#
# SPDX-License-Identifier: AGPL-3.0
-from __future__ import print_function
-
import arvados
import collections
import crunchstat_summary.dygraphs
# number of GiB. (Actual nodes tend to be sold in sizes like 8 GiB
# that have amounts like 7.5 GiB according to the kernel.)
AVAILABLE_RAM_RATIO = 0.95
-
+MB=2**20
# Workaround datetime.datetime.strptime() thread-safety bug by calling
# it once before starting threads. https://bugs.python.org/issue7980
class Task(object):
def __init__(self):
self.starttime = None
+ self.finishtime = None
self.series = collections.defaultdict(list)
continue
# 2017-12-02_17:15:08 e51c5-8i9sb-mfp68stkxnqdd6m 63676 0 stderr crunchstat: keepcalls 0 put 2576 get -- interval 10.0000 seconds 0 put 2576 get
- m = re.search(r'^(?P<timestamp>[^\s.]+)(\.\d+)? (?P<job_uuid>\S+) \d+ (?P<seq>\d+) stderr crunchstat: (?P<category>\S+) (?P<current>.*?)( -- interval (?P<interval>.*))?\n$', line)
+ m = re.search(r'^(?P<timestamp>[^\s.]+)(\.\d+)? (?P<job_uuid>\S+) \d+ (?P<seq>\d+) stderr (?P<crunchstat>crunchstat: )(?P<category>\S+) (?P<current>.*?)( -- interval (?P<interval>.*))?\n$', line)
if not m:
continue
else:
# crunch2
# 2017-12-01T16:56:24.723509200Z crunchstat: keepcalls 0 put 3 get -- interval 10.0000 seconds 0 put 3 get
- m = re.search(r'^(?P<timestamp>\S+) (crunchstat: )?(?P<category>\S+) (?P<current>.*?)( -- interval (?P<interval>.*))?\n$', line)
+ m = re.search(r'^(?P<timestamp>\S+) (?P<crunchstat>crunchstat: )?(?P<category>\S+) (?P<current>.*?)( -- interval (?P<interval>.*))?\n$', line)
if not m:
continue
raise ValueError("Cannot parse timestamp {!r}".format(
timestamp))
- if not task.starttime:
- task.starttime = timestamp
+ if task.starttime is None:
logger.debug('%s: task %s starttime %s',
self.label, task_id, timestamp)
- task.finishtime = timestamp
+ if task.starttime is None or timestamp < task.starttime:
+ task.starttime = timestamp
+ if task.finishtime is None or timestamp > task.finishtime:
+ task.finishtime = timestamp
- if not self.starttime:
+ if self.starttime is None or timestamp < task.starttime:
self.starttime = timestamp
- self.finishtime = timestamp
+ if self.finishtime is None or timestamp < task.finishtime:
+ self.finishtime = timestamp
+
+ if (not self.detected_crunch1) and task.starttime is not None and task.finishtime is not None:
+ elapsed = (task.finishtime - task.starttime).seconds
+ self.task_stats[task_id]['time'] = {'elapsed': elapsed}
+ if elapsed > self.stats_max['time']['elapsed']:
+ self.stats_max['time']['elapsed'] = elapsed
this_interval_s = None
for group in ['current', 'interval']:
else:
stats[stat] = int(val)
except ValueError as e:
- logger.warning(
- 'Error parsing value %r (stat %r, category %r): %r',
- val, stat, category, e)
- logger.warning('%s', line)
+ # If the line doesn't start with 'crunchstat:' we
+ # might have mistaken an error message for a
+ # structured crunchstat line.
+ if m.group("crunchstat") is None or m.group("category") == "crunchstat":
+ logger.warning("%s: log contains message\n %s", self.label, line)
+ else:
+ logger.warning(
+ '%s: Error parsing value %r (stat %r, category %r): %r',
+ self.label, val, stat, category, e)
+ logger.warning('%s', line)
continue
if 'user' in stats or 'sys' in stats:
stats['user+sys'] = stats.get('user', 0) + stats.get('sys', 0)
if 'tx' in stats or 'rx' in stats:
stats['tx+rx'] = stats.get('tx', 0) + stats.get('rx', 0)
- for stat, val in stats.iteritems():
+ for stat, val in stats.items():
if group == 'interval':
if stat == 'seconds':
this_interval_s = val
self.job_tot = collections.defaultdict(
functools.partial(collections.defaultdict, int))
- for task_id, task_stat in self.task_stats.iteritems():
- for category, stat_last in task_stat.iteritems():
- for stat, val in stat_last.iteritems():
+ for task_id, task_stat in self.task_stats.items():
+ for category, stat_last in task_stat.items():
+ for stat, val in stat_last.items():
if stat in ['cpus', 'cache', 'swap', 'rss']:
# meaningless stats like 16 cpu cores x 5 tasks = 80
continue
def _text_report_gen(self):
yield "\t".join(['category', 'metric', 'task_max', 'task_max_rate', 'job_total'])
- for category, stat_max in sorted(self.stats_max.iteritems()):
- for stat, val in sorted(stat_max.iteritems()):
+ for category, stat_max in sorted(self.stats_max.items()):
+ for stat, val in sorted(stat_max.items()):
if stat.endswith('__rate'):
continue
max_rate = self._format(stat_max.get(stat+'__rate', '-'))
self.stats_max['cpu']['user+sys__rate'],
lambda x: x * 100),
('Overall CPU usage: {}%',
- self.job_tot['cpu']['user+sys'] /
+ float(self.job_tot['cpu']['user+sys']) /
self.job_tot['time']['elapsed']
if self.job_tot['time']['elapsed'] > 0 else 0,
lambda x: x * 100),
