mirror of
https://hubproxy.babadafafafafa.cn/https://github.com/jxxghp/MoviePilot.git
synced 2026-09-20 08:03:34 +08:00
1115 lines
41 KiB
Python
1115 lines
41 KiB
Python
import base64
|
||
import pickle
|
||
import threading
|
||
import time
|
||
from types import SimpleNamespace
|
||
|
||
import pytest
|
||
|
||
from app import workflow as workflow_package
|
||
from app.chain import workflow as workflow_module
|
||
from app.runtime.correlation import correlation_scope, get_correlation_id
|
||
from app.schemas.types import EventType
|
||
from app.schemas.workflow import Action, ActionContext, ActionResult
|
||
|
||
|
||
def _build_workflow(current_action=None, context=None, actions=None, flows=None,
|
||
execution_config=None, execution_state=None):
|
||
"""构造最小工作流对象。"""
|
||
return SimpleNamespace(
|
||
id=1,
|
||
name="测试工作流",
|
||
actions=actions if actions is not None else [
|
||
{"id": "A", "type": "FakeAction", "name": "动作A", "data": {}},
|
||
{"id": "B", "type": "FakeAction", "name": "动作B", "data": {}},
|
||
],
|
||
flows=flows if flows is not None else [
|
||
{"id": "flow-1", "source": "A", "target": "B", "animated": True},
|
||
],
|
||
current_action=current_action,
|
||
context=context,
|
||
execution_config=execution_config or {},
|
||
execution_state=execution_state or {},
|
||
)
|
||
|
||
|
||
def _encoded_context(context: ActionContext) -> dict:
|
||
"""编码工作流恢复上下文。"""
|
||
return {
|
||
"content": base64.b64encode(pickle.dumps(context)).decode("utf-8"),
|
||
}
|
||
|
||
|
||
class _FakeWorkflowManager:
|
||
"""记录执行动作的工作流管理器。"""
|
||
|
||
def __init__(self, calls, results=None, contracts=None):
|
||
self.calls = calls
|
||
self.results = results or {}
|
||
self.contracts = contracts or {}
|
||
self.received_inputs = []
|
||
|
||
def execute(self, workflow_id, action, context=None, inputs=None, runtime=None, cancel_token=None):
|
||
"""执行伪动作并记录新版输入。"""
|
||
self.calls.append(action.id)
|
||
self.received_inputs.append((action.id, inputs or {}, runtime or {}, cancel_token))
|
||
result = self.results.get(action.id)
|
||
if callable(result):
|
||
return result(action, context or ActionContext())
|
||
if result:
|
||
return result
|
||
return ActionResult(success=True, message=f"{action.name}完成", context=context or ActionContext())
|
||
|
||
def excute(self, workflow_id, action, context=None):
|
||
"""兼容历史执行方法。"""
|
||
result = self.execute(workflow_id, action, context)
|
||
return result.success, result.message, result.context
|
||
|
||
def get_action_contract(self, action_type):
|
||
"""获取伪动作契约。"""
|
||
return self.contracts.get(action_type) or {}
|
||
|
||
|
||
class _FakeWorkflowOper:
|
||
"""记录工作流持久化调用。"""
|
||
|
||
def __init__(self, workflow):
|
||
self.workflow = workflow
|
||
self.steps = []
|
||
self.started = False
|
||
self.failed_result = None
|
||
self.succeeded = False
|
||
|
||
def reset(self, wid):
|
||
"""模拟重置工作流。"""
|
||
_ = wid
|
||
return True
|
||
|
||
def get(self, wid):
|
||
"""返回预置工作流。"""
|
||
_ = wid
|
||
return self.workflow
|
||
|
||
def start(self, wid):
|
||
"""记录启动调用。"""
|
||
_ = wid
|
||
self.started = True
|
||
return True
|
||
|
||
def step(self, wid, action_id, context, execution_state=None):
|
||
"""记录步骤持久化数据。"""
|
||
self.steps.append(
|
||
{
|
||
"wid": wid,
|
||
"action_id": action_id,
|
||
"context": context,
|
||
"execution_state": execution_state,
|
||
}
|
||
)
|
||
return True
|
||
|
||
def fail(self, wid, result):
|
||
"""记录失败结果。"""
|
||
_ = wid
|
||
self.failed_result = result
|
||
return True
|
||
|
||
def success(self, wid, result=None):
|
||
"""记录成功结果。"""
|
||
_ = wid, result
|
||
self.succeeded = True
|
||
return True
|
||
|
||
|
||
class _OpaqueValue:
|
||
"""模拟无法直接 JSON 序列化的值。"""
|
||
|
||
__slots__ = ()
|
||
|
||
def __str__(self):
|
||
return "opaque-value"
|
||
|
||
|
||
def test_workflow_executor_preserves_trigger_context(monkeypatch):
|
||
"""工作流节点及其完成回调应保留触发链路的关联 ID。"""
|
||
observed = []
|
||
release = threading.Event()
|
||
|
||
def run_action(_action, context):
|
||
observed.append(("node", get_correlation_id()))
|
||
assert release.wait(timeout=1)
