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https://github.com/fastapiadmin/FastapiAdmin.git
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refactor(task): 重构定时任务与存储路由
- 移除各路由文件顶部冗余注释 - 将 JobRouter/NodeRouter 重命名为 CornJobRouter/CornJobNodeRouter - 新增存储浏览、节点、传输、工作流路由注册 - 调整路由导入来源与任务表名 - 优化 main.py 启动方式及环境配置加载
This commit is contained in:
zhangtao
@@ -0,0 +1,489 @@
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from collections import defaultdict
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from collections.abc import Sequence
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from typing import cast
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.core.base_schema import AuthSchema, PageResultSchema
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from app.core.exceptions import CustomException
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from app.modules.task.storage.node.crud import StorageNodeCRUD
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from app.modules.task.storage.node.model import StorageNodeModel
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from app.modules.task.storage.node.service import StorageNodeService
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from app.modules.task.storage.transfer.schema import TransferMode, TransferTargetSchema, TransferTaskCreateSchema
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from app.modules.task.storage.transfer.service import StorageTransferService
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from app.utils.common_util import search_to_dict
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from .crud import WorkflowCRUD, WorkflowEdgeCRUD, WorkflowNodeCRUD
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from .model import WorkflowEdgeModel, WorkflowModel, WorkflowNodeModel
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from .schema import (
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WorkflowCreateSchema,
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WorkflowEdgeSchema,
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WorkflowGraphEdgeDataSchema,
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WorkflowGraphNodeDataSchema,
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WorkflowLayoutEdgeSchema,
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WorkflowLayoutNodeSchema,
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WorkflowNodeSchema,
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WorkflowOutSchema,
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WorkflowQueryParam,
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WorkflowSourceSchema,
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WorkflowSplitResultSchema,
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WorkflowTargetSchema,
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WorkflowTransferPlanSchema,
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WorkflowUpdateSchema,
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)
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class WorkflowService:
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"""传输流程服务（源节点 → 目标节点，支持 1对多 / 多对1）
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数据职责划分：
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- flow.graph：仅存画布布局与展示字段（节点位置、连线样式/动画），不做业务解析
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- flow_node / flow_edge 表：节点存储源与默认源目录、连线启用与传输方式（业务配置）
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- sources / targets：由连线明细实时派生，仅用于列表展示/校验
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保存时拆分画布、回显时组装画布、执行直接读明细表，避免对 graph 的重复解析。
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"""
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def __init__(self, auth: AuthSchema, db: AsyncSession) -> None:
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self.auth = auth
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self.db = db
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# ── 内部工具 ────────────────────────────────────────────────────
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def _crud(self) -> WorkflowCRUD:
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return WorkflowCRUD(self.auth, self.db)
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async def _validate_nodes(
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self, sources: list[WorkflowSourceSchema], targets: list[WorkflowTargetSchema]
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) -> None:
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"""校验源节点与目标节点均存在且启用。"""
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node_service = StorageNodeService(self.auth, self.db)
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ids = [s.source_id for s in sources] + [t.target_id for t in targets]
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if ids:
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await node_service.get_active_sources(ids)
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@staticmethod
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def _sources_from(
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edge_rows: Sequence[WorkflowEdgeModel], node_map: dict[str, WorkflowNodeModel]
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) -> list[WorkflowSourceSchema]:
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"""由连线明细派生源节点列表（去重）。"""
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out: list[WorkflowSourceSchema] = []
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seen: set[int] = set()
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for e in edge_rows:
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src_node = node_map.get(e.source_node_key)
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if not src_node:
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continue
