Merge branch 'master' into test

ad_out_v1_get_offline_score_item.py

@@ -1,5 +1,6 @@
 #coding utf-8
 import sys
+import json
 import datetime
 import traceback
 from threading import Timer
@@ -239,7 +240,7 @@ from candidate_item
             k = str(row['i_id'])
             item_features = get_item_features(row)
             item_h = lr_model.predict_h(item_features)
-            redis_helper.set_data_to_redis(f"{key_name_prefix}:{k}", item_h, expire_time)
+            #redis_helper.set_data_to_redis(f"{key_name_prefix}:{k}", item_h, expire_time)
             item_h_dict[k] = item_h
             mean_item_h += item_h
             count_item_h += 1
@@ -250,9 +251,9 @@ from candidate_item
     print(mean_item_h)
     print(count_item_h)
     k = 'mean'
-    redis_helper.set_data_to_redis(f"{key_name_prefix}:{k}", mean_item_h, expire_time)
-    # with open('{}.json'.format(key_name_prefix), 'w') as fout:
-    #     json.dump(item_h_dict, fout, indent=2, ensure_ascii=False, sort_keys=True)
+    #redis_helper.set_data_to_redis(f"{key_name_prefix}:{k}", mean_item_h, expire_time)
+    with open('{}.{}.json'.format(key_name_prefix, dt), 'w') as fout:
+         json.dump(item_h_dict, fout, indent=2, ensure_ascii=False, sort_keys=True)
 
 
 def timer_check(dt):

ad_out_v1_get_offline_score_item.sh

@@ -2,9 +2,9 @@ source /etc/profile
 echo $ROV_OFFLINE_ENV
 if [[ $ROV_OFFLINE_ENV == 'test' ]]; then
     cd /data2/rov-offline &&
-    /root/anaconda3/bin/python /data2/rov-offline/ad_out_v1_get_offline_score_item.py $*
+    /root/anaconda3/bin/python /data2/rov-offline/ad_out_v1_get_offline_score_item_new.py $*
 elif [[ $ROV_OFFLINE_ENV == 'pro' ]]; then
     cd /data/rov-offline &&
-    /root/anaconda3/bin/python /data/rov-offline/ad_out_v1_get_offline_score_item.py $*
+    /root/anaconda3/bin/python /data/rov-offline/ad_out_v1_get_offline_score_item_new.py $*
 fi
 

