strip()
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if not author_name:
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continue
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try:
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# 获取channel并查询author_id
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channel = self.convert_value(row.get('channel', '1'), 'int') or 1
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author_id = self.get_author_id(author_name, int(channel))
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if author_id == 0:
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self.logger.warning(f"跳过无效记录: {author_name}, author_id未找到")
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failed_count += 1
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continue
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stat_date = row.get('stat_date', '').strip()
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# 1. ai_statistics_days 表数据（仅当日数据）
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day_revenue = self.convert_value(row.get('day_revenue', '0'), 'decimal') or Decimal('0')
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daily_published_count = self.convert_value(row.get('daily_published_count', '0'), 'int') or 0
|
||
cumulative_published_count = self.convert_value(row.get('cumulative_published_count', '0'), 'int') or 0
|
||
|
||
days_record = {
|
||
'author_id': author_id,
|
||
'author_name': author_name,
|
||
'channel': channel,
|
||
'stat_date': stat_date,
|
||
'daily_published_count': daily_published_count,
|
||
'day_revenue': day_revenue,
|
||
'updated_at': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
|
||
}
|
||
|
||
# 2. ai_statistics_weekly 表数据
|
||
weekly_revenue = self.convert_value(row.get('weekly_revenue', '0'), 'decimal') or Decimal('0')
|
||
revenue_wow_growth_rate = self.convert_value(row.get('revenue_wow_growth_rate', '0'), 'decimal') or Decimal('0')
|
||
|
||
# 计算该日期所在周次（格式：WW，如51）
|
||
from datetime import datetime as dt, timedelta
|
||
date_obj = dt.strptime(stat_date, '%Y-%m-%d')
|
||
# 使用isocalendar()获取ISO周数（周一为一周开始）
|
||
year, week_num, _ = date_obj.isocalendar()
|
||
stat_weekly = week_num # 直接使用数字
|
||
|
||
weekly_record = {
|
||
'author_id': author_id,
|
||
'author_name': author_name,
|
||
'channel': channel,
|
||
'stat_weekly': stat_weekly,
|
||
'weekly_revenue': weekly_revenue,
|
||
'revenue_wow_growth_rate': revenue_wow_growth_rate,
|
||
'updated_at': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
|
||
}
|
||
|
||
# 3. ai_statistics_monthly 表数据
|
||
monthly_revenue = self.convert_value(row.get('monthly_revenue', '0'), 'decimal') or Decimal('0')
|
||
revenue_mom_growth_rate = self.convert_value(row.get('revenue_mom_growth_rate', '0'), 'decimal') or Decimal('0')
|
||
|
||
# 计算该日期所在月份（格式：YYYY-MM，如2025-12）
|
||
stat_monthly = date_obj.strftime('%Y-%m')
|
||
|
||
monthly_record = {
|
||
'author_id': author_id,
|
||
'author_name': author_name,
|
||
'channel': channel,
|
||
'stat_monthly': stat_monthly,
|
||
'monthly_revenue': monthly_revenue,
|
||
'revenue_mom_growth_rate': revenue_mom_growth_rate,
|
||
'updated_at': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
|
||
}
|
||
|
||
# 构建SQL模板（首次）
|
||
if days_sql is None:
|
||
days_keys = list(days_record.keys())
|
||
columns = ', '.join(days_keys)
|
||
placeholders = ', '.join(['%s'] * len(days_keys))
|
||
update_parts = [f"{key} = VALUES({key})" for key in days_keys if key not in ['author_name', 'channel', 'stat_date']]
|
||
days_sql = f"""
|
||
INSERT INTO ai_statistics_days ({columns})
|
||
VALUES ({placeholders})
|
||
ON DUPLICATE KEY UPDATE {', '.join(update_parts)}
|
||
"""
|
||
|
||
if weekly_sql is None:
|
||
weekly_keys = list(weekly_record.keys())
|
||
columns = ', '.join(weekly_keys)
|
||
placeholders = ', '.join(['%s'] * len(weekly_keys))
|
||
update_parts = [f"{key} = VALUES({key})" for key in weekly_keys if key not in ['author_name', 'channel', 'stat_weekly']]
|
||
weekly_sql = f"""
|
||
INSERT INTO ai_statistics_weekly ({columns})
|
||
