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 __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 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)