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 # FIXME: the default on this get won't work correctly
413 asked_cache = self.existing_constraints.get(constraint_key, 256) * self._runtime_constraint_mem_unit()
415 if utilization < 0.8:
417 '#!! {} Keep cache utilization was {:.2f}% -- '
418 'try runtime_constraints "{}":{} (or more)'
423 math.ceil(asked_cache * 2 / self._runtime_constraint_mem_unit()))
426 def _format(self, val):
427 """Return a string representation of a stat.
429 {:.2f} for floats, default format for everything else."""
430 if isinstance(val, float):
431 return '{:.2f}'.format(val)
433 return '{}'.format(val)
435 def _runtime_constraint_mem_unit(self):
436 if hasattr(self, 'runtime_constraint_mem_unit'):
437 return self.runtime_constraint_mem_unit
438 elif self.detected_crunch1:
439 return JobSummarizer.runtime_constraint_mem_unit
441 return ContainerSummarizer.runtime_constraint_mem_unit
443 def _map_runtime_constraint(self, key):
444 if hasattr(self, 'map_runtime_constraint'):
445 return self.map_runtime_constraint[key]
446 elif self.detected_crunch1:
447 return JobSummarizer.map_runtime_constraint[key]
452 class CollectionSummarizer(Summarizer):
453 def __init__(self, collection_id, **kwargs):
454 super(CollectionSummarizer, self).__init__(
455 crunchstat_summary.reader.CollectionReader(collection_id), **kwargs)
456 self.label = collection_id
459 def NewSummarizer(process_or_uuid, **kwargs):
460 """Construct with the appropriate subclass for this uuid/object."""
462 if isinstance(process_or_uuid, dict):
463 process = process_or_uuid
464 uuid = process['uuid']
466 uuid = process_or_uuid
468 arv = arvados.api('v1', model=OrderedJsonModel())
470 if '-dz642-' in uuid:
472 process = arv.containers().get(uuid=uuid).execute()
473 klass = ContainerTreeSummarizer
474 elif '-xvhdp-' in uuid:
476 process = arv.container_requests().get(uuid=uuid).execute()
477 klass = ContainerTreeSummarizer
478 elif '-8i9sb-' in uuid:
480 process = arv.jobs().get(uuid=uuid).execute()
481 klass = JobTreeSummarizer
482 elif '-d1hrv-' in uuid:
484 process = arv.pipeline_instances().get(uuid=uuid).execute()
485 klass = PipelineSummarizer
486 elif '-4zz18-' in uuid:
487 return CollectionSummarizer(collection_id=uuid)
489 raise ArgumentError("Unrecognized uuid %s", uuid)
490 return klass(process, uuid=uuid, **kwargs)
493 class ProcessSummarizer(Summarizer):
494 """Process is a job, pipeline, container, or container request."""
496 def __init__(self, process, label=None, **kwargs):
498 self.process = process
500 label = self.process.get('name', self.process['uuid'])
501 if self.process.get('log'):
503 rdr = crunchstat_summary.reader.CollectionReader(self.process['log'])
504 except arvados.errors.NotFoundError as e:
505 logger.warning("Trying event logs after failing to read "
506 "log collection %s: %s", self.process['log'], e)
508 rdr = crunchstat_summary.reader.LiveLogReader(self.process['uuid'])
509 label = label + ' (partial)'
510 super(ProcessSummarizer, self).__init__(rdr, label=label, **kwargs)
511 self.existing_constraints = self.process.get('runtime_constraints', {})
514 class JobSummarizer(ProcessSummarizer):
515 runtime_constraint_mem_unit = MB
516 map_runtime_constraint = {
517 'keep_cache_ram': 'keep_cache_mb_per_task',
518 'ram': 'min_ram_mb_per_node',
519 'vcpus': 'min_cores_per_node',
523 class ContainerSummarizer(ProcessSummarizer):
524 runtime_constraint_mem_unit = 1
527 class MultiSummarizer(object):
528 def __init__(self, children={}, label=None, threads=1, **kwargs):
529 self.throttle = threading.Semaphore(threads)
530 self.children = children
533 def run_and_release(self, target, *args, **kwargs):
535 return target(*args, **kwargs)
537 self.throttle.release()
541 for child in self.children.values():
542 self.throttle.acquire()
543 t = threading.Thread(target=self.run_and_release, args=(child.run, ))
550 def text_report(self):
552 d = self._descendants()
553 for child in d.values():
555 txt += '### Summary for {} ({})\n'.format(
556 child.label, child.process['uuid'])
557 txt += child.text_report()
561 def _descendants(self):
562 """Dict of self and all descendants.
