def module_task(self, params):
self.logger.info(
'Starting Hbase-HBase ETL using Hadoop to clean billing data...')
"""CHECK INCONSISTENCIES IN params"""
try:
result_companyId = params['result_companyId']
data_companyId = params[
'data_companyId'] if 'data_companyId' in params else []
ts_to = params['ts_to']
ts_from = params[
'ts_from'] if 'ts_from' in params else date_n_month(
ts_to, -96)
energyTypeList = params['type'] if 'type' in params else []
except KeyError as e:
raise Exception('Mandatory Parameter not provided: {}'.format(e))
######################################################################################################################################################################################
""" GET DATA FROM MONGO TO MAKE QUERYS """
######################################################################################################################################################################################
if not energyTypeList:
energyTypeList = list(
set([
x['type']
for x in self.mongo['readings'].find({}, {'type': 1})
]))
if not data_companyId:
data_companyId = list(
set([
x['companyId']
for x in self.mongo['companies'].find({}, {'companyId': 1})
]))
######################################################################################################################################################################################
""" HIVE QUERY TO GET HBASE DATA """
######################################################################################################################################################################################
for measure_config in self.config['measures']:
# Create temp tables with hbase data, add them to context_clean to be deleted after execution
tables = []
type_table_name = measure_config["hbase_table"]
tables_source = []
tables_energyType = []
self.logger.info('creating {} tables for {} and {}'.format(
type_table_name, energyTypeList, data_companyId))
tables_list = self.hbase.tables()
for energyType in energyTypeList:
for companyId in data_companyId:
try:
table_name = "{}_{}_{}".format(type_table_name,
energyType, companyId)
if table_name not in tables_list:
continue
hbase_table_name = "{}{}{}".format(
self.config['hbase']['db'],
self.config['hbase']['db_separator'], table_name)
keys = measure_config['hbase_keys']
columns = measure_config['hbase_columns']
temp_table = create_hive_table_from_hbase_table(
self.hive, table_name, hbase_table_name, keys,
columns, self.task_UUID)
tables.append(temp_table)
self.context.add_clean_hive_tables(temp_table)
tables_energyType.append(energyType)
tables_source.append(companyId)
self.logger.debug(
"Created table: {}".format(temp_table))
except Exception as e:
self.logger.debug("Error creating table: {}".format(e))
self.logger.debug(len(tables))
fields = measure_config["hive_fields"]
location = measure_config['measures'].format(UUID=self.task_UUID)
self.context.add_clean_hdfs_file(location)
input_table = create_hive_module_input_table(
self.hive, measure_config['temp_input_table'], location,
fields, self.task_UUID)
#add input table to be deleted after execution
self.context.add_clean_hive_tables(input_table)
qbr = RawQueryBuilder(self.hive)
select = ", ".join(
[f[0] for f in measure_config["sql_sentence_select"]])
sentence = """
INSERT OVERWRITE TABLE {input_table}
SELECT {select} FROM
( """.format(select=select, input_table=input_table)
letter = ["a{}".format(i) for i in range(len(tables) + 1)]
text = []
for index, tab in enumerate(tables):
var = letter[index]
energy_type = tables_energyType[index]
source = tables_source[index]
select = ", ".join([
f[1] for f in measure_config["sql_sentence_select"]
]).format(var=var, energy_type=energy_type, source=source)
where = measure_config["sql_where_select"].format(
var=var, ts_from=ts_from, ts_to=ts_to)
text.append(""" SELECT {select} FROM {tab} {var}
WHERE
{where}
""".format(var=var,
select=select,
tab=tab,
where=where))
sentence += """UNION
""".join(text)
sentence += """) unionResult """
self.logger.debug(sentence)
try:
qbr.execute_query(sentence)
except:
continue
#####################################################################################################################################################################################
""" SETUP MAP REDUCE JOB """
######################################################################################################################################################################################
# remove previous raw_data results
output_fields = self.config['output']['fields']
clean_tables = []
for measure_config in self.config['measures']:
clean_file_name = measure_config['clean_output_file'].format(
UUID=self.task_UUID)
self.context.add_clean_hdfs_file(clean_file_name)
clean_table_name = measure_config['clean_output_table']
self.logger.debug('Launching MR job to clean the daily data')
try:
# Launch MapReduce job
self.launcher_hadoop_job(
measure_config['type'],
measure_config['measures'].format(UUID=self.task_UUID),
clean_file_name, result_companyId)
except Exception as e:
raise Exception('MRJob process has failed: {}'.format(e))
clean_table = create_hive_module_input_table(
self.hive, clean_table_name, clean_file_name, output_fields,
self.task_UUID)
self.context.add_clean_hive_tables(clean_table)
clean_tables.append([clean_table, measure_config['type']])
self.logger.debug("MRJob finished for {}".format(
measure_config['type']))
######################################################################################################################################################################################
""" Join the output in a hive table """
######################################################################################################################################################################################
output_file_name = self.config['output']['output_file_name']
output_hive_name = self.config['output']['output_hive_table']
output_hive_table = create_hive_module_input_table(
self.hive, output_hive_name, output_file_name, output_fields)
try:
for i in self.hdfs.delete([output_file_name], recurse=True):
try:
i
except:
pass
except:
pass
select = ", ".join(
[f[0] for f in self.config['output']["sql_sentence_select"]])
sentence = """
INSERT OVERWRITE TABLE {output_table}
SELECT {select} FROM
( """.format(select=select,
output_table=output_hive_table)
letter = ["a{}".format(i) for i in range(len(clean_tables) + 1)]
text = []
for index, tab in enumerate(clean_tables):
var = letter[index]
select = ", ".join([
f[1] for f in self.config['output']["sql_sentence_select"]
]).format(var=var, data_type=tab[1])
text.append(""" SELECT {select} FROM {tab} {var}
""".format(var=var, select=select, tab=tab[0]))
sentence += """UNION
""".join(text)
sentence += """) unionResult """
self.logger.debug(sentence)
qbr = RawQueryBuilder(self.hive)
qbr.execute_query(sentence)
self.logger.info(
'MHbase-HBase ETL clean billing data execution finished...')
def module_task(self, params):
self.logger.info('Starting Module for edinet baseline...')
"""CHECK INCONSISTENCIES IN params"""
try:
result_companyId = params['result_companyId']
ts_to = params['ts_to']
ts_from = params[
'ts_from'] if 'ts_from' in params else date_n_month(
ts_to, -24)
energyTypeList = params['type'] if 'type' in params else []
except KeyError as e:
raise Exception(
'Not enough parameters provided to module: {}'.format(e))
######################################################################################################################################################################################
""" GET DATA FROM MONGO TO MAKE QUERYS """
######################################################################################################################################################################################
if not energyTypeList:
energyTypeList = list(
set([
x['type']
for x in self.mongo['readings'].find({}, {'type': 1})
]))
#####################################################################################################################################################################################
""" LOAD DATA FROM HIVE """
######################################################################################################################################################################################
self.logger.info('Extracting data from mongodb')
# setting variables for readability
collection = self.config['mongodb']['modelling_units_collection']
self.logger.debug('Querying for modelling units in MongoDB')
cursor = self.mongo[collection].find({})
device_key = {}
stations = {}
for item in cursor:
if len(item['devices']) > 0: # to avoid empty list of devices
for dev in item['devices']:
stations[str(dev['deviceId'].encode('utf-8'))] = str(
item['stationId']) if 'stationId' in item else None
if str(dev['deviceId'].encode(
'utf-8')) in device_key.keys():
device_key[str(
dev['deviceId'].encode('utf-8'))].append(
str(item['modellingUnitId']) + '~' +
str(item['devices']))
else:
device_key[str(dev['deviceId'].encode('utf-8'))] = [
str(item['modellingUnitId']) + '~' +
str(item['devices'])
]
self.logger.info('A mongo query process has loaded {} devices'.format(
len(device_key.keys())))
######################################################################################################################################################################################
""" HIVE QUERY TO PREPARE DATA FOR MRJOB """
######################################################################################################################################################################################
# create a table to link devices with stations
device_stations_df = pd.DataFrame(data={
"deviceId": stations.keys(),
