change test file

rerank_esmm.py

@@ -3,19 +3,24 @@ import datetime
 import json
 import redis
 import pandas as pd
+import time
 
 
 def get_esmm_users():
-    db = pymysql.connect(host='172.16.40.158', port=4000, user='root',
-                         passwd='3SYz54LS9#^9sBvC', db='jerry_prod')
-    cursor = db.cursor()
-    stat_date = (datetime.date.today() - datetime.timedelta(days=1)).strftime("%Y-%m-%d")
-    sql = "select distinct device_id,city_id from data_feed_exposure_precise " \
-          "where stat_date = '{}'".format(stat_date)
-    cursor.execute(sql)
-    result = list(cursor.fetchall())
-    db.close()
-    return result
+    try:
+        db = pymysql.connect(host='172.16.40.158', port=4000, user='root',
+                             passwd='3SYz54LS9#^9sBvC', db='jerry_prod')
+        cursor = db.cursor()
+        stat_date = (datetime.date.today() - datetime.timedelta(days=1)).strftime("%Y-%m-%d")
+        sql = "select distinct device_id,city_id from data_feed_exposure_precise " \
+              "where stat_date = '{}'".format(stat_date)
+        cursor.execute(sql)
+        result = list(cursor.fetchall())
+        db.close()
+        return result
+    except:
+        return []
+
 
 
 def get_user_profile(device_id):
@@ -46,8 +51,6 @@ def get_user_profile(device_id):
         return []
 
 
-
-
 def get_searchworlds_to_tagid():
     try:
         sql = 'select id, name from api_tag where is_online = 1 and tag_type < 4'
@@ -63,24 +66,28 @@ def get_searchworlds_to_tagid():
 
         return searchworlds_to_tagid
     except Exception as e:
-        print(e)
+        return {}
 
 
 def get_queues(device_id,city_id):
-    db = pymysql.connect(host='172.16.40.158', port=4000, user='root',
-                         passwd='3SYz54LS9#^9sBvC', db='jerry_test')
-    cursor = db.cursor()
-    sql = "select native_queue, nearby_queue, nation_queue, megacity_queue from esmm_device_diary_queue " \
-          "where device_id = '{}' and city_id = '{}'".format(device_id,city_id)
-    cursor.execute(sql)
-    result = cursor.fetchone()
-    db.close()
-    if len(result) > 0:
-        return list(result)
-    else:
+    try:
+        db = pymysql.connect(host='172.16.40.158', port=4000, user='root',
+                             passwd='3SYz54LS9#^9sBvC', db='jerry_test')
+        cursor = db.cursor()
+        sql = "select native_queue, nearby_queue, nation_queue, megacity_queue from esmm_device_diary_queue " \
+              "where device_id = '{}' and city_id = '{}'".format(device_id, city_id)
+        cursor.execute(sql)
+        result = cursor.fetchone()
+        db.close()
+        if result is not None:
+            return list(result)
+        else:
+            return []
+    except:
         return []
 
 
+
 def tag_boost(cid_str, tag_list):
     if cid_str is not None and cid_str != "":
         cids = cid_str.split(",")
@@ -96,6 +103,7 @@ def tag_boost(cid_str, tag_list):
                 cursor = db.cursor()
                 cursor.execute(sql)
                 result = cursor.fetchall()
+                db.close()
                 if len(result) > 0:
                     tag_cids = {}
                     left_cids = []
@@ -157,19 +165,19 @@ if __name__ == "__main__":
     for i in users_list[:6]:
         tag_list = get_user_profile(i[0])
         queues = get_queues(i[0],i[1])
-        if len(queues) > 0:
+        if len(queues) > 0 and len(tag_list) > 0:
             new_native = tag_boost(queues[0], tag_list)
             new_nearby = tag_boost(queues[1], tag_list)
 
-            sample = [i[0],i[1],new_native,new_nearby,queues[2],queues[3]]
+            sample = [i[0],i[1],new_native,new_nearby,queues[2],queues[3],int(time.time())]
             total_samples.append(sample)
 
     if len(total_samples) > 0:
         df = pd.DataFrame(total_samples)
         df = df.rename(columns={0: "device_id", 1: "city_id",2:"native_queue",
-                                3:"nearby_queue",4:"nation_queue",5:", megacity_queue"})
-        print(total_samples)
-        print(df.head(6))
+                                3:"nearby_queue",4:"nation_queue",5:", megacity_queue",6:"insert_time"})
+        print(total_samples[:2])
+        print(df.head(2))
     # to_data_base(df)
     # to kv