freedakgmail
2fc1b96f44
- 整合所有导入脚本到db/pipeline/目录 - config.py: 按月份动态配置数据源路径，支持4月/5月不同目录结构 - import_bill_records_may.py: 5月专用账单导入(51列全渠道订单明细→bill_records + 50列品项销售明细→dish_sales_details) - import_salary_attendance.py: 添加--salary-file/--attendance-file参数支持动态文件路径 - import_may_data.sh: 5月一键导入脚本(12步全流程) - run_all.py: 一键全量导入+物化视图刷新编排 - refresh_materialized_views.py: 按依赖顺序刷新38个物化视图 - verify.py/verify_import.py: 数据一致性验证 - README.md: 管线文档
242 lines
11 KiB
Python
242 lines
11 KiB
Python
#!/usr/bin/env python3
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"""
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薪资拆分明细表 + 考勤数据表 导入脚本
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遵循现有 import_log + records 模式
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"""
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import hashlib
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import psycopg2
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import os
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import sys
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import argparse
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import pandas as pd
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from psycopg2.extras import execute_values
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DB_CONFIG = {
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'host': 'localhost',
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'port': 5432,
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'dbname': 'bill_query',
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'user': 'freedak',
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'password': '',
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}
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BASE_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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SALARY_FILE = os.path.join(BASE_DIR, '数据', '4月', '薪资拆分明细表_脱敏.xlsx')
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ATTENDANCE_FILE = os.path.join(BASE_DIR, '数据', '4月', '考勤数据表-脱敏.xlsx')
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def file_sha256(filepath):
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h = hashlib.sha256()
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with open(filepath, 'rb') as f:
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for chunk in iter(lambda: f.read(8192), b''):
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h.update(chunk)
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return h.hexdigest()
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def to_num(val):
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if val is None or val == '':
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return None
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if isinstance(val, float) and pd.isna(val):
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return None
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try:
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return float(val)
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except (ValueError, TypeError):
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return None
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def to_text(val):
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if val is None:
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return None
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if isinstance(val, float) and pd.isna(val):
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return None
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s = str(val).strip()
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if s.lower() == 'nan' or s.lower() == 'none':
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return None
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return s if s else None
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def import_salary(conn, report_month='2026-04-01', salary_file=None):
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filepath = salary_file or SALARY_FILE
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print(f'导入薪资拆分明细表: {filepath}')
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sha = file_sha256(filepath)
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# Read with pandas (row 3 = headers, row 4+ = data)
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raw_df = pd.read_excel(filepath, header=None, engine='openpyxl')
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print(f' 总行数: {len(raw_df)}, 列数: {len(raw_df.columns)}')
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# Data starts from row 3 (0-indexed), filter rows with employee_code (col 9)
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df = raw_df.iloc[3:].copy()
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df = df[df[9].notna() & (df[9].astype(str).str.strip() != '') & (df[9].astype(str).str.strip() != 'nan')]
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data_rows = df.values.tolist()
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print(f' 数据行数: {len(data_rows)}')
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source_file = os.path.basename(filepath)
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cur = conn.cursor()
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cur.execute(
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'SELECT import_id FROM public.salary_import_log WHERE report_month = %s AND source_file = %s AND file_sha256 = %s',
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(report_month, source_file, sha)
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)
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existing = cur.fetchone()
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if existing:
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print(f' 已导入过, import_id={existing[0]}, 跳过')
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return
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cur.execute(
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'''INSERT INTO public.salary_import_log (report_month, source_file, file_sha256, workbook_rows, imported_rows)
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VALUES (%s, %s, %s, %s, %s) RETURNING import_id''',
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(report_month, source_file, sha, len(data_rows), len(data_rows))
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)
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import_id = cur.fetchone()[0]
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print(f' import_id={import_id}')
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# Column mapping: use execute_values for batch insert
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col_names = '''import_id, source_row,
