def test_optimize_step(self):
pg_num = 2048
size = 3
a = Ceph().constructor([
'optimize',
'--no-multithread',
'--replication-count',
str(size),
'--pool',
'3',
'--pg-num',
str(pg_num),
'--pgp-num',
str(pg_num),
'--rule',
'data',
'--choose-args',
'optimize',
'--step',
'64',
])
c = Crush(backward_compatibility=True)
c.parse('tests/test_optimize_small_cluster.json')
crushmap = c.get_crushmap()
converged = False
for i in range(20):
(count, crushmap) = a.optimize(crushmap)
if count <= 0:
converged = True
break
print("moved " + str(count) + " values")
assert converged
def analyze_failures(self, c, take, failure_domain):
if failure_domain == 0: # failure domain == device is a border case
return None
root = c.find_bucket(take)
worst = pd.DataFrame()
available_buckets = c.collect_buckets_by_type([root], failure_domain)
if len(available_buckets) <= self.args.replication_count:
log.error("there are not enough " + failure_domain +
" to sustain failure")
return None
for may_fail in available_buckets:
f = Crush(verbose=self.args.debug,
backward_compatibility=self.args.backward_compatibility)
f.crushmap = copy.deepcopy(c.get_crushmap())
root = f.find_bucket(take)
f.filter(lambda x: x.get('name') != may_fail.get('name'), root)
f.parse(f.crushmap)
try:
a = self.run_simulation(f, take, failure_domain)
a['~over filled %~'] = a['~over/under filled %~']
a = a[['~type~', '~over filled %~']]
worst = pd.concat([worst,
a]).groupby(['~type~']).max().reset_index()
except BadMapping:
log.error("mapping failed when removing {}".format(may_fail))
return worst.set_index('~type~')
def test_optimize_one_step(self):
pg_num = 2048
size = 3
a = Ceph().constructor([
'optimize',
'--no-multithread',
'--replication-count',
str(size),
'--pool',
'3',
'--pg-num',
str(pg_num),
'--pgp-num',
str(pg_num),
'--rule',
'data',
'--choose-args',
'optimize',
'--step',
'64',
])
c = Crush(backward_compatibility=True)
c.parse('tests/test_optimize_small_cluster.json')
crushmap = c.get_crushmap()
(count, crushmap) = a.optimize(crushmap)
assert 240 == count
def analyze(self):
self.pre_sanity_check_args()
c = Crush(backward_compatibility=self.args.backward_compatibility)
c.parse(self.main.convert_to_crushmap(self.args.crushmap))
self.post_sanity_check_args()
(take, failure_domain) = c.rule_get_take_failure_domain(self.args.rule)
d = self.run_simulation(c, take, failure_domain)
worst = self.analyze_failures(c, take, failure_domain)
return (d, worst, failure_domain)
def run_crush():
value = sys.argv[1]
rcount = sys.argv[2]
crushmap = open("./config/crushmap.json", "r").read()
c = Crush()
c.parse(json.loads(crushmap))
devices = c.map(rule="data",
value=int(value),
replication_count=int(rcount))
return devices
def define_crushmaps_1(self):
crushmap = self.define_crushmap_10()
pprint(crushmap)
c1 = Crush()
c1.parse(crushmap)
m2 = copy.deepcopy(crushmap)
del m2['trees'][0]['children'][2]['children'][1]
c2 = Crush()
c2.parse(m2)
return (c1, c2)
def run_optimize(self, p, rule_name, crushmap, with_positions=True):
pd.set_option('display.max_rows', None)
pd.set_option('display.width', 160)
p.extend(['--rule', rule_name])
a = Ceph().constructor([
'analyze',
] + p)
c = Crush(backward_compatibility=True)
c.parse(crushmap)
(take, failure_domain) = c.rule_get_take_failure_domain(rule_name)
crushmap = c.get_crushmap()
crushmap['choose_args'] = {
"optimize": [],
}
d = a.run_simulation(c, take, failure_domain)
if d['~overweight~'].any():
raise ValueError(
'no way to optimize when there is an overweight item')
print(str(d))
