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import os
import sys
sys.path.append(os.path.dirname(sys.path[0]))
from utils.hdfs_utils import HdfsUtils
from utils.common_util import CommonUtil
from utils.spark_util import SparkUtil
from pyspark.sql import functions as F
from pyspark.sql.window import Window
class DwtSTBaseReport(object):
def __init__(self, site_name, date_type, date_info):
self.site_name = site_name
self.date_type = date_type
self.date_info = date_info
self.hive_tb = "dwt_st_base_report"
app_name = f"{self.hive_tb}:{site_name} {date_type} {date_info}"
self.spark = SparkUtil.get_spark_session(app_name)
self.partitions_num = CommonUtil.reset_partitions(site_name, 1)
def run(self):
# 读ods_rank_search_rate_repeat表，获取排名+搜索量对应关系
if self.date_info <= '2022-08':
params = "date_info = '2022-08'"
else:
params = f"date_info = '{self.date_info}'"
sql1 = f"""
select
rank as st_rank,
search_num as st_volume,
search_sum as st_orders
from ods_rank_search_rate_repeat
where site_name = '{self.site_name}'
and date_type = 'month'
and {params};
"""
df_rank_sv = self.spark.sql(sql1).repartition(40, 'st_rank').cache()
print("排名+搜索量对应关系:")
df_rank_sv.show(10, False)
if df_rank_sv.count() == 0:
sql1 = f"""
select
rank as st_rank,
search_num as st_volume,
search_sum as st_orders,
date_info
from ods_rank_search_rate_repeat
where site_name = '{self.site_name}';
"""
df_rank_sv = self.spark.sql(sql1)
window = Window.partitionBy(["st_rank"]).orderBy(df_rank_sv.date_info.desc())
df_rank_sv = df_rank_sv.withColumn("rk", F.row_number().over(window=window))\
.filter("rk = 1")\
.drop("rk", "date_info")\
.repartition(40, 'st_rank').cache()
print("排名+搜索量对应关系:")
df_rank_sv.show(10, False)
# 搜索词主表
sql2 = f"""
select
id as st_key,
search_term
from dwt_aba_st_analytics
where site_name = '{self.site_name}'
and date_type = '{self.date_type}'
and date_info = '{self.date_info}';
"""
df_st_base = self.spark.sql(sql2).repartition(40, 'search_term').cache()
print("搜索词主表:")
df_st_base.show(10, False)
# 读ods_brand_analytics表，获取报告中的搜索词+排名
sql3 = f"""
select
search_term,
rank as st_rank
from ods_brand_analytics
where site_name = '{self.site_name}'
and date_type = '{self.date_type}'
and date_info = '{self.date_info}';
"""
df_st_rank = self.spark.sql(sql3).repartition(40, 'search_term').cache()
print("搜索词+排名对应关系，月:")
df_st_rank.show(10, False)
df_save = df_st_base \
.join(df_st_rank, 'search_term', 'inner') \
.repartition(40, 'st_rank') \
.join(df_rank_sv, 'st_rank', 'left') \
.withColumn('created_time', F.date_format(F.current_timestamp(), 'yyyy-MM-dd HH:mm:SS')) \
.withColumn('updated_time', F.date_format(F.current_timestamp(), 'yyyy-MM-dd HH:mm:SS')) \
.withColumn('years', F.lit(int(self.date_info.split("-")[0]))) \
.withColumn('site_name', F.lit(self.site_name)) \
.withColumn('date_type', F.lit(self.date_type)) \
.withColumn('date_info', F.lit(self.date_info))
hdfs_path = f"/home/{SparkUtil.DEF_USE_DB}/dwt/{self.hive_tb}/site_name={self.site_name}/date_type={self.date_type}/date_info={self.date_info}"
print(f"清除hdfs目录中数据:{hdfs_path}")
HdfsUtils.delete_hdfs_file(hdfs_path)
df_save = df_save.repartition(self.partitions_num)
partition_by = ["site_name", "date_type", "date_info"]
print(f"当前存储的表名为：{self.hive_tb},分区为{partition_by}", )
df_save.write.saveAsTable(name=self.hive_tb, format='hive', mode='append', partitionBy=partition_by)
print("success")
# 计算周维度的趋势图数据
# 读日期字典表，获取date_info对应的week_list
sql = f"""
select year_week from dim_date_20_to_30 where week_day = 1 and year_month = '{self.date_info}';
"""
df_week = self.spark.sql(sql)
week_list = sorted([row['year_week'] for row in df_week.collect()])
for year_week in week_list:
sql = f"""
select
search_term,
rank as st_rank
from ods_brand_analytics
where site_name = '{self.site_name}'
and date_type = 'week'
and date_info = '{year_week}'
and rank <= 1500000;
"""
df_st_rank_week = self.spark.sql(sql).repartition(40, 'search_term').cache()
print(f"搜索词+排名对应关系，{year_week}周:")
df_st_rank_week.show(10, False)
df_save = df_st_base\
.join(df_st_rank_week, 'search_term', 'inner')\
.repartition(40, 'st_rank')\
.join(df_rank_sv, 'st_rank', 'left')\
.withColumn('created_time', F.date_format(F.current_timestamp(), 'yyyy-MM-dd HH:mm:SS'))\
.withColumn('updated_time', F.date_format(F.current_timestamp(), 'yyyy-MM-dd HH:mm:SS'))\
.withColumn('years', F.lit(int(year_week.split("-")[0])))\
.withColumn('site_name', F.lit(self.site_name))\
.withColumn('date_type', F.lit('week'))\
.withColumn('date_info', F.lit(year_week))
hdfs_path = f"/home/{SparkUtil.DEF_USE_DB}/dwt/{self.hive_tb}/site_name={self.site_name}/date_type=week/date_info={year_week}"
print(f"清除hdfs目录中数据:{hdfs_path}")
HdfsUtils.delete_hdfs_file(hdfs_path)
df_save = df_save.repartition(self.partitions_num)
partition_by = ["site_name", "date_type", "date_info"]
print(f"当前存储的表名为：{self.hive_tb},分区为{partition_by}", )
df_save.write.saveAsTable(name=self.hive_tb, format='hive', mode='append', partitionBy=partition_by)
print("success")
if __name__ == '__main__':
site_name = CommonUtil.get_sys_arg(1, None)
date_type = CommonUtil.get_sys_arg(2, None)
date_info = CommonUtil.get_sys_arg(3, None)
obj = DwtSTBaseReport(site_name, date_type, date_info)
obj.run()