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if src_node.source_id in seen:
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continue
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seen.add(src_node.source_id)
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out.append(WorkflowSourceSchema(source_id=src_node.source_id))
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return out
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@staticmethod
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def _targets_from(
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edge_rows: Sequence[WorkflowEdgeModel], node_map: dict[str, WorkflowNodeModel]
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) -> list[WorkflowTargetSchema]:
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"""由连线明细派生目标列表（去重），目标目录取目标节点的默认源目录。"""
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out: list[WorkflowTargetSchema] = []
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seen: set[int] = set()
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for e in edge_rows:
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tgt_node = node_map.get(e.target_node_key)
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if not tgt_node:
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continue
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if tgt_node.source_id in seen:
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continue
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seen.add(tgt_node.source_id)
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out.append(
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WorkflowTargetSchema(target_id=tgt_node.source_id, target_path=tgt_node.source_path or "")
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)
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return out
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# ── 画布拆分/组装 ───────────────────────────────────────────────
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@staticmethod
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def _split_graph(graph: dict) -> WorkflowSplitResultSchema:
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"""校验并拆分提交的画布。
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- 业务配置（节点 source_id/source_path、连线启用与传输方式）→ FlowNode/FlowEdge 明细
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- 布局展示（位置、连线样式/动画）→ 精简后的 layout
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同时完成画布完整性校验（存在连线、源≠目标），支持 1对多 / 多对1 拓扑。
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"""
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nodes = {n["id"]: n for n in (graph.get("nodes") or [])}
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edges = graph.get("edges") or []
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if not edges:
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raise CustomException(msg="画布中未找到有效的传输连线（源节点 → 目标节点）")
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layout_nodes: list[dict] = []
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node_items: list[WorkflowNodeSchema] = []
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for nid, n in nodes.items():
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d = n.get("data") or {}
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layout_nodes.append(
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WorkflowLayoutNodeSchema(
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id=nid,
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type=n.get("type") or "storage",
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position=n.get("position") or {"x": 0, "y": 0},
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label=n.get("label") or d.get("label"),
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style=n.get("style"),
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).model_dump(exclude_none=True)
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)
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if d.get("source_id") is not None:
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node_items.append(
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WorkflowNodeSchema(node_key=nid, source_id=d["source_id"], source_path=d.get("source_path"))
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)
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node_map = {it.node_key: it for it in node_items}
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sources: list[WorkflowSourceSchema] = []
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targets: list[WorkflowTargetSchema] = []
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edge_items: list[WorkflowEdgeSchema] = []
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layout_edges: list[dict] = []
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seen_edges: set[tuple[int, int]] = set()
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seen_sources: set[int] = set()
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seen_targets: set[int] = set()
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for e in edges:
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src_n = node_map.get(e.get("source"))
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tgt_n = node_map.get(e.get("target"))
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if not src_n or not tgt_n:
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continue
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sid, tid = src_n.source_id, tgt_n.source_id
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src_label = (nodes.get(e.get("source")) or {}).get("data", {}).get("label") or sid
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tgt_label = (nodes.get(e.get("target")) or {}).get("data", {}).get("label") or tid
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if sid == tid:
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raise CustomException(msg=f"流程保存失败，连线「{src_label} → {tgt_label}」源与目标不能相同")
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pair = (sid, tid)
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if pair in seen_edges:
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continue
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seen_edges.add(pair)
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if sid not in seen_sources:
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seen_sources.add(sid)
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sources.append(WorkflowSourceSchema(source_id=sid))
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if tid not in seen_targets:
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seen_targets.add(tid)
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# 目标目录由目标节点默认源目录决定
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targets.append(WorkflowTargetSchema(target_id=tid, target_path=tgt_n.source_path or ""))
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ed = e.get("data") or {}
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edge_items.append(
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WorkflowEdgeSchema(
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edge_key=e["id"],
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source_node_key=e.get("source"),
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target_node_key=e.get("target"),
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enabled=ed.get("enabled", True),
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transfer_mode=ed.get("transfer_mode"),
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multipart_part_size=ed.get("multipart_part_size"),
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multipart_concurrency=ed.get("multipart_concurrency"),
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)
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)
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layout_edges.append(
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WorkflowLayoutEdgeSchema(
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id=e["id"],
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source=e.get("source"),
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target=e.get("target"),
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type=e.get("type") or "smoothstep",
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animated=e.get("animated"),
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style=e.get("style"),
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label=e.get("label"),
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).model_dump(exclude_none=True)
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)
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if not edge_items:
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raise CustomException(msg="画布中未找到有效的传输连线（源节点 → 目标节点）")
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return WorkflowSplitResultSchema(
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layout={"nodes": layout_nodes, "edges": layout_edges},
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nodes=node_items,
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edges=edge_items,
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sources=sources,
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targets=targets,
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)
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async def _save_graph(self, flow_id: int, nodes: list[WorkflowNodeSchema], edges: list[WorkflowEdgeSchema]) -> None:
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"""覆写流程的业务明细（附属表物理删除后重建，不做逻辑删除）。
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父流程行已在调用方经 WorkflowCRUD 做过数据权限校验，明细随父全量覆写，
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删除统一走子表 CRUD 的物理清理方法（基类软删 delete 不适用于逐次覆写场景）。
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"""
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await WorkflowNodeCRUD(self.auth, self.db).hard_delete_by_flow_ids([flow_id])
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await WorkflowEdgeCRUD(self.auth, self.db).hard_delete_by_flow_ids([flow_id])
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user_id = self.auth.user.id
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for it in nodes:
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self.db.add(WorkflowNodeModel(flow_id=flow_id, created_id=user_id, updated_id=user_id, **it.model_dump()))
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for it in edges:
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self.db.add(WorkflowEdgeModel(flow_id=flow_id, created_id=user_id, updated_id=user_id, **it.model_dump()))
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await self.db.flush()
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async def _load_flow_graph(self, flow_id: int) -> tuple[list[WorkflowNodeModel], list[WorkflowEdgeModel]]:
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result = await self.db.execute(
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select(WorkflowNodeModel)
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.where(WorkflowNodeModel.flow_id == flow_id)
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.order_by(WorkflowNodeModel.id)
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)
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node_rows = list(result.scalars().all())
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result = await self.db.execute(
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select(WorkflowEdgeModel)
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.where(WorkflowEdgeModel.flow_id == flow_id)
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.order_by(WorkflowEdgeModel.id)
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)