print(a._format_report(d, 'device'))
print(a._format_report(d, failure_domain))
print(a.analyze_failures(c, take, failure_domain))
p.extend(['--choose-args', 'optimize'])
pool = Pool()
children = [c.find_bucket(take)]
while len(children) > 0:
a = [(p, crushmap, item, with_positions) for item in children]
r = pool.map(o, a)
# r = map(o, a)
choose_args = filter(None, r)
crushmap['choose_args']['optimize'].extend(choose_args)
nc = []
for item in children:
nc.extend(item.get('children', []))
# fail if all children are not of the same type
children = nc
pprint.pprint(crushmap)
c.parse(crushmap)
a = Ceph().constructor([
'analyze',
] + p)
d = a.run_simulation(c, take, failure_domain)
print(a._format_report(d, 'device'))
print(a._format_report(d, failure_domain))
print(a.analyze_failures(c, take, failure_domain))
def define_crushmaps_2(self):
crushmap = {
"trees": [{
"type": "root",
"id": -1,
"name": "dc1",
"children": [],
}],
"rules": {
"firstn": [["take", "dc1"],
["chooseleaf", "firstn", 0, "type", "host"],
["emit"]],
"indep": [["take", "dc1"],
["chooseleaf", "indep", 0, "type", "host"],
["emit"]],
}
}
crushmap['trees'][0]['children'].extend([{
"type":
"host",
"id":
-(i + 2),
"name":
"host%d" % i,
"children": [
{
"id": (2 * i),
"name": "device%02d" % (2 * i),
"weight": 5.0
},
{
"id": (2 * i + 1),
"name": "device%02d" % (2 * i + 1),
"weight": 5.0
},
],
} for i in range(0, 5)])
first = crushmap['trees'][0]['children'][0]['children']
first[0]['weight'] = 0.5
first[1]['weight'] = 0.5
pprint(crushmap)
c1 = Crush(verbose=1)
c1.parse(crushmap)
m2 = copy.deepcopy(crushmap)
del m2['trees'][0]['children'][4]
c2 = Crush(verbose=1)
c2.parse(m2)
return (c1, c2)
def test_collect_dataframe(self):
tree = {
'name':
'rack0',
'type':
'rack',
'id':
-1,
'children': [
{
'name': 'host0',
'type': 'host',
'id': -2,
'children': [
{
'name': 'osd.3',
'id': 3
},
]
},
{
'name': 'host1',
'type': 'host',
'id': -3,
'children': [
{
'name': 'osd.4',
'id': 4
},
]
},
]
}
c = Crush(verbose=1)
c.parse({"trees": [tree]})
d = Analyze.collect_dataframe(c, tree)
expected = """\
~id~ ~weight~ ~type~ rack host device
~name~
rack0 -1 1.0 rack rack0 NaN NaN
host0 -2 1.0 host rack0 host0 NaN
osd.3 3 1.0 device rack0 host0 osd.3
host1 -3 1.0 host rack0 host1 NaN
osd.4 4 1.0 device rack0 host1 osd.4\
""" # noqa trailing whitespaces are expected
assert expected == str(d)
def test_filter_real(self):
name = 'cloud6-1429'
c = Crush()
c.parse('tests/test_crush_filter.json')
crushmap = c.get_crushmap()
optimize = sorted(crushmap['choose_args']['optimize'],
key=lambda v: v['bucket_id'])
assert 3 == len(optimize)
assert -1 == optimize[2]['bucket_id']
assert 7 == len(optimize[2]['weight_set'][0])
bucket = c.find_bucket(name)
assert name == bucket['name']
c.filter(lambda x: x.get('name') != name, crushmap['trees'][0])
optimize = crushmap['choose_args']['optimize']
assert 2 == len(optimize)
assert -1 == optimize[1]['bucket_id']
assert 6 == len(optimize[1]['weight_set'][0])
assert c.find_bucket(name) is None
def test_map(self):
crushmap = self.build_crushmap()
c = Crush(verbose=1)
assert c.parse(crushmap)
assert len(
c.map(rule="data",
value=1234,
replication_count=1,
weights={},
choose_args=[])) == 1
def optimize(self, crushmap):
c = Crush(backward_compatibility=self.args.backward_compatibility)
c.parse(crushmap)
crushmap = c.get_crushmap()
if 'choose_args' not in crushmap:
crushmap['choose_args'] = {}
c.parse(crushmap)
if self.args.choose_args not in crushmap['choose_args']:
crushmap['choose_args'][self.args.choose_args] = []
