1 # Copyright (C) The Arvados Authors. All rights reserved.
3 # SPDX-License-Identifier: AGPL-3.0
7 import crunchstat_summary.dygraphs
8 import crunchstat_summary.reader
18 from arvados.api import OrderedJsonModel
19 from crunchstat_summary import logger
21 # Recommend memory constraints that are this multiple of an integral
22 # number of GiB. (Actual nodes tend to be sold in sizes like 8 GiB
23 # that have amounts like 7.5 GiB according to the kernel.)
24 AVAILABLE_RAM_RATIO = 0.95
27 # Workaround datetime.datetime.strptime() thread-safety bug by calling
28 # it once before starting threads. https://bugs.python.org/issue7980
29 datetime.datetime.strptime('1999-12-31_23:59:59', '%Y-%m-%d_%H:%M:%S')
32 WEBCHART_CLASS = crunchstat_summary.dygraphs.DygraphsChart
38 self.finishtime = None
39 self.series = collections.defaultdict(list)
42 class Summarizer(object):
43 def __init__(self, logdata, label=None, skip_child_jobs=False, uuid=None, **kwargs):
44 self._logdata = logdata
49 self.finishtime = None
50 self._skip_child_jobs = skip_child_jobs
52 # stats_max: {category: {stat: val}}
53 self.stats_max = collections.defaultdict(
54 functools.partial(collections.defaultdict, lambda: 0))
55 # task_stats: {task_id: {category: {stat: val}}}
56 self.task_stats = collections.defaultdict(
57 functools.partial(collections.defaultdict, dict))
60 self.tasks = collections.defaultdict(Task)
62 # We won't bother recommending new runtime constraints if the
63 # constraints given when running the job are known to us and
64 # are already suitable. If applicable, the subclass
65 # constructor will overwrite this with something useful.
66 self.existing_constraints = {}
68 logger.debug("%s: logdata %s", self.label, logdata)
71 logger.debug("%s: parsing logdata %s", self.label, self._logdata)
72 with self._logdata as logdata:
75 def _run(self, logdata):
76 self.detected_crunch1 = False
78 if not self.detected_crunch1 and '-8i9sb-' in line:
79 self.detected_crunch1 = True
81 if self.detected_crunch1:
82 m = re.search(r'^\S+ \S+ \d+ (?P<seq>\d+) job_task (?P<task_uuid>\S+)$', line)
84 seq = int(m.group('seq'))
85 uuid = m.group('task_uuid')
86 self.seq_to_uuid[seq] = uuid
87 logger.debug('%s: seq %d is task %s', self.label, seq, uuid)
90 m = re.search(r'^\S+ \S+ \d+ (?P<seq>\d+) (success in|failure \(#., permanent\) after) (?P<elapsed>\d+) seconds', line)
92 task_id = self.seq_to_uuid[int(m.group('seq'))]
93 elapsed = int(m.group('elapsed'))
94 self.task_stats[task_id]['time'] = {'elapsed': elapsed}
95 if elapsed > self.stats_max['time']['elapsed']:
96 self.stats_max['time']['elapsed'] = elapsed
99 m = re.search(r'^\S+ \S+ \d+ (?P<seq>\d+) stderr Queued job (?P<uuid>\S+)$', line)
101 uuid = m.group('uuid')
102 if self._skip_child_jobs:
103 logger.warning('%s: omitting stats from child job %s'
104 ' because --skip-child-jobs flag is on',
107 logger.debug('%s: follow %s', self.label, uuid)
108 child_summarizer = ProcessSummarizer(uuid)
109 child_summarizer.stats_max = self.stats_max
110 child_summarizer.task_stats = self.task_stats
111 child_summarizer.tasks = self.tasks
112 child_summarizer.starttime = self.starttime
113 child_summarizer.run()
114 logger.debug('%s: done %s', self.label, uuid)
117 # 2017-12-02_17:15:08 e51c5-8i9sb-mfp68stkxnqdd6m 63676 0 stderr crunchstat: keepcalls 0 put 2576 get -- interval 10.0000 seconds 0 put 2576 get
118 m = re.search(r'^(?P<timestamp>[^\s.]+)(\.\d+)? (?P<job_uuid>\S+) \d+ (?P<seq>\d+) stderr (?P<crunchstat>crunchstat: )(?P<category>\S+) (?P<current>.*?)( -- interval (?P<interval>.*))?\n$', line)
123 # 2017-12-01T16:56:24.723509200Z crunchstat: keepcalls 0 put 3 get -- interval 10.0000 seconds 0 put 3 get
124 m = re.search(r'^(?P<timestamp>\S+) (?P<crunchstat>crunchstat: )?(?P<category>\S+) (?P<current>.*?)( -- interval (?P<interval>.*))?\n$', line)
128 if self.label is None:
130 self.label = m.group('job_uuid')
132 self.label = 'container'
133 if m.group('category').endswith(':'):
134 # "stderr crunchstat: notice: ..."