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org_level1, org_level2, org_level3, org_level4, org_level5, org_level6, org_level7, org_level8,
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employee_code, salary_period, position, work_type, employment_type, hire_date, leave_date,
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salary_standard, base_wage, overtime_subsidy, social_subsidy, position_wage, tenure_wage,
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hourly_rate, total_wage, brand_wage, rent_subsidy,
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month_days, expected_attend, calc_attend, actual_attend, actual_hours,
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expected_rest, actual_rest, expected_legal_holiday, actual_legal_holiday,
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comp_leave_days, annual_leave, marriage_leave, paid_leave, bereavement_leave,
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personal_leave_days, personal_leave_deduction, furlough_days, furlough_deduction,
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injury_leave_days, injury_leave_deduction, absent_days, absent_deduction, join_leave_absent,
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attendance_wage, rest_subsidy, overtime_pay, shift_subsidy, comp_leave_wage,
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cashier_amount, bonus, subsidy, subsidy_remark, rush_wage, total_supplement,
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late_deduction, no_punch_deduction, internal_social_total, internal_social_remark,
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loan, loan_remark, fine, compensation, total_deduction, injury_wage, singapore_subsidy,
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perf_standard, perf_score, perf_amount, phone_allowance, dorm_fee,
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net_salary, gross_pay, income_tax, net_pay,
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external_base_standard, external_overtime, external_absence, external_bonus,
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external_subsidy, external_other_deduction, external_gross,
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pension_deduction, medical_deduction, unemployment_deduction,
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external_social_total, external_tax, external_net, internal_tax, internal_net,
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external_unit, attendance_remark, employment_type_orig, salary_category'''
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batch = []
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for idx, r in enumerate(data_rows):
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source_row = idx + 4
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vals = [
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import_id, source_row,
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to_text(r[1]), to_text(r[2]), to_text(r[3]), to_text(r[4]), to_text(r[5]), to_text(r[6]), to_text(r[7]), to_text(r[8]),
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to_text(r[9]), to_text(r[10]), to_text(r[11]), to_text(r[12]), to_text(r[13]), to_text(r[14]), to_text(r[15]),
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to_num(r[16]), to_num(r[17]), to_num(r[18]), to_num(r[19]), to_num(r[20]), to_num(r[21]),
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to_num(r[22]), to_num(r[23]), to_num(r[24]), to_num(r[25]),
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to_num(r[26]), to_num(r[27]), to_num(r[28]), to_num(r[29]), to_num(r[30]),
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to_num(r[31]), to_num(r[32]), to_num(r[33]), to_num(r[34]),
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to_num(r[35]), to_num(r[36]), to_num(r[37]), to_num(r[38]), to_num(r[39]),
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to_num(r[40]), to_num(r[41]), to_num(r[42]), to_num(r[43]),
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to_num(r[44]), to_num(r[45]), to_num(r[46]), to_num(r[47]), to_num(r[48]),
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to_num(r[49]), to_num(r[50]), to_num(r[51]), to_num(r[52]), to_num(r[53]),
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to_num(r[54]), to_num(r[55]), to_num(r[56]), to_text(r[57]), to_num(r[58]), to_num(r[59]),
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to_num(r[60]), to_num(r[61]), to_num(r[62]), to_text(r[63]),
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to_num(r[64]), to_text(r[65]), to_num(r[66]), to_num(r[67]), to_num(r[68]), to_num(r[69]), to_num(r[70]),
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to_num(r[71]), to_num(r[72]), to_num(r[73]), to_num(r[74]), to_num(r[75]),
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to_num(r[76]), to_num(r[77]), to_num(r[78]), to_num(r[79]),
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to_num(r[80]), to_num(r[81]), to_num(r[82]), to_num(r[83]),
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to_num(r[84]), to_num(r[85]), to_num(r[86]),
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to_num(r[87]), to_num(r[88]), to_num(r[89]),
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to_num(r[90]), to_num(r[91]), to_num(r[92]), to_num(r[93]), to_num(r[94]),
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to_text(r[95]), to_text(r[96]), to_text(r[97]), to_text(r[98]),
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]
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batch.append(tuple(vals))
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if len(batch) >= 500:
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execute_values(cur, f'INSERT INTO public.salary_detail_records ({col_names}) VALUES %s', batch, page_size=500)
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conn.commit()
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print(f' 已导入 {idx + 1}/{len(data_rows)} 行')
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batch = []
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if batch:
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execute_values(cur, f'INSERT INTO public.salary_detail_records ({col_names}) VALUES %s', batch, page_size=500)
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conn.commit()
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print(f' 已导入 {len(data_rows)}/{len(data_rows)} 行')
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print(f' 薪资明细导入完成: {len(data_rows)} 行')
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def import_attendance(conn, report_month='2026-04-01', attendance_file=None):
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filepath = attendance_file or ATTENDANCE_FILE
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print(f'\n导入考勤数据表: {filepath}')
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sha = file_sha256(filepath)
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# Read with pandas (row 0 = headers, row 1+ = data)
