7edbd987f0
- Add sync.py for LAN file sync with atomic writes and mtime-based change detection - Add watch.py daemon that periodically syncs files and migrates only changed tables - Add TRUNCATE mode to migrate.py (--truncate flag) to preserve table structure - Update config.yaml schema with check_interval_minutes and lan_sources - Update README with new features documentation Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
532 lines
16 KiB
Python
532 lines
16 KiB
Python
"""
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Excel to SQL Server Migration Script
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Migrates production execution card data from Excel (.xlsm) to SQL Server.
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Configuration is loaded from config.yaml.
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"""
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import argparse
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import os
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import sys
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from collections import OrderedDict
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from datetime import datetime
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import openpyxl
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import pyodbc
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import yaml
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# ============================================================
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# 配置加载
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# ============================================================
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def load_config(config_path=None):
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"""加载 YAML 配置文件"""
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if config_path is None:
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config_path = os.path.join(
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os.path.dirname(os.path.abspath(__file__)), 'config.yaml'
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)
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with open(config_path, 'r', encoding='utf-8') as f:
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return yaml.safe_load(f)
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def get_connection_string(cfg):
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"""根据配置构建 pyodbc 连接字符串"""
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sql = cfg['sql_server']
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return (
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f"DRIVER={{{sql['driver']}}};"
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f"SERVER={sql['server']};"
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f"DATABASE={sql['database']};"
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f"UID={sql['username']};"
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f"PWD={sql['password']};"
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f"TrustServerCertificate={sql['TrustServerCertificate']};"
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)
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# ============================================================
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# Excel 读取与数据合并
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# ============================================================
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def sanitize_headers(headers):
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"""处理原始表头：
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1. 空列名 → col_N
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2. 重复列名 → 第二次出现加 _2 后缀
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返回处理后的列名列表
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"""
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seen = {}
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result = []
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for i, h in enumerate(headers):
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h = str(h).strip() if h else ''
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if not h:
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h = f'col_{i}'
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if h in seen:
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result.append(f'{h}_{seen[h] + 1}')
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seen[h] += 1
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else:
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seen[h] = 1
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result.append(h)
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return result
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def remap_headers(headers, mapping):
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"""根据 config.yaml 中的 column_mapping 重映射列名"""
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if not mapping:
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return headers
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return [mapping.get(h, h) for h in headers]
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def resolve_pk_index(headers, primary_key):
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"""找到主键列的索引。
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优先精确匹配 primary_key，其次匹配含 'BW' 的列，最后回退到第 0 列。
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"""
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for idx, h in enumerate(headers):
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if h == primary_key:
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return idx
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# 回退：首列含 BW（如 20BW序号）
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for idx, h in enumerate(headers):
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if 'BW' in h:
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return idx
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return 0
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def read_sheet(file_path, sheet_name, primary_key, cfg):
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"""读取单个工作表。
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返回 (列名列表, 实际主键列名, 数据列表)。
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每行数据为 dict {列名: 值}。
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跳过主键列为空的行。
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"""
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wb = openpyxl.load_workbook(file_path, read_only=True, data_only=True)
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ws = wb[sheet_name]
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rows_iter = ws.iter_rows(values_only=True)
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# 第一行为表头
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raw_headers = next(rows_iter)
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headers = sanitize_headers(raw_headers)
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headers = remap_headers(headers, cfg.get('column_mapping'))
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pk_idx = resolve_pk_index(headers, primary_key)
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actual_pk_col = headers[pk_idx]
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data = []
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for row in rows_iter:
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pk_val = row[pk_idx] if pk_idx < len(row) else None
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if pk_val is None or str(pk_val).strip() == '':
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continue
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record = {}
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for j, h in enumerate(headers):
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val = row[j] if j < len(row) else None
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record[h] = val
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data.append(record)
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wb.close()
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return headers, actual_pk_col, data
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def merge_sources(sources, excel_dir, primary_key, cfg):
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"""合并多个工作表的数据。
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- 列名取并集（按出现顺序）
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- 数据按主键去重，后读覆盖先读
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返回 (unified_headers, actual_pk_col, merged_data_ordered_dict)
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"""
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all_headers = [] # 保持顺序的列名并集
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header_set = set()
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merged = OrderedDict() # key=主键值, value=dict
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resolved_pk = primary_key # 实际使用的主键列名
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for src in sources:
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file_path = os.path.join(excel_dir, src['file'])
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sheet_name = src['sheet']
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headers, actual_pk_col, data = read_sheet(file_path, sheet_name, primary_key, cfg)
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resolved_pk = actual_pk_col
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# 扩展统一列名集
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for h in headers:
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if h not in header_set:
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all_headers.append(h)
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header_set.add(h)
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# 合并数据（后读覆盖先读）
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for record in data:
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pk_val = str(record.get(actual_pk_col, '')).strip()
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if not pk_val:
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continue
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if pk_val in merged:
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merged[pk_val].update(record)
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else:
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merged[pk_val] = record
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return all_headers, resolved_pk, merged
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# ============================================================
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# SQL Server 操作
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# ============================================================
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def get_connection(cfg):
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"""根据配置建立 SQL Server 连接"""
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conn_str = get_connection_string(cfg)
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return pyodbc.connect(conn_str, autocommit=False)
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def create_schema(cursor, schema_name):
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"""创建 schema（如不存在）"""
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sql = f"""
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IF NOT EXISTS (
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SELECT 1 FROM sys.schemas WHERE name = ?