ad_out_v1_get_offline_score_item_new.py

@@ -0,0 +1,291 @@
+#coding utf-8
+import sys
+import datetime
+import traceback
+from threading import Timer
+from tqdm import tqdm
+from utils import RedisHelper, data_check, get_feature_data, send_msg_to_feishu
+from config import set_config
+from log import Log
+from records_process import records_process
+
+config_, _ = set_config()
+log_ = Log()
+redis_helper = RedisHelper()
+
+from feature import get_item_features as get_features
+from lr_model import LrModel
+from utils import exe_sql
+
+model_key = 'ad_out_v1'
+lr_model = LrModel('model/{}.json'.format(model_key))
+item_h_dict = {}
+key_name_prefix = f"{config_.KEY_NAME_PREFIX_AD_OUT_MODEL_SCORE_ITEM}{model_key}"
+print(key_name_prefix)
+# 过期时间：一周
+expire_time = 7 * 24 * 3600
+
+def process_and_store(row):
+    k = str(row['k'])
+    features = get_features(row)
+    h = lr_model.predict_h(features)
+    redis_helper.set_data_to_redis(f"{key_name_prefix}:{k}", round(h, 6), expire_time)
+
+def update_offline_score_item(dt):
+    project = 'loghubods'
+    sql = """
+--odps sql 
+--********************************************************************--
+--author:研发
+--create time:2023-12-01 15:48:17
+--********************************************************************--
+with candidate as (
+select
+-- 基础特征_用户
+mid AS u_id
+,machineinfo_brand AS u_brand
+,machineinfo_model AS u_device
+,SPLIT(machineinfo_system,' ')[0] AS u_system
+,machineinfo_system AS u_system_ver
+-- 基础特征_视频
+,videoid AS i_id
+,i_up_id AS i_up_id
+,tags as i_tag
+,title as i_title
+,ceil(log2(i_title_len + 1)) as i_title_len
+,ceil(log2(total_time + 1)) as i_play_len
+,ceil(log2(i_days_since_upload + 1)) as i_days_since_upload -- 发布时间（距离现在天数）
+-- 基础特征_场景
+,apptype AS ctx_apptype
+,ctx_day AS ctx_day
+,ctx_week AS ctx_week
+,ctx_hour AS ctx_hour
+,ctx_region as ctx_region
+,ctx_city as ctx_city
+-- 基础特征_交叉
+,ui_is_out as ui_is_out
+,i_play_len as playtime
+-- ,IF(i_play_len > 1,'0','1') AS ui_is_out_new
+,rootmid AS ui_root_id
+,shareid AS ui_share_id
+-- 统计特征_用户
+,u_cycle_bucket_7days
+,u_cycle_bucket_30days
+,u_share_bucket_30days
+,ceil(log2(u_1day_exp_cnt + 1)) as u_1day_exp_cnt
+,ceil(log2(u_1day_click_cnt + 1)) as u_1day_click_cnt
+,ceil(log2(u_1day_share_cnt + 1)) as u_1day_share_cnt
+,ceil(log2(u_1day_return_cnt + 1)) as u_1day_return_cnt
+,ceil(log2(u_3day_exp_cnt + 1)) as u_3day_exp_cnt
+,ceil(log2(u_3day_click_cnt + 1)) as u_3day_click_cnt
+,ceil(log2(u_3day_share_cnt + 1)) as u_3day_share_cnt
+,ceil(log2(u_3day_return_cnt + 1)) as u_3day_return_cnt
+,ceil(log2(u_7day_exp_cnt + 1)) as u_7day_exp_cnt
+,ceil(log2(u_7day_click_cnt + 1)) as u_7day_click_cnt
+,ceil(log2(u_7day_share_cnt + 1)) as u_7day_share_cnt
+,ceil(log2(u_7day_return_cnt + 1)) as u_7day_return_cnt
+,ceil(log2(u_3month_exp_cnt + 1)) as u_3month_exp_cnt
+,ceil(log2(u_3month_click_cnt + 1)) as u_3month_click_cnt
+,ceil(log2(u_3month_share_cnt + 1)) as u_3month_share_cnt
+,ceil(log2(u_3month_return_cnt + 1)) as u_3month_return_cnt
+,round(if(u_ctr_1day > 10.0, 10.0, u_ctr_1day) / 10.0, 6) as u_ctr_1day
+,round(if(u_str_1day > 10.0, 10.0, u_str_1day) / 10.0, 6) as u_str_1day
+,round(if(u_rov_1day > 10.0, 10.0, u_rov_1day) / 10.0, 6) as u_rov_1day
+,round(if(u_ros_1day > 10.0, 10.0, u_ros_1day) / 10.0, 6) as u_ros_1day
+,round(if(u_ctr_3day > 10.0, 10.0, u_ctr_3day) / 10.0, 6) as u_ctr_3day
+,round(if(u_str_3day > 10.0, 10.0, u_str_3day) / 10.0, 6) as u_str_3day
+,round(if(u_rov_3day > 10.0, 10.0, u_rov_3day) / 10.0, 6) as u_rov_3day
+,round(if(u_ros_3day > 10.0, 10.0, u_ros_3day) / 10.0, 6) as u_ros_3day
+,round(if(u_ctr_7day > 10.0, 10.0, u_ctr_7day) / 10.0, 6) as u_ctr_7day
+,round(if(u_str_7day > 10.0, 10.0, u_str_7day) / 10.0, 6) as u_str_7day
+,round(if(u_rov_7day > 10.0, 10.0, u_rov_7day) / 10.0, 6) as u_rov_7day
+,round(if(u_ros_7day > 10.0, 10.0, u_ros_7day) / 10.0, 6) as u_ros_7day
+,round(if(u_ctr_3month > 10.0, 10.0, u_ctr_3month) / 10.0, 6) as u_ctr_3month
