import time
import traceback
from os import *

import pandas as pd
import pymysql
from pymysql.cursors import DictCursor
from sqlalchemy import create_engine

from utils.conf.read_conf import yaml_conf
from utils.log.trans_log import trans_print


class ConnectMysql:

    def __init__(self, connet_name):
        self.yaml_data = yaml_conf(environ.get('ETL_CONF'))
        self.connet_name = connet_name
        self.config = self.yaml_data[self.connet_name]
        self.database = self.config['database']

    # 从连接池中获取一个连接
    def get_conn(self):
        return pymysql.connect(**self.config, autocommit=True)

    # 使用连接执行sql
    def execute(self, sql, params=tuple()):

        with self.get_conn() as conn:
            with conn.cursor(cursor=DictCursor) as cursor:
                try:
                    cursor.execute(sql, params)
                    trans_print("开始执行SQL:", cursor._executed)
                    conn.commit()
                    result = cursor.fetchall()
                    return result
                except Exception as e:
                    trans_print(f"执行sql：{sql}，报错：{e}")
                    trans_print(traceback.format_exc())
                    conn.rollback()
                    raise e

    def get_engine(self):
        config = self.config
        username = config['user']
        password = config['password']
        host = config['host']
        port = config['port']
        dbname = config['database']
        return create_engine(f'mysql+pymysql://{username}:{password}@{host}:{port}/{dbname}',
                             pool_pre_ping=True,
                             isolation_level="READ COMMITTED",
                             connect_args={
                                 'connect_timeout': 30,
                                 'read_timeout': 120,
                                 'write_timeout': 7200
                             })

    def execute_df_save(self, df, table_name, chunksize=10000):
        engine = self.get_engine()
        try:
            retry_count = 0
            max_retries = 3
            while retry_count < max_retries:
                try:
                    df.to_sql(table_name, engine, if_exists='append', index=False, chunksize=chunksize)
                except Exception as e:
                    retry_count += 1
                    trans_print(f" 第 {retry_count} 次重试, 错误: {str(e)}")
                    time.sleep(5 * retry_count)  # 指数退避
                    if retry_count == max_retries:
                        trans_print(f"处理失败: {str(e)}")
                        raise
        except Exception as e:
            engine.dispose()
            raise e

    def read_sql_to_df(self, sql):
        df = pd.read_sql_query(sql, self.get_engine())
        return df