mirror of
https://github.com/jxxghp/MoviePilot.git
synced 2026-08-10 07:54:14 +08:00
refactor(cache): simplify recognition cache persistence
This commit is contained in:
jxxghp
@@ -1,166 +0,0 @@
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import asyncio
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import inspect
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from unittest.mock import Mock
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from app.api.endpoints import douban as douban_endpoint
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from app.db.user_oper import get_current_active_superuser_async
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from app.modules.douban.douban_cache import DoubanCache
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from app.schemas.types import MediaType, SystemConfigKey
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class _MemoryCacheStub:
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"""提供豆瓣缓存管理测试所需的最小内存后端。"""
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def __init__(self, data: dict):
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"""使用给定字典初始化测试缓存。"""
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self.data = data
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def items(self):
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"""返回全部缓存条目。"""
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return self.data.items()
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def get(self, key: str):
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"""读取指定缓存条目。"""
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return self.data.get(key)
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def delete(self, key: str):
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"""删除指定缓存条目。"""
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self.data.pop(key, None)
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def set(self, key: str, value):
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"""写入指定缓存条目。"""
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self.data[key] = value
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def clear(self):
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"""清空全部缓存条目。"""
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self.data.clear()
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def _build_douban_cache(data: dict) -> DoubanCache:
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"""构造绕过单例初始化的豆瓣缓存测试实例。"""
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cache = object.__new__(DoubanCache)
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cache._cache = _MemoryCacheStub(data)
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cache.save = lambda force=False: None
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return cache
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def test_douban_cache_management_endpoints_require_superuser():
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"""豆瓣识别缓存管理接口必须仅允许超级管理员访问。"""
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endpoints = [
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douban_endpoint.douban_recognition_cache,
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douban_endpoint.delete_douban_recognition_cache,
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douban_endpoint.clear_douban_recognition_cache,
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]
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for endpoint in endpoints:
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dependency = inspect.signature(endpoint).parameters["_"].default.dependency
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assert dependency is get_current_active_superuser_async
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def test_douban_cache_list_items_normalizes_media_type_and_sorting():
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"""豆瓣管理列表应输出稳定顺序和前端可识别的媒体类型。"""
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cache = _build_douban_cache({
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"[电视剧]Zulu-2024-1": {
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"id": "2",
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"title": "Zulu",
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"type": MediaType.TV,
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"year": "2024",
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},
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"[电影]Alpha-2023-None": {
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"id": "1",
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"title": "Alpha",
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"type": "电影",
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"year": "2023",
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"poster_path": "https://example.com/alpha.jpg",
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},
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"[电影]Missing-2022-None": {"id": 0},
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})
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items = cache.list_items()
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assert [item["title"] for item in items] == ["Alpha", "", "Zulu"]
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assert [item["media_type"] for item in items] == ["movie", "unknown", "tv"]
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assert items[0]["poster_path"] == "https://example.com/alpha.jpg"
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assert items[1]["douban_id"] == 0
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def test_douban_cache_infers_special_season_title_as_tv():
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"""缺少显式类型时，S00 标题仍应按电视剧写入缓存。"""
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cache = _build_douban_cache({})
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cache.update(
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meta=None,
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info={"id": "special", "title": "测试剧 S00", "year": "2024"},
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)
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cached = next(iter(cache._cache.data.values()))
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assert cached["type"] == MediaType.TV
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def test_douban_cache_delete_and_clear_persist_immediately(monkeypatch):
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"""豆瓣管理操作应修改运行时缓存并立即触发本地持久化。"""
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cache = _build_douban_cache({"first": {"id": "1"}, "second": {"id": "2"}})
