def rebuild_unblobbed_cache(username=None, db_name=None, taskname=None):
"""
Populate fews unblobbed cache for better user experience
"""
handler = get_handler(username=username, taskname=taskname)
logger = logging.getLogger(__name__)
logger.addHandler(handler)
logger.setLevel(20)
logger.info('Processing filter tree...')
fews_filters(ignore_cache=True)
logger.info('Processing Timeserie.has_data_dict...')
Timeserie.has_data_dict(ignore_cache=True)
logger.info('Processing filters...')
for f in Filter.objects.all():
f.parameters()
logger.info('Finished successfully.')
return 'OK'
def _timeseries(self):
"""
Get list of dicts of all timeseries. Optimized for performance.
"""
cache_key = 'lizard_fewsunblobbed.layers.timeseries_%s_%s' % (
self.filterkey, self.parameterkey)
result = cache.get(cache_key)
if result is None:
# fetching locationkey and parameterkey seems to be very expensive
parameter = Parameter.objects.get(pk=self.parameterkey)
# Pre load all used locations in a dictionary.
# In some cases it takes 3 seconds?
locations = dict([(location.pk, location)
for location in Location.objects.filter(
timeserie__filterkey=self.filterkey)])
result = []
related_timeseries = list(Timeserie.objects.filter(
filterkey=self.filterkey,
parameterkey=self.parameterkey))
# Fetch cached has_data dict for all timeseries.
timeseries_has_data = Timeserie.has_data_dict()
for timeserie in related_timeseries:
location = locations[timeserie.locationkey_id]
name = u'%s (%s): %s' % (parameter.name, parameter.unit,
location.name)
shortname = u'%s' % location.name
result.append(
{'rd_x': location.x,
'rd_y': location.y,
'longitude': location.longitude,
'latitude': location.latitude,
'object': timeserie,
'location_name': location.name.encode('ascii', 'replace'),
# ^^^ This used to be ``str(location.name)``.
# Which fails on non-ascii input.
# TODO: does it really need to be a string?
# It seems to be proper unicode to begin with.
'name': name,
'shortname': shortname,
'workspace_item': self.workspace_item,
'identifier': {'locationkey': location.pk},
'google_coords': location.google_coords(),
'has_data': timeserie.pk in timeseries_has_data,
})
cache.set(cache_key, result, 8 * 60 * 60)
else:
# the workspace_item can be different, so overwrite with our own
for row in result:
row['workspace_item'] = self.workspace_item
return copy.deepcopy(result)