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edge_rows = list(result.scalars().all())
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return node_rows, edge_rows
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async def _build_graph(
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self,
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flow: WorkflowModel,
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node_rows: Sequence[WorkflowNodeModel],
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edge_rows: Sequence[WorkflowEdgeModel],
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) -> dict:
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"""由布局（flow.graph）+ 业务明细（node/edge 表）+ 存储源组装完整画布，供前端直接回显。"""
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layout = flow.graph or {}
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layout_nodes = {n["id"]: n for n in (layout.get("nodes") or [])}
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layout_edges = {e["id"]: e for e in (layout.get("edges") or [])}
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nodes_by_key = {n.node_key: n for n in node_rows}
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edges_by_key = {e.edge_key: e for e in edge_rows}
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ids = {n.source_id for n in node_rows}
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src_map: dict[int, StorageNodeModel] = {}
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if ids:
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sources = await StorageNodeCRUD(self.auth, self.db).get_list(search={"id": ("in", sorted(ids))})
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src_map = {s.id: s for s in sources}
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out_nodes: list[dict] = []
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for key, ln in layout_nodes.items():
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node_row = nodes_by_key.get(key)
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if not node_row:
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continue
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src = src_map.get(node_row.source_id)
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node = dict(ln)
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node["data"] = WorkflowGraphNodeDataSchema(
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source_id=node_row.source_id,
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source_path=node_row.source_path,
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label=ln.get("label") or (src.name if src else None),
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protocol=src.protocol if src else None,
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host=src.host if src else None,
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bucket=src.bucket if src else None,
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endpoint=src.endpoint if src else None,
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region=src.region if src else None,
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path_prefix=src.path_prefix if src else None,
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).model_dump(exclude_none=True)
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out_nodes.append(node)
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out_edges: list[dict] = []
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for key, le in layout_edges.items():
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edge_row = edges_by_key.get(key)
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if not edge_row:
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continue
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src_node = nodes_by_key.get(edge_row.source_node_key)
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tgt_node = nodes_by_key.get(edge_row.target_node_key)
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src = src_map.get(src_node.source_id) if src_node else None
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tgt = src_map.get(tgt_node.source_id) if tgt_node else None
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edge = dict(le)
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edge["data"] = WorkflowGraphEdgeDataSchema(
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enabled=edge_row.enabled,
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transfer_mode=edge_row.transfer_mode,
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multipart_part_size=edge_row.multipart_part_size,
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multipart_concurrency=edge_row.multipart_concurrency,
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source_label=src.name if src else (str(src_node.source_id) if src_node else None),
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target_label=tgt.name if tgt else (str(tgt_node.source_id) if tgt_node else None),
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source_protocol=src.protocol if src else None,
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target_protocol=tgt.protocol if tgt else None,
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source_storage_id=src_node.source_id if src_node else None,
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target_storage_id=tgt_node.source_id if tgt_node else None,
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).model_dump(exclude_none=True)
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out_edges.append(edge)
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return {"nodes": out_nodes, "edges": out_edges}
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# ── 查询 ────────────────────────────────────────────────────────
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async def _to_out(self, obj: WorkflowModel) -> WorkflowOutSchema:
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out = WorkflowOutSchema.model_validate(obj)
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node_rows, edge_rows = await self._load_flow_graph(obj.id)
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out.graph_stats = {"node_count": len(node_rows), "edge_count": len(edge_rows)}
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if edge_rows:
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node_map = {n.node_key: n for n in node_rows}
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out.sources = self._sources_from(edge_rows, node_map)
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out.targets = self._targets_from(edge_rows, node_map)
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if obj.graph:
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out.graph = await self._build_graph(obj, node_rows, edge_rows)
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return out
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async def _to_out_list(self, objs: Sequence[WorkflowModel]) -> list[WorkflowOutSchema]:
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"""批量组装列表概览：源/目标列表、画布统计，一次查询避免 N+1。"""
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flow_ids = [o.id for o in objs]
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nodes_by_flow: dict[int, list[WorkflowNodeModel]] = defaultdict(list)
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edges_by_flow: dict[int, list[WorkflowEdgeModel]] = defaultdict(list)
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if flow_ids:
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result = await self.db.execute(
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select(WorkflowNodeModel).where(WorkflowNodeModel.flow_id.in_(flow_ids))
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)
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for n in result.scalars().all():
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nodes_by_flow[n.flow_id].append(n)
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result = await self.db.execute(
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select(WorkflowEdgeModel).where(WorkflowEdgeModel.flow_id.in_(flow_ids))
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)
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for e in result.scalars().all():
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edges_by_flow[e.flow_id].append(e)
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outs = [WorkflowOutSchema.model_validate(o) for o in objs]
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for obj, out in zip(objs, outs, strict=False):
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out.graph = None
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ns = nodes_by_flow.get(obj.id, [])
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es = edges_by_flow.get(obj.id, [])
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out.graph_stats = {"node_count": len(ns), "edge_count": len(es)}
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if es:
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node_map = {n.node_key: n for n in ns}
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out.sources = self._sources_from(es, node_map)
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out.targets = self._targets_from(es, node_map)
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return outs
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async def detail(self, id: int) -> WorkflowOutSchema:
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obj = await self._crud().get_or_404(id=id)
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return await self._to_out(obj)
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async def page(
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self,
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search: WorkflowQueryParam | None,
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page_no: int,
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page_size: int,
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order_by: list[dict] | None = None,
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) -> PageResultSchema[WorkflowOutSchema]:
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result = await self._crud().page(
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offset=(page_no - 1) * page_size,
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limit=page_size,
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order_by=order_by or [{"id": "asc"}],
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search=search_to_dict(search),
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)
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items = await self._to_out_list(result.items)
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return PageResultSchema[WorkflowOutSchema](
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page_no=result.page_no,
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page_size=result.page_size,
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total=result.total,
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has_next=result.has_next,
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items=items,
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)
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async def get_list(self, search: WorkflowQueryParam | None = None) -> list[WorkflowOutSchema]:
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objs = await self._crud().get_list(search=search_to_dict(search), order_by=[{"id": "asc"}])
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return await self._to_out_list(objs)
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# ── 写入 ────────────────────────────────────────────────────────
