def test_phedex_memory(self):
"Test phedex data memory usage"
print ""
phedex = PhEDExService(self.config)
api = 'blockreplicas'
params = [('node', 'T2_*'), ('create_since', 0.0), ('complete', 'y'), ('group', 'AnalysisOps'), ('show_dataset', 'y')]
phedex_data = phedex.fetch(api=api, params=params, cache=False)
total_size = total_size_of(phedex_data)
logger.info('Total size of PhEDEx data in memory is %d bytes (%dMB)', total_size, total_size/10**6)
def test_phedex(self):
"Test phedex functions"
print ""
phedex = PhEDExService(config=self.config)
api = 'data'
params = {'level':'block', 'dataset':'/DoubleElectron/Run2012D-22Jan2013-v1/AOD'}
expected = '/DoubleElectron/Run2012D-22Jan2013-v1/AOD'
json_data = phedex.fetch(api=api, params=params, cache=False)
result = json_data['phedex']['dbs'][0]['dataset'][0]['name']
self.assertEqual(result, expected)
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.popularity = PopularityManager(self.config)
self.storage = StorageManager(self.config)
self.rankings = Ranker(self.config)
self.max_gb = int(self.config['rocker_board']['max_gb'])
self.csv_data = list()
def test_phedex(self):
"Test phedex functions"
print ""
phedex = PhEDExService(config=self.config)
api = 'data'
params = {
'level': 'block',
'dataset': '/DoubleElectron/Run2012D-22Jan2013-v1/AOD'
}
expected = '/DoubleElectron/Run2012D-22Jan2013-v1/AOD'
json_data = phedex.fetch(api=api, params=params, cache=False)
result = json_data['phedex']['dbs'][0]['dataset'][0]['name']
self.assertEqual(result, expected)
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)
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.valid_tiers = config['tools']['valid_tiers'].split(',')
self.MAX_THREADS = int(config['threading']['max_threads'])
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.popularity = PopularityManager(self.config)
self.storage = StorageManager(self.config)
self.rankings = Ranker(self.config)
self.max_gb = int(self.config["rocker_board"]["max_gb"])
self.csv_data = list()
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.popularity = PopularityManager(self.config)
self.storage = StorageManager(self.config)
self.rankings = Ranker(self.config)
self.max_gb = int(self.config["rocker_board"]["max_gb"])
self.csv_data = list()
def start(self, date=datetime_day(datetime.datetime.utcnow())):
"""
Begin Rocker Board Algorithm
"""
t1 = datetime.datetime.utcnow()
# Get goals
dataset_rankings = self.rankings.get_dataset_rankings(date)
site_rankings = self.rankings.get_site_rankings(date)
self.change_dataset_rankings(dataset_rankings)
subscriptions = self.replicate(dataset_rankings, site_rankings)
self.logger.info("SUBSCRIPTIONS")
for subscription in subscriptions:
self.logger.info("site: %s\tdataset: %s", subscription[1], subscription[0])
# site_storage = self.rankings.get_site_storage_rankings(subscriptions)
# deletions = self.clean(dataset_rankings, site_storage)
# self.logger.info('DELETIONS')
# for deletion in deletions:
# self.logger.info('site: %s\tdataset: %s', deletion[1], deletion[0])
# self.delete(deletions)
self.subscribe(subscriptions)
# self.datasets.update_replicas(subscriptions, deletions)
t2 = datetime.datetime.utcnow()
td = t2 - t1
self.logger.info("Rocker Board took %s", str(td))
def change_dataset_rankings(self, dataset_rankings):
"""
Change the ranks from being the target number of replicas to being the
change in number of replicas required to reach the goal
"""
current_replicas = self.datasets.get_current_num_replicas()
for dataset in current_replicas:
dataset_rankings[dataset["name"]] -= dataset["n_replicas"]
def replicate(self, dataset_rankings, site_rankings):
"""
Balance system by creating new replicas based on popularity
"""
subscriptions = list()
subscribed_gb = 0
sites_available_storage_gb = self.sites.get_all_available_storage()
while (subscribed_gb < self.max_gb) and site_rankings:
tmp_site_rankings = dict()
for k, v in site_rankings.items():
tmp_site_rankings[k] = v
