class DataAnalysis(object):
"""
Data Analysis is collecting data and prints it to be used by visualization
software to better understand access patterns
"""
def __init__(self, config=dict()):
self.logger = logging.getLogger(__name__)
self.config = config
self.datasets = DatasetManager(self.config)
self.sites = SiteManager(self.config)
self.storage = StorageManager(self.config)
self.popularity = PopularityManager(self.config)
def start(self):
"""
Begin Data Analysis
"""
t1 = datetime.datetime.utcnow()
dataset_name = '/PAHighPt/HIRun2013-PromptReco-v1/RECO'
self.initiate_data(dataset_name)
self.export_data(dataset_name)
t2 = datetime.datetime.utcnow()
td = t2 - t1
self.logger.info('Data Analysis took %s', str(td))
def initiate_data(self, dataset_name):
"""
Initiate data about dataset(s)
"""
coll = 'dataset_data'
query = {'name':dataset_name}
data = {'$set':{'name':dataset_name}}
self.storage.update_data(coll=coll, query=query, data=data, upsert=True)
self.datasets.insert_phedex_data(dataset_name)
self.datasets.insert_dbs_data(dataset_name)
self.popularity.insert_dataset(dataset_name)
def export_data(self, dataset_name):
"""
Get data from DB and export to file for usage in visualization
"""
# get data from DB
coll = 'dataset_popularity'
pipeline = list()
match = {'$match':{'name':dataset_name}}
pipeline.append(match)
db_data = self.storage.get_data(coll=coll, pipeline=pipeline)
headers = ('dataset_name', 'date', 'popularity')
data = list()
for data_entry in db_data:
data.append(tuple(data_entry['name'], data_entry['date'], data_entry['n_accesses']*data_entry['n_cpus']*data_entry['n_users']))
export_csv(headers=headers, data=data, file_name='single_dataset')
class MiniAODAnalysis(object):
"""
Data Analysis is collecting data and prints it to be used by visualization
software to better understand access patterns
"""
def __init__(self, config=dict()):
self.logger = logging.getLogger(__name__)
self.config = config
self.storage = StorageManager(self.config)
#self.phedex = self.phedex = PhEDExService(self.config)
def start(self):
"""
Begin Data Analysis
"""
t1 = datetime.datetime.utcnow()
n_datasets, size_all = self.get_n_datasets()
print n_datasets
print size_all
#n_multiple, size_multiple = self.get_multiple_sites()
#n_replicas = self.get_n_replicas()
t2 = datetime.datetime.utcnow()
td = t2 - t1
self.logger.info('MINIAOD Analysis took %s', str(td))
def get_n_datasets(self):
"""
Collect the total number of MINIAOD[SIM] blocks
"""
regex = re.compile(".*MINIAOD(SIM)?$")
coll = 'dataset_data'
pipeline = list()
match = {'$match':{'name':{'$regex':regex}}}
pipeline.append(match)
group = {'$group':{'_id':None, 'count':{'$sum':1}, 'size_bytes':{'$sum':'$size_bytes'}}}
pipeline.append(group)
data = self.storage.get_data(coll, pipeline)
n_datasets = data[0]['count']
size_gb = data[0]['size_bytes']/10**9
return n_datasets, size_gb
class StorageTests(unittest.TestCase):
"""
A test class for service classes
"""
def setUp(self):
"Set up for test"
self.config = get_config(path=opt_path, file_name='test.cfg')
self.storage = StorageManager(config=self.config)
self.storage.drop_db()
def tearDown(self):
"Clean up"
coll = 'test'
query = dict()
self.storage.delete_data(coll=coll, query=query)
pipeline = list()
match = {'$match':{}}
pipeline.append(match)
expected = list()
result = self.storage.get_data(coll=coll, pipeline=pipeline)
self.assertEqual(result, expected)
self.storage.drop_db()
#@unittest.skip("Skip Test")
def test_cache(self):
"Test storage cache"
print ""
phedex = PhEDExService(config=self.config)
api = 'data'
params = {'level':'block', 'dataset':'/DoubleElectron/Run2012D-22Jan2013-v1/AOD'}
expected = '/DoubleElectron/Run2012D-22Jan2013-v1/AOD'
phedex.fetch(api=api, params=params, cache_only=True, force_cache=True)
cache_data = self.storage.get_cache(coll='phedex', api=api, params=params)
try:
result = cache_data['phedex']['dbs'][0]['dataset'][0]['name']
except KeyError:
self.assertTrue(False)
else:
self.assertEqual(result, expected)
#@unittest.skip("Skip Test")
def test_data(self):
"Test general collection manipulation functions"
coll = 'test'
# insert
data = [{'foo':'bar_1'}, {'foo':'bar_2'}]
self.storage.insert_data(coll=coll, data=data)
# get
pipeline = list()
match = {'$match':{'foo':'bar_2'}}
pipeline.append(match)
data = self.storage.get_data(coll=coll, pipeline=pipeline)
expected = 'bar_2'
result = data[0]['foo']
self.assertEqual(result, expected)
# update
query = {'foo':'bar_1'}
data = {'$set':{'foo':'bar_3'}}
self.storage.update_data(coll=coll, query=query, data=data)
pipeline = list()
match = {'$match':{'foo':'bar_3'}}
pipeline.append(match)
data = self.storage.get_data(coll=coll, pipeline=pipeline)
expected = 'bar_3'
result = data[0]['foo']
self.assertEqual(result, expected)
# last insert timestamp
data = [{'foo':'bar_4'}]
datetime_1 = datetime.utcnow().replace(microsecond=0)
self.storage.insert_data(coll=coll, data=data)
datetime_2 = self.storage.get_last_insert_time(coll)
self.assertTrue(datetime_1 <= datetime_2)
class RockerBoard(object):
"""
RockerBoard is a system balancing algorithm using popularity metrics to predict popularity
and make appropriate replications to keep the system balanced
"""
def __init__(self, config=dict()):
self.logger = logging.getLogger(__name__)
self.config = config
self.phedex = PhEDExService(self.config)
self.mit_db = MITDBService(self.config)
self.datasets = DatasetManager(self.config)
self.sites = SiteManager(self.config)
self.storage = StorageManager(self.config)
self.rankings = DeltaRanking(self.config)
self.max_gb = int(self.config['rocker_board']['max_gb'])
self.min_rank = float(self.config['rocker_board']['min_rank'])
def start(self):
"""
Begin Rocker Board Algorithm
"""
t1 = datetime.datetime.utcnow()
subscriptions = self.balance()
for subscription in subscriptions:
self.logger.info('site: %s\tdataset: %s', subscription[1], subscription[0])
self.subscribe(subscriptions)
t2 = datetime.datetime.utcnow()
td = t2 - t1
self.logger.info('Rocker Board took %s', str(td))
def balance(self):
"""
Balance system by creating new replicas based on popularity
"""
subscriptions = list()
dataset_rankings = self.rankings.dataset_rankings()
site_rankings = self.rankings.site_rankings()
subscribed_gb = 0
while subscribed_gb < self.max_gb:
tmp_site_rankings = site_rankings
dataset_name = weighted_choice(dataset_rankings)
if (not dataset_name) or (dataset_rankings[dataset_name] < self.min_rank):
break
size_gb = self.datasets.get_size(dataset_name)
unavailable_sites = set(self.datasets.get_sites(dataset_name))
for site_name in tmp_site_rankings.keys():
if (self.sites.get_available_storage(site_name) < size_gb) or (tmp_site_rankings[site_name] <= 0):
unavailable_sites.add(site_name)
for site_name in unavailable_sites:
try:
del tmp_site_rankings[site_name]
except:
continue
if not tmp_site_rankings:
break
site_name = weighted_choice(tmp_site_rankings)
subscription = (dataset_name, site_name)
subscriptions.append(subscription)
subscribed_gb += size_gb
avail_storage = self.sites.get_available_storage(site_name)
self.logger.info('rank: %s\tsize: %.2f\tdataset: %s', dataset_rankings[dataset_name], size_gb, dataset_name)
self.logger.info('rank: %s\tstorage: %d\site: %s', site_rankings[site_name], avail_storage, site_name)
new_avail_storage = avail_storage - self.datasets.get_size(dataset_name)
if new_avail_storage > 0:
new_rank = 0.0
else:
new_rank = (site_rankings[site_name]/avail_storage)*new_avail_storage
site_rankings[site_name] = new_rank
del dataset_rankings[dataset_name]
self.logger.info('Subscribed %dGB', subscribed_gb)
return subscriptions
def subscribe(self, subscriptions):
"""
Make subscriptions to phedex
subscriptions = [(dataset_name, site_name), ...]
