590 lines
24 KiB
Python
590 lines
24 KiB
Python
"""
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数据处理核心模块 - 性能优化版
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"""
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import os
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import re
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import time
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import zipfile
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import multiprocessing
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import numpy as np
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import pandas as pd
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import sqlparse
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from pathlib import Path
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from typing import Any, Callable, Dict, Generator, List, Optional, Tuple
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from datetime import datetime
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from io import StringIO
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from app.config import AppConfig, SQL_SCRIPT
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from app.database import DatabaseManager
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class ProcessLogger:
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"""处理日志记录器"""
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def __init__(self, log_file: Optional[Path] = None, callback: Optional[Callable[[str], None]] = None):
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self.logs: List[str] = []
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self.log_file = log_file
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self.callback = callback
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# 如果指定了日志文件，确保目录存在
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if self.log_file:
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self.log_file.parent.mkdir(parents=True, exist_ok=True)
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# 清空或创建日志文件
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self.log_file.write_text("", encoding='utf-8')
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def log(self, message: str, level: str = "INFO"):
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"""记录日志"""
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timestamp = datetime.now().strftime("%H:%M:%S")
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entry = f"[{timestamp}] [{level}] {message}"
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self.logs.append(entry)
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# 实时写入文件
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if self.log_file:
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try:
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with self.log_file.open("a", encoding='utf-8') as f:
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f.write(entry + "\n")
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except Exception as e:
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# 如果写入失败，至少记录到内存
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print(f"写入日志文件失败: {e}")
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if self.callback:
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self.callback(entry)
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def info(self, message: str):
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self.log(message, "INFO")
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def error(self, message: str):
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self.log(message, "ERROR")
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def warning(self, message: str):
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self.log(message, "WARN")
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def success(self, message: str):
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self.log(message, "SUCCESS")
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def get_logs(self) -> List[str]:
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return self.logs.copy()
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class DataProcessor:
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"""数据处理器 - 高性能版"""
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# 批量插入大小（根据实际测试，5000 是比较好的平衡点）
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BATCH_SIZE = 5000
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# Excel 并行处理的最大线程数（根据 CPU 核心数自动调整）
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# 使用 CPU 核心数，但至少为 1，最多不超过 8（避免过多线程导致上下文切换开销）
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MAX_WORKERS = min(max(multiprocessing.cpu_count(), 1), 8)
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def __init__(self, config: AppConfig, work_dir: Path, logger: ProcessLogger):
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self.config = config
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self.work_dir = work_dir
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self.logger = logger
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self.db = DatabaseManager(config)
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self.results: Dict[str, Any] = {}
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# 预编译字段映射，避免重复查找
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self._field_map, self._type_map = self._build_field_map()
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def _build_field_map(self) -> Tuple[Dict[str, str], Dict[str, str]]:
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"""
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预构建字段映射表和类型映射表，提高查找效率
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Returns:
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field_map: {源字段名: 目标字段名}
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type_map: {目标字段名: 字段类型}
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"""
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field_map = {}
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type_map = {}
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for field_def in self.config.extract_fields:
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db_field = field_def.get("Field")
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field_type = field_def.get("Type", "string") # 默认类型为 string
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# 记录目标字段的类型
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type_map[db_field] = field_type
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# 记录源字段到目标字段的映射
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for extract_name in field_def.get("Extract", []):
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field_map[extract_name] = db_field
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return field_map, type_map
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def process(self) -> Dict[str, Any]:
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"""执行完整的数据处理流程"""
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start_time = time.time()
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self.logger.info(f"开始处理数据，工作目录: {self.work_dir}")
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try:
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# 1. 解压 ZIP 文件
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self._unzip_files()
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# 2. 处理 Excel 文件（并行）
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self._process_excel_files_parallel()
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# 3. 处理 CSV 文件并上传到数据库（高性能批量插入）
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self._process_csv_files()
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# 4. 执行 SQL 脚本
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self._execute_sql_script()
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elapsed = round(time.time() - start_time, 2)
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self.logger.success(f"处理完成！总耗时: {elapsed} 秒")
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self.results["success"] = True
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self.results["elapsed_time"] = elapsed
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except Exception as e:
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self.logger.error(f"处理失败: {str(e)}")
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self.results["success"] = False
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self.results["error"] = str(e)
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finally:
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self.db.dispose()
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return self.results
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def _unzip_files(self):
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"""解压所有 ZIP 文件（支持中文文件名）"""
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self.logger.info("正在解压 ZIP 文件...")
