fix: 跳过推荐空缓存

2026-06-15 04:32:09 +08:00 · 2026-06-13 08:09:49 +08:00
parent 7d582cc4d8
commit ab9eeedb3e
2 changed files with 106 additions and 53 deletions
--- a/app/chain/recommend.py
+++ b/app/chain/recommend.py
@@ -105,7 +105,7 @@ class RecommendChain(ChainBase, metaclass=Singleton):
        ImageHelper().fetch_image(url=url)

    @log_execution_time(logger=logger)
-    @cached(ttl=recommend_ttl, region=recommend_cache_region)
+    @cached(ttl=recommend_ttl, region=recommend_cache_region, skip_empty=True)
    def tmdb_movies(self, sort_by: Optional[str] = "popularity.desc",
                    with_genres: Optional[str] = "",
                    with_original_language: Optional[str] = "",
@@ -131,7 +131,7 @@ class RecommendChain(ChainBase, metaclass=Singleton):
        return [movie.to_dict() for movie in movies] if movies else []

    @log_execution_time(logger=logger)
-    @cached(ttl=recommend_ttl, region=recommend_cache_region)
+    @cached(ttl=recommend_ttl, region=recommend_cache_region, skip_empty=True)
    def tmdb_tvs(self, sort_by: Optional[str] = "popularity.desc",
                 with_genres: Optional[str] = "",
                 with_original_language: Optional[str] = "zh|en|ja|ko",
@@ -166,7 +166,7 @@ class RecommendChain(ChainBase, metaclass=Singleton):
        return [info.to_dict() for info in infos] if infos else []

    @log_execution_time(logger=logger)
-    @cached(ttl=recommend_ttl, region=recommend_cache_region)
+    @cached(ttl=recommend_ttl, region=recommend_cache_region, skip_empty=True)
    def bangumi_calendar(self, page: Optional[int] = 1, count: Optional[int] = 30) -> List[dict]:
        """
        Bangumi每日放送
@@ -175,7 +175,7 @@ class RecommendChain(ChainBase, metaclass=Singleton):
        return [media.to_dict() for media in medias[(page - 1) * count: page * count]] if medias else []

    @log_execution_time(logger=logger)
-    @cached(ttl=recommend_ttl, region=recommend_cache_region)
+    @cached(ttl=recommend_ttl, region=recommend_cache_region, skip_empty=True)
    def douban_movie_showing(self, page: Optional[int] = 1, count: Optional[int] = 30) -> List[dict]:
        """
        豆瓣正在热映
@@ -184,7 +184,7 @@ class RecommendChain(ChainBase, metaclass=Singleton):
        return [media.to_dict() for media in movies] if movies else []

    @log_execution_time(logger=logger)
-    @cached(ttl=recommend_ttl, region=recommend_cache_region)
+    @cached(ttl=recommend_ttl, region=recommend_cache_region, skip_empty=True)
    def douban_movies(self, sort: Optional[str] = "R", tags: Optional[str] = "",
                      page: Optional[int] = 1, count: Optional[int] = 30) -> List[dict]:
        """
@@ -195,7 +195,7 @@ class RecommendChain(ChainBase, metaclass=Singleton):
        return [media.to_dict() for media in movies] if movies else []

    @log_execution_time(logger=logger)
-    @cached(ttl=recommend_ttl, region=recommend_cache_region)
+    @cached(ttl=recommend_ttl, region=recommend_cache_region, skip_empty=True)
    def douban_tvs(self, sort: Optional[str] = "R", tags: Optional[str] = "",
                   page: Optional[int] = 1, count: Optional[int] = 30) -> List[dict]:
        """
@@ -206,7 +206,7 @@ class RecommendChain(ChainBase, metaclass=Singleton):
        return [media.to_dict() for media in tvs] if tvs else []