"""
new_subscriptions = dict()
for subscription in subscriptions:
dataset_name = subscription[0]
site_name = subscription[1]
try:
new_subscriptions[site_name].append(dataset_name)
except:
new_subscriptions[site_name] = list()
new_subscriptions[site_name].append(dataset_name)
for site_name, dataset_names in new_subscriptions.items():
data = self.phedex.generate_xml(dataset_names)
comments = 'This dataset is predicted to become popular and has therefore been automatically replicated by cuadrnt'
api = 'subscribe'
params = [('node', site_name), ('data', data), ('level','dataset'), ('move', 'n'), ('custodial', 'n'), ('group', 'AnalysisOps'), ('request_only', 'n'), ('no_mail', 'n'), ('comments', comments)]
json_data = self.phedex.fetch(api=api, params=params, method='post')
# insert into db
group_name = 'AnalysisOps'
request_id = 0
request_type = 0
try:
request = json_data['phedex']
request_id = request['request_created'][0]['id']
request_created = timestamp_to_datetime(request['request_timestamp'])
except:
self.logger.warning('Subscription did not succeed\n\tSite:%s\n\tDatasets: %s', str(site_name), str(dataset_names))
continue
for dataset_name in dataset_names:
coll = 'dataset_popularity'
date = datetime_day(datetime.datetime.utcnow())
pipeline = list()
match = {'$match':{'name':dataset_name, 'date':date}}
pipeline.append(match)
project = {'$project':{'delta_popularity':1, '_id':0}}
pipeline.append(project)
data = self.storage.get_data(coll=coll, pipeline=pipeline)
dataset_rank = data[0]['delta_popularity']
query = "INSERT INTO Requests(RequestId, RequestType, DatasetId, SiteId, GroupId, Rank, Date) SELECT %s, %s, Datasets.DatasetId, Sites.SiteId, Groups.GroupId, %s, %s FROM Datasets, Sites, Groups WHERE Datasets.DatasetName=%s AND Sites.SiteName=%s AND Groups.GroupName=%s"
values = (request_id, request_type, dataset_rank, request_created, dataset_name, site_name, group_name)
self.mit_db.query(query=query, values=values, cache=False)
class DatasetManager(object):
"""
Handle all dataset related data
"""
def __init__(self, config=dict()):
self.logger = logging.getLogger(__name__)
self.config = config
self.phedex = PhEDExService(self.config)
self.dbs = DBSService(self.config)
self.storage = StorageManager(self.config)
self.sites = SiteManager(self.config)
self.MAX_THREADS = int(config['threading']['max_threads'])
def initiate_db(self):
"""
Initiate dataset data in database
Get general data and popularity data from beginning
"""
q = Queue.Queue()
for i in range(self.MAX_THREADS):
worker = threading.Thread(target=self.insert_dataset_data, args=(i, q))
worker.daemon = True
worker.start()
active_sites = self.sites.get_active_sites()
api = 'blockreplicas'
params = [('node', active_sites), ('create_since', 0.0), ('complete', 'y'), ('dist_complete', 'y'), ('group', 'AnalysisOps'), ('show_dataset', 'y')]
t1 = datetime.datetime.utcnow()
phedex_data = self.phedex.fetch(api=api, params=params)
t2 = datetime.datetime.utcnow()
td = t2 - t1
self.logger.info('Call to PhEDEx took %s', str(td))
count = 1
t1 = datetime.datetime.utcnow()
for dataset_data in get_json(get_json(phedex_data, 'phedex'), 'dataset'):
q.put((dataset_data, count))
count += 1
q.join()
t2 = datetime.datetime.utcnow()
td = t2 - t1
self.logger.info('Inserting PhEDEx data took %s', str(td))
