Update DAG-TACOMVENTAS-PROMOCIONESRESIDENCIAL-05-06-23

dags/app_conf.yml

@@ -2,7 +2,7 @@
 general:
   s3_parameters:
     s3_conn_id: "bcom_tp_connection"
-    bucket: "prueba1234568"
+    bucket: "prueba-ca"
 
 dags:
   dag1:
@@ -41,14 +41,14 @@ dags:
     relacion3pa2p:
       type: "INSUMO"
       pattern: "temporal_relacion3pa2p*.txt"
-      prefix: "pruebas_qa"
+      prefix: ""
       key_field: "TRESP"
       value_field: "DOSP"
       delimiter: ","
     relacionpoidpaquete:
       type: "INSUMO"
       pattern: "temporal_relacion_Paquete*.txt"
-      prefix: "pruebas_qa"
+      prefix: ""
       key_field: "POID_PRODUCT"
       value_field: "CD_PAQUETE"
       delimiter: ","

dags/components/S3Route.py

@@ -27,13 +27,13 @@ def get_df_from_s3(conn: str, bucket: str, key: str, period: str, delimiter: str
         response.update({'filename': s3_data["filename"]})
         file_type = get_type_file(s3_data["filename"])
         if file_type == FileTypeEnum.EXCEL:
-            dataframe = pd.read_excel(s3_data["data"], engine="openpyxl")
+            dataframe = pd.read_excel(s3_data["data"], engine="openpyxl", dtype='object')
         elif file_type == FileTypeEnum.OLD_EXCEL:
-            dataframe = pd.read_excel(s3_data["data"], engine="xlrd")
+            dataframe = pd.read_excel(s3_data["data"], engine="xlrd", dtype='object')
         elif file_type == FileTypeEnum.TEXT or file_type == FileTypeEnum.CSV:
             str_data = str(s3_data["data"].getvalue(), encoding='UTF-8', errors='ignore')
             data = StringIO(str_data)
-            dataframe = pd.read_csv(data, sep=delimiter)
+            dataframe = pd.read_csv(data, sep=delimiter, dtype='object')
         response.update({'df': dataframe})
     except Exception as e:
         logger.error(f"Error trayendo y transformando a DataFrame desde S3 con periodo {period}. {e}")

dags/dag_transformacion_tacomventas_promoresidencial.py

@@ -18,6 +18,11 @@ import logging
 logger = logging.getLogger()
 
 DAG_NAME = "BCOM_DAG_TRANSFORMACION_TACOMVENTAS_PROMOCIONESRESIDENCIAL"
+PROMOCION_3PA2P = "3P a 2P"
+PROMOCION_PARRILLA = "Parrilla"
+PROMOCION_HBO = "Promocion HBO"
+PROMOCION_DEFAULT = "Promocion"
+PROMOCIONES_NO_CONSIDERADAS_TV_CANALES = "Adicional|Soundbox|SOUNDBOX"
 
 DEFAULT_ARGS = {
     'owner': 'airflow',
@@ -56,25 +61,26 @@ def dag1_id2(promo: pd.DataFrame, relation_poid: pd.DataFrame, key_field: str, v
 def dag1_id3(tacom: pd.DataFrame, promo: pd.DataFrame) -> pd.DataFrame:
     result = pd.DataFrame()
     try:
-        promo["CUENTA"] = promo["CUENTA"].astype(int, errors='ignore')
         promo["CD_PAQUETE_PROMO"] = promo["CD_PAQUETE"].astype(int, errors='ignore')
         promo.drop("CD_PAQUETE", axis=1, inplace=True)
         promo = promo.drop_duplicates(["CUENTA", "CD_PAQUETE_PROMO"])
         result = tacom.merge(promo, how='left', left_on=["CD_CUENTA", "CD_PAQUETE"], right_on=["CUENTA", "CD_PAQUETE_PROMO"])
         result["CD_PAQUETE"] = result["CD_PAQUETE"].astype(int).astype(str)
 
