refactor: 重构执行引擎为上下文驱动架构
- 优化 WorkflowExecutor 与 Exchange支持 ExecutionEnv 资源注入。 - 实现 Session 级别连接复用与变量池内存镜像化，消除重复 I/O 开销。 - 引入 ChainMap 实现动态上下文切换，解决参数化变量与全局提取变量的优先级覆盖。 - 完善变量提取与断言逻辑，确保跨用例变量流转的可靠性。
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@@ -8,11 +8,12 @@
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import logging
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import importlib
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from typing import Any, List, Optional
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from collections import ChainMap
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from pydantic import TypeAdapter
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from core import settings
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from core.models import CaseInfo, ValidateItem, RequestModel, ApiActionModel
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from core.context import ExecutionEnv
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from core.models import RawSchema, ValidateItem, HttpAction, ApiActionModel
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from core.session import Session
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from core.exchange import Exchange
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from utils.case_validator import CaseValidator
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@@ -24,61 +25,54 @@ VALIDATE_LIST_ADAPTER = TypeAdapter(List[ValidateItem])
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class WorkflowExecutor:
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def __init__(self, session: Session, exchanger: Exchange):
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self.session = session
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self.exchanger = exchanger
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def perform(self, case_info: CaseInfo,context: Optional[dict[str, Any]] = None) -> Any:
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@classmethod
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def perform(cls, case_info: RawSchema, env: ExecutionEnv, context: Optional[dict[str, Any]] = None) -> Any:
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"""执行单个用例：支持直接请求和PO模式调用"""
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context = context or {}
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# --- 重点 1：备份并切换上下文 ---
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# 保存 Exchange 当前的全局字典引用
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original_cache = env.exchanger.global_vars
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# 1. 建立优先级变量池 (参数化变量 > 全局提取变量)
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# ChainMap 是实现“局部覆盖全局”性能最好的方案
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combined_vars = ChainMap(context, original_cache)
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# 将 Exchange 的内部缓存临时指向这个合并池
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env.exchanger.global_vars = combined_vars
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# 1. 局部变量优先级注入
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# 备份全局缓存，将当前行数据合并进去
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old_cache = self.exchanger._variable_cache.copy()
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self.exchanger._variable_cache.update(context)
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resp = None # 初始化 resp，避免异常时引用未定义
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try:
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# 2. 动态更新标题（如果 context 中包含 title）
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current_title = context.get("title") or case_info.title
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logger.info(f"🚀 执行用例: {current_title}")
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# raw_data = case_info.model_dump(by_alias=True, exclude_none=True)
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# 1. 变量替换（将 ${var} 替换为真实值）
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# rendered_dict = self.exchanger.replace(raw_data)
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# rendered_case = CaseInfo.model_validate(rendered_dict)
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raw_action_dict = case_info.action.model_dump(by_alias=True, exclude_none=True)
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rendered_action_dict = env.exchanger.replace(raw_action_dict)
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# --- 2. 决定执行模式 ---
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if case_info.is_po_mode():
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# PO 模式：仅渲染 api_action
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action_dict = case_info.api_action.model_dump(by_alias=True, exclude_none=True)
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rendered_action_dict = self.exchanger.replace(action_dict)
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# 重新校验以修复类型（如 params 里的 int）
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rendered_action = ApiActionModel.model_validate(rendered_action_dict)
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# PO 模式：反射调用
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resp = self._execute_po_method(action=rendered_action)
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resp = cls._execute_po_method(rendered_action, env)
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else:
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# 接口模式：直接请求
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# 直接将 RequestModel 转为字典传给 session.request
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request_kwargs = case_info.request.model_dump(by_alias=True, exclude_none=True)
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rendered_req_dict = self.exchanger.replace(request_kwargs)
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rendered_request = RequestModel.model_validate(rendered_req_dict)
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rendered_request = HttpAction.model_validate(rendered_action_dict)
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request_kwargs = rendered_request.model_dump(by_alias=True, exclude_none=True)
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resp = self.session.request(**request_kwargs)
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resp = env.session.request(**request_kwargs)