VALUES ({placeholders})
|
||
ON DUPLICATE KEY UPDATE {', '.join(update_parts)}
|
||
"""
|
||
|
||
if monthly_sql is None:
|
||
monthly_keys = list(monthly_record.keys())
|
||
columns = ', '.join(monthly_keys)
|
||
placeholders = ', '.join(['%s'] * len(monthly_keys))
|
||
update_parts = [f"{key} = VALUES({key})" for key in monthly_keys if key not in ['author_name', 'channel', 'stat_monthly']]
|
||
monthly_sql = f"""
|
||
INSERT INTO ai_statistics_monthly ({columns})
|
||
VALUES ({placeholders})
|
||
ON DUPLICATE KEY UPDATE {', '.join(update_parts)}
|
||
"""
|
||
|
||
# 添加到批量参数
|
||
days_batch.append(tuple(days_record[key] for key in days_keys))
|
||
weekly_batch.append(tuple(weekly_record[key] for key in weekly_keys))
|
||
monthly_batch.append(tuple(monthly_record[key] for key in monthly_keys))
|
||
|
||
# 批量提交
|
||
if len(days_batch) >= batch_size or idx == len(rows):
|
||
try:
|
||
# 提交 ai_statistics_days
|
||
result = self.db_manager.execute_many(days_sql, days_batch, autocommit=True)
|
||
days_success += result
|
||
print(f"[days] 已导入 {days_success} 条")
|
||
days_batch = []
|
||
except Exception as e:
|
||
print(f" [X] days表提交失败: {e}")
|
||
self.logger.error(f"ai_statistics_days批量提交失败: {e}")
|
||
failed_count += len(days_batch)
|
||
days_batch = []
|
||
|
||
try:
|
||
# 提交 ai_statistics_weekly
|
||
result = self.db_manager.execute_many(weekly_sql, weekly_batch, autocommit=True)
|
||
weekly_success += result
|
||
print(f"[weekly] 已导入 {weekly_success} 条")
|
||
weekly_batch = []
|
||
except Exception as e:
|
||
print(f" [X] weekly表提交失败: {e}")
|
||
self.logger.error(f"ai_statistics_weekly批量提交失败: {e}")
|
||
weekly_batch = []
|
||
|
||
try:
|
||
# 提交 ai_statistics_monthly
|
||
result = self.db_manager.execute_many(monthly_sql, monthly_batch, autocommit=True)
|
||
monthly_success += result
|
||
print(f"[monthly] 已导入 {monthly_success} 条")
|
||
monthly_batch = []
|
||
except Exception as e:
|
||
print(f" [X] monthly表提交失败: {e}")
|
||
self.logger.error(f"ai_statistics_monthly批量提交失败: {e}")
|
||
monthly_batch = []
|
||
|
||
except Exception as e:
|
||
failed_count += 1
|
||
print(f" [X] 处理失败 ({author_name}): {e}")
|
||
self.logger.error(f"数据处理失败: {author_name}, 错误: {e}")
|
||
continue
|
||
|
||
print("\n" + "="*70)
|
||
print(f"[OK] 数据导入完成（拆分到3个表）")
|
||
print(f" ai_statistics_days: {days_success} 条")
|
||
print(f" ai_statistics_weekly: {weekly_success} 条")
|
||
print(f" ai_statistics_monthly: {monthly_success} 条")
|
||
if failed_count > 0:
|
||
print(f" 失败: {failed_count} 条")
|
||
print("="*70)
|
||
|
||
self.logger.info(f"数据导入完成: days={days_success}, weekly={weekly_success}, monthly={monthly_success}, failed={failed_count}")
|
||
return days_success > 0
|
||
|
||
def import_all(self) -> bool:
|
||
"""导入所有CSV文件"""
|
||
print("\n" + "="*70)
|
||
print("CSV数据导入数据库")
|
||
print("="*70)
|
||
print("\n将导入以下三个CSV文件到数据库：")
|
||
print(" 1. ai_statistics.csv -> ai_statistics表")
|
||
print(" 2. ai_statistics_day.csv -> ai_statistics_day表")
|
||
print(" 3. ai_statistics_days.csv -> ai_statistics_days表")
|
||
print("\n唯一键：(author_name, channel, date/stat_date)")
|
||
print("="*70)
|
||
|
||
self.logger.info("开始执行CSV数据导入任务")
|
||
|
||
# 导入三个表
|
||
result1 = self.import_ai_statistics()
|
||
result2 = self.import_ai_statistics_day()
|
||
result3 = self.import_ai_statistics_days()
|
||
|
||
if result1 and result2 and result3:
|
||
print("\n" + "="*70)
|
||
print("✓ 所有CSV文件导入完成！")
|
||
print("="*70)
|
||
self.logger.info("所有CSV文件导入成功")
|
||
return True
|
||
else:
|
||
print("\n" + "="*70)
|
||
print("✗ 部分CSV文件导入失败")
|
||
print("="*70)
|
||
self.logger.warning("部分CSV文件导入失败")
|
||
return False
|
||
|
||
|
||
def main():
|
||
"""主函数"""
|
||
logger = None
|
||
try:
|
||
# 创建导入器
|
||
importer = CSVImporter()
|
||
logger = importer.logger
|
||
|
||
# 导入所有CSV文件
|
||
success = importer.import_all()
|
||
|
||
return 0 if success else 1
|
||
|
||
except Exception as e:
|
||
print(f"\n[X] 程序执行出错: {e}")
|
||
if logger:
|
||
logger.error(f"程序执行出错: {e}", exc_info=True)
|
||
return 1
|
||
|
||
|
||
if __name__ == '__main__':
|
||
sys.exit(main())
|