    @log_execution_time(logger=logger)
-    @cached(ttl=recommend_ttl, region=recommend_cache_region)
+    @cached(ttl=recommend_ttl, region=recommend_cache_region, skip_empty=True)
    def douban_movie_top250(self, page: Optional[int] = 1, count: Optional[int] = 30) -> List[dict]:
        """
        豆瓣电影TOP250
@@ -215,7 +215,7 @@ class RecommendChain(ChainBase, metaclass=Singleton):
        return [media.to_dict() for media in movies] if movies else []

    @log_execution_time(logger=logger)
-    @cached(ttl=recommend_ttl, region=recommend_cache_region)
+    @cached(ttl=recommend_ttl, region=recommend_cache_region, skip_empty=True)
    def douban_tv_weekly_chinese(self, page: Optional[int] = 1, count: Optional[int] = 30) -> List[dict]:
        """
        豆瓣国产剧集榜
@@ -224,7 +224,7 @@ class RecommendChain(ChainBase, metaclass=Singleton):
        return [media.to_dict() for media in tvs] if tvs else []

    @log_execution_time(logger=logger)
-    @cached(ttl=recommend_ttl, region=recommend_cache_region)
+    @cached(ttl=recommend_ttl, region=recommend_cache_region, skip_empty=True)
    def douban_tv_weekly_global(self, page: Optional[int] = 1, count: Optional[int] = 30) -> List[dict]:
        """
        豆瓣全球剧集榜
@@ -233,7 +233,7 @@ class RecommendChain(ChainBase, metaclass=Singleton):
        return [media.to_dict() for media in tvs] if tvs else []

    @log_execution_time(logger=logger)
-    @cached(ttl=recommend_ttl, region=recommend_cache_region)
+    @cached(ttl=recommend_ttl, region=recommend_cache_region, skip_empty=True)
    def douban_tv_animation(self, page: Optional[int] = 1, count: Optional[int] = 30) -> List[dict]:
        """
        豆瓣热门动漫
@@ -242,7 +242,7 @@ class RecommendChain(ChainBase, metaclass=Singleton):
        return [media.to_dict() for media in tvs] if tvs else []

    @log_execution_time(logger=logger)
-    @cached(ttl=recommend_ttl, region=recommend_cache_region)
+    @cached(ttl=recommend_ttl, region=recommend_cache_region, skip_empty=True)
    def douban_movie_hot(self, page: Optional[int] = 1, count: Optional[int] = 30) -> List[dict]:
        """
        豆瓣热门电影
@@ -251,7 +251,7 @@ class RecommendChain(ChainBase, metaclass=Singleton):
        return [media.to_dict() for media in movies] if movies else []

    @log_execution_time(logger=logger)
-    @cached(ttl=recommend_ttl, region=recommend_cache_region)
+    @cached(ttl=recommend_ttl, region=recommend_cache_region, skip_empty=True)
    def douban_tv_hot(self, page: Optional[int] = 1, count: Optional[int] = 30) -> List[dict]:
        """
        豆瓣热门电视剧
@@ -260,7 +260,7 @@ class RecommendChain(ChainBase, metaclass=Singleton):
        return [media.to_dict() for media in tvs] if tvs else []

    @log_execution_time(logger=logger)
-    @cached(ttl=recommend_ttl, region=recommend_cache_region)
+    @cached(ttl=recommend_ttl, region=recommend_cache_region, skip_empty=True)
    async def async_tmdb_movies(self, sort_by: Optional[str] = "popularity.desc",
                                with_genres: Optional[str] = "",
                                with_original_language: Optional[str] = "",
@@ -286,7 +286,7 @@ class RecommendChain(ChainBase, metaclass=Singleton):
        return [movie.to_dict() for movie in movies] if movies else []

    @log_execution_time(logger=logger)
-    @cached(ttl=recommend_ttl, region=recommend_cache_region)
+    @cached(ttl=recommend_ttl, region=recommend_cache_region, skip_empty=True)
    async def async_tmdb_tvs(self, sort_by: Optional[str] = "popularity.desc",
                             with_genres: Optional[str] = "",
                             with_original_language: Optional[str] = "zh|en|ja|ko",
@@ -321,7 +321,7 @@ class RecommendChain(ChainBase, metaclass=Singleton):
        return [info.to_dict() for info in infos] if infos else []

