import os
import re
import sys
import time
import traceback

sys.path.append("/opt/module/spark-3.2.0-bin-hadoop3.2/demo/py_demo/")

sys.path.append(os.path.dirname(sys.path[0]))  # 上级目录
from utils.templates import Templates
from pyspark.sql import functions as F
from pyspark.sql.types import *
from utils.db_util import DBUtil
from utils.common_util import CommonUtil
from utils.spark_util import SparkUtil
from datetime import datetime, timedelta
from functools import reduce
from utils.es_util import EsUtils
from pyspark.sql import Window
from pyspark.storagelevel import StorageLevel
from utils.DorisHelper import DorisHelper
from yswg_utils.common_df import get_node_first_id_df, get_first_id_from_category_desc_df
from yswg_utils.common_udf import udf_parse_bs_category, parse_weight_str, udf_extract_volume_dimensions, udf_get_package_quantity_with_flag as udf_get_package_quantity, udf_parse_seller_json


class KafkaFlowAsinDetail(Templates):
    def __init__(self, site_name='us', date_type="day", date_info='2022-10-01', consumer_type='latest', test_flag='normal', batch_size=100000):
        super().__init__()
        self.site_name = site_name
        self.date_type = date_type
        self.date_info = date_info
        self.consumer_type = consumer_type  # 消费实时还是消费历史
        self.test_flag = test_flag  # 正式环境跟测试环境
        self.year_month = str(self.date_info).replace("-", "_")
        self.repartition_num = 80
        # kafka相关参数
        self.topic_name = f"{self.site_name}_asin_detail_month_{self.year_month}"
        self.batch_size = batch_size
        self.schema = self.init_schema()
        self.batch_size_history = 20000
        self.processing_time = 900 if self.site_name == 'us' else 600
        self.history_batch_id = 0
        # doris相关参数
        self.doris_db = "test" if self.test_flag == "test" else "selection"
        self.max_bought_month_table = f"{self.site_name}_asin_max_bought_month_info"
        self.parent_asin_latest_detail_table = f"{self.site_name}_parent_asin_latest_detail"
        self.asin_latest_detail_table = f"{self.site_name}_asin_latest_detail"
        # elasticsearch相关参数
        self.client = EsUtils.get_es_client()
        self.es_index_name = f"{self.site_name}_flow_asin_30day_test" if self.test_flag == 'test' else f"{self.site_name}_flow_asin_30day"
        # self.es_index_alias_name = f"{self.site_name}_st_detail_last_4_week_test" if self.test_flag == 'test' else f"{self.site_name}_st_detail_last_4_week"
        self.es_index_body = EsUtils.get_es_30day_body()
        # 富集策略相关配置，用于更新 usr_mask_type 字段
        self.policy_name1 = "user_mask_asin_policy"
        self.policy_name2 = "user_mask_category_policy"
        self.pipeline_id = "user_asin_mask_enrich_pipeline"
        self.es_options = EsUtils.get_es_options(self.es_index_name, self.pipeline_id)
        self.db_save = 'kafka_flow_asin_detail'
        self.app_name = self.get_app_name()
        print(f"任务名称：{self.app_name}")
        # Spark实时消费相关参数
        self.spark = SparkUtil.get_stream_spark(app_name=self.app_name)
        self.check_path = f"/home/big_data_selection/tmp/kafka_checkpoint/{self.topic_name}_{self.consumer_type}_test" if self.test_flag == 'test' else f"/home/big_data_selection/tmp/kafka_checkpoint/{self.topic_name}_{self.consumer_type}"
        # self.previous_date = self.get_previous_date(self)
        # self.previous_two_date = self.get_previous_two_date(self)
        self.launch_time_interval_dict = self.get_launch_time_interval_dict()
        print("日期字典：", self.launch_time_interval_dict)
        self.initial_batch_id = self.get_initial_batch_id(self)
        print("当前消费的起始批次为： ", self.initial_batch_id)
        self.history_batch_id = self.initial_batch_id + 1
        # BSR分类解析模板
        self.pattern1_dict = {
            "us": "See Top 100 in ".lower(),
            "uk": "See Top 100 in ".lower(),
            "de": "Siehe Top 100 in ".lower(),
            "es": "Ver el Top 100 en ".lower(),
            "fr": "Voir les 100 premiers en ".lower(),
            "it": "Visualizza i Top 100 nella categoria ".lower(),
        }
        self.pattern_current_dict = {
            "us": "#(\d+) ",
            "uk": "(\d+) in ",
            "de": "(\d+) in ",
            "es": "(\d+) en ",
            "fr": "(\d+) en ",
            "it": "(\d+) in ",
        }
        # DataFrame初始化
        self.date_info_last_month = CommonUtil.get_month_offset(self.date_info, -1)
        self.date_info_last_year  = CommonUtil.get_month_offset(self.date_info, -12)
        self.df_previous_flow_asin = self.spark.sql("select 1+1;")
        self.df_previous_flow_asin_lastyear = self.spark.sql("select 1+1;")
        self.df_seller_info = self.spark.sql("select 1+1;")
        self.df_seller_country = self.spark.sql("select 1+1;")
        self.df_asin_seller = self.spark.sql("select 1+1;")
        self.df_self_asin_info = self.spark.sql("select 1+1;")
        self.df_alarm_brand_info = self.spark.sql("select 1+1;")
        self.df_asin_label_info = self.spark.sql("select 1+1;")
        self.df_asin_measure = self.spark.sql("select 1+1;")
        self.df_bs_report = self.spark.sql("select 1+1;")
        self.df_asin_keep_date = self.spark.sql("select 1+1;")
        self.df_asin_bsr_end = self.spark.sql("select 1+1;")
        self.df_hide_category = self.spark.sql("select 1+1;")
        self.df_bsr_category = self.spark.sql("select 1+1;")
        self.df_asin_new_cate = self.spark.sql("select 1+ 1;")
        self.df_user_package_num = self.spark.sql("select 1+1;")
        self.df_asin_category = self.spark.sql("select 1+1;")
        self.df_max_bought_month_info_update = self.spark.sql("select 1+1;")
        self.df_asin_source_flag = self.spark.sql("select 1+1;")
        self.df_keepa_asin = self.spark.sql("select 1+1;")
        self.df_asin_profit_rate = self.spark.sql("select 1+1;")
        self.color_set = set()
        # udf函数注册
        package_schema = StructType([
            StructField("parse_package_quantity", IntegerType(), True),
            StructField("is_package_quantity_abnormal", IntegerType(), True),
        ])
        self.u_parse_package_quantity = self.spark.udf.register('u_parse_package_quantity', udf_get_package_quantity, package_schema)
        bs_category_schema = StructType([
            StructField('asin_bs_cate_1_id', StringType(), True),
            StructField('asin_bs_cate_current_id', StringType(), True),
            StructField('asin_bs_cate_1_rank', IntegerType(), True),
            StructField('asin_bs_cate_current_rank', IntegerType(), True),
        ])
        self.u_parse_bs_category = self.spark.udf.register('u_parse_bs_category', udf_parse_bs_category, bs_category_schema)
        weight_schema = StructType([
            StructField('weight', FloatType(), True),
            StructField('weight_type', StringType(), True)
        ])
        self.u_parse_weight = self.spark.udf.register('u_parse_weight', parse_weight_str, weight_schema)
        volume_schema = StructType([
            StructField("length", FloatType(), True),
            StructField("width", FloatType(), True),
            StructField("height", FloatType(), True),
            StructField("asin_volume_type", StringType(), True)
        ])
        self.u_parse_volume = self.spark.udf.register('u_parse_volume', udf_extract_volume_dimensions, volume_schema)
        seller_schema = StructType([
            StructField("buy_box_seller_type", IntegerType(), True),
            StructField("account_name", StringType(), True),
            StructField("account_id", StringType(), True)
        ])
        self.u_parse_seller_info = self.spark.udf.register('u_parse_seller_info', udf_parse_seller_json, seller_schema)

    @staticmethod
    def init_schema():
        schema = StructType([
            StructField("asin", StringType(), True),
            StructField("title", StringType(), True),
            StructField("img_url", StringType(), True),
            StructField("rating", DoubleType(), True),
            StructField("total_comments", IntegerType(), True),
            StructField("price", FloatType(), True),
            StructField("category", StringType(), True),
            StructField("launch_time", StringType(), True),
            StructField("volume", StringType(), True),
            StructField("page_inventory", IntegerType(), True),
            StructField("asin_vartion_list", ArrayType(ArrayType(StringType()), True), True),
            StructField("title_len", IntegerType(), True),
            StructField("img_num", IntegerType(), True),
            StructField("img_type", StringType(), True),
            StructField("activity_type", StringType(), True),
            StructField("one_two_val", StringType(), True),
            StructField("three_four_val", StringType(), True),
            StructField("five_six_val", StringType(), True),
            StructField("eight_val", StringType(), True),
            StructField("node_id", StringType(), True),
            StructField("five_star", IntegerType(), True),
            StructField("four_star", IntegerType(), True),
            StructField("three_star", IntegerType(), True),
            StructField("two_star", IntegerType(), True),
            StructField("one_star", IntegerType(), True),
            StructField("low_star", IntegerType(), True),
            StructField("together_asin", StringType(), True),
            StructField("brand", StringType(), True),
            StructField("ac_name", StringType(), True),
            StructField("material", StringType(), True),
            StructField("data_type", IntegerType(), True),
            StructField("weight_str", StringType(), True),
            StructField("seller_id", StringType(), True),
            StructField("variat_num", IntegerType(), True),
            StructField("best_sellers_rank", StringType(), True),
            StructField("best_sellers_herf", StringType(), True),
            StructField("account_name", StringType(), True),
            StructField("parentAsin", StringType(), True),
            StructField("asinUpdateTime", StringType(), True),
            StructField("all_best_sellers_herf", StringType(), True),
            StructField("image_view", IntegerType(), True),
            StructField("product_description", StringType(), True),
            StructField("describe", StringType(), True),
            StructField("buy_sales", StringType(), True),
            StructField("lob_asin_json", StringType(), True),
            StructField("seller_json", StringType(), True),
            StructField("customer_reviews_json", StringType(), True),
            StructField("img_list", StringType(), True),
            StructField("follow_sellers", IntegerType(), True),
            StructField("fbm_delivery_price", FloatType(), True),
            StructField("product_json", StringType(), True),
            StructField("amazon_label", StringType(), True)
        ])
        return schema