self.logger.info('Done inserting datasets into DB')
def update_db(self):
"""
Get datasets currently in AnalysisOps and compare to database
Deactivate removed datasets and insert new
Update replicas
"""
# get all datasets in database
dataset_names = self.get_db_datasets()
dataset_names = set(dataset_names)
# get all active sites, only fetch replicas from these
active_sites = self.sites.get_active_sites()
api = 'blockreplicas'
params = [('node', active_sites), ('create_since', 0.0), ('complete', 'y'), ('group', 'AnalysisOps'), ('show_dataset', 'y')]
phedex_data = self.phedex.fetch(api=api, params=params)
current_datasets = set()
q = Queue.Queue()
for i in range(self.MAX_THREADS):
worker = threading.Thread(target=self.insert_dataset_data, args=(i, q))
worker.daemon = True
worker.start()
count = 1
for dataset_data in get_json(get_json(phedex_data, 'phedex'), 'dataset'):
dataset_name = get_json(dataset_data, 'name')
current_datasets.add(dataset_name)
if dataset_name not in dataset_names:
# this is a new dataset which need to be inserted into the database
q.put((dataset_data, count))
count += 1
else:
# update replicas
replicas = self.get_replicas(dataset_data)
coll = 'dataset_data'
query = {'name':dataset_name}
data = {'$set':{'replicas':replicas}}
data = self.storage.update_data(coll=coll, query=query, data=data, upsert=False)
q.join()
deprecated_datasets = dataset_names - current_datasets
for dataset_name in deprecated_datasets:
self.remove_dataset(dataset_name)
def insert_dataset_data(self, i, q):
"""
Insert a new dataset into the database and initiate all data
"""
while True:
data = q.get()
dataset_data = data[0]
count = data[1]
self.logger.debug('Inserting dataset number %d', count)
dataset_name = get_json(dataset_data, 'name')
replicas = self.get_replicas(dataset_data)
coll = 'dataset_data'
query = {'name':dataset_name}
data = {'$set':{'name':dataset_name, 'replicas':replicas}}
data = self.storage.update_data(coll=coll, query=query, data=data, upsert=True)
self.insert_phedex_data(dataset_name)
self.insert_dbs_data(dataset_name)
q.task_done()
def insert_phedex_data(self, dataset_name):
"""
Fetch phedex data about dataset and insert into database
"""
api = 'data'
params = {'dataset':dataset_name, 'level':'block', 'create_since':0.0}
phedex_data = self.phedex.fetch(api=api, params=params)
size_bytes = 0
n_files = 0
try:
dataset_data = get_json(get_json(get_json(phedex_data, 'phedex'), 'dbs')[0],'dataset')[0]
except:
coll = 'dataset_data'
query = {'name':dataset_name}
self.storage.delete_data(coll=coll, query=query)
return
for block_data in get_json(dataset_data, 'block'):
size_bytes += get_json(block_data, 'bytes')
n_files += get_json(block_data, 'files')
coll = 'dataset_data'
query = {'name':dataset_name}
data = {'$set':{'size_bytes':size_bytes, 'n_files':n_files}}
self.storage.update_data(coll=coll, query=query, data=data, upsert=False)
def insert_dbs_data(self, dataset_name):
"""
Fetch dbs data about dataset and insert into database
"""
api = 'datasets'
params = {'dataset':dataset_name, 'detail':True, 'dataset_access_type':'*'}
dbs_data = self.dbs.fetch(api=api, params=params)
try:
dataset_data = get_json(dbs_data, 'data')[0]
except:
coll = 'dataset_data'
query = {'name':dataset_name}
self.storage.delete_data(coll=coll, query=query)
return
ds_name = get_json(dataset_data, 'primary_ds_name')
