move to production

k8s/auth-service/values-prod.yaml

@@ -6,7 +6,7 @@ app:
 replicaCount: 3
 image:
   repository: eu.gcr.io/airqo-250220/airqo-auth-api
-  tag: prod-85a0a843-1739265138
+  tag: prod-2919fc05-1739268252
 nameOverride: ''
 fullnameOverride: ''
 podAnnotations: {}

k8s/device-registry/values-prod.yaml

@@ -6,7 +6,7 @@ app:
 replicaCount: 3
 image:
   repository: eu.gcr.io/airqo-250220/airqo-device-registry-api
-  tag: prod-85a0a843-1739265138
+  tag: prod-2919fc05-1739268252
 nameOverride: ''
 fullnameOverride: ''
 podAnnotations: {}

k8s/spatial/values-prod.yaml

@@ -6,7 +6,7 @@ app:
 replicaCount: 3
 image:
   repository: eu.gcr.io/airqo-250220/airqo-spatial-api
-  tag: prod-85a0a843-1739265138
+  tag: prod-2919fc05-1739268252
 nameOverride: ''
 fullnameOverride: ''
 podAnnotations: {}

k8s/workflows/values-prod.yaml

@@ -10,7 +10,7 @@ images:
     initContainer: eu.gcr.io/airqo-250220/airqo-workflows-xcom
     redisContainer: eu.gcr.io/airqo-250220/airqo-redis
     containers: eu.gcr.io/airqo-250220/airqo-workflows
-  tag: prod-85a0a843-1739265138
+  tag: prod-2919fc05-1739268252
 nameOverride: ''
 fullnameOverride: ''
 podAnnotations: {}

k8s/workflows/values-stage.yaml

@@ -10,7 +10,7 @@ images:
     initContainer: eu.gcr.io/airqo-250220/airqo-stage-workflows-xcom
     redisContainer: eu.gcr.io/airqo-250220/airqo-stage-redis
     containers: eu.gcr.io/airqo-250220/airqo-stage-workflows
-  tag: stage-96f29bf1-1739265098
+  tag: stage-44f5f46e-1739268213
 nameOverride: ''
 fullnameOverride: ''
 podAnnotations: {}

src/workflows/dags/airqo_measurements.py

@@ -18,6 +18,7 @@
     send_raw_measurements_to_bigquery_doc,
     extract_raw_airqo_gaseous_data_doc,
     extract_historical_device_measurements_doc,
+    extract_hourly_old_historical_data_doc,
 )
 from airqo_etl_utils.constants import DeviceNetwork, DeviceCategory, Frequency, DataType
 from datetime import datetime, timedelta
@@ -648,39 +649,30 @@ def send_raw_measurements_to_bigquery(airqo_data: pd.DataFrame):
 )
 def airqo_bigquery_data_measurements_to_api():
     import pandas as pd
+    from airflow.models import Variable
+    from airqo_etl_utils.date import date_to_str_hours
 
     @task(
+        doc_md=extract_hourly_old_historical_data_doc,
         provide_context=True,
         retries=3,
         retry_delay=timedelta(minutes=5),
     )
     def extract_hourly_data(**kwargs) -> pd.DataFrame:
-        from airqo_etl_utils.date import date_to_str_hours
-
         # Only used the first time
-        start = kwargs.get("params", {}).get("start_date", "2021-01-01T00:00:00Z")
-        start = datetime.strptime(start, "%Y-%m-%dT%H:%M:%SZ")
+        start = kwargs.get("params", {}).get("start_date", "2021-01-01T01:00:00Z")
         end = kwargs.get("params", {}).get("end_date", "2021-12-31T23:59:59Z")
         end = datetime.strptime(end, "%Y-%m-%dT%H:%M:%SZ")
 
-        previous_date = kwargs["ti"].xcom_pull(key="new_date")
-        if not previous_date:
-            previous_date = start
+        previous_date = Variable.get("new_date_2021", default_var=start)
 
-        hour_of_day = (
-            datetime.strptime(previous_date, "%Y-%m-%dT%H:%M:%SZ")
-            if not isinstance(previous_date, datetime)
-            else previous_date
-        )
+        hour_of_day = datetime.strptime(previous_date, "%Y-%m-%dT%H:%M:%SZ")
         start_date_time = date_to_str_hours(hour_of_day)
         end_date_time = datetime.strftime(hour_of_day, "%Y-%m-%dT%H:59:59Z")
 
         if hour_of_day > end or (hour_of_day + timedelta(hours=1)) > end:
             raise AirflowFailException(f"Run expired on {end}")
 
-        if previous_date == start:
-            kwargs["ti"].xcom_push(key="new_date", value=hour_of_day)
-
         return DataUtils.extract_data_from_bigquery(
             DataType.AVERAGED,
             start_date_time=start_date_time,
@@ -697,8 +689,11 @@ def send_hourly_measurements_to_api(data: pd.DataFrame, **kwargs):
 
         airqo_api = AirQoApi()
         airqo_api.save_events(measurements=data)
-        previous_date = kwargs["ti"].xcom_pull(key="new_date")
-        kwargs["ti"].xcom_push(key="new_date", value=previous_date + timedelta(hours=1))
+        previous_date = datetime.strptime(
+            Variable.get("new_date_2021"), "%Y-%m-%dT%H:%M:%SZ"
+        )
+        previous_date = date_to_str_hours(previous_date + timedelta(hours=1))
+        Variable.set("new_date_2021", previous_date)
 
     hourly_data = extract_hourly_data()
     send_hourly_measurements_to_api(hourly_data)

src/workflows/dags/task_docs.py

@@ -33,3 +33,14 @@
 #### Notes
 - <a href="https://airqo.africa/" target="_blank">AirQo</a>
 """
+
+extract_hourly_old_historical_data_doc = """
+#### Purpose
+Extracts hourly averaged data from BigQuery for a specified time window.
+
+- This task function retrieves the start and end date-times from the Airflow parameters, then uses an Airflow Variable to obtain the last processed timestamp (defaulting to the start date if not set).
+- It computes the current hourly window by converting the retrieved timestamp into a start and end string, with the end time representing the last second of the hour. If the computed hour exceeds the specified end date, an AirflowFailException is raised to halt further processing.
+
+#### Notes
+- <a href="https://airqo.africa/" target="_blank">AirQo</a>
+"""