def process_ed2nav():
models.db.session.add(job)
models.db.session.commit()
# We pass the job id to each tasks, but job need to be commited for having an id
for action in actions:
action.kwargs['job_id'] = job.id
# Create binary file (New .nav.lz4)
binarisation = [ed2nav.si(instance_config, job.id, custom_output_dir)]
actions.append(chain(*binarisation))
# Reload kraken with new data after binarisation (New .nav.lz4)
if reload:
actions.append(reload_data.si(instance_config, job.id))
if not skip_mimir:
for dataset in job.data_sets:
actions.extend(
send_to_mimir(instance, dataset.name, dataset.family_type))
else:
current_app.logger.info("skipping mimir import")
actions.append(finish_job.si(job.id))
# We should delete old backup directories related to this instance
actions.append(
purge_instance.si(
instance.id,
current_app.config['DATASET_MAX_BACKUPS_TO_KEEP']))
if asynchronous:
return chain(*actions).delay()
else:
# all job are run in sequence and import_data will only return when all the jobs are finish
return chain(*actions).apply()
def reload_kraken(instance_id):
instance = models.Instance.query.get(instance_id)
job = models.Job()
job.instance = instance
job.state = 'pending'
instance_config = load_instance_config(instance.name)
models.db.session.add(job)
models.db.session.commit()
chain(reload_data.si(instance_config, job.id), finish_job.si(job.id)).delay()
logging.info("Task reload kraken for instance {} queued".format(instance.name))
def reload_at(instance_id):
instance = models.Instance.query.get(instance_id)
job = models.Job()
job.instance = instance
job.state = 'pending'
instance_config = load_instance_config(instance.name)
models.db.session.add(job)
models.db.session.commit()
chain(nav2rt.si(instance_config, job.id),
reload_data.si(instance_config, job.id),
finish_job.si(job.id)).delay()
models.db.session.add(dataset)
job.data_sets.append(dataset)
if actions:
models.db.session.add(job)
models.db.session.commit()
for action in actions:
action.kwargs['job_id'] = job.id
#We pass the job id to each tasks, but job need to be commited for
#having an id
binarisation = [ed2nav.si(instance_config, job.id, custom_output_dir)]
#We pass the job id to each tasks, but job need to be commited for
#having an id
actions.append(chain(*binarisation))
if reload:
actions.append(reload_data.si(instance_config, job.id))
actions.append(finish_job.si(job.id))
if async:
chain(*actions).delay()
else:
# all job are run in sequence and import_data will only return when all the jobs are finish
chain(*actions).apply()
@celery.task()
def update_data():
for instance in models.Instance.query.all():
current_app.logger.debug("Update data of : {}".format(instance.name))
instance_config = load_instance_config(instance.name)
files = glob.glob(instance_config.source_directory + "/*")
import_data(files, instance, backup_file=True)
def import_data(
files, instance, backup_file, asynchronous=True, reload=True, custom_output_dir=None, skip_mimir=False
):
"""
import the data contains in the list of 'files' in the 'instance'
:param files: files to import
:param instance: instance to receive the data
:param backup_file: If True the files are moved to a backup directory, else they are not moved
:param asynchronous: If True all jobs are run in background, else the jobs are run in sequence the function
will only return when all of them are finish
:param reload: If True kraken would be reload at the end of the treatment
:param custom_output_dir: subdirectory for the nav file created. If not given, the instance default one is taken
:param skip_mimir: skip importing data into mimir
run the whole data import process:
- data import in bdd (fusio2ed, gtfs2ed, poi2ed, ...)
- export bdd to nav file
- update the jormungandr db with the new data for the instance
- reload the krakens
"""
actions = []
job = models.Job()
instance_config = load_instance_config(instance.name)
job.instance = instance
job.state = 'running'
task = {
'gtfs': gtfs2ed,
'fusio': fusio2ed,
'osm': osm2ed,
'geopal': geopal2ed,
'fare': fare2ed,
'poi': poi2ed,
'synonym': synonym2ed,
'shape': shape2ed,
}
for _file in files:
filename = None
dataset = models.DataSet()
# NOTE: for the moment we do not use the path to load the data here
# but we'll need to refactor this to take it into account
try:
dataset.type, _ = utils.type_of_data(_file)
dataset.family_type = utils.family_of_data(dataset.type)
except Exception:
if backup_file:
move_to_backupdirectory(_file, instance_config.backup_directory)
current_app.logger.debug(
"Corrupted source file : {} moved to {}".format(_file, instance_config.backup_directory)
)
continue
if dataset.type in task:
if backup_file:
filename = move_to_backupdirectory(_file, instance_config.backup_directory)
else:
filename = _file
actions.append(task[dataset.type].si(instance_config, filename, dataset_uid=dataset.uid))
else:
# unknown type, we skip it
current_app.logger.debug("unknown file type: {} for file {}".format(dataset.type, _file))
continue
# currently the name of a dataset is the path to it
dataset.name = filename
models.db.session.add(dataset)
job.data_sets.append(dataset)
if actions:
models.db.session.add(job)
models.db.session.commit()
# We pass the job id to each tasks, but job need to be commited for having an id
for action in actions:
action.kwargs['job_id'] = job.id
# Create binary file (New .nav.lz4)
binarisation = [ed2nav.si(instance_config, job.id, custom_output_dir)]
actions.append(chain(*binarisation))
# Reload kraken with new data after binarisation (New .nav.lz4)
if reload:
actions.append(reload_data.si(instance_config, job.id))
if not skip_mimir:
for dataset in job.data_sets:
actions.extend(send_to_mimir(instance, dataset.name, dataset.family_type))
else:
current_app.logger.info("skipping mimir import")
actions.append(finish_job.si(job.id))
if asynchronous:
return chain(*actions).delay()
else:
# all job are run in sequence and import_data will only return when all the jobs are finish
return chain(*actions).apply()
def update_data():
for instance in models.Instance.query.all():
current_app.logger.debug("Update data of : %s" % instance.name)
instance_config = load_instance_config(instance.name)
files = glob.glob(instance_config.source_directory + "/*")
actions = []
job = models.Job()
job.instance = instance
job.state = 'pending'
for _file in files:
dataset = models.DataSet()
filename = None
dataset.type = type_of_data(_file)
if dataset.type == 'gtfs':
filename = move_to_backupdirectory(
_file, instance_config.backup_directory)
actions.append(gtfs2ed.si(instance_config, filename))
elif dataset.type == 'fusio':
filename = move_to_backupdirectory(
_file, instance_config.backup_directory)
actions.append(fusio2ed.si(instance_config, filename))
elif dataset.type == 'osm':
filename = move_to_backupdirectory(
_file, instance_config.backup_directory)
actions.append(osm2ed.si(instance_config, filename))
elif dataset.type == 'geopal':
filename = move_to_backupdirectory(
_file, instance_config.backup_directory)
actions.append(geopal2ed.si(instance_config, filename))
elif dataset.type == 'fare':
filename = move_to_backupdirectory(
_file, instance_config.backup_directory)
actions.append(fare2ed.si(instance_config, filename))
elif dataset.type == 'poi':
filename = move_to_backupdirectory(
_file, instance_config.backup_directory)
actions.append(poi2ed.si(instance_config, filename))
elif dataset.type == 'synonym':
filename = move_to_backupdirectory(
_file, instance_config.backup_directory)
actions.append(synonym2ed.si(instance_config, filename))
else:
#unknown type, we skip it
continue
#currently the name of a dataset is the path to it
dataset.name = filename
models.db.session.add(dataset)
job.data_sets.append(dataset)
if actions:
models.db.session.add(job)
models.db.session.commit()
for action in actions:
action.kwargs['job_id'] = job.id
#We pass the job id to each tasks, but job need to be commited for
#having an id
binarisation = [
ed2nav.si(instance_config, job.id),
nav2rt.si(instance_config, job.id)
]
aggregate = aggregate_places.si(instance_config, job.id)
#We pass the job id to each tasks, but job need to be commited for
#having an id
actions.append(group(chain(*binarisation), aggregate))
actions.append(reload_data.si(instance_config, job.id))
actions.append(finish_job.si(job.id))
chain(*actions).delay()
def import_data(files, instance, backup_file):
"""
import the data contains in the list of 'files' in the 'instance'
:param files: files to import
:param instance: instance to receive the data
:param backup_file: If True the files are moved to a backup directory, else they are not moved
run the whole data import process:
- data import in bdd (fusio2ed, gtfs2ed, poi2ed, ...)
- export bdd to nav file
- update the jormungandr db with the new data for the instance
- reload the krakens
"""
actions = []
job = models.Job()
instance_config = load_instance_config(instance.name)
job.instance = instance
job.state = 'pending'
task = {
'gtfs': gtfs2ed,
'fusio': fusio2ed,
'osm': osm2ed,
'geopal': geopal2ed,
'fare': fare2ed,
'poi': poi2ed,
'synonym': synonym2ed,
}
for _file in files:
filename = None
dataset = models.DataSet()
dataset.type = type_of_data(_file)
if dataset.type in task:
if backup_file:
filename = move_to_backupdirectory(
_file, instance_config.backup_directory)
else:
filename = _file
actions.append(task[dataset.type].si(instance_config, filename))
else:
#unknown type, we skip it
current_app.logger.debug("unknwn file type: {} for file {}".format(
dataset.type, _file))
continue
#currently the name of a dataset is the path to it
dataset.name = filename
models.db.session.add(dataset)
job.data_sets.append(dataset)
if actions:
models.db.session.add(job)
models.db.session.commit()
for action in actions:
action.kwargs['job_id'] = job.id
#We pass the job id to each tasks, but job need to be commited for
#having an id
binarisation = [
ed2nav.si(instance_config, job.id),
nav2rt.si(instance_config, job.id)
]
aggregate = aggregate_places.si(instance_config, job.id)
#We pass the job id to each tasks, but job need to be commited for
#having an id
actions.append(group(chain(*binarisation), aggregate))
actions.append(reload_data.si(instance_config, job.id))
actions.append(finish_job.si(job.id))
chain(*actions).delay()