|
||
return ActionResult(success=True, message="ok", context=context)
|
||
|
||
fake_manager = _FakeWorkflowManager(
|
||
[],
|
||
results={"A": run_action},
|
||
)
|
||
monkeypatch.setattr(workflow_module, "get_workflow_manager", lambda: fake_manager)
|
||
monkeypatch.setattr(
|
||
workflow_module.runtime_stop_state,
|
||
"resume_workflow",
|
||
lambda _workflow_id: None,
|
||
)
|
||
monkeypatch.setattr(
|
||
workflow_module.runtime_stop_state,
|
||
"is_workflow_stopped",
|
||
lambda _workflow_id: False,
|
||
)
|
||
|
||
executor = workflow_module.WorkflowExecutor(
|
||
_build_workflow(
|
||
actions=[
|
||
{"id": "A", "type": "FakeAction", "name": "动作A", "data": {}}
|
||
],
|
||
flows=[],
|
||
),
|
||
step_callback=lambda _action, _context: observed.append(
|
||
("completion", get_correlation_id())
|
||
),
|
||
)
|
||
timer = threading.Timer(0.05, release.set)
|
||
try:
|
||
with correlation_scope("workflow-request"):
|
||
timer.start()
|
||
executor.execute()
|
||
finally:
|
||
release.set()
|
||
timer.join(timeout=1)
|
||
|
||
assert observed == [
|
||
("node", "workflow-request"),
|
||
("completion", "workflow-request"),
|
||
]
|
||
|
||
|
||
def test_workflow_executor_resumes_downstream_nodes(monkeypatch):
|
||
"""恢复执行时应释放已完成节点的后继节点。"""
|
||
calls = []
|
||
fake_manager = _FakeWorkflowManager(calls)
|
||
workflow = _build_workflow(
|
||
current_action="A",
|
||
context=_encoded_context(ActionContext()),
|
||
)
|
||
|
||
monkeypatch.setattr(workflow_module, "get_workflow_manager", lambda: fake_manager)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "resume_workflow", lambda workflow_id: None)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "is_workflow_stopped", lambda workflow_id: False)
|
||
|
||
executor = workflow_module.WorkflowExecutor(workflow)
|
||
executor.execute()
|
||
|
||
assert calls == ["B"]
|
||
assert executor.success is True
|
||
assert executor.context.progress == 100
|
||
|
||
|
||
def test_workflow_executor_restores_structured_context(monkeypatch):
|
||
"""恢复执行时应兼容新版结构化上下文存储格式。"""
|
||
calls = []
|
||
fake_manager = _FakeWorkflowManager(calls)
|
||
workflow = _build_workflow(
|
||
current_action="A",
|
||
context={
|
||
"workflow_context": {"trace_id": "wf-1"},
|
||
"node_outputs": {"A": {"items": ["movie"]}},
|
||
"progress": 50,
|
||
},
|
||
)
|
||
|
||
monkeypatch.setattr(workflow_module, "get_workflow_manager", lambda: fake_manager)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "resume_workflow", lambda workflow_id: None)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "is_workflow_stopped", lambda workflow_id: False)
|
||
|
||
executor = workflow_module.WorkflowExecutor(workflow)
|
||
executor.execute()
|
||
|
||
assert calls == ["B"]
|
||
assert executor.context.workflow_context["trace_id"] == "wf-1"
|
||
assert executor.context.node_outputs["A"]["items"] == ["movie"]
|
||
|
||
|
||
def test_workflow_executor_reports_incremental_progress(monkeypatch):
|
||
"""顺序工作流的中间进度应按已完成比例计算。"""
|
||
calls = []
|
||
progresses = []
|
||
fake_manager = _FakeWorkflowManager(calls)
|
||
|
||
monkeypatch.setattr(workflow_module, "get_workflow_manager", lambda: fake_manager)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "resume_workflow", lambda workflow_id: None)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "is_workflow_stopped", lambda workflow_id: False)
|
||
|
||
executor = workflow_module.WorkflowExecutor(
|
||
_build_workflow(),
|
||
step_callback=lambda action, context: progresses.append(context.progress),
|
||
)
|
||
executor.execute()
|
||
|
||
assert calls == ["A", "B"]
|
||
assert progresses == [50, 100]
|
||
|
||
|
||
def test_workflow_executor_skips_false_condition_branch(monkeypatch):
|
||
"""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。"""
|
||
calls = []
|
||
fake_manager = _FakeWorkflowManager(
|
||
calls,
|
||
results={
|
||
"A": lambda action, context: ActionResult(
|
||
success=True,
|
||
message=f"{action.name}完成",
|
||
context=context,
|
||
outputs={"items": ["movie"]}
|
||
)
|
||
}
|
||
)
|
||
workflow = _build_workflow(
