Kris
002e6a356b
1. 清理测试文件中未使用的导入与冗余代码 2. 修复审计日志与批量操作的空值约束，统一填充"global"作为默认渠道 3. 调整批量消息撤回的响应语义，对齐其他端点的部分成功契约 4. 修复访问规则批量克隆的唯一约束问题，新增后缀自动处理逻辑 5. 替换anyio为asyncio并行调用，修正时间UTC导入路径 6. 优化前端外部系统概览页的刷新状态提示与缓存逻辑 7. 修复测试用例中的断言与请求方式问题，适配httpx删除请求特性 8. 重构后端路由的依赖注入，移除冗余的数据库会话依赖 9. 调整测试用例的权限校验逻辑，修正强制登出的权限判断 10. 修复语义分块测试的numpy依赖问题，清理冗余导入
334 lines
14 KiB
Python
334 lines
14 KiB
Python
"""Execution 子域 Router。
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提供执行记录的查询 / 详情 / trace / 重试 / 清理 / 统计聚合 /
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慢执行 / 重试链 / 执行上下文 / 按 trace 查询 API。挂载到
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``/system/external-systems/executions`` 前缀下（根前缀由聚合 router 追加）。
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路径顺序约束：静态路径 ``/cleanup`` / ``/stats`` / ``/stats/grouped`` /
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``/stats/error-analysis`` / ``/stats/trend`` / ``/slow`` / ``/by-trace/{trace_id}``
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必须在 ``/{execution_id}`` 前声明，避免被路径参数捕获。
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设计依据：docs/vibe/v1.1/设计方案/2026-06-17-Router-API-设计方案.md
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扩展依据：docs/vibe/v1.1/设计方案/RouterAPI扩展设计/10-execution_router扩展设计方案.md
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"""
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from __future__ import annotations
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from datetime import datetime
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from typing import Any, Literal
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from fastapi import APIRouter, Depends, Path, Query
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from pydantic import BaseModel, ConfigDict, Field
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from yuxi.external_systems.infrastructure.container import UseCases
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from yuxi.external_systems.use_cases.dto.execution import (
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CleanupInput,
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ExecutionStatusLiteral,
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GetExecutionContextInput,
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GetExecutionDetailInput,
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GetExecutionStatsInput,
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GetExecutionTraceInput,
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GetExecutionTrendInput,
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GetSlowExecutionsInput,
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GetTraceExecutionsInput,
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ListRetriesInput,
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PaginateExecutionsInput,
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RetryExecutionInput,
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)
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from yuxi.storage.postgres.models_business import User
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from server.routers.external_systems import get_use_cases
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from server.utils.auth_middleware import get_admin_user, get_required_user
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execution_router = APIRouter(prefix="/executions", tags=["external-systems-execution"])
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# =============================================================================
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# === Request Schemas ===
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# =============================================================================
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class CleanupExecutionsRequest(BaseModel):
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"""清理历史执行记录请求体。字段对齐 ``CleanupInput``。"""
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model_config = ConfigDict(frozen=True)
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system_id: int | None = Field(default=None, ge=1)
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before_at: datetime | None = None
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status: ExecutionStatusLiteral | None = None
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batch_size: int = Field(default=1000, ge=1, le=10000)
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# =============================================================================
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# === 静态路径端点（必须在 /{execution_id} 之前声明） ===
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# =============================================================================
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@execution_router.post("/cleanup", response_model=dict)
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async def cleanup_old_executions(
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payload: CleanupExecutionsRequest,
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use_cases: UseCases = Depends(get_use_cases),
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current_user: User = Depends(get_admin_user),
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) -> dict[str, Any]:
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"""清理历史执行记录（管理员）。"""
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input_dto = CleanupInput(
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system_id=payload.system_id,
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before_at=payload.before_at,
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status=payload.status,
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batch_size=payload.batch_size,
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)
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output = await use_cases.execution_service.cleanup_old_executions(input_dto)
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return {"success": True, "data": output.model_dump()}
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@execution_router.get("", response_model=dict)
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async def paginate_executions(
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limit: int = Query(100, ge=1, le=500),
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offset: int = Query(0, ge=0),
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system_id: int | None = Query(None, ge=1),
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env_key: str | None = Query(None, max_length=32),
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tool_slug: str | None = Query(None, max_length=128),
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status: ExecutionStatusLiteral | None = Query(
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None, description="执行状态：pending/running/success/failed/timeout/throttled/cancelled"
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),
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caller: str | None = Query(None, max_length=32),
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caller_id: str | None = Query(None, max_length=64, description="按调用方实体过滤"),
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operation: str | None = Query(None, max_length=32, description="按操作类型过滤"),
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tag_key: str | None = Query(None, max_length=64, description="按业务标签键过滤"),
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tag_value: str | None = Query(None, max_length=256, description="按业务标签值过滤"),
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trace_id: str | None = Query(None, max_length=64),
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correlation_id: str | None = Query(None, max_length=64),
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start_time: datetime | None = Query(None, description="ISO8601 开始时间"),
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end_time: datetime | None = Query(None, description="ISO8601 结束时间"),
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use_cases: UseCases = Depends(get_use_cases),
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current_user: User = Depends(get_required_user),
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) -> dict[str, Any]:
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"""分页查询执行记录。"""
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input_dto = PaginateExecutionsInput(
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limit=limit,
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offset=offset,
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system_id=system_id,
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env_key=env_key,
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tool_slug=tool_slug,
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status=status,
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caller=caller,
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caller_id=caller_id,