136 elif m.group('category') in ('error', 'caught'):
138 elif m.group('category') in ('read', 'open', 'cgroup', 'CID', 'Running'):
139 # "stderr crunchstat: read /proc/1234/net/dev: ..."
140 # (old logs are less careful with unprefixed error messages)
143 if self.detected_crunch1:
144 task_id = self.seq_to_uuid[int(m.group('seq'))]
146 task_id = 'container'
147 task = self.tasks[task_id]
149 # Use the first and last crunchstat timestamps as
150 # approximations of starttime and finishtime.
151 timestamp = m.group('timestamp')
152 if timestamp[10:11] == '_':
153 timestamp = datetime.datetime.strptime(
154 timestamp, '%Y-%m-%d_%H:%M:%S')
155 elif timestamp[10:11] == 'T':
156 timestamp = datetime.datetime.strptime(
157 timestamp[:19], '%Y-%m-%dT%H:%M:%S')
159 raise ValueError("Cannot parse timestamp {!r}".format(
162 if task.starttime is None:
163 logger.debug('%s: task %s starttime %s',
164 self.label, task_id, timestamp)
165 if task.starttime is None or timestamp < task.starttime:
166 task.starttime = timestamp
167 if task.finishtime is None or timestamp > task.finishtime:
168 task.finishtime = timestamp
170 if self.starttime is None or timestamp < task.starttime:
171 self.starttime = timestamp
172 if self.finishtime is None or timestamp < task.finishtime:
173 self.finishtime = timestamp
175 if (not self.detected_crunch1) and task.starttime is not None and task.finishtime is not None:
176 elapsed = (task.finishtime - task.starttime).seconds
177 self.task_stats[task_id]['time'] = {'elapsed': elapsed}
178 if elapsed > self.stats_max['time']['elapsed']:
179 self.stats_max['time']['elapsed'] = elapsed
181 this_interval_s = None
182 for group in ['current', 'interval']:
183 if not m.group(group):
185 category = m.group('category')
186 words = m.group(group).split(' ')
189 for val, stat in zip(words[::2], words[1::2]):
191 stats[stat] = float(val)
193 stats[stat] = int(val)
194 except ValueError as e:
195 # If the line doesn't start with 'crunchstat:' we
196 # might have mistaken an error message for a
197 # structured crunchstat line.
198 if m.group("crunchstat") is None or m.group("category") == "crunchstat":
199 logger.warning("%s: log contains message\n %s", self.label, line)
202 '%s: Error parsing value %r (stat %r, category %r): %r',
203 self.label, val, stat, category, e)
204 logger.warning('%s', line)
206 if 'user' in stats or 'sys' in stats:
207 stats['user+sys'] = stats.get('user', 0) + stats.get('sys', 0)
208 if 'tx' in stats or 'rx' in stats:
209 stats['tx+rx'] = stats.get('tx', 0) + stats.get('rx', 0)
210 for stat, val in stats.items():
211 if group == 'interval':
212 if stat == 'seconds':
213 this_interval_s = val
215 elif not (this_interval_s > 0):
217 "BUG? interval stat given with duration {!r}".