|
||
actions=[
|
||
{"id": "A", "type": "FakeAction", "name": "动作A", "data": {}},
|
||
{"id": "B", "type": "FakeAction", "name": "动作B", "data": {}},
|
||
{"id": "C", "type": "FakeAction", "name": "动作C", "data": {}},
|
||
],
|
||
flows=[
|
||
{"id": "flow-ab", "source": "A", "target": "B", "condition": "outputs.A.items.count == 0"},
|
||
{"id": "flow-ac", "source": "A", "target": "C", "data": {"condition": "outputs.A.items.count > 0"}},
|
||
],
|
||
)
|
||
|
||
monkeypatch.setattr(workflow_module, "get_workflow_manager", lambda: fake_manager)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "resume_workflow", lambda workflow_id: None)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "is_workflow_stopped", lambda workflow_id: False)
|
||
|
||
executor = workflow_module.WorkflowExecutor(workflow)
|
||
executor.execute()
|
||
|
||
assert calls == ["A", "C"]
|
||
assert executor.success is True
|
||
assert executor.context.progress == 100
|
||
assert executor.context.node_outputs["A"]["items"] == ["movie"]
|
||
|
||
|
||
def test_workflow_executor_all_success_join_waits_parallel_branches(monkeypatch):
|
||
"""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。"""
|
||
calls = []
|
||
joined_outputs = {}
|
||
|
||
def run_join(action, context):
|
||
"""记录汇合节点读取到的上游输出。"""
|
||
joined_outputs.update(context.node_outputs)
|
||
return ActionResult(success=True, message=f"{action.name}完成", context=context)
|
||
|
||
fake_manager = _FakeWorkflowManager(
|
||
calls,
|
||
results={
|
||
"A": lambda action, context: ActionResult(
|
||
success=True,
|
||
message=f"{action.name}完成",
|
||
context=context,
|
||
outputs={"value": "A"}
|
||
),
|
||
"B": lambda action, context: ActionResult(
|
||
success=True,
|
||
message=f"{action.name}完成",
|
||
context=context,
|
||
outputs={"value": "B"}
|
||
),
|
||
"C": run_join,
|
||
}
|
||
)
|
||
workflow = _build_workflow(
|
||
actions=[
|
||
{"id": "A", "type": "FakeAction", "name": "动作A", "data": {}},
|
||
{"id": "B", "type": "FakeAction", "name": "动作B", "data": {}},
|
||
{"id": "C", "type": "FakeAction", "name": "动作C", "data": {}},
|
||
],
|
||
flows=[
|
||
{"id": "flow-ac", "source": "A", "target": "C"},
|
||
{"id": "flow-bc", "source": "B", "target": "C"},
|
||
],
|
||
)
|
||
|
||
monkeypatch.setattr(workflow_module, "get_workflow_manager", lambda: fake_manager)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "resume_workflow", lambda workflow_id: None)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "is_workflow_stopped", lambda workflow_id: False)
|
||
|
||
executor = workflow_module.WorkflowExecutor(workflow)
|
||
executor.execute()
|
||
|
||
assert set(calls) == {"A", "B", "C"}
|
||
assert calls[-1] == "C"
|
||
assert joined_outputs["A"] == {"value": "A"}
|
||
assert joined_outputs["B"] == {"value": "B"}
|
||
|
||
|
||
def test_workflow_executor_any_success_join_runs_after_available_branch(monkeypatch):
|
||
"""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。"""
|
||
calls = []
|
||
fake_manager = _FakeWorkflowManager(
|
||
calls,
|
||
results={
|
||
"A": lambda action, context: ActionResult(
|
||
success=True,
|
||
message=f"{action.name}完成",
|
||
context=context,
|
||
outputs={"items": ["movie"]}
|
||
)
|
||
}
|
||
)
|
||
workflow = _build_workflow(
|
||
actions=[
|
||
{"id": "A", "type": "FakeAction", "name": "动作A", "data": {}},
|
||
{"id": "B", "type": "FakeAction", "name": "动作B", "data": {}},
|
||
{"id": "C", "type": "FakeAction", "name": "动作C", "data": {}},
|
||
{"id": "D", "type": "FakeAction", "name": "动作D", "data": {"join_policy": "any_success"}},
|
||
],
|
||
flows=[
|
||
{"id": "flow-ab", "source": "A", "target": "B", "condition": "outputs.A.items.count == 0"},
|
||
{"id": "flow-ac", "source": "A", "target": "C", "condition": "outputs.A.items.count > 0"},
|
||
{"id": "flow-bd", "source": "B", "target": "D"},
|
||
{"id": "flow-cd", "source": "C", "target": "D"},
|
||
],
|
||
)
|
||
|
||
monkeypatch.setattr(workflow_module, "get_workflow_manager", lambda: fake_manager)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "resume_workflow", lambda workflow_id: None)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "is_workflow_stopped", lambda workflow_id: False)