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# --- 3. 后置处理 (提取 & 断言) ---
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self._post_process(resp, case_info)
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# --- 3. 后处理：提取与断言 ---
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cls._post_process(resp, case_info, env, original_cache)
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return resp
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except Exception as e:
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logger.error(f"用例执行失败: {case_info.title} | 原因: {e}", exc_info=True)
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raise
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finally:
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# 4. 关键：清理现场，还原全局变量池
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self.exchanger._variable_cache = old_cache
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# 兜底确保环境还原 (尽管 try 块中已经还原了一次，这里确保异常情况下也复位)
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env.exchanger.global_vars = original_cache
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def _execute_po_method(self, action: ApiActionModel):
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@staticmethod
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def _execute_po_method(action: ApiActionModel, env: ExecutionEnv):
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"""核心反射逻辑：根据字符串动态加载 api/ 目录下的类并执行方法"""
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class_name = action.api_class
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method_name = action.method
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@@ -94,15 +88,13 @@ class WorkflowExecutor:
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try:
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# 1. 动态导入模块（假设都在 api 目录下）
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# 例如 class_name 是 UserAPI，则尝试从 api.user 导入
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# 这里简单处理，你可以根据你的文件名约定进一步优化逻辑
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# module_name = f"api.{class_name.lower().replace('api', '')}"
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module = importlib.import_module(module_name)
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# 2. 获取类并实例化
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cls = getattr(module, class_name)
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api_instance = cls(self.session) # 传入 session 保持会话统一
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api_instance = cls(env.session) # 传入 session 保持会话统一
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# 3. 调用方法并返回结果
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method = getattr(api_instance, method_name)
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@@ -118,21 +110,33 @@ class WorkflowExecutor:
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logger.error(f"反射调用失败: {class_name}.{method_name} -> {e}")
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raise
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def _post_process(self, resp: Any, rendered_case: CaseInfo):
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# 3. 提取变量 (接口关联)
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if rendered_case.extract:
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for var_name, extract_info in rendered_case.extract.items():
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self.exchanger.extract(resp, var_name, *extract_info)
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@classmethod
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def _post_process(cls, resp: Any, case_info: RawSchema, env: ExecutionEnv, original_cache: dict):
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"""
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统一后处理逻辑：处理变量提取（写全局）和断言校验（读局部+全局）
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"""
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# 记录当前的混合上下文 (ChainMap)，供断言使用
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combined_vars = env.exchanger.global_vars
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# 4. 断言校验
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if rendered_case.validate_data:
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# raw_validate_list = [i.model_dump(by_alias=True) for i in rendered_case.validate_data]
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# 1. 变量提取 (Write Operation)
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if case_info.extract:
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try:
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# 必须切回 original_cache 才能持久化写入到全局变量池
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env.exchanger.global_vars = original_cache
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for var_name, extract_info in case_info.extract.items():
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env.exchanger.extract(resp, var_name, *extract_info)
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finally:
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# 提取完成后，切回 combined_vars，防止后续逻辑（如断言）丢失局部变量上下文
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env.exchanger.global_vars = combined_vars
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# 2. 断言校验 (Read Operation)
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if case_info.validate_data:
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raw_validate_list = [
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item.model_dump(by_alias=True) if isinstance(item, ValidateItem) else item
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for item in rendered_case.validate_data
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for item in case_info.validate_data
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]
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rendered_validate_list = self.exchanger.replace(raw_validate_list)
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rendered_validate_list = env.exchanger.replace(raw_validate_list)
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# 重新通过 Adapter 触发类型修复 (str -> int)
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final_validate_data = VALIDATE_LIST_ADAPTER.validate_python(rendered_validate_list)
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CaseValidator.validate(resp, final_validate_data)
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