218 format(this_interval_s))
221 stat = stat + '__rate'
222 val = val / this_interval_s
223 if stat in ['user+sys__rate', 'tx+rx__rate']:
224 task.series[category, stat].append(
225 (timestamp - self.starttime, val))
228 task.series[category, stat].append(
229 (timestamp - self.starttime, val))
230 self.task_stats[task_id][category][stat] = val
231 if val > self.stats_max[category][stat]:
232 self.stats_max[category][stat] = val
233 logger.debug('%s: done parsing', self.label)
235 self.job_tot = collections.defaultdict(
236 functools.partial(collections.defaultdict, int))
237 for task_id, task_stat in self.task_stats.items():
238 for category, stat_last in task_stat.items():
239 for stat, val in stat_last.items():
240 if stat in ['cpus', 'cache', 'swap', 'rss']:
241 # meaningless stats like 16 cpu cores x 5 tasks = 80
243 self.job_tot[category][stat] += val
244 logger.debug('%s: done totals', self.label)
246 def long_label(self):
248 if hasattr(self, 'process') and self.process['uuid'] not in label:
249 label = '{} ({})'.format(label, self.process['uuid'])
251 label += ' -- elapsed time '
252 s = (self.finishtime - self.starttime).total_seconds()
254 label += '{}d'.format(int(s/86400))
256 label += '{}h'.format(int(s/3600) % 24)
258 label += '{}m'.format(int(s/60) % 60)
259 label += '{}s'.format(int(s) % 60)
262 def text_report(self):
264 return "(no report generated)\n"
265 return "\n".join(itertools.chain(
266 self._text_report_gen(),
267 self._recommend_gen())) + "\n"
269 def html_report(self):
270 return WEBCHART_CLASS(self.label, [self]).html()
272 def _text_report_gen(self):
273 yield "\t".join(['category', 'metric', 'task_max', 'task_max_rate', 'job_total'])
274 for category, stat_max in sorted(self.stats_max.items()):
275 for stat, val in sorted(stat_max.items()):
276 if stat.endswith('__rate'):
278 max_rate = self._format(stat_max.get(stat+'__rate', '-'))
279 val = self._format(val)
280 tot = self._format(self.job_tot[category].get(stat, '-'))
281 yield "\t".join([category, stat, str(val), max_rate, tot])
283 ('Number of tasks: {}',
286 ('Max CPU time spent by a single task: {}s',
287 self.stats_max['cpu']['user+sys'],
289 ('Max CPU usage in a single interval: {}%',
290 self.stats_max['cpu']['user+sys__rate'],
292 ('Overall CPU usage: {}%',
293 float(self.job_tot['cpu']['user+sys']) /
294 self.job_tot['time']['elapsed']
295 if self.job_tot['time']['elapsed'] > 0 else 0,
297 ('Max memory used by a single task: {}GB',
298 self.stats_max['mem']['rss'],
300 ('Max network traffic in a single task: {}GB',
301 self.stats_max['net:eth0']['tx+rx'] +
302 self.stats_max['net:keep0']['tx+rx'],
304 ('Max network speed in a single interval: {}MB/s',
305 self.stats_max['net:eth0']['tx+rx__rate'] +
306 self.stats_max['net:keep0']['tx+rx__rate'],
308 ('Keep cache miss rate {}%',
309 (float(self.job_tot['keepcache']['miss']) /
310 float(self.job_tot['keepcalls']['get']))
311 if self.job_tot['keepcalls']['get'] > 0 else 0,
312 lambda x: x * 100.0),
313 ('Keep cache utilization {}%',
314 (float(self.job_tot['blkio:0:0']['read']) /
315 float(self.job_tot['net:keep0']['rx']))
316 if self.job_tot['net:keep0']['rx'] > 0 else 0,
317 lambda x: x * 100.0)):
318 format_string, val, transform = args
319 if val == float('-Inf'):
323 yield "# "+format_string.format(self._format(val))
325 def _recommend_gen(self):
326 return itertools.chain(
327 self._recommend_cpu(),
328 self._recommend_ram(),
329 self._recommend_keep_cache())
331 def _recommend_cpu(self):
332 """Recommend asking for 4 cores if max CPU usage was 333%"""
334 constraint_key = self._map_runtime_constraint('vcpus')
335 cpu_max_rate = self.stats_max['cpu']['user+sys__rate']
336 if cpu_max_rate == float('-Inf'):
337 logger.warning('%s: no CPU usage data', self.label)
339 used_cores = max(1, int(math.ceil(cpu_max_rate)))
340 asked_cores = self.existing_constraints.get(constraint_key)
341 if asked_cores is None or used_cores < asked_cores:
343 '#!! {} max CPU usage was {}% -- '
344 'try runtime_constraints "{}":{}'
347 math.ceil(cpu_max_rate*100),
351 def _recommend_ram(self):
352 """Recommend an economical RAM constraint for this job.
