3 # module to parse fio histogram log files, not using pandas
4 # runs in python v2 or v3
5 # to get help with the CLI: $ python fio-histo-log-pctiles.py -h
6 # this can be run standalone as a script but is callable
7 # assumes all threads run for same time duration
8 # assumes all threads are doing the same thing for the entire run
16 # separate read and write stats for randrw mixed workload
17 # report average latency if needed
18 # prove that it works (partially done with unit tests)
20 # to run unit tests, set UNITTEST environment variable to anything
21 # if you do this, don't pass normal CLI parameters to it
22 # otherwise it runs the CLI
24 import sys, os, math, copy
25 from copy import deepcopy
32 class FioHistoLogExc(Exception):
35 # if there is an error, print message, print syntax, and exit with error status
38 print('ERROR: ' + msg)
39 print('usage: fio-histo-log-pctiles.py ')
40 print(' [ --fio-version 2|3 (default 3) ]')
41 print(' [ --bucket-groups positive-int (default 29) ]')
42 print(' [ --bucket-bits small-positive-int (default 6) ]')
43 print(' [ --percentiles p1,p2,...,pN ] (default 0,50,95,99,100)')
44 print(' [ --time-quantum positive-int (default 1 sec) ]')
45 print(' [ --output-unit msec|usec|nsec (default msec) ]')
46 print(' log-file1 log-file2 ...')
50 # convert histogram log file into a list of
51 # (time_ms, direction, bsz, buckets) tuples where
52 # - time_ms is the time in msec at which the log record was written
53 # - direction is 0 (read) or 1 (write)
54 # - bsz is block size (not used)
55 # - buckets is a CSV list of counters that make up the histogram
56 # caller decides if the expected number of counters are present
59 def exception_suffix( record_num, pathname ):
60 return 'in histogram record %d file %s' % (record_num+1, pathname)
62 # log file parser raises FioHistoLogExc exceptions
63 # it returns histogram buckets in whatever unit fio uses
65 def parse_hist_file(logfn, buckets_per_interval):
66 max_timestamp_ms = 0.0
68 with open(logfn, 'r') as f:
69 records = [ l.strip() for l in f.readlines() ]
71 for k, r in enumerate(records):
76 int_tokens = [ int(t) for t in tokens ]
77 except ValueError as e:
78 raise FioHistoLogExc('non-integer value %s' % exception_suffix(k+1, logfn))
80 neg_ints = list(filter( lambda tk : tk < 0, int_tokens ))
82 raise FioHistoLogExc('negative integer value %s' % exception_suffix(k+1, logfn))
84 if len(int_tokens) < 3:
85 raise FioHistoLogExc('too few numbers %s' % exception_suffix(k+1, logfn))
87 time_ms = int_tokens[0]
88 if time_ms > max_timestamp_ms:
89 max_timestamp_ms = time_ms
91 direction = int_tokens[1]
92 if direction != 0 and direction != 1:
93 raise FioHistoLogExc('invalid I/O direction %s' % exception_suffix(k+1, logfn))
97 raise FioHistoLogExc('block size too large %s' % exception_suffix(k+1, logfn))
99 buckets = int_tokens[3:]
100 if len(buckets) != buckets_per_interval:
101 raise FioHistoLogExc('%d buckets per interval but %d expected in %s' %
102 (len(buckets), buckets_per_interval, exception_suffix(k+1, logfn)))
103 intervals.append((time_ms, direction, bsz, buckets))
104 if len(intervals) == 0:
105 raise FioHistoLogExc('no records in %s' % logfn)
106 return (intervals, max_timestamp_ms)
109 # compute time range for each bucket index in histogram record
110 # see comments in https://github.com/axboe/fio/blob/master/stat.h
111 # for description of bucket groups and buckets
112 # fio v3 bucket ranges are in nanosec (since response times are measured in nanosec)
113 # but we convert fio v3 nanosecs to floating-point microseconds
115 def time_ranges(groups, counters_per_group, fio_version=3):
118 bucket_intervals = []
119 for g in range(0, groups):
120 for b in range(0, counters_per_group):
121 rmin = float(bucket_base)
122 rmax = rmin + bucket_width
124 rmin /= nsec_per_usec
125 rmax /= nsec_per_usec
126 bucket_intervals.append( [rmin, rmax] )
127 bucket_base += bucket_width
130 return bucket_intervals
133 # compute number of time quantum intervals in the test
135 def get_time_intervals(time_quantum, max_timestamp_ms):
136 # round down to nearest second
137 max_timestamp = max_timestamp_ms // msec_per_sec
138 # round up to nearest whole multiple of time_quantum
139 time_interval_count = (max_timestamp + time_quantum) // time_quantum
140 end_time = time_interval_count * time_quantum
141 return (end_time, time_interval_count)
143 # align raw histogram log data to time quantum so
144 # we can then combine histograms from different threads with addition
145 # for randrw workload we count both reads and writes in same output bucket
146 # but we separate reads and writes for purposes of calculating
147 # end time for histogram record.