-        no_consider = "Adicional|Soundbox|SOUNDBOX"
         result["PROMOCION"] = np.where((result["CD_PAQUETE_PROMO"].isna()) | (result["CD_PAQUETE_PROMO"] == "None") |
                                        (result["CD_PAQUETE_PROMO"] == "nan"), None,
-                              np.where((result["CD_PAQUETE"].notna()) & (result["CD_PAQUETE"].str.len() <= 5), "3P a 2P",
+                              np.where((result["CD_PAQUETE"].notna()) & (result["CD_PAQUETE"].str.len() <= 5),
+                                       PROMOCION_3PA2P,
                               np.where((result["NOMBRE_PRODUCTO"].str.contains("TV", na=False)) &
-                                       (~result["NOMBRE_PRODUCTO"].str.contains(no_consider, na=False)), "Parrilla",
-                              np.where((result["NOMBRE_PRODUCTO"].str.contains("CANALES", na=False)) &
-                                       (~result["NOMBRE_PRODUCTO"].str.contains(no_consider, na=False)), "Parrilla",
-                              np.where(result["NOMBRE_PRODUCTO"].str.contains("HBO MAX", na=False), "Promocion HBO",
-                              np.where(result["NOMBRE_PRODUCTO"].str.contains("PAQUETE HBO", na=False), "Promocion HBO",
+                                       (~result["NOMBRE_PRODUCTO"].str.contains(PROMOCIONES_NO_CONSIDERADAS_TV_CANALES, na=False)),
+                                       PROMOCION_PARRILLA,
+                                       np.where((result["NOMBRE_PRODUCTO"].str.contains("CANALES", na=False)) &
+                                                (~result["NOMBRE_PRODUCTO"].str.contains(PROMOCIONES_NO_CONSIDERADAS_TV_CANALES, na=False)),
+                                                PROMOCION_PARRILLA,
+                                                np.where(result["NOMBRE_PRODUCTO"].str.contains("HBO MAX", na=False), PROMOCION_HBO,
+                              np.where(result["NOMBRE_PRODUCTO"].str.contains("PAQUETE HBO", na=False), PROMOCION_HBO,
                               np.where(result["NOMBRE_PRODUCTO"].str.contains("STAR PREMIUM", na=False),
-                                       "Promocion STAR PREMIUM", "PROMOCION")))))))
+                                       "Promocion STAR PREMIUM", PROMOCION_DEFAULT)))))))
 
         result["CD_PAQUETE_PROMO"] = np.where((result["CD_PAQUETE_PROMO"] == 'nan') |
                                               (result["CD_PAQUETE_PROMO"] == 'None'), None, result["CD_PAQUETE"])
@@ -91,7 +97,7 @@ def dag1_id4(df: pd.DataFrame, df_promo: pd.DataFrame, key_field: str, value_fie
         df_promo[value_field] = df_promo[value_field].astype(str, errors='ignore')
         df = df.merge(df_promo, how='outer', left_on="CD_PAQUETE", right_on=key_field)
         df = df.dropna(how='all', subset=["CD_EMPRESA", "CD_FOLIO", "CD_CUENTA"])
-        df["CD_PAQUETE"] = np.where((df["PROMOCION"] == "3P a 2P") & (df[key_field].notna()), df[value_field], df["CD_PAQUETE"])
+        df["CD_PAQUETE"] = np.where((df["PROMOCION"] == PROMOCION_3PA2P) & (df[key_field].notna()), df[value_field], df["CD_PAQUETE"])
         df = df.drop([key_field, value_field], axis=1)
     except Exception as e:
         logger.error(f"Error DAG1_ID4. {e}")
@@ -240,7 +246,7 @@ def set_dag_1():
     from yaml.loader import SafeLoader
     # Cambiar conf_path dependiendo del ambiente, en prod usando k8 y contenedores usar /opt/airflow/dags/app_conf.yml
     # En desarrollo, cualquiera que apunte a su carpeta dags
-    conf_path = "/root/airflow/dags/app_conf.yml"
+    conf_path = "/opt/airflow/dags/app_conf.yml"
     with open(conf_path) as f:
         data = yaml.load(f, Loader=SafeLoader)
         general_cnf = data["general"]

deploy-k8/airflow-envvars-configmap.yaml

@@ -24,5 +24,9 @@ data:
   _AIRFLOW_WWW_USER_CREATE: 'true'
   _AIRFLOW_WWW_USER_USERNAME: admin
   _AIRFLOW_WWW_USER_PASSWORD: admin
+  S3_DAGS_DIR: 's3://prueba1234568/dags'
+  SYNCHRONYZE_DAG_DIR: '30'
+  MINIO_SERVER: 'http://192.168.49.2:9000'
+  MINIO_DAGS_DIR: '/prueba-ca/dags'
     
