import threading
import _strptime
-from arvados.api import OrderedJsonModel
from crunchstat_summary import logger
# Recommend memory constraints that are this multiple of an integral
# 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
+AVAILABLE_RAM_RATIO = 0.90
MB=2**20
# Workaround datetime.datetime.strptime() thread-safety bug by calling
self.job_tot[category][stat] += val
logger.debug('%s: done totals', self.label)
+ missing_category = {
+ 'cpu': 'CPU',
+ 'mem': 'memory',
+ 'net:': 'network I/O',
+ 'statfs': 'storage space',
+ }
+ for task_stat in self.task_stats.values():
+ for category in task_stat.keys():
+ for checkcat in missing_category:
+ if checkcat.endswith(':'):
+ if category.startswith(checkcat):
+ missing_category.pop(checkcat)
+ break
+ else:
+ if category == checkcat:
+ missing_category.pop(checkcat)
+ break
+ for catlabel in missing_category.values():
+ logger.warning('%s: %s stats are missing -- possible cluster configuration issue',
+ self.label, catlabel)
+
def long_label(self):
label = self.label
if hasattr(self, 'process') and self.process['uuid'] not in label:
label = '{} ({})'.format(label, self.process['uuid'])
- if self.finishtime:
- label += ' -- elapsed time '
- s = (self.finishtime - self.starttime).total_seconds()
- if s > 86400:
- label += '{}d'.format(int(s/86400))
- if s > 3600:
- label += '{}h'.format(int(s/3600) % 24)
- if s > 60:
- label += '{}m'.format(int(s/60) % 60)
- label += '{}s'.format(int(s) % 60)
+ return label
+
+ def elapsed_time(self):
+ if not self.finishtime:
+ return ""
+ label = ""
+ s = (self.finishtime - self.starttime).total_seconds()
+ if s > 86400:
+ label += '{}d'.format(int(s/86400))
+ if s > 3600:
+ label += '{}h'.format(int(s/3600) % 24)
+ if s > 60:
+ label += '{}m'.format(int(s/60) % 60)
+ label += '{}s'.format(int(s) % 60)
return label
def text_report(self):
if not self.tasks:
return "(no report generated)\n"
return "\n".join(itertools.chain(
- self._text_report_gen(),
- self._recommend_gen())) + "\n"
+ self._text_report_table_gen(lambda x: "\t".join(x),
+ lambda x: "\t".join(x)),
+ self._text_report_agg_gen(lambda x: "# {}: {}{}".format(x[0], x[1], x[2])),
+ self._recommend_gen(lambda x: "#!! "+x))) + "\n"
def html_report(self):
return WEBCHART_CLASS(self.label, [self]).html()
- def _text_report_gen(self):
- yield "\t".join(['category', 'metric', 'task_max', 'task_max_rate', 'job_total'])
+ def _text_report_table_gen(self, headerformat, rowformat):
+ yield headerformat(['category', 'metric', 'task_max', 'task_max_rate', 'job_total'])
for category, stat_max in sorted(self.stats_max.items()):
for stat, val in sorted(stat_max.items()):
if stat.endswith('__rate'):
max_rate = self._format(stat_max.get(stat+'__rate', '-'))
val = self._format(val)
tot = self._format(self.job_tot[category].get(stat, '-'))
- yield "\t".join([category, stat, str(val), max_rate, tot])
- for args in (
- ('Number of tasks: {}',
- len(self.tasks),
- None),
- ('Max CPU time spent by a single task: {}s',
+ yield rowformat([category, stat, str(val), max_rate, tot])
+
+ def _text_report_agg_gen(self, aggformat):
+ by_single_task = ""
+ if len(self.tasks) > 1:
