"""
author: CT
description: 同步 Hive dwt_flow_asin 月维度数据到 Doris dwt.{site}_flow_asin_month
             流程：
               Step 0) 建 selection 月物化表 selection.{site}_flow_asin_month_{yyyy_mm}[_test]
               Step 1~3) Spark 读 Hive → 规范化 → 写 Doris dwt.{site}_flow_asin_month
               Step 4) Doris INSERT OVERWRITE 物化到 selection 月物化表
               Step 5) 更新 MySQL workflow_everyday 流程记录表（仅 formal 模式）
             支持 us / uk / de 三站点
             支持 formal / test 模式：
               - formal：selection 表名无后缀，更新流程记录表
               - test  ：selection 表名加 _test 后缀，不更新流程记录表（dwt 主表不变）
执行示例:
    spark-submit dwt_flow_asin_month.py us 2026-05            # 默认 formal
    spark-submit dwt_flow_asin_month.py us 2026-05 formal
    spark-submit dwt_flow_asin_month.py us 2026-05 test       # test 模式
"""
import os
import sys

sys.path.append(os.path.dirname(sys.path[0]))

import pandas as pd
from pyspark.sql import functions as F

from utils.spark_util import SparkUtil
from utils.DorisHelper import DorisHelper
from utils.db_util import DBUtil, DbTypes


DORIS_DB = "dwt"
SUPPORTED_SITES = ("us", "uk", "de")


def _exec_doris_sql(sql_list, use_type='selection'):
    """通过 pymysql 走 Doris jdbc_port 执行 DDL / INSERT 等
    指定 database=selection 让 UDF（如 stem_en）可见"""
    import pymysql
    conn_info = DorisHelper.get_connection_info(use_type)
    conn = pymysql.connect(
        host=conn_info['ip'],
        port=conn_info['jdbc_port'],
        user=conn_info['user'],
        password=conn_info['pwd'],
        database='selection',
        charset='utf8mb4',
        autocommit=True,
    )
    try:
        cur = conn.cursor()
        for sql in sql_list:
            print(f"[Doris SQL] {sql[:200]}{'...' if len(sql) > 200 else ''}")
            cur.execute(sql)
        cur.close()
    finally:
        conn.close()


