Merge branch 'developer' into 'developer_ev'

# Conflicts:
#   conf.yml

conf.yml

@@ -7,7 +7,7 @@ app:
     port: 3301
     user: root
     password: root
-    db: cusca
+    db: css_cuscatlan
     dialect: 'mysql+pymysql'
     # BD conexion configurations
     # https://docs.sqlalchemy.org/en/14/core/pooling.html
@@ -35,10 +35,4 @@ app:
     worker_connections: 50
     loglevel: 'debug'
     accesslog: '-'
-    capture_output: True
-
-  spark:
-    cores: '*'
-    memory: 16  # En gb y que sea par, de preferencia 2, 4, 6, 18, 32, etc.
-    jars_path:
-      mysql: 'jars/mysql-connector-java-8.0.30.jar'
\ No newline at end of file
+    capture_output: True
\ No newline at end of file

config.py

@@ -25,11 +25,6 @@ class Config(object):
     # Max threads allowed
     max_engine_threads = conf["max_engine_threads"]
 
-    # Spark config
-    spark_cores = conf["spark"]["cores"]
-    spark_mem = conf["spark"]["memory"]
-    spark_jars = conf["spark"]["jars_path"]
-
 
 class ProductionConfig(Config):
     DEBUG = False

scripts/match-and-exclude-records-actions_v1.py

 from typing import Any, Dict
 import importlib.util
+from itertools import combinations
+import time
+import numpy as np
+from concurrent.futures import ThreadPoolExecutor
 
 from app.main.engine.action.ActionInterface import ActionInterface
 
@@ -14,6 +18,11 @@ DRIVER_MEMORY = "4g"
 EXECUTOR_MEMORY = "4g"
 MYSQL_JAR_PATH = "jars/mysql-connector-java-8.0.30.jar"
 
+# EXCLUDE VALIDATION FIELD
+EXCLUDE_ROWS_FIELD = "EXCLUDE_VALID"
+# REDONDEO DE DECIMALES
+ROUND_DECIMAL = 2
+
 
 class MatchAndExcludeRecordsAction(ActionInterface):
 
@@ -27,6 +36,7 @@ class MatchAndExcludeRecordsAction(ActionInterface):
         self.exclude_pivot = None
         self.pivot_params = None
         self.ctp_params = None
+        self.output = None
         self.config_params = ["max-records-per-combinations", "max-timeout-per-combinations", "exclude-entity-pivot"]
 
     def parser(self, descriptor: Dict[str, Any]):
@@ -69,6 +79,7 @@ class MatchAndExcludeRecordsAction(ActionInterface):
         self.ctp_params = ctp_params
 
     def process(self, source_obj):
+
         # Inicializar la sesion de Spark
         session = self.createSession()
 
