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CSS-Engine-Python-Cusca
Commit c2d602b8 authored by Cristian Aguirre's avatar Cristian Aguirre
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Update action-exclude-records-v1-dask

parent 7086b4fa
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Showing with 229 additions and 481 deletions
app/main/engine/action/ActionInterface.py
View file @ c2d602b8
......@@ -14,7 +14,7 @@ class ActionInterface(ABC):
raise NotImplementedError
@abstractmethod
def process(self, source_obj, script_name, timezone, pattern):
def process(self, source_obj):
"""Método que ejecuta la lógica del script"""
raise NotImplementedError
......
app/main/engine/service/Process.py
View file @ c2d602b8
......@@ -46,16 +46,16 @@ class Process:
# Iniciando process
self.app.logger.info(f"Iniciando procesamiento de script")
obj_script.process(source, script_name, cfg.timezone, cfg.time_pattern)
obj_script.process(source)
print("1")
# Guardando resultado
self.app.logger.info(f"Generado y guardando resultado")
# _ = obj_script.response()
response = obj_script.response()
# response.show()
# result = self.utils.create_result(response, self.descriptor)
# save = self.utils.save_result(result, self.descriptor, db_session)
# if save["status"] == StatusEnum.ERROR.name:
# raise InterruptedError(save["message"])
result = self.utils.create_result(response, self.descriptor)
save = self.utils.save_result(result, self.descriptor, db_session)
if save["status"] == StatusEnum.ERROR.name:
raise InterruptedError(save["message"])
except TimeoutError as e:
self.app.logger.error(f"Error de Timeout. Error: {e}")
status, status_description = CodeResponseEnum.TIMEOUT, str(e)
......
deploy/Dockerfile
View file @ c2d602b8
......@@ -9,8 +9,6 @@ tar xzf Python-3.10.0.tgz && cd Python-3.10.0 && \
./configure --enable-optimizations && \
make altinstall
COPY subset_sum_linux /tmp/
COPY requirements.txt /
RUN python3 -m pip install numpy pandas py4j python-dateutil pytz six tzdata
......
scripts/emr_match-and-exclude-records-actions_v1.py deleted 100644 → 0
View file @ 7086b4fa
import os
import uuid
from typing import Dict, Any, List
import sys
import subprocess
import pandas as pd
import time
from datetime import datetime
from dateutil.parser import parse
import json
import pytz
import logging
from enum import Enum
from pyspark.sql import SparkSession
from pyspark.sql.functions import sum, collect_list, round, when, col, lit, size, udf, array_except, array
from pyspark.sql.types import ArrayType, IntegerType, LongType
class FixedFieldsEnum(Enum):
INTER_PIVOT_ID = "INTER_PIVOT_ID"
INTER_CTP_ID = "INTER_CTP_ID"
LISTA_DIFF = "LISTA_DIFF"
DIFF = "DIFF"
MATCH_RECORDS = "match-records"
class StatusEnum(Enum):
OK = 200
ERROR = 609
TIMEOUT = 610
logger = logging.getLogger(__name__)
# EXCLUDE VALIDATION FIELD
EXCLUDE_ROWS_FIELD = "EXCLUDE_VALID"
# REDONDEO DE DECIMALES
ROUND_DECIMAL = 2
# COLUMNAS TABLA RESULTADO
RESULT_TABLENAME = "CSS_RESULT_BY_ACTION"
RESULT_TABLE_FIELDS = ["ACTION_ID", "ID_PROCESS", "CREATE_DATE", "KEY", "RESULT_JSON"]
def process() -> Dict[str, Any]:
response = {"status": StatusEnum.ERROR.name}
start_time = time.time()
params = sys.argv
descriptor = params[1]
jdbc_url = params[2]
timezone = params[3]
pattern = params[4]
descriptor = json.loads(descriptor)
session = createSession()
configs = descriptor["config-params"]