+ by_single_task = " by a single task"
+ metrics = [
+ ('Elapsed time',
+ self.elapsed_time(),
+ None,
+ ''),
+ ('Max CPU time spent{}'.format(by_single_task),
self.stats_max['cpu']['user+sys'],
- None),
- ('Max CPU usage in a single interval: {}%',
+ None,
+ 's'),
+ ('Max CPU usage in a single interval',
self.stats_max['cpu']['user+sys__rate'],
- lambda x: x * 100),
- ('Overall CPU usage: {}%',
+ lambda x: x * 100,
+ '%'),
+ ('Overall CPU usage',
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),
- ('Max memory used by a single task: {}GB',
+ lambda x: x * 100,
+ '%'),
+ ('Max memory used{}'.format(by_single_task),
self.stats_max['mem']['rss'],
- lambda x: x / 1e9),
- ('Max network traffic in a single task: {}GB',
+ lambda x: x / 1e9,
+ 'GB'),
+ ('Max network traffic{}'.format(by_single_task),
self.stats_max['net:eth0']['tx+rx'] +
self.stats_max['net:keep0']['tx+rx'],
- lambda x: x / 1e9),
- ('Max network speed in a single interval: {}MB/s',
+ lambda x: x / 1e9,
+ 'GB'),
+ ('Max network speed in a single interval',
self.stats_max['net:eth0']['tx+rx__rate'] +
self.stats_max['net:keep0']['tx+rx__rate'],
- lambda x: x / 1e6),
- ('Keep cache miss rate {}%',
+ lambda x: x / 1e6,
+ 'MB/s'),
+ ('Keep cache miss rate',
(float(self.job_tot['keepcache']['miss']) /
float(self.job_tot['keepcalls']['get']))
if self.job_tot['keepcalls']['get'] > 0 else 0,
- lambda x: x * 100.0),
- ('Keep cache utilization {}%',
+ lambda x: x * 100.0,
+ '%'),
+ ('Keep cache utilization',
(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),
- ('Temp disk utilization {}%',
+ 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
+ lambda x: x * 100.0,
+ '%'),
+ ]
+
+ if len(self.tasks) > 1:
+ metrics.insert(0, ('Number of tasks',
+ len(self.tasks),
+ None,
+ ''))
+ for args in metrics:
+ format_string, val, transform, suffix = args
if val == float('-Inf'):
continue
if transform:
val = transform(val)
- yield "# "+format_string.format(self._format(val))
+ yield aggformat((format_string, self._format(val), suffix))
- def _recommend_gen(self):
+ def _recommend_gen(self, recommendformat):
# 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_temp_disk(),
+ self._recommend_cpu(recommendformat),
+ self._recommend_ram(recommendformat),
+ self._recommend_keep_cache(recommendformat),
+ self._recommend_temp_disk(recommendformat),
)
- def _recommend_cpu(self):
+ def _recommend_cpu(self, recommendformat):
"""Recommend asking for 4 cores if max CPU usage was 333%"""
constraint_key = self._map_runtime_constraint('vcpus')
asked_cores = 1
# TODO: This should be more nuanced in cases where max >> avg
if used_cores < asked_cores:
- yield (
- '#!! {} max CPU usage was {}% -- '
+ yield recommendformat(
+ '{} max CPU usage was {}% -- '
'try reducing runtime_constraints to "{}":{}'
).format(
self.label,
constraint_key,
int(used_cores))
- # FIXME: This needs to be updated to account for current nodemanager algorithms
- def _recommend_ram(self):
+ # FIXME: This needs to be updated to account for current a-d-c algorithms
+ def _recommend_ram(self, recommendformat):
"""Recommend an economical RAM constraint for this job.