"stationId": stations.values()
},
columns=["deviceId", "stationId"])
f = NamedTemporaryFile(delete=False, suffix='.csv')
device_stations_df.to_csv(f.name, header=None, index=None)
f.close()
call([
"hadoop", "fs", "-mkdir", "-p", f.name,
self.config['paths']['stations']
])
call([
"hadoop", "fs", "-copyFromLocal", f.name,
self.config['paths']['stations']
])
f.unlink(f.name)
device_stations = create_hive_module_input_table(
self.hive,
'edinet_device_stations_table',
self.config['paths']['stations'], [('deviceId', 'string'),
('stationId', 'string')],
self.task_UUID,
sep=",")
self.context.add_clean_hive_tables(device_stations)
# create a table with the devices values
fields = [('deviceId', 'string'), ('ts', 'int'), ('value', 'float'),
('energyType', 'string'), ('source', 'string'),
('temperature', 'string')]
location = self.config['paths']['measures']
input_table = create_hive_module_input_table(self.hive,
'edinet_baseline_input',
location, fields,
self.task_UUID)
#add input table to be deleted after execution
self.context.add_clean_hive_tables(input_table)
qbr = RawQueryBuilder(self.hive)
sentence = """
INSERT OVERWRITE TABLE {input_table}
SELECT a.deviceId, a.ts, a.value, a.energyType, a.source, c.temperature FROM
(SELECT ai.deviceid as deviceId, ai.ts as ts, ai.value as value, ai.energyType as energyType, ai.source as source FROM edinet_hourly_consumption ai
WHERE
ai.ts >= UNIX_TIMESTAMP("{ts_from}","yyyy-MM-dd HH:mm:ss") AND
ai.ts <= UNIX_TIMESTAMP("{ts_to}","yyyy-MM-dd HH:mm:ss")) a
JOIN {device_stations} b on a.deviceId==b.deviceId
JOIN edinet_meteo c on b.stationId==c.stationId and SUBSTR(FROM_UNIXTIME(a.ts), 1, 13) == SUBSTR(FROM_UNIXTIME(c.ts), 1, 13)
""".format(input_table=input_table,
ts_from=ts_from,
ts_to=ts_to,
device_stations=device_stations)
self.logger.debug(sentence)
qbr.execute_query(sentence)
######################################################################################################################################################################################
""" SETUP MAP REDUCE JOB """
######################################################################################################################################################################################
self.logger.info('Getting')
try:
# Launch MapReduce job
## Buffered measures to HBase
self.logger.debug('MRJob Align')
self.launcher_hadoop_job('align', location, result_companyId,
device_key, stations)
except Exception as e:
raise Exception('MRJob ALIGN process job has failed: {}'.format(e))
self.logger.info('Module EDINET_baseline execution finished...')
def module_task(self, params):
self.logger.info('Starting Module for edinet baseline...')
"""CHECK INCONSISTENCIES IN params"""
try:
result_companyId = params['result_companyId']
ts_to = params['ts_to']
ts_from = params[
'ts_from'] if 'ts_from' in params else date_n_month(
ts_to, -24)
energyTypeList = params['type'] if 'type' in params else []
save_data_debug = True if 'debug' in params and params[
'debug'] else False
except KeyError as e:
raise Exception(
'Not enough parameters provided to module: {}'.format(e))
#####################################################################################################################################################################################
""" LOAD DATA FROM MONGO MODELLING UNITS """
######################################################################################################################################################################################
self.logger.info('Extracting modelling_units from mongo')
# setting variables for readability
modelling_units_collection = self.config['mongodb'][
'modelling_units_collection']
weather_stations_collection = self.config['mongodb'][
'weather_stations_collection']
lon_lat_tz_dict = {}
tf = TimezoneFinder(in_memory=True)
self.logger.debug('Querying for weather station info in MongoDB')
cursor = self.mongo[weather_stations_collection].find({})
for station in cursor:
lon_lat_tz_dict[station['stationId']] = {
"lat":
station['latitude'],
"lon":
station['longitude'],
"tz":
tf.timezone_at(lat=station['latitude'],
lng=station['longitude'])
}
cursor.close()
tf = None
device_key = {}
stations = {}
solar_station = {}
self.logger.debug('Querying for modelling unit info in MongoDB')
cursor = self.mongo[modelling_units_collection].find({})
for item in cursor:
if len(item['devices']) > 0 and item[
'stationId'] != "Unknown": # to avoid empty list of devices
for dev in item['devices']:
stations[dev['deviceId']] = item[
'stationId'] if 'stationId' in item else None
solar_station[dev['deviceId']] = item[
'solar_station'] if 'solar_station' in item else None
key_str = "{modelling}~{devices}~{lat}~{lon}~{tz}".format(
modelling=item['modellingUnitId'],
devices=item['devices'],
lat=lon_lat_tz_dict[item['stationId']]['lat'],
lon=lon_lat_tz_dict[item['stationId']]['lon'],