c.parse(crushmap)
(take, failure_domain) = c.rule_get_take_failure_domain(self.args.rule)
parser = analyze.Analyze.get_parser()
self.main.hook_analyze_args(parser)
p = self.main.get_trimmed_argv(parser, self.args)
a = self.main.clone().constructor(['analyze'] + p)
if self.args.multithread:
from multiprocessing import Pool
pool = Pool()
children = [c.find_bucket(take)]
total_count = 0
over_step = False
n = self.main.value_name()
while not over_step and len(children) > 0:
a = [(self, p, c.get_crushmap(), item) for item in children]
if self.args.multithread:
r = list(pool.map(top_optimize, a))
else:
r = list(map(top_optimize, a))
for i in range(len(children)):
if r[i] is None:
continue
(count, choose_arg) = r[i]
total_count += count
c.update_choose_args(self.args.choose_args, [choose_arg])
log.info(children[i]['name'] + " weights updated with " + str(choose_arg))
if self.args.step and count > 0:
log.warning(children[i]['name'] + " will swap " +
str(count) + " " + n)
over_step = self.args.step and total_count > self.args.step
if over_step:
break
nc = []
for item in children:
nc.extend(item.get('children', []))
# fail if all children are not of the same type
children = nc
return (total_count, c.get_crushmap())
"children": [
{ "id": 2, "name": "device2", "weight": 1.0 },
{ "id": 3, "name": "device3", "weight": 2.0 }
]
},
{
"type": "host", "name": "host2", "id": -4,
"children": [
{ "id": 4, "name": "device4", "weight": 1.0 },
{ "id": 5, "name": "device5", "weight": 2.0 }
]
}
]
}
],
"rules": {
"data": [
[ "take", "dc1" ],
[ "chooseleaf", "firstn", 0, "type", "host" ],
[ "emit" ]
]
}
}
"""
c = Crush()
c.parse(json.loads(crushmap))
print c.map(rule="data", value=1234, replication_count=1)
print c.map(rule="data", value=1234, replication_count=2)
"chooseleaf",
"firstn",
0,
"type",
"host"
],
[
"emit"
]
]
}
}
"""
crush = Crush()
crush.parse(simplejson.loads(crushmap))
def find_part_for_inv(item):
#crush code
itemToBeSent = int(hashlib.sha1(item).hexdigest(), 16) % (10**8)
arr = crush.map(rule="InvRule", value=itemToBeSent, replication_count=1)
val = arr[0][-1:]
return int(val)
wb2 = load_workbook('sample.xlsx')
worksheet1 = wb2['Sheet1'] # one way to load a worksheet
items = []
def optimize_replica(a, crushmap, bucket, replication_count, choose_arg,
choose_arg_position):
a.args.replication_count = replication_count
id2weight = collections.OrderedDict([(i['id'], i['weight'])
for i in bucket['children']])
c = Crush(backward_compatibility=True)
c.parse(crushmap)
(take, failure_domain) = c.rule_get_take_failure_domain(a.args.rule)
pd.set_option('precision', 2)
c.parse(crushmap)
#
# initial simulation
#
i = a.run_simulation(c, take, failure_domain)
#print(str(i))
# select desired items
i = i.reset_index()
s = i['~name~'] == 'KKKK' # init to False, there must be a better way
for item in bucket['children']:
s |= (i['~name~'] == item['name'])
i['~delta~'] = i.loc[s, '~objects~'] - i.loc[s, '~expected~']
i.loc[s,
'~delta%~'] = (i.loc[s, '~objects~'] -
i.loc[s, '~expected~']) / i.loc[s, '~expected~'] * 100
i = i.sort_values('~delta~', ascending=False)
i = i[pd.notnull(i['~delta~'])]
previous_kl = None
improve_tolerance = 10
no_improvement = 0
max_iterations = 1000
for iterations in range(max_iterations):
# print(bucket['name'] + " weights " + str(id2weight.values()))
choose_arg['weight_set'][choose_arg_position] = id2weight.values()
# a.args.verbose = 1
c.parse(crushmap)
z = a.run_simulation(c, take, failure_domain)
z = z.reset_index()
d = z[s].copy()
