+# Copyright (C) The Arvados Authors. All rights reserved.
+#
+# SPDX-License-Identifier: AGPL-3.0
+
from __future__ import print_function
import arvados
-import gzip
+import collections
+import crunchstat_summary.chartjs
+import crunchstat_summary.reader
+import datetime
+import functools
+import itertools
+import math
import re
import sys
+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
+
+
+# Workaround datetime.datetime.strptime() thread-safety bug by calling
+# it once before starting threads. https://bugs.python.org/issue7980
+datetime.datetime.strptime('1999-12-31_23:59:59', '%Y-%m-%d_%H:%M:%S')
+
+
+class Task(object):
+ def __init__(self):
+ self.starttime = None
+ self.series = collections.defaultdict(list)
class Summarizer(object):
- def __init__(self, args):
- self.args = args
+ def __init__(self, logdata, label=None, skip_child_jobs=False):
+ self._logdata = logdata
+
+ self.label = label
+ self.starttime = None
+ self.finishtime = None
+ self._skip_child_jobs = skip_child_jobs
+
+ # stats_max: {category: {stat: val}}
+ self.stats_max = collections.defaultdict(
+ functools.partial(collections.defaultdict, lambda: 0))
+ # task_stats: {task_id: {category: {stat: val}}}
+ self.task_stats = collections.defaultdict(
+ functools.partial(collections.defaultdict, dict))
+
+ self.seq_to_uuid = {}
+ self.tasks = collections.defaultdict(Task)
+
+ # We won't bother recommending new runtime constraints if the
+ # constraints given when running the job are known to us and
+ # are already suitable. If applicable, the subclass
+ # constructor will overwrite this with something useful.
+ self.existing_constraints = {}
+
+ logger.debug("%s: logdata %s", self.label, logdata)
def run(self):
- stats_max = {}
- for line in self._logdata():
- m = re.search(r'^\S+ \S+ \d+ (?P<seq>\d+) stderr crunchstat: (?P<category>\S+) (?P<current>.*?)( -- interval (?P<interval>.*))?\n', line)
- if not m:
+ logger.debug("%s: parsing logdata %s", self.label, self._logdata)
+ for line in self._logdata:
+ m = re.search(r'^\S+ \S+ \d+ (?P<seq>\d+) job_task (?P<task_uuid>\S+)$', line)
+ if m:
+ seq = int(m.group('seq'))
+ uuid = m.group('task_uuid')
+ self.seq_to_uuid[seq] = uuid
+ logger.debug('%s: seq %d is task %s', self.label, seq, uuid)
continue
- if m.group('category').endswith(':'):
- # "notice:" etc.
+
+ m = re.search(r'^\S+ \S+ \d+ (?P<seq>\d+) (success in|failure \(#., permanent\) after) (?P<elapsed>\d+) seconds', line)
+ if m:
+ task_id = self.seq_to_uuid[int(m.group('seq'))]
+ elapsed = int(m.group('elapsed'))
+ self.task_stats[task_id]['time'] = {'elapsed': elapsed}
+ if elapsed > self.stats_max['time']['elapsed']:
+ self.stats_max['time']['elapsed'] = elapsed
continue
- this_interval_s = None
- for group in ['current', 'interval']:
- if not m.group(group):
+
+ m = re.search(r'^\S+ \S+ \d+ (?P<seq>\d+) stderr Queued job (?P<uuid>\S+)$', line)
+ if m:
+ uuid = m.group('uuid')
+ if self._skip_child_jobs:
+ logger.warning('%s: omitting stats from child job %s'
+ ' because --skip-child-jobs flag is on',
+ self.label, uuid)
continue
- category = m.group('category')
- if category not in stats_max:
- stats_max[category] = {}
- words = m.group(group).split(' ')
- for val, stat in zip(words[::2], words[1::2]):
- if '.' in val:
- val = float(val)
- else:
- val = int(val)
- if group == 'interval':
- if stat == 'seconds':
- this_interval_s = val
- continue
- elif not (this_interval_s > 0):
- print("BUG? interval stat given with duration {!r}".