dataset = max(dataset_rankings.iteritems(), key=operator.itemgetter(1))
dataset_name = dataset[0]
dataset_rank = dataset[1]
if (not dataset_name) or (dataset_rank < 1):
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:
del dataset_rankings[dataset_name]
continue
site_name = weighted_choice(tmp_site_rankings)
subscription = (dataset_name, site_name)
subscriptions.append(subscription)
subscribed_gb += size_gb
sites_available_storage_gb[site_name] -= size_gb
self.logger.info("%s : added", dataset_name)
if sites_available_storage_gb[site_name] <= 0:
del site_rankings[site_name]
dataset_rankings[dataset_name] -= 1
self.logger.info("Subscribed %dGB", subscribed_gb)
return subscriptions
def clean(self, dataset_rankings, site_rankings):
"""
Suggest deletions based on dataset and site rankings
"""
deletions = list()
deleted_gb = 0
while site_rankings:
tmp_site_rankings = dict()
dataset = min(dataset_rankings.iteritems(), key=operator.itemgetter(1))
dataset_name = dataset[0]
size_gb = self.datasets.get_size(dataset_name)
available_sites = set(self.datasets.get_sites(dataset_name))
for site_name in available_sites:
try:
tmp_site_rankings[site_name] = site_rankings[site_name]
except:
continue
if not tmp_site_rankings:
del dataset_rankings[dataset_name]
continue
site_name = weighted_choice(tmp_site_rankings)
deletion = (dataset_name, site_name)
deletions.append(deletion)
deleted_gb += size_gb
site_rankings[site_name] -= size_gb
dataset_rankings[dataset_name] += 1
if site_rankings[site_name] <= 0:
del site_rankings[site_name]
self.logger.info("Deleted %dGB", deleted_gb)
return deletions
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_rankings"
date = datetime_day(datetime.datetime.utcnow())
pipeline = list()
match = {"$match": {"name": dataset_name, "date": date}}
pipeline.append(match)
project = {"$project": {"delta_rank": 1, "_id": 0}}
pipeline.append(project)
data = self.storage.get_data(coll=coll, pipeline=pipeline)
dataset_rank = data[0]["delta_rank"]
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)
def delete(self, deletions):
"""
Make deletions to phedex
deletions = [(dataset_name, site_name), ...]
"""
new_deletions = dict()
for deletion in deletions:
dataset_name = deletion[0]
site_name = deletion[1]
try:
new_deletions[site_name].append(dataset_name)
except:
new_deletions[site_name] = list()
new_deletions[site_name].append(dataset_name)
for site_name, dataset_names in new_deletions.items():
data = self.phedex.generate_xml(dataset_names)
comments = "This dataset is predicted to become less popular and has therefore been automatically deleted by cuadrnt"
api = "delete"
params = [
("node", site_name),
("data", data),
("level", "dataset"),
("rm_subscriptions", "y"),
("comments", comments),
]
json_data = self.phedex.fetch(api=api, params=params, method="post")
# insert into db
group_name = "AnalysisOps"
request_id = 0
request_type = 1
try:
request = json_data["phedex"]
request_id = request["request_created"][0]["id"]
request_created = timestamp_to_datetime(request["request_timestamp"])
except:
self.logger.warning(
"Deletion did not succeed\n\tSite:%s\n\tDatasets: %s", str(site_name), str(dataset_names)
)
continue
for dataset_name in dataset_names:
coll = "dataset_rankings"
date = datetime_day(datetime.datetime.utcnow())
pipeline = list()
match = {"$match": {"name": dataset_name, "date": date}}
pipeline.append(match)
project = {"$project": {"delta_rank": 1, "_id": 0}}
pipeline.append(project)
data = self.storage.get_data(coll=coll, pipeline=pipeline)
dataset_rank = data[0]["delta_rank"]
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.valid_tiers = config['tools']['valid_tiers'].split(',')
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 dataset 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')]
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))
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
t1 = datetime.datetime.utcnow()
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 = {'$set':{'replicas':replicas}}