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saved_forces = []
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monkeypatch.setattr(cache, "save", lambda force=False: saved_forces.append(force))
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assert cache.delete("first") == {"id": "1"}
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assert cache.delete("missing") == {}
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cache.clear()
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assert cache.list_items() == []
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assert saved_forces == [True, True]
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def test_douban_cache_endpoint_returns_management_statistics(monkeypatch):
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"""豆瓣查询接口应返回识别成功和失败条目的统计。"""
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cache = _build_douban_cache({
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"recognized": {"id": "1", "title": "Alpha", "type": MediaType.MOVIE},
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"unrecognized": {"id": 0},
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})
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get_system_config = Mock(return_value=None)
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monkeypatch.setattr(douban_endpoint, "DoubanCache", lambda: cache)
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monkeypatch.setattr(
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douban_endpoint,
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"SystemConfigOper",
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lambda: type("SystemConfigStub", (), {"get": get_system_config})(),
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)
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monkeypatch.setattr(douban_endpoint.settings, "MEDIA_RECOGNIZE_SHARE", False)
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response = asyncio.run(douban_endpoint.douban_recognition_cache(None))
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assert response.success is True
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assert response.data["count"] == 2
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assert response.data["recognized"] == 1
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assert response.data["unrecognized"] == 1
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assert response.data["shared_recognized"] == 0
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assert response.data["shared_recognize_enabled"] is False
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get_system_config.assert_called_once_with(
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SystemConfigKey.MediaRecognizeShareCount
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)
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def test_douban_cache_delete_endpoint_reports_missing_item(monkeypatch):
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"""豆瓣删除接口应区分成功删除与缓存不存在。"""
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cache = _build_douban_cache({"existing": {"id": "1"}})
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monkeypatch.setattr(douban_endpoint, "DoubanCache", lambda: cache)
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deleted_response = asyncio.run(
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douban_endpoint.delete_douban_recognition_cache("existing", None)
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)
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missing_response = asyncio.run(
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douban_endpoint.delete_douban_recognition_cache("missing", None)
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)
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assert deleted_response.success is True
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assert missing_response.success is False
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def test_douban_cache_clear_endpoint_removes_all_items(monkeypatch):
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"""豆瓣清空接口应删除全部识别缓存。"""
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cache = _build_douban_cache({"existing": {"id": "1"}})
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monkeypatch.setattr(douban_endpoint, "DoubanCache", lambda: cache)
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response = asyncio.run(douban_endpoint.clear_douban_recognition_cache(None))
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assert response.success is True
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assert cache.list_items() == []
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80
tests/test_douban_recognition.py
Normal file
80
tests/test_douban_recognition.py
Normal file
@@ -0,0 +1,80 @@
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import asyncio
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from unittest.mock import Mock
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from unittest.mock import AsyncMock
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from app.core.meta import MetaBase
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from app.modules.douban import DoubanModule
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from app.schemas.types import MediaType
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def test_douban_recognize_does_not_keep_dedicated_mapping_cache():
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"""豆瓣识别应每次执行匹配，不再保留专用标题映射缓存。"""
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module = DoubanModule()
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meta = MetaBase("测试电影")
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meta.name = "测试电影"
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meta.type = MediaType.MOVIE
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meta.year = "2024"
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match_doubaninfo = Mock(return_value={"id": "200"})
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douban_info = Mock(return_value={
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"id": "200",
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"title": "测试电影",
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"type": "movie",
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"year": "2024",
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})
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first_result = module._recognize_media_core(
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meta=meta,
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source="douban",
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match_doubaninfo_func=match_doubaninfo,
