1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
import os
import sys
sys.path.append(os.path.dirname(sys.path[0]))
from utils.common_util import CommonUtil
from utils.hdfs_utils import HdfsUtils
from utils.spark_util import SparkUtil
"""
对nsr榜单（新品销售排行榜）ASIN历史纬度数据进行存档
"""
class DimNsrAsinRankHistory(object):
def __init__(self, site_name, date_info):
self.site_name = site_name
self.date_info = date_info
app_name = f"{self.__class__.__name__}:{site_name}:{date_info}"
self.spark = SparkUtil.get_spark_session(app_name)
self.hive_tb = "dim_nsr_asin_rank_history"
def run(self):
sql = f"""
select asin,
cate_current_id as old_category_id,
category_id as category_id,
bsr_rank ,
rating as asin_rating,
total_comments as asin_total_comments,
updated_at,
date_info,
site_name
from ods_new_releases_top100_asin
where 1 = 1
and date_info = '{self.date_info}'
and site_name='{self.site_name}'
"""
print("======================查询sql如下======================")
print(sql)
df_save = self.spark.sql(sql)
if df_save.first() == None:
print("============================无数据跳过===================================")
return
# 清除重复数据
df_save = df_save.dropDuplicates(['asin', 'category_id'])
# 分区重置
df_save = df_save.repartition(1)
partition_dict = {
"site_name": self.site_name,
"date_info": self.date_info,
}
CommonUtil.save_or_update_table(
spark_session=self.spark,
hive_tb_name=self.hive_tb,
partition_dict=partition_dict,
df_save=df_save,
drop_exist_tmp_flag=False
)
if __name__ == '__main__':
site_name = CommonUtil.get_sys_arg(1, None)
date_info = CommonUtil.get_sys_arg(2, None)
obj = DimNsrAsinRankHistory(site_name, date_info)
obj.run()