def build_create_table_sql(table_name, date_info):
    """构建 selection.{table_name} 建表语句（与 DDL 一致）；
    table_name 由外层拼接：{site}_flow_asin_month_{yyyy_mm}[_test]"""
    return f"""
CREATE TABLE IF NOT EXISTS `selection`.`{table_name}`
(
    `asin`                        VARCHAR(20)      NOT NULL COMMENT 'ASIN',
    `parent_asin`                 STRING           NULL     COMMENT '父ASIN',
    `collapse_asin`               STRING           NULL     COMMENT '聚合ASIN',
    `asin_crawl_date`             DATETIME         NULL     COMMENT 'Kafka消息抓取时间',
    `title`                       STRING           NULL     COMMENT '标题(lowercase)',
    `title_stem`                  STRING           NULL     COMMENT '标题词干(stem_en派生)',
    `title_len`                   INT              NULL     COMMENT '标题字符长度',
    `brand`                       STRING           NULL     COMMENT '品牌(lowercase)',
    `asin_describe`               STRING           NULL     COMMENT '五点描述',
    `describe_len`                INT              NULL     COMMENT '五点描述字符长度',
    `product_features`            STRING           NULL     COMMENT '产品评论/特色JSON',
    `together_asin`               STRING           NULL     COMMENT '组合购买推荐ASIN',
    `price`                       DECIMAL(20,2)    NULL,
    `fbm_price`                   DECIMAL(20,2)    NULL,
    `rating`                      DECIMAL(10,1)    NULL,
    `total_comments`              INT              NULL,
    `one_star`                    INT              NULL,
    `two_star`                    INT              NULL,
    `three_star`                  INT              NULL,
    `four_star`                   INT              NULL,
    `five_star`                   INT              NULL,
    `low_star`                    INT              NULL,
    `bsr_orders`                  INT              NULL,
    `bsr_orders_sale`             DECIMAL(20,2)    NULL,
    `asin_bought_month`           INT              NULL,
    `ao_val`                      DECIMAL(20,4)    NULL,
    `zr_counts`                   INT              NULL,
    `sp_counts`                   INT              NULL,
    `sb_counts`                   INT              NULL,
    `vi_counts`                   INT              NULL,
    `bs_counts`                   INT              NULL,
    `ac_counts`                   INT              NULL,
    `tr_counts`                   INT              NULL,
    `er_counts`                   INT              NULL,
    `zr_flow_proportion`          DECIMAL(20,4)    NULL,
    `matrix_flow_proportion`      DECIMAL(20,4)    NULL,
    `matrix_ao_val`               DECIMAL(20,4)    NULL,
    `one_two_val`                 DECIMAL(20,4)    NULL,
    `three_four_val`              DECIMAL(20,4)    NULL,
    `five_six_val`                DECIMAL(20,4)    NULL,
    `eight_val`                   DECIMAL(20,4)    NULL,
    `category_first_id`           STRING           NULL,
    `category_id`                 STRING           NULL,
    `first_category_rank`         INT              NULL,
    `current_category_rank`       INT              NULL,
    `weight`                      DECIMAL(20,4)    NULL,
    `volume`                      STRING           NULL,
    `asin_weight_ratio`           DECIMAL(20,4)    NULL,
    `color`                       STRING           NULL,
    `size`                        STRING           NULL,
    `style`                       STRING           NULL,
    `material`                    STRING           NULL,
    `package_quantity`            INT              NULL,
    `is_package_quantity_abnormal` TINYINT         NULL,
    `variation_num`               INT              NULL,
    `page_inventory`              INT              NULL,
    `activity_type`               STRING           NULL,
    `launch_time`                 DATETIME         NULL,
    `launch_time_type`            INT              NULL,
    `img_url`                     STRING           NULL,
    `img_num`                     INT              NULL,
    `img_type_arr`                ARRAY<INT>       NULL,
    `img_info`                    STRING           NULL,
    `account_id`                  STRING           NULL,
    `account_name`                STRING           NULL,
    `buy_box_seller_type`         TINYINT          NULL,
    `site_name`                   STRING           NULL,
    `follow_sellers_count`        INT              NULL,
    `asin_lob_info`               STRING           NULL,
    `is_contains_lob_info`        TINYINT          NULL,
    `asin_lqs_rating`             DECIMAL(20,1)    NULL,
    `asin_lqs_rating_detail`      STRING           NULL,
    `amazon_label`                STRING           NULL,
    `is_movie_label`              TINYINT          NULL,
    `is_brand_label`              TINYINT          NULL,
    `multi_color_flag`            TINYINT          NULL,
    `multi_color_str`             STRING           NULL,
    `rank_type`                   TINYINT          NULL,
    `ao_val_type`                 TINYINT          NULL,
    `price_type`                  TINYINT          NULL,
    `rating_type`                 TINYINT          NULL,
    `size_type`                   TINYINT          NULL,
    `weight_type`                 TINYINT          NULL,
    `site_name_type`              TINYINT          NULL,
    `quantity_variation_type`     TINYINT          NULL,
    `rank_rise`                   INT              NULL,
    `rank_change`                 DECIMAL(20,4)    NULL,
    `rank_yoy`                    DECIMAL(20,4)    NULL,
    `ao_rise`                     DECIMAL(20,4)    NULL,
    `ao_change`                   DECIMAL(20,4)    NULL,
    `ao_yoy`                      DECIMAL(20,4)    NULL,
    `price_rise`                  DECIMAL(20,2)    NULL,
    `price_change`                DECIMAL(20,4)    NULL,
    `price_yoy`                   DECIMAL(20,4)    NULL,
    `rating_rise`                 DECIMAL(20,1)    NULL,
    `rating_change`               DECIMAL(20,4)    NULL,
    `rating_yoy`                  DECIMAL(20,4)    NULL,
    `comments_rise`               INT              NULL,
    `comments_change`             DECIMAL(20,4)    NULL,
    `comments_yoy`                DECIMAL(20,4)    NULL,
    `bsr_orders_rise`             INT              NULL,
    `bsr_orders_change`           DECIMAL(20,4)    NULL,
    `bsr_orders_yoy`              DECIMAL(20,4)    NULL,
    `sales_rise`                  DECIMAL(20,2)    NULL,
    `sales_change`                DECIMAL(20,4)    NULL,
    `sales_yoy`                   DECIMAL(20,4)    NULL,
    `variation_rise`              INT              NULL,
    `variation_change`            DECIMAL(20,4)    NULL,
    `variation_yoy`               DECIMAL(20,4)    NULL,
    `bought_month_mom`            DECIMAL(20,4)    NULL,
    `bought_month_yoy`            DECIMAL(20,4)    NULL,
    `ocean_profit`                DECIMAL(20,4)    NULL,
    `air_profit`                  DECIMAL(20,4)    NULL,
    `tracking_since`              STRING           NULL,
    `tracking_since_type`         INT              NULL,
    `package_length`              INT              NULL,
    `package_width`               INT              NULL,
    `package_height`              INT              NULL,
    `item_weight`                 INT              NULL,
    `is_alarm_brand`              INT              NULL,
    `bsr_best_orders_type`        SMALLINT         NULL,
    `asin_source_flag`            ARRAY<INT>       NULL,
    `bsr_last_seen_at`            DATE             NULL,
    `bsr_seen_count_30d`          INT              NULL,
    `nsr_last_seen_at`            DATE             NULL,
    `nsr_seen_count_30d`          INT              NULL,
    `asin_type`                   SMALLINT         NULL,
    `usr_mask_progress`           STRING           NULL,
    `usr_mask_type`               STRING           NULL,
    `auctions_num`                INT              NULL,
    `auctions_num_all`            INT              NULL,
    `skus_num_creat`              INT              NULL,
    `skus_num_creat_all`          INT              NULL,
    `title_matching_degree`       DECIMAL(20,4)    NULL,
    `brand_badge_reason`          STRING           NULL,
    `title_stem_15`               STRING           NULL     COMMENT '标题词干前15个词(按空格分词截取)',
    INDEX idx_title      (`title`)      USING INVERTED PROPERTIES("parser" = "english") COMMENT '标题倒排索引',
    INDEX idx_title_stem (`title_stem`) USING INVERTED PROPERTIES("parser" = "english") COMMENT '标题词干倒排索引',
    INDEX idx_title_stem_15 (`title_stem_15`) USING INVERTED PROPERTIES("parser" = "english") COMMENT '标题词干前15个词倒排索引',
    INDEX idx_color      (`color`)      USING INVERTED PROPERTIES("parser" = "english") COMMENT '颜色倒排索引',
    INDEX idx_brand      (`brand`)      USING INVERTED PROPERTIES("parser" = "english") COMMENT '品牌倒排索引'
) ENGINE=OLAP
UNIQUE KEY(`asin`)
COMMENT '流量选品月维度物化表 {date_info}'
DISTRIBUTED BY HASH(`asin`) BUCKETS 32
PROPERTIES (
    "replication_num" = "3",
    "enable_unique_key_merge_on_write" = "true"
)
"""


