561 lines
19 KiB
Python
561 lines
19 KiB
Python
# -*- coding: utf-8 -*-
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import json
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import re
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from pathlib import Path
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from collections import defaultdict
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SQL_PATH = Path(r"C:\dev\LLTQ\ETL\feiqiu-ETL\etl_billiards\database\schema_ODS_doc.sql")
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DOC_DIR = Path(r"C:\dev\LLTQ\export\test-json-doc")
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TABLE_CN = {
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"member_profiles": "会员档案/会员账户信息",
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"member_balance_changes": "会员余额变更流水",
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"member_stored_value_cards": "会员储值/卡券账户列表",
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"recharge_settlements": "充值结算记录",
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"settlement_records": "结账/结算记录",
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"assistant_cancellation_records": "助教作废/取消记录",
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"assistant_accounts_master": "助教档案主数据",
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"assistant_service_records": "助教服务流水",
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"site_tables_master": "门店桌台主数据",
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"table_fee_discount_records": "台费折扣记录",
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"table_fee_transactions": "台费流水",
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"goods_stock_movements": "商品库存变动流水",
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"stock_goods_category_tree": "商品分类树",
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"goods_stock_summary": "商品库存汇总",
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"payment_transactions": "支付流水",
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"refund_transactions": "退款流水",
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"platform_coupon_redemption_records": "平台券核销/使用记录",
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"tenant_goods_master": "租户商品主数据",
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"group_buy_packages": "团购套餐主数据",
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"group_buy_redemption_records": "团购核销记录",
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"settlement_ticket_details": "结算小票明细",
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"store_goods_master": "门店商品主数据",
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"store_goods_sales_records": "门店商品销售流水",
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}
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COMMON_FIELD_PURPOSE = {
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"tenant_id": "租户/品牌 ID，用于商户维度过滤与关联。",
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"site_id": "门店 ID，用于门店维度过滤与关联。",
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"register_site_id": "会员注册门店 ID，用于归属门店维度关联。",
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"site_name": "门店名称快照，用于直接展示。",
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"id": "本表主键 ID，用于唯一标识一条记录。",
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"system_member_id": "系统级会员 ID（跨门店/跨卡种统一到‘人’的维度）。",
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"order_trade_no": "订单交易号，用于串联同一订单下的各类消费明细。",
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"order_settle_id": "订单结算/结账主键，用于关联结算记录与小票明细。",
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"order_pay_id": "关联支付流水的主键 ID，用于追溯支付明细。",
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"point": "积分余额，用于记录会员积分取值。",
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"growth_value": "成长值/成长积分，用于会员成长与等级评估。",
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"referrer_member_id": "推荐人会员 ID，用于记录会员推荐/拉新关系。",
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"create_time": "记录创建时间（业务侧产生时间）。",
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"status": "状态枚举，用于标识记录当前业务状态。",
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"user_status": "用户状态枚举，用于标识会员账户/用户可用状态。",
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"is_delete": "逻辑删除标记（0=否，1=是）。",
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"payload": "完整原始 JSON 记录快照，用于回溯与二次解析。",
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"source_file": "ETL 元数据：原始导出文件名，用于数据追溯。",
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"source_endpoint": "ETL 元数据：采集来源（接口/文件路径），用于数据追溯。",
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"fetched_at": "ETL 元数据：采集/入库时间戳，用于口径对齐与增量处理。",
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}
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ETL_META_FIELDS = {"source_file", "source_endpoint", "fetched_at"}
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def _first_sentence(text: str, max_len: int = 120) -> str:
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s = re.sub(r"\s+", " ", (text or "").strip())
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if not s:
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return ""
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parts = re.split(r"[。；;]\s*", s)
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s = parts[0].strip() if parts else s
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if len(s) > max_len:
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s = s[: max_len - 1] + "…"
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return s
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def _escape_sql(s: str) -> str:
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return (s or "").replace("'", "''")
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def normalize_key(s: str) -> str:
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return re.sub(r"[_\-\s]", "", (s or "").lower())
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def snake_to_lower_camel(s: str) -> str:
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parts = re.split(r"[_\-\s]+", s)
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if not parts:
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return s
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first = parts[0].lower()
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rest = "".join(p[:1].upper() + p[1:] for p in parts[1:] if p)
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return first + rest
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def snake_to_upper_camel(s: str) -> str:
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parts = re.split(r"[_\-\s]+", s)
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return "".join(p[:1].upper() + p[1:] for p in parts if p)
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def find_key_in_record(record: dict, token: str) -> str | None:
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if not isinstance(record, dict) or not token:
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return None
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if token in record:
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return token
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norm_to_key = {normalize_key(k): k for k in record.keys()}
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candidates = [
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token,
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token.lower(),
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token.upper(),
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snake_to_lower_camel(token),
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snake_to_upper_camel(token),
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]
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for c in candidates:
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nk = normalize_key(c)
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if nk in norm_to_key:
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return norm_to_key[nk]
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return None
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def _infer_purpose(_table: str, col: str) -> str:
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if col in COMMON_FIELD_PURPOSE:
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return COMMON_FIELD_PURPOSE[col]
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lower = col.lower()
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if lower.endswith("_id"):
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return "标识类 ID 字段，用于关联/定位相关实体。"
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if lower.endswith("_time") or lower.endswith("time"):
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return "时间字段，用于记录业务时间点/发生时间。"
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if any(k in lower for k in ["amount", "money", "fee", "price", "deduct", "cost"]):
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return "金额字段，用于计费/结算/分摊等金额计算。"
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if any(k in lower for k in ["count", "num", "number", "seconds", "qty"]):
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return "数量/时长字段，用于统计与计量。"
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if lower.endswith("_name") or lower.endswith("name"):
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return "名称字段，用于展示与辅助识别。"
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if lower.endswith("_code") or lower.endswith("code"):
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return "编码/枚举字段，用于表示类型、等级或业务枚举。"
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if lower.startswith("is_") or lower.startswith("able_") or lower.startswith("can_"):
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return "布尔/开关字段，用于表示权限、可用性或状态开关。"
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return "来自 JSON 导出的原始字段，用于保留业务取值。"
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def _format_example(value, max_len: int = 120) -> str:
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if value is None:
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return "NULL"
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if isinstance(value, bool):
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return "true" if value else "false"
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if isinstance(value, (int, float)):
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return str(value)
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if isinstance(value, str):
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s = value.strip()
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if len(s) > max_len:
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s = s[: max_len - 1] + "…"
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return s
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if isinstance(value, list):
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if not value:
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return "[]"
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sample = value[0]
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rendered = json.dumps(sample, ensure_ascii=False)
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if len(value) > 1:
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rendered = f"[{rendered}, …] (len={len(value)})"
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else:
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rendered = f"[{rendered}]"
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if len(rendered) > max_len:
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rendered = rendered[: max_len - 1] + "…"
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return rendered
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if isinstance(value, dict):
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keys = list(value)[:6]
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mini = {k: value.get(k) for k in keys}
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rendered = json.dumps(mini, ensure_ascii=False)
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if len(value) > len(keys):
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rendered = rendered[:-1] + ", …}"
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if len(rendered) > max_len:
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rendered = rendered[: max_len - 1] + "…"
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return rendered
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rendered = str(value)
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if len(rendered) > max_len:
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rendered = rendered[: max_len - 1] + "…"
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return rendered
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def _find_best_record_list(data, columns):
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cols = set(columns)
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best = None
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best_score = -1
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queue = [(data, 0)]
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visited = 0
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while queue and visited < 20000:
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node, depth = queue.pop(0)
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visited += 1
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if depth > 8:
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continue
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if isinstance(node, list):
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if node and all(isinstance(x, dict) for x in node[:3]):