Nodes that are advertised as "8 gibibytes" actually have what
logger.warning('%s: no memory usage data', self.label)
return
used_mib = math.ceil(float(used_bytes) / MB)
- asked_mib = self.existing_constraints.get(constraint_key)
+ asked_mib = self.existing_constraints.get(constraint_key) / MB
nearlygibs = lambda mebibytes: mebibytes/AVAILABLE_RAM_RATIO/1024
- if used_mib > 0 and (asked_mib is None or (
- math.ceil(nearlygibs(used_mib)) < nearlygibs(asked_mib))):
- yield (
- '#!! {} max RSS was {} MiB -- '
- 'try reducing runtime_constraints to "{}":{}'
+ ratio = 0.5
+ recommend_mib = int(math.ceil(nearlygibs(used_mib/ratio))*AVAILABLE_RAM_RATIO*1024)
+ if used_mib > 0 and (used_mib / asked_mib) < ratio and asked_mib > recommend_mib:
+ yield recommendformat(
+ '{} requested {} MiB of RAM but actual RAM usage was below {}% at {} MiB -- '
+ 'suggest reducing RAM request to {} MiB'
).format(
self.label,
+ int(asked_mib),
+ int(100*ratio),
int(used_mib),
- constraint_key,
- int(math.ceil(nearlygibs(used_mib))*AVAILABLE_RAM_RATIO*1024*(MB)/self._runtime_constraint_mem_unit()))
+ recommend_mib)
- def _recommend_keep_cache(self):
+ def _recommend_keep_cache(self, recommendformat):
"""Recommend increasing keep cache if utilization < 80%"""
constraint_key = self._map_runtime_constraint('keep_cache_ram')
if self.job_tot['net:keep0']['rx'] == 0:
asked_cache = self.existing_constraints.get(constraint_key, 256) * self._runtime_constraint_mem_unit()
if utilization < 0.8:
- yield (
- '#!! {} Keep cache utilization was {:.2f}% -- '
+ yield recommendformat(
+ '{} Keep cache utilization was {:.2f}% -- '
'try doubling runtime_constraints to "{}":{} (or more)'
).format(
self.label,
math.ceil(asked_cache * 2 / self._runtime_constraint_mem_unit()))
- def _recommend_temp_disk(self):
+ def _recommend_temp_disk(self, recommendformat):
"""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 -- '
+ yield recommendformat(
+ '{} max temp disk utilization was {:.0f}% of {:.0f} MiB -- '
'consider reducing "tmpdirMin" and/or "outdirMin"'
).format(
self.label,
else:
uuid = process_or_uuid
process = None
- arv = arvados.api('v1', model=OrderedJsonModel())
+ arv = arvados.api('v1')
if '-dz642-' in uuid:
if process is None:
return d
def html_report(self):
- return WEBCHART_CLASS(self.label, iter(self._descendants().values())).html()
+ tophtml = ""
+ bottomhtml = ""
+ label = self.label
+ if len(self._descendants()) == 1:
+ summarizer = next(iter(self._descendants().values()))
+ tophtml = """{}\n<table class='aggtable'><tbody>{}</tbody></table>\n""".format(
+ "\n".join(summarizer._recommend_gen(lambda x: "<p>{}</p>".format(x))),
+ "\n".join(summarizer._text_report_agg_gen(lambda x: "<tr><th>{}</th><td>{}{}</td></tr>".format(*x))))
+
+ bottomhtml = """<table class='metricstable'><tbody>{}</tbody></table>\n""".format(
+ "\n".join(summarizer._text_report_table_gen(lambda x: "<tr><th>{}</th><th>{}</th><th>{}</th><th>{}</th><th>{}</th></tr>".format(*x),
+ lambda x: "<tr><td>{}</td><td>{}</td><td>{}</td><td>{}</td><td>{}</td></tr>".format(*x))))
+ label = summarizer.long_label()
+
+ return WEBCHART_CLASS(label, iter(self._descendants().values())).html(tophtml, bottomhtml)
class JobTreeSummarizer(MultiSummarizer):
"""Summarizes a job and all children listed in its components field."""
def __init__(self, job, label=None, **kwargs):
- arv = arvados.api('v1', model=OrderedJsonModel())
+ arv = arvados.api('v1')
label = label or job.get('name', job['uuid'])
children = collections.OrderedDict()
children[job['uuid']] = JobSummarizer(job, label=label, **kwargs)
class ContainerRequestTreeSummarizer(MultiSummarizer):
def __init__(self, root, skip_child_jobs=False, **kwargs):
- arv = arvados.api('v1', model=OrderedJsonModel())
+ arv = arvados.api('v1')
label = kwargs.pop('label', None) or root.get('name') or root['uuid']
root['name'] = label
projects / arvados.git / blobdiff