def build_insert_overwrite_sql(site_name, table_name, date_info):
    """构建 INSERT OVERWRITE 到 selection.{table_name} 的 SQL；
    table_name 由外层拼接：{site}_flow_asin_month_{yyyy_mm}[_test]"""
    return f"""
INSERT OVERWRITE TABLE `selection`.`{table_name}`
SELECT
    f.asin,
    f.parent_asin,
    f.collapse_asin,
    f.asin_crawl_date,
    f.title,
    stem_en(f.title) AS title_stem,
    f.title_len,
    f.brand,
    f.asin_describe,
    f.describe_len,
    f.product_features,
    f.together_asin,
    f.price,
    f.fbm_price,
    f.rating,
    f.total_comments,
    f.one_star, f.two_star, f.three_star, f.four_star, f.five_star, f.low_star,
    f.bsr_orders,
    f.bsr_orders_sale,
    f.asin_bought_month,
    f.ao_val,
    f.zr_counts,
    f.sp_counts, f.sb_counts, f.vi_counts, f.bs_counts,
    f.ac_counts, f.tr_counts, f.er_counts,
    f.zr_flow_proportion,
    f.matrix_flow_proportion,
    f.matrix_ao_val,
    f.one_two_val, f.three_four_val, f.five_six_val, f.eight_val,
    f.category_first_id,
    f.category_id,
    f.first_category_rank,
    f.current_category_rank,
    f.weight,
    f.volume,
    f.asin_weight_ratio,
    f.color, f.size, f.style, f.material,
    COALESCE(uma.package_quantity, f.package_quantity) AS package_quantity,
    f.is_package_quantity_abnormal,
    f.variation_num,
    f.page_inventory,
    f.activity_type,
    COALESCE(f.launch_time, kp.keepa_launch_time) AS launch_time,
    CASE
        WHEN COALESCE(f.launch_time, kp.keepa_launch_time) IS NULL THEN 0
        WHEN DATEDIFF(f.asin_crawl_date, COALESCE(f.launch_time, kp.keepa_launch_time)) <= 30   THEN 1
        WHEN DATEDIFF(f.asin_crawl_date, COALESCE(f.launch_time, kp.keepa_launch_time)) <= 90   THEN 2
        WHEN DATEDIFF(f.asin_crawl_date, COALESCE(f.launch_time, kp.keepa_launch_time)) <= 180  THEN 3
        WHEN DATEDIFF(f.asin_crawl_date, COALESCE(f.launch_time, kp.keepa_launch_time)) <= 360  THEN 4
        WHEN DATEDIFF(f.asin_crawl_date, COALESCE(f.launch_time, kp.keepa_launch_time)) <= 720  THEN 5
        WHEN DATEDIFF(f.asin_crawl_date, COALESCE(f.launch_time, kp.keepa_launch_time)) <= 1080 THEN 6
        ELSE 7
    END AS launch_time_type,
    f.img_url,
    f.img_num,
    f.img_type AS img_type_arr,
    f.img_info,
    f.account_id,
    f.account_name,
    f.buy_box_seller_type,
    f.site_name,
    f.follow_sellers_count,
    f.asin_lob_info,
    f.is_contains_lob_info,
    f.asin_lqs_rating,
    f.asin_lqs_rating_detail,
    f.amazon_label,
    f.is_movie_label,
    f.is_brand_label,
    f.multi_color_flag,
    f.multi_color_str,
    f.rank_type, f.ao_val_type, f.price_type, f.rating_type,
    f.size_type, f.weight_type, f.site_name_type, f.quantity_variation_type,
    f.rank_rise,        f.rank_mom         AS rank_change,         f.rank_yoy,
    f.ao_rise,          f.ao_mom           AS ao_change,           f.ao_yoy,
    f.price_rise,       f.price_mom        AS price_change,        f.price_yoy,
    f.rating_rise,      f.rating_mom       AS rating_change,       f.rating_yoy,
    f.comments_rise,    f.comments_mom     AS comments_change,     f.comments_yoy,
    f.bsr_orders_rise,  f.bsr_orders_mom   AS bsr_orders_change,   f.bsr_orders_yoy,
    f.sales_rise,       f.sales_mom        AS sales_change,        f.sales_yoy,
    f.variation_rise,   f.variation_mom    AS variation_change,    f.variation_yoy,
    f.bought_month_mom, f.bought_month_yoy,
    pr.ocean_profit, pr.air_profit,
    FROM_UNIXTIME((CAST(kp.tracking_since AS BIGINT) + 21564000) * 60) AS tracking_since,
    CASE
        WHEN kp.tracking_since IS NULL OR kp.tracking_since <= 0 THEN 0
        WHEN DATEDIFF(f.asin_crawl_date, FROM_UNIXTIME((CAST(kp.tracking_since AS BIGINT) + 21564000) * 60)) <= 30   THEN 1
        WHEN DATEDIFF(f.asin_crawl_date, FROM_UNIXTIME((CAST(kp.tracking_since AS BIGINT) + 21564000) * 60)) <= 90   THEN 2
        WHEN DATEDIFF(f.asin_crawl_date, FROM_UNIXTIME((CAST(kp.tracking_since AS BIGINT) + 21564000) * 60)) <= 180  THEN 3
        WHEN DATEDIFF(f.asin_crawl_date, FROM_UNIXTIME((CAST(kp.tracking_since AS BIGINT) + 21564000) * 60)) <= 360  THEN 4
        WHEN DATEDIFF(f.asin_crawl_date, FROM_UNIXTIME((CAST(kp.tracking_since AS BIGINT) + 21564000) * 60)) <= 720  THEN 5
        WHEN DATEDIFF(f.asin_crawl_date, FROM_UNIXTIME((CAST(kp.tracking_since AS BIGINT) + 21564000) * 60)) <= 1080 THEN 6
        ELSE 7
    END AS tracking_since_type,