+,round(if(u_str_3month > 10.0, 10.0, u_str_3month) / 10.0, 6) as u_str_3month
+,round(if(u_rov_3month > 10.0, 10.0, u_rov_3month) / 10.0, 6) as u_rov_3month
+,round(if(u_ros_3month > 10.0, 10.0, u_ros_3month) / 10.0, 6) as u_ros_3month
+-- 统计特征_视频
+,ceil(log2(i_1day_exp_cnt + 1)) as i_1day_exp_cnt
+,ceil(log2(i_1day_click_cnt + 1)) as i_1day_click_cnt
+,ceil(log2(i_1day_share_cnt + 1)) as i_1day_share_cnt
+,ceil(log2(i_1day_return_cnt + 1)) as i_1day_return_cnt
+,ceil(log2(i_3day_exp_cnt + 1)) as i_3day_exp_cnt
+,ceil(log2(i_3day_click_cnt + 1)) as i_3day_click_cnt
+,ceil(log2(i_3day_share_cnt + 1)) as i_3day_share_cnt
+,ceil(log2(i_3day_return_cnt + 1)) as i_3day_return_cnt
+,ceil(log2(i_7day_exp_cnt + 1)) as i_7day_exp_cnt
+,ceil(log2(i_7day_click_cnt + 1)) as i_7day_click_cnt
+,ceil(log2(i_7day_share_cnt + 1)) as i_7day_share_cnt
+,ceil(log2(i_7day_return_cnt + 1)) as i_7day_return_cnt
+,ceil(log2(i_3month_exp_cnt + 1)) as i_3month_exp_cnt
+,ceil(log2(i_3month_click_cnt + 1)) as i_3month_click_cnt
+,ceil(log2(i_3month_share_cnt + 1)) as i_3month_share_cnt
+,ceil(log2(i_3month_return_cnt + 1)) as i_3month_return_cnt
+,round(if(i_ctr_1day > 10.0, 10.0, i_ctr_1day) / 10.0, 6) as i_ctr_1day
+,round(if(i_str_1day > 10.0, 10.0, i_str_1day) / 10.0, 6) as i_str_1day
+,round(if(i_rov_1day > 10.0, 10.0, i_rov_1day) / 10.0, 6) as i_rov_1day
+,round(if(i_ros_1day > 10.0, 10.0, i_ros_1day) / 10.0, 6) as i_ros_1day
+,round(if(i_ctr_3day > 10.0, 10.0, i_ctr_3day) / 10.0, 6) as i_ctr_3day
+,round(if(i_str_3day > 10.0, 10.0, i_str_3day) / 10.0, 6) as i_str_3day
+,round(if(i_rov_3day > 10.0, 10.0, i_rov_3day) / 10.0, 6) as i_rov_3day
+,round(if(i_ros_3day > 10.0, 10.0, i_ros_3day) / 10.0, 6) as i_ros_3day
+,round(if(i_ctr_7day > 10.0, 10.0, i_ctr_7day) / 10.0, 6) as i_ctr_7day
+,round(if(i_str_7day > 10.0, 10.0, i_str_7day) / 10.0, 6) as i_str_7day
+,round(if(i_rov_7day > 10.0, 10.0, i_rov_7day) / 10.0, 6) as i_rov_7day
+,round(if(i_ros_7day > 10.0, 10.0, i_ros_7day) / 10.0, 6) as i_ros_7day
+,round(if(i_ctr_3month > 10.0, 10.0, i_ctr_3month) / 10.0, 6) as i_ctr_3month
+,round(if(i_str_3month > 10.0, 10.0, i_str_3month) / 10.0, 6) as i_str_3month
+,round(if(i_rov_3month > 10.0, 10.0, i_rov_3month) / 10.0, 6) as i_rov_3month
+,round(if(i_ros_3month > 10.0, 10.0, i_ros_3month) / 10.0, 6) as i_ros_3month
+from
+loghubods.user_video_features_data_final
+where dt='{dt}'
+and ad_ornot = '0'
+and apptype != '13'
+), candidate_user as (
+    SELECT 
+    u_id,
+    max(u_brand) as u_brand,
+    max(u_device) as u_device,
+    max(u_system) as u_system,
+    max(u_system_ver) as u_system_ver,
+    max(ctx_region) as ctx_region,
+    max(ctx_city) as ctx_city,
+    max(u_cycle_bucket_7days) as u_cycle_bucket_7days,
+    max(u_cycle_bucket_30days) as u_cycle_bucket_30days,
+    max(u_share_bucket_30days) as u_share_bucket_30days,
+    max(u_1day_exp_cnt) as u_1day_exp_cnt,
+    max(u_1day_click_cnt) as u_1day_click_cnt,
+    max(u_1day_share_cnt) as u_1day_share_cnt,
+    max(u_1day_return_cnt) as u_1day_return_cnt,
+    max(u_3day_exp_cnt) as u_3day_exp_cnt,
+    max(u_3day_click_cnt) as u_3day_click_cnt,
+    max(u_3day_share_cnt) as u_3day_share_cnt,
+    max(u_3day_return_cnt) as u_3day_return_cnt,
+    max(u_7day_exp_cnt) as u_7day_exp_cnt,
+    max(u_7day_click_cnt) as u_7day_click_cnt,
+    max(u_7day_share_cnt) as u_7day_share_cnt,
+    max(u_7day_return_cnt) as u_7day_return_cnt,
+    max(u_3month_exp_cnt) as u_3month_exp_cnt,
+    max(u_3month_click_cnt) as u_3month_click_cnt,
+    max(u_3month_share_cnt) as u_3month_share_cnt,
+    max(u_3month_return_cnt) as u_3month_return_cnt,
+    max(u_ctr_1day) as u_ctr_1day,
+    max(u_str_1day) as u_str_1day,
+    max(u_rov_1day) as u_rov_1day,
+    max(u_ros_1day) as u_ros_1day,
+    max(u_ctr_3day) as u_ctr_3day,
+    max(u_str_3day) as u_str_3day,
+    max(u_rov_3day) as u_rov_3day,
+    max(u_ros_3day) as u_ros_3day,
+    max(u_ctr_7day) as u_ctr_7day,
+    max(u_str_7day) as u_str_7day,
+    max(u_rov_7day) as u_rov_7day,