|
||
|
||
executor = workflow_module.WorkflowExecutor(workflow)
|
||
executor.execute()
|
||
|
||
assert calls == ["A", "C", "D"]
|
||
assert executor.context.progress == 100
|
||
|
||
|
||
def test_workflow_executor_all_done_join_can_continue_after_failure(monkeypatch):
|
||
"""continue 失败策略配合 all_done 汇合时应继续执行收尾节点。"""
|
||
calls = []
|
||
fake_manager = _FakeWorkflowManager(
|
||
calls,
|
||
results={
|
||
"A": lambda action, context: ActionResult(success=False, message=f"{action.name}失败", context=context)
|
||
}
|
||
)
|
||
workflow = _build_workflow(
|
||
actions=[
|
||
{"id": "A", "type": "FakeAction", "name": "动作A", "data": {"fail_policy": "continue"}},
|
||
{"id": "B", "type": "FakeAction", "name": "动作B", "data": {}},
|
||
{"id": "C", "type": "FakeAction", "name": "动作C", "data": {"join_policy": "all_done"}},
|
||
],
|
||
flows=[
|
||
{"id": "flow-ac", "source": "A", "target": "C"},
|
||
{"id": "flow-bc", "source": "B", "target": "C"},
|
||
],
|
||
)
|
||
|
||
monkeypatch.setattr(workflow_module, "get_workflow_manager", lambda: fake_manager)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "resume_workflow", lambda workflow_id: None)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "is_workflow_stopped", lambda workflow_id: False)
|
||
|
||
executor = workflow_module.WorkflowExecutor(workflow)
|
||
executor.execute()
|
||
|
||
assert set(calls) == {"A", "B", "C"}
|
||
assert calls[-1] == "C"
|
||
assert executor.has_failure is True
|
||
assert executor.success is True
|
||
|
||
|
||
def test_workflow_executor_exclusive_branch_uses_first_matching_flow(monkeypatch):
|
||
"""互斥分支应只执行第一条满足条件的出边。"""
|
||
calls = []
|
||
fake_manager = _FakeWorkflowManager(
|
||
calls,
|
||
results={
|
||
"A": lambda action, context: ActionResult(
|
||
success=True,
|
||
message=f"{action.name}完成",
|
||
context=context,
|
||
outputs={"count": 2}
|
||
)
|
||
}
|
||
)
|
||
workflow = _build_workflow(
|
||
actions=[
|
||
{"id": "A", "type": "FakeAction", "name": "动作A", "data": {"branch_policy": "exclusive"}},
|
||
{"id": "B", "type": "FakeAction", "name": "动作B", "data": {}},
|
||
{"id": "C", "type": "FakeAction", "name": "动作C", "data": {}},
|
||
],
|
||
flows=[
|
||
{"id": "flow-ab", "source": "A", "target": "B", "condition": "outputs.A.count > 0"},
|
||
{"id": "flow-ac", "source": "A", "target": "C", "condition": "outputs.A.count > 1"},
|
||
],
|
||
)
|
||
|
||
monkeypatch.setattr(workflow_module, "get_workflow_manager", lambda: fake_manager)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "resume_workflow", lambda workflow_id: None)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "is_workflow_stopped", lambda workflow_id: False)
|
||
|
||
executor = workflow_module.WorkflowExecutor(workflow)
|
||
executor.execute()
|
||
|
||
assert calls == ["A", "B"]
|
||
assert executor.node_states["C"] == "skipped"
|
||
|
||
|
||
def test_workflow_executor_passes_declared_inputs(monkeypatch):
|
||
"""动作输入声明应从 node_outputs 中读取指定路径。"""
|
||
calls = []
|
||
fake_manager = _FakeWorkflowManager(
|
||
calls,
|
||
results={
|
||
"A": lambda action, context: ActionResult(
|
||
success=True,
|
||
message=f"{action.name}完成",
|
||
context=context,
|
||
outputs={"torrents": ["a", "b"]}
|
||
)
|
||
}
|
||
)
|
||
workflow = _build_workflow(
|
||
actions=[
|
||
{"id": "A", "type": "FakeAction", "name": "动作A", "data": {}},
|
||
{
|
||
"id": "B",
|
||
"type": "FakeAction",
|
||
"name": "动作B",
|
||
"data": {"inputs": ["A.torrents", "outputs.A.torrents.count"]},
|
||
},
|
||
],
|
||
)
|
||
|
||
monkeypatch.setattr(workflow_module, "get_workflow_manager", lambda: fake_manager)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "resume_workflow", lambda workflow_id: None)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "is_workflow_stopped", lambda workflow_id: False)
|
||
|
||
executor = workflow_module.WorkflowExecutor(workflow)
|
||
executor.execute()
|
||
|
||
