def generate_release(self):
'''
Get a list of data set to be processed and try to harmonised them into
one big data cube
'''
# Prepare a task list
tasks = []
for sheet_name in self._get_sheets_list():
output_file = self._conf.get_path('release') + sheet_name + '.ttl'
task = {
'sheet_name': sheet_name,
'output_file': output_file,
'endpoint': self._conf.get_SPARQL(),
'compress': self._conf.isCompress(),
'target': self._conf.get_namespace('data'),
'release_graph': self._conf.get_graph_name('release'),
'raw_data_graph': self._conf.get_graph_name('raw-data'),
'rules_graph': self._conf.get_graph_name('rules'),
'measure': self._conf.get_measure()
}
tasks.append(task)
# Call cube in parallel, avoid hammering the store too much
cpu_count = multiprocessing.cpu_count()
pool = multiprocessing.Pool(processes=min(4, cpu_count))
pool.map(generate_release_thread, tasks)
pool.close()
pool.join()
# Push all the data to the triple store
self._push_to_graph(self._conf.get_graph_name('release'),
self._conf.get_path('release'))
# Create an instance of CubeMaker
cubeMaker = CubeMaker(self._conf.get_SPARQL(),
self._conf.get_graph_name('release'),
self._conf.get_graph_name('raw-data'),
self._conf.get_graph_name('rules'))
cubeMaker.set_target_namespace(self._conf.get_namespace('data'))
cubeMaker.set_compress(self._conf.isCompress())
# Update the DSD
dsd_file_name = self._conf.get_path('release') + 'dsd.ttl'
log.info("Asking CubeMaker to generate the DSD")
cubeMaker.generate_dsd(self._conf.get_cube_title(),
self._conf.get_measure(),
self._conf.get_measureunit(),
self._conf.get_slices(), dsd_file_name)
# Load the DSD
pusher = Pusher(self._conf.get_SPARUL(), self._conf.get_user(),
self._conf.get_secret())
log.info("[{}] Adding the content of the DSD".format(
self._conf.get_graph_name('release')))
if self._conf.isCompress():
dsd_file_name = dsd_file_name + ".bz2"
pusher.upload_file(self._conf.get_graph_name('release'), dsd_file_name)
def generate_release(self):
'''
Get a list of data set to be processed and try to harmonised them into
one big data cube
'''
# Prepare a task list
tasks = []
for sheet_name in self._get_sheets_list():
output_file = self._conf.get_path('release') + sheet_name + '.ttl'
task = {'sheet_name' : sheet_name,
'output_file' : output_file,
'endpoint' : self._conf.get_SPARQL(),
'compress' : self._conf.isCompress(),
'target' : self._conf.get_namespace('data'),
'release_graph' : self._conf.get_graph_name('release'),
'raw_data_graph' : self._conf.get_graph_name('raw-data'),
'rules_graph' : self._conf.get_graph_name('rules'),
'measure' : self._conf.get_measure()}
tasks.append(task)
# Call cube in parallel, avoid hammering the store too much
cpu_count = multiprocessing.cpu_count()
pool = multiprocessing.Pool(processes=min(4, cpu_count))
pool.map(generate_release_thread, tasks)
pool.close()
pool.join()
# Push all the data to the triple store
self._push_to_graph(self._conf.get_graph_name('release'),
self._conf.get_path('release'))
# Create an instance of CubeMaker
cubeMaker = CubeMaker(self._conf.get_SPARQL(),
self._conf.get_graph_name('release'),
self._conf.get_graph_name('raw-data'),
self._conf.get_graph_name('rules'))
cubeMaker.set_target_namespace(self._conf.get_namespace('data'))
cubeMaker.set_compress(self._conf.isCompress())
# Update the DSD
dsd_file_name = self._conf.get_path('release') + 'dsd.ttl'
log.info("Asking CubeMaker to generate the DSD")
cubeMaker.generate_dsd(self._conf.get_cube_title(),
self._conf.get_measure(),
self._conf.get_measureunit(),
self._conf.get_slices(),
dsd_file_name)
# Load the DSD
pusher = Pusher(self._conf.get_SPARUL(),
self._conf.get_user(),
self._conf.get_secret())
log.info("[{}] Adding the content of the DSD".format(self._conf.get_graph_name('release')))
if self._conf.isCompress():
dsd_file_name = dsd_file_name + ".bz2"
pusher.upload_file(self._conf.get_graph_name('release'), dsd_file_name)
def generate_release_thread(parameters):
'''
Worker thread for generate_release
'''
sheet_name = parameters['sheet_name']
output_file = parameters['output_file']
log.info("[{}] Calling CubeMaker".format(sheet_name))
try:
cubeMaker = CubeMaker(parameters['endpoint'],
parameters['release_graph'],
parameters['raw_data_graph'],
parameters['rules_graph'])
cubeMaker.set_target_namespace(parameters['target'])
cubeMaker.set_compress(parameters['compress'])
cubeMaker.process(parameters['measure'], sheet_name, output_file)
except Exception as e:
log.error("[{}] Error in CubeMaker: {}".format(sheet_name, e))
def generate_release_thread(parameters):
'''
Worker thread for generate_release
'''
sheet_name = parameters['sheet_name']
output_file = parameters['output_file']
log.info("[{}] Calling CubeMaker".format(sheet_name))
try:
cubeMaker = CubeMaker(parameters['endpoint'],
parameters['release_graph'],
parameters['raw_data_graph'],
parameters['rules_graph'])
cubeMaker.set_target_namespace(parameters['target'])
cubeMaker.set_compress(parameters['compress'])
cubeMaker.process(parameters['measure'], sheet_name, output_file)
except Exception as e:
log.error("[{}] Error in CubeMaker: {}".format(sheet_name, e))