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douban_info_func=douban_info,
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)
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second_result = module._recognize_media_core(
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meta=meta,
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source="douban",
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match_doubaninfo_func=match_doubaninfo,
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douban_info_func=douban_info,
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)
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assert first_result.douban_id == "200"
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assert second_result.douban_id == "200"
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assert match_doubaninfo.call_count == 2
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assert douban_info.call_count == 2
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def test_async_douban_recognize_does_not_keep_dedicated_mapping_cache():
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"""异步豆瓣识别也应每次执行匹配，不使用专用标题映射缓存。"""
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module = DoubanModule()
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meta = MetaBase("测试剧集")
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meta.name = "测试剧集"
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meta.type = MediaType.TV
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meta.year = "2024"
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match_doubaninfo = AsyncMock(return_value={"id": "201"})
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douban_info = AsyncMock(return_value={
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"id": "201",
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"title": "测试剧集",
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"type": "tv",
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"year": "2024",
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})
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async def recognize_twice():
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"""连续执行两次异步豆瓣识别。"""
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first_result = await module._async_recognize_media_core(
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meta=meta,
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source="douban",
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async_match_doubaninfo_func=match_doubaninfo,
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async_douban_info_func=douban_info,
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)
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second_result = await module._async_recognize_media_core(
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meta=meta,
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source="douban",
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async_match_doubaninfo_func=match_doubaninfo,
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async_douban_info_func=douban_info,
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)
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return first_result, second_result
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first_result, second_result = asyncio.run(recognize_twice())
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assert first_result.douban_id == "201"
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assert second_result.douban_id == "201"
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assert match_doubaninfo.await_count == 2
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assert douban_info.await_count == 2
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79
tests/test_scheduler_cache_expiry.py
Normal file
79
tests/test_scheduler_cache_expiry.py
Normal file
@@ -0,0 +1,79 @@
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import threading
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from unittest.mock import Mock
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from app import scheduler as scheduler_module
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from app.scheduler import Scheduler
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class _BackgroundSchedulerStub:
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"""记录系统定时任务注册结果的调度器替身。"""
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def __init__(self):
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"""初始化任务记录。"""
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self.jobs = []
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self.started = False
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def add_job(self, func, trigger, **kwargs):
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"""记录一次任务注册。"""
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self.jobs.append({"func": func, "trigger": trigger, **kwargs})
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def start(self):
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"""记录调度器已启动。"""
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self.started = True
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def test_meta_cache_expire_does_not_schedule_bulk_cache_clear(monkeypatch):
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"""单条缓存 TTL 不应再被用于注册整批缓存清理任务。"""
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background_scheduler = _BackgroundSchedulerStub()
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generic_chain = Mock()
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for name in [
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"MediaServerChain",
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"RecommendChain",
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"SchedulerChain",
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"SiteChain",
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"SubscribeChain",
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"TransferChain",
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"WallpaperHelper",
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"WorkflowChain",
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"PluginManager",
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]:
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monkeypatch.setattr(scheduler_module, name, lambda: generic_chain)
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monkeypatch.setattr(
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scheduler_module.ServiceConfigHelper,
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"get_mediaserver_configs",
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lambda: [],
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)