- format(this_interval_s),
- file=sys.stderr)
- continue
+ logger.debug('%s: follow %s', self.label, uuid)
+ child_summarizer = JobSummarizer(uuid)
+ child_summarizer.stats_max = self.stats_max
+ child_summarizer.task_stats = self.task_stats
+ child_summarizer.tasks = self.tasks
+ child_summarizer.starttime = self.starttime
+ child_summarizer.run()
+ logger.debug('%s: done %s', self.label, uuid)
+ continue
+
+ 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)
+ if not m:
+ continue
+
+ try:
+ if self.label is None:
+ self.label = m.group('job_uuid')
+ logger.debug('%s: using job uuid as label', self.label)
+ if m.group('category').endswith(':'):
+ # "stderr crunchstat: notice: ..."
+ continue
+ elif m.group('category') in ('error', 'caught'):
+ continue
+ elif m.group('category') == 'read':
+ # "stderr crunchstat: read /proc/1234/net/dev: ..."
+ # (crunchstat formatting fixed, but old logs still say this)
+ continue
+ task_id = self.seq_to_uuid[int(m.group('seq'))]
+ task = self.tasks[task_id]
+
+ # Use the first and last crunchstat timestamps as
+ # approximations of starttime and finishtime.
+ timestamp = datetime.datetime.strptime(
+ m.group('timestamp'), '%Y-%m-%d_%H:%M:%S')
+ if not task.starttime:
+ task.starttime = timestamp
+ logger.debug('%s: task %s starttime %s',
+ self.label, task_id, timestamp)
+ task.finishtime = timestamp
+
+ if not self.starttime:
+ self.starttime = timestamp
+ self.finishtime = timestamp
+
+ this_interval_s = None
+ for group in ['current', 'interval']:
+ if not m.group(group):
+ continue
+ category = m.group('category')
+ words = m.group(group).split(' ')
+ stats = {}
+ for val, stat in zip(words[::2], words[1::2]):
+ try:
+ if '.' in val:
+ stats[stat] = float(val)
+ else:
+ stats[stat] = int(val)
+ except ValueError as e:
+ raise ValueError(
+ 'Error parsing {} stat: {!r}'.format(
+ stat, e))
+ 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():
+ if group == 'interval':
+ if stat == 'seconds':
+ this_interval_s = val
+ continue
+ elif not (this_interval_s > 0):
+ logger.error(
+ "BUG? interval stat given with duration {!r}".
+ format(this_interval_s))
+ continue
+ else:
+ stat = stat + '__rate'
+ val = val / this_interval_s
+ if stat in ['user+sys__rate', 'tx+rx__rate']:
+ task.series[category, stat].append(
+ (timestamp - self.starttime, val))
else:
- stat = stat + '__rate'
- val = val / this_interval_s
- if val > stats_max[category].get(stat, float('-Inf')):
- stats_max[category][stat] = val
- self.stats_max = stats_max
-
- def report(self):
- return "\n".join(self._report_gen()) + "\n"
-
- def _report_gen(self):
- yield "\t".join(['category', 'metric', 'max', 'max_rate'])
- for category, stat_max in self.stats_max.iteritems():
- for stat, val in stat_max.iteritems():
+ if stat in ['rss']:
+ task.series[category, stat].append(
+ (timestamp - self.starttime, val))
+ self.task_stats[task_id][category][stat] = val
+ if val > self.stats_max[category][stat]:
+ self.stats_max[category][stat] = val
+ except Exception as e:
+ logger.info('Skipping malformed line: {}Error was: {}\n'.format(line, e))
+ logger.debug('%s: done parsing', self.label)
+
+ 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():
+ if stat in ['cpus', 'cache', 'swap', 'rss']:
+ # meaningless stats like 16 cpu cores x 5 tasks = 80
+ continue
+ self.job_tot[category][stat] += val
+ logger.debug('%s: done totals', self.label)
+
+ def long_label(self):
+ label = self.label
+ 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 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"
+
+ def html_report(self):
+ return crunchstat_summary.chartjs.ChartJS(self.label, [self]).html()
+
+ 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()):
if stat.endswith('__rate'):
continue
- if stat+'__rate' in stat_max:
- max_rate = '{:.2f}'.format(stat_max[stat+'__rate'])
- else:
- max_rate = '-'
- if isinstance(val, float):
- val = '{:.2f}'.format(val)