exclude_pivot = configs["exclude-entity-pivot"]
max_combinations = configs["max-records-per-combinations"]
params_input = descriptor["params-input"]
pivot_params, ctp_params = params_input["pivot-config"], params_input["counterpart-config"]
pivot_table, ctp_table = pivot_params["tablename"], ctp_params["tablename"]
jdbc_properties = {"driver": "com.mysql.jdbc.Driver"}
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(pivot_params.keys(), ctp_params.keys()):
if isinstance(pivot_params[key_p], str):
pivot_params[key_p] = "PIVOT_" + pivot_params[key_p]
ctp_params[key_c] = "COUNTERPART_" + ctp_params[key_c]
else:
pivot_params[key_p] = ["PIVOT_" + column for column in pivot_params[key_p]]
ctp_params[key_c] = ["COUNTERPART_" + column for column in ctp_params[key_c]]
pivot_cols = pivot_params["columns-transaction"].copy()
if pivot_params["amount-column"] in pivot_cols:
pivot_cols.remove(pivot_params["amount-column"])
ctp_cols = ctp_params["columns-transaction"].copy()
if ctp_params["amount-column"] in ctp_cols:
ctp_cols.remove(ctp_params["amount-column"])
# Ejecutamos lógica de excluir registros
if len(pivot_params["columns-group"]) == 0 and len(ctp_params["columns-group"]) == 0:
raise RuntimeError(f"Debe haber al menos pivot o contraparte agrupado")
# Caso: 1 - Muchos
elif len(pivot_params["columns-group"]) == 0 and len(ctp_params["columns-group"]) > 0:
ctp_df2 = ctp_df.groupby(ctp_params["columns-group"]). \
agg(round(sum(ctp_params["amount-column"]), ROUND_DECIMAL).alias(ctp_params["amount-column"]),
collect_list(ctp_params["id-column"]).alias(ctp_params["id-column"]))
pivot_df2 = pivot_df
# Caso: Muchos - 1
elif len(pivot_params["columns-group"]) > 0 and len(ctp_params["columns-group"]) == 0:
pivot_df2 = pivot_df.groupby(pivot_params["columns-group"]).agg(
round(sum(pivot_params["amount-column"]), ROUND_DECIMAL).alias(pivot_params["amount-column"]),
collect_list(pivot_params["id-column"]).alias(pivot_params["id-column"]))
ctp_df2 = ctp_df
# Caso: Muchos - Muchos
elif len(pivot_params["columns-group"]) > 0 and len(ctp_params["columns-group"]) > 0:
pivot_df2 = pivot_df.groupby(pivot_params["columns-group"]).agg(
round(sum(pivot_params["amount-column"]), ROUND_DECIMAL).alias(pivot_params["amount-column"]),
collect_list(pivot_params["id-column"]).alias(pivot_params["id-column"]))
ctp_df2 = ctp_df.groupby(ctp_params["columns-group"]).agg(
round(sum(ctp_params["amount-column"]), ROUND_DECIMAL).alias(ctp_params["amount-column"]),
collect_list(ctp_params["id-column"]).alias(ctp_params["id-column"]))
condition = [pivot_df2[col1] == ctp_df2[col2] for col1, col2 in zip(pivot_params["columns-transaction"],
ctp_params["columns-transaction"])]
total_merged = pivot_df2.join(ctp_df2, condition, 'left')
total_merged = total_merged.withColumn("DIFF", when(col(ctp_params["columns-transaction"][0]).isNotNull(),
lit(0)).otherwise(lit(None)))
total_merged = total_merged.select(*pivot_df2.columns, "DIFF")
condition = [total_merged[col1] == ctp_df2[col2] for col1, col2 in zip(pivot_cols, ctp_cols)]
merged = total_merged.join(ctp_df2, condition)
merged = merged.withColumn("DIFF", when(col("DIFF").isNull(),
total_merged[pivot_params["amount-column"]] - ctp_df2[ctp_params["amount-column"]]).otherwise(col("DIFF")))
if len(pivot_params["columns-group"]) == 0 and len(ctp_params["columns-group"]) > 0:
merged = merged.sort(pivot_params["id-column"])