 

deploy-k8/airflow-scheduler-deployment.yaml

@@ -20,15 +20,11 @@ spec:
     spec:
       containers:
       - name: airflow-scheduler
-        image: cristianfernando/airflow_custom:0.0.1
+        image: apache/airflow:2.5.3
         args: ["scheduler"]
         envFrom:
           - configMapRef:
               name: airflow-envvars-configmap
-        resources:
-          limits:
-            memory: "512Mi"
-            # cpu: "100"
         volumeMounts:
           - name: dags-host-volume
             mountPath: /opt/airflow/dags

deploy-k8/airflow-secrets.yaml

+apiVersion: v1
+kind: Secret
+metadata:
+  name: credentials
+
+type:
+  Opaque
+
+data:
+  AWS_ACCESS_KEY: bWluaW9hZG1pbg==
+  AWS_SECRET_KEY: bWluaW9hZG1pbg==
+  MINIO_USER: bWluaW9hZG1pbg==
+  MINIO_PASSWORD: bWluaW9hZG1pbg==
+

deploy-k8/airflow-webserver-deployment.yaml

@@ -20,15 +20,11 @@ spec:
     spec:
       containers:
       - name: airflow-webserver
-        image: cristianfernando/airflow_custom:0.0.1
+        image: apache/airflow:2.5.3
         args: ["webserver"]
         envFrom:
           - configMapRef:
               name: airflow-envvars-configmap
-        resources:
-          limits:
-            memory: "512Mi"
-            # cpu: "100"
         ports:
           - containerPort: 8080
         volumeMounts:

deploy-k8/pod_template.yaml

@@ -6,21 +6,12 @@ spec:
   containers:
     - args: [ ]
       command: [ ]
+      envFrom:
+        - configMapRef:
+            name: airflow-envvars-configmap
       env:
         - name: AIRFLOW__CORE__EXECUTOR
           value: LocalExecutor
-        - name: DB_HOST
-          value: postgres
-        - name: DB_DATABASE
-          value: airflow
-        - name: DB_USER
-          value: airflow
-        - name: DB_PASSWORD
-          value: airflow
-        - name: AIRFLOW__DATABASE__SQL_ALCHEMY_CONN
-          value: postgresql+psycopg2://airflow:airflow@postgres/airflow
-        - name: AIRFLOW__LOGGING__LOGGING_LEVEL
-          value: INFO
       image: dumy-image
       imagePullPolicy: IfNotPresent
       name: base
@@ -30,7 +21,7 @@ spec:
         - name: logs-persistent-storage
           mountPath: /opt/airflow/logs
   hostNetwork: false
-  restartPolicy: Never
+  restartPolicy: IfNotPresent
   securityContext:
     runAsUser: 50000
   nodeSelector: { }

deploy-k8/script-apply.sh

@@ -3,6 +3,7 @@ kubectl apply -f airflow-rbac.yaml
 kubectl apply -f postgres-deployment.yaml
 kubectl apply -f postgres-service.yaml
 kubectl apply -f airflow-envvars-configmap.yaml
+kubectl apply -f airflow-secrets.yaml
 kubectl apply -f airflow-webserver-deployment.yaml
 kubectl apply -f airflow-webserver-service.yaml
 kubectl apply -f airflow-scheduler-deployment.yaml

deploy-k8/script-delete.sh

 kubectl delete -f airflow-rbac.yaml
 kubectl delete -f postgres-service.yaml
 kubectl delete -f postgres-deployment.yaml
+kubectl delete -f airflow-secrets.yaml
 kubectl delete -f airflow-envvars-configmap.yaml
 kubectl delete -f airflow-webserver-service.yaml
 kubectl delete -f airflow-webserver-deployment.yaml

deploy-k8/sync-dags-deployment.yaml

@@ -16,18 +16,29 @@ spec:
     spec:
       containers:
         - args:
-            - while true; aws s3 sync --exact-timestamps --delete 's3://prueba1234568/dags' '/dags'; do sleep 30; done;
+            - mc alias set minio ${MINIO_SERVER:-http://192.168.49.2:9000} ${MINIO_USER:-minioadmin} 
+              ${MINIO_PASSWORD:-minioadmin}; while true; mc mirror --remove --overwrite minio${MINIO_DAGS_DIR:-/prueba-ca/dags} /dags; 
+              do sleep ${SYNCHRONYZE_DAG_DIR:-30}; done;
           command:
             - /bin/bash
             - -c
             - --
-          name: sync-dags
-          image: amazon/aws-cli:2.1.34
+          name: sync-dags-minio
+          image: minio/mc
+          envFrom:
+            - configMapRef:
+                name: airflow-envvars-configmap
           env:
-            - name: AWS_ACCESS_KEY_ID
-              value: AKIAQAAMXO3Z4BHNKEIE
-            - name: AWS_SECRET_ACCESS_KEY
-              value: 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
+            - name: MINIO_USER
+              valueFrom:
+                secretKeyRef:
+                  key: MINIO_USER
+                  name: credentials
+            - name: MINIO_PASSWORD
+              valueFrom:
+                secretKeyRef:
+                  key: MINIO_PASSWORD
+                  name: credentials
           volumeMounts:
           - name: dags-host-volume
             mountPath: /dags