d['~delta~'] = d['~objects~'] - d['~expected~']
d['~delta%~'] = d['~delta~'] / d['~expected~']
kl = d['~delta~'].abs().sum()
# kl = entropy(d.loc[s, '~expected~'], d.loc[s, '~objects~'])
# stop when kl is small enough or when it increases meaning
# what we're doing is no longer reducing kl
if previous_kl is not None:
if previous_kl < kl:
no_improvement += 1
else:
previous_kl = kl
best_weights = id2weight.values()
no_improvement = 0
if no_improvement >= improve_tolerance:
choose_arg['weight_set'][choose_arg_position] = best_weights
break
else:
best_weights = id2weight.values()
previous_kl = kl
print(bucket['name'] + " kl " + str(kl) + " no_improvement " +
str(no_improvement))
# if kl < 1e-6:
# break
d = d.sort_values('~delta~', ascending=False)
# print(str(d))
if d.iloc[0]['~delta~'] <= 0 or d.iloc[-1]['~delta~'] >= 0:
break
# there should not be a need to keep the sum of the weights to the same value, they
# are only used locally for placement and have no impact on the upper weights
# nor are they derived from the weights from below *HOWEVER* in case of a failure
# the weights need to be as close as possible from the target weight to limit
# the negative impact
shift = id2weight[d.iloc[0]['~id~']] * min(0.01, d.iloc[0]['~delta%~'])
if id2weight[d.iloc[-1]['~id~']] < shift:
break
id2weight[d.iloc[0]['~id~']] -= shift
id2weight[d.iloc[-1]['~id~']] += shift
if iterations >= max_iterations - 1:
print("!!!!!!!! stoped after " + str(iterations))
print("Done " + str(no_improvement))
def test_get_item_by_(self):
crushmap = self.build_crushmap()
c = Crush(verbose=1)
assert c.parse(crushmap)
assert c.get_item_by_id(-2)['name'] == 'host0'
assert c.get_item_by_name('host0')['id'] == -2
def set_origin_crushmap(self, origin):
self.args.choose_args = self.args.origin_choose_args
o = Crush(backward_compatibility=self.args.backward_compatibility)
o.parse(self.main.convert_to_crushmap(origin))
self.set_origin(o)
def analyze_crushmap(self, crushmap):
c = Crush(backward_compatibility=self.args.backward_compatibility)
c.parse(crushmap)
(take, failure_domain) = c.rule_get_take_failure_domain(self.args.rule)
return self.run_simulation(c, take, failure_domain)
def set_destination_crushmap(self, destination):
self.args.choose_args = self.args.destination_choose_args
d = Crush(backward_compatibility=self.args.backward_compatibility)
d.parse(self.main.convert_to_crushmap(destination))
self.set_destination(d)
def optimize_replica(self, p, origin_crushmap,
crushmap, bucket,
replication_count, choose_arg_position):
a = self.main.clone().constructor(['analyze'] + p)
a.args.replication_count = replication_count
parser = compare.Compare.get_parser()
self.main.hook_compare_args(parser)
cp = self.main.get_trimmed_argv(parser, self.args)
compare_instance = self.main.clone().constructor(['compare'] + cp)
compare_instance.args.replication_count = replication_count
compare_instance.set_origin_crushmap(origin_crushmap)
choose_arg = self.get_choose_arg(crushmap, bucket)
self.set_choose_arg_position(choose_arg, bucket, choose_arg_position)
id2weight = collections.OrderedDict()
for pos in range(len(bucket['children'])):
v = choose_arg['weight_set'][choose_arg_position][pos]
id2weight[bucket['children'][pos]['id']] = v
log.info(bucket['name'] + " optimizing replica " + str(replication_count) + " " +
str(list(id2weight.values())))
log.debug(bucket['name'] + " optimizing replica " + str(replication_count) + " " +
str(dict(id2weight)))
c = Crush(backward_compatibility=self.args.backward_compatibility)
