导入文件建表接口
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364
import_database_utils.py
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364
import_database_utils.py
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"""
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数据库导入工具模块
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用于通过HTTP接口导入文件到数据库
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"""
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import os
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import sys
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import sqlite3
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import pandas as pd
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from datetime import datetime
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from pathlib import Path
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from typing import Optional, Dict, Any, Tuple, List
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import openpyxl
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import io
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# 添加sqlite-mcp-server到路径以使用其工具函数
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current_dir = os.path.dirname(__file__)
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project_root = os.path.dirname(os.path.dirname(current_dir))
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sqlite_mcp_path = os.path.join(project_root, 'sqlite-mcp-server')
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if os.path.exists(sqlite_mcp_path) and sqlite_mcp_path not in sys.path:
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sys.path.insert(0, sqlite_mcp_path)
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from tools.common import (
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clean_table_name, clean_column_name, infer_data_types,
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CHUNK_SIZE, SQLITE_MAX_VARIABLES
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)
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def _clean_cell_value(value: Any) -> Any:
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"""
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清理单元格内容：去除空格、换行、制表符等
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Args:
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value: 单元格的值
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Returns:
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清理后的值
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"""
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if value is None:
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return None
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if isinstance(value, str):
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# 去除各种空白字符：空格、换行、回车、制表符、全角空格
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cleaned = value.replace(' ', '').replace('\n', '').replace('\r', '').replace('\t', '').replace('\u3000', '')
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return cleaned if cleaned else None
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return value
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def _process_merged_cells(ws: openpyxl.worksheet.worksheet.Worksheet) -> Dict[Tuple[int, int], Any]:
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"""
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处理合并单元格，将所有合并单元格打散并填充原值
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Args:
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ws: openpyxl worksheet对象
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Returns:
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单元格坐标到值的映射
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"""
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merged_map = {}
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# 处理所有合并单元格范围
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for merged_range in ws.merged_cells.ranges:
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# 获取左上角单元格的值
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min_row, min_col = merged_range.min_row, merged_range.min_col
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value = ws.cell(row=min_row, column=min_col).value
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# 填充整个合并范围
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for row in range(merged_range.min_row, merged_range.max_row + 1):
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for col in range(merged_range.min_col, merged_range.max_col + 1):
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merged_map[(row, col)] = value
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return merged_map
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def _worksheet_is_empty(ws: openpyxl.worksheet.worksheet.Worksheet) -> bool:
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"""
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判断工作表是否为空（无任何有效数据）
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"""
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max_row = ws.max_row or 0
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max_col = ws.max_column or 0
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if max_row == 0 or max_col == 0:
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return True
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# 快速路径：单个单元且为空
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if max_row == 1 and max_col == 1:
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return _clean_cell_value(ws.cell(row=1, column=1).value) is None
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# 扫描是否存在任一非空单元格
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for row in ws.iter_rows(min_row=1, max_row=max_row, min_col=1, max_col=max_col, values_only=True):
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if any(_clean_cell_value(v) is not None for v in row):
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return False
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return True
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def _read_excel_with_merged_cells(table_name: str, file_content: bytes) -> List[Dict[str, Any]]:
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"""
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从内存中读取Excel文件，处理合并单元格
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Args:
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table_name: 表名
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file_content: Excel文件的二进制内容
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Returns:
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处理后的DataFrame
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"""
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# 从字节流加载Excel文件
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wb = openpyxl.load_workbook(io.BytesIO(file_content), data_only=True)
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wss = wb.sheetnames
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data = []
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df = None
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for ws_name in wss:
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ws = wb[ws_name]
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# 跳过空工作表
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if _worksheet_is_empty(ws):
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continue
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# 处理合并单元格
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# merged_cells_map = _process_merged_cells(ws)
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# 读取所有数据
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all_data = []
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# 跳过空列
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none_raw = []
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max_row = ws.max_row
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max_col = ws.max_column
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for row_idx in range(1, max_row + 1):
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row_data = []
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for col_idx in range(1, max_col + 1):