tz=lon_lat_tz_dict[item['stationId']]['tz'])
if dev['deviceId'] in device_key.keys():
device_key[dev['deviceId']].append(key_str)
else:
device_key[dev['deviceId']] = [key_str]
cursor.close()
self.logger.info('A mongo query process has loaded {} devices'.format(
len(device_key.keys())))
######################################################################################################################################################################################
""" CREATE INPUT DATA FROM HIVE TABLES """
######################################################################################################################################################################################
# create a table to link devices with stations
self.logger.debug('creating weather hive table')
weather_stations_df = pd.DataFrame(data={
"deviceId": list(stations.keys()),
"stationId": list(stations.values())
},
columns=["deviceId", "stationId"])
f_station = NamedTemporaryFile(delete=False, suffix='.csv')
weather_stations_df.to_csv(f_station.name, header=None, index=None)
call([
"hadoop", "fs", "-mkdir", "-p", f_station.name,
self.config['paths']['stations']
])
call([
"hadoop", "fs", "-copyFromLocal", f_station.name,
self.config['paths']['stations']
])
weather_stations = create_hive_module_input_table(
self.hive,
'edinet_weather_stations_table',
self.config['paths']['stations'], [('deviceId', 'string'),
('stationId', 'string')],
self.task_UUID,
sep=",")
self.context.add_clean_hive_tables(weather_stations)
# create a table to link devices with solar_stations
self.logger.debug('creating solar hive table')
solar_stations_df = pd.DataFrame(data={
"deviceId":
list(solar_station.keys()),
"stationId":
list(solar_station.values())
},
columns=["deviceId", "stationId"])
f_solar_station = NamedTemporaryFile(delete=False, suffix='.csv')
solar_stations_df.to_csv(f_solar_station.name, header=None, index=None)
call([
"hadoop", "fs", "-mkdir", "-p", f_solar_station.name,
self.config['paths']['solar_stations']
])
call([
"hadoop", "fs", "-copyFromLocal", f_solar_station.name,
self.config['paths']['solar_stations']
])
solar_stations = create_hive_module_input_table(
self.hive,
'edinet_solar_stations_table',
self.config['paths']['solar_stations'], [('deviceId', 'string'),
('stationId', 'string')],
self.task_UUID,
sep=",")
self.context.add_clean_hive_tables(solar_stations)
# create a table with the devices values
self.logger.debug('creating input table')
final_table_fields = [
[x[0], x[1]] for x in self.config['hive']['final_table_fields']
]
location = self.config['paths']['measures']
input_table = create_hive_module_input_table(
self.hive, self.config['hive']['job_table_name'], location,
final_table_fields, self.task_UUID)
#add input table to be deleted after execution
self.context.add_clean_hive_tables(input_table)
self.logger.debug('creating hive query')
qbr = RawQueryBuilder(self.hive)
total_select_joint = ", ".join([
"{}.{}".format(x[2], x[0])
for x in self.config['hive']['final_table_fields']
])
sentence = """
INSERT OVERWRITE TABLE {input_table}
SELECT {total_select_joint} FROM
(SELECT ai.deviceid as deviceId, ai.ts as ts, ai.value as value, ai.energyType as energyType FROM edinet_hourly_consumption ai
WHERE
ai.ts >= UNIX_TIMESTAMP("{ts_from}","yyyy-MM-dd HH:mm:ss") AND
ai.ts <= UNIX_TIMESTAMP("{ts_to}","yyyy-MM-dd HH:mm:ss") AND
ai.deviceid IN ({devices})) a
JOIN {weather_stations} b on a.deviceId==b.deviceId
JOIN {solar_stations} b1 on a.deviceId==b1.deviceId
JOIN edinet_meteo c on b.stationId==c.stationId and SUBSTR(FROM_UNIXTIME(a.ts), 1, 13) == SUBSTR(FROM_UNIXTIME(c.ts), 1, 13)
JOIN edinet_meteo d on b1.stationId==d.stationId and SUBSTR(FROM_UNIXTIME(a.ts), 1, 13) == SUBSTR(FROM_UNIXTIME(d.ts), 1, 13)
""".format(input_table=input_table,
total_select_joint=total_select_joint,
ts_from=ts_from,
ts_to=ts_to,
weather_stations=weather_stations,
solar_stations=solar_stations,
devices=", ".join("\"{}\"".format(x)
for x in list(device_key.keys())))
self.logger.debug(sentence)
qbr.execute_query(sentence)
#####################################################################################################################################################################################
""" LOAD from MONGO to HBASE """
######################################################################################################################################################################################
self.logger.info('Getting')
try:
# Launch MapReduce job
## Buffered measures to HBase
self.logger.debug('Baseline Calculation')
self.launcher_hadoop_job(location, device_key, result_companyId,
save_data_debug)
except Exception as e:
raise Exception('MRJob ALIGN process job has failed: {}'.format(e))
self.logger.info('Module EDINET_baseline execution finished...')
def module_task(self, params):
self.logger.info('Starting Module for edinet comparisons ...')