+    max(u_ros_7day) as u_ros_7day,
+    max(u_ctr_3month) as u_ctr_3month,
+    max(u_str_3month) as u_str_3month,
+    max(u_rov_3month) as u_rov_3month,
+    max(u_ros_3month) as u_ros_3month
+    FROM 
+    candidate
+    group by u_id
+), candidate_item as (
+    select
+    i_id,
+    max(i_up_id) as i_up_id,
+    max(i_title_len) as i_title_len,
+    max(i_play_len) as i_play_len,
+    max(i_days_since_upload) as i_days_since_upload,
+    max(i_1day_exp_cnt) as i_1day_exp_cnt,
+    max(i_1day_click_cnt) as i_1day_click_cnt,
+    max(i_1day_share_cnt) as i_1day_share_cnt,
+    max(i_1day_return_cnt) as i_1day_return_cnt,
+    max(i_3day_exp_cnt) as i_3day_exp_cnt,
+    max(i_3day_click_cnt) as i_3day_click_cnt,
+    max(i_3day_share_cnt) as i_3day_share_cnt,
+    max(i_3day_return_cnt) as i_3day_return_cnt,
+    max(i_7day_exp_cnt) as i_7day_exp_cnt,
+    max(i_7day_click_cnt) as i_7day_click_cnt,
+    max(i_7day_share_cnt) as i_7day_share_cnt,
+    max(i_7day_return_cnt) as i_7day_return_cnt,
+    max(i_3month_exp_cnt) as i_3month_exp_cnt,
+    max(i_3month_click_cnt) as i_3month_click_cnt,
+    max(i_3month_share_cnt) as i_3month_share_cnt,
+    max(i_3month_return_cnt) as i_3month_return_cnt,
+    max(i_ctr_1day) as i_ctr_1day,
+    max(i_str_1day) as i_str_1day,
+    max(i_rov_1day) as i_rov_1day,
+    max(i_ros_1day) as i_ros_1day,
+    max(i_ctr_3day) as i_ctr_3day,
+    max(i_str_3day) as i_str_3day,
+    max(i_rov_3day) as i_rov_3day,
+    max(i_ros_3day) as i_ros_3day,
+    max(i_ctr_7day) as i_ctr_7day,
+    max(i_str_7day) as i_str_7day,
+    max(i_rov_7day) as i_rov_7day,
+    max(i_ros_7day) as i_ros_7day,
+    max(i_ctr_3month) as i_ctr_3month,
+    max(i_str_3month) as i_str_3month,
+    max(i_rov_3month) as i_rov_3month,
+    max(i_ros_3month) as i_ros_3month
+    FROM 
+    candidate
+    group by i_id
+)
+SELECT
+i_id as k,
+*
+from candidate_item
+    """.format(dt=dt)
+    # log_.info(sql)
+    records = exe_sql(project, sql)
+    log_.info('sql_done')
+    records_process(records, process_and_store, max_size=50, num_workers=10)
+
+def timer_check(dt):
+    try:
+        project = config_.ad_model_data['ad_out_v1'].get('project')
+        table = config_.ad_model_data['ad_out_v1'].get('table')
+        now_date = datetime.datetime.today()
+        yesterday_date = now_date - datetime.timedelta(days=1)
+        now_dt = datetime.datetime.strftime(now_date, '%Y%m%d')
+        yesterday_dt = datetime.datetime.strftime(yesterday_date, '%Y%m%d')
+        log_.info(f"now_dt: {now_dt}")
+        if dt is not None:
+            yesterday_dt = dt
+        log_.info(f"update_dt: {yesterday_dt}")
+        now_min = datetime.datetime.now().minute
+        # 查看当前更新的数据是否已准备好
+        data_count = data_check(project=project, table=table, dt=yesterday_dt)
+        if data_count > 0:
+            log_.info('update_offline_score_item start! {}'.format(data_count))
+            # 数据准备好，进行更新
+            update_offline_score_item(dt=yesterday_dt)
+            log_.info('update_offline_score_item end!')
+        else:
+            # 数据没准备好，5分钟后重新检查
+            wait_seconds = 5 * 60
+            log_.info('data not ready, wait {}s'.format(wait_seconds))
+            Timer(wait_seconds, timer_check).start()
+
+    except Exception as e:
+        log_.error(f"用户广告跳出率预估离线item数据更新失败 exception: {e}, traceback: {traceback.format_exc()}")
+        send_msg_to_feishu(
+            webhook=config_.FEISHU_ROBOT['server_robot'].get('webhook'),
+            key_word=config_.FEISHU_ROBOT['server_robot'].get('key_word'),
+            msg_text=f"rov-offline{config_.ENV_TEXT} - 用户广告跳出率预估离线item数据更新失败\n"
+                     f"exception: {e}\n"
+                     f"traceback: {traceback.format_exc()}"
+        )
+
+
+if __name__ == "__main__":
+    dt = None
+    if len(sys.argv) > 1:
+        dt = sys.argv[1]
+        log_.info('## 手动更新:{}'.format(dt))
+    else:
+        log_.info('## 自动更新')
+    timer_check(dt)
+
+