b_inputs = [item for action_id, item, _, _ in fake_manager.received_inputs if action_id == "B"][0]
|
||
assert b_inputs == {
|
||
"A.torrents": ["a", "b"],
|
||
"outputs.A.torrents.count": 2,
|
||
}
|
||
|
||
|
||
def test_workflow_executor_uses_contract_inputs(monkeypatch):
|
||
"""未手写输入声明时应按动作契约读取上下文字段。"""
|
||
calls = []
|
||
fake_manager = _FakeWorkflowManager(
|
||
calls,
|
||
contracts={
|
||
"NeedsTorrentsAction": {
|
||
"inputs": [{"name": "torrents", "label": "资源", "kind": "list"}],
|
||
"outputs": [],
|
||
}
|
||
},
|
||
results={
|
||
"A": lambda action, context: ActionResult(
|
||
success=True,
|
||
message=f"{action.name}完成",
|
||
context=context,
|
||
outputs={"torrents": ["a", "b"]}
|
||
)
|
||
}
|
||
)
|
||
workflow = _build_workflow(
|
||
actions=[
|
||
{"id": "A", "type": "FakeAction", "name": "动作A", "data": {}},
|
||
{"id": "B", "type": "NeedsTorrentsAction", "name": "动作B", "data": {}},
|
||
],
|
||
)
|
||
|
||
monkeypatch.setattr(workflow_module, "get_workflow_manager", lambda: fake_manager)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "resume_workflow", lambda workflow_id: None)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "is_workflow_stopped", lambda workflow_id: False)
|
||
|
||
executor = workflow_module.WorkflowExecutor(workflow)
|
||
executor.execute()
|
||
|
||
b_inputs = [item for action_id, item, _, _ in fake_manager.received_inputs if action_id == "B"][0]
|
||
assert b_inputs == {"torrents": ["a", "b"]}
|
||
|
||
|
||
def test_workflow_executor_persists_structured_state(monkeypatch):
|
||
"""步骤回调应收到可持久化的结构化执行状态。"""
|
||
calls = []
|
||
states = []
|
||
fake_manager = _FakeWorkflowManager(
|
||
calls,
|
||
results={
|
||
"A": lambda action, context: ActionResult(
|
||
success=True,
|
||
message=f"{action.name}完成",
|
||
context=context,
|
||
outputs={"items": ["movie"]}
|
||
)
|
||
}
|
||
)
|
||
|
||
monkeypatch.setattr(workflow_module, "get_workflow_manager", lambda: fake_manager)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "resume_workflow", lambda workflow_id: None)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "is_workflow_stopped", lambda workflow_id: False)
|
||
|
||
executor = workflow_module.WorkflowExecutor(
|
||
_build_workflow(actions=[{"id": "A", "type": "FakeAction", "name": "动作A", "data": {}}], flows=[]),
|
||
step_callback=lambda action, context, execution_state, completed: states.append(execution_state),
|
||
)
|
||
executor.execute()
|
||
|
||
assert states[-1]["nodes"]["A"]["state"] == "success"
|
||
assert states[-1]["outputs"]["A"]["items"] == ["movie"]
|
||
assert states[-1]["runtime"]["progress"] == 100
|
||
|
||
|
||
def test_workflow_executor_restores_outputs_from_execution_state(monkeypatch):
|
||
"""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。"""
|
||
calls = []
|
||
fake_manager = _FakeWorkflowManager(calls)
|
||
workflow = _build_workflow(
|
||
execution_state={
|
||
"nodes": {
|
||
"A": {"state": "success", "attempt": 1},
|
||
},
|
||
"outputs": {
|
||
"A": {"torrents": ["movie"]},
|
||
},
|
||
},
|
||
flows=[
|
||
{"id": "flow-ab", "source": "A", "target": "B", "condition": "A.torrents.count > 0"},
|
||
],
|
||
)
|
||
|
||
monkeypatch.setattr(workflow_module, "get_workflow_manager", lambda: fake_manager)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "resume_workflow", lambda workflow_id: None)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "is_workflow_stopped", lambda workflow_id: False)
|
||
|
||
executor = workflow_module.WorkflowExecutor(workflow)
|
||
executor.execute()
|
||
|
||
assert calls == ["B"]
|
||
assert executor.context.node_outputs["A"]["torrents"] == ["movie"]
|
||
|
||
|
||
def test_workflow_executor_keeps_execution_state_dict_for_non_json_leaf(monkeypatch):
|
||
"""结构化状态遇到不可序列化叶子节点时仍应保持字典结构。"""
|
||
calls = []
|
||
states = []
|
||
fake_manager = _FakeWorkflowManager(
|
||
calls,
|
||
results={
|
||
"A": lambda action, context: ActionResult(
|
||
success=True,
|
||
message=f"{action.name}完成",
|
||
context=context,
|
||
outputs={"opaque": _OpaqueValue()}
|
||
)
|
||
}
|
||
)
|
||
|
||
