DAG: cv202308_cleaning

schedule: 0 15 * * *


Task Instance: getMongoDB


Task Instance Details

Dependencies Blocking Task From Getting Scheduled
Dependency Reason
Dagrun Running Task instance's dagrun was not in the 'running' state but in the state 'success'.
Task Instance State Task is in the 'success' state which is not a valid state for execution. The task must be cleared in order to be run.
Attribute: python_callable
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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/657bff6ce782da1a574b1706?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 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 'Checklist' in key:
                        total_report = 0
                        total_x = 0
                        for item in x['data'][key]:
                            for item_key in item:
                                if item[item_key] == 'N/A' or item[item_key] == '✓':
                                    total_report += 1
                                if item[item_key] == '✘':
                                    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_cv202308', con=conn, if_exists='replace', index= False)
Task Instance Attributes
Attribute Value
dag_id cv202308_cleaning
duration 23.494084
end_date 2024-09-12 04:57:53.112739+00:00
execution_date 2024-09-10T15:00:00+00:00
executor_config {}
generate_command <function TaskInstance.generate_command at 0x7f152f9bf320>
hostname 63fbafbc3109
is_premature False
job_id 176
key ('cv202308_cleaning', 'getMongoDB', <Pendulum [2024-09-10T15:00:00+00:00]>, 2)
log <Logger airflow.task (INFO)>
log_filepath /usr/local/airflow/logs/cv202308_cleaning/getMongoDB/2024-09-10T15:00:00+00:00.log
log_url http://localhost:8080/admin/airflow/log?execution_date=2024-09-10T15%3A00%3A00%2B00%3A00&task_id=getMongoDB&dag_id=cv202308_cleaning
logger <Logger airflow.task (INFO)>
mark_success_url http://localhost:8080/success?task_id=getMongoDB&dag_id=cv202308_cleaning&execution_date=2024-09-10T15%3A00%3A00%2B00%3A00&upstream=false&downstream=false
max_tries 1
metadata MetaData(bind=None)
next_try_number 2
operator PythonOperator
pid 3471
pool default_pool
prev_attempted_tries 1
previous_execution_date_success None
previous_start_date_success None
previous_ti None
previous_ti_success None
priority_weight 2
queue default
queued_dttm 2024-09-12 04:57:12.410366+00:00
raw False
run_as_user None
start_date 2024-09-12 04:57:29.618655+00:00
state success
task <Task(PythonOperator): getMongoDB>
task_id getMongoDB
test_mode False
try_number 2
unixname airflow
Task Attributes
Attribute Value
dag <DAG: cv202308_cleaning>
dag_id cv202308_cleaning
depends_on_past False
deps {<TIDep(Not In Retry Period)>, <TIDep(Trigger Rule)>, <TIDep(Previous Dagrun State)>}
do_xcom_push True
downstream_list [<Task(PythonOperator): getMongoDB2>]
downstream_task_ids {'getMongoDB2'}
email None
email_on_failure True
email_on_retry True
end_date None
execution_timeout None
executor_config {}
extra_links []
global_operator_extra_link_dict {}
inlets []
lineage_data None
log <Logger airflow.task.operators (INFO)>
logger <Logger airflow.task.operators (INFO)>
max_retry_delay None
on_failure_callback None
on_retry_callback None
on_success_callback None
op_args []
op_kwargs {'name': 'Dylan'}
operator_extra_link_dict {}
operator_extra_links ()
outlets []
owner airflow
params {}
pool default_pool
priority_weight 1
priority_weight_total 2
provide_context True
queue default
resources None
retries 1
retry_delay 0:05:00
retry_exponential_backoff False
run_as_user None
schedule_interval 0 15 * * *
shallow_copy_attrs ('python_callable', 'op_kwargs')
sla None
start_date 2023-01-17T00:00:00+00:00
subdag None
task_concurrency None
task_id getMongoDB
task_type PythonOperator
template_ext []
template_fields ('templates_dict', 'op_args', 'op_kwargs')
templates_dict None
trigger_rule all_success
ui_color #ffefeb
ui_fgcolor #000
upstream_list [<Task(PythonOperator): getDrowToken>]
upstream_task_ids {'getDrowToken'}
wait_for_downstream False
weight_rule downstream