新增表结构 + excel导入到数据库
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ExcelToMysql.py
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209
ExcelToMysql.py
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import pandas as pd
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from sqlalchemy import create_engine, text
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import logging
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import math
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import re
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import time
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# --- 配置 (与之前相同) ---
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EXCEL_FILE_PATH = 'Z:\\xiaohu\\web_data40000-50000.xlsx'
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SHEET_NAME = 0
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DB_USER = 'zsjie'
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DB_PASSWORD = 'xRekX6Cc3RRK6mBe'
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DB_HOST = '111.180.203.166'
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DB_PORT = 25506
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DB_NAME = 'zsjie'
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TABLE_NAME = 'resource'
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UNIQUE_KEY_COLUMNS = ['id']
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DATE_COLUMN_TO_CONVERT = 'update_date'
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DEFAULT_FOR_STRING = ''
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DEFAULT_FOR_NUMERIC = 0
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COMMIT_BATCH_SIZE = 300
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# --- 新增：用于条件判断的列名 ---
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CONDITION_COLUMN = 'resource_url' # 基于此列在数据库中的值来决定是否更新
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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def excel_to_mysql_upsert_conditional_on_url():
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engine = None
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connection = None
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transaction = None
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rows_processed = 0
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rows_since_last_commit = 0
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total_inserted = 0
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total_updated = 0 # 注意：此计数现在可能包含实际未更改但匹配了重复键的行
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start_time = time.time()
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try:
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# 1. 创建引擎 (带 pool_recycle)
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engine_url = f"mysql+mysqlconnector://{DB_USER}:{DB_PASSWORD}@{DB_HOST}:{DB_PORT}/{DB_NAME}"
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engine = create_engine(engine_url, pool_recycle=3600)
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logging.info(f"数据库引擎创建成功: {DB_HOST}:{DB_PORT}/{DB_NAME}")
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# 2. 读取 Excel
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read_start_time = time.time()
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logging.info(f"开始读取 Excel: {EXCEL_FILE_PATH} (Sheet: {SHEET_NAME})")
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df = pd.read_excel(EXCEL_FILE_PATH, sheet_name=SHEET_NAME, dtype='object', keep_default_na=True)
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logging.info(f"Excel 读取完成，共 {len(df)} 行。耗时: {time.time() - read_start_time:.2f} 秒。")
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# --- 3. 数据清理和准备 ---
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transform_start_time = time.time()
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logging.info("开始清理和转换数据...")
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# a. 重命名列
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rename_map = {
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'URL': 'url', 'Title': 'title', 'Tags': 'tags',
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'source': 'resource_url', 'password': 'resource_pd',
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}
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df.rename(columns=rename_map, inplace=True)
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# b. 删除 Unnamed 列
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unnamed_cols = [col for col in df.columns if str(col).startswith('Unnamed:')]
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if unnamed_cols: df.drop(columns=unnamed_cols, inplace=True)
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# c. 清理列名空格
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df.columns = [str(col).strip() for col in df.columns]
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# d. 检查 'id' 和 条件列 存在
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if 'id' not in df.columns: raise ValueError("错误：唯一键列 'id' 在 DataFrame 中未找到。")
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if CONDITION_COLUMN not in df.columns:
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# 如果条件列必须存在，则报错
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raise ValueError(f"错误：条件更新所需的列 '{CONDITION_COLUMN}' 在 DataFrame 中未找到。")
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# 如果条件列是可选的，可以只记录警告并构建不带条件的SQL
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# logging.warning(f"警告: 列 '{CONDITION_COLUMN}' 未找到，将执行无条件 Upsert。")
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# build_conditional_sql = False # 控制下方 SQL 构建逻辑
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# e. update_date 转换
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if DATE_COLUMN_TO_CONVERT in df.columns:
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date_series = df[DATE_COLUMN_TO_CONVERT].astype(str)
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date_series_numeric_str = date_series.str.replace(r'\D', '', regex=True)
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df[DATE_COLUMN_TO_CONVERT] = pd.to_numeric(date_series_numeric_str, errors='coerce')
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# f. 确定列类型
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col_types = {}
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for col in df.columns:
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if pd.api.types.is_string_dtype(df[col]) or pd.api.types.is_object_dtype(df[col]): col_types[col] = 'string'
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elif pd.api.types.is_numeric_dtype(df[col]): col_types[col] = 'numeric'
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else: col_types[col] = 'other'
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logging.info(f"数据清理转换完成。耗时: {time.time() - transform_start_time:.2f} 秒。")
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# g. *** 修改：构建带条件的 SQL 模板 ***
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all_columns = df.columns.tolist()
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update_columns = [col for col in all_columns if col not in UNIQUE_KEY_COLUMNS]
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cols_str = ", ".join([f"`{col}`" for col in all_columns])
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placeholders_str = ", ".join([f":{col}" for col in all_columns])
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# 构建 ON DUPLICATE KEY UPDATE 部分
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if update_columns:
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update_clause_list = []
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for col in update_columns:
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# --- 核心条件逻辑 ---
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# 如果数据库现有的 resource_url 是 NULL 或空, 则更新为新值, 否则保持旧值
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update_clause = f"`{col}` = IF(`{CONDITION_COLUMN}` IS NULL OR `{CONDITION_COLUMN}` = '', VALUES(`{col}`), `{col}`)"
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update_clause_list.append(update_clause)
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# --- 结束核心条件逻辑 ---
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update_str = ", ".join(update_clause_list)
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sql_template = f"""
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INSERT INTO `{TABLE_NAME}` ({cols_str})
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VALUES ({placeholders_str})
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ON DUPLICATE KEY UPDATE {update_str}
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"""
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logging.info(f"将使用带条件 (基于数据库 '{CONDITION_COLUMN}' 值) 的 INSERT ... ON DUPLICATE KEY UPDATE 模式。")
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else:
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# 如果只有 id 列，则使用 INSERT IGNORE
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sql_template = f"INSERT IGNORE INTO `{TABLE_NAME}` ({cols_str}) VALUES ({placeholders_str})"
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logging.info("仅配置了唯一键，将使用 INSERT IGNORE 模式。")
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# --- 结束 SQL 构建 ---
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# --- 4. 数据库交互与周期性提交 (与之前类似) ---
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db_interaction_start_time = time.time()
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connection = engine.connect()
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logging.info("数据库连接成功。")
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transaction = connection.begin()
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logging.info("已开始第一个事务。")
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logging.info(f"开始处理 {len(df)} 行数据 (每 {COMMIT_BATCH_SIZE} 行提交一次)...")