ad_out_v1_get_offline_score_user.py

@@ -1,5 +1,6 @@
 #coding utf-8
 import sys
+import json
 import datetime
 import traceback
 from threading import Timer
@@ -229,7 +230,6 @@ from candidate_user
     lr_model = LrModel('model/{}.json'.format(model_key))
     user_h_dict = {}
     k_col = 'u_id'
-    dt = datetime
     key_name_prefix = f"{config_.KEY_NAME_PREFIX_AD_OUT_MODEL_SCORE_USER}{model_key}"
     print(key_name_prefix)
     mean_user_h = 0.0
@@ -241,7 +241,7 @@ from candidate_user
             k = str(row['u_id'])
             user_features = get_user_features(row)
             user_h = lr_model.predict_h(user_features)
-            redis_helper.set_data_to_redis(f"{key_name_prefix}:{k}", user_h, expire_time)
+            # redis_helper.set_data_to_redis(f"{key_name_prefix}:{k}", user_h, expire_time)
             user_h_dict[k] = user_h
             mean_user_h += user_h
             count_user_h += 1
@@ -252,9 +252,9 @@ from candidate_user
     print(mean_user_h)
     print(count_user_h)
     k = 'mean'
-    redis_helper.set_data_to_redis(f"{key_name_prefix}:{k}", mean_user_h, expire_time)
-    # with open('{}.json'.format(key_name_prefix), 'w') as fout:
-    #     json.dump(user_h_dict, fout, indent=2, ensure_ascii=False, sort_keys=True)
+    #redis_helper.set_data_to_redis(f"{key_name_prefix}:{k}", mean_user_h, expire_time)
+    with open('{}.{}.json'.format(key_name_prefix, dt), 'w') as fout:
+        json.dump(user_h_dict, fout, indent=2, ensure_ascii=False, sort_keys=True)
 
 
 def timer_check(dt):

ad_out_v1_get_offline_score_user.sh

@@ -2,9 +2,9 @@ source /etc/profile
 echo $ROV_OFFLINE_ENV
 if [[ $ROV_OFFLINE_ENV == 'test' ]]; then
     cd /data2/rov-offline &&
-    /root/anaconda3/bin/python /data2/rov-offline/ad_out_v1_get_offline_score_user.py $*
+    /root/anaconda3/bin/python /data2/rov-offline/ad_out_v1_get_offline_score_user_new.py $*
 elif [[ $ROV_OFFLINE_ENV == 'pro' ]]; then
     cd /data/rov-offline &&
-    /root/anaconda3/bin/python /data/rov-offline/ad_out_v1_get_offline_score_user.py $*
+    /root/anaconda3/bin/python /data/rov-offline/ad_out_v1_get_offline_score_user_new.py $*
 fi
 

