Metodos para la base de datos

run.py

 from app import MainApplication
 import warnings
-from sqlalchemy.orm import sessionmaker, scoped_session
-from pyspark.sql import SparkSession
-from decimal import Decimal
-from pyspark.sql.types import StructType, StructField, StringType, DecimalType, ArrayType
 warnings.filterwarnings("ignore")
-from sqlalchemy import create_engine
-from app.main.engine.util.Utils import Utils
+
 base = MainApplication()
 app = base.create_app()
-#
-# if __name__ == "__main__":
-#     base.run(port=8000)
-
-spark = SparkSession.builder \
-    .appName("Crear DataFrame en PySpark") \
-    .getOrCreate()
-
-# Especificar el esquema del DataFrame
-schema = StructType([
-    StructField("PIVOT_Fecha", StringType(), True),
-    StructField("COUNTERPART_Fecha", StringType(), True),
-    StructField("Cuenta", StringType(), True),
-    StructField("Account", StringType(), True),
-    StructField("DIFF", DecimalType(10, 2), True),
-    StructField("LISTA_DIFF", ArrayType(StringType()), True),
-    StructField("INTER_PIVOT_ID", StringType(), True),
-    StructField("INTER_CTP_ID", StringType(), True),
-    StructField("PIVOT_Valor", DecimalType(10, 2), True),
-    StructField("COUNTERPART_Valor", DecimalType(10, 2), True)
-])
-
-# Crear el DataFrame con datos de ejemplo
-data = [
-    ("2024-04-01", "2024-04-01", "Cuenta1", "Account1", Decimal('10.50'), ['1', '2', '3'], "ID1", "ID2", Decimal('100.00'), Decimal('95.00')),
-    ("2024-04-02", "2024-04-02", "Cuenta2", "Account2", Decimal('15.75'), ['4', '5', '6'], "ID3", "ID4", Decimal('200.00'), Decimal('190.00')),
-    ("2024-04-03", "2024-04-03", "Cuenta3", "Account3", Decimal('20.25'), ['7', '8', '9'], "ID5", "ID6", Decimal('300.00'), Decimal('280.00'))
-]
-df = spark.createDataFrame(data, schema)
 
-# Mostrar el DataFrame
-df.show()
-descriptor = {
-    "idProcess" : 500240,
-    "idScript": "match-and-exclude",
-    "config-params":{
-        "max-records-per-combination": 10,
-        "max-timeout-per-combination": 1000,
-        "exclude-entity-pivot": True
-    },
-    "params-input": {
-        "pivot-config": {
-            "tablename" : "PIVOT_TEMPORAL",
-            "id-column" : "ID",
-            "amount-columns" : "Valor",
-            "columns-group" : ["Fecha", "Cuenta"],
-            "columns-transaction" : ["Fecha", "Cuenta", "Valor"]
-        },
-        "counterpart-config": {
-            "tablename" : "PIVOT_TEMPORAL",
-            "id-column" : "ID",
-            "amount-columns" : "Valor",
-            "columns-group" : ["Fecha", "Account"],
-            "columns-transaction" : ["Fecha", "Account", "Valor"]
-        }
-    }
-}
-a = Utils(app).create_result(df, descriptor)
-print(a)
-engine = create_engine("mysql+pymysql://root:root@192.168.0.11:3301/cusca")
-session_factory = sessionmaker(bind=engine)
-session = session_factory()
-b = Utils(app).save_result(a["detail"],descriptor, session)
+if __name__ == "__main__":
+    base.run(port=8000)
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