asin_find_st.py

import os
import sys

import pandas as pd
from pyspark.sql.window import Window

sys.path.append(os.path.dirname(sys.path[0]))  # 上级目录
from pyspark.storagelevel import StorageLevel
from utils.templates import Templates
# from ..utils.templates import Templates
from pyspark.sql import functions as F
from pyspark.sql.types import StructType,StructField, StringType, IntegerType


class DwdStMeasure(Templates):

    def __init__(self, site_name='us', date_type="month", date_info='2022-01'):
        super().__init__()
        self.site_name = site_name
        self.date_type = date_type
        self.date_info = date_info
        self.db_save = f'dwd_st_rank'
        self.spark = self.create_spark_object(
            app_name=f"{self.db_save}: {self.site_name}, {self.date_type}, {self.date_info}")
        # self.df_date = self.get_year_week_tuple()  # pandas的df对象
        self.df_st_month = self.spark.sql(f"select 1+1;")
        self.df_st_week = self.spark.sql(f"select 1+1;")

    def read_data(self):
        sql_month = f"select search_term , asin, st_asin_zr_page, st_asin_zr_page_row , st_ao_val from dwt_st_asin_reverse where site_name ='us' and date_type='month' and date_info='2023-10';"
        # and st_asin_zr_page=1 and st_asin_zr_page_row<=3
        print("sql:", sql_month)
        self.df_st_month = self.spark.sql(sql_month).cache()
        self.df_st_month.show(20, truncate=False)
        print("month:", self.df_st_month.count())
        pdf = pd.read_excel(f"/root/asin.xlsx")
        schema = StructType([StructField("asin", StringType(), True)])
        self.df_st_week = self.spark.createDataFrame(pdf, schema=schema).cache()

        # sql_week = f"SELECT distinct(trim(asin_brand_name)) as asin_brand_name from dim_asin_detail WHERE site_name ='us' and date_type ='week' and date_info BETWEEN '2023-14' and '2023-39';"
        # self.df_st_week = self.spark.sql(sql_week).cache()
        self.df_st_week.show(20, truncate=False)
        print("week:", self.df_st_week.count())

    def handle_data(self):
        # # 合并两个DataFrame
        # df_combined = self.df_st_month.union(self.df_st_week)
        #
        # # 只选择 row_number 为 1 的行
        # df_unique = df_combined.drop_duplicates(['asin_brand_name'])
        #
        # self.df_combined_unique = df_unique
        # self.df_combined_unique.show(20, truncate=False)
        self.df_combined_unique = self.df_st_month.join(self.df_st_week, on='asin', how='inner')

        # , 'search_term'
        window = Window.partitionBy(['asin']).orderBy(
            self.df_combined_unique.st_asin_zr_page_row.asc(),
        )
        self.df_combined_unique = self.df_combined_unique. \
            withColumn("page_rank_top", F.row_number().over(window=window))
        # print("self.df_st_asin_info, 开窗去重前:", self.df_st_asin_info.count())
        self.df_combined_unique = self.df_combined_unique.filter("page_rank_top<=3")
        print("combined:", self.df_combined_unique.count())

    def save_data(self):
        # 转换为 Pandas DataFrame
        pdf = self.df_combined_unique.toPandas()

        # 根据需求将每100万行数据保存为一个CSV文件
        num_rows_per_file = 1000000
        num_files = (len(pdf) // num_rows_per_file) + (1 if len(pdf) % num_rows_per_file != 0 else 0)

        for i in range(num_files):
            start_idx = i * num_rows_per_file
            end_idx = start_idx + num_rows_per_file
            output_path = os.path.join("/root", f"asin_find_st_{i + 1}.csv")

            # 将子集保存为CSV
            pdf.iloc[start_idx:end_idx].to_csv(output_path, index=False)

        print(f"Data saved into {num_files} CSV files.")


if __name__ == '__main__':
    handle_obj = DwdStMeasure()
    handle_obj.run()