redis-opt-20220810: update rule_rank_h_by_24h & region_rule_rank_h_by24h

config.py

@@ -105,16 +105,6 @@ class BaseConfig(object):
         'rule2': {'cal_score_func': 2, 'return_count': 100, 'platform_return_rate': 0.001},
     }
 
-    # 小时级更新过去24h数据
-    PROJECT_24H = 'loghubods'
-    TABLE_24H = 'video_data_each_hour_dataset_24h_total'
-
-    # 小时级更新过去24h数据规则参数
-    RULE_PARAMS_24H = {
-        # 'rule1': {'cal_score_func': 2, 'return_count': 100, 'platform_return_rate': 0.001},
-        'rule2': {'cal_score_func': 2, 'return_count': 100, 'platform_return_rate': 0.001, 'view_type': 'preview'},
-    }
-
     REGION_CODE = {
         '北京': '110000', '天津': '120000', '河北省': '130000', '山西省': '140000', '内蒙古': '150000',
         '辽宁省': '210000', '吉林省': '220000', '黑龙江省': '230000',
@@ -126,16 +116,6 @@ class BaseConfig(object):
         'None': '-1'
     }
 
-    # 地域分组小时级规则更新使用数据
-    PROJECT_REGION = 'loghubods'
-    TABLE_REGION = 'video_each_hour_update_province'
-
-    # 地域分组小时级规则参数
-    RULE_PARAMS_REGION = {
-        # 'rule1': {'view_type': 'pre-view', 'platform_return_rate': 0.001, 'region_24h_rule_key': 'rule1'},
-        'rule2': {'view_type': 'video-show', 'platform_return_rate': 0.001, 'region_24h_rule_key': 'rule2'},
-        'rule3': {'view_type': 'video-show-region', 'platform_return_rate': 0.001, 'region_24h_rule_key': 'rule2'},
-    }
 
     # 地域分组天级规则更新使用数据
     PROJECT_REGION_DAY = 'loghubods'
@@ -146,16 +126,6 @@ class BaseConfig(object):
         'rule1': {'view_type': 'pre-view', 'return_count': 21, 'score_rule': 0},
     }
 
-    # 地域分组小时级更新24h使用数据
-    PROJECT_REGION_24H = 'loghubods'
-    TABLE_REGION_24H = 'video_each_day_update_province_24h_total'
-
-    # 地域分组小时级更新24h规则参数
-    RULE_PARAMS_REGION_24H = {
-        # 'rule1': {'view_type': 'pre-view', 'return_count': 21, 'score_rule': 0, 'platform_return_rate': 0.001},
-        'rule2': {'view_type': 'video-show', 'return_count': 21, 'score_rule': 0, 'platform_return_rate': 0.001},
-    }
-
     # ##### 区分appType数据
     # 小时级更新过去48h数据 loghubods.video_data_each_hour_dataset_48h_total_apptype
     PROJECT_48H_APP_TYPE = 'loghubods'
@@ -163,18 +133,15 @@ class BaseConfig(object):
 
     # 小时级更新过去48h数据规则参数
     RULE_PARAMS_48H_APP_TYPE = {
-        APP_TYPE['VLOG']: {
-            'rule_params': {
-                'rule1': {'cal_score_func': 2, 'return_count': 100, 'platform_return_rate': 0.001,
-                          'view_type': 'preview'},
-            },
-            'data_params': {
-                'data1': [APP_TYPE['VLOG'], ],
-            },
-            'params_list': [
-                {'data': 'data1', 'rule': 'rule1'},
-            ],
+        'rule_params': {
+            'rule1': {'cal_score_func': 2, 'return_count': 100, 'platform_return_rate': 0.001, 'view_type': 'preview'},
+        },
+        'data_params': {
+            'data1': [APP_TYPE['VLOG'], ],
         },
+        'params_list': [
+            {'data': 'data1', 'rule': 'rule1'},
+        ],
     }
 