- yield "\t".join([category, stat, str(val), max_rate])
-
- def _logdata(self):
- if self.args.log_file:
- if self.args.log_file.endswith('.gz'):
- return gzip.open(self.args.log_file)
- else:
- return open(self.args.log_file)
- elif self.args.job:
- arv = arvados.api('v1')
- job = arv.jobs().get(uuid=self.args.job).execute()
- if not job['log']:
- raise ValueError(
- "job {} has no log; live summary not implemented".format(
- self.args.job))
- collection = arvados.collection.CollectionReader(job['log'])
- filenames = [filename for filename in collection]
- if len(filenames) != 1:
- raise ValueError(
- "collection {} has {} files; need exactly one".format(
- job.log, len(filenames)))
- return collection.open(filenames[0])
+ 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',
+ self.stats_max['cpu']['user+sys'],
+ None),
+ ('Max CPU usage in a single interval: {}%',
+ self.stats_max['cpu']['user+sys__rate'],
+ lambda x: x * 100),
+ ('Overall CPU usage: {}%',
+ 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',
+ self.stats_max['mem']['rss'],
+ lambda x: x / 1e9),
+ ('Max network traffic in a single task: {}GB',
+ 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',
+ self.stats_max['net:eth0']['tx+rx__rate'] +
+ self.stats_max['net:keep0']['tx+rx__rate'],
+ lambda x: x / 1e6),
+ ('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 {}%',
+ (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)):
+ format_string, val, transform = args
+ if val == float('-Inf'):
+ continue
+ if transform:
+ val = transform(val)
+ yield "# "+format_string.format(self._format(val))
+
+ def _recommend_gen(self):
+ return itertools.chain(
+ self._recommend_cpu(),
+ self._recommend_ram(),
+ self._recommend_keep_cache())
+
+ def _recommend_cpu(self):
+ """Recommend asking for 4 cores if max CPU usage was 333%"""
+
+ cpu_max_rate = self.stats_max['cpu']['user+sys__rate']
+ if cpu_max_rate == float('-Inf'):
+ logger.warning('%s: no CPU usage data', self.label)
+ return
+ used_cores = max(1, int(math.ceil(cpu_max_rate)))
+ asked_cores = self.existing_constraints.get('min_cores_per_node')
+ if asked_cores is None or used_cores < asked_cores:
+ yield (
+ '#!! {} max CPU usage was {}% -- '
+ 'try runtime_constraints "min_cores_per_node":{}'
+ ).format(
+ self.label,
+ int(math.ceil(cpu_max_rate*100)),
+ int(used_cores))
+
+ def _recommend_ram(self):
+ """Recommend an economical RAM constraint for this job.
+
+ Nodes that are advertised as "8 gibibytes" actually have what
+ we might call "8 nearlygibs" of memory available for jobs.
+ Here, we calculate a whole number of nearlygibs that would
+ have sufficed to run the job, then recommend requesting a node
+ with that number of nearlygibs (expressed as mebibytes).
+
+ Requesting a node with "nearly 8 gibibytes" is our best hope
+ of getting a node that actually has nearly 8 gibibytes
+ available. If the node manager is smart enough to account for
+ the discrepancy itself when choosing/creating a node, we'll
+ get an 8 GiB node with nearly 8 GiB available. Otherwise, the
+ advertised size of the next-size-smaller node (say, 6 GiB)
+ will be too low to satisfy our request, so we will effectively
+ get rounded up to 8 GiB.
+
+ For example, if we need 7500 MiB, we can ask for 7500 MiB, and
+ we will generally get a node that is advertised as "8 GiB" and
+ has at least 7500 MiB available. However, asking for 8192 MiB
+ would either result in an unnecessarily expensive 12 GiB node
+ (if node manager knows about the discrepancy), or an 8 GiB
+ node which has less than 8192 MiB available and is therefore
+ considered by crunch-dispatch to be too small to meet our
+ constraint.
+
+ When node manager learns how to predict the available memory
+ for each node type such that crunch-dispatch always agrees
+ that a node is big enough to run the job it was brought up
+ for, all this will be unnecessary. We'll just ask for exactly
+ the memory we want -- even if that happens to be 8192 MiB.
