mirror of
https://github.com/jxxghp/MoviePilot.git
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635 lines
24 KiB
Python
635 lines
24 KiB
Python
import base64
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import pickle
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import threading
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import time
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from types import SimpleNamespace
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from app.chain import workflow as workflow_module
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from app.schemas import Action, ActionContext, ActionResult
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from app.schemas.types import EventType
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from app import workflow as workflow_package
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def _build_workflow(current_action=None, context=None, actions=None, flows=None,
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execution_config=None, execution_state=None):
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"""构造最小工作流对象。"""
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return SimpleNamespace(
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id=1,
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name="测试工作流",
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actions=actions if actions is not None else [
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{"id": "A", "type": "FakeAction", "name": "动作A", "data": {}},
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{"id": "B", "type": "FakeAction", "name": "动作B", "data": {}},
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],
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flows=flows if flows is not None else [
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{"id": "flow-1", "source": "A", "target": "B", "animated": True},
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],
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current_action=current_action,
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context=context,
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execution_config=execution_config or {},
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execution_state=execution_state or {},
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)
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def _encoded_context(context: ActionContext) -> dict:
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"""编码工作流恢复上下文。"""
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return {
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"content": base64.b64encode(pickle.dumps(context)).decode("utf-8"),
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}
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class _FakeWorkflowManager:
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"""记录执行动作的工作流管理器。"""
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def __init__(self, calls, results=None, contracts=None):
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self.calls = calls
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self.results = results or {}
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self.contracts = contracts or {}
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self.received_inputs = []
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def execute(self, workflow_id, action, context=None, inputs=None, runtime=None, cancel_token=None):
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"""执行伪动作并记录新版输入。"""
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self.calls.append(action.id)
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self.received_inputs.append((action.id, inputs or {}, runtime or {}, cancel_token))
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result = self.results.get(action.id)
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if callable(result):
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return result(action, context or ActionContext())
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if result:
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return result
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return ActionResult(success=True, message=f"{action.name}完成", context=context or ActionContext())
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def excute(self, workflow_id, action, context=None):
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"""兼容历史执行方法。"""
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result = self.execute(workflow_id, action, context)
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return result.success, result.message, result.context
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def get_action_contract(self, action_type):
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"""获取伪动作契约。"""
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return self.contracts.get(action_type) or {}
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def test_workflow_executor_resumes_downstream_nodes(monkeypatch):
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"""恢复执行时应释放已完成节点的后继节点。"""
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calls = []
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fake_manager = _FakeWorkflowManager(calls)
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workflow = _build_workflow(
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current_action="A",
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context=_encoded_context(ActionContext()),
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)
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monkeypatch.setattr(workflow_module, "WorkFlowManager", lambda: fake_manager)
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monkeypatch.setattr(workflow_module.global_vars, "workflow_resume", lambda workflow_id: None)
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monkeypatch.setattr(workflow_module.global_vars, "is_workflow_stopped", lambda workflow_id: False)
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executor = workflow_module.WorkflowExecutor(workflow)
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executor.execute()
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assert calls == ["B"]
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assert executor.success is True
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assert executor.context.progress == 100
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def test_workflow_executor_reports_incremental_progress(monkeypatch):