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async def _prepare_and_save(
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self, flow_id: int | None, data: WorkflowCreateSchema | WorkflowUpdateSchema
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) -> tuple[dict, list[WorkflowNodeSchema], list[WorkflowEdgeSchema]]:
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"""拆分画布：业务配置写入明细表，布局存入 flow.graph。"""
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data_dict = data.model_dump(exclude_unset=True, exclude_none=True)
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graph = data_dict.pop("graph", None)
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if not graph:
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raise CustomException(msg="创建失败，请至少添加一条传输连线")
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split = self._split_graph(graph)
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await self._validate_nodes(split.sources, split.targets)
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||||
data_dict["graph"] = split.layout
|
||||
if flow_id is not None:
|
||||
await self._save_graph(flow_id, split.nodes, split.edges)
|
||||
return data_dict, split.nodes, split.edges
|
||||
|
||||
async def create(self, data: WorkflowCreateSchema) -> WorkflowOutSchema:
|
||||
exist = await self._crud().get(name=data.name)
|
||||
if exist:
|
||||
raise CustomException(msg="创建失败，流程名称已存在")
|
||||
data_dict, node_items, edge_items = await self._prepare_and_save(None, data)
|
||||
obj = await self._crud().create(data=data_dict)
|
||||
await self._save_graph(obj.id, node_items, edge_items)
|
||||
return await self._to_out(obj)
|
||||
|
||||
async def update(self, id: int, data: WorkflowUpdateSchema) -> WorkflowOutSchema:
|
||||
await self._crud().get_or_404(id=id, msg="更新失败，该流程不存在")
|
||||
exist = await self._crud().get(name=data.name)
|
||||
if exist and exist.id != id:
|
||||
raise CustomException(msg="更新失败，流程名称已存在")
|
||||
# _prepare_and_save 在 flow_id 非空时已覆写明细，无需再次 _save_graph
|
||||
data_dict, _, _ = await self._prepare_and_save(id, data)
|
||||
await self._crud().update(id=id, data=data_dict)
|
||||
obj = await self._crud().get_or_404(id=id)
|
||||
return await self._to_out(obj)
|
||||
|
||||
async def delete(self, ids: list[int]) -> None:
|
||||
if not ids:
|
||||
raise CustomException(msg="删除失败，删除对象不能为空")
|
||||
await self._crud().delete(ids=ids)
|
||||
# 子表随父流程一并物理清理（无软删消费场景）
|
||||
await WorkflowNodeCRUD(self.auth, self.db).hard_delete_by_flow_ids(ids)
|
||||
await WorkflowEdgeCRUD(self.auth, self.db).hard_delete_by_flow_ids(ids)
|
||||
|
||||
# ── 执行 ────────────────────────────────────────────────────────
|
||||
|
||||
async def execute(self, id: int, source_paths: dict[str, str] | None = None) -> list[int]:
|
||||
"""执行传输流程：直接读取业务明细表，每条启用的连线生成一个传输任务。
|
||||
|
||||
支持 1对多 / 多对1 拓扑；源文件/目录优先取执行时传入的 source_paths
|
||||
（按源存储源ID映射），未传入时回退使用节点配置的默认源目录；
|
||||
传输方式未配置时默认流式传输；禁用的连线不参与执行。
|
||||
"""
|
||||
obj = await self._crud().get_or_404(id=id, msg="执行失败，该流程不存在")
|
||||
node_rows, edge_rows = await self._load_flow_graph(id)
|
||||
# 只执行启用的连线（禁用的连线不生成传输任务）
|
||||
enabled_edges = [e for e in edge_rows if e.enabled]
|
||||
if not enabled_edges:
|
||||
raise CustomException(msg="执行失败，该流程画布没有启用的传输连线")
|
||||
nodes_by_key = {n.node_key: n for n in node_rows}
|
||||
source_paths = source_paths or {}
|
||||
|
||||
# 阶段一：解析并校验每条连线
|
||||
plans: list[WorkflowTransferPlanSchema] = []
|
||||
for e in enabled_edges:
|
||||
src_node = nodes_by_key.get(e.source_node_key)
|
||||
tgt_node = nodes_by_key.get(e.target_node_key)
|
||||
if not src_node or not tgt_node:
|
||||
raise CustomException(msg=f"执行失败，连线 {e.edge_key} 对应的节点不存在")
|
||||
src_id, tgt_id = src_node.source_id, tgt_node.source_id
|
||||
src_path = (source_paths.get(str(src_id)) or (src_node.source_path or "")).strip()
|
||||
# 目标目录由目标节点默认源目录决定
|
||||
tgt_path = (tgt_node.source_path or "").strip()
|
||||
edge_label = f"「存储源{src_id} → 存储源{tgt_id}」"
|
||||
if not src_id or not tgt_id:
|
||||
raise CustomException(msg=f"执行失败，连线 {edge_label} 存在无效的存储源节点")
|
||||
if src_id == tgt_id:
|
||||
raise CustomException(msg=f"执行失败，连线 {edge_label} 源与目标不能相同")
|
||||
if not src_path or not tgt_path:
|
||||
missing = "未选择源文件/目录" if not src_path else "目标节点未配置默认目录"
|
||||
raise CustomException(msg=f"执行失败，连线 {edge_label} {missing}")
|
||||
plans.append(
|
||||
WorkflowTransferPlanSchema(
|
||||
src_id=src_id,
|
||||
tgt_id=tgt_id,
|
||||
src_path=src_path,
|
||||
tgt_path=tgt_path,
|
||||
transfer_mode=e.transfer_mode or "stream",
|
||||
multipart_part_size=e.multipart_part_size,
|
||||
multipart_concurrency=e.multipart_concurrency,
|
||||
)
|
||||
)
|
||||
|
||||
# 阶段二：统一校验所有涉及的存储源可用，再逐个生成传输任务
|
||||
node_service = StorageNodeService(self.auth, self.db)
|
||||
sources = await node_service.get_active_sources(
|
||||
[p.src_id for p in plans] + [p.tgt_id for p in plans]
|
||||
)
|
||||
name_map = {s.id: s.name for s in sources}
|
||||
|
||||
transfer_service = StorageTransferService(self.auth, self.db)
|
||||
task_ids: list[int] = []
|
||||
for p in plans:
|
||||
src_label = name_map.get(p.src_id) or f"存储源{p.src_id}"
|
||||
tgt_label = name_map.get(p.tgt_id) or f"存储源{p.tgt_id}"
|
||||
# 任务名最大 128 字符，超长时截断避免 422
|
||||
task_name = f"{obj.name}-{src_label}→{tgt_label}"[:128]
|
||||
task_id = await transfer_service.create(
|
||||
TransferTaskCreateSchema(
|
||||
name=task_name,
|
||||
task_type="parallel",
|
||||
source_type="remote",
|
||||
source_id=p.src_id,
|
||||
source_path=p.src_path,
|
||||
targets=[TransferTargetSchema(target_id=p.tgt_id, target_path=p.tgt_path)],
|
||||
# flow 侧以 str 保存传输方式，接口侧字面量校验由 pydantic 兜底，此处仅收窄静态类型
|
||||
transfer_mode=cast(TransferMode | None, p.transfer_mode),
|
||||
multipart_part_size=p.multipart_part_size,
|
||||
multipart_concurrency=p.multipart_concurrency,
|
||||
)
|
||||
)
|
||||
task_ids.append(task_id)
|
||||
return task_ids
|
||||
Reference in New Issue
Block a user