yield "# "+format_string.format(self._format(val))
def _recommend_gen(self):
+ # TODO recommend fixing job granularity if elapsed time is too short
return itertools.chain(
self._recommend_cpu(),
self._recommend_ram(),
constraint_key = self._map_runtime_constraint('vcpus')
cpu_max_rate = self.stats_max['cpu']['user+sys__rate']
- if cpu_max_rate == float('-Inf'):
+ if cpu_max_rate == float('-Inf') or cpu_max_rate == 0.0:
logger.warning('%s: no CPU usage data', self.label)
return
+ # TODO Don't necessarily want to recommend on isolated max peak
+ # take average CPU usage into account as well or % time at max
used_cores = max(1, int(math.ceil(cpu_max_rate)))
asked_cores = self.existing_constraints.get(constraint_key)
- if asked_cores is None or used_cores < asked_cores:
+ if asked_cores is None:
+ asked_cores = 1
+ if used_cores < asked_cores:
yield (
'#!! {} max CPU usage was {}% -- '
'try runtime_constraints "{}":{}'
).format(
self.label,
- int(math.ceil(cpu_max_rate*100)),
+ math.ceil(cpu_max_rate*100),
constraint_key,
int(used_cores))
if used_bytes == float('-Inf'):
logger.warning('%s: no memory usage data', self.label)
return
- used_mib = math.ceil(float(used_bytes) / 1048576)
+ used_mib = math.ceil(float(used_bytes) / MB)
asked_mib = self.existing_constraints.get(constraint_key)
nearlygibs = lambda mebibytes: mebibytes/AVAILABLE_RAM_RATIO/1024
- if asked_mib is None or (
- math.ceil(nearlygibs(used_mib)) < nearlygibs(asked_mib)):
+ if used_mib > 0 and (asked_mib is None or (
+ math.ceil(nearlygibs(used_mib)) < nearlygibs(asked_mib))):
yield (
'#!! {} max RSS was {} MiB -- '
'try runtime_constraints "{}":{}'
self.label,
int(used_mib),
constraint_key,
- int(math.ceil(nearlygibs(used_mib))*AVAILABLE_RAM_RATIO*1024*(2**20)/self._runtime_constraint_mem_unit()))
+ int(math.ceil(nearlygibs(used_mib))*AVAILABLE_RAM_RATIO*1024*(MB)/self._runtime_constraint_mem_unit()))
def _recommend_keep_cache(self):
"""Recommend increasing keep cache if utilization < 80%"""
return
utilization = (float(self.job_tot['blkio:0:0']['read']) /
float(self.job_tot['net:keep0']['rx']))
- asked_mib = self.existing_constraints.get(constraint_key, 256)
+ # FIXME: the default on this get won't work correctly
+ asked_cache = self.existing_constraints.get(constraint_key, 256) * self._runtime_constraint_mem_unit()
if utilization < 0.8:
yield (
self.label,
utilization * 100.0,
constraint_key,
- asked_mib*2*(2**20)/self._runtime_constraint_mem_unit())
+ math.ceil(asked_cache * 2 / self._runtime_constraint_mem_unit()))
def _format(self, val):
class JobSummarizer(ProcessSummarizer):
- runtime_constraint_mem_unit = 1048576
+ runtime_constraint_mem_unit = MB
map_runtime_constraint = {
'keep_cache_ram': 'keep_cache_mb_per_task',
'ram': 'min_ram_mb_per_node',
def run(self):
threads = []
- for child in self.children.itervalues():
+ for child in self.children.values():
self.throttle.acquire()
t = threading.Thread(target=self.run_and_release, args=(child.run, ))
t.daemon = True
def text_report(self):
txt = ''
d = self._descendants()
- for child in d.itervalues():
+ for child in d.values():
if len(d) > 1:
txt += '### Summary for {} ({})\n'.format(
child.label, child.process['uuid'])
MultiSummarizers) are omitted.
"""
d = collections.OrderedDict()
- for key, child in self.children.iteritems():
+ for key, child in self.children.items():
if isinstance(child, Summarizer):
d[key] = child
if isinstance(child, MultiSummarizer):
return d
def html_report(self):
- return WEBCHART_CLASS(self.label, self._descendants().itervalues()).html()
+ return WEBCHART_CLASS(self.label, iter(self._descendants().values())).html()
class JobTreeSummarizer(MultiSummarizer):
preloaded = {}
for j in arv.jobs().index(
limit=len(job['components']),
- filters=[['uuid','in',job['components'].values()]]).execute()['items']:
+ filters=[['uuid','in',list(job['components'].values())]]).execute()['items']:
preloaded[j['uuid']] = j
for cname in sorted(job['components'].keys()):
child_uuid = job['components'][cname]
class PipelineSummarizer(MultiSummarizer):
def __init__(self, instance, **kwargs):
children = collections.OrderedDict()
- for cname, component in instance['components'].iteritems():
+ for cname, component in instance['components'].items():
if 'job' not in component:
logger.warning(
"%s: skipping component with no job assigned", cname)
cr['name'] = cr.get('name') or cr['uuid']
todo.append(cr)
sorted_children = collections.OrderedDict()
- for uuid in sorted(children.keys(), key=lambda uuid: children[uuid].sort_key):
+ for uuid in sorted(list(children.keys()), key=lambda uuid: children[uuid].sort_key):
sorted_children[uuid] = children[uuid]
super(ContainerTreeSummarizer, self).__init__(
children=sorted_children,
projects / arvados.git / blobdiff