+ """
+
+ used_bytes = self.stats_max['mem']['rss']
+ if used_bytes == float('-Inf'):
+ logger.warning('%s: no memory usage data', self.label)
+ return
+ used_mib = math.ceil(float(used_bytes) / 1048576)
+ asked_mib = self.existing_constraints.get('min_ram_mb_per_node')
+
+ nearlygibs = lambda mebibytes: mebibytes/AVAILABLE_RAM_RATIO/1024
+ if asked_mib is None or (
+ math.ceil(nearlygibs(used_mib)) < nearlygibs(asked_mib)):
+ yield (
+ '#!! {} max RSS was {} MiB -- '
+ 'try runtime_constraints "min_ram_mb_per_node":{}'
+ ).format(
+ self.label,
+ int(used_mib),
+ int(math.ceil(nearlygibs(used_mib))*AVAILABLE_RAM_RATIO*1024))
+
+ def _recommend_keep_cache(self):
+ """Recommend increasing keep cache if utilization < 80%"""
+ if self.job_tot['net:keep0']['rx'] == 0:
+ return
+ utilization = (float(self.job_tot['blkio:0:0']['read']) /
+ float(self.job_tot['net:keep0']['rx']))
+ asked_mib = self.existing_constraints.get('keep_cache_mb_per_task', 256)
+
+ if utilization < 0.8:
+ yield (
+ '#!! {} Keep cache utilization was {:.2f}% -- '
+ 'try runtime_constraints "keep_cache_mb_per_task":{} (or more)'
+ ).format(
+ self.label,
+ utilization * 100.0,
+ asked_mib*2)
+
+
+ def _format(self, val):
+ """Return a string representation of a stat.
+
+ {:.2f} for floats, default format for everything else."""
+ if isinstance(val, float):
+ return '{:.2f}'.format(val)
+ else:
+ return '{}'.format(val)
+
+
+class CollectionSummarizer(Summarizer):
+ def __init__(self, collection_id, **kwargs):
+ super(CollectionSummarizer, self).__init__(
+ crunchstat_summary.reader.CollectionReader(collection_id), **kwargs)
+ self.label = collection_id
+
+
+class JobSummarizer(Summarizer):
+ def __init__(self, job, **kwargs):
+ arv = arvados.api('v1')
+ if isinstance(job, basestring):
+ self.job = arv.jobs().get(uuid=job).execute()
else:
- return sys.stdin
+ self.job = job
+ rdr = None
+ if self.job.get('log'):
+ try:
+ rdr = crunchstat_summary.reader.CollectionReader(self.job['log'])
+ except arvados.errors.NotFoundError as e:
+ logger.warning("Trying event logs after failing to read "
+ "log collection %s: %s", self.job['log'], e)
+ else:
+ label = self.job['uuid']
+ if rdr is None:
+ rdr = crunchstat_summary.reader.LiveLogReader(self.job['uuid'])
+ label = self.job['uuid'] + ' (partial)'
+ super(JobSummarizer, self).__init__(rdr, **kwargs)
+ self.label = label
+ self.existing_constraints = self.job.get('runtime_constraints', {})
+
+
+class PipelineSummarizer(object):
+ def __init__(self, pipeline_instance_uuid, **kwargs):
+ arv = arvados.api('v1', model=OrderedJsonModel())
+ instance = arv.pipeline_instances().get(
+ uuid=pipeline_instance_uuid).execute()
+ self.summarizers = collections.OrderedDict()
+ for cname, component in instance['components'].iteritems():
+ if 'job' not in component:
+ logger.warning(
+ "%s: skipping component with no job assigned", cname)
+ else:
+ logger.info(
+ "%s: job %s", cname, component['job']['uuid'])
+ summarizer = JobSummarizer(component['job'], **kwargs)
+ summarizer.label = '{} {}'.format(
+ cname, component['job']['uuid'])
+ self.summarizers[cname] = summarizer
+ self.label = pipeline_instance_uuid
+
+ def run(self):
+ threads = []
+ for summarizer in self.summarizers.itervalues():
+ t = threading.Thread(target=summarizer.run)
+ t.daemon = True
+ t.start()
+ threads.append(t)
+ for t in threads:
+ t.join()
+
+ def text_report(self):
+ txt = ''
+ for cname, summarizer in self.summarizers.iteritems():
+ txt += '### Summary for {} ({})\n'.format(
+ cname, summarizer.job['uuid'])
+ txt += summarizer.text_report()
+ txt += '\n'
+ return txt
+
+ def html_report(self):
+ return crunchstat_summary.chartjs.ChartJS(
+ self.label, self.summarizers.itervalues()).html()
projects / arvados.git / blobdiff