def getMongoDB(**context):
    token = context.get("ti").xcom_pull(key="token")
    response = requests.get(
    url=f"{dRoW_api_end_url}/api/module/document-export/airflow/workflow/62bb4cea0e814e2e260a9795?export_type=0",
    headers={
    "x-access-token": f"Bearer {token}",
    "ICWPxAccessKey": "nd@201907ICWP_[1AG:4UdI){n=b~"
    }
    )
    #print('got_data')
    RISC_Data = json.loads(response.text)
    Mapping= {
    # "f2_checked_by_supd_on_date" : "f2_checked_by_supd_on_date", 
    "Date of Inspection" : "a01a_inspection_date",
    # "D4 Submission Date" : "d4_submission_date",
    "Daily Cleaning or Weekly Cleaning?": "a2_daily_or_weekly"
    }
    #print('start transform')
    host                  = 'drowdatewarehouse.crlwwhgepgi7.ap-east-1.rds.amazonaws.com'  

        # User name of the database server
    dbUserName            = 'dRowAdmin'  

    # Password for the database user
    dbUserPassword        = 'drowsuper'  

    # Name of the database 
    database              = 'drowDateWareHouse'

    # Character set
    charSet               = "utf8mb4"  

    port                  = "5432"

    # #cursor Type
    # cusrsorType            = pymysql.cursors.DictCursor



    #create_engine('mysql+mysqldb://root:password@localhost:3306/mydbname', echo = False)
    conn_string = ('postgres://' +
                           dbUserName + ':' + 
                           dbUserPassword +
                           '@' + host + ':' + port +
                           '/' + database)

    # df = context.get("ti").xcom_pull(key="InsertData")
    # print(df)
    # conn_string = 'postgres://user:password@host/data1'
    
    db = create_engine(conn_string)
    conn = db.connect()
    #print('db connected')
    with conn as conn:
        df = pd.DataFrame()
        for x in RISC_Data:
            #print(x)
            df_nested_list = json_normalize(x['data'])
            #print('process 1')
            # df2["a10_request_submission_date_time"] = request_date
            
            df2 = df_nested_list.reindex(columns=Mapping.keys())
            if len(x['ApproveLogSummary']) > 0:
                # request_date = pd.to_datetime(df2["C1 - Inspect on Date Time"]) - pd.Timedelta(days=1)
                request_data = [data for data in x['ApproveLogSummary'] if data.get('statusName')=="B : RSS check"]
                if len(request_data) > 0 and 'from' in request_data[-1]:
                    df2['f2_checked_by_supd_on_date'] = request_data[-1]['from']
                else:
                    df2['f2_checked_by_supd_on_date'] = None
                if len(request_data) > 0 and 'to' in request_data[-1]:
                    df2['d4_submission_date'] = request_data[-1]['to']
                else:
                    df2['d4_submission_date'] = None
            else:
                df2['f2_checked_by_supd_on_date'] = None
                df2['d4_submission_date'] = None
                
            df2["report_name"] = df2["Date of Inspection"].astype(str).str[:10] + df2["Daily Cleaning or Weekly Cleaning?"]
            if len([data for data in x['ApproveLogSummary'] if data.get('statusName')=="Z : END"])>0 or len([data for data in x['ApproveLogSummary'] if data.get('statusName')=="C : Contractor Acknowledge and Archive"])>0:
                df2['report_complete_or_incomplete'] = 'complete'
            else:
                df2['report_complete_or_incomplete'] = 'incomplete'
            if 'data' in x and isinstance(x['data'], dict):
                if x['data']["Daily Cleaning or Weekly Cleaning?"] == "Weekly Cleaning":
                    total_report = 0
                    total_x = 0
                    for item in x['data']['Weekly']:
                        for item_key in item:
                            total_report += 1
                            if item_key.endswith('Condition') and item[item_key] == 'Unsatisfactory':
                                total_x += 1
                    df2['nc_report_item'] = total_x
                    df2['total_report_item'] = total_report
                else: 
                    total_report = 0
                    total_x = 0
                    for item in x['data']['Daily']:
                        for item_key in item:
                            total_report += 1
                            if item_key.endswith('Condition') and item[item_key] == 'Unsatisfactory':
                                total_x += 1
                    df2['nc_report_item'] = total_x
                    df2['total_report_item'] = total_report

            if (not df2['f2_checked_by_supd_on_date'].isnull().bool() and not df2['Date of Inspection'].isnull().bool()):
                df2['complete_time_in_days'] = (((df2['f2_checked_by_supd_on_date'].astype('datetime64[ns]') - 
                df2['Date of Inspection'].astype('datetime64[ns]'))/ np.timedelta64(1, 'h'))/24).round(2)
                if df2['complete_time_in_days'].isnull().bool() or df2['complete_time_in_days'].lt(0).bool():
                    df2['complete_time_in_days'] = 0
            else:
                df2['complete_time_in_days'] = 0

            df2.rename(columns=Mapping, inplace=True)
            df = df.append(df2)
        df['a01a_inspection_date']=df['a01a_inspection_date'].apply(pd.to_datetime)
        df['d4_submission_date']=df['d4_submission_date'].apply(pd.to_datetime)
        df['f2_checked_by_supd_on_date']=df['f2_checked_by_supd_on_date'].apply(pd.to_datetime)
        df.to_sql('cleansing_6wsd21', con=conn, if_exists='replace', index= False)