    @log_execution_time(logger=logger)
-    @cached(ttl=recommend_ttl, region=recommend_cache_region)
+    @cached(ttl=recommend_ttl, region=recommend_cache_region, skip_empty=True)
    async def async_bangumi_calendar(self, page: Optional[int] = 1, count: Optional[int] = 30) -> List[dict]:
        """
        异步Bangumi每日放送
@@ -330,7 +330,7 @@ class RecommendChain(ChainBase, metaclass=Singleton):
        return [media.to_dict() for media in medias[(page - 1) * count: page * count]] if medias else []

    @log_execution_time(logger=logger)
-    @cached(ttl=recommend_ttl, region=recommend_cache_region)
+    @cached(ttl=recommend_ttl, region=recommend_cache_region, skip_empty=True)
    async def async_douban_movie_showing(self, page: Optional[int] = 1, count: Optional[int] = 30) -> List[dict]:
        """
        异步豆瓣正在热映
@@ -339,7 +339,7 @@ class RecommendChain(ChainBase, metaclass=Singleton):
        return [media.to_dict() for media in movies] if movies else []

    @log_execution_time(logger=logger)
-    @cached(ttl=recommend_ttl, region=recommend_cache_region)
+    @cached(ttl=recommend_ttl, region=recommend_cache_region, skip_empty=True)
    async def async_douban_movies(self, sort: Optional[str] = "R", tags: Optional[str] = "",
                                  page: Optional[int] = 1, count: Optional[int] = 30) -> List[dict]:
        """
@@ -350,7 +350,7 @@ class RecommendChain(ChainBase, metaclass=Singleton):
        return [media.to_dict() for media in movies] if movies else []

    @log_execution_time(logger=logger)
-    @cached(ttl=recommend_ttl, region=recommend_cache_region)
+    @cached(ttl=recommend_ttl, region=recommend_cache_region, skip_empty=True)
    async def async_douban_tvs(self, sort: Optional[str] = "R", tags: Optional[str] = "",
                               page: Optional[int] = 1, count: Optional[int] = 30) -> List[dict]:
        """
@@ -361,7 +361,7 @@ class RecommendChain(ChainBase, metaclass=Singleton):
        return [media.to_dict() for media in tvs] if tvs else []

    @log_execution_time(logger=logger)
-    @cached(ttl=recommend_ttl, region=recommend_cache_region)
+    @cached(ttl=recommend_ttl, region=recommend_cache_region, skip_empty=True)
    async def async_douban_movie_top250(self, page: Optional[int] = 1, count: Optional[int] = 30) -> List[dict]:
        """
        异步豆瓣电影TOP250
@@ -370,7 +370,7 @@ class RecommendChain(ChainBase, metaclass=Singleton):
        return [media.to_dict() for media in movies] if movies else []

    @log_execution_time(logger=logger)
-    @cached(ttl=recommend_ttl, region=recommend_cache_region)
+    @cached(ttl=recommend_ttl, region=recommend_cache_region, skip_empty=True)
    async def async_douban_tv_weekly_chinese(self, page: Optional[int] = 1, count: Optional[int] = 30) -> List[dict]:
        """
        异步豆瓣国产剧集榜
@@ -379,7 +379,7 @@ class RecommendChain(ChainBase, metaclass=Singleton):
        return [media.to_dict() for media in tvs] if tvs else []

    @log_execution_time(logger=logger)
-    @cached(ttl=recommend_ttl, region=recommend_cache_region)
+    @cached(ttl=recommend_ttl, region=recommend_cache_region, skip_empty=True)
    async def async_douban_tv_weekly_global(self, page: Optional[int] = 1, count: Optional[int] = 30) -> List[dict]:
        """
        异步豆瓣全球剧集榜
@@ -388,7 +388,7 @@ class RecommendChain(ChainBase, metaclass=Singleton):
        return [media.to_dict() for media in tvs] if tvs else []