    kp.package_length,
    kp.package_width,
    kp.package_height,
    CASE WHEN kp.item_weight > 0 THEN kp.item_weight ELSE kp.package_weight END AS item_weight,
    CASE WHEN ba.brand_name_norm IS NOT NULL THEN 1 ELSE 0 END AS is_alarm_brand,
    CAST(-1 AS SMALLINT) AS bsr_best_orders_type,
    COALESCE(cf.asin_cate_flag, ARRAY(0))                    AS asin_source_flag,
    COALESCE(cf.bsr_latest_date, CAST('1970-01-01' AS DATE)) AS bsr_last_seen_at,
    COALESCE(cf.bsr_30day_count, 0)                          AS bsr_seen_count_30d,
    COALESCE(cf.nsr_latest_date, CAST('1970-01-01' AS DATE)) AS nsr_last_seen_at,
    COALESCE(cf.nsr_30day_count, 0)                          AS nsr_seen_count_30d,
    CAST(
        CASE
            WHEN sa.asin IS NOT NULL OR ab.brand_lower IS NOT NULL THEN 1
            WHEN (
                (COALESCE(chf.is_need_cat, 0) = 1 AND COALESCE(chd.is_need_cat, 0) = 1)
                OR f.asin NOT LIKE 'B0%'
            ) THEN 2
            WHEN (COALESCE(chf.is_hide_cat, 0) = 1 OR COALESCE(chc.is_hide_cat, 0) = 1)
                 AND NOT (COALESCE(chf.is_white_cat, 0) = 1 OR COALESCE(chc.is_white_cat, 0) = 1) THEN 3
            ELSE 0
        END
    AS SMALLINT) AS asin_type,
    uma.usr_mask_progress,
    COALESCE(uma.usr_mask_type, umc.usr_mask_type) AS usr_mask_type,
    COALESCE(aa.auctions_num,       0) AS auctions_num,
    COALESCE(aa.auctions_num_all,   0) AS auctions_num_all,
    COALESCE(aa.skus_num_creat,     0) AS skus_num_creat,
    COALESCE(aa.skus_num_creat_all, 0) AS skus_num_creat_all,
    f.title_matching_degree,
    bb.brand_badge_reason,
    ARRAY_JOIN(ARRAY_SLICE(SPLIT_BY_STRING(stem_en(f.title), ' '), 1, 15), ' ') AS title_stem_15
FROM `dwt`.`{site_name}_flow_asin_month` f
LEFT JOIN `dwd`.`dwd_asin_profit_rate_latest` pr
    ON f.asin = pr.asin AND f.price = pr.price AND pr.site_name = '{site_name}'
LEFT JOIN `dwd`.`dwd_keepa_asin_detail` kp
    ON f.asin = kp.asin AND kp.site_name = '{site_name}'
LEFT JOIN (
    SELECT DISTINCT LOWER(TRIM(brand_name)) AS brand_name_norm
    FROM `selection`.`brand_alert_erp`
    WHERE brand_name IS NOT NULL
) ba ON f.brand = ba.brand_name_norm
LEFT JOIN (
    SELECT asin FROM `mysql_selection`.`selection`.`us_self_asin` GROUP BY asin
) sa ON f.asin = sa.asin
LEFT JOIN (
    SELECT DISTINCT LOWER(TRIM(brand_name)) AS brand_lower
    FROM `mysql_selection`.`selection`.`amazon_brand` WHERE brand_type = '1'
) ab ON LOWER(f.brand) = ab.brand_lower
LEFT JOIN (
    SELECT category_id_base,
        MAX(CASE WHEN hide_type = 'is_need'  THEN 1 ELSE 0 END) AS is_need_cat,
        MAX(CASE WHEN hide_type = 'is_hide'  THEN 1 ELSE 0 END) AS is_hide_cat,
        MAX(CASE WHEN hide_type = 'is_white' THEN 1 ELSE 0 END) AS is_white_cat
    FROM `mysql_selection`.`selection`.`us_bs_category_hide` GROUP BY category_id_base
) chf ON f.category_first_id = chf.category_id_base
LEFT JOIN (
    SELECT category_id_base,
        MAX(CASE WHEN hide_type = 'is_need'  THEN 1 ELSE 0 END) AS is_need_cat,
        MAX(CASE WHEN hide_type = 'is_hide'  THEN 1 ELSE 0 END) AS is_hide_cat,
        MAX(CASE WHEN hide_type = 'is_white' THEN 1 ELSE 0 END) AS is_white_cat
    FROM `mysql_selection`.`selection`.`us_bs_category_hide` GROUP BY category_id_base
) chc ON f.category_id = chc.category_id_base
LEFT JOIN (
    SELECT category_id_base,
        MAX(CASE WHEN hide_type = 'is_need'  THEN 1 ELSE 0 END) AS is_need_cat,
        MAX(CASE WHEN hide_type = 'is_hide'  THEN 1 ELSE 0 END) AS is_hide_cat,
        MAX(CASE WHEN hide_type = 'is_white' THEN 1 ELSE 0 END) AS is_white_cat
    FROM `mysql_selection`.`selection`.`us_bs_category_hide` GROUP BY category_id_base
) chd ON f.desc_category_first_id = chd.category_id_base
LEFT JOIN `selection`.`user_mask_asin` uma ON f.asin = uma.asin
LEFT JOIN `selection`.`user_mask_category` umc ON f.category_id = umc.category_id
LEFT JOIN (
    SELECT asin, asin_cate_flag, bsr_latest_date, bsr_30day_count, nsr_latest_date, nsr_30day_count
    FROM `dwd`.`dwd_asin_source_flag`
    WHERE site_name = '{site_name}' AND date_type = 'month' AND date_info = '{date_info}'
) cf ON f.asin = cf.asin
LEFT JOIN `dwd`.`dwd_asin_auction` aa ON f.asin = aa.asin
LEFT JOIN `dwd`.`dwd_st_brand_badge` bb
    ON f.brand = bb.brand AND bb.site_name = '{site_name}' AND bb.date_info = '{date_info}'
WHERE f.date_info = '{date_info}'
"""


