add

aiocache.py

@@ -15,7 +15,7 @@ P = aiopg.connect(database=pg_info['dbname'],
                                user=pg_info['user'],
                                password=pg_info['password'],
                                host=pg_info['host'],
-                                 port=pg_info['port'])
+                               port=pg_info['port'])
 
 
 R = aioredis.from_url(redis_info['host'], password=redis_info['password'])

recommend.py

@@ -27,8 +27,6 @@ async def video_recommend(mid, uid, size, app_type, algo_type):
     # ####### 多进程召回
     start_recall = time.time()
     log_.info('====== recall')
-    cores = multiprocessing.cpu_count()
-    pool = multiprocessing.Pool(processes=cores)
     pool_recall = PoolRecall(app_type=app_type, mid=mid, uid=uid, ab_code=ab_code)
     _, last_rov_recall_key, _ = await pool_recall.get_video_last_idx()
 

utils.py

@@ -145,7 +145,6 @@ class FilterVideos(object):
         :param video_ids: 视频id列表 type-list
         :return: filtered_videos
         """
-        b_time = time.time()
         if len(video_ids) == 1:
             sql = "set hg_experimental_enable_shard_pruning=off; " \
                   "SELECT video_id " \
@@ -171,8 +170,6 @@ class FilterVideos(object):
 
         data = await aiocache.pg_getdata(sql=sql)
         filtered_videos = [temp[0] for temp in data]
-        e_time = time.time()
-        print('holo time',e_time-b_time)
         return filtered_videos
 
     async def filter_video_viewed(self, video_ids, types=(1,)):

video_rank.py

@@ -21,7 +21,6 @@ def video_rank(data, size):
         return None
     # 将各路召回的视频按照score从大到小排序
     # ROV召回池
-    print('---------a',data['rov_pool_recall'])
     rov_recall_rank = sorted(data['rov_pool_recall'], key=lambda k: (k.get('rovScore'), 0), reverse=True)
     # 流量池
     flow_recall_rank = sorted(data['flow_pool_recall'], key=lambda k: (k.get('rovScore'), 0), reverse=True)