     # 小时级更新过去24h数据 loghubods.video_data_each_hour_dataset_24h_total_apptype
@@ -182,6 +149,104 @@ class BaseConfig(object):
     TABLE_24H_APP_TYPE = 'video_data_each_hour_dataset_24h_total_apptype'
 
     # 小时级更新过去24h数据规则参数
+    RULE_PARAMS_24H_APP_TYPE = {
+        'rule_params': {
+            'rule2': {'cal_score_func': 2, 'return_count': 40, 'platform_return_rate': 0.001,
+                      'view_type': 'preview'},
+            'rule3': {'cal_score_func': 2, 'return_count': 100, 'platform_return_rate': 0.001,
+                      'view_type': 'preview'},
+        },
+        'data_params': {
+            'data1': [APP_TYPE['VLOG'], ],
+            'data2': [APP_TYPE['VLOG'], APP_TYPE['LONG_VIDEO'], ],
+            'data3': [APP_TYPE['VLOG'], APP_TYPE['LOVE_LIVE'], ],
+            'data4': [APP_TYPE['VLOG'], APP_TYPE['SHORT_VIDEO'], ],
+            'data5': [APP_TYPE['VLOG'], APP_TYPE['ZUI_JING_QI']],
+            'data6': [APP_TYPE['VLOG'], APP_TYPE['LOVE_LIVE'], APP_TYPE['LONG_VIDEO'], APP_TYPE['SHORT_VIDEO']],
+            'data7': [APP_TYPE['VLOG'], APP_TYPE['LOVE_LIVE'], APP_TYPE['LONG_VIDEO'], APP_TYPE['SHORT_VIDEO'],
+                      APP_TYPE['APP']],
+        },
+        'params_list': [
+            {'data': 'data1', 'rule': 'rule2'},
+            {'data': 'data1', 'rule': 'rule3'},
+            {'data': 'data2', 'rule': 'rule2'},
+            {'data': 'data3', 'rule': 'rule2'},
+            {'data': 'data4', 'rule': 'rule2'},
+            {'data': 'data7', 'rule': 'rule2'},
+            {'data': 'data6', 'rule': 'rule2'},
+        ]
+    }
+
+    # 地域分组小时级更新24h使用数据  loghubods.video_each_day_update_province_24h_total_apptype
+    PROJECT_REGION_24H_APP_TYPE = 'loghubods'
+    TABLE_REGION_24H_APP_TYPE = 'video_each_day_update_province_24h_total_apptype'
+
+    # 地域分组小时级更新24h规则参数
+    RULE_PARAMS_REGION_24H_APP_TYPE = {
+        'rule_params': {
+            'rule2': {'view_type': 'video-show', 'return_count': 21, 'score_rule': 0,
+                      'platform_return_rate': 0.001},
+            'rule3': {'view_type': 'preview', 'return_count': 21, 'score_rule': 0,
+                      'platform_return_rate': 0.001},
+        },
+        'data_params': {
+            'data1': [APP_TYPE['VLOG'], ],
+            'data2': [APP_TYPE['VLOG'], APP_TYPE['LONG_VIDEO'], ],
+            'data3': [APP_TYPE['VLOG'], APP_TYPE['LOVE_LIVE'], ],
+            'data4': [APP_TYPE['VLOG'], APP_TYPE['SHORT_VIDEO'], ],
+            'data5': [APP_TYPE['VLOG'], APP_TYPE['ZUI_JING_QI']],