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"""顺序工作流的中间进度应按已完成比例计算。"""
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calls = []
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progresses = []
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fake_manager = _FakeWorkflowManager(calls)
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monkeypatch.setattr(workflow_module, "WorkFlowManager", lambda: fake_manager)
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monkeypatch.setattr(workflow_module.global_vars, "workflow_resume", lambda workflow_id: None)
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monkeypatch.setattr(workflow_module.global_vars, "is_workflow_stopped", lambda workflow_id: False)
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executor = workflow_module.WorkflowExecutor(
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_build_workflow(),
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step_callback=lambda action, context: progresses.append(context.progress),
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)
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executor.execute()
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assert calls == ["A", "B"]
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assert progresses == [50, 100]
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def test_workflow_executor_skips_false_condition_branch(monkeypatch):
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"""条件边不满足时应跳过对应分支，并继续执行满足条件的分支。"""
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calls = []
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fake_manager = _FakeWorkflowManager(
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calls,
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results={
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"A": lambda action, context: ActionResult(
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success=True,
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message=f"{action.name}完成",
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context=context,
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outputs={"items": ["movie"]}
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)
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}
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)
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workflow = _build_workflow(
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actions=[
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{"id": "A", "type": "FakeAction", "name": "动作A", "data": {}},
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{"id": "B", "type": "FakeAction", "name": "动作B", "data": {}},
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{"id": "C", "type": "FakeAction", "name": "动作C", "data": {}},
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],
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flows=[
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{"id": "flow-ab", "source": "A", "target": "B", "condition": "outputs.A.items.count == 0"},
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{"id": "flow-ac", "source": "A", "target": "C", "data": {"condition": "outputs.A.items.count > 0"}},
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],
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)
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monkeypatch.setattr(workflow_module, "WorkFlowManager", lambda: fake_manager)
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monkeypatch.setattr(workflow_module.global_vars, "workflow_resume", lambda workflow_id: None)
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monkeypatch.setattr(workflow_module.global_vars, "is_workflow_stopped", lambda workflow_id: False)
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executor = workflow_module.WorkflowExecutor(workflow)
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executor.execute()
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assert calls == ["A", "C"]
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assert executor.success is True
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assert executor.context.progress == 100
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assert executor.context.node_outputs["A"]["items"] == ["movie"]
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def test_workflow_executor_all_success_join_waits_parallel_branches(monkeypatch):
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"""默认汇合策略应等待所有上游分支成功后再执行目标节点。"""
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calls = []
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joined_outputs = {}
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def run_join(action, context):
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"""记录汇合节点读取到的上游输出。"""
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joined_outputs.update(context.node_outputs)
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return ActionResult(success=True, message=f"{action.name}完成", context=context)
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fake_manager = _FakeWorkflowManager(
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calls,
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results={
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"A": lambda action, context: ActionResult(
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success=True,
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message=f"{action.name}完成",
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context=context,
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outputs={"value": "A"}
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),
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"B": lambda action, context: ActionResult(
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success=True,
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message=f"{action.name}完成",
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context=context,
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outputs={"value": "B"}
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),
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"C": run_join,
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}
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)
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workflow = _build_workflow(
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actions=[
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{"id": "A", "type": "FakeAction", "name": "动作A", "data": {}},