physics_group = get_json(dataset_data, 'physics_group_name')
data_tier = get_json(dataset_data, 'data_tier_name')
creation_date = datetime_day(timestamp_to_datetime(get_json(dataset_data, 'creation_date')))
ds_type = get_json(dataset_data, 'primary_ds_type')
coll = 'dataset_data'
query = {'name':dataset_name}
data = {'$set':{'ds_name':ds_name, 'physics_group':physics_group, 'data_tier':data_tier, 'creation_date':creation_date, 'ds_type':ds_type}}
self.storage.update_data(coll=coll, query=query, data=data, upsert=False)
def get_replicas(self, dataset_data):
"""
Generator function to get all replicas of a dataset
"""
replicas = list()
for block_data in get_json(dataset_data, 'block'):
for replica_data in get_json(block_data, 'replica'):
if get_json(replica_data, 'files') > 0:
replicas.append(get_json(replica_data, 'node'))
return replicas
def get_db_datasets(self):
"""
Get all datasets currently in database
"""
coll = 'dataset_data'
pipeline = list()
project = {'$project':{'name':1, '_id':0}}
pipeline.append(project)
data = self.storage.get_data(coll=coll, pipeline=pipeline)
dataset_names = [dataset_data['name'] for dataset_data in data]
self.logger.info('%d datasets present in database', len(dataset_names))
return dataset_names
def remove_dataset(self, dataset_name):
"""
Remove dataset from database
"""
coll = 'dataset_data'
query = {'name':dataset_name}
self.storage.delete_data(coll=coll, query=query)
def get_sites(self, dataset_name):
"""
Get all sites with a replica of the dataset
"""
coll = 'dataset_data'
pipeline = list()
match = {'$match':{'name':dataset_name}}
pipeline.append(match)
project = {'$project':{'replicas':1, '_id':0}}
pipeline.append(project)
data = self.storage.get_data(coll=coll, pipeline=pipeline)
site_names = data[0]['replicas']
return site_names
def get_size(self, dataset_name):
"""
Get size in GB of dataset
"""
coll = 'dataset_data'
pipeline = list()
match = {'$match':{'name':dataset_name}}
pipeline.append(match)
project = {'$project':{'size_bytes':1, '_id':0}}
pipeline.append(project)
data = self.storage.get_data(coll=coll, pipeline=pipeline)
size_gb = float(data[0]['size_bytes'])/10**9
return size_gb
class PopularityManager(object):
"""
Generate popularity metrics for datasets and sites
"""
def __init__(self, config=dict()):
self.logger = logging.getLogger(__name__)
self.config = config
self.pop_db = PopDBService(self.config)
self.sites = SiteManager(self.config)
self.datasets = DatasetManager(self.config)
self.storage = StorageManager(self.config)
self.MAX_THREADS = int(config['threading']['max_threads'])
def initiate_db(self):
"""
Collect popularity data
"""
q = Queue.Queue()
for i in range(self.MAX_THREADS):
worker = threading.Thread(target=self.insert_popularity_data, args=(i, q))
worker.daemon = True
worker.start()
start_date = datetime_day(datetime.datetime.utcnow() - datetime.timedelta(days=90))
end_date = datetime_day(datetime.datetime.utcnow())
# fetch popularity data
t1 = datetime.datetime.utcnow()
for date in daterange(start_date, end_date):
q.put(date)
q.join()
t2 = datetime.datetime.utcnow()
td = t2 - t1
self.logger.info('Inserting Pop DB data took %s', str(td))
def insert_popularity_data(self, i, q):
"""
Insert popularity data for one dataset into db
"""
coll = 'dataset_popularity'
while True:
date = q.get()