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# 检查是否在合并单元格映射中
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# if (row_idx, col_idx) in merged_cells_map:
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# value = merged_cells_map[(row_idx, col_idx)]
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# else:
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value = ws.cell(row=row_idx, column=col_idx).value
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if (value is None and row_idx == 1) or (col_idx in none_raw):
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none_raw.append(col_idx)
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continue
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# 清理单元格内容
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value = _clean_cell_value(value)
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row_data.append(value)
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# 跳过完全空白的行
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if any(v is not None for v in row_data):
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all_data.append(row_data)
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# 转换为DataFrame
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if not all_data:
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# 空表，跳过
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continue
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# 第一行作为列名
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columns = [str(col) if col is not None else f"列{i+1}"
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for i, col in enumerate(all_data[0])]
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# 其余行作为数据
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data_rows = all_data[1:] if len(all_data) > 1 else []
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# 创建DataFrame
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df = pd.DataFrame(data_rows, columns=columns)
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if len(wss) > 1:
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normalized_sheet = ws_name.replace(' ', '').replace('-', '_')
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data.append({"table_name": f"{table_name}_{normalized_sheet}", "df": df})
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else:
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data.append({"table_name": table_name, "df": df})
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if not data:
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return []
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return data
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def import_to_database(
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db_path: str,
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file_content: bytes,
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filename: str,
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table_name: Optional[str] = None,
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force_overwrite: bool = False
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) -> Dict[str, Any]:
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"""
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导入文件内容到指定的SQLite数据库
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Args:
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db_path: 数据库文件路径
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file_content: 文件的二进制内容
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filename: 原始文件名
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table_name: 指定的表名（可选，默认从文件名生成）
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force_overwrite: 是否强制覆盖已存在的表
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Returns:
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导入结果信息
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"""
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try:
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# 检查数据库是否存在
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if not os.path.exists(db_path):
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return {"success": False, "error": f"数据库不存在: {db_path}"}
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# 读取文件
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file_ext = Path(filename).suffix.lower()
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table_name = filename.split('.')[0]
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if file_ext == '.csv':
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# 检测编码
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encodings = ['utf-8', 'gbk', 'gb2312', 'gb18030']
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df = None
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data = []
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for encoding in encodings:
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try:
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# 尝试从内存读取
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df = pd.read_csv(io.BytesIO(file_content), encoding=encoding, nrows=5)
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# 如果成功，用同样的编码读取完整内容
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df = pd.read_csv(io.BytesIO(file_content), encoding=encoding)
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data.append({"table_name": table_name, "df": df})
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break
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except:
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continue
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if df is None:
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conn.close()
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return {"success": False, "error": "无法识别文件编码"}
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elif file_ext in ['.xlsx', '.xls']:
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# 使用自定义函数读取Excel，处理合并单元格
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data = _read_excel_with_merged_cells(table_name, file_content)
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else:
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conn.close()
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return {"success": False, "error": f"不支持的文件类型: {file_ext}"}
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conn = sqlite3.connect(db_path)
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cursor = conn.cursor()
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for item in data:
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table_name = item["table_name"]
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df = item["df"]
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# 检查表是否存在
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cursor.execute(f"""
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SELECT name FROM sqlite_master
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WHERE type='table' AND name='{table_name}'
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""")
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table_exists = cursor.fetchone() is not None
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if table_exists and not force_overwrite:
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# 获取表的基本信息
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cursor.execute(f"SELECT COUNT(*) FROM \"{table_name}\"")
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row_count = cursor.fetchone()[0]
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cursor.execute(f"PRAGMA table_info(\"{table_name}\")")
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columns = [col[1] for col in cursor.fetchall() if col[1] != '_row_id']
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conn.close()
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return {
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"success": False,
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"error": "table_exists",
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"error_type": "TABLE_EXISTS",
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"message": f"表 {table_name} 已存在",