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monkeypatch.setattr(
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scheduler_module,
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"BackgroundScheduler",
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lambda **kwargs: background_scheduler,
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)
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monkeypatch.setattr(Scheduler, "stop", lambda self: None)
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monkeypatch.setattr(Scheduler, "init_workflow_jobs", lambda self: None)
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monkeypatch.setattr(Scheduler, "init_agent_task_jobs", lambda self: None)
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monkeypatch.setattr(Scheduler, "init_plugin_jobs", lambda self: None)
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monkeypatch.setattr(scheduler_module.settings, "DEV", False)
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monkeypatch.setattr(scheduler_module.settings, "COOKIECLOUD_INTERVAL", 0)
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monkeypatch.setattr(scheduler_module.settings, "SUBSCRIBE_SEARCH", False)
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monkeypatch.setattr(scheduler_module.settings, "SUBSCRIBE_MODE", "rss")
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monkeypatch.setattr(scheduler_module.settings, "SUBSCRIBE_RSS_INTERVAL", 30)
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monkeypatch.setattr(scheduler_module.settings, "SITEDATA_REFRESH_INTERVAL", 0)
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monkeypatch.setattr(scheduler_module.settings, "MEMORY_GC_INTERVAL", 0)
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monkeypatch.setattr(scheduler_module.settings, "AI_AGENT_ENABLE", False)
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monkeypatch.setattr(scheduler_module.settings, "DATA_CLEANUP_ENABLE", False)
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monkeypatch.setattr(scheduler_module.settings, "USAGE_STATISTIC_SHARE", False)
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scheduler = object.__new__(Scheduler)
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scheduler._scheduler = None
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scheduler._event = threading.Event()
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scheduler._lock = threading.RLock()
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scheduler._jobs = {}
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scheduler._auth_count = 0
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scheduler._auth_message = False
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scheduler.init()
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scheduled_job_ids = {job["id"] for job in background_scheduler.jobs}
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assert "clear_cache" not in scheduled_job_ids
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assert "clear_cache" in scheduler._jobs
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assert background_scheduler.started is True
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@@ -1,9 +1,11 @@
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import asyncio
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import inspect
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import pickle
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from unittest.mock import Mock
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from app.api.endpoints import tmdb as tmdb_endpoint
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from app.db.user_oper import get_current_active_superuser_async
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from app.modules.themoviedb import tmdb_cache as tmdb_cache_module
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from app.modules.themoviedb.tmdb_cache import TmdbCache
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from app.schemas.types import MediaType, SystemConfigKey
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@@ -27,19 +29,83 @@ class _MemoryCacheStub:
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"""删除指定缓存条目。"""
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self.data.pop(key, None)
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def set(self, key: str, value, ttl=None):
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"""写入指定缓存条目。"""
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self.data[key] = value
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def clear(self):
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"""清空全部缓存条目。"""
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self.data.clear()
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class _FileCacheStub:
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"""提供 TMDB 持久化测试所需的统一文件缓存替身。"""
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def __init__(self, content: bytes = None):
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"""使用预置序列化内容初始化文件缓存。"""
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self.content = content
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self.set_calls = []
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self.delete_calls = []
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def get(self, key: str, region: str):
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"""读取预置缓存内容。"""
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return self.content
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def set(self, key: str, value: bytes, region: str):
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"""记录统一文件缓存写入。"""
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self.content = value
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self.set_calls.append((key, region))
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def delete(self, key: str, region: str):
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"""记录统一文件缓存删除。"""
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self.content = None
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self.delete_calls.append((key, region))
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class _TTLCacheStub(_MemoryCacheStub):
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"""记录每条数据恢复时剩余 TTL 的内存缓存替身。"""
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def __init__(self):
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"""初始化空缓存和 TTL 记录。"""
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super().__init__({})
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self.ttls = {}
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@staticmethod