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{"id": "B", "type": "FakeAction", "name": "动作B", "data": {}},
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{"id": "C", "type": "FakeAction", "name": "动作C", "data": {}},
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],
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flows=[
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{"id": "flow-ac", "source": "A", "target": "C"},
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{"id": "flow-bc", "source": "B", "target": "C"},
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],
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)
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monkeypatch.setattr(workflow_module, "WorkFlowManager", lambda: fake_manager)
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monkeypatch.setattr(workflow_module.global_vars, "workflow_resume", lambda workflow_id: None)
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monkeypatch.setattr(workflow_module.global_vars, "is_workflow_stopped", lambda workflow_id: False)
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executor = workflow_module.WorkflowExecutor(workflow)
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executor.execute()
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assert set(calls) == {"A", "B", "C"}
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assert calls[-1] == "C"
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assert joined_outputs["A"] == {"value": "A"}
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assert joined_outputs["B"] == {"value": "B"}
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def test_workflow_executor_any_success_join_runs_after_available_branch(monkeypatch):
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"""any_success 汇合策略应允许任一满足条件的上游分支触发目标节点。"""
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calls = []
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fake_manager = _FakeWorkflowManager(
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calls,
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results={
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"A": lambda action, context: ActionResult(
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success=True,
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message=f"{action.name}完成",
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context=context,
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outputs={"items": ["movie"]}
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)
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}
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)
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workflow = _build_workflow(
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actions=[
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{"id": "A", "type": "FakeAction", "name": "动作A", "data": {}},
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{"id": "B", "type": "FakeAction", "name": "动作B", "data": {}},
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{"id": "C", "type": "FakeAction", "name": "动作C", "data": {}},
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{"id": "D", "type": "FakeAction", "name": "动作D", "data": {"join_policy": "any_success"}},
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],
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flows=[
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{"id": "flow-ab", "source": "A", "target": "B", "condition": "outputs.A.items.count == 0"},
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{"id": "flow-ac", "source": "A", "target": "C", "condition": "outputs.A.items.count > 0"},
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{"id": "flow-bd", "source": "B", "target": "D"},
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{"id": "flow-cd", "source": "C", "target": "D"},
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],
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)
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monkeypatch.setattr(workflow_module, "WorkFlowManager", lambda: fake_manager)
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monkeypatch.setattr(workflow_module.global_vars, "workflow_resume", lambda workflow_id: None)
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monkeypatch.setattr(workflow_module.global_vars, "is_workflow_stopped", lambda workflow_id: False)
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executor = workflow_module.WorkflowExecutor(workflow)
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executor.execute()
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assert calls == ["A", "C", "D"]
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assert executor.context.progress == 100
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def test_workflow_executor_all_done_join_can_continue_after_failure(monkeypatch):
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"""continue 失败策略配合 all_done 汇合时应继续执行收尾节点。"""
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calls = []
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fake_manager = _FakeWorkflowManager(
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calls,
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results={
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"A": lambda action, context: ActionResult(success=False, message=f"{action.name}失败", context=context)
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}
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)
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workflow = _build_workflow(
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actions=[
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{"id": "A", "type": "FakeAction", "name": "动作A", "data": {"fail_policy": "continue"}},
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{"id": "B", "type": "FakeAction", "name": "动作B", "data": {}},
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{"id": "C", "type": "FakeAction", "name": "动作C", "data": {"join_policy": "all_done"}},
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],
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flows=[
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{"id": "flow-ac", "source": "A", "target": "C"},
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{"id": "flow-bc", "source": "B", "target": "C"},
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],
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)
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monkeypatch.setattr(workflow_module, "WorkFlowManager", lambda: fake_manager)
