# 用于钉钉监控T+1任务是否需要重跑
import argparse
import sys
import re
import os
import requests
import json
from pyhive import hive
import pandas as pd

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 configparser import ConfigParser
from datetime import time
from pymongo import MongoClient
from dw_base import *
from dw_base.scheduler.polling_scheduler import get_mongo_client
from dw_base.utils.config_utils import parse_args
from dw_base.scheduler.mg2es.conf_reader import ConfReader
from dw_base.scheduler.mg2es.es_operator import ESOperator
from elasticsearch.exceptions import NotFoundError


# sql = "SELECT mgdb, mgtbl_name FROM tmp.tmp_zjh_1011"
# spark = SparkSQL()
# res = spark.query(sql)[0].collect()

def parse_arguments():
    # 创建 ArgumentParser 对象
    parser = argparse.ArgumentParser(description='Process some parameters.')

    # 添加参数
    parser.add_argument('-mgdb', dest='mgdb', required=True, help='Parameter 1')

    # 解析参数
    return parser.parse_args()

def get_mongo_client(conf_path):
    config_parser = ConfigParser()
    config_parser.read(root_path + conf_path)
    url = config_parser.get('base', 'address')
    return MongoClient(url)



def get_count(client, mgdb):
    # 选择数据库
    db = client[mgdb]
    # 选择集合
    collection1 = db['shipments_imports']
    collection2 = db['shipments_exports']

    # 使用聚合管道进行分组和计数
    pipeline = [
        {
            "$group": {
                "_id": "$ysfs",  # 按ysfs字段分组
                "count": {"$sum": 1}  # 计算每个组的数量
            }
        }
    ]

    # 执行聚合查询
    results1 = list(collection1.aggregate(pipeline))
    results2 = list(collection2.aggregate(pipeline))

    pretty_print(f'开始合并结果-------------------------------------------------------------------------')

    # 合并结果
    combined_results = list(results1) + list(results2)


    # 假设 combined_results 是一个字典列表
    # 将结果转换为 DataFrame
    df = pd.DataFrame(combined_results)
    df1 = pd.DataFrame(results1)
    df2 = pd.DataFrame(results2)

    # 连接到 Hive
    hive_conn = hive.Connection(host='192.168.30.3', port=10000, username='hive', database='dim')

    # 写入 Hive 表
    cursor = hive_conn.cursor()

    pretty_print(f'开始插入结果-------------------------------------------------------------------------')

    # 插入数据
    for index, row in df1.iterrows():
        insert_query = f"""
        INSERT INTO dim.cts_ysfs_global_old (ysfs, cnt, gj, jck)
        VALUES ('{row['_id']}' , '{row['count']}','{mgdb}', 'im')
        """
        pretty_print(f'{insert_query}')
        cursor.execute(insert_query)
    # 插入数据
    for index, row in df2.iterrows():
        insert_query = f"""
        INSERT INTO dim.cts_ysfs_global_old (ysfs, cnt, gj, jck)
        VALUES ('{row['_id']}' , '{row['count']}','{mgdb}', 'ex')
        """
        cursor.execute(insert_query)

    # 关闭连接
    cursor.close()
    hive_conn.close()

    jgj = ('----------------------'+
           '\n结果1-->' + str(results1) +
           '结果1end\n结果2-->' + str(results2) +
           '结果2end\n合并后结果-->'+str(combined_results)+
           '\n----------------------'
    )
    pretty_print(f'{jgj}')
    return jgj

def get_old_count(mgdb):
    client = get_mongo_client('/../datasource/mongo/mongo-cts-prod-old.ini')
    result = get_count(client, mgdb)
    pretty_print(f'{NORM_MGT} old source mongo: {NORM_GRN}{mgdb} '
                 f'{NORM_MGT} old data count: {NORM_GRN}')
    return result


def main():
    # CONFIG, _ = parse_args(sys.argv[1:])
    # for record in res:
    # mgtbl = record['mgtbl_name']

    # 解析命令行参数
    args = parse_arguments()
    mgdb = args.mgdb
    old_cnt = get_old_count(mgdb)
    return 0

if __name__ == '__main__':
    main()