+            'data6': [APP_TYPE['VLOG'], APP_TYPE['LOVE_LIVE'], APP_TYPE['LONG_VIDEO'], APP_TYPE['SHORT_VIDEO']],
+            'data7': [APP_TYPE['VLOG'], APP_TYPE['LOVE_LIVE'], APP_TYPE['LONG_VIDEO'], APP_TYPE['SHORT_VIDEO'],
+                      APP_TYPE['APP']],
+        },
+        'params_list': [
+            {'data': 'data1', 'rule': 'rule2'},
+            {'data': 'data2', 'rule': 'rule2'},
+            {'data': 'data3', 'rule': 'rule2'},
+            {'data': 'data4', 'rule': 'rule2'},
+            {'data': 'data6', 'rule': 'rule2'},
+            {'data': 'data7', 'rule': 'rule3'},
+        ]
+    }
+
+    # 地域分组小时级规则更新使用数据
+    PROJECT_REGION_APP_TYPE = 'loghubods'
+    TABLE_REGION_APP_TYPE = 'video_each_hour_update_province_apptype'
+
+    # 地域分组小时级规则参数
+    RULE_PARAMS_REGION_APP_TYPE = {
+        'rule_params': {
+            # 'rule2': {'view_type': 'video-show', 'platform_return_rate': 0.001, 'region_24h_rule_key': 'rule2'},
+            'rule3': {'view_type': 'video-show-region', 'platform_return_rate': 0.001,
+                      'region_24h_rule_key': 'rule2', '24h_rule_key': 'rule2'},
+            'rule4': {'view_type': 'video-show-region', 'platform_return_rate': 0.001,
+                      'region_24h_rule_key': 'rule2', '24h_rule_key': 'rule3'},
+            'rule6': {'view_type': 'preview', 'platform_return_rate': 0.001,
+                      'region_24h_rule_key': 'rule3', '24h_rule_key': 'rule2'},
+        },
+        'data_params': {
+            'data1': [APP_TYPE['VLOG'], ],
+            'data2': [APP_TYPE['VLOG'], APP_TYPE['LONG_VIDEO'], ],
+            'data3': [APP_TYPE['VLOG'], APP_TYPE['LOVE_LIVE'], ],
+            'data4': [APP_TYPE['VLOG'], APP_TYPE['SHORT_VIDEO'], ],
+            'data5': [APP_TYPE['VLOG'], APP_TYPE['ZUI_JING_QI']],
+            'data6': [APP_TYPE['VLOG'], APP_TYPE['LOVE_LIVE'], APP_TYPE['LONG_VIDEO'], APP_TYPE['SHORT_VIDEO']],
+            'data7': [APP_TYPE['VLOG'], APP_TYPE['LOVE_LIVE'], APP_TYPE['LONG_VIDEO'], APP_TYPE['SHORT_VIDEO'],
+                      APP_TYPE['APP']],
+        },
+        'params_list': [
+            {'data': 'data1', 'rule': 'rule3'},
+            {'data': 'data1', 'rule': 'rule4'},
+            {'data': 'data2', 'rule': 'rule3'},
+            {'data': 'data3', 'rule': 'rule3'},
+            {'data': 'data4', 'rule': 'rule3'},
+            {'data': 'data6', 'rule': 'rule3'},
+            {'data': 'data7', 'rule': 'rule6'},
+        ],
+    }
+
+
+    """
     RULE_PARAMS_24H_APP_TYPE = {
         APP_TYPE['VLOG']: {
             'rule_params': {
@@ -297,11 +362,8 @@ class BaseConfig(object):
         },
     }
 