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"table_info": {
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"table_name": table_name,
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"row_count": row_count,
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"columns": columns,
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"column_count": len(columns)
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},
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"suggestion": "您可以选择：1) 覆盖现有表（force_overwrite=true）2) 使用其他表名 3) 取消操作"
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}
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total_rows_all = 0
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imported_rows_all = 0
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error_count_all = 0
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table_name_all = []
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for item in data:
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df = item["df"]
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table_name = item["table_name"]
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# 清理列名
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df.columns = [clean_column_name(col) for col in df.columns]
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# 检查是否有重复列名
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if len(df.columns) != len(set(df.columns)):
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# 为重复列名添加序号
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new_cols = []
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col_count = {}
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for col in df.columns:
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if col in col_count:
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col_count[col] += 1
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new_cols.append(f"{col}_{col_count[col]}")
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else:
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col_count[col] = 0
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new_cols.append(col)
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df.columns = new_cols
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total_rows = len(df)
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# 如果表存在且需要覆盖，先删除表
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if table_exists and force_overwrite:
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cursor.execute(f'DROP TABLE IF EXISTS "{table_name}"')
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conn.commit()
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# 创建新表
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data_types = infer_data_types(df)
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columns_def = []
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for col in df.columns:
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col_type = data_types.get(col, 'TEXT')
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columns_def.append(f'"{col}" {col_type}')
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create_table_sql = f"""
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CREATE TABLE IF NOT EXISTS "{table_name}" (
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_row_id INTEGER PRIMARY KEY AUTOINCREMENT,
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{', '.join(columns_def)}
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)
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"""
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cursor.execute(create_table_sql)
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conn.commit()
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# 数据清洗和类型转换
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for col in df.columns:
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# 对于CSV文件，去除前后空格
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if file_ext == '.csv' and df[col].dtype == 'object':
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df[col] = df[col].apply(lambda x: x.strip() if isinstance(x, str) else x)
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# 将 NaN 替换为 None (SQLite NULL)
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df[col] = df[col].where(pd.notna(df[col]), None)
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# 日期类型转换为 ISO 格式
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# try:
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# # 尝试识别日期列
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# if df[col].dtype == 'object' and df[col].notna().any():
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# sample = df[col].dropna().head(100)
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# dates = pd.to_datetime(sample, errors='coerce')
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# if dates.notna().sum() / len(sample) > 0.9:
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# df[col] = pd.to_datetime(df[col], errors='coerce')
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# df[col] = df[col].dt.strftime('%Y-%m-%d %H:%M:%S')
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# except:
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# pass
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# 计算安全的批次大小（考虑 SQLite 变量限制）
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num_columns = len(df.columns)
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safe_batch_size = min(CHUNK_SIZE, SQLITE_MAX_VARIABLES // num_columns - 1)
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# 批量导入数据
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error_count = 0
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imported_rows = 0
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for batch_start in range(0, total_rows, safe_batch_size):
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batch_end = min(batch_start + safe_batch_size, total_rows)
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batch_df = df.iloc[batch_start:batch_end]
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try:
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# 批量插入
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batch_df.to_sql(table_name, conn, if_exists='append',
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index=False, method='multi', chunksize=100)
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imported_rows = batch_end
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except Exception as e:
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error_count += 1
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print(f"批次导入错误 [{batch_start}-{batch_end}]: {str(e)}")
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# 优化表
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cursor.execute(f'ANALYZE "{table_name}"')
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total_rows_all += total_rows
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imported_rows_all += imported_rows
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error_count_all += error_count
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table_name_all.append(table_name)
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conn.commit()
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conn.close()
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table_name_all = set(table_name_all)
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return {
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"success": True,
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"table_name": table_name,
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"total_rows": total_rows_all,
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"imported_rows": imported_rows_all,
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"error_count": error_count_all,
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"message": f"成功导入 {imported_rows}/{total_rows} 行数据到表 {table_name_all}"
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}
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except Exception as e:
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return {"success": False, "error": str(e)}
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