@@ -77,11 +88,111 @@ class MatchAndExcludeRecordsAction(ActionInterface):
         jdbc_conn = source_obj.create_spark_connection()
         jdbc_url = jdbc_conn["url"]
         jdbc_properties = jdbc_conn["properties"]
-        print(jdbc_url, jdbc_properties)
         pivot_df = session.read.jdbc(url=jdbc_url, table=pivot_table, properties=jdbc_properties)
+        ctp_df = session.read.jdbc(url=jdbc_url, table=ctp_table, properties=jdbc_properties)
+
+        # Agregar un prefijo a cada columna, tanto del pivot como contraparte. Actualizar campos del input
+        # pivot: 'PIVOT_', contraparte: 'COUNTERPART_'
+        for column in pivot_df.columns:
+            if column == EXCLUDE_ROWS_FIELD:
+                continue
+            pivot_df = pivot_df.withColumnRenamed(column, "PIVOT_"+column)
+
+        for column in ctp_df.columns:
+            if column == EXCLUDE_ROWS_FIELD:
+                continue
+            ctp_df = ctp_df.withColumnRenamed(column, "COUNTERPART_"+column)
+
+        for key_p, key_c in zip(self.pivot_params.keys(), self.ctp_params.keys()):
+            if isinstance(self.pivot_params[key_p], str):
+                self.pivot_params[key_p] = "PIVOT_"+self.pivot_params[key_p]
+                self.ctp_params[key_c] = "COUNTERPART_"+self.ctp_params[key_c]
+            else:
+                self.pivot_params[key_p] = ["PIVOT_"+column for column in self.pivot_params[key_p]]
+                self.ctp_params[key_c] = ["COUNTERPART_" + column for column in self.ctp_params[key_c]]
+
+        from pyspark.sql.functions import sum, collect_list, round, udf, array_except, size, col, array
+        from pyspark.sql.types import ArrayType, IntegerType, LongType
+
+        pivot_cols = self.pivot_params["columns-transaction"].copy()
+        if self.pivot_params["amount-column"] in pivot_cols:
+            pivot_cols.remove(self.pivot_params["amount-column"])
+
+        ctp_cols = self.ctp_params["columns-transaction"].copy()
+        if self.ctp_params["amount-column"] in ctp_cols:
+            ctp_cols.remove(self.ctp_params["amount-column"])
+
+        timeout = self.timeout
+        max_combinations = self.max_combinations
+
+        # Ejecutamos lógica de excluir registros
+        if len(self.pivot_params["columns-group"]) == 0 and len(self.ctp_params["columns-group"]) == 0:
+            raise RuntimeError(f"Debe haber al menos pivot o contraparte agrupado")
+
+        # Caso: 1 - Muchos
+        elif len(self.pivot_params["columns-group"]) == 0 and len(self.ctp_params["columns-group"]) > 0:
+            ctp_df2 = ctp_df.groupby(self.ctp_params["columns-group"]). \
+                agg(round(sum(self.ctp_params["amount-column"]), ROUND_DECIMAL).alias(self.ctp_params["amount-column"]),
+                    collect_list(self.ctp_params["id-column"]).alias(self.ctp_params["id-column"]))
+
+            pivot_df2 = pivot_df.dropDuplicates(subset=pivot_cols)
+
+        # Caso: Muchos - 1
+        elif len(self.pivot_params["columns-group"]) > 0 and len(self.ctp_params["columns-group"]) == 0:
+            pivot_df2 = pivot_df.groupby(self.pivot_params["columns-group"]).\
+                agg(round(sum(self.pivot_params["amount-column"]), ROUND_DECIMAL).alias(self.pivot_params["amount-column"]),
+                    collect_list(self.pivot_params["id-column"]).alias(self.pivot_params["id-column"]))
+
+            ctp_df2 = ctp_df.dropDuplicates(subset=ctp_cols)
+
+        # Caso: Muchos - Muchos
+        elif len(self.pivot_params["columns-group"]) > 0 and len(self.ctp_params["columns-group"]) > 0:
+            pivot_df2 = pivot_df.groupby(self.pivot_params["columns-group"]). \
+                agg(round(sum(self.pivot_params["amount-column"]), ROUND_DECIMAL).alias(self.pivot_params["amount-column"]),
+                    collect_list(self.pivot_params["id-column"]).alias(self.pivot_params["id-column"]))
+            ctp_df2 = ctp_df.groupby(self.ctp_params["columns-group"]). \
+                agg(round(sum(self.ctp_params["amount-column"]), ROUND_DECIMAL).alias(self.ctp_params["amount-column"]),
+                    collect_list(self.ctp_params["id-column"]).alias(self.ctp_params["id-column"]))
+
+        condition = [pivot_df2[col1] == ctp_df2[col2] for col1, col2 in zip(pivot_cols, ctp_cols)]
+        merged = pivot_df2.join(ctp_df2, condition)
+
+        merged = merged.withColumn("DIFF", pivot_df2[self.pivot_params["amount-column"]] - ctp_df2[self.ctp_params["amount-column"]])
+
+        merged_df = merged.withColumn("DIFF", round(merged["DIFF"], ROUND_DECIMAL))
+        if self.exclude_pivot:
+            df_pandas = pivot_df.toPandas()
+            group_cols = self.pivot_params["columns-group"]
+            amount_col = self.pivot_params["amount-column"]
+            id_col = self.pivot_params["id-column"]
+        else:
+            df_pandas = ctp_df.toPandas()
+            group_cols = self.ctp_params["columns-group"]
+            amount_col = self.ctp_params["amount-column"]
+            id_col = self.ctp_params["id-column"]
+
+        custom_udf = udf(lambda diff: custom_apply(diff, df_pandas, group_cols, amount_col, id_col, timeout,
+                                                   max_combinations), ArrayType(IntegerType()))
+        merged_df = merged_df.withColumn("LISTA_DIFF", custom_udf(merged_df["DIFF"]))
+        merged_df = merged_df.filter((col("DIFF") == 0) | ((col("DIFF") != 0) & (size(col("LISTA_DIFF")) > 0)))
+
+        if self.exclude_pivot:
+            merged_df = merged_df.withColumn("INTER_PIVOT_ID", array_except(merged_df[self.pivot_params["id-column"]],
+                                                                            merged_df["LISTA_DIFF"]))
+            merged_df = merged_df.withColumnRenamed(self.ctp_params["id-column"], "INTER_CTP_ID")
+            if merged_df.schema["INTER_CTP_ID"].dataType == LongType():
+                merged_df = merged_df.withColumn("INTER_CTP_ID", array(col("INTER_CTP_ID")).cast(ArrayType(LongType())))
+        else:
+            merged_df = merged_df.withColumn("INTER_CTP_ID", array_except(merged_df[self.ctp_params["id-column"]],
+                                                                          merged_df["LISTA_DIFF"]))
+            merged_df = merged_df.withColumnRenamed(self.pivot_params["id-column"], "INTER_PIVOT_ID")
+            if merged_df.schema["INTER_PIVOT_ID"].dataType == LongType():
+                merged_df = merged_df.withColumn("INTER_PIVOT_ID", array(col("INTER_PIVOT_ID")).cast(ArrayType(LongType())))
+        self.output = merged_df
 