-    # 地域分组小时级更新24h使用数据  loghubods.video_each_day_update_province_24h_total_apptype
-    PROJECT_REGION_24H_APP_TYPE = 'loghubods'
-    TABLE_REGION_24H_APP_TYPE = 'video_each_day_update_province_24h_total_apptype'
+    
 
-    # 地域分组小时级更新24h规则参数
     RULE_PARAMS_REGION_24H_APP_TYPE = {
         APP_TYPE['VLOG']: {
             'rule_params': {
@@ -416,11 +478,8 @@ class BaseConfig(object):
         },
     }
 
-    # 地域分组小时级规则更新使用数据
-    PROJECT_REGION_APP_TYPE = 'loghubods'
-    TABLE_REGION_APP_TYPE = 'video_each_hour_update_province_apptype'
-
-    # 地域分组小时级规则参数
+    
+    
     RULE_PARAMS_REGION_APP_TYPE = {
         APP_TYPE['VLOG']: {
             'rule_params': {
@@ -543,21 +602,21 @@ class BaseConfig(object):
             ],
         },
     }
+    """
+
 
     # 不区分地域数据使用相对48h数据
     RULE_PARAMS_REGION_APP_TYPE_48H = {
-        APP_TYPE['VLOG']: {
-            'rule_params': {
-                'rule5': {'view_type': 'video-show-region', 'platform_return_rate': 0.001,
-                          'region_24h_rule_key': 'rule2', '48h_rule_key': 'rule1'},
-            },
-            'data_params': {
-                'data1': [APP_TYPE['VLOG'], ],
-            },
-            'params_list': [
-                {'data': 'data1', 'rule': 'rule5'},
-            ],
+        'rule_params': {
+            'rule5': {'view_type': 'video-show-region', 'platform_return_rate': 0.001,
+                      'region_24h_rule_key': 'rule2', '48h_rule_key': 'rule1'},
+        },
+        'data_params': {
+            'data1': [APP_TYPE['VLOG'], ],
         },
+        'params_list': [
+            {'data': 'data1', 'rule': 'rule5'},
+        ],
     }
 
     # 老视频更新使用数据
@@ -602,11 +661,11 @@ class BaseConfig(object):
     RECALL_KEY_NAME_PREFIX_BY_48H_OTHER = 'recall:item:score:apptype:48h:other:'
 
     # 小程序小时级24h数据更新结果存放 redis key前缀，
-    # 完整格式：recall:item:score:apptype:24h:{appType}:{data_key}:{rule_key}:{date}:{h}
-    RECALL_KEY_NAME_PREFIX_BY_24H = 'recall:item:score:apptype:24h:'
+    # 完整格式：recall:item:score:24h:{data_key}:{rule_key}:{date}:{h}
+    RECALL_KEY_NAME_PREFIX_BY_24H = 'recall:item:score:24h:'
     # 小程序小时级24h数据 筛选后的剩余数据 更新结果存放 redis key前缀，
-    # 完整格式：recall:item:score:apptype:24h:other:{appType}:{data_key}:{rule_key}:{date}:{h}
-    RECALL_KEY_NAME_PREFIX_BY_24H_OTHER = 'recall:item:score:apptype:24h:other:'
+    # 完整格式：recall:item:score:24h:other:{data_key}:{rule_key}:{date}:{h}
+    RECALL_KEY_NAME_PREFIX_BY_24H_OTHER = 'recall:item:score:24h:other:'
     # 小程序离线ROV模型结果与小程序小时级24h更新结果去重后 存放 redis key前缀，
     # 完整格式：com.weiqu.video.recall.hot.item.score.dup.24h.{rule_key}.{date}.{h}
     RECALL_KEY_NAME_PREFIX_DUP_24H = 'com.weiqu.video.recall.hot.item.score.dup.24h.'
@@ -651,8 +710,8 @@ class BaseConfig(object):
     RECALL_KEY_NAME_PREFIX_REGION_BY_DAY = 'com.weiqu.video.recall.item.score.region.day.'
 
     # 小程序地域分组小时级更新24h结果存放 redis key前缀，
-    # 完整格式：recall:item:score:apptype:region:24h:{region}:{appType}:{data_key}:{rule_key}:{date}:{h}
-    RECALL_KEY_NAME_PREFIX_REGION_BY_24H = 'recall:item:score:apptype:region:24h:'
+    # 完整格式：recall:item:score:region:24h:{region}:{data_key}:{rule_key}:{date}:{h}
+    RECALL_KEY_NAME_PREFIX_REGION_BY_24H = 'recall:item:score:region:24h:'
     # 小程序天级更新结果与 小程序地域分组小时级更新24h结果 去重后 存放 redis key前缀，
     # 完整格式：com.weiqu.video.recall.hot.item.score.dup.region.day.24h.{region}.{rule_key}.{date}.{h}
     RECALL_KEY_NAME_PREFIX_DUP_REGION_DAY_24H = 'com.weiqu.video.recall.hot.item.score.dup.region.day.24h.'
@@ -1082,8 +1141,8 @@ class ProductionConfig(BaseConfig):
 