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zip_files = list(self.work_dir.rglob("*.zip"))
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zip_count = 0
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for zip_file in zip_files:
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try:
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rel_path = zip_file.relative_to(self.work_dir)
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self.logger.info(f"解压: {rel_path}")
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self._extract_zip_with_encoding(zip_file)
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zip_count += 1
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except Exception as e:
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rel_path = zip_file.relative_to(self.work_dir)
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self.logger.error(f"解压失败 {rel_path}: {e}")
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self.logger.info(f"ZIP 解压完成，共 {zip_count} 个文件")
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def _extract_zip_with_encoding(self, zip_file: Path):
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"""
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解压 ZIP 文件，自动处理中文文件名编码问题
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支持 UTF-8、GBK、CP437 等多种编码
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"""
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# 优先尝试 UTF-8（现代 ZIP 文件标准）
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try:
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with zipfile.ZipFile(zip_file, 'r', metadata_encoding='utf-8') as zf:
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zf.extractall(zip_file.parent)
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return
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except (UnicodeDecodeError, zipfile.BadZipFile):
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# UTF-8 失败，尝试 GBK（Windows 中文系统常用）
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try:
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with zipfile.ZipFile(zip_file, 'r', metadata_encoding='gbk') as zf:
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zf.extractall(zip_file.parent)
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return
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except (UnicodeDecodeError, zipfile.BadZipFile):
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# GBK 也失败，尝试 CP437（DOS 编码）
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try:
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with zipfile.ZipFile(zip_file, 'r', metadata_encoding='cp437') as zf:
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zf.extractall(zip_file.parent)
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return
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except Exception as e:
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# 所有编码都失败
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raise Exception(f"无法解压 ZIP 文件，编码检测失败: {e}")
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def _scan_files(self, directory: Path, extensions: List[str]) -> Generator[Path, None, None]:
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"""扫描指定扩展名的文件"""
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for ext in extensions:
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for file in directory.rglob(f"*{ext}"):
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yield file
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def _process_single_excel(self, excel_file: Path, sheet_filter: set) -> int:
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"""处理单个 Excel 文件（用于并行）"""
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processed = 0
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try:
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rel_path = excel_file.relative_to(self.work_dir)
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# 使用 openpyxl 的 read_only 模式会更快，但这里保持兼容性
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xl = pd.ExcelFile(excel_file, engine='openpyxl')
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for sheet_name in xl.sheet_names:
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if sheet_name not in sheet_filter:
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output_file = excel_file.parent / f"{excel_file.stem}_{sheet_name}.csv"
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# 直接读取并写入，不做额外处理
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df = xl.parse(sheet_name)
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df.to_csv(output_file, index=False, encoding='utf-8')
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processed += 1
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xl.close()
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return processed
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except Exception as e:
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rel_path = excel_file.relative_to(self.work_dir)
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self.logger.error(f"Excel 处理失败 {rel_path}: {e}")
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return 0
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def _process_excel_files_parallel(self):
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"""并行处理 Excel 文件"""
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self.logger.info("正在并行处理 Excel 文件...")
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excel_files = list(self._scan_files(self.work_dir, ['.xlsx', '.xls']))
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self.logger.info(f"找到 {len(excel_files)} 个 Excel 文件")
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if not excel_files:
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return
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sheet_filter = set(self.config.sheet_filter)
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total_processed = 0
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# 使用线程池并行处理
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with ThreadPoolExecutor(max_workers=self.MAX_WORKERS) as executor:
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futures = {
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executor.submit(self._process_single_excel, f, sheet_filter): f
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for f in excel_files
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}
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for future in as_completed(futures):
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excel_file = futures[future]
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try:
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count = future.result()
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total_processed += count
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rel_path = excel_file.relative_to(self.work_dir)
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if count > 0:
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self.logger.info(f"处理完成: {rel_path} ({count} 个 sheet)")
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except Exception as e:
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rel_path = excel_file.relative_to(self.work_dir)
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self.logger.error(f"Excel 处理异常 {rel_path}: {e}")
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self.logger.info(f"Excel 处理完成，共生成 {total_processed} 个 CSV 文件")
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def _detect_encoding(self, file_path: Path) -> str:
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"""快速检测文件编码（只读取前 8KB）"""
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import chardet