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operation=operation,
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tag_key=tag_key,
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tag_value=tag_value,
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trace_id=trace_id,
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correlation_id=correlation_id,
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start_at=start_time,
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end_at=end_time,
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)
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output = await use_cases.execution_service.paginate_executions(input_dto)
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return {"success": True, "data": output.model_dump()}
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# =============================================================================
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# === 扩展：统计聚合 / 慢执行 / 按 trace 查询（静态路径，必须在 /{execution_id} 之前） ===
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# =============================================================================
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@execution_router.get("/stats", response_model=dict)
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async def get_execution_stats(
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system_id: int | None = Query(None, ge=1, description="系统过滤"),
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env_key: str | None = Query(None, max_length=32, description="环境过滤"),
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adapter_type: str | None = Query(None, max_length=32, description="适配器类型过滤"),
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start_time: datetime | None = Query(None, description="起始时间"),
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end_time: datetime | None = Query(None, description="结束时间"),
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tool_limit: int = Query(20, ge=1, le=100, description="Top 工具数量"),
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use_cases: UseCases = Depends(get_use_cases),
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current_user: User = Depends(get_required_user),
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) -> dict[str, Any]:
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"""执行统计聚合。含按状态/工具分组。"""
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input_dto = GetExecutionStatsInput(
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system_id=system_id,
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env_key=env_key,
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adapter_type=adapter_type,
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start=start_time,
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end=end_time,
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limit=tool_limit,
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)
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output = await use_cases.execution_service.get_execution_stats(input_dto)
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return {"success": True, "data": output.model_dump()}
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@execution_router.get("/stats/grouped", response_model=dict)
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async def get_grouped_stats(
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group_by: Literal["tool"] = Query(..., description="分组维度，仅支持 tool"),
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system_id: int | None = Query(None, ge=1, description="系统过滤"),
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env_key: str | None = Query(None, max_length=32, description="环境过滤"),
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adapter_type: str | None = Query(None, max_length=32, description="适配器类型过滤"),
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start_time: datetime | None = Query(None, description="起始时间"),
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end_time: datetime | None = Query(None, description="结束时间"),
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limit: int = Query(20, ge=1, le=100, description="返回数量"),
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use_cases: UseCases = Depends(get_use_cases),
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current_user: User = Depends(get_required_user),
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) -> dict[str, Any]:
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"""分组统计。仅支持 group_by=tool（仓储仅 aggregate_by_tool 支持分组）。"""
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input_dto = GetExecutionStatsInput(
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system_id=system_id,
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env_key=env_key,
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adapter_type=adapter_type,
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start=start_time,
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end=end_time,
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limit=limit,
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)
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output = await use_cases.execution_service.get_grouped_stats(input_dto)
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return {"success": True, "data": output.model_dump()}
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@execution_router.get("/stats/error-analysis", response_model=dict)
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async def get_error_analysis(
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system_id: int = Query(..., ge=1, description="系统 ID（必填）"),
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start_time: datetime | None = Query(None, description="起始时间"),
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end_time: datetime | None = Query(None, description="结束时间"),
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limit: int = Query(5, ge=1, le=50, description="返回数量"),
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use_cases: UseCases = Depends(get_use_cases),
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current_user: User = Depends(get_required_user),
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) -> dict[str, Any]:
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"""错误分析。按错误码分组统计失败执行数量。"""
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input_dto = GetExecutionStatsInput(
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system_id=system_id,
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start=start_time,
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end=end_time,
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limit=limit,
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)
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output = await use_cases.execution_service.get_error_analysis(input_dto)
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return {"success": True, "data": output.model_dump()}
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@execution_router.get("/stats/trend", response_model=dict)
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async def get_execution_trend(
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tool_slug: str = Query(..., min_length=1, max_length=128, description="工具标识（必填）"),
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system_id: int | None = Query(None, ge=1, description="系统过滤"),
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env_key: str | None = Query(None, max_length=32, description="环境过滤"),
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start_time: datetime | None = Query(None, description="起始时间"),
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end_time: datetime | None = Query(None, description="结束时间"),
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interval: Literal["hour", "day"] = Query("day", description="时间桶粒度：hour / day"),
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use_cases: UseCases = Depends(get_use_cases),
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current_user: User = Depends(get_required_user),
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) -> dict[str, Any]:
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"""执行趋势。按时间桶 + 工具聚合。"""
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input_dto = GetExecutionTrendInput(
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tool_slug=tool_slug,
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system_id=system_id,
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env_key=env_key,
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start=start_time,
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end=end_time,
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interval=interval,
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)
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output = await use_cases.execution_service.get_execution_trend(input_dto)
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return {"success": True, "data": output.model_dump()}