 def set_config():
     # 获取环境变量 ROV_OFFLINE_ENV
-    env = os.environ.get('ROV_OFFLINE_ENV')
-    # env = 'dev'
+    # env = os.environ.get('ROV_OFFLINE_ENV')
+    env = 'dev'
     if env is None:
         # log_.error('ENV ERROR: is None!')
         return

region_rule_rank_h_by24h.py

@@ -129,7 +129,7 @@ def cal_score(df, param):
     return df
 
 
-def video_rank(df, now_date, now_h, rule_key, param, region, app_type, data_key):
+def video_rank(df, now_date, now_h, rule_key, param, region, data_key):
     """
     获取符合进入召回源条件的视频
     :param df:
@@ -168,7 +168,7 @@ def video_rank(df, now_date, now_h, rule_key, param, region, app_type, data_key)
         h_video_ids.append(int(video_id))
 
     day_recall_key_name = \
-        f"{config_.RECALL_KEY_NAME_PREFIX_REGION_BY_24H}{region}:{app_type}:{data_key}:{rule_key}:" \
+        f"{config_.RECALL_KEY_NAME_PREFIX_REGION_BY_24H}{region}:{data_key}:{rule_key}:" \
         f"{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
     if len(day_recall_result) > 0:
         redis_helper.add_data_with_zset(key_name=day_recall_key_name, data=day_recall_result, expire_time=2 * 3600)
@@ -197,15 +197,14 @@ def merge_df(df_left, df_right):
     return df_merged[feature_list]
 
 
-def process_with_region(region, df_merged, app_type, data_key, rule_key, rule_param, now_date, now_h):
+def process_with_region(region, df_merged, data_key, rule_key, rule_param, now_date, now_h):
     log_.info(f"region = {region} start...")
     # 计算score
     region_df = df_merged[df_merged['code'] == region]
     log_.info(f'region = {region}, region_df count = {len(region_df)}')
     score_df = cal_score(df=region_df, param=rule_param)
-    video_rank(df=score_df, now_date=now_date, now_h=now_h,
-               rule_key=rule_key, param=rule_param, region=region,
-               app_type=app_type, data_key=data_key)
+    video_rank(df=score_df, now_date=now_date, now_h=now_h, region=region,
+               rule_key=rule_key, param=rule_param, data_key=data_key)
     log_.info(f"region = {region} end!")
 
 
@@ -232,17 +231,52 @@ def process_with_app_type(app_type, params, region_code_list, feature_df, now_da
     log_.info(f"app_type = {app_type} end!")
 
 
+def process_with_param(param, data_params_item, rule_params_item, region_code_list, feature_df, now_date, now_h):
+    log_.info(f"param = {param} start...")
+
+    data_key = param.get('data')
+    data_param = data_params_item.get(data_key)
+    log_.info(f"data_key = {data_key}, data_param = {data_param}")
+    df_list = [feature_df[feature_df['apptype'] == apptype] for apptype in data_param]
+    df_merged = reduce(merge_df, df_list)
+
+    rule_key = param.get('rule')
+    rule_param = rule_params_item.get(rule_key)
+    log_.info(f"rule_key = {rule_key}, rule_param = {rule_param}")
+    task_list = [
+        gevent.spawn(process_with_region, region, df_merged, data_key, rule_key, rule_param, now_date, now_h)
+        for region in region_code_list
+    ]
+    gevent.joinall(task_list)
+
+    log_.info(f"param = {param} end!")
+
+
 def rank_by_24h(project, table, now_date, now_h, rule_params, region_code_list):
     # 获取特征数据
     feature_df = get_feature_data(project=project, table=table, now_date=now_date)
     feature_df['apptype'] = feature_df['apptype'].astype(int)
     # rank
+    data_params_item = rule_params.get('data_params')
+    rule_params_item = rule_params.get('rule_params')
+    params_list = rule_params.get('params_list')
+    pool = multiprocessing.Pool(processes=len(params_list))
+    for param in params_list:
+        pool.apply_async(
+            func=process_with_param,
+            args=(param, data_params_item, rule_params_item, region_code_list, feature_df, now_date, now_h)
+        )
+    pool.close()
+    pool.join()
+
+    """
     pool = multiprocessing.Pool(processes=len(config_.APP_TYPE))
     for app_type, params in rule_params.items():
         pool.apply_async(func=process_with_app_type,
                          args=(app_type, params, region_code_list, feature_df, now_date, now_h))
     pool.close()
     pool.join()
+    """
 