api = 'DSStatInTimeWindow/'
tstart = datetime_to_string(date)
tstop = tstart
params = {'sitename':'summary', 'tstart':tstart, 'tstop':tstop}
json_data = self.pop_db.fetch(api=api, params=params)
# sort it in dictionary for easy fetching
for dataset in json_data['DATA']:
dataset_name = dataset['COLLNAME']
popularity_data = {'name':dataset_name, 'date':date}
popularity_data['n_accesses'] = dataset['NACC']
popularity_data['n_cpus'] = dataset['TOTCPU']
popularity_data['n_users'] = dataset['NUSERS']
query = {'name':dataset_name, 'data':date}
data = {'$set':popularity_data}
self.storage.update_data(coll=coll, query=query, data=data, upsert=True)
q.task_done()
def update_db(self):
"""
Fetch latest popularity data not in database
"""
# get dates
coll = 'dataset_popularity'
pipeline = list()
sort = {'$sort':{'date':-1}}
pipeline.append(sort)
limit = {'$limit':1}
pipeline.append(limit)
project = {'$project':{'date':1, '_id':0}}
pipeline.append(project)
data = self.storage.get_data(coll=coll, pipeline=pipeline)
try:
start_date = data[0]['date']
except:
self.logger.warning('Popularity needs to be initiated')
self.initiate_db()
return
q = Queue.Queue()
for i in range(self.MAX_THREADS):
worker = threading.Thread(target=self.insert_popularity_data, args=(i, q))
worker.daemon = True
worker.start()
end_date = datetime_day(datetime.datetime.utcnow())
# fetch popularity data
t1 = datetime.datetime.utcnow()
for date in daterange(start_date, end_date):
q.put(date)
t2 = datetime.datetime.utcnow()
td = t2 - t1
self.logger.info('Updating Pop DB data took %s', str(td))
def insert_dataset(self, dataset_name):
"""
Fetch all popularity data for dataset
"""
api = 'getSingleDSstat'
sitename = 'summary'
name = dataset_name
aggr = 'day'
orderbys = ['totcpu', 'naccess']
coll = 'dataset_popularity'
for orderby in orderbys:
params = {'sitename':sitename, 'name':name, 'aggr':aggr, 'orderby':orderby}
json_data = self.pop_db.fetch(api=api, params=params)
data = get_json(json_data, 'data')
for pop_data in get_json(data, 'data'):
date = pop_db_timestamp_to_datetime(pop_data[0])
query = {'name':dataset_name, 'data':date}
popularity_data = {'name':dataset_name, 'date':date}
popularity_data[orderby] = pop_data[1]
data = {'$set':popularity_data}
self.storage.update_data(coll=coll, query=query, data=data, upsert=True)
class SiteManager(object):
"""
Keep track of site data
"""
def __init__(self, config=dict()):
self.logger = logging.getLogger(__name__)
self.config = config
self.intelroccs = IntelROCCSService(self.config)
self.crab = CRABService(self.config)
self.storage = StorageManager(self.config)
def initiate_db(self):
"""
Initiate Site database
Does exactly the same as update_db
"""
self.update_db()
def update_db(self):
"""
Initiate site data in database
Get general data about all sites
"""
api = 'Detox'
file_ = 'SitesInfo.txt'
intelroccs_data = self.intelroccs.fetch(api=api, params=file_, secure=False)
for site_data in get_json(intelroccs_data, 'data'):
self.insert_site_data(site_data)
def insert_site_data(self, site_data):
"""
Insert site into database
"""
coll = 'site_data'
site_name = str(site_data[4])
site_status = int(site_data[0])
site_quota = int(site_data[1])*10**3
query = {'name':site_name}
data = {'$set':{'name':site_name, 'status':site_status, 'quota_gb':site_quota}}