c.parse(crushmap)
(take, failure_domain) = c.rule_get_take_failure_domain(a.args.rule)
#
# initial simulation
#
i = a.run_simulation(c, take, failure_domain)
i = i.reset_index()
s = i['~name~'] == 'KKKK' # init to False, there must be a better way
for item in bucket['children']:
s |= (i['~name~'] == item['name'])
previous_delta = None
improve_tolerance = 10
no_improvement = 0
max_iterations = 1000
from_to_count = 0
best_weights = list(id2weight.values())
n = self.main.value_name()
for iterations in range(max_iterations):
choose_arg['weight_set'][choose_arg_position] = list(id2weight.values())
c.parse(crushmap)
z = a.run_simulation(c, take, failure_domain)
z = z.reset_index()
d = z[s].copy()
d['~delta~'] = d['~' + n + '~'] - d['~expected~']
d['~delta%~'] = d['~delta~'] / d['~expected~']
delta = d['~delta~'].abs().sum()
if previous_delta is not None:
if previous_delta < delta:
no_improvement += 1
else:
previous_delta = delta
best_weights = list(id2weight.values())
no_improvement = 0
if no_improvement >= improve_tolerance:
log.info("stop because " + str(no_improvement) + " tries")
break
else:
best_weights = list(id2weight.values())
previous_delta = delta
if delta == 0:
log.info("stop because the distribution is perfect")
break
log.info(bucket['name'] + " delta " + str(delta))
if self.args.step and no_improvement == 0:
compare_instance.set_destination(c)
(from_to, in_out) = compare_instance.compare_bucket(bucket)
from_to_count = sum(map(lambda x: sum(x.values()), from_to.values()))
in_out_count = sum(map(lambda x: sum(x.values()), in_out.values()))
log.debug("moved from_to " + str(from_to_count) +
" in_out " + str(in_out_count))
if from_to_count > self.args.step:
log.info("stopped because moved " + str(from_to_count) +
" --step " + str(self.args.step))
break
d = d.sort_values('~delta~', ascending=False)
if d.iloc[0]['~delta~'] <= 0 or d.iloc[-1]['~delta~'] >= 0:
log.info("stop because [" + str(d.iloc[0]['~delta~']) + "," +
str(d.iloc[-1]['~delta~']) + "]")
break
# there should not be a need to keep the sum of the weights to the same value, they
# are only used locally for placement and have no impact on the upper weights
# nor are they derived from the weights from below *HOWEVER* in case of a failure
# the weights need to be as close as possible from the target weight to limit
# the negative impact
shift = int(id2weight[d.iloc[0]['~id~']] * min(0.01, abs(d.iloc[0]['~delta%~'])))
if shift <= 0:
log.info("stop because shift is zero")
break
log.debug("shift from " + str(d.iloc[0]['~id~']) +
" to " + str(d.iloc[-1]['~id~']))
id2weight[d.iloc[0]['~id~']] -= shift
id2weight[d.iloc[-1]['~id~']] += shift
choose_arg['weight_set'][choose_arg_position] = best_weights
c.parse(crushmap)
compare_instance.set_destination(c)
(from_to, in_out) = compare_instance.compare_bucket(bucket)
from_to_count = sum(map(lambda x: sum(x.values()), from_to.values()))
if iterations >= max_iterations - 1:
log.info("stopped after " + str(iterations))
log.info(bucket['name'] + " replica " + str(replication_count) + " optimized")
log.info(bucket['name'] + " weights " + str(choose_arg['weight_set'][choose_arg_position]))
return from_to_count
def convert_to_crushmap(self, crushmap):
c = Crush(verbose=self.args.debug,
backward_compatibility=self.args.backward_compatibility)
c.parse(crushmap)
return c.get_crushmap()
def crushmap_to_file(self, crushmap):
c = Crush(verbose=self.args.debug,
backward_compatibility=self.args.backward_compatibility)
c.parse(crushmap)
c.to_file(self.args.out_path)