     # for app_type, params in rule_params.items():
     #     log_.info(f"app_type = {app_type}")
@@ -331,6 +365,28 @@ def h_rank_bottom(now_date, now_h, rule_params, region_code_list):
 
     # 以上一小时的地域分组数据作为当前小时的数据
     key_prefix = config_.RECALL_KEY_NAME_PREFIX_REGION_BY_24H
+    for param in rule_params.get('params_list'):
+        data_key = param.get('data')
+        rule_key = param.get('rule')
+        log_.info(f"data_key = {data_key}, rule_key = {rule_key}")
+        for region in region_code_list:
+            log_.info(f"region = {region}")
+            key_name = f"{key_prefix}{region}:{data_key}:{rule_key}:{redis_dt}:{redis_h}"
+            initial_data = redis_helper.get_all_data_from_zset(key_name=key_name, with_scores=True)
+            if initial_data is None:
+                initial_data = []
+            final_data = dict()
+            h_video_ids = []
+            for video_id, score in initial_data:
+                final_data[video_id] = score
+                h_video_ids.append(int(video_id))
+            # 存入对应的redis
+            final_key_name = \
+                f"{key_prefix}{region}:{data_key}:{rule_key}:{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
+            if len(final_data) > 0:
+                redis_helper.add_data_with_zset(key_name=final_key_name, data=final_data, expire_time=2 * 3600)
+
+    """
     for app_type, params in rule_params.items():
         log_.info(f"app_type = {app_type}")
         for param in params.get('params_list'):
@@ -358,6 +414,7 @@ def h_rank_bottom(now_date, now_h, rule_params, region_code_list):
 
                 # 与其他召回视频池去重，存入对应的redis
                 # dup_to_redis(h_video_ids=h_video_ids, now_date=now_date, now_h=now_h, rule_key=rule_key, region=region)
+    """
 
 
 def h_timer_check():
@@ -379,8 +436,7 @@ def h_timer_check():
         log_.info(f"region_24h_data end!")
     elif now_min > 50:
         log_.info('24h_recall data is None, use bottom data!')
-        for key, _ in rule_params.items():
-            h_rank_bottom(now_date=now_date, now_h=now_h, rule_params=rule_params, region_code_list=region_code_list)
+        h_rank_bottom(now_date=now_date, now_h=now_h, rule_params=rule_params, region_code_list=region_code_list)
         log_.info(f"region_24h_data end!")
     else:
         # 数据没准备好，1分钟后重新检查

