# 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
try:
self.label = m.group('job_uuid')
except IndexError:
- self.label = 'container'
- if m.group('category').endswith(':'):
+ self.label = 'label #1'
+ category = m.group('category')
+ if category.endswith(':'):
# "stderr crunchstat: notice: ..."
continue
- elif m.group('category') in ('error', 'caught'):
+ elif category in ('error', 'caught'):
continue
- elif m.group('category') in ('read', 'open', 'cgroup', 'CID', 'Running'):
+ elif category in ('read', 'open', 'cgroup', 'CID', 'Running'):
# "stderr crunchstat: read /proc/1234/net/dev: ..."
# (old logs are less careful with unprefixed error messages)
continue
if task.finishtime is None or timestamp > task.finishtime:
task.finishtime = timestamp
- if self.starttime is None or timestamp < task.starttime:
+ if self.starttime is None or timestamp < self.starttime:
self.starttime = timestamp
- if self.finishtime is None or timestamp < task.finishtime:
+ if self.finishtime is None or timestamp > self.finishtime:
self.finishtime = timestamp
if (not self.detected_crunch1) and task.starttime is not None and task.finishtime is not None:
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.items():
- if group == 'interval':
- if stat == 'seconds':
- this_interval_s = val
- continue
- elif not (this_interval_s > 0):
+ if group == 'interval':
+ if 'seconds' in stats:
+ this_interval_s = stats.get('seconds',0)
+ del stats['seconds']
+ if this_interval_s <= 0:
logger.error(
"BUG? interval stat given with duration {!r}".
format(this_interval_s))
- continue
- else:
+ else:
+ logger.error('BUG? interval stat missing duration')
+ for stat, val in stats.items():
+ if group == 'interval' and this_interval_s:
stat = stat + '__rate'
val = val / this_interval_s
- if stat in ['user+sys__rate', 'tx+rx__rate']:
+ if stat in ['user+sys__rate', 'user__rate', 'sys__rate', 'tx+rx__rate', 'rx__rate', 'tx__rate']:
task.series[category, stat].append(
(timestamp - self.starttime, val))
else:
- if stat in ['rss']:
+ if stat in ['rss','used','total']:
task.series[category, stat].append(
(timestamp - self.starttime, val))
self.task_stats[task_id][category][stat] = val
(float(self.job_tot['blkio:0:0']['read']) /
float(self.job_tot['net:keep0']['rx']))
if self.job_tot['net:keep0']['rx'] > 0 else 0,
- lambda x: x * 100.0)):
+ lambda x: x * 100.0),
+ ('Temp disk utilization {}%',
+ (float(self.job_tot['statfs']['used']) /
+ float(self.job_tot['statfs']['total']))
+ if self.job_tot['statfs']['total'] > 0 else 0,
+ lambda x: x * 100.0),
+ ):
format_string, val, transform = args
if val == float('-Inf'):
continue
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(),
- self._recommend_keep_cache())
+ self._recommend_keep_cache(),
+ self._recommend_temp_disk(),
+ )
def _recommend_cpu(self):
"""Recommend asking for 4 cores if max CPU usage was 333%"""
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
+ # TODO: This should be more nuanced in cases where max >> avg
+ if used_cores < asked_cores:
yield (
'#!! {} max CPU usage was {}% -- '
- 'try runtime_constraints "{}":{}'
+ 'try reducing runtime_constraints to "{}":{}'
).format(
self.label,
math.ceil(cpu_max_rate*100),
constraint_key,
int(used_cores))
+ # FIXME: This needs to be updated to account for current nodemanager algorithms
def _recommend_ram(self):
"""Recommend an economical RAM constraint for this job.
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 "{}":{}'
+ 'try reducing runtime_constraints to "{}":{}'
).format(
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 (
'#!! {} Keep cache utilization was {:.2f}% -- '
- 'try runtime_constraints "{}":{} (or more)'
+ 'try doubling runtime_constraints to "{}":{} (or more)'
).format(
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 _recommend_temp_disk(self):
+ """Recommend decreasing temp disk if utilization < 50%"""
+ total = float(self.job_tot['statfs']['total'])
+ utilization = (float(self.job_tot['statfs']['used']) / total) if total > 0 else 0.0
+
+ if utilization < 50.8 and total > 0:
+ yield (
+ '#!! {} max temp disk utilization was {:.0f}% of {:.0f} MiB -- '
+ 'consider reducing "tmpdirMin" and/or "outdirMin"'
+ ).format(
+ self.label,
+ utilization * 100.0,
+ total / MB)
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',