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monkeypatch.setattr(workflow_module.global_vars, "workflow_resume", lambda workflow_id: None)
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monkeypatch.setattr(workflow_module.global_vars, "is_workflow_stopped", lambda workflow_id: False)
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executor = workflow_module.WorkflowExecutor(workflow)
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executor.execute()
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assert set(calls) == {"A", "B", "C"}
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assert calls[-1] == "C"
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assert executor.has_failure is True
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assert executor.success is True
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def test_workflow_executor_exclusive_branch_uses_first_matching_flow(monkeypatch):
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"""互斥分支应只执行第一条满足条件的出边。"""
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calls = []
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fake_manager = _FakeWorkflowManager(
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calls,
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results={
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"A": lambda action, context: ActionResult(
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success=True,
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message=f"{action.name}完成",
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context=context,
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outputs={"count": 2}
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)
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}
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)
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workflow = _build_workflow(
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actions=[
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{"id": "A", "type": "FakeAction", "name": "动作A", "data": {"branch_policy": "exclusive"}},
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{"id": "B", "type": "FakeAction", "name": "动作B", "data": {}},
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{"id": "C", "type": "FakeAction", "name": "动作C", "data": {}},
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],
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flows=[
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{"id": "flow-ab", "source": "A", "target": "B", "condition": "outputs.A.count > 0"},
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{"id": "flow-ac", "source": "A", "target": "C", "condition": "outputs.A.count > 1"},
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],
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)
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monkeypatch.setattr(workflow_module, "WorkFlowManager", lambda: fake_manager)
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monkeypatch.setattr(workflow_module.global_vars, "workflow_resume", lambda workflow_id: None)
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monkeypatch.setattr(workflow_module.global_vars, "is_workflow_stopped", lambda workflow_id: False)
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executor = workflow_module.WorkflowExecutor(workflow)
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executor.execute()
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assert calls == ["A", "B"]
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assert executor.node_states["C"] == "skipped"
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def test_workflow_executor_passes_declared_inputs(monkeypatch):
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"""动作输入声明应从 node_outputs 中读取指定路径。"""
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calls = []
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fake_manager = _FakeWorkflowManager(
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calls,
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results={
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"A": lambda action, context: ActionResult(
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success=True,
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message=f"{action.name}完成",
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context=context,
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outputs={"torrents": ["a", "b"]}
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)
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}
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)
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workflow = _build_workflow(
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actions=[
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{"id": "A", "type": "FakeAction", "name": "动作A", "data": {}},
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{
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"id": "B",
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"type": "FakeAction",
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"name": "动作B",
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"data": {"inputs": ["A.torrents", "outputs.A.torrents.count"]},
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},
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],
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)
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monkeypatch.setattr(workflow_module, "WorkFlowManager", lambda: fake_manager)
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monkeypatch.setattr(workflow_module.global_vars, "workflow_resume", lambda workflow_id: None)
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monkeypatch.setattr(workflow_module.global_vars, "is_workflow_stopped", lambda workflow_id: False)
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executor = workflow_module.WorkflowExecutor(workflow)
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executor.execute()
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b_inputs = [item for action_id, item, _, _ in fake_manager.received_inputs if action_id == "B"][0]
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assert b_inputs == {
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"A.torrents": ["a", "b"],
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"outputs.A.torrents.count": 2,
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}
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def test_workflow_executor_uses_contract_inputs(monkeypatch):
