# 用来解析字段
#
# -tbl cts_turkey_im

import sys
import re
import os

abspath = os.path.abspath(__file__)
root_path = re.sub(r"tendata-warehouse.*", "tendata-warehouse", abspath)
sys.path.append(root_path)
from dw_base.spark.spark_sql import SparkSQL
from dw_base.utils.log_utils import pretty_print
from datetime import time
from dw_base import *
from dw_base.utils.config_utils import parse_args

NORM_RED: str = '\033[0;31m'
NORM_GRN: str = '\033[0;32m'
NORM_YEL: str = '\033[0;33m'
NORM_BLU: str = '\033[0;34m'
NORM_MGT: str = '\033[0;35m'
NORM_CYN: str = '\033[0;36m'


def get_cols_with_type(dt: str):
    global_output = {}
    spark = SparkSQL()
    spark._final_spark_config = {'hive.exec.dynamic.partition': 'true',
                                 'hive.exec.dynamic.partition.mode': 'nonstrict',
                                 'spark.yarn.queue': 'cts',
                                 'spark.sql.crossJoin.enabled': 'true',
                                 'spark.executor.memory': '6g',
                                 'spark.executor.memoryOverhead': '2024',
                                 'spark.driver.memory': '4g',
                                 'spark.executor.instances': "10",
                                 'spark.executor.cores': '4'
                                 }
    # sql = (f"select concat('cts_',mgdb,'_',catalog) as  `tbl` from task.mg_count_monitor "
    #        f"where is_deleted = '0'")
    # tbls = spark.query(sql)[0].collect()
    # for tbl2 in tbls:
    #     tbl = tbl2['tbl']
    tbls = [
        'cts_argentina_im',
        ]
    for tbl in tbls:
        sql = f'SHOW CREATE TABLE from_mongo.{tbl}_incr'
        if tbl == "cts_sao_tome_and_principe_im":
            sql = f"SHOW CREATE TABLE from_mongo.cts_stp_im_incr"
        if tbl == "cts_sao_tome_and_principe_ex":
            sql = f"SHOW CREATE TABLE from_mongo.cts_stp_ex_incr"
        ctbl = spark.query(sql)[0].collect()[0]['createtab_stmt']
        cols_with_type = re.findall(r'`([^`]+)` ([A-Z]+)', ctbl)
        if cols_with_type[0][0] != 'id':
            exit('请检查表结构，id列必须是第一个字段')

        fields = {tup[0] for tup in cols_with_type if tup[0] != 'id'}
        # 对每个字段名加上反引号
        quoted_fields = [f"`{field}`" for field in fields]
        group_str = ', '.join(quoted_fields)
        query0 = (f" select count(1) as `all_cnt`,'all_cnt2' from dwd.{tbl} where dt in('19700101','{dt}') "
                  f"union all select count(1) as `unique_cnt`,'dwd.{tbl}' as `tbl` "
                  f"from ( select count(1) as `cnt` FROM (select {group_str} FROM dwd.{tbl} where dt in('19700101','{dt}')) t2  "
                  f"GROUP BY {group_str} ) b ")
        cnt = spark.query(query0)[0].collect()
        all_cnt = cnt[0].all_cnt
        unique_cnt = cnt[1].all_cnt
        duplicate_cnt = all_cnt - unique_cnt
        #
        # print(all_cnt )
        # print( unique_cnt)
        # print(unique_cnt/all_cnt)
        cts_dict = {
            "country": tbl,
            "all_cnt": all_cnt,
            "group_cnt": unique_cnt,
            "duplicate_cnt": duplicate_cnt,
            "duplicate_ratio": (all_cnt - unique_cnt) / all_cnt
        }

        duplicate_ratio = (all_cnt - unique_cnt) / all_cnt
        # queryend = (f" insert into table tmp.cts_distinct"
        #             f" values('{tbl}','{all_cnt}','{unique_cnt}','{duplicate_cnt}','{duplicate_ratio}') ")
        # spark.query(queryend)[0].collect()
        # print(cts_dict)
        global_output[tbl] = cts_dict
        pretty_print(f'{NORM_CYN}{time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())} '
                     f'{NORM_MGT} 大数据dwd表名: {NORM_GRN}  {tbl} '
                     f'{NORM_MGT} 本表的指标  {cts_dict}'
                     )

    pretty_print(f'{NORM_CYN}{time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())} '
                 f'{NORM_MGT} 大数据dwd表名: {NORM_GRN}  {tbl} '
                 f'{NORM_MGT} 这是最终的  {global_output}'
                 )


if __name__ == '__main__':
    get_cols_with_type('20240812')