354 Nodes that are advertised as "8 gibibytes" actually have what
355 we might call "8 nearlygibs" of memory available for jobs.
356 Here, we calculate a whole number of nearlygibs that would
357 have sufficed to run the job, then recommend requesting a node
358 with that number of nearlygibs (expressed as mebibytes).
360 Requesting a node with "nearly 8 gibibytes" is our best hope
361 of getting a node that actually has nearly 8 gibibytes
362 available. If the node manager is smart enough to account for
363 the discrepancy itself when choosing/creating a node, we'll
364 get an 8 GiB node with nearly 8 GiB available. Otherwise, the
365 advertised size of the next-size-smaller node (say, 6 GiB)
366 will be too low to satisfy our request, so we will effectively
367 get rounded up to 8 GiB.
369 For example, if we need 7500 MiB, we can ask for 7500 MiB, and
370 we will generally get a node that is advertised as "8 GiB" and
371 has at least 7500 MiB available. However, asking for 8192 MiB
372 would either result in an unnecessarily expensive 12 GiB node
373 (if node manager knows about the discrepancy), or an 8 GiB
374 node which has less than 8192 MiB available and is therefore
375 considered by crunch-dispatch to be too small to meet our
378 When node manager learns how to predict the available memory
379 for each node type such that crunch-dispatch always agrees
380 that a node is big enough to run the job it was brought up
381 for, all this will be unnecessary. We'll just ask for exactly
382 the memory we want -- even if that happens to be 8192 MiB.
385 constraint_key = self._map_runtime_constraint('ram')
386 used_bytes = self.stats_max['mem']['rss']
387 if used_bytes == float('-Inf'):
388 logger.warning('%s: no memory usage data', self.label)
390 used_mib = math.ceil(float(used_bytes) / MB)
391 asked_mib = self.existing_constraints.get(constraint_key)
393 nearlygibs = lambda mebibytes: mebibytes/AVAILABLE_RAM_RATIO/1024
394 if asked_mib is None or (
395 math.ceil(nearlygibs(used_mib)) < nearlygibs(asked_mib)):
397 '#!! {} max RSS was {} MiB -- '
398 'try runtime_constraints "{}":{}'
403 int(math.ceil(nearlygibs(used_mib))*AVAILABLE_RAM_RATIO*1024*(MB)/self._runtime_constraint_mem_unit()))
405 def _recommend_keep_cache(self):
406 """Recommend increasing keep cache if utilization < 80%"""
407 constraint_key = self._map_runtime_constraint('keep_cache_ram')
408 if self.job_tot['net:keep0']['rx'] == 0:
410 utilization = (float(self.job_tot['blkio:0:0']['read']) /
411 float(self.job_tot['net:keep0']['rx']))
412 asked_cache = self.existing_constraints.get(constraint_key, 256)
414 if utilization < 0.8:
416 '#!! {} Keep cache utilization was {:.2f}% -- '
417 'try runtime_constraints "{}":{} (or more)'
422 asked_cache*2*(MB)/self._runtime_constraint_mem_unit())
425 def _format(self, val):
426 """Return a string representation of a stat.