def modify_mission_record_status(site_name, date_info, result_type):
    """流程记录表更新：仅 month + formal 模式才入库 mysql workflow_everyday
    参考 export_es/es_flow_asin.py modify_mission_record_status"""
    if result_type != 'formal':
        print(f"[Step 5] result_type={result_type}，跳过流程记录表更新")
        return
    record_table            = 'workflow_everyday'
    record_table_name_field = f'{site_name}_flow_asin_last_month'
    record_type             = 'month'
    cur_date                = date_info
    engine_mysql            = DBUtil.get_db_engine(db_type=DbTypes.mysql.name, site_name='us')
    select_sql = (
        f"select id from {record_table} where site_name='{site_name}' and date_type='month' "
        f"and report_date='{cur_date}' and page='流量选品' and status_val=14 and is_end='是'"
    )
    df_is_finished = pd.read_sql(select_sql, engine_mysql)
    if df_is_finished.empty:
        replace_sql = f"""
            replace into {record_table} (site_name, report_date, status, status_val, table_name, date_type, page, is_end, remark, export_db_type)
            VALUES ('{site_name}', '{cur_date}', '流量选品计算完毕', 14, '{record_table_name_field}', '{record_type}', '流量选品', '是', '流量选品计算完毕', 'doris')
        """
        DBUtil.exec_sql('mysql', 'us', replace_sql)
        print(f"[Step 5] 流程记录表 workflow_everyday 已写入：{site_name} {cur_date}")
    else:
        print(f"[Step 5] 流程记录表已存在该记录，跳过")