    # @staticmethod
    # def get_previous_date(self):
    #     self.df_date = self.spark.sql(f"select * from dim_date_20_to_30")
    #     df = self.df_date.toPandas()
    #     df_loc = df.loc[(df.year_month == f'{self.date_info}') & (df.day == 1)]
    #     cur_month_id = int(list(df_loc.id)[0])
    #     previous_date_id = cur_month_id - 1
    #     df_loc = df.loc[df.id == previous_date_id]
    #     previous_date = str(list(df_loc.year_month)[0])
    #     return previous_date

    # @staticmethod
    # def get_previous_two_date(self):
    #     self.df_date = self.spark.sql(f"select * from dim_date_20_to_30")
    #     df = self.df_date.toPandas()
    #     df_loc = df.loc[(df.year_month == f'{self.date_info}') & (df.day == 1)]
    #     cur_month_id = int(list(df_loc.id)[0])
    #     previous_two_date_id = cur_month_id - 40
    #     df_loc = df.loc[df.id == previous_two_date_id]
    #     prvious_two_date = str(list(df_loc.year_month)[0])
    #     return prvious_two_date

    @staticmethod
    def get_launch_time_interval_dict():
        cur_date = datetime.now().date()
        return {
            "one_month": (cur_date + timedelta(days=-30)).strftime('%Y-%m-%d'),
            "three_month": (cur_date + timedelta(days=-90)).strftime('%Y-%m-%d'),
            "six_month": (cur_date + timedelta(days=-180)).strftime('%Y-%m-%d'),
            "twelve_month": (cur_date + timedelta(days=-360)).strftime('%Y-%m-%d'),
            "twenty_four_month": (cur_date + timedelta(days=-720)).strftime('%Y-%m-%d'),
            "thirty_six_month": (cur_date + timedelta(days=-1080)).strftime('%Y-%m-%d')
        }

    @staticmethod
    def get_initial_batch_id(self):
        max_bought_month_batch_id_sql = f"""
            SELECT MAX(batch_id) as initial_batch_id from {self.doris_db}.{self.max_bought_month_table} WHERE date_info='{self.date_info}' AND consumer_type='{self.consumer_type}' 
               """
        df_max_bought_month_batch_id = DorisHelper.spark_import_with_sql(
            self.spark, query=max_bought_month_batch_id_sql)
        max_bought_month_batch_id = 0 if df_max_bought_month_batch_id.take(1)[0]['initial_batch_id'] is None else \
        df_max_bought_month_batch_id.take(1)[0]['initial_batch_id']
        return max_bought_month_batch_id

    # 1. 处理asin分类及排名以及排名类型字段
    def handle_asin_bs_category_info(self, df):
        df = df.withColumnRenamed("parentAsin", "parent_asin")
        cate_current_pattern = self.pattern_current_dict[self.site_name]
        cate_1_pattern = self.pattern1_dict[self.site_name]
        df = df.withColumn("asin_bs_sellers_rank_lower", F.lower("best_sellers_rank"))
        df = df.withColumn("asin_bs", self.u_parse_bs_category(
            "asin_bs_sellers_rank_lower", "best_sellers_herf", "all_best_sellers_herf", F.lit(cate_current_pattern), F.lit(cate_1_pattern), "node_id"))
        df = df.withColumn("asin_bs_cate_1_id", df.asin_bs.getField("asin_bs_cate_1_id")) \
            .withColumn("asin_bs_cate_current_id", df.asin_bs.getField("asin_bs_cate_current_id")) \
            .withColumn("asin_bs_cate_1_rank", df.asin_bs.getField("asin_bs_cate_1_rank")) \
            .withColumn("asin_bs_cate_current_rank", df.asin_bs.getField("asin_bs_cate_current_rank")) \
            .drop("asin_bs", "asin_bs_sellers_rank_lower", "best_sellers_herf", "all_best_sellers_herf",
                  "best_sellers_rank")
        df = df.withColumn("rank_type", F.expr(""" 
        CASE WHEN asin_bs_cate_1_rank IS NOT NULL AND asin_bs_cate_1_rank BETWEEN 0 AND 1000 THEN 1 
        WHEN asin_bs_cate_1_rank BETWEEN 1000 AND 5000 THEN 2 WHEN asin_bs_cate_1_rank BETWEEN 5000 AND 10000 THEN 3 
        WHEN asin_bs_cate_1_rank BETWEEN 10000 AND 20000 THEN 4 WHEN asin_bs_cate_1_rank BETWEEN 20000 AND 30000 THEN 5 
        WHEN asin_bs_cate_1_rank BETWEEN 30000 AND 50000 THEN 6 WHEN asin_bs_cate_1_rank BETWEEN 50000 AND 70000 THEN 7 
        WHEN asin_bs_cate_1_rank >= 70000 THEN 8 ELSE 0 END"""))
        return df

    # 2. 利用node_id以及分类描述进行分类补充（此时无排名信息）
    def handle_asin_category_supplement(self, df):
        df = df.join(self.df_asin_new_cate, on=['node_id'], how='left')
        df = df.withColumn("asin_bs_cate_current_id", F.coalesce(F.col("asin_bs_cate_current_id"), F.col("node_id"))). \
            withColumn("asin_bs_cate_1_id", F.coalesce(F.col("asin_bs_cate_1_id"), F.col("category_first_id"))). \
            drop("category_first_id", "node_id")
        df_with_category = df.filter("asin_bs_cate_1_id is null and category is not null").select("asin", "category")
        df_with_category = df_with_category.withColumn(
            "category_split", F.split(F.col("category"), "›")
        ).withColumn(
            "category_first_name", F.lower(F.col("category_split").getItem(0))
        ).drop("category_split", "category")
        df_with_category = df_with_category.join(self.df_asin_category, on=['category_first_name'], how='inner')
        df_with_category = df_with_category.withColumnRenamed("category_first_id", "category_first_id_with_name").drop("category_first_name")
        df = df.join(df_with_category, on=['asin'], how='left')
        df = df.withColumn("asin_bs_cate_1_id", F.coalesce(F.col("asin_bs_cate_1_id"), F.col("category_first_id_with_name"))).drop("category_first_id_with_name")
        return df

    # 3. 处理bsr销量、价格类型字段以及BSR销售额信息
    def handle_asin_bsr_orders(self, df):
        df = df.join(self.df_bs_report, on=['asin_bs_cate_1_id', 'asin_bs_cate_1_rank'], how='left')
        df = df.withColumn("price_type", F.expr("""
        CASE WHEN price IS NOT NULL AND price > 0 AND price < 10 THEN 1 WHEN price >= 10 AND price < 15 THEN 2 
        WHEN price >= 15 AND price < 20 THEN 3 WHEN price >= 20 AND price < 30 THEN 4 
        WHEN price >= 30 AND price < 50 THEN 5 WHEN price >= 50 THEN 6 ELSE 0 END""")).\
            withColumn("bsr_orders_sale", F.round(F.col("bsr_orders") * F.col("price"), 2))
        return df

    # 4.解析Make-It-A-Bundle信息
    def handle_asin_lob_info(self, df):
        df = df.withColumn("is_contains_lob_info",
                           F.when(F.col("lob_asin_json").isNotNull(), F.lit(1)).otherwise(F.lit(0)))
        df_parsed = df.withColumn("parse_asin_lob",
                                  F.when(F.col("is_contains_lob_info") == 1, F.from_json("lob_asin_json", "array<struct<lob_asin:string>>")))
        df_result = df_parsed.withColumn("asin_lob_info", F.expr("transform(parse_asin_lob, x -> x.lob_asin)"))
        df = df_result.withColumn(
            "asin_lob_info", F.regexp_replace(F.concat_ws(",", "asin_lob_info"), "[{}]", "")).drop(
            "parse_asin_lob", "lob_asin_json")
        return df

    # 5. 处理配送方式、卖家所在地以及卖家所在地类型
    def handle_asin_buy_box_seller_type(self, df):
        df = df.withColumn("seller_json_parsed", self.u_parse_seller_info(df.seller_json))
        df = df.withColumn("buy_box_seller_type", df.seller_json_parsed.buy_box_seller_type).withColumn(
            "account_name", df.seller_json_parsed.account_name).drop("seller_json_parsed")

        # 1. 关联全局缓存的df_asin_seller，用于填充seller_id为空的情况
        df = df.join(self.df_asin_seller, on=['asin'], how='left')

        # 2. 优先使用kafka消息中的seller_id，为空则使用df_asin_seller的数据
        df = df.withColumn(
            "seller_id", F.coalesce(F.col("seller_id"), F.col("fd_seller_id"))
        ).withColumn(
            "account_name", F.coalesce(F.col("account_name"), F.col("fd_account_name"))
        ).drop("fd_seller_id", "fd_account_name")

        # 3. 关联全局缓存的df_seller_country获取seller_country_name
        df = df.join(self.df_seller_country, on=['seller_id'], how='left')

        df = df.withColumn("site_name_type", F.expr("""
        CASE WHEN buy_box_seller_type = 1 THEN 4
        WHEN buy_box_seller_type != 1 AND seller_country_name is not null AND seller_country_name like '%US%' THEN 1
        WHEN buy_box_seller_type != 1 AND seller_country_name is not null AND seller_country_name like '%CN%' THEN 2
        WHEN buy_box_seller_type != 1 AND seller_country_name is not null AND seller_country_name like '%HK%' THEN 5
        WHEN buy_box_seller_type != 1 AND seller_country_name is not null AND seller_country_name like '%TW%' THEN 6
        ELSE 3 END"""))
        return df