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for record_original in df.to_dict(orient='records'):
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rows_processed += 1
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record_processed = {}
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# --- 应用默认值 (与之前相同) ---
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for col_name, value in record_original.items():
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processed_value = value
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if pd.isna(processed_value):
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if col_name not in UNIQUE_KEY_COLUMNS:
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column_type = col_types.get(col_name, 'other')
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if column_type == 'string': processed_value = DEFAULT_FOR_STRING
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elif column_type == 'numeric': processed_value = DEFAULT_FOR_NUMERIC
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else: processed_value = None
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record_processed[col_name] = processed_value
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# --- 检查 'id' (与之前相同) ---
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if record_processed.get('id') is None:
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logging.warning(f"跳过第 {rows_processed} 行，因为 'id' 列为空或无效: {record_original}")
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continue # 跳过该行
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# 增加有效行批次计数器
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rows_since_last_commit += 1
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# --- 执行 SQL (使用新的条件模板) ---
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try:
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result = connection.execute(text(sql_template), record_processed)
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# --- 解释 rowcount (可能有歧义) ---
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# 1: 插入了新行
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# 2: 匹配了重复键，并执行了 UPDATE 子句（即使所有 IF 条件都为 false，导致无实际更改）
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# 0: 匹配了重复键，但某些 MySQL 版本/配置下，无实际更改的 UPDATE 可能报告 0
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if result.rowcount == 1:
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total_inserted += 1
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elif result.rowcount == 2: # 认为匹配了重复键并尝试了更新
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total_updated += 1
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# 注意：total_updated 不再精确代表“实际发生值改变的更新行数”
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except Exception as row_error:
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logging.error(f"处理行数据时出错 (行号约 {rows_processed}):\n 原始: {record_original}\n 处理后: {record_processed}\n 错误: {row_error}")
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if transaction:
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try: transaction.rollback()
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except Exception as rb_err: logging.error(f"回滚事务时也出错: {rb_err}")
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raise
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# --- 周期性提交 (与之前相同) ---
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if rows_since_last_commit >= COMMIT_BATCH_SIZE:
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try:
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commit_start = time.time()
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transaction.commit()
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commit_duration = time.time() - commit_start
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logging.info(f"已提交 {rows_since_last_commit} 行 (处理总数: {rows_processed})。本次提交耗时: {commit_duration:.2f} 秒。")
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transaction = connection.begin()
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rows_since_last_commit = 0
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except Exception as commit_error:
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logging.error(f"提交事务时出错 (行号约 {rows_processed}): {commit_error}")
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raise
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# --- 循环结束后的最终提交 (与之前相同) ---
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if rows_since_last_commit > 0:
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try:
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final_commit_start = time.time()
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logging.info(f"准备提交最后 {rows_since_last_commit} 行...")
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transaction.commit()
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final_commit_duration = time.time() - final_commit_start
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logging.info(f"最后 {rows_since_last_commit} 行已成功提交。耗时: {final_commit_duration:.2f} 秒。")
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except Exception as final_commit_error:
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logging.error(f"提交最后批次事务时出错: {final_commit_error}")
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raise
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total_db_time = time.time() - db_interaction_start_time
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logging.info(f"数据库交互完成。总耗时: {total_db_time:.2f} 秒。")
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logging.info(f"处理完成。总处理行: {rows_processed}, 总插入: {total_inserted}, 总匹配重复键(尝试更新): {total_updated}.")
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except ValueError as ve:
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logging.error(f"配置或数据错误: {ve}")
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except Exception as e:
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logging.error(f"发生严重错误，脚本已停止: {e}", exc_info=False)
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finally:
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if connection:
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connection.close()
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logging.info("数据库连接已关闭。")
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total_script_time = time.time() - start_time
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logging.info(f"脚本总运行时间: {total_script_time:.2f} 秒。")
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if __name__ == "__main__":
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excel_to_mysql_upsert_conditional_on_url()
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12
ddl.mysql.sql
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12
ddl.mysql.sql
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CREATE TABLE resource (
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id int not null default 0 comment '资源ID',
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url varchar(100) binary not null default ' ' comment '资源URL',
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title varchar(100) binary not null default ' ' comment '标题',
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tags varchar(50) binary not null default ' ' comment '标签',
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update_date int not null default 0 comment '更新日期',
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resource_url varchar(100) binary not null default ' ' comment '资源地址',
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resource_pd varchar(10) binary not null default ' ' comment '资源密码',
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alias_name varchar(100) binary not null default ' ' comment '资源别名',
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available_flag varchar(1) binary not null default ' ' comment '是否可用',
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constraint pk_resource primary key (id)
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)COMMENT='资源表';
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