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@execution_router.get("/slow", response_model=dict)
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async def list_slow_executions(
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system_id: int = Query(..., ge=1, description="系统 ID（必填）"),
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min_duration_ms: int = Query(..., ge=0, description="耗时阈值（毫秒，必填）"),
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limit: int = Query(50, ge=1, le=200, description="返回数量"),
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use_cases: UseCases = Depends(get_use_cases),
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current_user: User = Depends(get_required_user),
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) -> dict[str, Any]:
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"""慢执行查询。按耗时降序。"""
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input_dto = GetSlowExecutionsInput(
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system_id=system_id,
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min_duration_ms=min_duration_ms,
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limit=limit,
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)
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output = await use_cases.execution_service.list_slow_executions(input_dto)
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return {"success": True, "data": output.model_dump()}
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@execution_router.get("/by-trace/{trace_id}", response_model=dict)
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async def list_by_trace(
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trace_id: str = Path(..., min_length=1, max_length=64, description="调用链路 ID"),
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limit: int = Query(50, ge=1, le=200, description="返回数量"),
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use_cases: UseCases = Depends(get_use_cases),
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current_user: User = Depends(get_required_user),
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) -> dict[str, Any]:
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"""按 trace 查询。返回 execution + trace 组合。"""
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input_dto = GetTraceExecutionsInput(
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trace_id=trace_id,
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limit=limit,
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)
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output = await use_cases.execution_service.list_by_trace(input_dto)
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return {"success": True, "data": output.model_dump()}
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# =============================================================================
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# === 动态路径端点 /{execution_id} ===
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# =============================================================================
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@execution_router.get("/{execution_id}", response_model=dict)
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async def get_execution_detail(
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execution_id: str = Path(..., min_length=1, max_length=128, description="执行记录 ID"),
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use_cases: UseCases = Depends(get_use_cases),
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current_user: User = Depends(get_required_user),
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) -> dict[str, Any]:
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"""获取执行详情。"""
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input_dto = GetExecutionDetailInput(execution_id=execution_id)
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output = await use_cases.execution_service.get_execution_detail(input_dto)
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return {"success": True, "data": output.model_dump()}
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@execution_router.get("/{execution_id}/trace", response_model=dict)
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async def get_execution_trace(
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execution_id: str = Path(..., min_length=1, max_length=128, description="执行记录 ID"),
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use_cases: UseCases = Depends(get_use_cases),
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current_user: User = Depends(get_required_user),
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) -> dict[str, Any]:
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"""获取执行 trace。"""
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input_dto = GetExecutionTraceInput(execution_id=execution_id)
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output = await use_cases.execution_service.get_execution_trace(input_dto)
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return {"success": True, "data": output.model_dump()}
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@execution_router.get("/{execution_id}/retries", response_model=dict)
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async def list_retries(
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execution_id: str = Path(..., min_length=1, max_length=128, description="执行记录 ID"),
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limit: int = Query(50, ge=1, le=200, description="返回数量"),
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use_cases: UseCases = Depends(get_use_cases),
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current_user: User = Depends(get_required_user),
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) -> dict[str, Any]:
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"""重试链查询。基于 retry_of 字段，按 started_at 升序。"""
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input_dto = ListRetriesInput(
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execution_id=execution_id,
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limit=limit,
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)
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output = await use_cases.execution_service.list_retries(input_dto)
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return {"success": True, "data": output.model_dump()}
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@execution_router.get("/{execution_id}/context", response_model=dict)
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async def get_execution_context(
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execution_id: str = Path(..., min_length=1, max_length=128, description="执行记录 ID"),
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use_cases: UseCases = Depends(get_use_cases),
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current_user: User = Depends(get_required_user),
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) -> dict[str, Any]:
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"""执行上下文。含 trace + 关联告警。"""
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input_dto = GetExecutionContextInput(execution_id=execution_id)
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output = await use_cases.execution_service.get_execution_context(input_dto)
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return {"success": True, "data": output.model_dump()}
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@execution_router.post("/{execution_id}/retry", response_model=dict)
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async def retry_execution(
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execution_id: str = Path(..., min_length=1, max_length=128, description="执行记录 ID"),
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use_cases: UseCases = Depends(get_use_cases),
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current_user: User = Depends(get_admin_user),
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) -> dict[str, Any]:
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"""重试执行（管理员）。
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``caller`` / ``caller_id`` 均使用当前管理员 uid，用于审计日志。
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执行记录的 ``caller`` 由 service 层固定为 ``retry``，确保重试语义准确。
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"""
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input_dto = RetryExecutionInput(
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execution_id=execution_id,
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caller=current_user.uid,
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caller_id=current_user.uid,
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)
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output = await use_cases.execution_service.retry_execution(input_dto)
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return {"success": True, "data": output.model_dump()}
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