data = self.storage.update_data(coll=coll, query=query, data=data)
q.join()
deprecated_datasets = dataset_names - current_datasets
for dataset_name in deprecated_datasets:
self.remove_dataset(dataset_name)
t2 = datetime.datetime.utcnow()
td = t2 - t1
self.logger.info('Updating dataset data took %s', str(td))
self.logger.info('Done updating datasets in DB')
def insert_dataset_data(self, i, q):
"""
Insert a new dataset into the database and initiate all data
"""
while True:
data = q.get()
dataset_data = data[0]
count = data[1]
self.logger.debug('Inserting dataset number %d', count)
dataset_name = get_json(dataset_data, 'name')
coll = 'dataset_data'
query = {'name':dataset_name}
data = {'$set':{'name':dataset_name}}
data = self.storage.update_data(coll=coll, query=query, data=data, upsert=True)
try:
self.insert_phedex_data(dataset_name)
self.insert_dbs_data(dataset_name)
replicas = self.get_replicas(dataset_data)
query = {'name':dataset_name}
data = {'$set':{'name':dataset_name, 'replicas':replicas}}
data = self.storage.update_data(coll=coll, query=query, data=data)
except:
coll = 'dataset_data'
query = {'name':dataset_name}
self.storage.delete_data(coll=coll, query=query)
q.task_done()
def insert_phedex_data(self, dataset_name):
"""
Fetch phedex data about dataset and insert into database
"""
api = 'data'
params = {'dataset':dataset_name, 'level':'block', 'create_since':0.0}
phedex_data = self.phedex.fetch(api=api, params=params)
size_bytes = 0
n_files = 0
dataset_data = get_json(get_json(get_json(phedex_data, 'phedex'), 'dbs')[0],'dataset')[0]
for block_data in get_json(dataset_data, 'block'):
size_bytes += get_json(block_data, 'bytes')
n_files += get_json(block_data, 'files')
coll = 'dataset_data'
query = {'name':dataset_name}
data = {'$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)
dataset_data = get_json(dbs_data, 'data')[0]
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 = {'$set':{'ds_name':ds_name, 'physics_group':physics_group, 'data_tier':data_tier, 'creation_date':creation_date, 'ds_type':ds_type}}
self.storage.update_data(coll=coll, query=query, data=data, upsert=False)
def get_replicas(self, dataset_data):
"""
Generator function to get all replicas of a dataset
"""
replicas_check = dict()
dataset_name = get_json(dataset_data, 'name')
for block_data in get_json(dataset_data, 'block'):
for replica_data in get_json(block_data, 'replica'):
try:
replicas_check[get_json(replica_data, 'node')] += get_json(replica_data, 'files')
except:
replicas_check[get_json(replica_data, 'node')] = get_json(replica_data, 'files')
replicas = list()
n_files = self.get_n_files(dataset_name)
for site, site_files in replicas_check.items():
if site_files == n_files:
replicas.append(site)
return replicas
def get_db_datasets(self):
"""
Get all datasets currently in database
"""
coll = 'dataset_data'
pipeline = list()
match = {'$match':{'data_tier': {'$in':self.valid_tiers}}}
pipeline.append(match)
project = {'$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 get_removed_db_datasets(self):
"""
Get all datasets currently in database with more than one replica
"""
coll = 'dataset_data'
pipeline = list()
match = {'$match':{'data_tier': {'$in':self.valid_tiers}}}
pipeline.append(match)
group = {'$group':{'_id':'$name', 'n_replicas':{'$first':{'$size':'$replicas'}}}}
pipeline.append(group)
project = {'$project':{'name':'$_id', 'n_replicas':1, '_id':0}}
pipeline.append(project)
match = {'$match': {'n_replicas':{'$gt':1}}}
pipeline.append(match)
project = {'$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 valid 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_dataset_features(self, dataset_name):
"""
Get dataset features for dataset from db
"""
coll = 'dataset_data'
pipeline = list()
match = {'$match':{'name':dataset_name}}
pipeline.append(match)
project = {'$project':{'dataset_name':'$name', 'size_gb':{'$multiply':['$size_bytes', 0.000000001]}, 'n_files':1, 'physics_group':1, 'ds_type':1, 'data_tier':1, '_id':0}}