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def is_redis() -> bool:
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"""测试替身固定使用非 Redis 后端。"""
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return False
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def set(self, key: str, value, ttl=None):
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"""写入缓存并记录本次设置的 TTL。"""
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super().set(key, value, ttl=ttl)
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self.ttls[key] = ttl
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def _build_tmdb_cache(data: dict) -> TmdbCache:
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"""构造绕过单例初始化的 TMDB 缓存测试实例。"""
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cache = object.__new__(TmdbCache)
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cache._cache = _MemoryCacheStub(data)
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cache._expires_at = {key: float("inf") for key in data}
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cache._dirty = False
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cache._file_cache = None
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cache._legacy_file_cache = None
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cache._legacy_cache_found = False
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cache.save = lambda force=False: None
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return cache
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def _build_initialized_tmdb_cache(monkeypatch, file_cache: _FileCacheStub,
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runtime_cache: _TTLCacheStub,
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now: float = 1000) -> TmdbCache:
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"""使用可控时间和缓存替身初始化完整 TMDB 缓存实例。"""
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monkeypatch.setattr(tmdb_cache_module, "time", lambda: now)
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monkeypatch.setattr(tmdb_cache_module, "TTLCache", lambda **kwargs: runtime_cache)
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monkeypatch.setattr(tmdb_cache_module, "FileCache", lambda **kwargs: file_cache)
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cache = object.__new__(TmdbCache)
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cache.__init__()
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return cache
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|
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|
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def test_tmdb_cache_management_endpoints_require_superuser():
|
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"""识别缓存管理接口必须仅允许超级管理员访问。"""
|
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endpoints = [
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@@ -92,6 +158,115 @@ def test_tmdb_cache_delete_and_clear_persist_immediately(monkeypatch):
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assert saved_forces == [True, True]
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||||
|
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|
||||
def test_tmdb_cache_restores_only_unexpired_persisted_items(monkeypatch):
|
||||
"""TMDB 持久化恢复应保留每条数据原有期限并跳过已过期条目。"""
|
||||
payload = {
|
||||
"version": tmdb_cache_module.PERSISTENCE_VERSION,
|
||||
"items": {
|
||||
"fresh": {
|
||||
"value": {"id": 1, "title": "有效"},
|
||||
"expires_at": 1030,
|
||||
},
|
||||
"expired": {
|
||||
"value": {"id": 2, "title": "过期"},
|
||||
"expires_at": 999,
|
||||
},
|
||||
},
|
||||
}
|
||||
file_cache = _FileCacheStub(pickle.dumps(payload))
|
||||
runtime_cache = _TTLCacheStub()
|
||||
|
||||
cache = _build_initialized_tmdb_cache(
|
||||
monkeypatch=monkeypatch,
|
||||
file_cache=file_cache,
|
||||
runtime_cache=runtime_cache,
|
||||
)
|
||||
|
||||
assert runtime_cache.data == {"fresh": {"id": 1, "title": "有效"}}
|
||||
assert runtime_cache.ttls == {"fresh": 30}
|
||||
assert cache._expires_at == {"fresh": 1030}
|
||||
assert cache._dirty is True
|
||||
|
||||
|
||||
def test_tmdb_cache_persists_individual_expiration_with_file_cache(monkeypatch):
|
||||
"""TMDB 持久化应通过统一文件缓存保存每条数据的独立过期时间。"""
|
||||
file_cache = _FileCacheStub()
|
||||
runtime_cache = _TTLCacheStub()
|
||||
cache = _build_initialized_tmdb_cache(
|
||||
monkeypatch=monkeypatch,
|
||||
file_cache=file_cache,
|
||||
runtime_cache=runtime_cache,
|
||||
)
|
||||
runtime_cache.data = {
|
||||
"recognized": {"id": 1, "title": "有效"},
|
||||
"unrecognized": {"id": 0},
|
||||
}
|
||||
cache._expires_at = {
|
||||
"recognized": 1060,
|
||||
"unrecognized": 1070,
|
||||
}
|
||||
cache._dirty = True
|
||||
|
||||
cache.save()
|
||||
|
||||
payload = pickle.loads(file_cache.content)
|
||||
assert file_cache.set_calls == [(
|
||||
tmdb_cache_module.PERSISTENCE_KEY,
|
||||
tmdb_cache_module.PERSISTENCE_REGION,
|
||||
)]
|
||||
assert payload == {
|
||||
"version": tmdb_cache_module.PERSISTENCE_VERSION,
|
||||
"items": {
|
||||
"recognized": {
|
||||
"value": {"id": 1, "title": "有效"},
|
||||
"expires_at": 1060,
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def test_tmdb_cache_migrates_legacy_file_to_global_file_cache(monkeypatch):
|
||||
"""旧 TMDB 缓存应迁移到全局文件缓存并删除旧文件。"""
|
||||
primary_cache = _FileCacheStub()
|
||||
legacy_cache = _FileCacheStub(pickle.dumps({
|
||||
"legacy": {"id": 1, "title": "旧缓存"},
|
||||
}))
|
||||
file_caches = iter([primary_cache, legacy_cache])
|
||||
file_cache_calls = []
|
||||
|
||||
def build_file_cache(**kwargs):
|
||||
"""记录全局文件缓存构造参数并返回对应替身。"""
|
||||
file_cache_calls.append(kwargs)
|
||||
return next(file_caches)
|
||||
|
||||
runtime_cache = _TTLCacheStub()
|
||||
monkeypatch.setattr(tmdb_cache_module, "time", lambda: 1000)
|
||||
monkeypatch.setattr(
|
||||
tmdb_cache_module,
|
||||
"TTLCache",
|
||||
lambda **kwargs: runtime_cache,
|
||||
)
|
||||
monkeypatch.setattr(tmdb_cache_module, "FileCache", build_file_cache)
|
||||
|
||||
cache = object.__new__(TmdbCache)
|
||||
cache.__init__()
|
||||
cache.save()
|
||||
|
||||
assert file_cache_calls == [
|
||||
{"base": tmdb_cache_module.settings.CACHE_PATH, "ttl": cache.ttl},
|
||||
{"base": tmdb_cache_module.settings.TEMP_PATH.parent, "ttl": cache.ttl},
|
||||
]
|
||||
assert runtime_cache.data == {"legacy": {"id": 1, "title": "旧缓存"}}
|
||||
assert primary_cache.set_calls == [(
|
||||
tmdb_cache_module.PERSISTENCE_KEY,
|
||||
tmdb_cache_module.PERSISTENCE_REGION,
|
||||
)]
|
||||
assert legacy_cache.delete_calls == [(
|
||||
cache.region,
|
||||
tmdb_cache_module.settings.TEMP_PATH.name,
|
||||
)]
|
||||
|
||||
|
||||
def test_tmdb_cache_endpoint_returns_management_statistics(monkeypatch):
|
||||
"""查询接口应返回识别成功和失败条目的统计。"""
|
||||
cache = _build_tmdb_cache({
|
||||
|
||||
Reference in New Issue
Block a user