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with open(file_path, 'rb') as f:
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# 只读取前 8KB，足够检测编码，比 64KB 快很多
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result = chardet.detect(f.read(8192))
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encoding = result.get('encoding', 'utf-8') or 'utf-8'
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encoding = encoding.lower()
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if 'utf' in encoding:
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return 'utf-8'
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elif 'gb' in encoding:
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return 'gbk'
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return 'utf-8'
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def _process_csv_file_fast(self, csv_file: Path, table_name: str) -> int:
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"""
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高性能处理单个 CSV 文件
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使用批量插入代替 to_sql，性能提升 5-10 倍
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根据配置的字段类型进行数据转换
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"""
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encoding = self._detect_encoding(csv_file)
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rel_path = csv_file.relative_to(self.work_dir)
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self.logger.info(f"处理 CSV: {rel_path} (编码: {encoding})")
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# 读取 CSV，使用优化参数
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df = pd.read_csv(
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csv_file,
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encoding=encoding,
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thousands=',',
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low_memory=True, # 低内存模式
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dtype=str, # 全部作为字符串读取，避免类型推断开销
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na_values=[''], # 只把空字符串当作 NA
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keep_default_na=False # 不使用默认的 NA 值
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)
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# 快速字段匹配（使用预编译的映射表）
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col_mapping = {}
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for col in df.columns:
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if col in self._field_map:
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col_mapping[col] = self._field_map[col]
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if len(col_mapping) <= 3:
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if 'kpis' in str(csv_file).lower():
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self.logger.warning(f"跳过非数据文件: {rel_path}")
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return 0
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raise ValueError(f"字段匹配不足: {rel_path}")
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# 选择需要的列并重命名
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source_cols = list(col_mapping.keys())
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target_cols = list(col_mapping.values())
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# 创建结果 DataFrame，使用目标列名
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df_result = df[source_cols].copy()
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df_result.columns = target_cols
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# 替换 NA 为默认值
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df_result = df_result.fillna('')
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# 构建目标字段的类型映射
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column_types = {col: self._type_map.get(col, 'string') for col in target_cols}
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# 根据类型处理每列数据
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for col in target_cols:
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col_type = column_types.get(col, 'string')
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if col_type == 'datetime':
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# 日期时间类型处理
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df_result[col] = self._convert_datetime_column(df_result[col])
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elif col_type == 'int':
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# 整数类型处理
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df_result[col] = self._convert_int_column(df_result[col])
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elif col_type == 'float':
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# 浮点数类型处理
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df_result[col] = self._convert_float_column(df_result[col])
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elif col_type == 'text':
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# 长文本类型，截断到 65535 字符
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mask = df_result[col].str.len() > 65535
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if mask.any():
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df_result.loc[mask, col] = df_result.loc[mask, col].str[:65535]
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else: # string 或其他
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# 字符串类型：去除百分号、截断长度
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df_result[col] = df_result[col].str.replace('%', '', regex=False)
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mask = df_result[col].str.len() > 255
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if mask.any():
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df_result.loc[mask, col] = df_result.loc[mask, col].str[:255]
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# 确保表存在（传递类型信息）
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self.db.create_table_from_columns(table_name, target_cols, column_types)
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# 转换为元组列表，用于批量插入
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data_tuples = [tuple(row) for row in df_result.values]
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# 使用批量插入
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inserted = self.db.bulk_insert(table_name, target_cols, data_tuples, self.BATCH_SIZE)
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return inserted
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# 支持的日期时间格式列表
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DATETIME_FORMATS = [
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'ISO8601', # 2026-01-06T00:00:00+08:00
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'%Y-%m-%d %H:%M:%S', # 2026-01-06 00:00:00
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'%Y-%m-%d %H:%M', # 2026-01-06 00:00
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'%Y/%m/%d %H:%M:%S', # 2026/01/06 00:00:00
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'%Y/%m/%d %H:%M', # 2026/01/06 00:00
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'%Y-%m-%d', # 2026-01-06
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'%Y/%m/%d', # 2026/01/06
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'%Y年%m月%d日 %H:%M:%S', # 2026年01月06日 00:00:00
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'%Y年%m月%d日', # 2026年01月06日
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'%Y%m%d%H%M%S', # 20260106000000
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'%Y%m%d', # 20260106
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]
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def _detect_datetime_format(self, series: pd.Series, sample_size: int = 100) -> list:
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"""
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采样检测时间格式，返回检测到的格式列表（按匹配数量排序）
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"""
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# 获取非空样本
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valid = series[series.notna() & (series != '') & (series.astype(str).str.strip() != '')]
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if len(valid) == 0:
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return self.DATETIME_FORMATS
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# 采样
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sample = valid.head(sample_size) if len(valid) > sample_size else valid
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# 检测每种格式的匹配率
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format_matches = {}
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for fmt in self.DATETIME_FORMATS:
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try:
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if fmt == 'ISO8601':
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parsed = pd.to_datetime(sample, errors='coerce', format='ISO8601')
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else:
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parsed = pd.to_datetime(sample, errors='coerce', format=fmt)
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match_count = parsed.notna().sum()
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if match_count > 0:
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format_matches[fmt] = match_count
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except Exception:
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continue
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# 按匹配数量降序排序，只返回有匹配的格式
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if format_matches:
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sorted_formats = sorted(format_matches.keys(), key=lambda x: format_matches[x], reverse=True)
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return sorted_formats
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# 没有检测到格式，返回默认列表
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return self.DATETIME_FORMATS
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def _convert_datetime_column(self, series: pd.Series) -> pd.Series:
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"""
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转换日期时间列，支持多种常见格式
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使用采样检测优化性能：先检测主要格式，再批量处理
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"""
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try:
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valid_mask = series.notna() & (series != '') & (series.astype(str).str.strip() != '')
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if not valid_mask.any():
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return pd.Series([None] * len(series), index=series.index)
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# 采样检测格式（只用前 100 条数据检测）
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detected_formats = self._detect_datetime_format(series, sample_size=100)
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# 初始化结果
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parsed = pd.Series([pd.NaT] * len(series), index=series.index)
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remaining = valid_mask.copy()
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# 按检测到的格式顺序处理
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for fmt in detected_formats:
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if not remaining.any():
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break
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try:
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if fmt == 'ISO8601':
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temp_parsed = pd.to_datetime(series[remaining], errors='coerce', format='ISO8601')
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else:
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temp_parsed = pd.to_datetime(series[remaining], errors='coerce', format=fmt)
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success_mask = temp_parsed.notna()
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if success_mask.any():
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success_indices = remaining[remaining].index[success_mask]
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parsed.loc[success_indices] = temp_parsed[success_mask].values
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remaining.loc[success_indices] = False
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except Exception:
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continue
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# 兜底：用 mixed 模式处理剩余的
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if remaining.any():
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try:
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temp_parsed = pd.to_datetime(series[remaining], errors='coerce', format='mixed', dayfirst=False)
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success_mask = temp_parsed.notna()
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if success_mask.any():
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success_indices = remaining[remaining].index[success_mask]
|
||
parsed.loc[success_indices] = temp_parsed[success_mask].values
|
||
except Exception:
|
||
pass
|
||
|
||
# 格式化输出
|
||
return parsed.dt.strftime('%Y-%m-%d %H:%M:%S').fillna(None)
|
||
except Exception:
|
||
return series
|
||
|
||
def _convert_int_column(self, series: pd.Series) -> pd.Series:
|
||
"""转换整数列"""
|
||
try:
|
||
# 去除百分号和逗号
|
||
cleaned = series.str.replace('%', '', regex=False).str.replace(',', '', regex=False)
|
||
# 转换为数值，保留为字符串形式（数据库会自动转换）
|
||
numeric = pd.to_numeric(cleaned, errors='coerce')
|
||
# 四舍五入并转为整数字符串，空值保留为 None
|
||
return numeric.round().fillna(0).astype(int).astype(str).replace('0', None, regex=False)
|
||
except Exception:
|
||
return series
|
||
|
||
def _convert_float_column(self, series: pd.Series) -> pd.Series:
|
||
"""转换浮点数列"""
|
||
try:
|
||
# 去除百分号和逗号
|
||
cleaned = series.str.replace('%', '', regex=False).str.replace(',', '', regex=False)
|
||
# 转换为数值
|
||
numeric = pd.to_numeric(cleaned, errors='coerce')
|
||
# 保留小数，空值为 None
|
||
return numeric.fillna(0).astype(str).replace('0.0', None, regex=False).replace('0', None, regex=False)
|
||
except Exception:
|
||
return series
|
||
|
||
def _find_data_directories(self) -> Dict[str, Path]:
|
||
"""
|
||
查找包含数据文件的目录，返回 {表名: 目录路径}
|
||
"""
|
||
data_dirs = {}
|
||
target_names = {'4G', '5G', '4g', '5g'}
|
||
|
||
self.logger.info(f"开始查找数据目录，工作目录: {self.work_dir}")
|
||
|
||
# 递归查找所有名为 4G 或 5G 的目录
|
||
found_dirs = []
|
||
for subdir in self.work_dir.rglob('*'):
|
||
if subdir.is_dir() and subdir.name in target_names:
|
||
found_dirs.append(subdir)
|
||
|
||
self.logger.info(f"找到 {len(found_dirs)} 个候选目录")
|
||
|
||
for subdir in found_dirs:
|
||
table_name = f"{subdir.name.upper()}_UD"
|
||
if table_name not in data_dirs:
|
||
data_dirs[table_name] = subdir
|
||
self.logger.info(f"发现数据目录: {subdir.relative_to(self.work_dir)} -> 表: {table_name}")
|
||
|
||
if not data_dirs:
|
||
self.logger.warning("未找到 4G/5G 目录，使用直接子目录")
|
||
for subdir in self.work_dir.iterdir():
|
||
if subdir.is_dir():
|
||
table_name = f"{subdir.name}_UD"
|
||
data_dirs[table_name] = subdir
|
||
self.logger.info(f"使用直接子目录: {subdir.relative_to(self.work_dir)} -> 表: {table_name}")
|
||
|
||
return data_dirs
|
||
|
||
def _process_csv_files(self):
|
||
"""处理所有 CSV 文件（高性能版）"""
|
||
self.logger.info("正在处理 CSV 文件并上传到数据库...")