rule_rank_h_by_24h.py

@@ -125,7 +125,7 @@ def cal_score2(df, param):
     return df
 
 
-def video_rank_h(df, now_date, now_h, rule_key, param, app_type, data_key):
+def video_rank_h(df, now_date, now_h, rule_key, param, data_key):
     """
     获取符合进入召回源条件的视频，与每日更新的rov模型结果视频列表进行合并
     :param df:
@@ -133,18 +133,11 @@ def video_rank_h(df, now_date, now_h, rule_key, param, app_type, data_key):
     :param now_h:
     :param rule_key: 天级规则数据进入条件
     :param param: 天级规则数据进入条件参数
-    :param app_type:
     :param data_key: 使用数据标识
     :return:
     """
     redis_helper = RedisHelper()
-    # 获取rov模型结果
-    # key_name = get_rov_redis_key(now_date=now_date)
-    # initial_data = redis_helper.get_all_data_from_zset(key_name=key_name, with_scores=True)
-    # if initial_data is None:
-    #     initial_data = []
-    # log_.info(f'initial data count = {len(initial_data)}')
-    log_.info(f"app_type = {app_type}, videos_count = {len(df)}")
+    log_.info(f"videos_count = {len(df)}")
 
     # videoid重复时，保留分值高
     df = df.sort_values(by=['score'], ascending=False)
@@ -159,11 +152,6 @@ def video_rank_h(df, now_date, now_h, rule_key, param, app_type, data_key):
         day_recall_df = df
     platform_return_rate = param.get('platform_return_rate', 0)
     day_recall_df = day_recall_df[day_recall_df['platform_return_rate'] > platform_return_rate]
-
-    # videoid重复时，保留分值高
-    # day_recall_df = day_recall_df.sort_values(by=['score'], ascending=False)
-    # day_recall_df = day_recall_df.drop_duplicates(subset=['videoid'], keep='first')
-    # day_recall_df['videoid'] = day_recall_df['videoid'].astype(int)
     day_recall_videos = day_recall_df['videoid'].to_list()
     log_.info(f'h_by24h_recall videos count = {len(day_recall_videos)}')
 