428 {:.2f} for floats, default format for everything else."""
429 if isinstance(val, float):
430 return '{:.2f}'.format(val)
432 return '{}'.format(val)
434 def _runtime_constraint_mem_unit(self):
435 if hasattr(self, 'runtime_constraint_mem_unit'):
436 return self.runtime_constraint_mem_unit
437 elif self.detected_crunch1:
438 return JobSummarizer.runtime_constraint_mem_unit
440 return ContainerSummarizer.runtime_constraint_mem_unit
442 def _map_runtime_constraint(self, key):
443 if hasattr(self, 'map_runtime_constraint'):
444 return self.map_runtime_constraint[key]
445 elif self.detected_crunch1:
446 return JobSummarizer.map_runtime_constraint[key]
451 class CollectionSummarizer(Summarizer):
452 def __init__(self, collection_id, **kwargs):
453 super(CollectionSummarizer, self).__init__(
454 crunchstat_summary.reader.CollectionReader(collection_id), **kwargs)
455 self.label = collection_id
458 def NewSummarizer(process_or_uuid, **kwargs):
459 """Construct with the appropriate subclass for this uuid/object."""
461 if isinstance(process_or_uuid, dict):
462 process = process_or_uuid
463 uuid = process['uuid']
465 uuid = process_or_uuid
467 arv = arvados.api('v1', model=OrderedJsonModel())
469 if '-dz642-' in uuid:
471 process = arv.containers().get(uuid=uuid).execute()
472 klass = ContainerTreeSummarizer
473 elif '-xvhdp-' in uuid:
475 process = arv.container_requests().get(uuid=uuid).execute()
476 klass = ContainerTreeSummarizer
477 elif '-8i9sb-' in uuid:
479 process = arv.jobs().get(uuid=uuid).execute()
480 klass = JobTreeSummarizer
481 elif '-d1hrv-' in uuid:
483 process = arv.pipeline_instances().get(uuid=uuid).execute()
484 klass = PipelineSummarizer
485 elif '-4zz18-' in uuid:
486 return CollectionSummarizer(collection_id=uuid)
488 raise ArgumentError("Unrecognized uuid %s", uuid)
489 return klass(process, uuid=uuid, **kwargs)
492 class ProcessSummarizer(Summarizer):
493 """Process is a job, pipeline, container, or container request."""
495 def __init__(self, process, label=None, **kwargs):
497 self.process = process
499 label = self.process.get('name', self.process['uuid'])
500 if self.process.get('log'):
502 rdr = crunchstat_summary.reader.CollectionReader(self.process['log'])
503 except arvados.errors.NotFoundError as e:
504 logger.warning("Trying event logs after failing to read "
505 "log collection %s: %s", self.process['log'], e)
507 rdr = crunchstat_summary.reader.LiveLogReader(self.process['uuid'])
508 label = label + ' (partial)'
509 super(ProcessSummarizer, self).__init__(rdr, label=label, **kwargs)
510 self.existing_constraints = self.process.get('runtime_constraints', {})
513 class JobSummarizer(ProcessSummarizer):
514 runtime_constraint_mem_unit = MB
515 map_runtime_constraint = {
516 'keep_cache_ram': 'keep_cache_mb_per_task',
517 'ram': 'min_ram_mb_per_node',
518 'vcpus': 'min_cores_per_node',
522 class ContainerSummarizer(ProcessSummarizer):
523 runtime_constraint_mem_unit = 1
526 class MultiSummarizer(object):
527 def __init__(self, children={}, label=None, threads=1, **kwargs):
528 self.throttle = threading.Semaphore(threads)
529 self.children = children
532 def run_and_release(self, target, *args, **kwargs):
534 return target(*args, **kwargs)
536 self.throttle.release()
540 for child in self.children.values():
541 self.throttle.acquire()
542 t = threading.Thread(target=self.run_and_release, args=(child.run, ))
549 def text_report(self):
551 d = self._descendants()
552 for child in d.values():
554 txt += '### Summary for {} ({})\n'.format(
555 child.label, child.process['uuid'])
556 txt += child.text_report()
560 def _descendants(self):
561 """Dict of self and all descendants.