ad_out_v1_get_offline_score_user_new.py

@@ -0,0 +1,291 @@
+#coding utf-8
+import sys
+import datetime
+import traceback
+from threading import Timer
+from tqdm import tqdm
+from utils import RedisHelper, data_check, get_feature_data, send_msg_to_feishu
+from config import set_config
+from log import Log
+from records_process import records_process
+
+config_, _ = set_config()
+log_ = Log()
+redis_helper = RedisHelper()
+
+from feature import get_user_features as get_features
+from lr_model import LrModel
+from utils import exe_sql
+
+model_key = 'ad_out_v1'
+lr_model = LrModel('model/{}.json'.format(model_key))
+item_h_dict = {}
+key_name_prefix = f"{config_.KEY_NAME_PREFIX_AD_OUT_MODEL_SCORE_USER}{model_key}"
+print(key_name_prefix)
+# 过期时间：一周
+expire_time = 7 * 24 * 3600
+
+def process_and_store(row):
+    k = str(row['k'])
+    features = get_features(row)
+    h = lr_model.predict_h(features)
+    redis_helper.set_data_to_redis(f"{key_name_prefix}:{k}", round(h, 6), expire_time)
+
+def update_offline_score_user(dt):
+    project = 'loghubods'
+    sql = """
+--odps sql 
+--********************************************************************--
+--author:研发
+--create time:2023-12-01 15:48:17
+--********************************************************************--
+with candidate as (
+select
+-- 基础特征_用户
+mid AS u_id
+,machineinfo_brand AS u_brand
+,machineinfo_model AS u_device
+,SPLIT(machineinfo_system,' ')[0] AS u_system
+,machineinfo_system AS u_system_ver
+-- 基础特征_视频
+,videoid AS i_id
+,i_up_id AS i_up_id
+,tags as i_tag
+,title as i_title
+,ceil(log2(i_title_len + 1)) as i_title_len
+,ceil(log2(total_time + 1)) as i_play_len
+,ceil(log2(i_days_since_upload + 1)) as i_days_since_upload -- 发布时间（距离现在天数）
+-- 基础特征_场景
+,apptype AS ctx_apptype
+,ctx_day AS ctx_day
+,ctx_week AS ctx_week
+,ctx_hour AS ctx_hour
+,ctx_region as ctx_region
+,ctx_city as ctx_city
+-- 基础特征_交叉
+,ui_is_out as ui_is_out
+,i_play_len as playtime
+-- ,IF(i_play_len > 1,'0','1') AS ui_is_out_new
+,rootmid AS ui_root_id
+,shareid AS ui_share_id
+-- 统计特征_用户
+,u_cycle_bucket_7days
+,u_cycle_bucket_30days
+,u_share_bucket_30days
+,ceil(log2(u_1day_exp_cnt + 1)) as u_1day_exp_cnt
+,ceil(log2(u_1day_click_cnt + 1)) as u_1day_click_cnt
+,ceil(log2(u_1day_share_cnt + 1)) as u_1day_share_cnt
+,ceil(log2(u_1day_return_cnt + 1)) as u_1day_return_cnt
+,ceil(log2(u_3day_exp_cnt + 1)) as u_3day_exp_cnt
+,ceil(log2(u_3day_click_cnt + 1)) as u_3day_click_cnt
+,ceil(log2(u_3day_share_cnt + 1)) as u_3day_share_cnt
+,ceil(log2(u_3day_return_cnt + 1)) as u_3day_return_cnt
+,ceil(log2(u_7day_exp_cnt + 1)) as u_7day_exp_cnt
+,ceil(log2(u_7day_click_cnt + 1)) as u_7day_click_cnt
+,ceil(log2(u_7day_share_cnt + 1)) as u_7day_share_cnt
+,ceil(log2(u_7day_return_cnt + 1)) as u_7day_return_cnt
+,ceil(log2(u_3month_exp_cnt + 1)) as u_3month_exp_cnt
+,ceil(log2(u_3month_click_cnt + 1)) as u_3month_click_cnt
+,ceil(log2(u_3month_share_cnt + 1)) as u_3month_share_cnt
+,ceil(log2(u_3month_return_cnt + 1)) as u_3month_return_cnt
+,round(if(u_ctr_1day > 10.0, 10.0, u_ctr_1day) / 10.0, 6) as u_ctr_1day
+,round(if(u_str_1day > 10.0, 10.0, u_str_1day) / 10.0, 6) as u_str_1day
+,round(if(u_rov_1day > 10.0, 10.0, u_rov_1day) / 10.0, 6) as u_rov_1day
+,round(if(u_ros_1day > 10.0, 10.0, u_ros_1day) / 10.0, 6) as u_ros_1day
+,round(if(u_ctr_3day > 10.0, 10.0, u_ctr_3day) / 10.0, 6) as u_ctr_3day
+,round(if(u_str_3day > 10.0, 10.0, u_str_3day) / 10.0, 6) as u_str_3day
+,round(if(u_rov_3day > 10.0, 10.0, u_rov_3day) / 10.0, 6) as u_rov_3day
+,round(if(u_ros_3day > 10.0, 10.0, u_ros_3day) / 10.0, 6) as u_ros_3day
+,round(if(u_ctr_7day > 10.0, 10.0, u_ctr_7day) / 10.0, 6) as u_ctr_7day
+,round(if(u_str_7day > 10.0, 10.0, u_str_7day) / 10.0, 6) as u_str_7day
+,round(if(u_rov_7day > 10.0, 10.0, u_rov_7day) / 10.0, 6) as u_rov_7day
+,round(if(u_ros_7day > 10.0, 10.0, u_ros_7day) / 10.0, 6) as u_ros_7day
+,round(if(u_ctr_3month > 10.0, 10.0, u_ctr_3month) / 10.0, 6) as u_ctr_3month
+,round(if(u_str_3month > 10.0, 10.0, u_str_3month) / 10.0, 6) as u_str_3month
+,round(if(u_rov_3month > 10.0, 10.0, u_rov_3month) / 10.0, 6) as u_rov_3month
+,round(if(u_ros_3month > 10.0, 10.0, u_ros_3month) / 10.0, 6) as u_ros_3month
+-- 统计特征_视频
+,ceil(log2(i_1day_exp_cnt + 1)) as i_1day_exp_cnt
+,ceil(log2(i_1day_click_cnt + 1)) as i_1day_click_cnt