    @log_execution_time(logger=logger)
-    @cached(ttl=recommend_ttl, region=recommend_cache_region)
+    @cached(ttl=recommend_ttl, region=recommend_cache_region, skip_empty=True)
    async def async_douban_tv_animation(self, page: Optional[int] = 1, count: Optional[int] = 30) -> List[dict]:
        """
        异步豆瓣热门动漫
@@ -397,7 +397,7 @@ class RecommendChain(ChainBase, metaclass=Singleton):
        return [media.to_dict() for media in tvs] if tvs else []

    @log_execution_time(logger=logger)
-    @cached(ttl=recommend_ttl, region=recommend_cache_region)
+    @cached(ttl=recommend_ttl, region=recommend_cache_region, skip_empty=True)
    async def async_douban_movie_hot(self, page: Optional[int] = 1, count: Optional[int] = 30) -> List[dict]:
        """
        异步豆瓣热门电影
@@ -406,7 +406,7 @@ class RecommendChain(ChainBase, metaclass=Singleton):
        return [media.to_dict() for media in movies] if movies else []

    @log_execution_time(logger=logger)
-    @cached(ttl=recommend_ttl, region=recommend_cache_region)
+    @cached(ttl=recommend_ttl, region=recommend_cache_region, skip_empty=True)
    async def async_douban_tv_hot(self, page: Optional[int] = 1, count: Optional[int] = 30) -> List[dict]:
        """
        异步豆瓣热门电视剧
--- a/tests/test_recommend_chain.py
+++ b/tests/test_recommend_chain.py
@@ -1,42 +1,95 @@
 import asyncio
-from unittest import TestCase
+from typing import Generator
 from unittest.mock import AsyncMock, patch

+import pytest
+
 from app.chain.recommend import RecommendChain
 from app.core.cache import TTLCache


-class RecommendChainTest(TestCase):
-    def tearDown(self):
-        """
-        清理推荐缓存，避免缓存装饰器状态影响其他用例。
-        """
-        RecommendChain.tmdb_trending.cache_clear()
-        asyncio.run(RecommendChain.async_tmdb_trending.cache_clear())
-        TTLCache(region=RecommendChain.recommend_cache_region).clear()
+SYNC_EMPTY_CACHE_CASES = [
+    ("tmdb_movies", "app.chain.recommend.TmdbChain", "tmdb_discover"),
+    ("tmdb_tvs", "app.chain.recommend.TmdbChain", "tmdb_discover"),
+    ("tmdb_trending", "app.chain.recommend.TmdbChain", "tmdb_trending"),
+    ("bangumi_calendar", "app.chain.recommend.BangumiChain", "calendar"),
+    ("douban_movie_showing", "app.chain.recommend.DoubanChain", "movie_showing"),
+    ("douban_movies", "app.chain.recommend.DoubanChain", "douban_discover"),
+    ("douban_tvs", "app.chain.recommend.DoubanChain", "douban_discover"),
+    ("douban_movie_top250", "app.chain.recommend.DoubanChain", "movie_top250"),
+    ("douban_tv_weekly_chinese", "app.chain.recommend.DoubanChain", "tv_weekly_chinese"),
+    ("douban_tv_weekly_global", "app.chain.recommend.DoubanChain", "tv_weekly_global"),
+    ("douban_tv_animation", "app.chain.recommend.DoubanChain", "tv_animation"),
+    ("douban_movie_hot", "app.chain.recommend.DoubanChain", "movie_hot"),
+    ("douban_tv_hot", "app.chain.recommend.DoubanChain", "tv_hot"),
+]