"""CHECK INCONSISTENCIES IN params"""
try:
result_companyId = params['result_companyId']
ts_to = params['ts_to']
ts_from = params[
'ts_from'] if 'ts_from' in params else date_n_month(
ts_to, -48)
energyTypeDict = params['type'] if 'type' in params else {
'heatConsumption': 'gasConsumption',
'gasConsumption': 'gasConsumption',
'monthlyElectricityConsumption': 'electricityConsumption',
'electricityConsumption': 'electricityConsumption'
}
except KeyError as e:
raise Exception(
'Not enough parameters provided to module: {}'.format(e))
#####################################################################################################################################################################################
""" LOAD from MONGO """
######################################################################################################################################################################################
# Get the link between the devices and the modelling units. In the form of a dict {"device":{modelling_unit~{device:multiplier}}
self.logger.info('Extracting data from mongodb')
modelling_units_collection = self.config['mongodb'][
'modelling_units_collection']
cursor = self.mongo[modelling_units_collection].find({})
device_key = {}
def get_building(modelling_unit, mongo, building_collection,
reporting_collection):
building = mongo[building_collection].find_one(
{"modellingUnits": modelling_unit})
if not building:
reporting = mongo[reporting_collection].find_one(
{"modelling_Units": modelling_unit})
if reporting and "reportingUnitId" in reporting:
building = mongo[building_collection].find_one(
{"buildingId": reporting['reportingUnitId']})
if not building:
return None
return building
building_collection = self.config['mongodb']['buildings_collection']
reporting_collection = self.config['mongodb']['reporting_collection']
self.logger.debug("generating the device_key dict")
for item in cursor:
#self.logger.debug(item)
#self.logger.debug("gettinng item building {}".format(item['modellingUnitId']))
building = get_building(item['modellingUnitId'], self.mongo,
building_collection, reporting_collection)
#self.logger.debug("obtained building {}".format(building))
if building and 'data' in building and 'areaBuild' in building[
'data']:
surface = building["data"]["areaBuild"]
else:
surface = None
#self.logger.debug("area of building: {}".format(surface))
if len(item['devices']
) > 0 and surface: # to avoid empty list of devices
#self.logger.debug("list of devices {}".format(item['devices']))
for dev in item['devices']:
key_str = "{modelling}~{devices}~{area}".format(
modelling=item['modellingUnitId'],
devices=item['devices'],
area=surface)
if dev['deviceId'] in device_key.keys():
device_key[dev['deviceId']].append(key_str)
else:
device_key[dev['deviceId']] = [key_str]
#self.logger.debug("finished for {}".format(item['modellingUnitId']))
cursor.close()
self.logger.info('A mongo query process has loaded {} devices'.format(
len(device_key.keys())))
######################################################################################################################################################################################
""" HIVE QUERY TO PREPARE DATA THAT HAS TO BE LOADED INTO MONGO """
######################################################################################################################################################################################