+,ceil(log2(i_1day_share_cnt + 1)) as i_1day_share_cnt
+,ceil(log2(i_1day_return_cnt + 1)) as i_1day_return_cnt
+,ceil(log2(i_3day_exp_cnt + 1)) as i_3day_exp_cnt
+,ceil(log2(i_3day_click_cnt + 1)) as i_3day_click_cnt
+,ceil(log2(i_3day_share_cnt + 1)) as i_3day_share_cnt
+,ceil(log2(i_3day_return_cnt + 1)) as i_3day_return_cnt
+,ceil(log2(i_7day_exp_cnt + 1)) as i_7day_exp_cnt
+,ceil(log2(i_7day_click_cnt + 1)) as i_7day_click_cnt
+,ceil(log2(i_7day_share_cnt + 1)) as i_7day_share_cnt
+,ceil(log2(i_7day_return_cnt + 1)) as i_7day_return_cnt
+,ceil(log2(i_3month_exp_cnt + 1)) as i_3month_exp_cnt
+,ceil(log2(i_3month_click_cnt + 1)) as i_3month_click_cnt
+,ceil(log2(i_3month_share_cnt + 1)) as i_3month_share_cnt
+,ceil(log2(i_3month_return_cnt + 1)) as i_3month_return_cnt
+,round(if(i_ctr_1day > 10.0, 10.0, i_ctr_1day) / 10.0, 6) as i_ctr_1day
+,round(if(i_str_1day > 10.0, 10.0, i_str_1day) / 10.0, 6) as i_str_1day
+,round(if(i_rov_1day > 10.0, 10.0, i_rov_1day) / 10.0, 6) as i_rov_1day
+,round(if(i_ros_1day > 10.0, 10.0, i_ros_1day) / 10.0, 6) as i_ros_1day
+,round(if(i_ctr_3day > 10.0, 10.0, i_ctr_3day) / 10.0, 6) as i_ctr_3day
+,round(if(i_str_3day > 10.0, 10.0, i_str_3day) / 10.0, 6) as i_str_3day
+,round(if(i_rov_3day > 10.0, 10.0, i_rov_3day) / 10.0, 6) as i_rov_3day
+,round(if(i_ros_3day > 10.0, 10.0, i_ros_3day) / 10.0, 6) as i_ros_3day
+,round(if(i_ctr_7day > 10.0, 10.0, i_ctr_7day) / 10.0, 6) as i_ctr_7day
+,round(if(i_str_7day > 10.0, 10.0, i_str_7day) / 10.0, 6) as i_str_7day
+,round(if(i_rov_7day > 10.0, 10.0, i_rov_7day) / 10.0, 6) as i_rov_7day
+,round(if(i_ros_7day > 10.0, 10.0, i_ros_7day) / 10.0, 6) as i_ros_7day
+,round(if(i_ctr_3month > 10.0, 10.0, i_ctr_3month) / 10.0, 6) as i_ctr_3month
+,round(if(i_str_3month > 10.0, 10.0, i_str_3month) / 10.0, 6) as i_str_3month
+,round(if(i_rov_3month > 10.0, 10.0, i_rov_3month) / 10.0, 6) as i_rov_3month
+,round(if(i_ros_3month > 10.0, 10.0, i_ros_3month) / 10.0, 6) as i_ros_3month
+from
+loghubods.user_video_features_data_final
+where dt='{dt}'
+and ad_ornot = '0'
+and apptype != '13'
+), candidate_user as (
+    SELECT 
+    u_id,
+    max(u_brand) as u_brand,
+    max(u_device) as u_device,
+    max(u_system) as u_system,
+    max(u_system_ver) as u_system_ver,
+    max(ctx_region) as ctx_region,
+    max(ctx_city) as ctx_city,
+    max(u_cycle_bucket_7days) as u_cycle_bucket_7days,
+    max(u_cycle_bucket_30days) as u_cycle_bucket_30days,
+    max(u_share_bucket_30days) as u_share_bucket_30days,
+    max(u_1day_exp_cnt) as u_1day_exp_cnt,
+    max(u_1day_click_cnt) as u_1day_click_cnt,
+    max(u_1day_share_cnt) as u_1day_share_cnt,
+    max(u_1day_return_cnt) as u_1day_return_cnt,
+    max(u_3day_exp_cnt) as u_3day_exp_cnt,
+    max(u_3day_click_cnt) as u_3day_click_cnt,
+    max(u_3day_share_cnt) as u_3day_share_cnt,
+    max(u_3day_return_cnt) as u_3day_return_cnt,
+    max(u_7day_exp_cnt) as u_7day_exp_cnt,
+    max(u_7day_click_cnt) as u_7day_click_cnt,
+    max(u_7day_share_cnt) as u_7day_share_cnt,
+    max(u_7day_return_cnt) as u_7day_return_cnt,
+    max(u_3month_exp_cnt) as u_3month_exp_cnt,
+    max(u_3month_click_cnt) as u_3month_click_cnt,
+    max(u_3month_share_cnt) as u_3month_share_cnt,
+    max(u_3month_return_cnt) as u_3month_return_cnt,
+    max(u_ctr_1day) as u_ctr_1day,
+    max(u_str_1day) as u_str_1day,
+    max(u_rov_1day) as u_rov_1day,
+    max(u_ros_1day) as u_ros_1day,
+    max(u_ctr_3day) as u_ctr_3day,
+    max(u_str_3day) as u_str_3day,
+    max(u_rov_3day) as u_rov_3day,
+    max(u_ros_3day) as u_ros_3day,
+    max(u_ctr_7day) as u_ctr_7day,
+    max(u_str_7day) as u_str_7day,
+    max(u_rov_7day) as u_rov_7day,
+    max(u_ros_7day) as u_ros_7day,
+    max(u_ctr_3month) as u_ctr_3month,
+    max(u_str_3month) as u_str_3month,
+    max(u_rov_3month) as u_rov_3month,
+    max(u_ros_3month) as u_ros_3month
+    FROM 
+    candidate
+    group by u_id
+), candidate_item as (
+    select
+    i_id,
+    max(i_up_id) as i_up_id,
+    max(i_title_len) as i_title_len,
+    max(i_play_len) as i_play_len,
+    max(i_days_since_upload) as i_days_since_upload,
+    max(i_1day_exp_cnt) as i_1day_exp_cnt,
+    max(i_1day_click_cnt) as i_1day_click_cnt,
+    max(i_1day_share_cnt) as i_1day_share_cnt,
+    max(i_1day_return_cnt) as i_1day_return_cnt,
+    max(i_3day_exp_cnt) as i_3day_exp_cnt,