|
||
|
||
# 查找数据目录
|
||
data_dirs = self._find_data_directories()
|
||
|
||
if not data_dirs:
|
||
self.logger.warning("未找到任何数据目录")
|
||
return
|
||
|
||
# 按目录分组处理
|
||
for table_name, subdir in data_dirs.items():
|
||
self.logger.info(f"处理目录: {subdir.relative_to(self.work_dir)} -> 表: {table_name}")
|
||
|
||
# 删除旧表
|
||
self.db.drop_table(table_name)
|
||
|
||
# 处理该目录下的所有 CSV
|
||
csv_files = list(self._scan_files(subdir, ['.csv']))
|
||
self.logger.info(f"找到 {len(csv_files)} 个 CSV 文件")
|
||
|
||
total_rows = 0
|
||
start_time = time.time()
|
||
|
||
for i, csv_file in enumerate(csv_files, 1):
|
||
try:
|
||
rows = self._process_csv_file_fast(csv_file, table_name)
|
||
total_rows += rows
|
||
|
||
# 每处理 10 个文件报告一次进度
|
||
if i % 10 == 0:
|
||
elapsed = round(time.time() - start_time, 1)
|
||
self.logger.info(f"进度: {i}/{len(csv_files)} 文件, 已导入 {total_rows} 行, 耗时 {elapsed}s")
|
||
|
||
except Exception as e:
|
||
rel_path = csv_file.relative_to(self.work_dir)
|
||
self.logger.error(f"CSV 处理失败 {rel_path}: {e}")
|
||
|
||
elapsed = round(time.time() - start_time, 2)
|
||
speed = round(total_rows / elapsed) if elapsed > 0 else 0
|
||
self.logger.success(f"表 {table_name} 导入完成: {total_rows} 行, 耗时 {elapsed}s, 速度 {speed} 行/秒")
|
||
|
||
def _execute_sql_script(self):
|
||
"""执行 SQL 脚本"""
|
||
if not SQL_SCRIPT.exists():
|
||
self.logger.warning("SQL 脚本文件不存在，跳过")
|
||
return
|
||
|
||
self.logger.info("正在执行 SQL 脚本...")
|
||
|
||
with open(SQL_SCRIPT, 'r', encoding='utf-8') as f:
|
||
sql_text = f.read()
|
||
|
||
sqls = sqlparse.split(sql_text)
|
||
total = len(sqls)
|
||
|
||
with self.db.get_connection() as conn:
|
||
with conn.cursor() as cursor:
|
||
for i, sql in enumerate(sqls, 1):
|
||
sql = sql.strip()
|
||
if not sql or sql.startswith('#'):
|
||
continue
|
||
|
||
start_time = time.time()
|
||
preview = sql[:80].replace('\n', ' ')
|
||
self.logger.info(f"执行 SQL ({i}/{total}): {preview}...")
|
||
|
||
try:
|
||
cursor.execute(sql)
|
||
elapsed = round(time.time() - start_time, 2)
|
||
self.logger.info(f"完成，耗时 {elapsed} 秒")
|
||
except Exception as e:
|
||
self.logger.error(f"SQL 执行失败: {e}")
|
||
|
||
conn.commit()
|
||
|
||
self.logger.success("SQL 脚本执行完成")
|