564 Nodes with nothing of their own to report (like
565 MultiSummarizers) are omitted.
567 d = collections.OrderedDict()
568 for key, child in self.children.items():
569 if isinstance(child, Summarizer):
571 if isinstance(child, MultiSummarizer):
572 d.update(child._descendants())
575 def html_report(self):
576 return WEBCHART_CLASS(self.label, iter(self._descendants().values())).html()
579 class JobTreeSummarizer(MultiSummarizer):
580 """Summarizes a job and all children listed in its components field."""
581 def __init__(self, job, label=None, **kwargs):
582 arv = arvados.api('v1', model=OrderedJsonModel())
583 label = label or job.get('name', job['uuid'])
584 children = collections.OrderedDict()
585 children[job['uuid']] = JobSummarizer(job, label=label, **kwargs)
586 if job.get('components', None):
588 for j in arv.jobs().index(
589 limit=len(job['components']),
590 filters=[['uuid','in',list(job['components'].values())]]).execute()['items']:
591 preloaded[j['uuid']] = j
592 for cname in sorted(job['components'].keys()):
593 child_uuid = job['components'][cname]
594 j = (preloaded.get(child_uuid) or
595 arv.jobs().get(uuid=child_uuid).execute())
596 children[child_uuid] = JobTreeSummarizer(job=j, label=cname, **kwargs)
598 super(JobTreeSummarizer, self).__init__(
604 class PipelineSummarizer(MultiSummarizer):
605 def __init__(self, instance, **kwargs):
606 children = collections.OrderedDict()
607 for cname, component in instance['components'].items():
608 if 'job' not in component:
610 "%s: skipping component with no job assigned", cname)
613 "%s: job %s", cname, component['job']['uuid'])
614 summarizer = JobTreeSummarizer(component['job'], label=cname, **kwargs)
615 summarizer.label = '{} {}'.format(
616 cname, component['job']['uuid'])
617 children[cname] = summarizer
618 super(PipelineSummarizer, self).__init__(
620 label=instance['uuid'],
624 class ContainerTreeSummarizer(MultiSummarizer):
625 def __init__(self, root, skip_child_jobs=False, **kwargs):
626 arv = arvados.api('v1', model=OrderedJsonModel())
628 label = kwargs.pop('label', None) or root.get('name') or root['uuid']
631 children = collections.OrderedDict()
632 todo = collections.deque((root, ))
634 current = todo.popleft()
635 label = current['name']
636 sort_key = current['created_at']
637 if current['uuid'].find('-xvhdp-') > 0:
638 current = arv.containers().get(uuid=current['container_uuid']).execute()
640 summer = ContainerSummarizer(current, label=label, **kwargs)
641 summer.sort_key = sort_key
642 children[current['uuid']] = summer
646 child_crs = arv.container_requests().index(
648 filters=page_filters+[
649 ['requesting_container_uuid', '=', current['uuid']]],
651 if not child_crs['items']:
653 elif skip_child_jobs:
654 logger.warning('%s: omitting stats from %d child containers'
655 ' because --skip-child-jobs flag is on',
656 label, child_crs['items_available'])
658 page_filters = [['uuid', '>', child_crs['items'][-1]['uuid']]]
659 for cr in child_crs['items']:
660 if cr['container_uuid']:
661 logger.debug('%s: container req %s', current['uuid'], cr['uuid'])
662 cr['name'] = cr.get('name') or cr['uuid']
664 sorted_children = collections.OrderedDict()
665 for uuid in sorted(list(children.keys()), key=lambda uuid: children[uuid].sort_key):
666 sorted_children[uuid] = children[uuid]
667 super(ContainerTreeSummarizer, self).__init__(
668 children=sorted_children,