merged = merged.dropDuplicates([pivot_cols])
elif len(pivot_params["columns-group"]) > 0 and len(ctp_params["columns-group"]) == 0:
merged = merged.sort(ctp_params["id-column"])
merged = merged.dropDuplicates([ctp_cols])
merged_df = merged.withColumn("DIFF", round(merged["DIFF"], ROUND_DECIMAL))
if exclude_pivot:
df = pivot_df
group_cols = pivot_params["columns-group"]
amount_col = pivot_params["amount-column"]
id_col = pivot_params["id-column"]
else:
df = ctp_df
group_cols = ctp_params["columns-group"]
amount_col = ctp_params["amount-column"]
id_col = ctp_params["id-column"]
total_tmp_cols = group_cols + ["DIFF"]
df3 = df.join(merged_df.select(*total_tmp_cols), group_cols)
columns = [col(column) for column in group_cols]
custom = udf(custom_func_udf, ArrayType(IntegerType()))
# Fitlrar solo los que tienen S en el campo de exclusión - No tomaria los matches
# df3 = df3.filter(col(EXCLUDE_ROWS_FIELD) == 'S')
resultado = df3.groupby(*columns).agg(
custom(collect_list(amount_col), collect_list(id_col), collect_list(EXCLUDE_ROWS_FIELD), collect_list("DIFF"), lit(max_combinations)).alias("LISTA_DIFF"))
meged2 = resultado.join(merged_df, group_cols, 'left')
handle_array_udf = udf(handle_array, ArrayType(IntegerType()))
meged2 = meged2.withColumn("LISTA_DIFF", handle_array_udf("LISTA_DIFF"))
meged2 = meged2.filter((col("DIFF") == 0) | ((col("DIFF") != 0) & (size(col("LISTA_DIFF")) > 0)))
if exclude_pivot:
meged2 = meged2.withColumn("INTER_PIVOT_ID", array_except(meged2[pivot_params["id-column"]], meged2["LISTA_DIFF"]))
meged2 = meged2.withColumnRenamed(ctp_params["id-column"], "INTER_CTP_ID")
if meged2.schema["INTER_CTP_ID"].dataType == LongType():
meged2 = meged2.withColumn("INTER_CTP_ID", array(col("INTER_CTP_ID")).cast(ArrayType(LongType())))
else:
meged2 = meged2.withColumn("INTER_CTP_ID", array_except(meged2[ctp_params["id-column"]], meged2["LISTA_DIFF"]))
meged2 = meged2.withColumnRenamed(pivot_params["id-column"], "INTER_PIVOT_ID")
if meged2.schema["INTER_PIVOT_ID"].dataType == LongType():
meged2 = meged2.withColumn("INTER_PIVOT_ID", array(col("INTER_PIVOT_ID")).cast(ArrayType(LongType())))
meged2.show()
print("SHOW:", time.time() - start_time)
meged2 = meged2.toPandas()
print("SOLO ALGORITMO:", time.time() - start_time)
# Guardado en la BD
print("creando result")
result = create_result(meged2, descriptor)
print("emepce a guardar")
if result["status"] == StatusEnum.ERROR:
raise InterruptedError(f"Error generando el json resultado. {result['message']}")
save = save_result(result, session, jdbc_url, descriptor, timezone, pattern)
if save["status"] == StatusEnum.ERROR:
raise InterruptedError(f"Error guardando registro resultado en la BD. {result['message']}")
response["status"] = StatusEnum.OK.name
return response
def createSession(name: str = "app_engine_spark"):
try:
session = SparkSession.builder \
.appName(name) \
.getOrCreate()
return session
except Exception as e:
raise Exception(f"Error creando sesion Spark. {e}")
def handle_array(x):
if isinstance(x, List):
return x
else:
return []
def custom_func_udf(amount_values, id_values, excludes, diffs, max_combinations):
diff = diffs[0]
if pd.isna(diff) or diff == 0:
return None
diff = int(diff * (10**ROUND_DECIMAL))
amount_values = [int(value * (10**ROUND_DECIMAL)) for value, exclude in zip(amount_values, excludes) if exclude == 'S']