    # 6. 处理asin基础属性信息(长宽高重量等)
    def handle_asin_basic_attribute_info(self, df):
        # 1.解析ASIN重量相关信息
        df = df.withColumn("weight_str", F.lower(F.col("weight_str"))).withColumn("asin_weight", self.u_parse_weight("weight_str", F.lit(self.site_name))).drop("weight_str")
        df = df.withColumn(
            "weight", F.when(df.asin_weight.getField("weight_type") == 'pounds', df.asin_weight.getField("weight")).otherwise(F.lit(0))).drop("asin_weight")
        # 2.处理重量类型
        df = df.withColumn("weight_type", F.expr("""
        CASE WHEN weight BETWEEN 0 AND 0.2 THEN 1 WHEN weight BETWEEN 0.2 AND 0.4 THEN 2 
        WHEN weight BETWEEN 0.4 AND 0.6 THEN 3 WHEN weight BETWEEN 0.6 AND 1 THEN 4 
        WHEN weight BETWEEN 1 AND 2 THEN 5 WHEN weight >= 2 THEN 6 ELSE 0 END"""))
        # 3.解析ASIN体积相关信息
        df = df.withColumn("asin_volume", self.u_parse_volume("volume"))
        df = df.withColumn("asin_volume_type", df.asin_volume.getField("asin_volume_type")) \
            .withColumn("asin_length", F.when(F.col("asin_volume_type") == 'inches', df.asin_volume.getField("length"))) \
            .withColumn("asin_width", F.when(F.col("asin_volume_type") == 'inches', df.asin_volume.getField("width"))) \
            .withColumn("asin_height", F.when(F.col("asin_volume_type") == 'inches', df.asin_volume.getField("height"))) \
            .drop("asin_volume", "asin_volume_type")
        # 4.获取体积重/毛重相关信息
        df = df.withColumn(
            "asin_weight_ratio", F.when(
                F.col("asin_length").isNotNull() & (F.col("asin_width").isNotNull()) &
                (F.col("asin_height").isNotNull()) &
                (F.col("weight") > 0), F.round(F.col("asin_length") * F.col("asin_width") * F.col("asin_height") * 3.2774128 / (F.col("weight") * 453.59), 3))
            .otherwise(F.lit(-1)))
        # 5.处理尺寸类型
        if self.site_name == 'us':
            expr_str = f"""
            CASE WHEN weight > 0 AND weight * 16 <= 16 AND asin_length > 0 AND asin_length <= 15 AND asin_width > 0 AND asin_width <= 12 AND asin_height > 0 AND asin_height <= 0.75 THEN 1 
            WHEN weight > 0 AND weight <= 20 AND asin_length > 0 AND asin_length <= 18 AND  asin_width > 0 AND asin_width <= 14 AND asin_height > 0 AND asin_height <= 8 THEN 2 
            WHEN weight > 0 AND weight <= 70 AND asin_length > 0 AND asin_length <= 60 AND asin_width > 0 AND asin_width <= 30 AND asin_length + asin_length + (asin_width + asin_height) * 2 <= 130  THEN 3 
            WHEN weight > 0 AND weight <= 150 AND asin_length > 0 AND asin_length <= 108 AND asin_length + asin_length + (asin_width + asin_height) * 2 <= 130  THEN 4 
            WHEN weight > 0 AND weight <= 150 AND asin_length > 0 AND asin_length <= 108 AND asin_length + asin_length + (asin_width + asin_height) * 2 <= 165  THEN 5 
            WHEN weight > 150 AND asin_length > 108 AND asin_length + asin_length + (asin_width + asin_height) * 2 > 165  THEN 6 ELSE 0 END"""
        else:
            expr_str = f"""
            CASE WHEN weight > 0 AND weight <= 100 AND asin_length > 0 AND asin_length <= 20 AND asin_width > 0 AND asin_width <= 15 AND asin_height > 0 AND asin_height <= 1 THEN 1 
            WHEN weight > 0 AND weight <= 500 AND asin_length > 0 AND asin_length <= 33 AND asin_width > 0 AND asin_width <= 23 AND asin_height > 0 AND asin_height <= 2.5 THEN 2 
            WHEN weight > 0 AND weight <= 1000 AND asin_length > 0 AND asin_length <= 33 AND asin_width > 0 AND asin_width <= 23 AND asin_height > 0 AND asin_height <= 5 THEN 3 
            WHEN weight > 0 AND weight <= 12000 AND asin_length > 0 AND asin_length <= 45 AND asin_width > 0 AND asin_width <= 34 AND asin_height > 0 AND asin_height <= 26 THEN 4 
            WHEN weight > 0 AND weight <= 2000 AND asin_length > 0 AND asin_length <= 61 AND asin_width > 0 AND asin_width <= 46 AND asin_height > 0 AND asin_height <= 46 THEN 5 
            WHEN asin_length > 0 AND asin_length <= 150 AND asin_length + asin_length + (asin_width + asin_height) <= 300 THEN 6 
            WHEN asin_length > 150 AND asin_length + asin_length + (asin_width + asin_height) > 300 THEN 7 ELSE 0 END"""
        df = df.withColumn("size_type", F.expr(expr_str)).drop("asin_length", "asin_width", "asin_height")
        # 6.处理五点描述长度
        df = df.withColumn("describe_len", F.length(F.regexp_replace(F.col("describe"), "\\|-\\|", "")))
        return df

    # 7. 处理asin图片信息
    def handle_asin_img_info(self, df):
        img_schema = ArrayType(ArrayType(StringType()))
        df = df.withColumn("img_list", F.from_json(F.col("img_list"), img_schema))
        df_with_img = df.filter(F.size("img_list") > 0).select("asin", "img_list")
        df_with_img_attribute = df_with_img.select(
            "asin", F.explode("img_list").alias("img_attributes")
        ).select(
            "asin", F.col("img_attributes")[1].alias("img_url"), F.col("img_attributes")[2].alias("img_order_by"),
            F.col("img_attributes")[3].alias("data_type")
        )
        df_with_img_attribute_agg = df_with_img_attribute.groupby("asin").agg(
            F.to_json(F.collect_list(F.struct(F.col("img_url"), F.col("img_order_by"), F.col("data_type")))).alias(
                "img_info")
        )
        df = df.drop("img_list")
        df = df.join(df_with_img_attribute_agg, on=['asin'], how='left')
        return df

    # 8. 处理变体相关（ao及母体相关，自然占比及母体自然占比，各类型数量，月销信息等）
    def handle_asin_measure(self, df):
        df = CommonUtil.get_asin_variant_attribute(df_asin_detail=df, df_asin_measure=self.df_asin_measure,
                                                   partition_num=self.repartition_num, use_type=1)
        # 是否数量变体类型和ao的类型
        df = df.withColumn("quantity_variation_type", F.expr("""
        CASE WHEN size is not null and size != '' and lower(size) like '%quantity%' THEN 1 ELSE 0 END""")).withColumn(
            "ao_val_type", F.expr("""
            CASE WHEN asin_ao_val BETWEEN 0 AND 0.1 THEN 1 WHEN asin_ao_val BETWEEN 0.1 AND 0.2 THEN 2 
            WHEN asin_ao_val BETWEEN 0.2 AND 0.4 THEN 3 WHEN asin_ao_val BETWEEN 0.4 AND 0.8 THEN 4 
            WHEN asin_ao_val BETWEEN 0.8 AND 1.2 THEN 5 WHEN asin_ao_val BETWEEN 1.2 AND 2 THEN 6 
            WHEN asin_ao_val >= 2 THEN 7 ELSE 0 END"""))
        df = df.withColumnRenamed("asin_zr_counts", "zr_counts").withColumnRenamed("asin_ao_val", "ao_val") \
            .withColumnRenamed("asin_zr_flow_proportion", "zr_flow_proportion") \
            .withColumnRenamed("asin_amazon_orders", "asin_bought_month").drop("asin_st_counts", "asin_adv_counts")
        # 获取parent_asin下最新ASIN信息
        df_parent_asin_info = df.filter("parent_asin is not null").select("parent_asin", "asin_vartion_list", "asinUpdateTime")
        parent_asin_window = Window.partitionBy(['parent_asin']).orderBy(
            F.desc_nulls_last("asinUpdateTime")
        )
        df_parent_asin_info = df_parent_asin_info.withColumn("u_rank", F.row_number().over(window=parent_asin_window))
        df_parent_asin_info = df_parent_asin_info.repartition(self.repartition_num)
        df_parent_asin_info = df_parent_asin_info.filter("u_rank = 1").drop("u_rank")
        df_asin_variat = df_parent_asin_info.filter(F.size("asin_vartion_list") > 0).\
            select("parent_asin", "asinUpdateTime", F.explode("asin_vartion_list").alias("variant_attribute")).\
            select("parent_asin", F.col("asinUpdateTime").alias("asin_crawl_date"),
                   F.col("variant_attribute")[0].alias("asin"), F.col("variant_attribute")[1].alias("color"),
                   F.col("variant_attribute")[3].alias("size"), F.col("variant_attribute")[5].alias("style"))
        df_asin_variat_agg = df_asin_variat.groupby(['parent_asin']).agg(
            F.first("asin_crawl_date").alias("asin_crawl_date"),
            F.concat_ws(',', F.collect_list("asin")).alias("variation_info"),
            F.to_json(F.collect_list(F.struct(F.col("color"), F.col("size"), F.col("style")))).alias("attr_info")
        )
        print("导出父ASIN最新变体信息到doris：")
        df_doris = df_asin_variat_agg.select(
            "parent_asin", F.lit(self.date_info).alias("date_info"), "asin_crawl_date", "variation_info", "attr_info",
            F.current_timestamp().alias("updated_at"))
        table_columns = "parent_asin, date_info, asin_crawl_date, variation_info, attr_info, updated_at"
        DorisHelper.spark_export_with_columns(df_save=df_doris, db_name=self.doris_db, table_name=self.parent_asin_latest_detail_table, table_columns=table_columns)
        df_doris.unpersist()
        return df