monkeypatch.setattr(workflow_module, "get_workflow_manager", lambda: fake_manager)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "resume_workflow", lambda workflow_id: None)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "is_workflow_stopped", lambda workflow_id: False)
|
||
|
||
executor = workflow_module.WorkflowExecutor(
|
||
_build_workflow(actions=[{"id": "A", "type": "FakeAction", "name": "动作A", "data": {}}], flows=[]),
|
||
step_callback=lambda action, context, execution_state, completed: states.append(execution_state),
|
||
)
|
||
executor.execute()
|
||
|
||
assert isinstance(states[-1], dict)
|
||
assert states[-1]["outputs"]["A"]["opaque"] == "opaque-value"
|
||
|
||
|
||
def test_workflow_chain_process_serializes_circular_context(monkeypatch):
|
||
"""工作流步骤持久化应清洗循环引用和不可序列化上下文。"""
|
||
calls = []
|
||
|
||
def run_action(action, context):
|
||
"""构造包含循环引用的上下文。"""
|
||
context.workflow_context["self"] = context.workflow_context
|
||
context.workflow_context["opaque"] = _OpaqueValue()
|
||
return ActionResult(success=True, message=f"{action.name}完成", context=context)
|
||
|
||
fake_manager = _FakeWorkflowManager(calls, results={"A": run_action})
|
||
workflow = _build_workflow(
|
||
actions=[{"id": "A", "type": "FakeAction", "name": "动作A", "data": {}}],
|
||
flows=[{"id": "flow-end", "source": "A", "target": "END", "animated": True}],
|
||
)
|
||
fake_oper = _FakeWorkflowOper(workflow)
|
||
port_calls = []
|
||
|
||
def get_execution_port():
|
||
"""记录单次执行获取事务端口的次数。"""
|
||
port_calls.append(True)
|
||
return fake_oper
|
||
|
||
monkeypatch.setattr(workflow_module, "get_workflow_manager", lambda: fake_manager)
|
||
monkeypatch.setattr(
|
||
workflow_module,
|
||
"get_configured_workflow_execution",
|
||
get_execution_port,
|
||
)
|
||
monkeypatch.setattr(
|
||
workflow_module,
|
||
"get_configured_workflow_query",
|
||
lambda: SimpleNamespace(get_sync=lambda _workflow_id: workflow),
|
||
)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "resume_workflow", lambda workflow_id: None)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "is_workflow_stopped", lambda workflow_id: False)
|
||
|
||
success, message = workflow_module.WorkflowChain.process(workflow_id=1)
|
||
|
||
assert success is True
|
||
assert message == ""
|
||
assert port_calls == [True]
|
||
assert fake_oper.succeeded is True
|
||
saved_workflow_context = fake_oper.steps[-1]["context"]["workflow_context"]
|
||
saved_self = saved_workflow_context["self"]
|
||
|
||
assert saved_workflow_context["opaque"] == "opaque-value"
|
||
if isinstance(saved_self, dict):
|
||
assert saved_self["self"] == workflow_module.CIRCULAR_REFERENCE_PLACEHOLDER
|
||
assert saved_self["opaque"] == "opaque-value"
|
||
else:
|
||
assert saved_self == workflow_module.CIRCULAR_REFERENCE_PLACEHOLDER
|
||
|
||
|
||
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, "get_workflow_manager", lambda: fake_manager)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "resume_workflow", lambda workflow_id: None)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "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, "get_workflow_manager", lambda: fake_manager)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "resume_workflow", lambda workflow_id: None)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "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_filter_action_replaces_with_empty_outputs(monkeypatch):
|
||
"""过滤节点结果为空时也应清空上游资源，避免后续继续下载。"""
|
||
calls = []
|
||
stale_torrents = ["old", "drop"]
|
||
filtered_torrents = []
|
||
|
||
def run_fetch(action, context):
|
||
"""模拟上游搜索节点产出资源池。"""
|
||
context.torrents = stale_torrents.copy()
|
||
return ActionResult(
|
||
success=True,
|
||
message=f"{action.name}完成",
|
||
context=context,
|
||
outputs={"torrents": stale_torrents.copy()}
|
||
)
|
||
|
||
def run_filter(action, context):
|
||
"""模拟过滤节点把资源全部过滤掉。"""
|
||
context.torrents = filtered_torrents.copy()
|
||
return ActionResult(
|
||
success=True,
|
||
message=f"{action.name}完成",
|
||
context=context,
|
||
outputs={"torrents": filtered_torrents.copy()}
|
||
)
|
||
|
||