dir_name = str(uuid.uuid4())
prefix = "/tmp/" + dir_name + "_"
tmp_file_arr1, tmp_file_arr2 = "values.txt", "target.txt"
full_path_arr1, full_path_arr2 = prefix + tmp_file_arr1, prefix + tmp_file_arr2
with open(full_path_arr1, 'w') as archivo:
archivo.writelines([f'{entero}\n' for entero in amount_values])
with open(full_path_arr2, 'w') as archivo:
archivo.write(str(diff))
executable_path = '/tmp/subset_sum_linux'
indices = []
for comb in range(1, max_combinations+1):
argumentos = [full_path_arr1, full_path_arr2, str(comb), '1', '1', 'false', 'false']
result = subprocess.run([executable_path] + argumentos, check=True, capture_output=True, text=True)
result = str(result)
if "keys:[" in result:
match = result[result.index("keys:[") + 5:result.index("keys remainder") - 20]
match = match.replace("targets:", "").replace("+", ",")
match = match.split("==")[0].replace(" ", "")
match = json.loads(match)
for idx, val in zip(id_values, amount_values):
if val in match:
indices.append(idx)
match.remove(val)
break
os.remove(full_path_arr1), os.remove(full_path_arr2)
return indices
def create_result(data, descriptor):
result = []
response = {"detail": result}
try:
exclude_pivot = descriptor["config-params"]["exclude-entity-pivot"]
pivot_params = descriptor["params-input"]["pivot-config"]
ctp_params = descriptor["params-input"]["counterpart-config"]
group_pivot_match = pivot_params["columns-group"]
transaction_pivot_match = pivot_params["columns-transaction"]
group_counterpart_match = ctp_params["columns-group"]
transaction_counterpart_match = ctp_params["columns-transaction"]
used_list = transaction_counterpart_match if exclude_pivot else transaction_pivot_match
if data is None or data.empty:
logger.info(f"El dataframe resultado esta vacio")
else:
for idx, i in data.iterrows():
input_data = {}
key_transaction = None
key_group_pivot = None
key_group_counterpart = None
for element in used_list:
if key_transaction is None:
key_transaction = str(i[element])
else:
key_transaction = key_transaction + "-" + str(i[element])
for element_g in group_pivot_match:
if key_group_pivot is None:
key_group_pivot = str(i[element_g])
else:
key_group_pivot = key_group_pivot + "-" + str(i[element_g])
for element_c in group_counterpart_match:
if key_group_counterpart is None:
key_group_counterpart = str(i[element_c])
else:
key_group_counterpart = key_group_counterpart + "-" + str(i[element_c])
input_data["key-transaction"] = str(key_transaction)
input_data["key-group-pivot"] = "" if key_group_pivot is None else str(key_group_pivot)
input_data["key-group-counterpart"] = "" if key_group_counterpart is None else str(
key_group_counterpart)
input_data["list-ids-pivot"] = str(i[FixedFieldsEnum.INTER_PIVOT_ID.value])
input_data["list-ids-counterpart"] = str(i[FixedFieldsEnum.INTER_CTP_ID.value])
input_data["exclude-ids"] = str(i[FixedFieldsEnum.LISTA_DIFF.value])
input_data["difference-amount"] = str(i[FixedFieldsEnum.DIFF.value])
result.append(input_data)
response['status'] = StatusEnum.OK
response["detail"] = result
except Exception as e:
logger.error(f"Error al crear el diccionario de resultados. {e}")
response["status"] = StatusEnum.ERROR
response["message"] = str(e)
finally:
return response
def save_result(result, session, jdbc_url, descriptor, timezone, pattern):
response = {}
try:
d1 = datetime_by_tzone(timezone, pattern)
result_json = json.dumps(result["detail"])