    # 9. 提取打包数量字段
    def handle_asin_package_quantity(self, df):
        df = df.withColumn(
            "variat_attribute", F.concat_ws("&&&%", F.col("color"), F.col("style"), F.col("size"), F.col("material")))
        df = df.withColumn("title_parse", self.u_parse_package_quantity(df.title)).withColumn(
            "variat_parse", self.u_parse_package_quantity(df.variat_attribute))
        df = df.withColumn("title_package_quantity", df.title_parse.getField("parse_package_quantity")). \
            withColumn("variat_package_quantity", df.variat_parse.getField("parse_package_quantity")). \
            withColumn("title_package_quantity_is_abnormal", df.title_parse.getField("is_package_quantity_abnormal")). \
            withColumn("variat_package_quantity_is_abnormal", df.variat_parse.getField("is_package_quantity_abnormal")). \
            drop("title_parse", "variat_parse", "variat_attribute")
        # 从 product_json 额外提取 Color 字段作为颜色来源备用
        df = df.withColumn(
            "_product_json_color",
            F.lower(F.get_json_object(F.col("product_json"), "$.Color"))
        )
        # Number of Items：从 product_json 提取，cast 失败（脏数据）自动为 null，提取后立即 drop
        df = df.withColumn(
            "number_of_items",
            F.get_json_object(F.col("product_json"), "$.Number of Items").cast("int")
        ).drop("product_json")
        # 优先级：Number of Items > 属性字段 > 标题解析 > 默认1
        df = df.withColumn(
            "package_quantity", F.expr("""
            CASE WHEN number_of_items IS NOT NULL AND number_of_items > 0 THEN number_of_items
                 WHEN variat_package_quantity IS NOT NULL THEN variat_package_quantity
                 WHEN title_package_quantity IS NOT NULL THEN title_package_quantity
                 ELSE 1 END""")
        ).withColumn(
            "is_package_quantity_abnormal", F.expr("""
            CASE WHEN number_of_items IS NOT NULL AND number_of_items > 0 THEN 0
                 WHEN variat_package_quantity IS NOT NULL THEN variat_package_quantity_is_abnormal
                 WHEN title_package_quantity IS NOT NULL THEN title_package_quantity_is_abnormal
                 ELSE 2 END""")
        ).drop("number_of_items", "title_package_quantity", "variat_package_quantity",
               "title_package_quantity_is_abnormal", "variat_package_quantity_is_abnormal")
        df = df.withColumn("title", F.lower(F.col("title")))
        # color 优先使用变体属性颜色，fallback 到 product_json 中的 Color 字段
        df = df.withColumn("color", F.coalesce(F.col("color"), F.col("_product_json_color"))).drop("_product_json_color")
        df = df.join(self.df_user_package_num, on=['asin', 'title'], how='left')
        df = df.withColumn("package_quantity", F.coalesce(F.col("user_package_num"), F.col("package_quantity"))). \
            withColumn(
            "is_package_quantity_abnormal", F.coalesce(F.col("user_is_package_quantity_abnormal"), F.col("is_package_quantity_abnormal"))
        ).drop("user_package_num", "user_is_package_quantity_abnormal")
        return df

    # 10. 处理品牌标签、是否告警品牌、处理asin_lqs_rating信息
    def handle_asin_lqs_and_brand(self, df):
        # 1.品牌标签以及是否告警品牌
        df = df.withColumn("is_brand_label", F.expr("""CASE WHEN brand is not null THEN 1 ELSE 0 END"""))
        df = df.withColumn("brand", F.lower("brand"))
        df = df.join(self.df_alarm_brand_info, on=['brand'], how='left')
        df = df.withColumn("is_alarm_brand",
                           F.when(F.col("is_alarm_brand").isNotNull(), F.col("is_alarm_brand")).otherwise(F.lit(0)))
        # 2. lqs评分
        df = df.withColumn("category_node_rating",
                           F.expr(f"""CASE WHEN asin_bs_cate_current_id is not null THEN 1 ELSE 0 END""")) \
            .withColumn("zr_rating", F.expr(f"""CASE WHEN zr_counts > 0 THEN 0.5 ELSE 0 END""")) \
            .withColumn("sp_rating", F.expr(f"""CASE WHEN sp_counts > 0 THEN 1 ELSE 0 END""")) \
            .withColumn("a_add_rating", F.expr(f"""CASE WHEN img_type like '%3%' THEN 1 ELSE 0 END""")) \
            .withColumn("video_rating", F.expr(f"""CASE WHEN img_type like '%2%' THEN 0.5 ELSE 0 END""")) \
            .withColumn("brand_rating", F.expr(f"""CASE WHEN is_brand_label = 1 THEN 0.2 ELSE 0 END""")) \
            .withColumn("product_describe_rating",
                        F.expr(f"""CASE WHEN product_description is not null THEN 0.2 ELSE 0 END""")) \
            .withColumn("highlight_rating", F.expr(f"""
            CASE WHEN describe is not null AND size(split(describe, '\\|-\\|')) <= 4 THEN size(split(describe, '\\|-\\|')) * 0.4 
            WHEN describe is not null AND size(split(describe, '\\|-\\|')) > 4 THEN 1.6 ELSE 0 END""")) \
            .withColumn("title_len_rating", F.expr(f"""CASE WHEN title_len >= 50 AND title_len <=200 THEN 0.5 ELSE 0 END""")) \
            .withColumn("title_brand_rating", F.expr(f"""
            CASE WHEN brand is not null AND lower(regexp_replace(title, '[^a-zA-Z0-9\\s]', '')) LIKE lower(regexp_replace(brand, '[^a-zA-Z0-9\\s]', '')) || '%' THEN 0.5 
            ELSE 0 END""")) \
            .withColumn("img_num_rating", F.expr(f"""
            CASE WHEN img_num <= 4 THEN img_num * 0.5 WHEN img_num >4 THEN 2 ELSE 0 END""")) \
            .withColumn("img_enlarge_rating", F.expr(f"""CASE WHEN image_view = 1 THEN 0.5 ELSE 0 END"""))
        df = df.withColumn(
            "asin_lqs_rating",
            (F.col("category_node_rating") + F.col("zr_rating") + F.col("sp_rating") + F.col("a_add_rating") +
             F.col("video_rating") + F.col("brand_rating") + F.col("product_describe_rating") +
             F.col("highlight_rating") + F.col("title_len_rating") + F.col("title_brand_rating") +
             F.col("img_num_rating") + F.col("img_enlarge_rating")).cast("double")).withColumn(
            "asin_lqs_rating_detail", F.to_json(
                F.struct(F.col("category_node_rating"), F.col("zr_rating"), F.col("sp_rating"), F.col("a_add_rating"),
                         F.col("video_rating"), F.col("brand_rating"), F.col("product_describe_rating"),
                         F.col("highlight_rating"), F.col("title_len_rating"), F.col("title_brand_rating"),
                         F.col("img_num_rating"), F.col("img_enlarge_rating")))
        )
        df = df.drop("product_description", "image_view", "category_node_rating", "zr_rating", "sp_rating",
                     "a_add_rating", "video_rating", "brand_rating", "product_describe_rating", "highlight_rating",
                     "title_len_rating", "title_brand_rating", "img_num_rating", "img_enlarge_rating")
        return df

    def handle_multi_color_flag(self, df):
        """判断 ASIN 是否为颜色组合产品
        multi_color_flag:
          0 = 非多色
          1 = 从颜色变体属性字段（color）解析为多色
          2 = 从标题或五点描述解析为多色（降级 fallback）
        颜色变体属性三层判断：
          1. 命中颜色组合关键词
          2. 含分隔符 (/ + & ; , and) 或 "数字 color"
          3. 含 2 个及以上颜色表中的单色词
        """
        # ── 第1层：关键词正则（支持 color/colors/colour/colours）──
        KEYWORD_PATTERN = (
            r"(?i)("
            r"\bmulticolou?rs?\b|\bmulti[\s\-]colou?rs?\b|\bmulti[\s\-]colored\b|\bmulticolored\b|"
            r"\bassorted\b|\bmorandi\b|\bpastel\b|\bvibrant\b|\bvintage\b|\bboho\b|\bgradient\b|"
            r"\bcandy\b|\bdusty\b|\bfluorescent\b|\bgentle\b|\bneutral\b|\bsoft\b|\bmuted\b|"
            r"\brainbow\b|\bmaillard\b|\bcolorful\b|\bcolourful\b|\bmulti\b|"
            r"\baesthetic colou?rs?\b|\bdreamy colou?rs?\b|\bearthy colou?rs?\b|\bshades of\b|"
            r"\bvarious colou?rs?\b|\bsolid colou?rs?\b|\bmix colou?rs?\b|\bmixed colou?rs?\b|"
            r"\bbasic colou?rs?\b|\blightcolor\b|\bdarkcolor\b|\battractive colou?rs?\b|"
            r"\bmultiple colou?rs?\b|\bbright colorful\b|\bdifferent colou?rs?\b|\bclassic colou?rs?\b|"
            r"\bfriendly colou?rs?\b|\bwarm colou?rs?\b|\bfun colou?rs?\b|\bmetallic colou?rs?\b|"
            r"\bbright colou?rs?\b|\bdark colou?rs?\b|\blight colou?rs?\b|"
            r"\bautumn colou?rs?\b|\bsummer colou?rs?\b|\bwinter colou?rs?\b|\bspring colou?rs?\b"
            r")"
        )
        # ── 第2层：分隔符正则（/ + & ; 逗号 and 数字+color）──
        SEPARATOR_PATTERN = r"(?i)[/+&；;，,]|\band\b|\d+\s*colou?rs?"