@@ -179,10 +167,13 @@ def video_rank_h(df, now_date, now_h, rule_key, param, app_type, data_key):
         score = day_recall_df[day_recall_df['videoid'] == video_id]['score']
         day_recall_result[int(video_id)] = float(score)
         day_video_ids.append(int(video_id))
+    # h_24h_recall_key_name = \
+    #     f"{config_.RECALL_KEY_NAME_PREFIX_BY_24H}{app_type}:{data_key}:{rule_key}:{now_dt}:{now_h}"
     h_24h_recall_key_name = \
-        f"{config_.RECALL_KEY_NAME_PREFIX_BY_24H}{app_type}:{data_key}:{rule_key}:{now_dt}:{now_h}"
+        f"{config_.RECALL_KEY_NAME_PREFIX_BY_24H}{data_key}:{rule_key}:{now_dt}:{now_h}"
+
     if len(day_recall_result) > 0:
-        log_.info(f"count = {len(day_recall_result)}")
+        log_.info(f"count = {len(day_recall_result)}, key = {h_24h_recall_key_name}")
         redis_helper.add_data_with_zset(key_name=h_24h_recall_key_name, data=day_recall_result, expire_time=2 * 3600)
         # 清空线上过滤应用列表
         # redis_helper.del_keys(key_name=f"{config_.H_VIDEO_FILER_24H}{app_type}.{data_key}.{rule_key}")
@@ -202,8 +193,10 @@ def video_rank_h(df, now_date, now_h, rule_key, param, app_type, data_key):
         for video_id in other_videos:
             score = df[df['videoid'] == video_id]['score']
             other_24h_recall_result[int(video_id)] = float(score)
+        # other_h_24h_recall_key_name = \
+        #     f"{config_.RECALL_KEY_NAME_PREFIX_BY_24H_OTHER}{app_type}:{data_key}:{rule_key}:{now_dt}:{now_h}"
         other_h_24h_recall_key_name = \
-            f"{config_.RECALL_KEY_NAME_PREFIX_BY_24H_OTHER}{app_type}:{data_key}:{rule_key}:{now_dt}:{now_h}"
+            f"{config_.RECALL_KEY_NAME_PREFIX_BY_24H_OTHER}{data_key}:{rule_key}:{now_dt}:{now_h}"
         if len(other_24h_recall_result) > 0:
             log_.info(f"count = {len(other_24h_recall_result)}")
             redis_helper.add_data_with_zset(key_name=other_h_24h_recall_key_name, data=other_24h_recall_result,
@@ -244,6 +237,28 @@ def rank_by_h(now_date, now_h, rule_params, project, table):
     feature_df = get_feature_data(now_date=now_date, now_h=now_h, project=project, table=table)
     feature_df['apptype'] = feature_df['apptype'].astype(int)
     # rank
+    data_params_item = rule_params.get('data_params')
+    rule_params_item = rule_params.get('rule_params')
+    for param in rule_params.get('params_list'):
+        data_key = param.get('data')
+        data_param = data_params_item.get(data_key)
+        log_.info(f"data_key = {data_key}, data_param = {data_param}")
+        df_list = [feature_df[feature_df['apptype'] == apptype] for apptype in data_param]
+        df_merged = reduce(merge_df, df_list)
+
+        rule_key = param.get('rule')
+        rule_param = rule_params_item.get(rule_key)
+        log_.info(f"rule_key = {rule_key}, rule_param = {rule_param}")
+        # 计算score
+        cal_score_func = rule_param.get('cal_score_func', 1)
+        if cal_score_func == 2:
+            score_df = cal_score2(df=df_merged, param=rule_param)
+        else:
+            score_df = cal_score1(df=df_merged)
+        video_rank_h(df=score_df, now_date=now_date, now_h=now_h,
+                     rule_key=rule_key, param=rule_param, data_key=data_key)
+
+    """
     for app_type, params in rule_params.items():
         log_.info(f"app_type = {app_type}")
         data_params_item = params.get('data_params')
@@ -266,7 +281,7 @@ def rank_by_h(now_date, now_h, rule_params, project, table):
                 score_df = cal_score1(df=df_merged)
             video_rank_h(df=score_df, now_date=now_date, now_h=now_h, rule_key=rule_key, param=rule_param,
                          app_type=app_type, data_key=data_key)
-
+    """
     #     # to-csv
     #     score_filename = f"score_by24h_{key}_{datetime.strftime(now_date, '%Y%m%d%H')}.csv"
     #     score_df.to_csv(f'./data/{score_filename}')
@@ -288,6 +303,26 @@ def h_rank_bottom(now_date, now_h, rule_params):
         redis_dt = datetime.strftime(now_date, '%Y%m%d')
         redis_h = now_h - 1
     key_prefix_list = [config_.RECALL_KEY_NAME_PREFIX_BY_24H, config_.RECALL_KEY_NAME_PREFIX_BY_24H_OTHER]
+
+    for param in rule_params.get('params_list'):
+        data_key = param.get('data')
+        rule_key = param.get('rule')
+        log_.info(f"data_key = {data_key}, rule_key = {rule_key}")
+        for key_prefix in key_prefix_list:
+            key_name = f"{key_prefix}{data_key}:{rule_key}:{redis_dt}:{redis_h}"
+            initial_data = redis_helper.get_all_data_from_zset(key_name=key_name, with_scores=True)
+            if initial_data is None:
+                initial_data = []
+            final_data = dict()
+            for video_id, score in initial_data:
+                final_data[video_id] = score
+            # 存入对应的redis
+            final_key_name = \
+                f"{key_prefix}{data_key}:{rule_key}:{datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
+            if len(final_data) > 0:
+                redis_helper.add_data_with_zset(key_name=final_key_name, data=final_data, expire_time=2 * 3600)
+
+    """
     for app_type, params in rule_params.items():
         log_.info(f"app_type = {app_type}")
         for param in params.get('params_list'):
@@ -309,6 +344,7 @@ def h_rank_bottom(now_date, now_h, rule_params):
                     redis_helper.add_data_with_zset(key_name=final_key_name, data=final_data, expire_time=2 * 3600)
                 # 清空线上过滤应用列表
                 # redis_helper.del_keys(key_name=f"{config_.H_VIDEO_FILER_24H}{app_type}.{data_key}.{rule_key}")
+    """
 
 
 def h_timer_check():