def main(site_name, date_info, result_type='formal'):
    assert site_name in SUPPORTED_SITES, f"不支持的站点：{site_name}，仅支持 us/uk/de"
    assert result_type in ('formal', 'test'), f"不支持的 result_type：{result_type}，仅支持 formal/test"

    doris_table          = f"{site_name}_flow_asin_month"   # dwt 主表，不区分 test/formal
    date_info_underscore = date_info.replace('-', '_')
    # selection 月物化表：test 模式加 _test 后缀
    env_suffix      = '_test' if result_type == 'test' else ''
    selection_table = f"{site_name}_flow_asin_month_{date_info_underscore}{env_suffix}"

    print(f"启动：site={site_name}, date_info={date_info}, result_type={result_type}")
    print(f"  dwt 主表：dwt.{doris_table}（不区分 test/formal）")
    print(f"  selection 物化表：selection.{selection_table}")

    spark = SparkUtil.get_spark_session(
        f"DwtFlowAsinMonth: {site_name} {date_info} {result_type}"
    )

    # ===== Step 0:Doris 端建 selection 月物化表(IF NOT EXISTS) =====
    print(f"[Step 0] Doris 建表 selection.{selection_table}")
    _exec_doris_sql([build_create_table_sql(selection_table, date_info)])

    # ===== Step 1：读 Hive dwt_flow_asin 月数据 =====
    sql = f"""
        SELECT
            asin,
            asin_ao_val,
            asin_zr_counts, asin_sp_counts, asin_sb_counts, asin_vi_counts,
            asin_bs_counts, asin_ac_counts, asin_tr_counts, asin_er_counts,
            bsr_orders,
            sales AS bsr_orders_sale,
            asin_title, asin_title_len,
            asin_price,
            asin_rating, asin_total_comments,
            asin_buy_box_seller_type,
            asin_page_inventory,
            asin_volume, asin_weight,
            asin_color, asin_size, asin_style, asin_material,
            asin_launch_time,
            asin_img_num,
            parent_asin,
            asin_img_type,
            asin_img_url,
            asin_activity_type,
            act_one_two_val, act_three_four_val, act_five_six_val, act_eight_val,
            asin_brand_name,
            variation_num,
            one_star, two_star, three_star, four_star, five_star, low_star,
            together_asin,
            account_name, account_id,
            asin_rank_rise, asin_rank_mom, asin_rank_yoy,
            asin_ao_rise, asin_ao_mom, asin_ao_yoy,
            asin_price_rise, asin_price_mom, asin_price_yoy,
            asin_rating_rise, asin_rating_mom, asin_rating_yoy,
            asin_comments_rise, asin_comments_mom, asin_comments_yoy,
            asin_bsr_orders_rise, asin_bsr_orders_mom, asin_bsr_orders_yoy,
            asin_sales_rise, asin_sales_mom, asin_sales_yoy,
            asin_variation_rise, asin_variation_mom, asin_variation_yoy,
            asin_bought_mom, asin_bought_yoy,
            asin_size_type, asin_rating_type, asin_site_name_type, asin_weight_type,
            asin_ao_val_type, asin_rank_type, asin_price_type,
            asin_quantity_variation_type, package_quantity,
            is_movie_label, is_brand_label,
            asin_crawl_date,
            category_first_id, category_id, desc_category_first_id,
            first_category_rank, current_category_rank,
            asin_weight_ratio,
            seller_country_name,
            asin_bought_month,
            asin_lqs_rating, asin_lqs_rating_detail,
            asin_lob_info, is_contains_lob_info,
            is_package_quantity_abnormal,
            zr_flow_proportion, matrix_flow_proportion, matrix_ao_val,
            customer_reviews_json,
            img_info,
            coalesce(parent_asin, asin) AS collapse_asin,
            follow_sellers_count,
            asin_describe, asin_fbm_price, describe_len,
            title_matching_degree,
            multi_color_flag, multi_color_str,
            amazon_label
        FROM dwt_flow_asin
        WHERE site_name = '{site_name}' AND date_type = 'month' AND date_info = '{date_info}'
    """
    print(f"sql=\n{sql}")
    df_raw = spark.sql(sqlQuery=sql).repartition(40, 'asin')

    # ===== Step 2：字段规范化（与 dwt.us_flow_asin_30day DDL 对齐）=====
    def _norm_dt(col_name):
        """text → DATETIME 容错：先 yyyy-MM-dd HH:mm:ss，失败退到 yyyy-MM-dd"""
        c = F.col(col_name)
        return F.coalesce(
            F.to_timestamp(c, 'yyyy-MM-dd HH:mm:ss'),
            F.to_timestamp(c, 'yyyy-MM-dd'),
        )