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scores = []
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for x in node[:5]:
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scores.append(len(set(x.keys()) & cols))
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score = sum(scores) / max(1, len(scores))
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if score > best_score:
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best_score = score
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best = node
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for x in node[:10]:
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queue.append((x, depth + 1))
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else:
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for x in node[:50]:
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queue.append((x, depth + 1))
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elif isinstance(node, dict):
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for v in list(node.values())[:80]:
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queue.append((v, depth + 1))
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return best
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def _find_best_record_list_and_node(data, columns):
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cols = set(columns)
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best = None
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best_score = -1
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best_path = []
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queue = [(data, 0, [])]
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visited = 0
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while queue and visited < 25000:
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node, depth, path = queue.pop(0)
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visited += 1
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if depth > 10:
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continue
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if isinstance(node, list):
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if node and all(isinstance(x, dict) for x in node[:3]):
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scores = []
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for x in node[:5]:
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scores.append(len(set(x.keys()) & cols))
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score = sum(scores) / max(1, len(scores))
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if score > best_score:
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best_score = score
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best = node
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best_path = path
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for x in node[:10]:
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queue.append((x, depth + 1, path))
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else:
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for x in node[:80]:
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queue.append((x, depth + 1, path))
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elif isinstance(node, dict):
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for k, v in list(node.items())[:120]:
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queue.append((v, depth + 1, path + [str(k)]))
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node_str = ".".join(best_path) if best_path else "$"
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return best or [], node_str
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def _choose_examples(records, columns):
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examples = {}
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if not records:
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return examples
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for col in columns:
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val = None
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for r in records[:120]:
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if isinstance(r, dict) and col in r and r[col] not in (None, ""):
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val = r[col]
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break
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examples[col] = val
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return examples
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def _extract_header_fields(line: str, columns_set):
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s = line.strip()
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if not s:
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return []
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# 支持 1. id / 1.1 siteProfile / 8. tenant_id
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m = re.match(r"^\d+(?:\.\d+)*[\.)]?\s+(.+)$", s)
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if m:
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s = m.group(1).strip()
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parts = re.split(r"\s*[/、,，]\s*", s)
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fields = [p.strip() for p in parts if p.strip() in columns_set]
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if not fields and s in columns_set:
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fields = [s]
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if fields and len(line) <= 120:
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return fields
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return []
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def _parse_field_purpose_from_block(block_lines):
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lines = [l.rstrip() for l in block_lines]
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def pick_after_label(labels):
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for i, l in enumerate(lines):
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for lab in labels:
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if lab in l:
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after = l.split(lab, 1)[1].strip()
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if after:
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return after
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buf = []
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j = i + 1
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while j < len(lines) and not lines[j].strip():
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j += 1
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for k in range(j, len(lines)):