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)
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BEGIN
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EXEC('CREATE SCHEMA [{schema_name}]')
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END
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"""
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cursor.execute(sql, (schema_name,))
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cursor.commit()
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def infer_sql_type(col_name, type_rules, primary_key):
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"""根据 config.yaml 中的 type_rules 推断 SQL Server 数据类型"""
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name = col_name
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# 主键列（含 TM_2022 的 20BW序号）
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if name == primary_key or '20BW' in name:
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return type_rules['primary_key_type']
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# DATETIME
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if any(kw in name for kw in type_rules.get('datetime', [])):
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return 'DATETIME'
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# INT（精确匹配）
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for rule in type_rules.get('int', []):
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if rule.get('exact') and rule['name'] == name:
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return 'INT'
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elif not rule.get('exact') and rule['name'] in name:
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return 'INT'
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# DECIMAL（精确匹配）
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for rule in type_rules.get('decimal', []):
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if rule.get('exact') and rule['name'] == name:
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return 'DECIMAL(18,2)'
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elif not rule.get('exact') and rule['name'] in name:
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return 'DECIMAL(18,2)'
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# NVARCHAR(MAX) 长文本
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if any(kw in name for kw in type_rules.get('long_text', [])):
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return 'NVARCHAR(MAX)'
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return type_rules.get('default_type', 'NVARCHAR(500)')
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def table_exists(cursor, schema, table_name):
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"""检查表是否已存在"""
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full_name = f'[{schema}].[{table_name}]'
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cursor.execute(f"SELECT OBJECT_ID('{full_name}', 'U')")
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row = cursor.fetchone()
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return row[0] is not None
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def get_existing_columns(cursor, schema, table_name):
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"""查询现有表的列名集合（排除 id 自增列）"""
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full_name = f'[{schema}].[{table_name}]'
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cursor.execute(f"""
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SELECT COLUMN_NAME
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FROM INFORMATION_SCHEMA.COLUMNS
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WHERE TABLE_SCHEMA = ? AND TABLE_NAME = ?
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ORDER BY ORDINAL_POSITION
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""", (schema, table_name))
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cols = {row[0] for row in cursor.fetchall()}
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cols.discard('id')
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return cols
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def ensure_table(cursor, table_name, headers, cfg, actual_pk, use_truncate=False):
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"""确保目标表就绪。
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- use_truncate=False（默认）→ 调用 create_table()，DROP + CREATE
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- use_truncate=True →
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1. 表不存在 → CREATE TABLE
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2. 表存在且列名集合一致 → TRUNCATE TABLE
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3. 表存在但列名有变化 → DROP + CREATE（兜底）
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"""
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if not use_truncate:
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return create_table(cursor, table_name, headers, cfg, actual_pk)
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schema = cfg['schema']
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full_name = f'[{schema}].[{table_name}]'
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if not table_exists(cursor, schema, table_name):
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# 表不存在，走正常 CREATE
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return create_table(cursor, table_name, headers, cfg, actual_pk)
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# 比较列名集合
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existing_cols = get_existing_columns(cursor, schema, table_name)
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new_cols = set(headers)
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# 主键列也应包含在比较中
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new_cols.add(actual_pk)
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if existing_cols == new_cols:
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# 列名一致，TRUNCATE
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cursor.execute(f"TRUNCATE TABLE {full_name}")
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cursor.commit()
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return f"TRUNCATE TABLE {full_name}"
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else:
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# 列名有变化，兜底 DROP + CREATE
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return create_table(cursor, table_name, headers, cfg, actual_pk)
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def create_table(cursor, table_name, headers, cfg, actual_pk):
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"""动态创建表。如已存在则先 DROP。"""
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schema = cfg['schema']
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type_rules = cfg['type_rules']
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full_name = f'[{schema}].[{table_name}]'
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# DROP if exists
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cursor.execute(f"IF OBJECT_ID('{full_name}', 'U') IS NOT NULL DROP TABLE {full_name}")
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cursor.commit()
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# 构建列定义
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col_defs = ['[id] INT IDENTITY(1,1)']
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for h in headers:
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sql_type = infer_sql_type(h, type_rules, actual_pk)
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col_defs.append(f'[{h}] {sql_type}')
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# 主键约束
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col_defs.append(f'CONSTRAINT [PK_{table_name}] PRIMARY KEY ([{actual_pk}])')
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create_sql = f"CREATE TABLE {full_name} ({', '.join(col_defs)})"
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cursor.execute(create_sql)
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cursor.commit()
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return create_sql
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def build_typed_cols(headers, type_rules, actual_pk):
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"""构建需要做类型转换的列映射 {列名: 'datetime'|'int'|'decimal'}"""
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typed = {}
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for h in headers:
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if h == actual_pk or '20BW' in h:
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continue
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if any(kw in h for kw in type_rules.get('datetime', [])):
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typed[h] = 'datetime'
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for rule in type_rules.get('int', []):
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match = (rule['name'] == h) if rule.get('exact') else (rule['name'] in h)
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if match:
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typed[h] = 'int'
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for rule in type_rules.get('decimal', []):
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match = (rule['name'] == h) if rule.get('exact') else (rule['name'] in h)
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if match:
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typed[h] = 'decimal'
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return typed
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def safe_datetime(val):
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"""将值转换为 datetime，失败则返回 None"""
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if val is None:
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return None
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if isinstance(val, str) and val.strip() == '':
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return None
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if isinstance(val, datetime):
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if val.year < 1753:
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return None
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return val
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if isinstance(val, str):
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for fmt in ('%Y-%m-%d %H:%M:%S', '%Y-%m-%d %H:%M', '%Y-%m-%d',
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'%Y/%m/%d %H:%M:%S', '%Y/%m/%d %H:%M', '%Y/%m/%d'):
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try:
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dt = datetime.strptime(val.strip(), fmt)
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if dt.year >= 1753:
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return dt
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return None
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except ValueError:
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continue
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return None
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return None
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def safe_int(val):
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"""将值转换为 int，失败则返回 None"""
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if val is None:
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return None
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if isinstance(val, str) and val.strip() == '':
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return None
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if isinstance(val, (int, float)):
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return int(val)
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try:
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return int(float(str(val).strip()))
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except (ValueError, TypeError):
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return None
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def safe_decimal(val):
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"""将值转换为 float（pyodbc 会映射到 DECIMAL），失败则返回 None"""
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if val is None:
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return None
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if isinstance(val, str) and val.strip() == '':
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return None
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if isinstance(val, (int, float)):
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return float(val)
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try:
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return float(str(val).strip())
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except (ValueError, TypeError):
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return None
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TYPE_CONVERTERS = {
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'datetime': safe_datetime,
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'int': safe_int,
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'decimal': safe_decimal,
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}
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def batch_insert(cursor, table_name, headers, data_dict, cfg, actual_pk):
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"""批量插入数据到 SQL Server。"""
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schema = cfg['schema']
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batch_size = cfg.get('batch_size', 500)
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type_rules = cfg['type_rules']
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full_name = f'[{schema}].[{table_name}]'
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typed_cols = build_typed_cols(headers, type_rules, actual_pk)
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col_list = ', '.join(f'[{h}]' for h in headers)
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placeholders = ', '.join(['?'] * len(headers))
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insert_sql = f"INSERT INTO {full_name} ({col_list}) VALUES ({placeholders})"
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batch = []
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total = 0
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for pk, record in data_dict.items():
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row = []
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for h in headers:
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val = record.get(h)
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if isinstance(val, str) and val.strip() == '':
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val = None
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# 强类型列清洗
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if h in typed_cols and val is not None:
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converter = TYPE_CONVERTERS[typed_cols[h]]
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val = converter(val)
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row.append(val)
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batch.append(row)
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total += 1
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if len(batch) >= batch_size:
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cursor.executemany(insert_sql, batch)
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cursor.commit()
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batch = []
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if batch:
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cursor.executemany(insert_sql, batch)
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cursor.commit()
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return total
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# ============================================================
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# 主流程
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# ============================================================
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def migrate_table(cursor, table_config, cfg, dry_run=False, use_truncate=False):
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"""迁移单个表"""
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table_name = table_config['table']
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sources = table_config['sources']
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primary_key = cfg['primary_key']
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excel_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'Excel')
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print(f"\n 迁移 {table_name} ...")
