import os
import sys

sys.path.append(os.path.dirname(sys.path[0]))
from utils.ssh_util import SSHUtil
from utils.common_util import CommonUtil, DateTypes
from utils.db_util import DBUtil
from utils.hdfs_utils import HdfsUtils

if __name__ == '__main__':
    site_name = CommonUtil.get_sys_arg(1, None)
    date_type = CommonUtil.get_sys_arg(2, None)
    date_info = CommonUtil.get_sys_arg(3, None)
    # 最后一个参数为 test 标志
    test_flag = CommonUtil.get_sys_arg(len(sys.argv) - 1, None)
    print(f"执行参数为{sys.argv}")

    assert date_type == DateTypes.month.name, "本脚本仅支持 month 类型导出"

    if test_flag == 'test':
        db_type = 'postgresql_test'
        print("导出到测试库中")
    else:
        # 工时校验（与新 ABA 流程其它导出脚本一致）：非工作时段或负责人不在班则跳过
        CommonUtil.judge_is_work_hours(
            site_name=site_name, date_type=date_type, date_info=date_info,
            principal='hejiangming',
            priority=2,
            export_tools_type=1,
            belonging_to_process=f'分类词频_{date_type}'
        )
        db_type = 'postgresql_cluster'
        print("导出到PG集群中")

    # 1) 校验 Hive 分区有数据，避免空分区导出后 PG 数据被清空
    hive_partition_path = (
        f"/home/big_data_selection/dws/dws_aba_word_freq_cate/"
        f"site_name={site_name}/date_type={date_type}/date_info={date_info}"
    )
    hive_files = HdfsUtils.read_list(hive_partition_path)
    if not hive_files:
        print(f"[ERROR] Hive 分区无数据文件，路径：{hive_partition_path}，跳过导出！")
        sys.exit(1)
    print(f"Hive 分区文件数：{len(hive_files)}，路径：{hive_partition_path}，继续导出")

    engine = DBUtil.get_db_engine(db_type, site_name)

    # 2) 表名拼装
    # master 表（DBA 按 DDL 建好，PARTITION BY RANGE(date_info)）：us_aba_word_freq_cate_2026
    # 子分区表：us_aba_word_freq_cate_2026_04
    # copy 表：us_aba_word_freq_cate_2026_04_copy
    suffix = str(date_info).replace("-", "_")
    year_str = CommonUtil.safeIndex(date_info.split("-"), 0, None)
    year_before = str(int(year_str) - 1)          # 去年，跨年时复制其结构建当年 master
    next_val = CommonUtil.get_next_val(date_type, date_info)

    export_base_tb = f"{site_name}_aba_word_freq_cate"
    export_master_tb = f"{export_base_tb}_{year_str}"        # 当年主表，如 us_aba_word_freq_cate_2027
    export_tb_before = f"{export_base_tb}_{year_before}"     # 去年主表，如 ..._2026，复制结构用
    export_table = f"{export_base_tb}_{suffix}"
    export_tb_copy = f"{export_table}_copy"

    # 3) 跨年自动建当年 master：用 LIKE 复制去年主表结构
    #    再在当年 master 上建当月子分区（if not exists，重跑幂等）。
    sql_create_partition = f"""
        create table if not exists {export_master_tb}
            ( like {export_tb_before} including all )
        partition by range (date_info);

        create table if not exists {export_table} partition of {export_master_tb}
        for values from ('{date_info}') to ('{next_val}');
    """
    DBUtil.engine_exec_sql(engine, sql_create_partition)

    # 4) 创建 copy 表（继承子分区结构 like ... including all），并清空
    # copy 表是独立普通表，Sqoop 先写到这里，最后通过分区交换替换正式子分区，避免空窗期
    sql_copy = f"""
        create table if not exists {export_tb_copy}
        (
            like {export_table} including all
        );
        truncate table {export_tb_copy};
    """
    DBUtil.engine_exec_sql(engine, sql_copy)

    # 5) ALTER copy 表的 attr_dim 列类型 VARCHAR[] → VARCHAR(500)
    # 原因：Sqoop 不支持向 PG 数组类型写数据，先临时改普通 VARCHAR，
    # 让 Sqoop 把 Hive 端 "材质,颜色" 逗号串原样写进来；交换前再 ALTER 回 VARCHAR[]
    sql_alter_to_varchar = f"""
        ALTER TABLE {export_tb_copy} ALTER COLUMN attr_dim TYPE VARCHAR(500);
    """
    DBUtil.engine_exec_sql(engine, sql_alter_to_varchar)

    # 6) 拼装 Sqoop 导出脚本（字段顺序与 Hive dws_aba_word_freq_cate schema 一致；
    #    site_name/date_type 编进表名不导出列，date_info 作为普通列导出）
    export_cols = [
        "scope",
        "category_id",
        "category_level",
        "base_word",
        "display_word",
        "word_freq",
        "word_heat",
        "relate_st_num",
        "min_rank",
        "avg_rank",
        "new_st_num",
        "word_heat_last_year",
        "word_heat_change_rate",
        "word_heat_last_month",
        "word_heat_rate_of_change",
        "yoy_up_ratio",
        "mom_up_ratio",
        "word_heat_change_last_1_month",
        "word_heat_change_1_month_ago",
        "word_heat_change_2_month_ago",
        "word_heat_change_3_month_ago",
        "word_heat_change_4_month_ago",
        "word_heat_change_5_month_ago",
        "top_aba_example",
        "brand_word_flag",
        "new_word_flag",
        "attr_dim",
        "word_cn",
        "heat_trend",
        "created_time",
        "updated_time",
        "date_info",
    ]

    sh = CommonUtil.build_export_sh(
        site_name=site_name,
        db_type=db_type,
        hive_tb="dws_aba_word_freq_cate",
        export_tb=export_tb_copy,
        col=export_cols,
        partition_dict={
            "site_name": site_name,
            "date_type": date_type,
            "date_info": date_info
        }
    )

    client = SSHUtil.get_ssh_client()
    SSHUtil.exec_command_async(client, sh, ignore_err=False)
    client.close()

    # 7) Sqoop 写完后，ALTER copy 表的 attr_dim 回 VARCHAR[]
    # USING string_to_array(...) 把逗号串 "材质,颜色" 拆成数组 {材质,颜色}
    # 词典无匹配的词 PySpark 已填空串 ''：nullif 把 '' 归 NULL → string_to_array 得 NULL → coalesce 兜成空数组 {}
    #   （兼顾 Sqoop 可能把空串写成 NULL 的情况，'' 和 NULL 最终都落成 {}）
    sql_alter_back = f"""
        ALTER TABLE {export_tb_copy}
        ALTER COLUMN attr_dim TYPE VARCHAR[]
        USING coalesce(string_to_array(nullif(attr_dim, ''), ','), '{{}}')::varchar[];
    """
    DBUtil.engine_exec_sql(engine, sql_alter_back)

    # 8) 分区交换：copy 表替换正式子分区，无空窗期
    DBUtil.exchange_pg_part_tb(
        engine,
        source_tb_name=export_tb_copy,
        part_master_tb=export_master_tb,
        part_target_tb=export_table,
        cp_index_flag=False,
        part_val={"from": [date_info], "to": [next_val]}
    )

    # 9) 删除 copy 表（交换后 copy 表里是旧数据，留着没意义）
    DBUtil.engine_exec_sql(engine, f"drop table if exists {export_tb_copy};")

    print(f"==================表 {export_table} 导出完成==================================")
    print("success")
