move to production

k8s/analytics/values-prod.yaml

@@ -8,7 +8,7 @@ images:
     celeryWorker: eu.gcr.io/airqo-250220/airqo-analytics-celery-worker
     reportJob: eu.gcr.io/airqo-250220/airqo-analytics-report-job
     devicesSummaryJob: eu.gcr.io/airqo-250220/airqo-analytics-devices-summary-job
-  tag: prod-a75ee27d-1729690126
+  tag: prod-9cf6d750-1729798017
 api:
   name: airqo-analytics-api
   label: analytics-api

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-a75ee27d-1729690126
+  tag: prod-9cf6d750-1729798017
 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-a75ee27d-1729690126
+  tag: prod-9cf6d750-1729798017
 nameOverride: ''
 fullnameOverride: ''
 podAnnotations: {}

k8s/exceedance/values-prod-airqo.yaml

@@ -4,6 +4,6 @@ app:
   configmap: env-exceedance-production
 image:
   repository: eu.gcr.io/airqo-250220/airqo-exceedance-job
-  tag: prod-a75ee27d-1729690126
+  tag: prod-9cf6d750-1729798017
 nameOverride: ''
 fullnameOverride: ''

k8s/exceedance/values-prod-kcca.yaml

@@ -4,6 +4,6 @@ app:
   configmap: env-exceedance-production
 image:
   repository: eu.gcr.io/airqo-250220/kcca-exceedance-job
-  tag: prod-a75ee27d-1729690126
+  tag: prod-9cf6d750-1729798017
 nameOverride: ''
 fullnameOverride: ''

k8s/predict/values-prod.yaml

@@ -7,7 +7,7 @@ images:
     predictJob: eu.gcr.io/airqo-250220/airqo-predict-job
     trainJob: eu.gcr.io/airqo-250220/airqo-train-job
     predictPlaces: eu.gcr.io/airqo-250220/airqo-predict-places-air-quality
-  tag: prod-a75ee27d-1729690126
+  tag: prod-9cf6d750-1729798017
 api:
   name: airqo-prediction-api
   label: prediction-api

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-a75ee27d-1729690126
+  tag: prod-9cf6d750-1729798017
 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-8e811f9a-1729782865
+  tag: stage-cf11c99f-1729797943
 nameOverride: ''
 fullnameOverride: ''
 podAnnotations: {}

src/workflows/airqo_etl_utils/data_validator.py

@@ -277,8 +277,23 @@ def process_data_for_api(data: pd.DataFrame) -> list:
         return restructured_data
 
     def transform_devices(devices: List[Dict[str, Any]], task_instance) -> pd.DataFrame:
+        """
+        Transforms and processes the devices DataFrame. If the checksum of the
+        devices data has not changed since the last execution, it returns an empty DataFrame.
+        Otherwise, it updates the checksum in XCom and returns the transformed DataFrame.
+
+        Args:
+            devices (pd.DataFrame): A Pandas DataFrame containing the devices data.
+            task_instance: The Airflow task instance used to pull and push XCom values.
+
+        Returns:
+            pd.DataFrame: Transformed DataFrame if the devices data has changed since
+                        the last execution; otherwise, an empty DataFrame.
+        """
+        from .airqo_utils import (
+            AirQoDataUtils,
+        ) 
         import hashlib
-        from .airqo_utils import AirQoDataUtils
 
         devices.rename(
             columns={
@@ -290,12 +305,15 @@ def transform_devices(devices: List[Dict[str, Any]], task_instance) -> pd.DataFr
             inplace=True,
         )
 
+        # Convert devices DataFrame to JSON for consistency since JSON stores metadata and compute checksum
         devices_json = devices.to_json(orient="records", date_format="iso")
         api_devices_checksum = hashlib.md5(devices_json.encode()).hexdigest()
-        previous_cheksum = task_instance.xcom_pull(key="devices_checksum")
 
-        if previous_cheksum and previous_cheksum == api_devices_checksum:
+        previous_checksum = task_instance.xcom_pull(key="devices_checksum")
+
+        if previous_checksum == api_devices_checksum:
             return pd.DataFrame()
 
         task_instance.xcom_push(key="devices_checksum", value=api_devices_checksum)
+
         return devices

src/workflows/dags/airqo_kafka_workflows.py

@@ -12,7 +12,7 @@
     doc_md=extract_store_devices_data_in_kafka,
     default_args=AirflowUtils.dag_default_configs(),
     catchup=False,
-    tags=["devices", "hourly", "low cost"],
+    tags=["devices", "kafka"],
 )
 def airqo_devices_data():
     import pandas as pd
@@ -30,10 +30,16 @@ def send_device_data_to_broker(devices: pd.DataFrame, **kwargs) -> None:
         devices = DataValidationUtils.transform_devices(
             devices=devices, taskinstance=kwargs["ti"]
         )
-        broker = MessageBrokerUtils()
-        broker.publish_to_topic(
-            data=devices, topic=configuration.DEVICES_TOPIC, column_key="device_name"
-        )
+        if not devices.empty:
+            broker = MessageBrokerUtils()
+            broker.publish_to_topic(
+                data=devices,
+                topic=configuration.DEVICES_TOPIC,
+                column_key="device_name",
+            )
 
     extracted_device = extract_devices()
     send_device_data_to_broker(extracted_device)
+
+
+airqo_devices_data()