        # ── 第3层：颜色表多色词检测 UDF ──
        single_colors = sorted(
            [c for c in self.color_set
             if c.strip() and not re.search(r'[/+&;；,，]|\band\b', c)],
            key=len, reverse=True
        )
        if single_colors:
            color_regex = re.compile(
                r'(?i)\b(' + '|'.join(re.escape(c) for c in single_colors) + r')\b'
            )
        else:
            color_regex = None

        def _get_matched_colors(color_str):
            if not color_str or color_regex is None:
                return None
            matched = sorted({m.group(1).lower() for m in color_regex.finditer(color_str)})
            return "/".join(matched) if len(matched) >= 2 else None

        udf_matched_colors = F.udf(_get_matched_colors, StringType())

        # ── 降级：标题 + 五点描述关键词正则 ──
        FALLBACK_PATTERN = (
            r"(?i)("
            r"\bmulticolou?rs?\b|\bmulti[\s\-]colou?rs?\b|\bmulti[\s\-]colored\b|\bmulticolored\b|"
            r"\bassorted colou?rs?\b|\bfluorescent colou?rs?\b|\bdifferent colou?rs?\b|"
            r"\bbright colou?rs?\b|\bcolorful\b|\bcolourful\b|\battractive colou?rs?\b|"
            r"\bvibrant colou?rs?\b|\d+\s*colou?rs?"
            r")"
        )

        # 提前计算 UDF 结果，避免 multi_color_flag / multi_color_str 各调用一次（性能优化）
        df = df.withColumn("_matched_colors_str", udf_matched_colors(F.col("color")))
        df = df.withColumn(
            "multi_color_flag",
            F.when(
                F.col("color").isNotNull() & F.col("color").rlike(KEYWORD_PATTERN), 1
            ).when(
                F.col("color").isNotNull() & F.col("color").rlike(SEPARATOR_PATTERN), 1
            ).when(
                F.col("color").isNotNull() & F.col("_matched_colors_str").isNotNull(), 1
            ).when(
                F.lower(F.concat_ws(" ", F.col("title"), F.col("describe"))).rlike(FALLBACK_PATTERN), 2
            ).otherwise(0)
        ).withColumn(
            "multi_color_str",
            F.when(
                F.col("color").isNotNull() & F.col("color").rlike(KEYWORD_PATTERN),
                F.regexp_extract(F.col("color"), KEYWORD_PATTERN, 1)
            ).when(
                F.col("color").isNotNull() & F.col("color").rlike(SEPARATOR_PATTERN),
                F.col("color")
            ).when(
                F.col("color").isNotNull() & F.col("_matched_colors_str").isNotNull(),
                F.col("_matched_colors_str")
            ).when(
                F.lower(F.concat_ws(" ", F.col("title"), F.col("describe"))).rlike(FALLBACK_PATTERN),
                F.regexp_extract(
                    F.lower(F.concat_ws(" ", F.col("title"), F.col("describe"))),
                    FALLBACK_PATTERN, 1
                )
            ).otherwise(F.lit(None))
        ).drop("_matched_colors_str")
        return df

    @staticmethod
    def build_time_interval_type_expr(col_name, interval_dict):
        one_month = interval_dict['one_month']
        three_month = interval_dict['three_month']
        six_month = interval_dict['six_month']
        twelve_month = interval_dict['twelve_month']
        twenty_four_month = interval_dict['twenty_four_month']
        thirty_six_month = interval_dict['thirty_six_month']
        return (f"CASE WHEN {col_name} >= '{one_month}' THEN 1 "
                f"WHEN {col_name} >= '{three_month}' AND {col_name} < '{one_month}' THEN 2 "
                f"WHEN {col_name} >= '{six_month}' AND {col_name} < '{three_month}' THEN 3 "
                f"WHEN {col_name} >= '{twelve_month}' AND {col_name} < '{six_month}' THEN 4 "
                f"WHEN {col_name} >= '{twenty_four_month}' AND {col_name} < '{twelve_month}' THEN 5 "
                f"WHEN {col_name} >= '{thirty_six_month}' AND {col_name} < '{twenty_four_month}' THEN 6 "
                f"WHEN {col_name} < '{thirty_six_month}' THEN 7 ELSE 0 END")

    # 11. 通过ASIN页面信息处理(评分类型、上架时间类型、电影标签、是否内部asin、是否隐藏分类、有效类型、必需ASIN、asin_type)
    def handle_asin_detail_all_type(self, df):
        # 1. 评分类型
        df = df.withColumn("rating_type", F.expr("""
        CASE WHEN rating >= 4.5 THEN 1 WHEN rating >= 4 AND rating < 4.5 THEN 2 WHEN rating >= 3.5 AND rating < 4 THEN 3 
        WHEN rating >= 3 AND rating < 3.5 THEN 4 WHEN rating < 3 AND rating >= 0 THEN 5 ELSE 0 END"""))
        # 2. 上架时间类型
        df = df.join(self.df_asin_keep_date, on=['asin'], how='left')
        df = df.withColumn("launch_time", F.when(F.col("launch_time").isNull(), F.col("new_launch_time")).otherwise(
            F.col("launch_time")))
        df = df.withColumn(
            "launch_time_type",
            F.expr(self.build_time_interval_type_expr("launch_time", self.launch_time_interval_dict))
        )
        # 3. 电影标签
        movie_label_list = ['prime video', 'dvd', 'blu-ray', 'kindle', 'app', 'paperback', 'audible audiobook',
                            'kindle edition', 'kindle & comixology', 'hardcover', 'comic', 'multi-format', '4k',
                            'library binding', 'vinyl', 'audio cd', 'mp3 music', 'single issue magazine',
                            'print magazine', 'unknown binding']
        df = df.join(self.df_asin_label_info, on=['asin'], how='left')
        condition = reduce(
            lambda acc, keyword: acc | F.expr(f"exists(asin_label_list, x -> x like '%{keyword}%')"),
            movie_label_list,
            F.lit(False)
        )
        df = df.withColumn("is_movie_label", condition.cast("int")).drop("asin_label_list")
        # 4. 是否内部asin、是否隐藏分类
        df = df.join(self.df_self_asin_info, on=['asin'], how='left')
        df = df.withColumn(
            "is_self_asin", F.when(F.col("is_self_asin").isNotNull(), F.col("is_self_asin")).otherwise(F.lit(0)))
        df = df.join(self.df_hide_category, on=['asin_bs_cate_current_id'], how='left')
        df = df.na.fill({"hide_flag": 0})
        df = df.withColumn("is_hide_asin", F.expr("""
        CASE WHEN hide_flag = 1 THEN 1 WHEN asin_bs_cate_1_id = 'grocery' and asin_bs_cate_current_id != '6492272011' THEN 1  
        WHEN asin_bs_cate_current_id in ('21393128011', '21377129011', '21377127011', '21377130011', '21388218011', '21377132011') THEN 1 
        ELSE 0 END""")).drop("hide_flag")
        # 5. 有效类型
        df = df.join(self.df_asin_bsr_end, on=['asin_bs_cate_1_id'], how='left')
        df = df.withColumn("bsr_type", F.expr("""
        CASE WHEN limit_rank is null and asin_bs_cate_1_rank <= 500000 THEN 1 WHEN limit_rank is not null and asin_bs_cate_1_rank <= limit_rank THEN 1 ELSE 0 END"""
                                              )).drop("limit_rank")
        # 5. 是否必需ASIN（双重确认：BSR分类 + 页面描述分类，两者均在排除列表才标记为1）
        df = df.withColumn(
            "desc_category_first_name",
            F.lower(F.trim(F.split(F.col("category"), "›").getItem(0)))
        ).join(self.df_bsr_category, on=['desc_category_first_name'], how='left')
        need_categories = "('mobile-apps', 'audible', 'books', 'music', 'dmusic', 'digital-text', 'magazines', 'movies-tv', 'software', 'videogames', 'amazon-devices', 'boost', 'us-live-explorations', 'amazon-renewed')"
        df = df.withColumn("is_need_asin", F.expr(f"""
        CASE WHEN asin_bs_cate_1_id in {need_categories}
                  AND desc_category_first_id in {need_categories} THEN 1
             WHEN asin NOT LIKE 'B0%' THEN 1
        ELSE 0 END"""))
        df = df.drop("desc_category_first_name", "desc_category_first_id")
        # 6. asin_type
        df = df.withColumn("asin_type", F.expr("""
        CASE WHEN is_self_asin=1 THEN 1 WHEN is_need_asin=1 THEN 2 WHEN is_hide_asin=1 THEN 3 ELSE 0 END"""
        )).drop("is_self_asin", "is_need_asin", "is_hide_asin")
        return df

    # 12. 处理变化率相关字段（环比_mom / 同比_yoy 统一处理）
    def handle_asin_attribute_change(self, df):
        df = df.join(self.df_previous_flow_asin, on=['asin'], how='left')
        df = df.join(self.df_previous_flow_asin_lastyear, on=['asin'], how='left')
        # (current_col, prev_col, lastyear_col, suffix, rise_round, use_sentinel)
        # rise_round: None=不计算_rise; 整数=round精度; 'int'=IntegerType
        columns_to_change = [
            ("ao_val", "previous_asin_ao_val", "lastyear_asin_ao_val", "ao", 3, False),
            ("price", "previous_asin_price", "lastyear_asin_price", "price", 2, False),
            ("asin_bs_cate_1_rank", "previous_first_category_rank", "lastyear_first_category_rank", "rank", "int", False),
            ("bsr_orders", "previous_asin_bsr_orders", "lastyear_asin_bsr_orders", "bsr_orders", "int", False),
            ("rating", "previous_asin_rating", "lastyear_asin_rating", "rating", 1, False),
            ("total_comments", "previous_asin_total_comments", "lastyear_asin_total_comments", "comments", "int", False),
            ("variat_num", "previous_asin_variation_num", "lastyear_asin_variation_num", "variation", "int", False),
            ("bsr_orders_sale", "previous_sales", "lastyear_sales", "sales", 2, False),
            ("asin_bought_month", "previous_asin_bought_month", "lastyear_asin_bought_month", "bought_month", None, True),
        ]

        def calculate_change(current_col, previous_col, use_sentinel=False):
            rise_col = F.col(current_col) - F.col(previous_col)
            if use_sentinel:
                change_col = F.when(
                    F.col(current_col).isNull() & F.col(previous_col).isNull(), F.lit(None)
                ).when(
                    F.col(current_col).isNull(), F.lit(-1000.0)
                ).when(
                    F.col(previous_col).isNull(), F.lit(1000.0)
                ).otherwise(
                    F.round((F.col(current_col) - F.col(previous_col)) / F.col(previous_col), 4)
                )
            else:
                change_col = F.when(
                    (F.col(previous_col).isNotNull()) & (F.col(previous_col) != 0),
                    F.round((F.col(current_col) - F.col(previous_col)) / F.col(previous_col), 4)
                ).otherwise(None)
            return rise_col, change_col

        for current_col, prev_col, lastyear_col, suffix, rise_round, use_sentinel in columns_to_change:
            rise_col, mom_col = calculate_change(current_col, prev_col, use_sentinel=use_sentinel)
            _, yoy_col = calculate_change(current_col, lastyear_col, use_sentinel=use_sentinel)
            if rise_round is not None:
                if rise_round == "int":
                    df = df.withColumn(f"{suffix}_rise", rise_col.cast(IntegerType()))
                else:
                    df = df.withColumn(f"{suffix}_rise", F.round(rise_col, rise_round))
            mom_field = f"{suffix}_mom" if suffix == "bought_month" else f"{suffix}_change"
            df = df.withColumn(mom_field, F.round(mom_col, 4)) \
                   .withColumn(f"{suffix}_yoy", F.round(yoy_col, 4))
        df = df.drop('previous_asin_bought_month', 'lastyear_asin_bought_month')
        return df

