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258 | try:
import json
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
import requests
from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from pandas.io.json import json_normalize
from sqlalchemy import create_engine
except Exception as e:
print("Error {} ".format(e))
dRoW_api_end_url = "https://drow.cloud"
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'
# Port number
port = "5432"
# Character set
charSet = "utf8mb4"
conn_string = ('postgres://' +
dbUserName + ':' +
dbUserPassword +
'@' + host + ':' + port +
'/' + database)
def getDrowToken(**context):
response = requests.post(
url=f"{dRoW_api_end_url}/api/auth/authenticate",
data={
"username": "keexiansuen@drow.cloud",
"password": "c3UxMTk5a3ghIQ==",
}).json()
context["ti"].xcom_push(key="token", value=response['token'])
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/66175ed0dac217ba1408964b?export_type=0",
headers={
"x-access-token": f"Bearer {token}",
"ICWPxAccessKey": "nd@201907ICWP_[1AG:4UdI){n=b~"
}
)
RISC_Data = json.loads(response.text)
Mapping= {
"Date of Inspection" : "a01a_inspection_date",
# "SupD signature time": "f2_checked_by_supd_on_date",
}
db = create_engine(conn_string)
conn = db.connect()
with conn as conn:
df = pd.DataFrame()
for x in RISC_Data:
df_nested_list = json_normalize(x['data'])
df2 = df_nested_list.reindex(columns=Mapping.keys())
df2.rename(columns=Mapping, inplace=True)
df2["a2_daily_or_weekly"] = "daily"
if len(x['ApproveLogSummary']) > 0:
request_data = [data for data in x['ApproveLogSummary'] if data.get('statusName')=="B : RSS(IOW) Check & Sign"]
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["a01a_inspection_date"].astype(str).str[:10]
if len([data for data in x['ApproveLogSummary'] if data.get('statusName')=="Z : END"])>0 :
df2['report_complete_or_incomplete'] = 'complete'
else:
df2['report_complete_or_incomplete'] = 'incomplete'
if 'data' in x and isinstance(x['data'], dict):
for key in x['data']:
if key == 'Daily':
total_report = 0
total_x = 0
for item in x['data'][key]:
nc_report = False
for item_key in item:
if item[item_key] == 'UA':
nc_report = True
break
if nc_report:
total_x += 1
total_report += 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['a01a_inspection_date'].isnull().bool()):
df2['complete_time_in_days'] = (((df2['f2_checked_by_supd_on_date'].astype('datetime64[ns]') -
df2['a01a_inspection_date'].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
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_cv202303', con=conn, if_exists='replace', index= False)
def getMongoDB2(**context):
token = context.get("ti").xcom_pull(key="token")
response = requests.get(
url=f"{dRoW_api_end_url}/api/module/document-export/airflow/workflow/664eb513b8d4129ad5dea4f1?export_type=0",
headers={
"x-access-token": f"Bearer {token}",
"ICWPxAccessKey": "nd@201907ICWP_[1AG:4UdI){n=b~"
}
)
RISC_Data = json.loads(response.text)
Mapping= {
"Date of Inspection" : "a01a_inspection_date",
# "SupD signature time": "f2_checked_by_supd_on_date",
}
db = create_engine(conn_string)
conn = db.connect()
with conn as conn:
df = pd.DataFrame()
for x in RISC_Data:
df_nested_list = json_normalize(x['data'])
df2 = df_nested_list.reindex(columns=Mapping.keys())
df2.rename(columns=Mapping, inplace=True)
df2["a2_daily_or_weekly"] = "weekly"
if len(x['ApproveLogSummary']) > 0:
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["a01a_inspection_date"].astype(str).str[:10]
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) :
for key in x['data']:
if key == 'Weekly':
total_report = 0
total_x = 0
for item in x['data'][key]:
nc_report = False
for item_key in item:
if item[item_key] == 'UA':
nc_report = True
break
if nc_report:
total_x += 1
total_report += 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['a01a_inspection_date'].isnull().bool()):
df2['complete_time_in_days'] = (((df2['f2_checked_by_supd_on_date'].astype('datetime64[ns]') -
df2['a01a_inspection_date'].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
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_cv202303', con=conn, if_exists='append', index= False)
# id SERIAL
create_table_sql_query = """
CREATE TABLE IF NOT EXISTS cleansing_cv202303 (
f2_checked_by_supd_on_date TIMESTAMP,
a01a_inspection_date TIMESTAMP,
d4_submission_date TIMESTAMP,
report_name VARCHAR (100),
report_complete_or_incomplete VARCHAR (100),
nc_report BOOLEAN,
complete_time_in_days NUMERIC(10,2)
);
"""
# */2 * * * * Execute every two minute
with DAG(
dag_id="cv202303_cleaning",
schedule_interval="0 15 * * *",
default_args={
"owner": "airflow",
"retries": 1,
"retry_delay": timedelta(minutes=5),
"start_date": datetime(2023, 1, 17)
},
catchup=False) as f:
getMongoDB = PythonOperator(
task_id="getMongoDB",
python_callable=getMongoDB,
op_kwargs={"name": "Dylan"},
provide_context=True,
)
getMongoDB2 = PythonOperator(
task_id="getMongoDB2",
python_callable=getMongoDB2,
op_kwargs={"name": "Dylan"},
provide_context=True,
)
getDrowToken = PythonOperator(
task_id="getDrowToken",
python_callable=getDrowToken,
provide_context=True,
)
# create_table = PostgresOperator(
# sql = create_table_sql_query,
# task_id = "create_table_task",
# postgres_conn_id = "postgres_rds",
# )
# create_table >> getDrowToken >> getMongoDB
getDrowToken >> getMongoDB >> getMongoDB2
|