148 # this requires us to weight a raw histogram bucket by the
149 # fraction of time quantum that the bucket overlaps the current
150 # time quantum interval
151 # for example, if we have a bucket with 515 samples for time interval
152 # [ 1010, 2014 ] msec since start of test, and time quantum is 1 sec, then
153 # for time quantum interval [ 1000, 2000 ] msec, the overlap is
154 # (2000 - 1010) / (2000 - 1000) = 0.99
155 # so the contribution of this bucket to this time quantum is
156 # 515 x 0.99 = 509.85
158 def align_histo_log(raw_histogram_log, time_quantum, bucket_count, max_timestamp_ms):
160 # slice up test time int intervals of time_quantum seconds
162 (end_time, time_interval_count) = get_time_intervals(time_quantum, max_timestamp_ms)
163 time_qtm_ms = time_quantum * msec_per_sec
164 end_time_ms = end_time * msec_per_sec
165 aligned_intervals = []
166 for j in range(0, time_interval_count):
167 aligned_intervals.append((
169 [ 0.0 for j in range(0, bucket_count) ] ))
171 log_record_count = len(raw_histogram_log)
172 for k, record in enumerate(raw_histogram_log):
174 # find next record with same direction to get end-time
175 # have to avoid going past end of array
176 # for fio randrw workload,
177 # we have read and write records on same time interval
178 # sometimes read and write records are in opposite order
179 # assertion checks that next read/write record
180 # can be separated by at most 2 other records
182 (time_msec, direction, sz, interval_buckets) = record
183 if k+1 < log_record_count:
184 (time_msec_end, direction2, _, _) = raw_histogram_log[k+1]
185 if direction2 != direction:
186 if k+2 < log_record_count:
187 (time_msec_end, direction2, _, _) = raw_histogram_log[k+2]
188 if direction2 != direction:
189 if k+3 < log_record_count:
190 (time_msec_end, direction2, _, _) = raw_histogram_log[k+3]
191 assert direction2 == direction
193 time_msec_end = end_time_ms
195 time_msec_end = end_time_ms
197 time_msec_end = end_time_ms
199 # calculate first quantum that overlaps this histogram record
201 qtm_start_ms = (time_msec // time_qtm_ms) * time_qtm_ms
202 qtm_end_ms = ((time_msec + time_qtm_ms) // time_qtm_ms) * time_qtm_ms
203 qtm_index = qtm_start_ms // time_qtm_ms
205 # for each quantum that overlaps this histogram record's time interval
207 while qtm_start_ms < time_msec_end: # while quantum overlaps record
209 # calculate fraction of time that this quantum
210 # overlaps histogram record's time interval
212 overlap_start = max(qtm_start_ms, time_msec)
213 overlap_end = min(qtm_end_ms, time_msec_end)
214 weight = float(overlap_end - overlap_start)
215 weight /= (time_msec_end - time_msec)
216 (_,aligned_histogram) = aligned_intervals[qtm_index]
217 for bx, b in enumerate(interval_buckets):
218 weighted_bucket = weight * b
219 aligned_histogram[bx] += weighted_bucket
221 # advance to the next time quantum
223 qtm_start_ms += time_qtm_ms
224 qtm_end_ms += time_qtm_ms
227 return aligned_intervals
229 # add histogram in "source" to histogram in "target"
230 # it is assumed that the 2 histograms are precisely time-aligned
232 def add_to_histo_from( target, source ):
233 for b in range(0, len(source)):
234 target[b] += source[b]
236 # compute percentiles
238 # buckets: histogram bucket array
239 # wanted: list of floating-pt percentiles to calculate
240 # time_ranges: [tmin,tmax) time interval for each bucket
241 # returns None if no I/O reported.