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if not lines[k].strip():
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break
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if re.match(r"^[\w\u4e00-\u9fff]+[:：]", lines[k].strip()):
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break
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buf.append(lines[k].strip())
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if buf:
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return " ".join(buf)
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return ""
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# 兼容「含义（结合其它文件）：」「含义（推测）：」等变体
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picked = pick_after_label(["含义：", "含义:"])
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if not picked:
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for i, l in enumerate(lines):
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s = l.strip()
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m = re.match(r"^含义.*[:：]\s*(.*)$", s)
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if m:
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after = m.group(1).strip()
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if after:
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picked = after
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else:
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buf = []
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j = i + 1
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while j < len(lines) and not lines[j].strip():
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j += 1
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for k in range(j, len(lines)):
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if not lines[k].strip():
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break
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if re.match(r"^[\w\u4e00-\u9fff]+[:：]", lines[k].strip()):
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break
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buf.append(lines[k].strip())
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if buf:
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picked = " ".join(buf)
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break
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if not picked:
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picked = pick_after_label(["作用：", "作用:"])
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if not picked:
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for i, l in enumerate(lines):
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s = l.strip()
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m = re.match(r"^作用.*[:：]\s*(.*)$", s)
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if m:
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after = m.group(1).strip()
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if after:
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picked = after
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break
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if not picked:
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# 兜底：尽量避开“类型：/唯一值个数：”这类描述
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for l in lines:
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s = l.strip()
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if not s:
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continue
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if any(
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s.startswith(prefix)
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for prefix in [
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"类型：",
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"非空：",
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"唯一值",
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"观测",
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"特征",
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"统计",
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"分布",
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"说明：",
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"关联：",
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"结构关系",
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"和其它表",
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"重复记录",
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"全部为",
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]
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):
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continue
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picked = s
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break
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return _first_sentence(picked, 160)
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def _is_poor_purpose(purpose: str) -> bool:
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s = (purpose or "").strip()
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if not s:
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return True
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if s.endswith("：") or s.endswith(":"):
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return True
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if s.startswith("全部为"):
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return True
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if s.startswith("含义") and ("：" in s or ":" in s) and len(s) <= 12:
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return True
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return False
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def parse_analysis(analysis_text: str, columns):
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columns_set = set(columns)
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blocks = defaultdict(list)
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current_fields = []
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buf = []
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for raw in analysis_text.splitlines():
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fields = _extract_header_fields(raw, columns_set)
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if fields:
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if current_fields and buf:
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for f in current_fields:
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blocks[f].extend(buf)
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current_fields = fields
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buf = []
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else:
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if current_fields:
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buf.append(raw)