    df_save = df_raw.select(
        F.lit(date_info).alias('date_info'),
        F.col('asin'),
        # ===== 核心选品指标 =====
        F.round(F.col('asin_ao_val').cast('double'), 4).cast('decimal(20,4)').alias('ao_val'),
        F.col('asin_zr_counts').cast('int').alias('zr_counts'),
        F.col('asin_sp_counts').cast('int').alias('sp_counts'),
        F.col('asin_sb_counts').cast('int').alias('sb_counts'),
        F.col('asin_vi_counts').cast('int').alias('vi_counts'),
        F.col('asin_bs_counts').cast('int').alias('bs_counts'),
        F.col('asin_ac_counts').cast('int').alias('ac_counts'),
        F.col('asin_tr_counts').cast('int').alias('tr_counts'),
        F.col('asin_er_counts').cast('int').alias('er_counts'),
        F.col('bsr_orders').cast('int').alias('bsr_orders'),
        F.round(F.col('bsr_orders_sale').cast('double'), 2).cast('decimal(20,2)').alias('bsr_orders_sale'),
        # ===== ASIN 基础属性 =====
        F.col('asin_title').alias('title'),
        F.col('asin_title_len').cast('int').alias('title_len'),
        F.round(F.col('asin_price').cast('double'), 2).cast('decimal(20,2)').alias('price'),
        F.round(F.col('asin_rating').cast('double'), 1).cast('decimal(10,1)').alias('rating'),
        F.col('asin_total_comments').cast('int').alias('total_comments'),
        F.col('asin_buy_box_seller_type').cast('tinyint').alias('buy_box_seller_type'),
        F.col('asin_page_inventory').cast('int').alias('page_inventory'),
        F.col('asin_volume').alias('volume'),
        F.round(F.col('asin_weight').cast('double'), 4).cast('decimal(20,4)').alias('weight'),
        F.col('asin_color').alias('color'),
        F.col('asin_size').alias('size'),
        F.col('asin_style').alias('style'),
        F.col('asin_material').alias('material'),
        _norm_dt('asin_launch_time').alias('launch_time'),
        F.col('asin_img_num').cast('int').alias('img_num'),
        F.col('parent_asin'),
        # asin_img_type string "1,2,3" → ARRAY<INT> → 再 to_json 成 "[1,2,3]"（Doris StreamLoad ARRAY<INT> 要求）
        F.to_json(F.expr("transform(split(asin_img_type, ','), x -> cast(x as int))")).alias('img_type'),
        F.col('asin_img_url').alias('img_url'),
        F.col('asin_activity_type').alias('activity_type'),
        F.round(F.col('act_one_two_val').cast('double'), 4).cast('decimal(20,4)').alias('one_two_val'),
        F.round(F.col('act_three_four_val').cast('double'), 4).cast('decimal(20,4)').alias('three_four_val'),
        F.round(F.col('act_five_six_val').cast('double'), 4).cast('decimal(20,4)').alias('five_six_val'),
        F.round(F.col('act_eight_val').cast('double'), 4).cast('decimal(20,4)').alias('eight_val'),
        F.col('asin_brand_name').alias('brand'),
        F.col('variation_num').cast('int').alias('variation_num'),
        F.col('one_star').cast('int').alias('one_star'),
        F.col('two_star').cast('int').alias('two_star'),
        F.col('three_star').cast('int').alias('three_star'),
        F.col('four_star').cast('int').alias('four_star'),
        F.col('five_star').cast('int').alias('five_star'),
        F.col('low_star').cast('int').alias('low_star'),
        F.col('together_asin'),
        F.col('account_name'),
        F.col('account_id'),
        # ===== 环比/同比变化率 =====
        F.col('asin_rank_rise').cast('int').alias('rank_rise'),
        F.round(F.col('asin_rank_mom').cast('double'), 4).cast('decimal(20,4)').alias('rank_mom'),
        F.round(F.col('asin_rank_yoy').cast('double'), 4).cast('decimal(20,4)').alias('rank_yoy'),
        F.round(F.col('asin_ao_rise').cast('double'), 4).cast('decimal(20,4)').alias('ao_rise'),
        F.round(F.col('asin_ao_mom').cast('double'), 4).cast('decimal(20,4)').alias('ao_mom'),
        F.round(F.col('asin_ao_yoy').cast('double'), 4).cast('decimal(20,4)').alias('ao_yoy'),
        F.round(F.col('asin_price_rise').cast('double'), 2).cast('decimal(20,2)').alias('price_rise'),
        F.round(F.col('asin_price_mom').cast('double'), 4).cast('decimal(20,4)').alias('price_mom'),
        F.round(F.col('asin_price_yoy').cast('double'), 4).cast('decimal(20,4)').alias('price_yoy'),
        F.round(F.col('asin_rating_rise').cast('double'), 1).cast('decimal(20,1)').alias('rating_rise'),
        F.round(F.col('asin_rating_mom').cast('double'), 4).cast('decimal(20,4)').alias('rating_mom'),
        F.round(F.col('asin_rating_yoy').cast('double'), 4).cast('decimal(20,4)').alias('rating_yoy'),
        F.col('asin_comments_rise').cast('int').alias('comments_rise'),
        F.round(F.col('asin_comments_mom').cast('double'), 4).cast('decimal(20,4)').alias('comments_mom'),
        F.round(F.col('asin_comments_yoy').cast('double'), 4).cast('decimal(20,4)').alias('comments_yoy'),
        F.col('asin_bsr_orders_rise').cast('int').alias('bsr_orders_rise'),
        F.round(F.col('asin_bsr_orders_mom').cast('double'), 4).cast('decimal(20,4)').alias('bsr_orders_mom'),
        F.round(F.col('asin_bsr_orders_yoy').cast('double'), 4).cast('decimal(20,4)').alias('bsr_orders_yoy'),
        F.round(F.col('asin_sales_rise').cast('double'), 2).cast('decimal(20,2)').alias('sales_rise'),
        F.round(F.col('asin_sales_mom').cast('double'), 4).cast('decimal(20,4)').alias('sales_mom'),
        F.round(F.col('asin_sales_yoy').cast('double'), 4).cast('decimal(20,4)').alias('sales_yoy'),
        F.col('asin_variation_rise').cast('int').alias('variation_rise'),
        F.round(F.col('asin_variation_mom').cast('double'), 4).cast('decimal(20,4)').alias('variation_mom'),
        F.round(F.col('asin_variation_yoy').cast('double'), 4).cast('decimal(20,4)').alias('variation_yoy'),
        F.round(F.col('asin_bought_mom').cast('double'), 4).cast('decimal(20,4)').alias('bought_month_mom'),
        F.round(F.col('asin_bought_yoy').cast('double'), 4).cast('decimal(20,4)').alias('bought_month_yoy'),
        # ===== 类型枚举字段 =====
        F.col('asin_size_type').cast('tinyint').alias('size_type'),
        F.col('asin_rating_type').cast('tinyint').alias('rating_type'),
        F.col('asin_site_name_type').cast('tinyint').alias('site_name_type'),
        F.col('asin_weight_type').cast('tinyint').alias('weight_type'),
        F.col('asin_ao_val_type').cast('tinyint').alias('ao_val_type'),
        F.col('asin_rank_type').cast('tinyint').alias('rank_type'),
        F.col('asin_price_type').cast('tinyint').alias('price_type'),
        F.col('asin_quantity_variation_type').cast('tinyint').alias('quantity_variation_type'),
        F.col('package_quantity').cast('int').alias('package_quantity'),
        F.col('is_movie_label').cast('tinyint').alias('is_movie_label'),
        F.col('is_brand_label').cast('tinyint').alias('is_brand_label'),
        # ===== 分类信息 =====
        _norm_dt('asin_crawl_date').alias('asin_crawl_date'),
        F.col('category_first_id'),
        F.col('category_id'),
        F.col('desc_category_first_id'),
        F.col('first_category_rank').cast('int').alias('first_category_rank'),
        F.col('current_category_rank').cast('int').alias('current_category_rank'),
        F.round(F.col('asin_weight_ratio').cast('double'), 4).cast('decimal(20,4)').alias('asin_weight_ratio'),
        F.col('seller_country_name').alias('site_name'),
        # ===== 月销及母体信息 =====
        F.col('asin_bought_month').cast('int').alias('asin_bought_month'),
        F.round(F.col('asin_lqs_rating').cast('double'), 1).cast('decimal(20,1)').alias('asin_lqs_rating'),
        F.col('asin_lqs_rating_detail'),
        F.col('asin_lob_info'),
        F.col('is_contains_lob_info').cast('tinyint').alias('is_contains_lob_info'),
        F.col('is_package_quantity_abnormal').cast('tinyint').alias('is_package_quantity_abnormal'),
        F.round(F.col('zr_flow_proportion').cast('double'), 4).cast('decimal(20,4)').alias('zr_flow_proportion'),
        F.round(F.col('matrix_flow_proportion').cast('double'), 4).cast('decimal(20,4)').alias('matrix_flow_proportion'),
        F.round(F.col('matrix_ao_val').cast('double'), 4).cast('decimal(20,4)').alias('matrix_ao_val'),
        F.col('customer_reviews_json').alias('product_features'),
        F.col('img_info'),
        F.col('collapse_asin'),
        F.col('follow_sellers_count').cast('int').alias('follow_sellers_count'),
        F.col('asin_describe'),
        F.round(F.col('asin_fbm_price').cast('double'), 2).cast('decimal(20,2)').alias('fbm_price'),
        F.col('describe_len').cast('int').alias('describe_len'),
        F.round(F.col('title_matching_degree').cast('double'), 4).cast('decimal(20,4)').alias('title_matching_degree'),
        # ===== 颜色组合识别 =====
        F.col('multi_color_flag').cast('tinyint').alias('multi_color_flag'),
        F.col('multi_color_str'),
        # ===== Amazon标签 =====
        F.col('amazon_label'),
    ).cache()