fake_manager = _FakeWorkflowManager(
|
||
calls,
|
||
results={
|
||
"A": run_fetch,
|
||
"B": run_filter,
|
||
}
|
||
)
|
||
workflow = _build_workflow(
|
||
actions=[
|
||
{"id": "A", "type": "FetchTorrentsAction", "name": "搜索站点资源", "data": {}},
|
||
{"id": "B", "type": "FilterTorrentsAction", "name": "过滤资源", "data": {}},
|
||
],
|
||
)
|
||
|
||
monkeypatch.setattr(workflow_module, "get_workflow_manager", lambda: fake_manager)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "resume_workflow", lambda workflow_id: None)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "is_workflow_stopped", lambda workflow_id: False)
|
||
|
||
executor = workflow_module.WorkflowExecutor(workflow)
|
||
executor.execute()
|
||
|
||
assert executor.context.torrents == filtered_torrents
|
||
assert executor.context.artifacts["torrents"] == filtered_torrents
|
||
assert executor.context.node_outputs["B"]["torrents"] == filtered_torrents
|
||
|
||
|
||
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, "get_workflow_manager", lambda: fake_manager)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "resume_workflow", lambda workflow_id: None)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "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_manager_shutdown_retains_blocked_execution_for_retry(monkeypatch):
|
||
"""阻塞动作超时时保留 manager owner，释放后同一执行可重试收敛。"""
|
||
entered = threading.Event()
|
||
release = threading.Event()
|
||
|
||
class BlockingAction:
|
||
"""模拟不响应取消令牌的第三方同步工作流动作。"""
|
||
|
||
def __init__(self, action_id):
|
||
"""保存动作标识。"""
|
||
self.action_id = action_id
|
||
self.success = True
|
||
self.message = ""
|
||
|
||
def execute_with_inputs(self, workflow_id, params, inputs, runtime, context):
|
||
"""阻塞到测试显式释放，并返回原工作流上下文。"""
|
||
_ = workflow_id, params, inputs, runtime
|
||
entered.set()
|
||
release.wait()
|
||
return ActionResult(success=True, context=context)
|
||
|
||
manager = object.__new__(workflow_package.WorkflowManager)
|
||
manager._lock = threading.RLock()
|
||
manager._actions = {"BlockingAction": BlockingAction}
|
||
manager._event_workflows = {}
|
||
manager._accepting_executions = True
|
||
manager._executions = {}
|
||
workflow = _build_workflow(
|
||
actions=[{
|
||
"id": "A",
|
||
"type": "BlockingAction",
|
||
"name": "阻塞动作",
|
||
"data": {},
|
||
}],
|
||
flows=[],
|
||
)
|
||
monkeypatch.setattr(workflow_module, "get_workflow_manager", lambda: manager)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "resume_workflow", lambda _workflow_id: None)
|
||
monkeypatch.setattr(
|
||
workflow_module.runtime_stop_state,
|
||
"is_workflow_stopped",
|
||
lambda _workflow_id: False,
|
||
)
|
||
|
||
executor = workflow_module.WorkflowExecutor(workflow)
|
||
execution_thread = threading.Thread(target=executor.execute, daemon=True)
|
||
execution_thread.start()
|
||
try:
|
||
assert entered.wait(timeout=1)
|
||
started_at = time.monotonic()
|
||
|
||
assert manager.stop(timeout=0.01) is False
|
||
|
||
assert time.monotonic() - started_at < 1
|
||
assert executor.cancel_token.is_cancelled() is True
|
||
assert manager._actions == {"BlockingAction": BlockingAction}
|
||
assert manager._executions == {id(executor): executor}
|
||
|
||
rejected = workflow_module.WorkflowExecutor(workflow)
|
||
assert rejected.admit() is False
|
||
assert rejected.errmsg == "工作流服务正在停止"
|
||
finally:
|
||
release.set()
|
||
execution_thread.join(timeout=2)
|
||
|
||
assert not execution_thread.is_alive()
|
||
assert executor.wait_stopped(timeout=1) is True
|
||
assert manager.stop(timeout=1) is True
|
||
assert manager._executions == {}
|
||
assert manager._actions == {}
|
||
|
||
|
||
def test_workflow_manager_shutdown_continues_across_owner_failures():
|
||
"""单个执行 owner 抛错不得跳过其它活动工作流的停止和等待。"""
|
||
|
||
class ObservedOwner:
|
||
"""记录 manager 对多个 owner 的关闭调用。"""
|
||
|
||
def __init__(self, manager, *, fail: bool = False):
|
||
"""保存管理器、失败开关和调用计数。"""
|
||
self.manager = manager
|
||
self.fail = fail
|
||
self.stop_calls = 0
|
||
self.wait_calls = 0
|
||
|
||
def request_stop(self) -> None:
|
||