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"""未手写输入声明时应按动作契约读取上下文字段。"""
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calls = []
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fake_manager = _FakeWorkflowManager(
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calls,
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contracts={
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"NeedsTorrentsAction": {
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"inputs": [{"name": "torrents", "label": "资源", "kind": "list"}],
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"outputs": [],
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}
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},
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results={
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"A": lambda action, context: ActionResult(
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success=True,
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message=f"{action.name}完成",
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context=context,
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outputs={"torrents": ["a", "b"]}
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)
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}
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)
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workflow = _build_workflow(
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actions=[
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{"id": "A", "type": "FakeAction", "name": "动作A", "data": {}},
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{"id": "B", "type": "NeedsTorrentsAction", "name": "动作B", "data": {}},
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],
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)
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monkeypatch.setattr(workflow_module, "WorkFlowManager", lambda: fake_manager)
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monkeypatch.setattr(workflow_module.global_vars, "workflow_resume", lambda workflow_id: None)
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monkeypatch.setattr(workflow_module.global_vars, "is_workflow_stopped", lambda workflow_id: False)
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executor = workflow_module.WorkflowExecutor(workflow)
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executor.execute()
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b_inputs = [item for action_id, item, _, _ in fake_manager.received_inputs if action_id == "B"][0]
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assert b_inputs == {"torrents": ["a", "b"]}
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def test_workflow_executor_persists_structured_state(monkeypatch):
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"""步骤回调应收到可持久化的结构化执行状态。"""
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calls = []
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states = []
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fake_manager = _FakeWorkflowManager(
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calls,
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results={
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"A": lambda action, context: ActionResult(
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success=True,
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message=f"{action.name}完成",
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context=context,
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outputs={"items": ["movie"]}
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)
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}
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)
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monkeypatch.setattr(workflow_module, "WorkFlowManager", lambda: fake_manager)
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monkeypatch.setattr(workflow_module.global_vars, "workflow_resume", lambda workflow_id: None)
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monkeypatch.setattr(workflow_module.global_vars, "is_workflow_stopped", lambda workflow_id: False)
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executor = workflow_module.WorkflowExecutor(
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_build_workflow(actions=[{"id": "A", "type": "FakeAction", "name": "动作A", "data": {}}], flows=[]),
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step_callback=lambda action, context, execution_state, completed: states.append(execution_state),
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)
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executor.execute()
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assert states[-1]["nodes"]["A"]["state"] == "success"
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assert states[-1]["outputs"]["A"]["items"] == ["movie"]
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assert states[-1]["runtime"]["progress"] == 100
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def test_workflow_executor_restores_outputs_from_execution_state(monkeypatch):
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"""恢复执行时应从结构化状态读取节点输出并继续判断条件边。"""
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calls = []
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fake_manager = _FakeWorkflowManager(calls)
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workflow = _build_workflow(
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execution_state={
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"nodes": {
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"A": {"state": "success", "attempt": 1},
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},
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"outputs": {
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"A": {"torrents": ["movie"]},
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},
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},
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flows=[
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{"id": "flow-ab", "source": "A", "target": "B", "condition": "A.torrents.count > 0"},
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],
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)
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monkeypatch.setattr(workflow_module, "WorkFlowManager", lambda: fake_manager)
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monkeypatch.setattr(workflow_module.global_vars, "workflow_resume", lambda workflow_id: None)