data = [descriptor["idScript"], descriptor["idProcess"], d1, FixedFieldsEnum.MATCH_RECORDS.value, result_json]
df = pd.DataFrame([data], columns=RESULT_TABLE_FIELDS)
df = session.createDataFrame(df)
df.write.format("jdbc").option("url", jdbc_url).option("dbtable", RESULT_TABLENAME). \
option("driver", "com.mysql.cj.jdbc.Driver").mode("append").save()
response['status'] = StatusEnum.OK
except Exception as e:
response["status"] = StatusEnum.ERROR
response["message"] = str(e)
logger.error(f"Error al guardar registro en la base de datos {e}")
finally:
return response
def datetime_by_tzone(timezone, pattern):
tzone = timezone
offset = None
# Algunos casos donde el timezone es de la forma 4:30 y no se encuentra en timezones de pytz (GMT)
if ":" in tzone:
offset = tzone.split(":")[1]
tzone = tzone.split(":")[0]
if "+" in tzone:
tzone = tzone.replace(tzone[-1], str(int(tzone[-1]) + 1))
timezones_list = pytz.all_timezones
tzones = [x if tzone in x else None for x in timezones_list]
tzones = list(filter(None, tzones))
server_timezone = pytz.timezone(tzones[0])
logger.debug("Zona Horaria : {}".format(server_timezone))
server_time = server_timezone.localize(datetime.utcnow())
current_time = parse(server_time.strftime('%Y-%m-%d %H:%M:%S.%f %Z'))
if offset:
offset = pytz.FixedOffset((current_time.utcoffset().total_seconds() / 60 + float(offset)) * -1)
offset = offset.utcoffset(datetime.utcnow())
current_time = datetime.utcnow() + offset
else:
current_time = current_time.replace(tzinfo=None) - current_time.utcoffset()
current_time = parse(current_time.strftime(pattern))
logger.debug("Hora actual: {}".format(current_time))
return current_time
# Ejecución de proceso
process()
scripts/match-and-exclude-records-actions_v1.py
View file @ c2d602b8
from typing import Any, Dict, List
import numpy as np
import pandas as pd
import json
from typing import Any, Dict
import sys
import os
import subprocess
import uuid
from dpss import find_subset
from dask import dataframe as dd
from numba import jit, types, typed
from wrapt_timeout_decorator import timeout
from app.main.engine.util.EMRServerless import EMRServerless
import multiprocessing as mp
from app.main.engine.action.ActionInterface import ActionInterface
......@@ -21,7 +28,6 @@ class MatchAndExcludeRecordsAction(ActionInterface):
def __init__(self, app) -> None:
super().__init__(app)
self.descriptor = None
self.max_combinations = None
self.timeout = None
self.exclude_pivot = None
......@@ -55,65 +61,176 @@ class MatchAndExcludeRecordsAction(ActionInterface):
if param not in pivot_params.keys() or param not in ctp_params.keys():
raise ReferenceError(f"Parámetro *{param}* no encontrado en pivot o contraparte")
self.descriptor = descriptor
self.max_combinations = configs["max-records-per-combinations"]
self.timeout = configs["max-timeout-per-combinations"]
self.exclude_pivot = configs["exclude-entity-pivot"]
self.pivot_params = pivot_params
self.ctp_params = ctp_params
def process(self, source_obs, script_name, timezone, pattern):
def process(self, source_obs):
try:
@timeout(self.timeout)
def __process(source_obj):
descriptor = DESCRIPTOR
serverless_job_role_arn = descriptor["job_role_arn"]
s3_bucket_name = descriptor["bucket_base"]
search_emr = descriptor["emr_started"]
end_app = descriptor["terminated_app"]
emr_serverless = EMRServerless(search_app=search_emr)