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if current_fields and buf:
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for f in current_fields:
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blocks[f].extend(buf)
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purposes = {}
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for col in columns:
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if col in blocks and blocks[col]:
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p = _parse_field_purpose_from_block(blocks[col])
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if p:
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purposes[col] = p
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return purposes
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def parse_columns_from_ddl(create_sql: str):
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start = create_sql.find("(")
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end = create_sql.rfind(")")
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body = create_sql[start + 1 : end]
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cols = []
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for line in body.splitlines():
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s = line.strip().rstrip(",")
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if not s:
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continue
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if s.startswith(")"):
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continue
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if s.upper().startswith("CONSTRAINT "):
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continue
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m = re.match(r"^([A-Za-z_][A-Za-z0-9_]*)\s+", s)
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if not m:
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continue
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name = m.group(1)
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if name.upper() in {"PRIMARY", "UNIQUE", "FOREIGN", "CHECK"}:
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continue
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cols.append(name)
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return cols
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def build_comment_block(table: str, columns, analysis_text: str, records):
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# records_node: 由外部确定，避免这里重复遍历 JSON
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records, records_node = records
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purposes = parse_analysis(analysis_text, columns)
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examples = _choose_examples(records, columns)
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table_cn = TABLE_CN.get(table, table)
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table_comment = (
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f"ODS 原始明细表：{table_cn}。"
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f"来源：C:/dev/LLTQ/export/test-json-doc/{table}.json；分析：{table}-Analysis.md。"
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f"字段以导出原样为主；ETL 补充 source_file/source_endpoint/fetched_at，并保留 payload 为原始记录快照。"
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)
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lines = []
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lines.append(f"COMMENT ON TABLE billiards_ods.{table} IS '{_escape_sql(table_comment)}';")
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for col in columns:
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json_file = f"{table}.json"
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if col in ETL_META_FIELDS:
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||
json_field = f"{json_file} - ETL元数据 - 无"
|
||
elif col == "payload":
|
||
json_field = f"{json_file} - {records_node} - $"
|
||
else:
|
||
actual = None
|
||
for r in records[:50]:
|
||
if isinstance(r, dict):
|
||
actual = find_key_in_record(r, col)
|
||
if actual:
|
||
break
|
||
field_name = actual or col
|
||
json_field = f"{json_file} - {records_node} - {field_name}"
|
||
|
||
purpose = purposes.get(col) or _infer_purpose(table, col)
|
||
purpose = _first_sentence(purpose, 140) or _infer_purpose(table, col)
|
||
if _is_poor_purpose(purpose):
|
||
purpose = COMMON_FIELD_PURPOSE.get(col) or _infer_purpose(table, col)
|
||
|
||
if col in ETL_META_FIELDS:
|
||
if col == "source_file":
|
||
ex = f"{table}.json"
|
||
elif col == "source_endpoint":
|
||
ex = f"C:/dev/LLTQ/export/test-json-doc/{table}.json"
|
||
else:
|
||
ex = "2025-11-10T00:00:00+08:00"
|
||
elif col == "payload":
|
||
ex = "{...}"
|
||
else:
|
||
ex = _format_example(examples.get(col))
|
||
|
||
func = purpose
|
||
if "用于" not in func:
|
||
func = "用于" + func.rstrip("。")
|
||
|
||
# ODS来源：表名-字段名（ODS自身字段）；ETL补充字段标记
|
||
if col in ETL_META_FIELDS:
|
||
ods_src = f"{table} - {col}（ETL补充）"
|
||
else:
|
||
ods_src = f"{table} - {col}"
|
||
|
||
comment = (
|
||
f"【说明】{purpose}。"
|
||
f" 【示例】{ex}（{func}）。"
|
||
f" 【ODS来源】{ods_src}。"
|
||
f" 【JSON字段】{json_field}。"
|
||
)
|
||
lines.append(
|
||
f"COMMENT ON COLUMN billiards_ods.{table}.{col} IS '{_escape_sql(comment)}';"
|
||
)
|
||
|
||
return "\n".join(lines)
|
||
|
||
|
||
text = SQL_PATH.read_text(encoding="utf-8")
|
||
newline = "\r\n" if "\r\n" in text else "\n"
|
||
|
||
kept = []
|
||
for raw_line in text.splitlines(True):
|
||
stripped = raw_line.lstrip()
|
||
if stripped.startswith("--"):
|
||
continue
|
||
if re.match(r"^\s*COMMENT ON\s+(TABLE|COLUMN)\s+", raw_line):
|
||
continue
|
||
kept.append(raw_line)
|
||
|
||
clean = "".join(kept)
|
||
|
||
create_re = re.compile(
|
||
r"(CREATE TABLE IF NOT EXISTS\s+billiards_ods\.(?P<table>[A-Za-z0-9_]+)\s*\([\s\S]*?\)\s*;)" ,
|
||
re.M,
|
||
)
|
||
|
||
out_parts = []
|
||
last = 0
|
||
count = 0
|
||
for m in create_re.finditer(clean):
|
||
out_parts.append(clean[last : m.end()])
|
||
table = m.group("table")
|
||
create_sql = m.group(1)
|
||
|
||
cols = parse_columns_from_ddl(create_sql)
|
||
analysis_text = (DOC_DIR / f"{table}-Analysis.md").read_text(encoding="utf-8")
|
||
data = json.loads((DOC_DIR / f"{table}.json").read_text(encoding="utf-8"))
|
||
record_list, record_node = _find_best_record_list_and_node(data, cols)
|
||
|
||
out_parts.append(newline + newline + build_comment_block(table, cols, analysis_text, (record_list, record_node)) + newline + newline)
|
||
last = m.end()
|
||
count += 1
|
||
|
||
out_parts.append(clean[last:])
|
||
|
||
result = "".join(out_parts)
|
||
result = re.sub(r"(?:\r?\n){4,}", newline * 3, result)
|
||
|
||
backup = SQL_PATH.with_suffix(SQL_PATH.suffix + ".rewrite2.bak")
|
||
backup.write_text(text, encoding="utf-8")
|
||
SQL_PATH.write_text(result, encoding="utf-8")
|
||
|
||
print(f"Rewrote comments for {count} tables. Backup: {backup}")
|