-    def test_tmdb_trending_does_not_cache_empty_result(self):
-        """
-        TMDB流行趋势返回空列表时不应缓存，避免一次接口异常后长时间固定为空。
-        """
-        chain = RecommendChain()
-        with patch("app.chain.recommend.TmdbChain") as tmdb_chain:
-            tmdb_chain.return_value.tmdb_trending.side_effect = [[], []]
+ASYNC_EMPTY_CACHE_CASES = [
+    ("async_tmdb_movies", "app.chain.recommend.TmdbChain"),
+    ("async_tmdb_tvs", "app.chain.recommend.TmdbChain"),
+    ("async_tmdb_trending", "app.chain.recommend.TmdbChain"),
+    ("async_bangumi_calendar", "app.chain.recommend.BangumiChain"),
+    ("async_douban_movie_showing", "app.chain.recommend.DoubanChain"),
+    ("async_douban_movies", "app.chain.recommend.DoubanChain"),
+    ("async_douban_tvs", "app.chain.recommend.DoubanChain"),
+    ("async_douban_movie_top250", "app.chain.recommend.DoubanChain"),
+    ("async_douban_tv_weekly_chinese", "app.chain.recommend.DoubanChain"),
+    ("async_douban_tv_weekly_global", "app.chain.recommend.DoubanChain"),
+    ("async_douban_tv_animation", "app.chain.recommend.DoubanChain"),
+    ("async_douban_movie_hot", "app.chain.recommend.DoubanChain"),
+    ("async_douban_tv_hot", "app.chain.recommend.DoubanChain"),
+]

-            self.assertEqual(chain.tmdb_trending(page=1), [])
-            self.assertEqual(chain.tmdb_trending(page=1), [])

-        self.assertEqual(tmdb_chain.return_value.tmdb_trending.call_count, 2)
+def clear_recommend_cache() -> None:
+    """清理推荐缓存，避免缓存装饰器状态影响用例。"""
+    TTLCache(region=RecommendChain.recommend_cache_region).clear()

-    def test_async_tmdb_trending_does_not_cache_empty_result(self):
-        """
-        异步TMDB流行趋势返回空列表时也不应缓存。
-        """
-        chain = RecommendChain()
-        with patch("app.chain.recommend.TmdbChain") as tmdb_chain:
-            tmdb_chain.return_value.async_run_module = AsyncMock(side_effect=[[], []])

-            self.assertEqual(asyncio.run(chain.async_tmdb_trending(page=1)), [])
-            self.assertEqual(asyncio.run(chain.async_tmdb_trending(page=1)), [])
+@pytest.fixture(autouse=True)
+def isolated_recommend_cache() -> Generator[None, None, None]:
+    """每个用例前后都清空推荐缓存。"""
+    clear_recommend_cache()
+    yield
+    clear_recommend_cache()

-        self.assertEqual(tmdb_chain.return_value.async_run_module.call_count, 2)
+
+@pytest.mark.parametrize(
+    ("method_name", "chain_target", "backend_method"),
+    SYNC_EMPTY_CACHE_CASES,
+)
+def test_sync_recommend_methods_do_not_cache_empty_result(
+    method_name: str,
+    chain_target: str,
+    backend_method: str,
+) -> None:
+    """同步推荐来源返回空列表时不应缓存。"""
+    chain = RecommendChain()
+    recommend_method = getattr(chain, method_name)
+
+    with patch(chain_target) as backend_chain:
+        backend_call = getattr(backend_chain.return_value, backend_method)
+        backend_call.side_effect = [[], []]
+
+        assert recommend_method(page=1) == []
+        assert recommend_method(page=1) == []
+
+    assert backend_call.call_count == 2
+
+
+@pytest.mark.parametrize(("method_name", "chain_target"), ASYNC_EMPTY_CACHE_CASES)
+def test_async_recommend_methods_do_not_cache_empty_result(
+    method_name: str,
+    chain_target: str,
+) -> None:
+    """异步推荐来源返回空列表时不应缓存。"""
+    chain = RecommendChain()
+    recommend_method = getattr(chain, method_name)
+
+    with patch(chain_target) as backend_chain:
+        backend_chain.return_value.async_run_module = AsyncMock(side_effect=[[], []])
+
+        assert asyncio.run(recommend_method(page=1)) == []
+        assert asyncio.run(recommend_method(page=1)) == []
+
+    assert backend_chain.return_value.async_run_module.call_count == 2