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monkeypatch.setattr(workflow_module.global_vars, "is_workflow_stopped", lambda workflow_id: False)
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executor = workflow_module.WorkflowExecutor(workflow)
|
|
executor.execute()
|
|
|
|
assert calls == ["B"]
|
|
assert executor.context.node_outputs["A"]["torrents"] == ["movie"]
|
|
|
|
|
|
def test_workflow_executor_concurrency_key_serializes_parallel_nodes(monkeypatch):
|
|
"""相同 concurrency_key 的并行节点不应同时运行。"""
|
|
calls = []
|
|
active_count = 0
|
|
max_active_count = 0
|
|
lock = threading.Lock()
|
|
|
|
def run_action(action, context):
|
|
"""记录同一并发键下的同时运行数量。"""
|
|
nonlocal active_count, max_active_count
|
|
with lock:
|
|
active_count += 1
|
|
max_active_count = max(max_active_count, active_count)
|
|
time.sleep(0.05)
|
|
with lock:
|
|
active_count -= 1
|
|
return ActionResult(success=True, message=f"{action.name}完成", context=context)
|
|
|
|
fake_manager = _FakeWorkflowManager(calls, results={"A": run_action, "B": run_action})
|
|
workflow = _build_workflow(
|
|
actions=[
|
|
{"id": "A", "type": "FakeAction", "name": "动作A", "data": {"concurrency_key": "download"}},
|
|
{"id": "B", "type": "FakeAction", "name": "动作B", "data": {"concurrency_key": "download"}},
|
|
],
|
|
flows=[],
|
|
execution_config={"max_workers": 2},
|
|
)
|
|
|
|
monkeypatch.setattr(workflow_module, "WorkFlowManager", lambda: fake_manager)
|
|
monkeypatch.setattr(workflow_module.global_vars, "workflow_resume", lambda workflow_id: None)
|
|
monkeypatch.setattr(workflow_module.global_vars, "is_workflow_stopped", lambda workflow_id: False)
|
|
|
|
executor = workflow_module.WorkflowExecutor(workflow)
|
|
executor.execute()
|
|
|
|
assert set(calls) == {"A", "B"}
|
|
assert max_active_count == 1
|
|
|
|
|
|
def test_workflow_executor_filter_action_replaces_artifact_outputs(monkeypatch):
|
|
"""过滤类动作默认应替换列表输出，避免把过滤前数据重新合并回来。"""
|
|
calls = []
|
|
fake_manager = _FakeWorkflowManager(
|
|
calls,
|
|
results={
|
|
"A": lambda action, context: ActionResult(
|
|
success=True,
|
|
message=f"{action.name}完成",
|
|
context=context,
|
|
outputs={"torrents": ["old", "keep"]}
|
|
),
|
|
"B": lambda action, context: ActionResult(
|
|
success=True,
|
|
message=f"{action.name}完成",
|
|
context=context,
|
|
outputs={"torrents": ["keep"]}
|
|
),
|
|
}
|
|
)
|
|
workflow = _build_workflow(
|
|
actions=[
|
|
{"id": "A", "type": "FakeAction", "name": "动作A", "data": {}},
|
|
{"id": "B", "type": "FilterTorrentsAction", "name": "过滤资源", "data": {}},
|
|
],
|
|
)
|
|
|
|
monkeypatch.setattr(workflow_module, "WorkFlowManager", lambda: fake_manager)
|
|
monkeypatch.setattr(workflow_module.global_vars, "workflow_resume", lambda workflow_id: None)
|
|
monkeypatch.setattr(workflow_module.global_vars, "is_workflow_stopped", lambda workflow_id: False)
|
|
|
|
executor = workflow_module.WorkflowExecutor(workflow)
|
|
executor.execute()
|
|
|
|
assert executor.context.torrents == ["keep"]
|
|
assert executor.context.artifacts["torrents"] == ["keep"]
|
|
|
|
|
|
def test_workflow_executor_stop_is_not_success(monkeypatch):
|
|
"""停止信号不应被执行器汇报为成功完成。"""
|
|
calls = []
|
|
fake_manager = _FakeWorkflowManager(calls)
|
|
|
|
monkeypatch.setattr(workflow_module, "WorkFlowManager", lambda: fake_manager)
|
|
monkeypatch.setattr(workflow_module.global_vars, "workflow_resume", lambda workflow_id: None)
|
|
monkeypatch.setattr(workflow_module.global_vars, "is_workflow_stopped", lambda workflow_id: True)
|
|
|
|
executor = workflow_module.WorkflowExecutor(_build_workflow())
|
|
executor.execute()
|
|
|
|
assert calls == []
|
|
assert executor.stopped is True
|
|
assert executor.success is False
|
|
assert executor.errmsg == "工作流已停止"
|
|
|
|
|
|
def test_workflow_context_merge_preserves_runtime_objects():
|
|
"""合并上下文时应保留运行时对象，而不是转成字典。"""
|
|
executor = object.__new__(workflow_module.WorkflowExecutor)
|
|
executor.context = ActionContext()
|
|
runtime_torrent = SimpleNamespace(title="runtime torrent")
|
|
result_context = ActionContext()
|
|
result_context.torrents.append(runtime_torrent)
|
|
|
|
executor.merge_context(result_context)
|
|
|
|
assert executor.context.torrents[0] is runtime_torrent
|
|
|
|
|
|
class _FakeEventManager:
|
|
"""记录事件监听器注册和移除次数。"""
|
|
|
|
def __init__(self):
|
|
self.added = []
|
|
self.removed = []
|
|
|
|
def add_event_listener(self, event_type, handler):
|
|
self.added.append(event_type)
|
|
|
|
def remove_event_listener(self, event_type, handler):
|
|
self.removed.append(event_type)
|
|
|
|
|
|
def test_workflow_event_listener_keeps_shared_handler_until_last_workflow(monkeypatch):
|
|
"""同一事件下移除单个工作流时不应断开其他工作流监听。"""
|
|
fake_eventmanager = _FakeEventManager()
|
|
manager = object.__new__(workflow_package.WorkFlowManager)
|
|
manager._lock = threading.Lock()
|
|
manager._event_workflows = {}
|
|
|
|
monkeypatch.setattr(workflow_package, "eventmanager", fake_eventmanager)
|
|
|
|
manager.register_workflow_event(1, EventType.DownloadAdded.value)
|
|
manager.register_workflow_event(2, EventType.DownloadAdded.value)
|
|
manager.remove_workflow_event(1, EventType.DownloadAdded.value)
|
|
|
|
assert fake_eventmanager.added == [EventType.DownloadAdded]
|
|
assert fake_eventmanager.removed == []
|
|
assert manager.get_event_workflows() == {EventType.DownloadAdded.value: [2]}
|
|
|
|
manager.remove_workflow_event(2, EventType.DownloadAdded.value)
|
|
|
|
assert fake_eventmanager.removed == [EventType.DownloadAdded]
|
|
assert manager.get_event_workflows() == {}
|
|
|
|
|
|
def test_workflow_manager_retries_action_until_success(monkeypatch):
|
|
"""动作管理器应按 retry 配置重试失败动作。"""
|
|
|
|
class RetryAction:
|
|
"""模拟第二次才成功的动作。"""
|
|
|
|
call_count = 0
|
|
|
|
def __init__(self, action_id):
|
|
self.action_id = action_id
|
|
|
|
def execute_with_inputs(self, workflow_id, params, inputs, runtime, context):
|
|
"""执行动作并在第二次返回成功。"""
|
|
_ = workflow_id, params, inputs, runtime
|
|
RetryAction.call_count += 1
|
|
if RetryAction.call_count == 1:
|
|
return ActionResult(success=False, message="第一次失败", context=context)
|
|
return ActionResult(success=True, message="第二次成功", context=context, outputs={"ok": True})
|
|
|
|
manager = object.__new__(workflow_package.WorkFlowManager)
|
|
manager._actions = {"RetryAction": RetryAction}
|
|
monkeypatch.setattr(workflow_package.global_vars, "is_workflow_stopped", lambda workflow_id: False)
|
|
|
|
result = manager.execute(
|
|
workflow_id=1,
|
|
action=Action(
|
|
id="retry",
|
|
type="RetryAction",
|
|
name="重试动作",
|
|
data={"retry": {"max_attempts": 2, "interval": 0}},
|
|
),
|
|
context=ActionContext(),
|
|
)
|
|
|
|
assert result.success is True
|
|
assert result.attempts == 2
|
|
assert result.outputs == {"ok": True}
|
|
assert RetryAction.call_count == 2
|