import os import sys sys.path.append(os.path.dirname(sys.path[0])) # 上级目录 from utils.templates import Templates # from ..utils.templates import Templates from pyspark.sql import functions as F class DwdStInfoDay(Templates): def __init__(self, site_name='us', date_type="month", date_info='2022-1'): super(DwdStInfoDay, self).__init__() self.site_name = site_name self.date_type = date_type self.date_info = date_info self.db_save = f'dwd_st_info_day' self.spark = self.create_spark_object(app_name=f"{self.db_save}: {self.site_name}, {self.date_info}") self.df_date = self.get_year_week_tuple() self.df_save = self.spark.sql(f"select 1+1;") self.df_brand_day = self.spark.sql(f"select 1+1;") self.df_rank_repeat = self.spark.sql(f"select 1+1;") self.partitions_by = ['site_name'] self.reset_partitions(partitions_num=5) def read_data(self): sql = f"select year, month, search_term, rank, report_date from ods_brand_analytics_day where site_name='{self.site_name}' " \ f"and dm in ('2021-9', '2021-10', '2021-11', '2021-12', '2022-1', '2022-2', '2022-3', '2022-4', '2022-5', '2022-6', '2022-7', '2022-8')" self.df_brand_day = self.spark.sql(sqlQuery=sql) sql = f"select rank, search_sum as st_orders from ods_rank_search_rate_repeat where site_name='{self.site_name}'" self.df_rank_repeat = self.spark.sql(sqlQuery=sql) def handle_data(self): self.df_brand_day = self.df_brand_day.join(self.df_rank_repeat, on='rank', how='left') self.df_brand_day = self.df_brand_day.withColumn('st_orders_day', self.df_brand_day.st_orders / 30) self.df_brand_day = self.df_brand_day.withColumn( "st_orders_pivot", F.concat(F.lit("st_orders_month"), self.df_brand_day.month, ) ).withColumn( "st_counts_pivot", F.concat(F.lit("st_counts_month"), self.df_brand_day.month, ) ) self.df_brand_day.show(10, truncate=False) df_orders = self.df_brand_day.groupby(['search_term']).pivot( "st_orders_pivot" ).agg(F.sum('st_orders_day')) df_orders = df_orders.fillna(0) df_orders = df_orders.withColumn( "st_orders_quarter1", df_orders.st_orders_month1 + df_orders.st_orders_month2 + df_orders.st_orders_month3 ).withColumn( "st_orders_quarter2", df_orders.st_orders_month4 + df_orders.st_orders_month5 + df_orders.st_orders_month6 ).withColumn( "st_orders_quarter3", df_orders.st_orders_month7 + df_orders.st_orders_month8 + df_orders.st_orders_month9 ).withColumn( "st_orders_quarter4", df_orders.st_orders_month10 + df_orders.st_orders_month11 + df_orders.st_orders_month12 ) df_orders = df_orders.withColumn( "st_orders_year", df_orders.st_orders_quarter1 + df_orders.st_orders_quarter2 + df_orders.st_orders_quarter3 + df_orders.st_orders_quarter4 ) df_orders.filter("search_term='sageholm leggings'").show(10, truncate=False) # df_orders.show(10, truncate=False) # print(df_orders.count()) df_counts = self.df_brand_day.groupby(['search_term']).pivot( "st_counts_pivot" ).agg(F.count('search_term')) df_counts = df_counts.fillna(0) df_counts = df_counts.withColumn( "st_counts_quarter1", df_counts.st_counts_month1 + df_counts.st_counts_month2 + df_counts.st_counts_month3 ).withColumn( "st_counts_quarter2", df_counts.st_counts_month4 + df_counts.st_counts_month5 + df_counts.st_counts_month6 ).withColumn( "st_counts_quarter3", df_counts.st_counts_month7 + df_counts.st_counts_month8 + df_counts.st_counts_month9 ).withColumn( "st_counts_quarter4", df_counts.st_counts_month10 + df_counts.st_counts_month11 + df_counts.st_counts_month12 ) df_counts = df_counts.withColumn( "st_counts_year", df_counts.st_counts_quarter1 + df_counts.st_counts_quarter2 + df_counts.st_counts_quarter3 + df_counts.st_counts_quarter4 ) # df_counts.show(10, truncate=False) # print(df_counts.count()) self.df_save = df_orders.join(df_counts, on='search_term', how='left') self.df_save = self.df_save.withColumn("site_name", F.lit(self.site_name)) # print(self.df_save.count()) # print(self.df_save.columns) # self.df_save.show(10, truncate=False) if __name__ == '__main__': handle_obj = DwdStInfoDay() handle_obj.run()