# create a table with the devices values that will be the input of the MRJob that creates the monthly datatable.
self.logger.debug('creating input table to aggregate monthly')
final_table_fields = [
[x[0], x[1]] for x in self.config['hive']['final_table_fields']
]
location = self.config['paths']['monthly_aggregation']
input_table = create_hive_module_input_table(
self.hive, self.config['hive']['job_table_name'], location,
final_table_fields, self.task_UUID)
#add input table to be deleted after execution
self.context.add_clean_hive_tables(input_table)
self.logger.debug('creating hive query')
qbr = RawQueryBuilder(self.hive)
total_select_joint = ", ".join([
"{}.{}".format(x[2], x[0])
for x in self.config['hive']['final_table_fields']
])
sentence = """
INSERT OVERWRITE TABLE {input_table}
SELECT {total_select_joint} FROM
(SELECT ai.deviceid as deviceId, ai.ts as ts, ai.value as value, ai.energyType as energyType, ai.source as source FROM edinet_daily_consumption ai
WHERE
ai.ts >= UNIX_TIMESTAMP("{ts_from}","yyyy-MM-dd HH:mm:ss") AND
ai.ts <= UNIX_TIMESTAMP("{ts_to}","yyyy-MM-dd HH:mm:ss") AND
ai.deviceid IN ({devices})) a
""".format(input_table=input_table,
total_select_joint=total_select_joint,
ts_from=ts_from,
ts_to=ts_to,
devices=", ".join("\"{}\"".format(x)
for x in list(device_key.keys())))
self.logger.debug(sentence)
qbr.execute_query(sentence)
self.hive.close()
gc.collect()
######################################################################################################################################################################################
""" MAPREDUCE TO AGGREGATE MONTHLY DATA """
######################################################################################################################################################################################
self.logger.info('Running Mapreduce for Montly Aggregation')
output_location = self.config['paths']['output_monthly_aggregation']
try:
# Launch MapReduce job
## Buffered measures to HBase
self.logger.debug('Montly Aggregation')
self.aggregate_hadoop_job(location, output_location, device_key,
result_companyId)
except Exception as e:
raise Exception('MRJob ALIGN process job has failed: {}'.format(e))
output_fields = [["modellingUnit", "string"], ["ts", "bigint"],
["value", "float"], ["energyType", "string"]]
aggregated_table_name = self.config['hive'][
'output_monthly_aggregation']
aggregated_table = create_hive_module_input_table(
self.hive, aggregated_table_name, output_location, output_fields,
self.task_UUID)
self.context.add_clean_hive_tables(aggregated_table)
self.logger.debug("MRJob for monthly aggregation finished")
######################################################################################################################################################################################
""" MAPREDUCE TO CALCULATE BENCHMARKING """
######################################################################################################################################################################################
self.logger.debug('creating benchmarking information table')
building_collection = self.config['mongodb']['buildings_collection']
cursor = self.mongo[building_collection].find({})
buildings_list = []
for item in cursor:
if not 'modellingUnits' in item or not 'data' in item:
continue
if not 'useType' in item[
'data'] or not 'organizationLevel1' in item['data']:
continue
for modelling in item['modellingUnits']:
b_dic = {
"modellingunit": modelling,
"type": item['data']['useType'],
"organization": item['data']['organizationLevel1']
}
buildings_list.append(b_dic)
cursor.close()
buildings_df = pd.DataFrame.from_records(
buildings_list, columns=['modellingunit', 'type', 'organization'])
f_station = NamedTemporaryFile(delete=False, suffix='.csv')
buildings_df.to_csv(f_station.name, header=None, index=None)
call([
"hadoop", "fs", "-mkdir", "-p", f_station.name,
self.config['paths']['building_info']
])
call([
"hadoop", "fs", "-copyFromLocal", f_station.name,
self.config['paths']['building_info']
])
building_table = create_hive_module_input_table(
self.hive,
self.config['hive']['building_info_table'],
self.config['paths']['building_info'],
[('modellingunit', 'string'), ('type', 'string'),
('organization', 'string')],
self.task_UUID,
sep=",")
self.context.add_clean_hive_tables(building_table)
self.logger.debug('creating hive query to join data with information')
qbr = RawQueryBuilder(self.hive)
location = self.config['paths']['benchmarking_data']
benchmarking_field = self.config['hive']['benchmarking_table_fields']
benchmarking_table = create_hive_module_input_table(
self.hive, self.config['hive']['benchmarking_table'], location,
benchmarking_field, self.task_UUID)
total_select_joint = ", ".join(
["{}.{}".format(x[2], x[0]) for x in benchmarking_field])
sentence = """
INSERT OVERWRITE TABLE {input_table}
SELECT {total_select_joint} FROM
(SELECT * FROM {aggregated_table}) a
JOIN {building_table} b on a.modellingUnit==b.modellingUnit
""".format(input_table=benchmarking_table,
total_select_joint=total_select_joint,
aggregated_table=aggregated_table,
building_table=building_table)
self.logger.debug(sentence)
qbr.execute_query(sentence)
self.logger.info('Running Mapreduce for Benchmarking')
try:
# Launch MapReduce job
## Buffered measures to HBase
self.logger.debug('Benchmarking_calculation')
self.benchmarking_hadoop_job(location, energyTypeDict,
result_companyId)
except Exception as e:
raise Exception('MRJob ALIGN process job has failed: {}'.format(e))
self.logger.debug("MRJob for benchmarking finished")