self.app.logger.info("Validando si exite una aplicación ya en curso")
exists_app = emr_serverless.valid_application()
if not exists_app["exists"]:
self.app.logger.info("Creando e inicializando la aplicación EMR Serverless")
emr_serverless.create_application(descriptor["application_name"], descriptor["emr_version"],
descriptor["app_args"])
# Traer la data desde BD tanto pivot como contraparte
pivot_table, ctp_table = self.pivot_params["tablename"], self.ctp_params["tablename"]
dialect = source_obj.get_dialect()
pivot_df = dd.read_sql_table(pivot_table, dialect, index_col=self.pivot_params["id-column"],
npartitions=mp.cpu_count())
pivot_df = pivot_df.reset_index()
ctp_df = dd.read_sql_table(ctp_table, dialect, index_col=self.ctp_params["id-column"],
npartitions=mp.cpu_count())
ctp_df = ctp_df.reset_index()
# Agregar un prefijo a cada columna, tanto del pivot como contraparte. Actualizar campos del input
# pivot: 'PIVOT_', contraparte: 'COUNTERPART_'
# Iterar sobre las columnas del DataFrame
for column in pivot_df.columns:
if column == EXCLUDE_ROWS_FIELD:
continue
new_column_name = "PIVOT_" + column
pivot_df = pivot_df.rename(columns={column: new_column_name})
for column in ctp_df.columns:
if column == EXCLUDE_ROWS_FIELD:
continue
new_column_name = "COUNTERPART_" + column
ctp_df = ctp_df.rename(columns={column: new_column_name})
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]]
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"])
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({
self.ctp_params["amount-column"]: 'sum', # Sumar la columna de cantidades
self.ctp_params["id-column"]: list
})
ctp_df2 = ctp_df2.reset_index()
pivot_df2 = pivot_df
# 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({
self.pivot_params["amount-column"]: 'sum',
self.pivot_params["id-column"]: list
})
pivot_df2 = pivot_df2.reset_index()
ctp_df2 = ctp_df
# 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({
self.pivot_params["amount-column"]: 'sum',
self.pivot_params["id-column"]: list
})
pivot_df2 = pivot_df2.reset_index()
ctp_df2 = ctp_df.groupby(self.ctp_params["columns-group"]).agg({
self.ctp_params["amount-column"]: 'sum', # Sumar la columna de cantidades
self.ctp_params["id-column"]: list
})
ctp_df2 = ctp_df2.reset_index()
pivot_df2[self.pivot_params["amount-column"]] = pivot_df2[self.pivot_params["amount-column"]].round(
ROUND_DECIMAL)
ctp_df2[self.ctp_params["amount-column"]] = ctp_df2[self.ctp_params["amount-column"]].round(
ROUND_DECIMAL)
total_merged = pivot_df2.merge(ctp_df2, 'left', left_on=self.pivot_params["columns-transaction"],
right_on=self.ctp_params["columns-transaction"])
total_merged = total_merged.map_partitions(self.add_diff_column)
selected_columns = list(pivot_df2.columns) + ['DIFF']
total_merged = total_merged[selected_columns]
merged = total_merged.merge(ctp_df2, 'inner', left_on=pivot_cols, right_on=ctp_cols)
merged['DIFF'] = merged['DIFF'].where(merged['DIFF'].notnull(),
merged[self.pivot_params["amount-column"]] - merged[
self.ctp_params["amount-column"]])
if len(self.pivot_params["columns-group"]) == 0 and len(self.ctp_params["columns-group"]) > 0:
merged = merged.set_index(self.pivot_params["id-column"])
merged = merged.map_partitions(lambda df_: df_.sort_values([self.pivot_params["id-column"]]))
merged = merged.drop_duplicates(subset=pivot_cols)
elif len(self.pivot_params["columns-group"]) > 0 and len(self.ctp_params["columns-group"]) == 0:
merged = merged.set_index(self.ctp_params["id-column"])
merged = merged.map_partitions(lambda df_: df_.sort_values([self.ctp_params["id-column"]]))
merged = merged.drop_duplicates(subset=ctp_cols)
merged = merged.reset_index()
merged_df = merged.assign(DIFF=lambda partition: partition["DIFF"].round(ROUND_DECIMAL))
if self.exclude_pivot:
df = pivot_df
group_cols = self.pivot_params["columns-group"]
amount_col = self.pivot_params["amount-column"]
id_col = self.pivot_params["id-column"]
else:
emr_serverless.application_id = exists_app["app"]
emr_serverless.start_application()
self.app.logger.info(emr_serverless)
job = descriptor["job"]
script_location = job["script_location"] + "emr_" + script_name
jdbc_conn = source_obj.create_spark_connection()
# jdbc_url = jdbc_conn["url"]
jdbc_url = "jdbc:mysql://admin:awsadmin@database-2.cgcfmoce13qq.us-east-1.rds.amazonaws.com:3306/cusca"
# jdbc_properties = jdbc_conn["properties"]
arguments = [json.dumps(self.descriptor), jdbc_url, timezone, pattern]
self.app.logger.info("Lanzando nuevo job Spark")
self.app.logger.info(script_location)
self.app.logger.info(serverless_job_role_arn)
self.app.logger.info(arguments)
self.app.logger.info(job["sparkArgs"])
self.app.logger.info(s3_bucket_name)
job_run_id = emr_serverless.run_spark_job(
script_location=script_location,
job_role_arn=serverless_job_role_arn,
arguments=arguments,
sparkArguments=job["sparkArgs"],
s3_bucket_name=s3_bucket_name,
)
job_status = emr_serverless.get_job_run(job_run_id)
self.app.logger.info(f"Job terminado: {job_run_id}, Estado: {job_status.get('state')}")
# Fetch and print the logs
spark_driver_logs = emr_serverless.fetch_driver_log(s3_bucket_name, job_run_id)
self.app.logger.info("Archivo de salida:\n----\n", spark_driver_logs, "\n----")
if end_app:
# Now stop and delete your application
self.app.logger.info("Deteniendo y borrando aplicación Spark")
emr_serverless.stop_application()
emr_serverless.delete_application()
self.app.logger.info("Hecho! 👋")
df = ctp_df
group_cols = self.ctp_params["columns-group"]
amount_col = self.ctp_params["amount-column"]
id_col = self.ctp_params["id-column"]
total_tmp_cols = group_cols + ["DIFF"]
df3 = df.merge(merged_df[total_tmp_cols], 'inner', on=group_cols)
df3 = df3.compute()
total_cols = group_cols + [amount_col, id_col, EXCLUDE_ROWS_FIELD, "DIFF"]
resultado = df3.groupby(group_cols)[total_cols].apply(lambda x: custom_func(x, amount_col, id_col, max_combinations))
resultado = resultado.reset_index()
if len(resultado.columns) == 1:
resultado = pd.DataFrame([], columns=group_cols + ["LISTA_DIFF"])
else:
resultado.columns = group_cols + ["LISTA_DIFF"]
resultado = dd.from_pandas(resultado, npartitions=mp.cpu_count())
meged2 = resultado.merge(merged_df, 'left', group_cols)
meged2 = meged2.map_partitions(lambda partition: partition.assign(
LISTA_DIFF=partition['LISTA_DIFF'].apply(lambda x: [] if pd.isna(x) else x)), meta=meged2.dtypes.to_dict())
meged2 = meged2[
(meged2['DIFF'] == 0) |
((meged2['DIFF'] != 0) & meged2['LISTA_DIFF'].apply(
lambda x: True if not pd.isna(x) and ((isinstance(x, List) and len(x) > 0) or (isinstance(x, str) and len(x) > 2)) else False))
]
meged2 = meged2.compute()
if meged2.empty:
pass
elif self.exclude_pivot:
meged2['INTER_PIVOT_ID'] = meged2.apply(lambda row: self.array_except(row[self.pivot_params["id-column"]], row['LISTA_DIFF']), axis=1)
