import time import os import sys import pyodbc from config import SQL_SERVER_CONN, ACCESS_DRIVER, SYNC_MAPPING, POLL_INTERVAL, LOG_TABLE_CONFIG, UPTIME_KUMA_CONFIG import db_utils from log_utils import (LoggerManager, log_success, log_error, log_warning, log_info, log_processing, log_skip, log_critical, log_start, log_stop, log_file, log_database, log_sync) from uptime_kuma_utils import UptimeKumaMonitor # 初始化日志管理器 LoggerManager("run_incremental_sync", log_prefix="incremental") # 初始化 Uptime Kuma 监控器 uptime_monitor = UptimeKumaMonitor(UPTIME_KUMA_CONFIG) uptime_monitor.set_logger(log_warning) # 提交后是否再做一次独立复核 (额外保险, 略增开销; 若不需要可设为 False) ENABLE_POST_COMMIT_VERIFY = True # IN 子句单批最大参数数 (SQL Server 上限约 2100, 留足余量) IN_CLAUSE_BATCH = 900 class SyncVerificationError(Exception): """数据落库校验未通过时抛出, 触发事务回滚并保留 Synced=0 以便下一轮重试""" pass # ================= 辅助函数 ================= def has_identity_column(sql_cursor, schema, table): """检查表是否包含标识列""" query = """ SELECT COUNT(*) FROM sys.columns c JOIN sys.tables t ON c.object_id = t.object_id JOIN sys.schemas s ON t.schema_id = s.schema_id WHERE s.name = ? AND t.name = ? AND c.is_identity = 1 """ sql_cursor.execute(query, (schema, table)) return sql_cursor.fetchone()[0] > 0 def _chunked(values, size=IN_CLAUSE_BATCH): """将集合/列表分批, 避免 IN 子句参数数量超过 SQL Server 上限""" items = list(values) for i in range(0, len(items), size): yield items[i:i + size] def _norm_key(v): """主键归一化, 用于跨来源(日志侧 / Access 侧 / SQL 侧)的集合比较, 规避类型差异""" return str(v).strip() if v is not None else None def fetch_existing_pks(sql_cursor, full_name, pk_col, pk_values): """ 拿一批主键去目标表查询, 返回其中【实际存在】的主键集合 (分批查询)。 这是逐主键校验的基础: 用 "查到了哪些" 来精确判断存在 / 不存在, 而不是用数量相减 (数量法会被 "一边少插一边漏删" 互相抵消而误判)。 """ found = set() for chunk in _chunked(pk_values): if not chunk: continue placeholders = ','.join(['?'] * len(chunk)) sql = f"SELECT [{pk_col}] FROM {full_name} WHERE [{pk_col}] IN ({placeholders})" sql_cursor.execute(sql, list(chunk)) for row in sql_cursor.fetchall(): found.add(row[0]) return found def verify_sync_result(sql_cursor, full_name, pk_col, record_ids, inserted_pk_set): """ 逐主键校验本批同步结果 (不依赖数量比较): - 删除校验: 源端已读不到的记录 (record_ids 中不在 inserted_pk_set 的部分), 删除后必须【全部不存在】于目标表; 只要还查得到任意一条即判失败。 - 插入校验: 从 Access 实际读到并重新插入的主键 (inserted_pk_set), 必须【全部存在】于目标表; 只要有一条查不到即判失败。 校验不通过抛出 SyncVerificationError, 并给出具体出问题的主键样例。 返回值: 本批 "随源删除" 的记录条数 (供日志展示)。 """ inserted_keys = {_norm_key(pk) for pk in inserted_pk_set} # 1) 删除校验: 应删除的记录, 删除后必须不在目标表 to_delete = [rid for rid in record_ids if _norm_key(rid) not in inserted_keys] if to_delete: leftover = fetch_existing_pks(sql_cursor, full_name, pk_col, to_delete) if leftover: sample = list(leftover)[:5] raise SyncVerificationError( f"删除校验失败: {len(leftover)} 条记录删除后仍存在于目标表, 例如 {sample}") # 2) 插入校验: 应插入的记录, 插入后必须存在于目标表 if inserted_pk_set: present_keys = {_norm_key(p) for p in fetch_existing_pks(sql_cursor, full_name, pk_col, inserted_pk_set)} missing = [pk for pk in inserted_pk_set if _norm_key(pk) not in present_keys] if missing: raise SyncVerificationError( f"插入校验失败: {len(missing)} 条记录插入后在目标表查不到, 例如 {missing[:5]}") return len(to_delete) def find_pk_index(acc_cols, pk_col): """在 Access 结果列中定位主键列下标 (大小写不敏感兜底)""" if pk_col in acc_cols: return acc_cols.index(pk_col) lower_map = {c.lower(): i for i, c in enumerate(acc_cols)} return lower_map.get(pk_col.lower()) # ================= 主逻辑 ================= def process_sync_task(): """ 逻辑重构: 遍历 SYNC_MAPPING 中的每一个文件 -> 去日志表查询该文件下特定表的未同步记录。 校验机制 (逐主键确认): 删旧插新后, 在提交前对本批数据做删除校验 + 插入校验 —— 应删除的主键确认已不在目标表、应插入的主键确认已在目标表, 二者都通过才标记 Synced=1 并提交; 否则回滚整批, 保持 Synced=0, 下一轮自动重试。 """ sql_conn = db_utils.get_sql_conn() sql_cursor = sql_conn.cursor() sql_cursor.fast_executemany = True # 高性能开关 # 标记是否有工作被处理（用于控制轮询休眠时间） work_done = False cols = LOG_TABLE_CONFIG log_full_name = db_utils.fmt_table(cols['schema'], cols['table_name']) try: # === 核心循环：以配置文件为驱动 === for clean_path, tables_map in SYNC_MAPPING.items(): # 1. 准备查询条件 target_tables = list(tables_map.keys()) if not target_tables: continue # 构造 TableAddress 的精确匹配条件 # 注意：将路径转为 Windows 标准反斜杠 win_path = os.path.normpath(clean_path) addr_candidates = [ f";DATABASE={win_path}", # 情况1 f"LOCAL={win_path}", # 情况2 f"LOCAL:{win_path}", # 情况3 win_path # 情况4 (兼容没有前缀的情况) ] # 2. 构造动态 SQL 查询 placeholders_addr = ','.join(['?'] * len(addr_candidates)) placeholders_tbl = ','.join(['?'] * len(target_tables)) query_log = f""" SELECT TOP 1000 {cols['col_log_id']}, {cols['col_table_name']}, {cols['col_record_id']} FROM {log_full_name} WHERE {cols['col_synced']} = 0 AND TableType = 'LINKED_ACCESS' AND {cols['col_address']} IN ({placeholders_addr}) AND {cols['col_table_name']} IN ({placeholders_tbl}) ORDER BY {cols['col_log_id']} ASC """ # 参数列表：先放地址，再放表名 params = addr_candidates + target_tables sql_cursor.execute(query_log, params) logs = sql_cursor.fetchall() if not logs: continue # 这个文件没有需要同步的记录，检查下一个文件 work_done = True # 标记有工作 log_file(f"{os.path.basename(clean_path)} 发现 {len(logs)} 条待同步变更") # 3. 本地分组 (按表名) # 结构: table_tasks[TableName] = { record_ids: set(), log_ids: [] } table_tasks = {} for row in logs: log_id, acc_table, record_id = row if acc_table not in table_tasks: table_tasks[acc_table] = {'record_ids': set(), 'log_ids': []} table_tasks[acc_table]['record_ids'].add(record_id) table_tasks[acc_table]['log_ids'].append(log_id) # 4. 执行同步 (连接一次 Access，处理多张表) if not os.path.exists(clean_path): log_error(f"无法访问文件: {clean_path}") continue acc_conn = None try: acc_conn = db_utils.get_access_conn(clean_path) acc_cursor = acc_conn.cursor() log_database(f"已连接 Access 文件: {os.path.basename(clean_path)}") except Exception as conn_err: log_error(f"连接 Access 失败 [{os.path.basename(clean_path)}]: {conn_err}") continue # 统计每个文件的同步情况 file_success_count = 0 file_error_count = 0 file_total_records = 0 try: for acc_table, data in table_tasks.items(): record_ids = list(data['record_ids']) log_ids = data['log_ids'] # 读取目标配置 target_conf = tables_map[acc_table] target_schema = target_conf['target_schema'] target_table = target_conf['target_table'] pk_col = target_conf['pk_col'] target_full_name = db_utils.fmt_table(target_schema, target_table) # 检查目标表是否存在，不存在则自动创建 if not db_utils.table_exists(sql_cursor, target_schema, target_table): db_utils.ensure_schema(sql_cursor, target_schema) acc_cursor.execute(f"SELECT TOP 1 * FROM [{acc_table}]") db_utils.create_table_from_access( sql_cursor, target_schema, target_table, acc_cursor.description, pk_col ) log_info(f"目标表 [{target_table}] 不存在，已自动创建") log_processing(f"正在同步表 [{acc_table}] → [{target_table}] ({len(record_ids)} 条记录)") identity_enabled = False try: # --- A. 从 Access 读取最新数据 --- ids_placeholders = ','.join(['?'] * len(record_ids)) acc_sql = f"SELECT * FROM [{acc_table}] WHERE [{pk_col}] IN ({ids_placeholders})" acc_cursor.execute(acc_sql, record_ids) new_rows = acc_cursor.fetchall() acc_cols = [col[0] for col in acc_cursor.description] # 定位主键列, 取出 Access 实际读到的主键集合 (校验基准) pk_index = find_pk_index(acc_cols, pk_col) if new_rows and pk_index is None: raise SyncVerificationError( f"在 Access 表 [{acc_table}] 中找不到主键列 [{pk_col}]") inserted_pk_set = (set(row[pk_index] for row in new_rows) if pk_index is not None else set()) # --- B. SQL Server 删除旧记录 --- del_sql = f"DELETE FROM {target_full_name} WHERE [{pk_col}] IN ({ids_placeholders})" sql_cursor.execute(del_sql, record_ids) # --- C. 