缓存优化

backend/app/api/endpoints/preferences.py

@@ -1,4 +1,5 @@
-from typing import List
+import time
+from typing import Any, Dict, List, Tuple
 
 from fastapi import APIRouter, Depends, HTTPException, Query, status
 from sqlalchemy.exc import IntegrityError
@@ -16,6 +17,16 @@ from app.services.matching_service import recommend_events_for_user
 
 router = APIRouter()
 
+# --- 轻量级接口缓存配置 ---
+_RECOMMEND_CACHE: Dict[str, Tuple[float, Any]] = {}
+CACHE_TTL_SECONDS = 60
+
+def _invalidate_user_cache(user_id: int):
+    """清除某个用户的推荐结果缓存（当用户新增或删除关键词时调用）"""
+    keys_to_delete = [k for k in _RECOMMEND_CACHE.keys() if k.startswith(f"{user_id}:")]
+    for k in keys_to_delete:
+        _RECOMMEND_CACHE.pop(k, None)
+# ---------------------------
 
 def _ensure_self_access(path_user_id: int, current_user: AppUser) -> None:
     """校验路径 user_id 是否为当前登录用户本人。"""
@@ -79,6 +90,7 @@ def create_user_preference(
         )
 
     db.refresh(db_obj)
+    _invalidate_user_cache(user_id)  # 失效推荐缓存
     return db_obj
 
 
@@ -107,6 +119,7 @@ def delete_user_preference(
 
     db.delete(preference)
     db.commit()
+    _invalidate_user_cache(user_id)  # 失效推荐缓存
     return None
 
 
@@ -127,6 +140,16 @@ def recommend_events(
     """基于用户兴趣词推荐事件（精确匹配 + 语义匹配）。"""
     _ensure_self_access(user_id, current_user)
 
+    # --- 1. 尝试从缓存读取 ---
+    cache_key = f"{user_id}:{min_hot}:{hours}:{limit}:{semantic_threshold}:{sort_by}"
+    current_time = time.time()
+    
+    if cache_key in _RECOMMEND_CACHE:
+        expire_time, cached_data = _RECOMMEND_CACHE[cache_key]
+        if current_time < expire_time:
+            return cached_data
+    # -----------------------
+
     matched = recommend_events_for_user(
         db,
         user_id=user_id,
@@ -155,8 +178,18 @@ def recommend_events(
             )
         )
 
-    return UserPreferenceRecommendationResponse(
+    response = UserPreferenceRecommendationResponse(
         user_id=user_id,
         total=len(result_data),
         data=result_data,
     )
+
+    # --- 2. 写入缓存 ---
+    if len(_RECOMMEND_CACHE) > 2000:
+        # 防止内存无限增长
+        _RECOMMEND_CACHE.clear()
+        
+    _RECOMMEND_CACHE[cache_key] = (current_time + CACHE_TTL_SECONDS, response)
+    # ------------------
+
+    return response