meged2 = meged2.rename(columns={self.ctp_params["id-column"]: "INTER_CTP_ID"})
if meged2['INTER_CTP_ID'].dtype == 'int64':
meged2['INTER_CTP_ID'] = meged2['INTER_CTP_ID'].apply(lambda x: [x]).astype('object')
else:
meged2['INTER_CTP_ID'] = meged2.apply(lambda row: self.array_except(row[self.ctp_params["id-column"]], row['LISTA_DIFF']), axis=1)
meged2 = meged2.rename(columns={self.pivot_params["id-column"]: "INTER_PIVOT_ID"})
if meged2['INTER_PIVOT_ID'].dtype == 'int64':
meged2['INTER_PIVOT_ID'] = meged2['INTER_PIVOT_ID'].apply(lambda x: [x]).astype('object')
return meged2
except TimeoutError as e:
raise TimeoutError(f"Tiempo límite superado. {e}")
......@@ -123,57 +240,56 @@ class MatchAndExcludeRecordsAction(ActionInterface):
def response(self):
return self.output
def add_diff_column(self, partition):
partition['DIFF'] = np.where(partition[self.ctp_params["columns-transaction"][0]].notnull(), 0, np.nan)
return partition
def handle_array(self, x):
if isinstance(x, np.ndarray):
return x
else:
return []
def array_except(self, arr1, arr2):
if arr2 is None:
return arr1
elif not isinstance(arr2, List):
cadena_sin_corchetes = arr2.replace(" ", "").strip('[]')
partes = cadena_sin_corchetes.split(",")
# print(partes)
arr2 = [int(numero) for numero in partes]
arr1 = json.loads(arr1.replace(" ", ""))
return [item for item in arr1 if item not in arr2]
def custom_func(group, amount_field, id_field, max_combinations):
diff_value = group["DIFF"].values[0]
if np.isnan(diff_value):
return None
diff = int(diff_value*(10**ROUND_DECIMAL))
if pd.isna(diff) or diff == 0:
return None
group = group[group[EXCLUDE_ROWS_FIELD] == 'S']
group[amount_field] = group[amount_field].astype(float)
group = group.reset_index(drop=True)
values = group[amount_field].values
values *= (10**ROUND_DECIMAL)
values = values.astype(np.int64)
ids = group[id_field].values
tam = len(values)
tam = tam if tam <= max_combinations else max_combinations
result = find_subset(values, diff, tam)
indices = []
if len(result) > 0:
result = result[0]
for idx, val in zip(ids, values):
if val in result:
indices.append(idx)
result.remove(val)
else:
return None
return indices
DESCRIPTOR = {
"application_name": "css_cuscatlan",
"emr_version": "emr-7.0.0",
"emr_started": True,
"terminated_app": False,
"job_role_arn": "arn:aws:iam::000026703603:role/emr-serverless-job-role",
"bucket_base": "bucket-emr-example",
"app_args": {
"initial_capacity": {
"DRIVER": {
"workerCount": 1,
"workerConfiguration": {
"cpu": "16vCPU",
"memory": "32GB"
}
},
"EXECUTOR": {
"workerCount": 12,
"workerConfiguration": {
"cpu": "16vCPU",
"memory": "32GB"
}
}
},
"maximun_capacity": {
"cpu": "208vCPU",
"memory": "416GB",
"disk": "1000GB"
},
"imageConfiguration": {
"imageUri": "000026703603.dkr.ecr.us-east-1.amazonaws.com/css_spark_custom:0.0.5"
},
"networkConfiguration": {
"subnetIds": [
"subnet-0f86499848ec99861", "subnet-078fe716da8b53818", "subnet-0a7d0a8bc3b623474"
],
"securityGroupIds": [
"sg-02154713a3639f7ce"
]
}
},
"job": {
"script_location": "s3://bucket-emr-example/css_cusca/endpoint/",
"sparkArgs": {
"driver-cores": 16,
"driver-memory": "14g",
"executor-cores": 16,
"executor-memory": "14g",
"executor-instances": 12,
"others": "--jars s3://bucket-emr-example/bcom_spark/jars/mysql-connector-java-8.0.30.jar"
}
}
}
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