     def response(self):
-        print("Llegue al response3")
+        self.output.show()
+        return self.output
 
     def createSession(self, name: str = "app_engine_spark"):
         try:
@@ -100,10 +211,66 @@ class MatchAndExcludeRecordsAction(ActionInterface):
             raise Exception(f"Error creando sesion Spark. {e}")
 
 
-
-
-
-
+def custom_apply(raw, df_pandas, group_cols, amount_col, id_col, timeout, max_combinations):
+    if raw == 0:
+        return []
+    else:
+        total_cols = group_cols.copy()
+        total_cols.append(amount_col)
+        total_cols.append(id_col)
+        total_cols.append(EXCLUDE_ROWS_FIELD)
+        result = df_pandas[total_cols].groupby(group_cols).apply(custom_func, diff=raw, amount_field=amount_col, id_field=id_col,
+                                                                 timeout=timeout, max_combinations=max_combinations).values
+        resp = ",".join(map(str, result)) if len(result) > 0 else ""
+        return [int(elem) for elem in resp[1:-1].split(",")] if resp != "" else []
+
+
+def custom_func(group, diff, amount_field, id_field, timeout, max_combinations):
+    group[amount_field] = group[amount_field].astype(float)
+    group = group.reset_index(drop=True)
+    values = group[amount_field].values
+    indexs = group.index.values
+    tam = len(values)
+    rang = range(1, tam + 1) if tam <= max_combinations else range(1, max_combinations + 1)
+
+    final = None
+    stop_event = False
+
+    def buscar_combinacion(i):
+        nonlocal final, stop_event
+
+        if not stop_event:  # Solo continuar si no se ha encontrado una combinación aún
+            for comb in combinations(indexs, i):
+                if stop_event:  # Verificar si se ha establecido la señal de detención
+                    break
+                if np.sum(values[list(comb)]).round(ROUND_DECIMAL) == diff:
+                    # Validamos si la combinación son de registros validados 'EXCLUDE_VALID'
+                    final = group[group.index.isin(comb)]
+                    if (final[EXCLUDE_ROWS_FIELD] == 'S').all():
+                        final = final[id_field].tolist()
+                        stop_event = True  # Establecer la señal de detención
+                        break
+
+        return None
+
+    start_time = time.time()
+
+    with ThreadPoolExecutor() as executor:
+        futures = [executor.submit(buscar_combinacion, i) for i in rang]
+
+        for future in futures:
+            try:
+                future.result(timeout=timeout)
+            except TimeoutError:
+                stop_event = True  # Establecer la señal de detención si se alcanza el tiempo límite
+                break
+
+            if stop_event or final is not None:
+                break
+
+    if time.time() - start_time >= timeout:
+        return None  # Devolver None si se alcanzó el tiempo límite
+    return final