"""记录停止请求，并按需模拟第三方 owner 异常。"""
|
||
self.stop_calls += 1
|
||
if self.fail:
|
||
raise RuntimeError("stop failed")
|
||
|
||
def wait_stopped(self, timeout: float) -> bool:
|
||
"""记录等待；正常 owner 从 manager 注册表释放自身。"""
|
||
_ = timeout
|
||
self.wait_calls += 1
|
||
if self.fail:
|
||
raise RuntimeError("wait failed")
|
||
self.manager.unregister_execution(self)
|
||
return True
|
||
|
||
manager = object.__new__(workflow_package.WorkflowManager)
|
||
manager._lock = threading.RLock()
|
||
action_marker = object()
|
||
manager._actions = {"FakeAction": action_marker}
|
||
manager._event_workflows = {}
|
||
manager._accepting_executions = True
|
||
manager._executions = {}
|
||
failing_owner = ObservedOwner(manager, fail=True)
|
||
healthy_owner = ObservedOwner(manager)
|
||
assert manager.register_execution(failing_owner) is True
|
||
assert manager.register_execution(healthy_owner) is True
|
||
|
||
assert manager.stop(timeout=0.01) is False
|
||
|
||
assert failing_owner.stop_calls == 1
|
||
assert failing_owner.wait_calls == 1
|
||
assert healthy_owner.stop_calls == 1
|
||
assert healthy_owner.wait_calls == 1
|
||
assert manager._executions == {id(failing_owner): failing_owner}
|
||
assert manager._actions == {"FakeAction": action_marker}
|
||
|
||
|
||
def test_workflow_chain_rejects_execution_before_persisting_running_state(monkeypatch):
|
||
"""停机封口后的新执行不得先把数据库状态写成运行中。"""
|
||
|
||
class RejectingWorkflowManager(_FakeWorkflowManager):
|
||
"""模拟已经封口的 concrete 工作流运行时。"""
|
||
|
||
def register_execution(self, _owner) -> bool:
|
||
"""拒绝停机后的新执行 owner。"""
|
||
return False
|
||
|
||
workflow = _build_workflow()
|
||
workflowoper = _FakeWorkflowOper(workflow)
|
||
manager = RejectingWorkflowManager([])
|
||
monkeypatch.setattr(workflow_module, "get_workflow_manager", lambda: manager)
|
||
monkeypatch.setattr(
|
||
workflow_module,
|
||
"get_configured_workflow_execution",
|
||
lambda: workflowoper,
|
||
)
|
||
monkeypatch.setattr(
|
||
workflow_module,
|
||
"get_configured_workflow_query",
|
||
lambda: SimpleNamespace(get_sync=lambda _workflow_id: workflow),
|
||
)
|
||
|
||
def unexpected_resume(_workflow_id: int) -> None:
|
||
"""拒绝准入时若仍恢复停止标记则立即暴露回归。"""
|
||
raise AssertionError("拒绝准入时不得恢复工作流")
|
||
|
||
monkeypatch.setattr(
|
||
workflow_module.runtime_stop_state,
|
||
"resume_workflow",
|
||
unexpected_resume,
|
||
)
|
||
|
||
success, message = workflow_module.WorkflowChain.process(workflow_id=1)
|
||
|
||
assert success is False
|
||
assert message == "工作流服务正在停止"
|
||
assert workflowoper.started is False
|
||
|
||
|
||
def test_workflow_chain_releases_admitted_owner_when_start_fails(monkeypatch):
|
||
"""数据库启动状态写入异常时不得遗留尚未执行的 manager owner。"""
|
||
|
||
class FailingWorkflowOper(_FakeWorkflowOper):
|
||
"""模拟执行状态 start 事务失败。"""
|
||
|
||
def start(self, wid):
|
||
"""在 owner 已准入后抛出持久化异常。"""
|
||
_ = wid
|
||
raise RuntimeError("start failed")
|
||
|
||
manager = object.__new__(workflow_package.WorkflowManager)
|
||
manager._lock = threading.RLock()
|
||
manager._actions = {"FakeAction": object()}
|
||
manager._event_workflows = {}
|
||
manager._accepting_executions = True
|
||
manager._executions = {}
|
||
workflowoper = FailingWorkflowOper(_build_workflow())
|
||
monkeypatch.setattr(workflow_module, "get_workflow_manager", lambda: manager)
|
||
monkeypatch.setattr(
|
||
workflow_module,
|
||
"get_configured_workflow_execution",
|
||
lambda: workflowoper,
|
||
)
|
||
monkeypatch.setattr(
|
||
workflow_module,
|
||
"get_configured_workflow_query",
|
||
lambda: SimpleNamespace(
|
||
get_sync=lambda _workflow_id: workflowoper.workflow
|
||
),
|
||
)
|
||
monkeypatch.setattr(workflow_module.runtime_stop_state, "resume_workflow", lambda _workflow_id: None)
|
||
|
||
with pytest.raises(RuntimeError, match="start failed"):
|
||
workflow_module.WorkflowChain.process(workflow_id=1)
|
||
|
||
assert manager._executions == {}
|
||
assert manager._accepting_executions is True
|
||
|
||
|
||
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.runtime_stop_state, "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
|