    # 13. 处理不同来源asin
    def handle_asin_different_source(self, df):
        df = df.join(self.df_asin_source_flag, on=['asin'], how='left').fillna({"asin_source_flag": "0"}).withColumn(
            "asin_source_flag", F.split(F.col("asin_source_flag"), ",")
        ).withColumn(
            "asin_source_flag", F.expr("transform(asin_source_flag, x -> cast(x as int))")
        )
        return df

    # 14. 处理keepa追踪时间和利润率
    def handle_asin_keepa_profit(self, df):
        df = df.join(
            self.df_keepa_asin, on=['asin'], how='left'
        ).join(
            self.df_asin_profit_rate, on=['asin', 'price'], how='left'
        )
        df = df.withColumn(
            "tracking_since",
            F.when(F.col("tracking_since").isNull(), F.lit(None)).otherwise(
                F.date_format(F.from_unixtime((F.col("tracking_since") + F.lit(21564000)) * 60), "yyyy-MM-dd")
            )
        ).withColumn(
            "tracking_since_type",
            F.expr(self.build_time_interval_type_expr("tracking_since", self.launch_time_interval_dict))
        ).withColumn(
            'profit_key', F.concat_ws("_", F.col("asin"), F.col("price"))
        ).withColumn(
            "profit_rate_extra",
            F.when(
                F.col("ocean_profit").isNull() & F.col("air_profit").isNull(),
                F.lit(None)
            ).otherwise(
                F.struct(
                    F.col("ocean_profit").alias("ocean_profit"),
                    F.col("air_profit").alias("air_profit")
                )
            )
        ).drop('ocean_profit', 'air_profit')
        return df

    # 15. 字段标准化
    def handle_column_name(self, df):
        df = df.withColumnRenamed("asin_bs_cate_1_id", "category_first_id")\
            .withColumnRenamed("asin_bs_cate_current_id", "category_id") \
            .withColumnRenamed("asin_bs_cate_1_rank", "first_category_rank")\
            .withColumnRenamed("asin_bs_cate_current_rank", "current_category_rank") \
            .withColumnRenamed("variat_num", "variation_num")\
            .withColumnRenamed("seller_id", "account_id").withColumnRenamed("seller_country_name", "site_name") \
            .withColumnRenamed("asinUpdateTime", "asin_crawl_date")\
            .withColumnRenamed("customer_reviews_json", "product_features")\
            .withColumn("collapse_asin", F.coalesce(F.col("parent_asin"), F.col("asin")))\
            .withColumn("bsr_best_orders_type", F.lit(-1))\
            .withColumn("img_type_arr", F.split(F.col("img_type"), ","))\
            .withColumn("img_type_arr", F.expr("transform(img_type_arr, x -> cast(x as int))"))\
            .withColumn("amazon_label", F.when(
                F.coalesce(F.get_json_object(F.col("amazon_label"), "$[0].badge_type"),
                           F.get_json_object(F.col("amazon_label"), "$.badge_type")) != "unknown",
                F.coalesce(F.get_json_object(F.col("amazon_label"), "$[0].badge_type"),
                           F.get_json_object(F.col("amazon_label"), "$.badge_type"))
            ))
        df_save = df.select("asin", "ao_val", "zr_counts", "sp_counts", "sb_counts", "vi_counts", "bs_counts", "ac_counts",
                       "tr_counts", "er_counts", "bsr_orders", "bsr_orders_sale", "title", "title_len", "price",
                       "rating", "total_comments", "buy_box_seller_type", "page_inventory", "volume", "weight", "color",
                       "size", "style", "material", "launch_time", "img_num", "parent_asin", "img_type", "img_url",
                       "activity_type", "one_two_val", "three_four_val", "five_six_val", "eight_val", "brand",
                       "variation_num", "one_star", "two_star", "three_star", "four_star", "five_star", "low_star",
                       "together_asin", "account_name", "account_id", "rank_rise", "rank_change", "rank_yoy", "ao_rise",
                       "ao_change", "ao_yoy", "price_rise", "price_change", "price_yoy", "rating_rise", "rating_change", "rating_yoy", "comments_rise",
                       "comments_change", "comments_yoy", "bsr_orders_rise", "bsr_orders_change", "bsr_orders_yoy", "sales_rise", "sales_change", "sales_yoy",
                       "variation_rise", "variation_change", "variation_yoy", "size_type", "rating_type", "site_name_type",
                       "weight_type", "launch_time_type", "ao_val_type", "rank_type", "price_type", "bsr_type",
                       "bsr_best_orders_type", "quantity_variation_type", "package_quantity", "is_movie_label",
                       "is_brand_label", "is_alarm_brand", "asin_type", "asin_crawl_date", "category_first_id",
                       "category_id", "first_category_rank", "current_category_rank", "asin_weight_ratio",
                       "site_name", "asin_bought_month", "bought_month_mom", "bought_month_yoy", "asin_lqs_rating", "asin_lqs_rating_detail",
                       "asin_lob_info", "is_contains_lob_info", "is_package_quantity_abnormal", "category",
                       "zr_flow_proportion", "matrix_flow_proportion", "matrix_ao_val", "product_features", "img_info",
                       "collapse_asin", F.col("follow_sellers").alias("follow_sellers_count"), "seller_json",
                       F.col("describe").alias("asin_describe"), F.round("fbm_delivery_price", 2).alias("fbm_price"), 
                       "asin_source_flag", "bsr_last_seen_at", "bsr_seen_count_30d", "nsr_last_seen_at", "nsr_seen_count_30d", 
                       "describe_len", "tracking_since", "tracking_since_type", "profit_key", "profit_rate_extra", "img_type_arr",
                       "multi_color_flag", "multi_color_str", "amazon_label")
        df_save = df_save.na.fill(
            {"zr_counts": 0, "sp_counts": 0, "sb_counts": 0, "vi_counts": 0, "bs_counts": 0, "ac_counts": 0,
             "tr_counts": 0, "er_counts": 0, "title_len": 0, "total_comments": 0, "variation_num": 0, "img_num": 0,
             "one_two_val": 0.0, "three_four_val": 0.0, "five_six_val": 0.0, "eight_val": 0.0,
             "one_star": 0, "two_star": 0, "three_star": 0, "four_star": 0, "five_star": 0, "low_star": 0,
             "size_type": 0, "rating_type": 0, "site_name_type": 0, "weight_type": 0, "launch_time_type": 0,
             "ao_val_type": 0, "rank_type": 0, "price_type": 0, "quantity_variation_type": 0, "package_quantity": 1,
             "is_movie_label": 0, "is_brand_label": 0, "is_alarm_brand": 0, "asin_lqs_rating": 0.0, "follow_sellers_count": -1,
             "bsr_last_seen_at": "1970-01-01", "bsr_seen_count_30d": 0, "nsr_last_seen_at": "1970-01-01", "nsr_seen_count_30d": 0,
             "describe_len": 0, "multi_color_flag": 0}
        )
        print("asin的标准信息：")
        df_save.show(10, truncate=False)
        return df_save

    def read_data(self):
        # previous/lastyear 共用：单表读取 + PySpark DataFrame join，不再从 dwt_flow_asin 取数据
        # date_type 固定 month；date_info 必传（previous=上个月，lastyear=去年同月）
        def _load_baseline(date_info_target, alias_prefix):
            where_clause = f"site_name = '{self.site_name}' and date_type = 'month' and date_info = '{date_info_target}'"
            sql_measure = f"""
                select asin, asin_ao_val, asin_bsr_orders
                from dwd_asin_measure
                where {where_clause}
            """
            sql_detail = f"""
                select asin, asin_price, variation_num, asin_rating, asin_total_comments, asin_bought_month
                from dim_asin_detail
                where {where_clause}
            """
            sql_bs = f"""
                select asin, asin_bs_cate_1_rank
                from dim_asin_bs_info
                where {where_clause}
            """
            print(f"sql_measure({alias_prefix})=", sql_measure)
            print(f"sql_detail ({alias_prefix})=", sql_detail)
            print(f"sql_bs     ({alias_prefix})=", sql_bs)
            df_m = self.spark.sql(sqlQuery=sql_measure).repartition(self.repartition_num, 'asin')
            df_d = self.spark.sql(sqlQuery=sql_detail).repartition(self.repartition_num, 'asin')
            df_b = self.spark.sql(sqlQuery=sql_bs).repartition(self.repartition_num, 'asin')
            df = df_m.join(df_d, on='asin', how='left').join(df_b, on='asin', how='left')
            return df.select(
                F.col('asin'),
                F.round(F.col('asin_ao_val'), 3).alias(f'{alias_prefix}_asin_ao_val'),
                F.col('asin_price').alias(f'{alias_prefix}_asin_price'),
                F.col('variation_num').alias(f'{alias_prefix}_asin_variation_num'),
                F.col('asin_rating').alias(f'{alias_prefix}_asin_rating'),
                F.col('asin_total_comments').alias(f'{alias_prefix}_asin_total_comments'),
                F.col('asin_bs_cate_1_rank').alias(f'{alias_prefix}_first_category_rank'),
                F.col('asin_bsr_orders').alias(f'{alias_prefix}_asin_bsr_orders'),
                F.round(F.col('asin_bsr_orders') * F.col('asin_price'), 2).alias(f'{alias_prefix}_sales'),
                F.col('asin_bought_month').alias(f'{alias_prefix}_asin_bought_month'),
            ).persist(StorageLevel.DISK_ONLY)