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raw_df = pd.read_excel(filepath, header=None, engine='openpyxl')
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print(f' 总行数: {len(raw_df)}, 列数: {len(raw_df.columns)}')
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# Data starts from row 1
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df = raw_df.iloc[1:].copy()
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df = df[df[0].notna() & (df[0].astype(str).str.strip() != '') & (df[0].astype(str).str.strip() != 'nan')]
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data_rows = df.values.tolist()
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print(f' 数据行数: {len(data_rows)}')
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source_file = os.path.basename(filepath)
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cur = conn.cursor()
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cur.execute(
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'SELECT import_id FROM public.attendance_import_log WHERE report_month = %s AND source_file = %s AND file_sha256 = %s',
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(report_month, source_file, sha)
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)
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existing = cur.fetchone()
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if existing:
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print(f' 已导入过, import_id={existing[0]}, 跳过')
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return
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cur.execute(
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'''INSERT INTO public.attendance_import_log (report_month, source_file, file_sha256, workbook_rows, imported_rows)
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VALUES (%s, %s, %s, %s, %s) RETURNING import_id''',
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(report_month, source_file, sha, len(data_rows), len(data_rows))
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)
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import_id = cur.fetchone()[0]
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print(f' import_id={import_id}')
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# 34 columns: import_id, source_row, employee_code, position, department, day_01..day_31
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cols = ['import_id', 'source_row', 'employee_code', 'position', 'department']
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cols += [f'day_{str(i).zfill(2)}' for i in range(1, 32)]
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col_names = ','.join(cols)
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batch = []
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for idx, r in enumerate(data_rows):
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source_row = idx + 2
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vals = [import_id, source_row, to_text(r[0]), to_text(r[1]), to_text(r[2])]
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# Days 1-31 (columns 3-33)
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for d in range(31):
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vals.append(to_text(r[3 + d]) if 3 + d < len(r) else None)
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batch.append(tuple(vals))
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if len(batch) >= 500:
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execute_values(cur, f'INSERT INTO public.attendance_records ({col_names}) VALUES %s', batch, page_size=500)
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conn.commit()
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print(f' 已导入 {idx + 1}/{len(data_rows)} 行')
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batch = []
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if batch:
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execute_values(cur, f'INSERT INTO public.attendance_records ({col_names}) VALUES %s', batch, page_size=500)
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conn.commit()
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print(f' 已导入 {len(data_rows)}/{len(data_rows)} 行')
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print(f' 考勤数据导入完成: {len(data_rows)} 行')
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def main():
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parser = argparse.ArgumentParser(description='薪资考勤数据导入')
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parser.add_argument('--month', default='2026-04-01', help='报告月份')
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parser.add_argument('--db', default='bill_query', help='数据库名')
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parser.add_argument('--skip-salary', action='store_true', help='跳过薪资导入')
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parser.add_argument('--skip-attendance', action='store_true', help='跳过考勤导入')
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parser.add_argument('--salary-file', default=None, help='薪资Excel文件路径 (默认使用内置路径)')
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parser.add_argument('--attendance-file', default=None, help='考勤Excel文件路径 (默认使用内置路径)')
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args = parser.parse_args()
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print(f'导入月份: {args.month}, 数据库: {args.db}')
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cfg = DB_CONFIG.copy()
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cfg['dbname'] = args.db
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conn = psycopg2.connect(**cfg)
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conn.autocommit = False
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try:
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if not args.skip_salary:
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import_salary(conn, args.month, args.salary_file)
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if not args.skip_attendance:
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import_attendance(conn, args.month, args.attendance_file)
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print('\n=== 导入完成 ===')
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except Exception as e:
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conn.rollback()
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print(f'错误: {e}', file=sys.stderr)
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raise
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finally:
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conn.close()
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if __name__ == '__main__':
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main()
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