+    max(i_3day_click_cnt) as i_3day_click_cnt,
+    max(i_3day_share_cnt) as i_3day_share_cnt,
+    max(i_3day_return_cnt) as i_3day_return_cnt,
+    max(i_7day_exp_cnt) as i_7day_exp_cnt,
+    max(i_7day_click_cnt) as i_7day_click_cnt,
+    max(i_7day_share_cnt) as i_7day_share_cnt,
+    max(i_7day_return_cnt) as i_7day_return_cnt,
+    max(i_3month_exp_cnt) as i_3month_exp_cnt,
+    max(i_3month_click_cnt) as i_3month_click_cnt,
+    max(i_3month_share_cnt) as i_3month_share_cnt,
+    max(i_3month_return_cnt) as i_3month_return_cnt,
+    max(i_ctr_1day) as i_ctr_1day,
+    max(i_str_1day) as i_str_1day,
+    max(i_rov_1day) as i_rov_1day,
+    max(i_ros_1day) as i_ros_1day,
+    max(i_ctr_3day) as i_ctr_3day,
+    max(i_str_3day) as i_str_3day,
+    max(i_rov_3day) as i_rov_3day,
+    max(i_ros_3day) as i_ros_3day,
+    max(i_ctr_7day) as i_ctr_7day,
+    max(i_str_7day) as i_str_7day,
+    max(i_rov_7day) as i_rov_7day,
+    max(i_ros_7day) as i_ros_7day,
+    max(i_ctr_3month) as i_ctr_3month,
+    max(i_str_3month) as i_str_3month,
+    max(i_rov_3month) as i_rov_3month,
+    max(i_ros_3month) as i_ros_3month
+    FROM 
+    candidate
+    group by i_id
+)
+SELECT
+u_id as k,
+*
+from candidate_user
+    """.format(dt=dt)
+    # log_.info(sql)
+    records = exe_sql(project, sql)
+    log_.info('sql_done')
+    records_process(records, process_and_store, max_size=50, num_workers=10)
+
+def timer_check(dt):
+    try:
+        project = config_.ad_model_data['ad_out_v1'].get('project')
+        table = config_.ad_model_data['ad_out_v1'].get('table')
+        now_date = datetime.datetime.today()
+        yesterday_date = now_date - datetime.timedelta(days=1)
+        now_dt = datetime.datetime.strftime(now_date, '%Y%m%d')
+        yesterday_dt = datetime.datetime.strftime(yesterday_date, '%Y%m%d')
+        log_.info(f"now_dt: {now_dt}")
+        if dt is not None:
+            yesterday_dt = dt
+        log_.info(f"update_dt: {yesterday_dt}")
+        now_min = datetime.datetime.now().minute
+        # 查看当前更新的数据是否已准备好
+        data_count = data_check(project=project, table=table, dt=yesterday_dt)
+        if data_count > 0:
+            log_.info('update_offline_score_user start! {}'.format(data_count))
+            # 数据准备好，进行更新
+            update_offline_score_user(dt=yesterday_dt)
+            log_.info('update_offline_score_user end!')
+        else:
+            # 数据没准备好，5分钟后重新检查
+            wait_seconds = 5 * 60
+            log_.info('data not ready, wait {}s'.format(wait_seconds))
+            Timer(wait_seconds, timer_check).start()
+
+    except Exception as e:
+        log_.error(f"用户广告跳出率预估离线user数据更新失败 exception: {e}, traceback: {traceback.format_exc()}")
+        send_msg_to_feishu(
+            webhook=config_.FEISHU_ROBOT['server_robot'].get('webhook'),
+            key_word=config_.FEISHU_ROBOT['server_robot'].get('key_word'),
+            msg_text=f"rov-offline{config_.ENV_TEXT} - 用户广告跳出率预估离线user数据更新失败\n"
+                     f"exception: {e}\n"
+                     f"traceback: {traceback.format_exc()}"
+        )
+
+
+if __name__ == "__main__":
+    dt = None
+    if len(sys.argv) > 1:
+        dt = sys.argv[1]
+        log_.info('## 手动更新:{}'.format(dt))
+    else:
+        log_.info('## 自动更新')
+    timer_check(dt)
+
+

records_process.py

@@ -0,0 +1,39 @@
+#coding utf-8
+import threading
+from concurrent.futures import ThreadPoolExecutor
+from queue import Queue
+from tqdm import tqdm
+
+def worker(queue, executor):
+    while True:
+        row = queue.get()
+        if row is None:  # 结束信号
+            queue.task_done()
+            break
+        executor(row)
+        queue.task_done()
+
+def records_process(records, executor, max_size=50, num_workers=10):
+    # 创建一个线程安全的队列
+    queue = Queue(maxsize=max_size)  # 可以调整 maxsize 以控制内存使用
+    # 设置线程池大小
+    num_workers = num_workers 
+    # 启动工作线程
+    threads = []
+    for _ in range(num_workers):
+        t = threading.Thread(target=worker, args=(queue, executor))
+        t.start()
+        threads.append(t)
+    # 读取数据并放入队列
+    with records.open_reader() as reader:
+        for row in tqdm(reader):
+            queue.put(row)
+    # 发送结束信号
+    for _ in range(num_workers):
+        queue.put(None)
+    # 等待所有任务完成
+    queue.join()
+    # 等待所有工作线程结束
+    for t in threads:
+        t.join()
+