        print(f"1. 读取上个月维度的flow_asin（date_type=month, date_info={self.date_info_last_month}）")
        self.df_previous_flow_asin = _load_baseline(self.date_info_last_month, 'previous')
        self.df_previous_flow_asin.show(10, truncate=False)
        print(f"1b. 读取同比去年的flow_asin（date_type=month, date_info={self.date_info_last_year}）")
        self.df_previous_flow_asin_lastyear = _load_baseline(self.date_info_last_year, 'lastyear')
        self.df_previous_flow_asin_lastyear.show(10, truncate=False)
        print("2. 获取卖家相关信息")
        sql = f"""
            select fd_unique as seller_id, fd_account_name as account_name, upper(fd_country_name) as seller_country_name, asin, updated_at
            from dim_fd_asin_info_30day where site_name='{self.site_name}' and date_type = '30day' and fd_unique is not null"""
        print("sql=", sql)
        self.df_seller_info = self.spark.sql(sqlQuery=sql)
        self.df_seller_info = self.df_seller_info.repartition(self.repartition_num).persist(StorageLevel.DISK_ONLY)
        self.df_seller_info.show(10, truncate=False)
        # df_seller_country: seller_id + seller_country_name 去重（全局缓存）
        self.df_seller_country = self.df_seller_info.select('seller_id', 'seller_country_name').dropDuplicates(['seller_id']).persist(StorageLevel.DISK_ONLY)
        # df_asin_seller: asin 去重，保留最新的 seller_id + account_name（全局缓存）
        window = Window.partitionBy('asin').orderBy(F.col('updated_at').desc())
        self.df_asin_seller = self.df_seller_info.select('asin', 'seller_id', 'account_name', 'updated_at') \
            .withColumn('rank', F.row_number().over(window)) \
            .filter(F.col('rank') == 1) \
            .drop('rank', 'updated_at') \
            .withColumnRenamed('seller_id', 'fd_seller_id') \
            .withColumnRenamed('account_name', 'fd_account_name') \
            .persist(StorageLevel.DISK_ONLY)
        print("3. 读取内部asin信息")
        sql = f"""select asin, 1 as is_self_asin from {self.site_name}_self_asin group by asin"""
        print("sql=", sql)
        mysql_con_info = DBUtil.get_connection_info(db_type='mysql', site_name=self.site_name)
        if mysql_con_info is not None:
            df_self_asin_info = SparkUtil.read_jdbc_query(
                session=self.spark, url=mysql_con_info['url'], pwd=mysql_con_info['pwd'],
                username=mysql_con_info['username'], query=sql)
            self.df_self_asin_info = F.broadcast(df_self_asin_info)
            self.df_self_asin_info.show(10, truncate=False)
        print("4. 读取告警品牌信息")
        sql = f"""
               select brand, 1 as is_alarm_brand
               from (select lower(trim(brand_name)) as brand
               from brand_alert_erp where brand_name is not null) t group by brand"""
        print("sql=", sql)
        if self.site_name == 'us':
            pg_cluster_con_info = DBUtil.get_connection_info(db_type="postgresql_cluster", site_name=self.site_name)
            if pg_cluster_con_info is not None:
                df_alarm_brand_info = SparkUtil.read_jdbc_query(
                    session=self.spark, url=pg_cluster_con_info['url'], pwd=pg_cluster_con_info['pwd'],
                    username=pg_cluster_con_info['username'], query=sql)
                self.df_alarm_brand_info = F.broadcast(df_alarm_brand_info)
                self.df_alarm_brand_info.show(10, truncate=False)
        else:
            schema = StructType([
                StructField("brand", StringType(), True),
                StructField("is_alarm_brand", IntegerType(), True)
            ])
            self.df_alarm_brand_info = self.spark.createDataFrame([], schema)
        print("5. 读取隐藏分类信息")
        sql = f"""
            select category_id_base as asin_bs_cate_current_id, 1 as hide_flag from us_bs_category_hide group by category_id_base
           """
        print("sql=", sql)
        us_mysql_con_info = DBUtil.get_connection_info(db_type='mysql', site_name='us')
        if us_mysql_con_info is not None:
            df_hide_category = SparkUtil.read_jdbc_query(
                session=self.spark, url=us_mysql_con_info['url'], pwd=us_mysql_con_info['pwd'],
                username=us_mysql_con_info['username'], query=sql)
            self.df_hide_category = F.broadcast(df_hide_category)
            self.df_hide_category.show(10, truncate=False)
        print("5.1 读取BSR分类树（用于双重确认is_need_asin）")
        sql = f"""
            select lower(trim(en_name)) as desc_category_first_name, category_first_id as desc_category_first_id
            from dim_bsr_category_tree where site_name = '{self.site_name}' and category_parent_id = 0 and leaf_node = 2
        """
        self.df_bsr_category = F.broadcast(self.spark.sql(sqlQuery=sql))
        self.df_bsr_category.show(10, truncate=False)
        print("6. 读取asin_label信息")
        sql = f"""
           select asin, label from 
           (select asin, lower(label) as label, created_time,row_number() over(partition by asin,label order by updated_time desc) as crank
           from ods_other_search_term_data where site_name='{self.site_name}' and date_type='30day' and trim(label) not in ('null','') and label is not null) t where t.crank=1
        """
        print("sql=", sql)
        self.df_asin_label_info = self.spark.sql(sqlQuery=sql)
        self.df_asin_label_info = self.df_asin_label_info.groupby(['asin']).agg(
            F.collect_set("label").alias("asin_label_list"))
        self.df_asin_label_info = self.df_asin_label_info.repartition(self.repartition_num).persist(StorageLevel.DISK_ONLY)
        self.df_asin_label_info.show(10, truncate=False)
        print("7. 读取dwd_asin_measure拿取ao及各类型数量")
        sql = f"""
       select asin, asin_sp_counts as sp_counts, (asin_sb1_counts + asin_sb2_counts) as sb_counts, asin_sb3_counts as vi_counts,
       asin_bs_counts as bs_counts, asin_ac_counts as ac_counts, asin_tr_counts as tr_counts, asin_er_counts as er_counts, 
       asin_st_counts, asin_zr_counts, asin_adv_counts, round(asin_zr_flow_proportion, 3) as asin_zr_flow_proportion, 
       round(asin_ao_val, 3) as asin_ao_val, asin_amazon_orders 
       from dwd_asin_measure where site_name='{self.site_name}' and date_type='30day'
           """
        print("sql=", sql)
        self.df_asin_measure = self.spark.sql(sqlQuery=sql)
        self.df_asin_measure = self.df_asin_measure.repartition(self.repartition_num).persist(StorageLevel.DISK_ONLY)
        self.df_asin_measure.show(10, truncate=False)
        print("8. 读取one_category_report表")
        sql = f"select category_id as asin_bs_cate_1_id, rank as asin_bs_cate_1_rank, orders as bsr_orders from ods_one_category_report " \
              f"where site_name='{self.site_name}' and date_type='30day'"
        print("sql=", sql)
        self.df_bs_report = self.spark.sql(sqlQuery=sql)
        self.df_bs_report = self.df_bs_report.repartition(self.repartition_num).persist(StorageLevel.DISK_ONLY)
        self.df_bs_report.show(10, truncate=False)
        print("9. 读取keep_date获取上架时间")
        sql = f"""
           select asin, new_launch_time from
           (select asin, launch_time as new_launch_time,
           row_number() over(partition by asin order by updated_at desc) as trank
           from ods_asin_keep_date_30day where site_name='{self.site_name}' and date_type='30day' and state=3) t where t.trank=1
               """
        print("sql=", sql)
        self.df_asin_keep_date = self.spark.sql(sqlQuery=sql)
        self.df_asin_keep_date = self.df_asin_keep_date.repartition(self.repartition_num).persist(StorageLevel.DISK_ONLY)
        self.df_asin_keep_date.show(10, truncate=False)
        print("10. 读取bsr有效排名信息")
        sql = f"""select rank as limit_rank, category_id as asin_bs_cate_1_id from {self.site_name}_bsr_end"""
        print("sql=", sql)
        if mysql_con_info is not None:
            df_asin_bsr_end = SparkUtil.read_jdbc_query(
                session=self.spark, url=mysql_con_info['url'], pwd=mysql_con_info['pwd'],
                username=mysql_con_info['username'], query=sql)
            self.df_asin_bsr_end = F.broadcast(df_asin_bsr_end)
            self.df_asin_bsr_end.show(10, truncate=False)
        print("11. 通过node_id获取一级分类进行分类补充")
        df_asin_new_cate = get_node_first_id_df(self.site_name, self.spark)
        self.df_asin_new_cate = F.broadcast(df_asin_new_cate)
        self.df_asin_new_cate.show(10, truncate=False)
        print("12. 获取用户修改打包数量信息")
        pg_con_info = DBUtil.get_connection_info("postgresql", "us")
        sql = f""" 
        WITH ranked_edit_logs AS (SELECT edit_key_id, lower(val_related_info) as val_related_info, val_after, 
        ROW_NUMBER() OVER (PARTITION BY edit_key_id ORDER BY create_time DESC) AS rn FROM sys_edit_log 
        WHERE module = '流量选品' AND filed = 'package_quantity' AND site_name='{self.site_name}') 
        SELECT edit_key_id as asin, val_related_info as title, cast(val_after as int) as user_package_num, 
        0 as user_is_package_quantity_abnormal FROM ranked_edit_logs WHERE rn = 1"""
        if pg_con_info is not None:
            df_user_package_num = SparkUtil.read_jdbc_query(
                session=self.spark, url=pg_con_info['url'], pwd=pg_con_info['pwd'],
                username=pg_con_info['username'], query=sql)
            self.df_user_package_num = F.broadcast(df_user_package_num)
            self.df_user_package_num.show(10, truncate=False)
        print("14. 获取分类ID与分类名称的对应关系")
        self.df_asin_category = get_first_id_from_category_desc_df(self.site_name, self.spark)
        self.df_asin_category = self.df_asin_category.withColumn(
            "category_first_name", F.lower("category_first_name")
        ).repartition(self.repartition_num).persist(StorageLevel.DISK_ONLY)
        self.df_asin_category.show(10, truncate=False)