pipeline.append(project)
data = self.storage.get_data(coll=coll, pipeline=pipeline)
return data[0]
def get_n_files(self, dataset_name):
"""
Get the number of files in the block
"""
coll = 'dataset_data'
pipeline = list()
match = {'$match':{'name':dataset_name}}
pipeline.append(match)
project = {'$project':{'n_files':1, '_id':0}}
pipeline.append(project)
data = self.storage.get_data(coll=coll, pipeline=pipeline)
return data[0]['n_files']
def get_data_tiers(self, dataset_names):
"""
Get the data tiers of all datasets
"""
dataset_tiers = dict()
coll = 'dataset_data'
pipeline = list()
match = {'$match':{'name':{'$in':dataset_names}}}
pipeline.append(match)
project = {'$project':{'data_tier':1, 'name':1, '_id':0}}
pipeline.append(project)
data = self.storage.get_data(coll=coll, pipeline=pipeline)
for dataset in data:
dataset_tiers[dataset['name']] = dataset['data_tier']
return dataset_tiers
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
def get_current_num_replicas(self):
"""
Get the current number of replicas for all datasets
"""
datasets = self.get_db_datasets()
coll = 'dataset_data'
pipeline = list()
match = {'$match': {'name': {'$in':datasets}}}
pipeline.append(match)
group = {'$group':{'_id':'$name', 'n_replicas':{'$first':{'$size':'$replicas'}}}}
pipeline.append(group)
project = {'$project':{'name':'$_id', 'n_replicas':1, '_id':0}}
pipeline.append(project)
data = self.storage.get_data(coll=coll, pipeline=pipeline)
return data
def get_num_replicas(self, dataset_name):
"""
Get the current number of replicas for one dataset
"""
coll = 'dataset_data'
pipeline = list()
match = {'$match': {'name': dataset_name}}
pipeline.append(match)
group = {'$group':{'_id':'$name', 'n_replicas':{'$first':{'$size':'$replicas'}}}}
pipeline.append(group)
project = {'$project':{'n_replicas':1, '_id':0}}
pipeline.append(project)
data = self.storage.get_data(coll=coll, pipeline=pipeline)
return data[0]['n_replicas']
def update_replicas(self, subscriptions, deletions):
"""
Maually update the replicas based on subscriptions and deletions
"""
coll = 'dataset_data'
for subscription in subscriptions:
dataset_name = subscription[0]
site_name = subscription[1]
query = {'name':dataset_name}
data = {'$push':{'replicas':site_name}}
self.storage.update_data(coll=coll, query=query, data=data)
for deletion in deletions:
dataset_name = deletion[0]
site_name = deletion[1]
query = {'name':dataset_name}
data = {'$pull':{'replicas':site_name}}
self.storage.update_data(coll=coll, query=query, data=data)
def get_total_size(self, dataset_name):
"""
Get the total storage used in the system
"""
coll = 'dataset_data'
pipeline = list()
match = {'$match': {'name':dataset_name}}
pipeline.append(match)
group = {'$group':{'_id':'$name', 'n_replicas':{'$first':{'$size':'$replicas'}}}}
pipeline.append(group)
project = {'$project':{'n_replicas':1, '_id':0}}
pipeline.append(project)
data = self.storage.get_data(coll=coll, pipeline=pipeline)
n_replicas = data[0]['n_replicas']
size_gb = self.get_size(dataset_name)
total_size = n_replicas * size_gb
return total_size
def get_all_dataset_size(self, dataset_names):
"""
Get the total storage used in the system
"""
coll = 'dataset_data'
pipeline = list()
match = {'$match': {'name':{'$in':dataset_names}}}
pipeline.append(match)
group = {'$group':{'_id':'$name', 'size_bytes':{'$sum':'$size_bytes'}, 'n_replicas':{'$first':{'$size':'$replicas'}}}}
pipeline.append(group)
project = {'$project':{'size_bytes':1, 'n_replicas':1, '_id':1}}
pipeline.append(project)
data = self.storage.get_data(coll=coll, pipeline=pipeline)
sizes = dict()
for dataset in data:
sizes[dataset['_id']] = (float(dataset['size_bytes'])/10**12)*dataset['n_replicas']
return sizes
def get_all_site_size(self, site_names):
"""
Get the total storage used in the system
"""
sites_sizes = dict()
for site_name in site_names:
# get all datasets with a replica at the site and how many replicas it has
coll = 'dataset_data'
pipeline = list()
match = {'$match':{'replicas':site_name}}
