186 lines
6.6 KiB
Python
186 lines
6.6 KiB
Python
"""契约快照测试公共工具。
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契约 = 前端/数据依赖的硬不变量：
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- 路由 (METHOD /path)
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- 权限字符串 (@permission_required("sys:..."))
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- 数据库表名
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用法：设置环境变量 UPDATE_GOLDEN=1 运行一次以重新生成 golden。
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"""
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import glob
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import json
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import os
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import re
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from sqlalchemy import BigInteger, DateTime, Date, Float, Integer, String, Text
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GOLDEN_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "golden")
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PERMISSION_RE = re.compile(r'@permission_required\(\s*["\'](sys:[^"\']+)["\']')
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UPDATE_GOLDEN = os.getenv("UPDATE_GOLDEN") == "1"
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def collect_routes():
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"""通过 openapi schema 收集全部 (METHOD /path)，去重排序。"""
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from core.app import create_app
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app = create_app()
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spec = app.openapi()
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return sorted(
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f"{method.upper()} {path}"
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for path, methods in spec["paths"].items()
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for method in methods
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)
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def collect_permissions():
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"""静态扫描源码中的 @permission_required("sys:...") 权限串。"""
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perms = set()
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for root in ("src/api/v1/endpoints", "src/modules"):
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for path in glob.glob(os.path.join(root, "**", "*.py"), recursive=True):
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with open(path, encoding="utf-8") as f:
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perms.update(PERMISSION_RE.findall(f.read()))
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return sorted(perms)
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def collect_tables():
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"""从 SQLAlchemy metadata 收集全部表名。"""
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from core.database import Base
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return sorted(Base.metadata.tables.keys())
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def compare_snapshot(name: str, actual: list):
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"""与 golden 对比；UPDATE_GOLDEN=1 时重新生成并跳过断言。"""
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path = os.path.join(GOLDEN_DIR, name)
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if UPDATE_GOLDEN:
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os.makedirs(GOLDEN_DIR, exist_ok=True)
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with open(path, "w", encoding="utf-8") as f:
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json.dump(actual, f, ensure_ascii=False, indent=2)
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return
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with open(path, encoding="utf-8") as f:
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golden = json.load(f)
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assert actual == golden, (
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f"契约快照 [{name}] 不一致！\n"
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f"新增: {sorted(set(actual) - set(golden))}\n"
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f"缺失: {sorted(set(golden) - set(actual))}"
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)
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# ============================================================
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# 模型 ↔ SQL dump 建表结构一致性
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# ============================================================
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PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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SQL_SCHEMA_PATH = os.path.join(PROJECT_ROOT, "document", "djangoadmin.fastapi.elevue.sql")
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def parse_sql_type(type_tail: str):
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"""从列定义中解析 SQL 类型，归一化到与模型一致的 (类型族, 长度)。
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SQL 方言与归一化规则：
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int/int(0)/tinyint(1)/smallint/mediumint/bit(1) -> "int"
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bigint -> "bigint"
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datetime(0)/datetime(6)/timestamp -> "datetime"
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date -> "date"
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varchar(n)/char(n) -> ("varchar", n)
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text/tinytext/mediumtext/longtext -> "text"
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float/double/decimal -> "float"
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"""
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m = re.match(r"([a-z]+)(?:\((\d+)\))?", type_tail.strip().lower())
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if not m:
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raise ValueError(f"无法解析 SQL 类型: {type_tail!r}")
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name, length = m.group(1), m.group(2)
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if name in ("int", "tinyint", "smallint", "mediumint", "bit"):
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return ("int", None)
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if name == "bigint":
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return ("bigint", None)
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if name in ("datetime", "timestamp"):
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return ("datetime", None)
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if name == "date":
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return ("date", None)
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if name in ("varchar", "char"):
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return ("varchar", int(length))
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if name in ("text", "tinytext", "mediumtext", "longtext"):
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return ("text", None)
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if name in ("float", "double", "decimal"):
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return ("float", None)
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raise ValueError(f"无法解析 SQL 类型: {type_tail!r}")
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def collect_sql_schema():
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"""解析 SQL dump 中的 CREATE TABLE，产出 {表名: {列名: (类型族, 长度, 可空)}} 与 {表名: 主键集}。
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兼容 Navicat 导出（Style A：`` `col` int(0) NULL DEFAULT NULL ``）与
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手写（Style B：`` `col` int NOT NULL ``）两种格式。
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"""
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with open(SQL_SCHEMA_PATH, encoding="utf-8") as f:
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content = f.read()
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schema, pk_map = {}, {}
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for m in re.finditer(r"CREATE TABLE\s+`([^`]+)`\s*\((.*?)\)\s*ENGINE", content, re.S):
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table = m.group(1)
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columns, pks = {}, set()
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for line in m.group(2).splitlines():
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s = line.strip().rstrip(",")
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if not s or s.startswith("PRIMARY KEY"):
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if s.startswith("PRIMARY KEY"):
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pk_m = re.search(r"`([^`]+)`", s)
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if pk_m:
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pks.add(pk_m.group(1))
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continue
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if s.startswith(("KEY ", "UNIQUE KEY", "INDEX ", "FULLTEXT ")):
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continue
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col_m = re.match(r"`([^`]+)`\s+(.+)$", s)
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if not col_m:
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continue
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col, type_tail = col_m.group(1), col_m.group(2)
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# 忽略 COMMENT 子句后再判断可空，避免注释文本误含 NOT NULL
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pre = type_tail.split("COMMENT")[0]
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nullable = "NOT NULL" not in pre.upper()
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columns[col] = (*parse_sql_type(type_tail), nullable)
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schema[table] = columns
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pk_map[table] = pks
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return schema, pk_map
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def model_type(col):
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"""将 SQLAlchemy 列类型映射到与 parse_sql_type 相同的 (类型族, 长度)。"""
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t = col.type
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if isinstance(t, BigInteger):
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return ("bigint", None)
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if isinstance(t, Integer):
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return ("int", None)
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if isinstance(t, DateTime):
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return ("datetime", None)
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if isinstance(t, Date):
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return ("date", None)
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# 注意：Text 是 String 的子类，必须先判断 Text
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if isinstance(t, Text):
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return ("text", None)
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if isinstance(t, String):
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return ("varchar", t.length)
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if isinstance(t, Float):
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return ("float", None)
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raise ValueError(f"无法映射模型列类型: {col} ({t!r})")
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def collect_model_schema():
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"""通过 create_app 强制加载全部模型，从 Base.metadata 产出与 collect_sql_schema 同构的数据。"""
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from core.app import create_app
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create_app()
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from core.database import Base
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schema, pk_map = {}, {}
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for table_name, table in Base.metadata.tables.items():
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columns, pks = {}, set()
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for col in table.columns:
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columns[col.name] = (*model_type(col), col.nullable)
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if col.primary_key:
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pks.add(col.name)
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schema[table_name] = columns
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pk_map[table_name] = pks
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return schema, pk_map
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