    count = df_save.count()
    print(f"写入数据量：{count:,}")
    df_save.show(5, truncate=False)

    # ===== Step 3：写入 Doris dwt 主表 =====
    table_columns = (
        "date_info, 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_mom, rank_yoy, ao_rise, ao_mom, ao_yoy, price_rise, price_mom, price_yoy, "
        "rating_rise, rating_mom, rating_yoy, comments_rise, comments_mom, comments_yoy, "
        "bsr_orders_rise, bsr_orders_mom, bsr_orders_yoy, sales_rise, sales_mom, sales_yoy, "
        "variation_rise, variation_mom, variation_yoy, bought_month_mom, bought_month_yoy, "
        "size_type, rating_type, site_name_type, weight_type, ao_val_type, rank_type, price_type, "
        "quantity_variation_type, package_quantity, is_movie_label, is_brand_label, "
        "asin_crawl_date, category_first_id, category_id, desc_category_first_id, "
        "first_category_rank, current_category_rank, asin_weight_ratio, site_name, "
        "asin_bought_month, asin_lqs_rating, asin_lqs_rating_detail, asin_lob_info, is_contains_lob_info, "
        "is_package_quantity_abnormal, zr_flow_proportion, matrix_flow_proportion, matrix_ao_val, "
        "product_features, img_info, collapse_asin, follow_sellers_count, asin_describe, fbm_price, describe_len, "
        "title_matching_degree, multi_color_flag, multi_color_str, amazon_label"
    )
    print(f"[Step 3] 写入 Doris {DORIS_DB}.{doris_table}")
    DorisHelper.spark_export_with_columns(
        df_save=df_save,
        db_name=DORIS_DB,
        table_name=doris_table,
        table_columns=table_columns,
    )
    df_save.unpersist()

    # ===== Step 4：Doris 端 INSERT OVERWRITE 到 selection 月物化表 =====
    print(f"[Step 4] Doris INSERT OVERWRITE selection.{selection_table}")
    _exec_doris_sql([build_insert_overwrite_sql(site_name, selection_table, date_info)])

    # ===== Step 5：流程记录表更新（仅 formal 模式）=====
    modify_mission_record_status(site_name, date_info, result_type)

    print("success!")


if __name__ == "__main__":
    site_name = sys.argv[1]
    date_info = sys.argv[2]
    result_type = sys.argv[3] if len(sys.argv) > 3 else 'formal'
    main(site_name, date_info, result_type)