pipeline.append(match)
project = {'$project':{'name':1, '_id':0}}
pipeline.append(project)
data = self.storage.get_data(coll=coll, pipeline=pipeline)
dataset_names = [dataset_data['name'] for dataset_data in data]
# get the popularity of the dataset and decide by number of replicas
coll = 'dataset_data'
pipeline = list()
match = {'$match': {'name':{'$in':dataset_names}}}
pipeline.append(match)
group = {'$group':{'_id':'$name', 'size_bytes':{'$sum':'$size_bytes'}, 'n_replicas':{'$first':{'$size':'$replicas'}}}}
pipeline.append(group)
project = {'$project':{'size_bytes':1, 'n_replicas':1, '_id':1}}
pipeline.append(project)
data = self.storage.get_data(coll=coll, pipeline=pipeline)
size = 0.0
for dataset in data:
size += (float(dataset['size_bytes'])/10**12)*dataset['n_replicas']
sites_sizes[site_name] = size
return sites_sizes
def get_total_storage(self):
"""
Get the total storage used in the system
"""
datasets = self.get_removed_db_datasets()
coll = 'dataset_data'
pipeline = list()
match = {'$match': {'name': {'$in':datasets}}}
pipeline.append(match)
group = {'$group':{'_id':'$name', 'size_bytes':{'$sum':'$size_bytes'}, 'n_replicas':{'$first':{'$size':'$replicas'}}}}
pipeline.append(group)
project = {'$project':{'name':'$_id', 'size_bytes':1, 'n_replicas':1, '_id':0}}
pipeline.append(project)
data = self.storage.get_data(coll=coll, pipeline=pipeline)
total_storage = 0.0
for dataset in data:
total_storage += (float(dataset['size_bytes'])/10**9)*dataset['n_replicas']
return total_storage
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.popularity = PopularityManager(self.config)
self.storage = StorageManager(self.config)
self.rankings = Ranker(self.config)
self.max_gb = int(self.config['rocker_board']['max_gb'])
self.csv_data = list()
def start(self, date=datetime_day(datetime.datetime.utcnow())):
"""
Begin Rocker Board Algorithm
"""
t1 = datetime.datetime.utcnow()
# Get goals
dataset_rankings = self.rankings.get_dataset_rankings(date)
site_rankings = self.rankings.get_site_rankings(date)
self.change_dataset_rankings(dataset_rankings)
subscriptions = self.replicate(dataset_rankings, site_rankings)
self.logger.info('SUBSCRIPTIONS')
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 change_dataset_rankings(self, dataset_rankings):
"""
Change the ranks from being the target number of replicas to being the
change in number of replicas required to reach the goal
"""
current_replicas = self.datasets.get_current_num_replicas()
for dataset in current_replicas:
dataset_rankings[dataset['name']] -= dataset['n_replicas']
def replicate(self, dataset_rankings, site_rankings):
"""
Balance system by creating new replicas based on popularity
"""
subscriptions = list()
subscribed_gb = 0
sites_available_storage_gb = self.sites.get_all_available_storage()
while (subscribed_gb < self.max_gb) and site_rankings:
tmp_site_rankings = dict()
for k, v in site_rankings.items():
tmp_site_rankings[k] = v
dataset = max(dataset_rankings.iteritems(), key=operator.itemgetter(1))
dataset_name = dataset[0]
dataset_rank = dataset[1]
if (not dataset_name) or (dataset_rank < 1):
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:
del dataset_rankings[dataset_name]
continue
site_name = weighted_choice(tmp_site_rankings)
subscription = (dataset_name, site_name)
subscriptions.append(subscription)
subscribed_gb += size_gb
sites_available_storage_gb[site_name] -= size_gb
self.logger.info('%s : added', dataset_name)
if sites_available_storage_gb[site_name] <= 0:
del site_rankings[site_name]
dataset_rankings[dataset_name] -= 1
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_rankings'
date = datetime_day(datetime.datetime.utcnow())
pipeline = list()
match = {'$match':{'name':dataset_name, 'date':date}}
pipeline.append(match)
project = {'$project':{'delta_rank':1, '_id':0}}
pipeline.append(project)
data = self.storage.get_data(coll=coll, pipeline=pipeline)
dataset_rank = data[0]['delta_rank']
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)