        print("15. 获取asin不同来源标识")
        sql = f"""
        select asin, asin_cate_flag as asin_source_flag, bsr_latest_date as bsr_last_seen_at, bsr_30day_count as bsr_seen_count_30d, 
        nsr_latest_date as nsr_last_seen_at, nsr_30day_count as nsr_seen_count_30d from dwd_asin_cate_flag 
        where site_name='{self.site_name}' and date_type='30day'
        """
        self.df_asin_source_flag = self.spark.sql(sqlQuery=sql)
        self.df_asin_source_flag = self.df_asin_source_flag.repartition(self.repartition_num).persist(StorageLevel.DISK_ONLY)
        self.df_asin_source_flag.show(10, truncate=False)

        print("16. 获取keepa数据")
        sql = f"""
        select asin, tracking_since from dim_keepa_asin_info where site_name = '{self.site_name}'
        """
        self.df_keepa_asin = self.spark.sql(sqlQuery=sql)
        self.df_keepa_asin = self.df_keepa_asin.repartition(self.repartition_num).persist(StorageLevel.DISK_ONLY)
        self.df_keepa_asin.show(10, truncate=False)

        print("17. 获取asin利润率数据")
        sql = f"""
        select asin, price, ocean_profit, air_profit from dim_asin_profit_rate_info where site_name='{self.site_name}'
        """
        self.df_asin_profit_rate = self.spark.sql(sqlQuery=sql)
        self.df_asin_profit_rate = self.df_asin_profit_rate.repartition(self.repartition_num).persist(StorageLevel.DISK_ONLY)
        self.df_asin_profit_rate.show(10, truncate=False)

        print("18. 读取颜色词表 dim_asin_color_info")
        color_rows = self.spark.sql(
            f"SELECT lower(en_name) as en_name FROM dim_asin_color_info WHERE site_name='{self.site_name}'"
        ).collect()
        self.color_set = {row.en_name for row in color_rows}
        print(f"颜色词表共 {len(self.color_set)} 条")

    # 字段处理逻辑综合
    def handle_all_field(self, df):
        # 1. 处理asin分类及排名以及排名类型字段
        df = self.handle_asin_bs_category_info(df)
        # 2. 利用node_id进行分类补充
        df = self.handle_asin_category_supplement(df)
        # 3. 处理bsr销量及销售额信息以及价格类型字段
        df = self.handle_asin_bsr_orders(df)
        # 4. 解析Make-It-A-Bundle信息
        df = self.handle_asin_lob_info(df)
        # 5. 处理配送方式、卖家所在地以及卖家所在地类型
        df = self.handle_asin_buy_box_seller_type(df)
        # 6. 处理asin基础属性信息(长宽高重量等)
        df = self.handle_asin_basic_attribute_info(df)
        # 7. 处理asin图片信息
        df = self.handle_asin_img_info(df)
        # 8. 处理变体相关（ao及母体相关，自然占比及母体自然占比，各类型数量，月销信息等）
        df = self.handle_asin_measure(df)
        # 9. 提取打包数量字段
        df = self.handle_asin_package_quantity(df)
        # 9.5. 多色判断（依赖 color 字段已从变体属性+product_json 补全）
        df = self.handle_multi_color_flag(df)
        # 10. 处理品牌标签、是否告警品牌、处理asin_lqs_rating信息
        df = self.handle_asin_lqs_and_brand(df)
        # 11.通过ASIN页面信息处理(评分类型、上架时间类型、电影标签、ASIN类型、有效类型)
        df = self.handle_asin_detail_all_type(df)
        # 12. 处理变化率相关字段
        df = self.handle_asin_attribute_change(df)
        # 13. 处理不同来源asin
        df = self.handle_asin_different_source(df)
        # 14. 处理keepa追踪时间和利润率
        df = self.handle_asin_keepa_profit(df)
        # 15. 字段标准化
        df_save = self.handle_column_name(df)
        return df_save

    # 写入es前的准备工作
    def es_prepare(self):
        # 创建对应es索引
        EsUtils.create_index(self.es_index_name, self.client, self.es_index_body)
        print("索引名称为：", self.es_index_name)
        # 执行富集策略
        # if self.site_name == 'us':
        #     self.client.enrich.execute_policy(name=self.policy_name1)
        #     self.client.enrich.execute_policy(name=self.policy_name2)
        # else:
        #     pass
        # EsUtils.user_enrich_pipeline(self.client, self.pipeline_id, self.policy_name1, self.policy_name2)
        # if not EsUtils.exist_index_alias(self.es_index_alias_name, self.client):
        #     EsUtils.create_index_alias(self.es_index_name, self.es_index_alias_name, self.client)
        # else:
        #     index_name_list = EsUtils.get_index_names_associated_alias(self.es_index_alias_name, self.client)
        #     if self.es_index_name not in index_name_list:
        #         EsUtils.delete_index_alias(self.es_index_alias_name, self.client)
        #         EsUtils.create_index_alias(self.es_index_name, self.es_index_alias_name, self.client)
        #     else:
        #         pass

    # 写入elasticsearch逻辑
    def save_to_es(self, df, batch_num):
        print("插入当前批次数据, 插入的数量量为： " + str(batch_num))
        start_time = time.time()
        df_asin_latest_detail = df. \
            select("asin", F.col("ao_val").alias("asin_ao_val"), F.col("title").alias("asin_title"),
                   F.col("title_len").alias("asin_title_len"), F.col("category").alias("asin_category_desc"),
                   F.col("volume").alias("asin_volume"), F.col("weight").alias("asin_weight"),
                   F.col("launch_time").alias("asin_launch_time"), F.col("brand").alias("asin_brand_name"), "one_star",
                   "two_star", "three_star", "four_star", "five_star", "low_star", "account_name", "account_id",
                   F.col("site_name").alias("seller_country_name"), "category_first_id", "parent_asin", "variation_num",
                   "img_info", "asin_crawl_date", F.col("price").alias("asin_price"),
                   F.col("rating").alias("asin_rating"),
                   F.col("total_comments").alias("asin_total_comments"), "matrix_ao_val", "zr_flow_proportion",
                   "matrix_flow_proportion", F.lit(self.date_info).alias("date_info"), "img_url",
                   F.col("category_id").alias("category_current_id"),
                   F.col("first_category_rank").alias("category_first_rank"),
                   F.col("current_category_rank").alias("category_current_rank"), "asin_type",
                   "bsr_orders", "bsr_orders_sale", "page_inventory", "asin_bought_month", "seller_json",
                   "buy_box_seller_type", "asin_describe", F.col("fbm_price").alias("asin_fbm_price"), 
                   F.col("describe_len").alias("asin_describe_len")
                   )
        df = df.drop("category", "seller_json")
        df = df.withColumn("date_info_del", F.lit(self.date_info))
        df.write.format("org.elasticsearch.spark.sql").options(**self.es_options).mode("append").save()
        end_time = time.time()
        elapsed_time = end_time - start_time
        print("当前插入时长为：" + str(elapsed_time))
        # ASIN最新详情的信息
        if self.consumer_type == 'latest' and self.test_flag == 'normal':
            print("导出ASIN最新详情信息到doris：")
            table_columns = """asin, asin_ao_val, asin_title, asin_title_len, asin_category_desc, asin_volume, 
            asin_weight, asin_launch_time, asin_brand_name, one_star, two_star, three_star, four_star, five_star, low_star, 
            account_name, account_id, seller_country_name, category_first_id, parent_asin, variation_num, img_info, 
            asin_crawl_date, asin_price, asin_rating, asin_total_comments, matrix_ao_val, zr_flow_proportion, matrix_flow_proportion, 
            date_info, img_url, category_current_id, category_first_rank, category_current_rank, asin_type, bsr_orders, bsr_orders_sale, 
            page_inventory, asin_bought_month, seller_json, buy_box_seller_type, asin_describe, asin_fbm_price, asin_describe_len"""
            DorisHelper.spark_export_with_columns(df_save=df_asin_latest_detail, db_name=self.doris_db, table_name=self.asin_latest_detail_table, table_columns=table_columns)
            df_asin_latest_detail.unpersist()

    # 实时消费中批次数据的处理逻辑
    def handle_kafka_stream(self, df, batch_id):
        try:
            batch_num = df.count()
            if batch_num > 0:
                start_time = time.time()
                print("当前批次:" + str(batch_id) + "; 该批次数据量为：" + str(batch_num))
                df = df.repartition(self.repartition_num)
                batch_id = int(batch_id) + self.initial_batch_id
                df_save = self.handle_all_field(df)
                self.es_prepare()
                self.save_to_es(df_save, batch_num)
                df_save.unpersist()
                end_time = time.time()
                print("当前批次:" + str(batch_id) + "执行完毕, 执行时长为：" + str(end_time - start_time))
            else:
                print("当前批次没有数据")
        except Exception as e:
            print(e, traceback.format_exc())

    # 消费主题下的所有历史数据
    def handle_kafka_history(self, kafka_df):
        print("处理kafka历史数据")
        batch_num = kafka_df.count()
        if batch_num > 0:
            self.history_batch_id = self.history_batch_id + 1
            start_time = time.time()
            kafka_df = kafka_df.repartition(self.repartition_num)
            kafka_df = self.handle_all_field(kafka_df)
            self.es_prepare()
            self.save_to_es(kafka_df, batch_num)
            end_time = time.time()
            print("该批次数据处理完毕, 执行时长为：" + str(end_time - start_time))
        else:
            raise ValueError("当前主题中没有数据,请注意检查！")


if __name__ == '__main__':
    arguments = sys.argv[1:]
    site_name = sys.argv[1]  # 参数1：站点
    date_type = sys.argv[2]  # 参数2：类型：week/4_week/month/quarter/day
    date_info = sys.argv[3]  # 参数3：年-周/年-月/年-季/年-月-日, 比如: 2022-1
    consumer_type = sys.argv[4]  # 参数3：年-周/年-月/年-季/年-月-日, 比如: 2022-1
    if len(arguments) == 5:
        test_flag = sys.argv[5]
    else:
        test_flag = 'normal'
    handle_obj = KafkaFlowAsinDetail(site_name=site_name, date_type=date_type, date_info=date_info, consumer_type=consumer_type, test_flag=test_flag, batch_size=200000)
    handle_obj.run_kafka()