self.storage.update_data(coll=coll, query=query, data=data, upsert=True)
def update_cpu(self):
"""
Update maximum CPU capacity for site
"""
active_sites = self.get_active_sites()
for site_name in active_sites:
# remove older values
date = datetime.datetime.utcnow() - datetime.timedelta(days=30)
coll = 'site_data'
query = {'name':site_name}
data = {'$pull':{'cpu_data':{'date':{'$lt':date}}}}
self.storage.update_data(coll=coll, query=query, data=data)
# get CRAB data about site
query = 'GLIDEIN_CMSSite =?= "%s" && CPUs > 0' % (site_name)
attributes = ['GLIDEIN_CMSSite', 'CPUs']
ads = self.crab.fetch_cluster_ads(query, attributes=attributes)
cpus = 0
for ad in ads:
cpus += ad['CPUs']
# insert new data
date = datetime.datetime.utcnow()
query = {'name':site_name}
data = {'$push':{'cpu_data':{'date':date, 'cpus':cpus}}}
self.storage.update_data(coll=coll, query=query, data=data)
def get_active_sites(self):
"""
Get all sites which are active, includes sites which are not available for replication
"""
coll = 'site_data'
pipeline = list()
match = {'$match':{'status':{'$in':[1, 2]}}}
pipeline.append(match)
project = {'$project':{'name':1, '_id':0}}
pipeline.append(project)
sites_data = self.storage.get_data(coll=coll, pipeline=pipeline)
return [site_data['name'] for site_data in sites_data]
def get_available_sites(self):
"""
Get all sites which are available for replication
"""
coll = 'site_data'
pipeline = list()
match = {'$match':{'status':1}}
pipeline.append(match)
project = {'$project':{'name':1, '_id':0}}
pipeline.append(project)
data = self.storage.get_data(coll=coll, pipeline=pipeline)
return [site['name'] for site in data]
def get_available_storage(self, site_name):
"""
Get total AnalysisOps storage available at the site
"""
coll = 'dataset_data'
pipeline = list()
match = {'$match':{'replicas':site_name}}
pipeline.append(match)
group = {'$group':{'_id':None, 'size_bytes':{'$sum':'$size_bytes'}}}
pipeline.append(group)
project = {'$project':{'size_bytes':1, '_id':0}}
pipeline.append(project)
data = self.storage.get_data(coll=coll, pipeline=pipeline)
try:
size = data[0]['size_bytes']/10**9
except:
return 0
coll = 'site_data'
pipeline = list()
match = {'$match':{'name':site_name}}
pipeline.append(match)
project = {'$project':{'quota_gb':1, '_id':0}}
pipeline.append(project)
data = self.storage.get_data(coll=coll, pipeline=pipeline)
quota = data[0]['quota_gb']
available_gb = (0.95*quota) - size
return available_gb
def get_performance(self, site_name):
"""
Get the maximum number of CPU's for site in last 30 days
"""
# get maximum number of CPU's and quota
coll = 'site_data'
pipeline = list()
match = {'$match':{'name':site_name}}
pipeline.append(match)
unwind = {'$unwind':'$cpu_data'}
pipeline.append(unwind)
group = {'$group':{'_id':'$name', 'quota_gb':{'$max':'$quota_gb'}, 'max_cpus':{'$max':'$cpu_data.cpus'}}}
pipeline.append(group)
project = {'$project':{'quota_gb':1, 'max_cpus':1, '_id':0}}
pipeline.append(project)
data = self.storage.get_data(coll=coll, pipeline=pipeline)
try:
max_cpus = data[0]['max_cpus']
quota = float(data[0]['quota_gb'])/10**3
except:
self.logger.warning('Could not get site performance for %s', site_name)
max_cpus = 0
quota = 0
try:
performance = float(max_cpus)/float(quota)
except:
performance = 0.0
if not (performance > 0):
performance = 0.0
return performance