插入新记录 --- if new_rows: insert_sql = db_utils.generate_insert_sql(target_schema, target_table, acc_cols) # 检查并启用 IDENTITY_INSERT if has_identity_column(sql_cursor, target_schema, target_table): try: sql_cursor.execute(f"SET IDENTITY_INSERT {target_full_name} ON") identity_enabled = True except Exception as id_err: log_error(f"无法启用 IDENTITY_INSERT: {id_err}") raise sql_cursor.executemany(insert_sql, new_rows) # 立即关闭 IDENTITY_INSERT (会话级设置, 不随事务回滚) if identity_enabled: sql_cursor.execute(f"SET IDENTITY_INSERT {target_full_name} OFF") identity_enabled = False # --- D. 提交前校验 (逐主键确认: 应删的已不在 / 应插的已在) --- removed_count = verify_sync_result( sql_cursor, target_full_name, pk_col, record_ids, inserted_pk_set) # --- E. 校验通过 -> 标记日志 Synced = 1 --- log_placeholders = ','.join(['?'] * len(log_ids)) update_log_sql = f""" UPDATE {log_full_name} SET {cols['col_synced']} = 1 WHERE {cols['col_log_id']} IN ({log_placeholders}) """ sql_cursor.execute(update_log_sql, log_ids) # --- F. 提交事务 (删/插/标记 原子生效) --- sql_conn.commit() # --- G. 提交后独立复核 (可选, 防 "提交成功但未持久化" 等极端情况) --- if ENABLE_POST_COMMIT_VERIFY: try: verify_sync_result( sql_cursor, target_full_name, pk_col, record_ids, inserted_pk_set) except SyncVerificationError as post_err: log_critical( f"严重: 表 [{target_table}] 提交后复核失败! {post_err}; " f"撤销同步标记以便重试") revert_sql = f""" UPDATE {log_full_name} SET {cols['col_synced']} = 0 WHERE {cols['col_log_id']} IN ({log_placeholders}) """ sql_cursor.execute(revert_sql, log_ids) sql_conn.commit() file_error_count += 1 continue # 同步成功 msg = f"表 [{target_table}] 同步并校验通过: {len(inserted_pk_set)} 条入库" if removed_count > 0: msg += f", {removed_count} 条随源删除" log_success(msg) file_success_count += 1 file_total_records += len(record_ids) except Exception as tbl_err: # 清理可能残留的 IDENTITY_INSERT 会话状态 if identity_enabled: try: sql_cursor.execute(f"SET IDENTITY_INSERT {target_full_name} OFF") except: pass # 回滚本表的 删/插/标记, 保持 Synced=0, 下一轮自动重试 try: sql_conn.rollback() except: pass log_error(f"表 [{acc_table}] 同步失败 (已回滚, 将重试): {tbl_err}") file_error_count += 1 finally: # 确保 Access 连接关闭 try: if acc_conn: acc_conn.close() log_info(f"已关闭 Access 连接: {os.path.basename(clean_path)}") except Exception as close_err: log_warning(f"关闭 Access 连接时出错: {close_err}") # 输出文件级别的汇总 if file_success_count > 0 or file_error_count > 0: summary = f"文件 [{os.path.basename(clean_path)}] 同步汇总: " summary += f"成功 {file_success_count} 张表 ({file_total_records} 条记录)" if file_error_count > 0: summary += f" | 失败 {file_error_count} 张表" log_sync(summary) return work_done except Exception as e: log_critical(f"全局异常: {e}") return False finally: try: sql_conn.close() except: pass # ================= Uptime Kuma 心跳 ================= # 使用 uptime_kuma_utils.UptimeKumaMonitor 替代原有实现 if __name__ == "__main__": log_start("增量同步服务已启动 (配置驱动模式 + 逐主键落库校验)") log_info(f"轮询间隔: {POLL_INTERVAL} 秒") log_info(f"监控配置: {len(SYNC_MAPPING)} 个文件") log_info(f"提交后复核: {'开启' if ENABLE_POST_COMMIT_VERIFY else '关闭'}") if UPTIME_KUMA_CONFIG.get('enabled', False): log_info(f"心跳间隔: {UPTIME_KUMA_CONFIG['heartbeat_interval']} 秒") log_info("=" * 70) # 启动时发送第一次心跳 uptime_monitor.send_heartbeat() try: while True: try: has_work = process_sync_task() # 检查是否需要发送心跳 uptime_monitor.check_and_send_heartbeat() # 如果有工作，说明可能还有积压，休息短一点(0.1s) # 如果没工作，休息标准间隔 time.sleep(0.1 if has_work else POLL_INTERVAL) except KeyboardInterrupt: log_info("=" * 70) log_stop("收到停止信号，服务正在关闭...") break except Exception as e: log_critical(f"主循环崩溃: {e}") time.sleep(5) finally: # 停止时发送心跳停止信号（可选） uptime_monitor.send_stop_signal()