563 Nodes with nothing of their own to report (like
564 MultiSummarizers) are omitted.
566 d = collections.OrderedDict()
567 for key, child in self.children.items():
568 if isinstance(child, Summarizer):
570 if isinstance(child, MultiSummarizer):
571 d.update(child._descendants())
574 def html_report(self):
575 return WEBCHART_CLASS(self.label, iter(self._descendants().values())).html()
578 class JobTreeSummarizer(MultiSummarizer):
579 """Summarizes a job and all children listed in its components field."""
580 def __init__(self, job, label=None, **kwargs):
581 arv = arvados.api('v1', model=OrderedJsonModel())
582 label = label or job.get('name', job['uuid'])
583 children = collections.OrderedDict()
584 children[job['uuid']] = JobSummarizer(job, label=label, **kwargs)
585 if job.get('components', None):
587 for j in arv.jobs().index(
588 limit=len(job['components']),
589 filters=[['uuid','in',list(job['components'].values())]]).execute()['items']:
590 preloaded[j['uuid']] = j
591 for cname in sorted(job['components'].keys()):
592 child_uuid = job['components'][cname]
593 j = (preloaded.get(child_uuid) or
594 arv.jobs().get(uuid=child_uuid).execute())
595 children[child_uuid] = JobTreeSummarizer(job=j, label=cname, **kwargs)
597 super(JobTreeSummarizer, self).__init__(
603 class PipelineSummarizer(MultiSummarizer):
604 def __init__(self, instance, **kwargs):
605 children = collections.OrderedDict()
606 for cname, component in instance['components'].items():
607 if 'job' not in component:
609 "%s: skipping component with no job assigned", cname)
612 "%s: job %s", cname, component['job']['uuid'])
613 summarizer = JobTreeSummarizer(component['job'], label=cname, **kwargs)
614 summarizer.label = '{} {}'.format(
615 cname, component['job']['uuid'])
616 children[cname] = summarizer
617 super(PipelineSummarizer, self).__init__(
619 label=instance['uuid'],
623 class ContainerTreeSummarizer(MultiSummarizer):
624 def __init__(self, root, skip_child_jobs=False, **kwargs):
625 arv = arvados.api('v1', model=OrderedJsonModel())
627 label = kwargs.pop('label', None) or root.get('name') or root['uuid']
630 children = collections.OrderedDict()
631 todo = collections.deque((root, ))
633 current = todo.popleft()
634 label = current['name']
635 sort_key = current['created_at']
636 if current['uuid'].find('-xvhdp-') > 0:
637 current = arv.containers().get(uuid=current['container_uuid']).execute()
639 summer = ContainerSummarizer(current, label=label, **kwargs)
640 summer.sort_key = sort_key
641 children[current['uuid']] = summer
645 child_crs = arv.container_requests().index(
647 filters=page_filters+[
648 ['requesting_container_uuid', '=', current['uuid']]],
650 if not child_crs['items']:
652 elif skip_child_jobs:
653 logger.warning('%s: omitting stats from %d child containers'
654 ' because --skip-child-jobs flag is on',
655 label, child_crs['items_available'])
657 page_filters = [['uuid', '>', child_crs['items'][-1]['uuid']]]
658 for cr in child_crs['items']:
659 if cr['container_uuid']:
660 logger.debug('%s: container req %s', current['uuid'], cr['uuid'])
661 cr['name'] = cr.get('name') or cr['uuid']
663 sorted_children = collections.OrderedDict()
664 for uuid in sorted(list(children.keys()), key=lambda uuid: children[uuid].sort_key):
665 sorted_children[uuid] = children[uuid]
666 super(ContainerTreeSummarizer, self).__init__(
667 children=sorted_children,