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# 1. 读取并合并数据
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headers, actual_pk, merged = merge_sources(sources, excel_dir, primary_key, cfg)
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# 打印各源行数
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for src in sources:
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fpath = os.path.join(excel_dir, src['file'])
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_, _, data = read_sheet(fpath, src['sheet'], primary_key, cfg)
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print(f" 读取: {src['file']} > {src['sheet']} → {len(data):,} 行")
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print(f" 合并去重后: {len(merged):,} 行, {len(headers)} 列 (主键列: {actual_pk})")
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if dry_run:
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print(f" [DRY-RUN] 建表 SQL:")
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type_rules = cfg['type_rules']
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col_defs = ['[id] INT IDENTITY(1,1)']
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for h in headers:
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sql_type = infer_sql_type(h, type_rules, actual_pk)
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col_defs.append(f' [{h}] {sql_type}')
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col_defs.append(f' CONSTRAINT [PK_{table_name}] PRIMARY KEY ([{actual_pk}])')
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print(f" CREATE TABLE [{cfg['schema']}].[{table_name}] (")
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print(',\n'.join(col_defs))
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print(" );")
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return len(merged)
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# 2. 建表
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create_sql = ensure_table(cursor, table_name, headers, cfg, actual_pk, use_truncate=use_truncate)
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action = "TRUNCATE" if use_truncate and "TRUNCATE" in (create_sql or "") else "建表"
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print(f" {action}: [{cfg['schema']}].[{table_name}] ({len(headers)} 列)")
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# 3. 插入数据
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total = batch_insert(cursor, table_name, headers, merged, cfg, actual_pk)
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print(f" 插入: {total:,} 行 done")
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return total
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def main():
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parser = argparse.ArgumentParser(description='Excel to SQL Server Migration')
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parser.add_argument('--config', default=None, help='配置文件路径 (默认: config.yaml)')
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parser.add_argument('--dry-run', action='store_true', help='仅预览，不写入数据库')
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parser.add_argument('--truncate', action='store_true',
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help='使用 TRUNCATE 模式（表存在时截断而非重建）')
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parser.add_argument('--table', nargs='+', default=None,
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help='只迁移指定的表，例如 --table PG_2022 TM_2025')
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args = parser.parse_args()
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# 加载配置
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cfg = load_config(args.config)
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# 按参数筛选表
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if args.table:
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specified = set(args.table)
|
||
tables = [t for t in cfg['tables'] if t['table'] in specified]
|
||
missing = specified - {t['table'] for t in tables}
|
||
if missing:
|
||
print(f" 错误: 未找到表 {', '.join(missing)}")
|
||
available = ', '.join(t['table'] for t in cfg['tables'])
|
||
print(f" 可用表: {available}")
|
||
sys.exit(1)
|
||
else:
|
||
tables = cfg['tables']
|
||
|
||
total_tables = len(tables)
|
||
primary_key = cfg['primary_key']
|
||
|
||
print(f"{'=' * 60}")
|
||
print(f" Excel → SQL Server 迁移工具")
|
||
print(f" 目标: [{cfg['schema']}], {total_tables} 张表")
|
||
print(f" 模式: {'DRY-RUN' if args.dry_run else 'LIVE'}")
|
||
print(f"{'=' * 60}")
|
||
|
||
conn = None
|
||
cursor = None
|
||
|
||
if not args.dry_run:
|
||
conn = get_connection(cfg)
|
||
cursor = conn.cursor()
|
||
create_schema(cursor, cfg['schema'])
|
||
print(f"\n Schema [{cfg['schema']}] 已就绪")
|
||
|
||
total_rows = 0
|
||
for i, table_cfg in enumerate(tables, 1):
|
||
print(f"\n[{i}/{total_tables}]", end='')
|
||
rows = migrate_table(cursor, table_cfg, cfg, dry_run=args.dry_run,
|
||
use_truncate=args.truncate)
|
||
total_rows += rows
|
||
|
||
if not args.dry_run:
|
||
cursor.close()
|
||
conn.close()
|
||
|
||
print(f"\n{'=' * 60}")
|
||
print(f" 完成! 共迁移 {total_tables} 张表, {total_rows:,} 行")
|
||
print(f"{'=' * 60}")
|
||
|
||
|
||
if __name__ == '__main__':
|
||
main()
|