242 # otherwise we would be dividing by zero
243 # think of buckets as probability distribution function
244 # and this loop is integrating to get cumulative distribution function
246 def get_pctiles(buckets, wanted, time_ranges):
248 # get total of IO requests done
250 for io_count in buckets:
251 total_ios += io_count
253 # don't return percentiles if no I/O was done during interval
257 pctile_count = len(wanted)
259 # results returned as dictionary keyed by percentile
262 # index of next percentile in list
266 next_pctile = wanted[pctile_index]
268 # no one is interested in percentiles bigger than this but not 100.0
269 # this prevents floating-point error from preventing loop exit
273 for b, io_count in enumerate(buckets):
274 total_so_far += io_count
275 pct_lt = 100.0 * float(total_so_far) / total_ios
276 # a single bucket could satisfy multiple pctiles
277 # so this must be a while loop
278 # consider both the 0-percentile (min latency)
279 # and 100-percentile (max latency) case here
280 while ((next_pctile == 100.0 and pct_lt >= almost_100) or
281 (next_pctile < 100.0 and pct_lt > next_pctile)):
282 # FIXME: interpolate between these fractions
283 range_max_time = time_ranges[b][1]
284 pctile_result[next_pctile] = range_max_time
286 if pctile_index == pctile_count:
288 next_pctile = wanted[pctile_index]
289 if pctile_index == pctile_count:
291 assert pctile_index == pctile_count
296 # returns a tuple of command line parameters
297 # parameters have default values unless otherwise shown
299 def parse_cli_params():
301 # default values for input parameters
303 fio_version = 3 # we are using fio 3.x now
304 bucket_groups = None # defaulting comes later
305 bucket_bits = 6 # default in fio 3.x
306 pctiles_wanted = [ 0, 50, 90, 95, 99, 100 ]
310 # parse command line parameters and display them
313 argct = len(sys.argv)
315 usage('must supply at least one histogram log file')
316 while argindex < argct:
317 if argct < argindex + 2:
319 pname = sys.argv[argindex]
320 pval = sys.argv[argindex+1]
321 if not pname.startswith('--'):
326 if pname == 'bucket-groups':
327 bucket_groups = int(pval)
328 elif pname == 'bucket-bits':
329 bucket_bits = int(pval)
330 elif pname == 'time-quantum':
331 time_quantum = int(pval)
332 elif pname == 'percentiles':
333 pctiles_wanted = [ float(p) for p in pval.split(',') ]
334 elif pname == 'output-unit':
335 if pval == 'msec' or pval == 'usec':
338 usage('output-unit must be usec (microseconds) or msec (milliseconds)')
339 elif pname == 'fio-version':
340 if pval != '2' and pval != '3':
341 usage('invalid fio version, must be 2 or 3')
342 fio_version = int(pval)
344 usage('invalid parameter name --%s' % pname)
346 if not bucket_groups:
347 # default changes based on fio version
354 filename_list = sys.argv[argindex:]
355 for f in filename_list:
356 if not os.path.exists(f):
357 usage('file %s does not exist' % f)
358 return (bucket_groups, bucket_bits, fio_version, pctiles_wanted,
359 filename_list, time_quantum, output_unit)
362 # this is really the main program
364 def compute_percentiles_from_logs():
365 (bucket_groups, bucket_bits, fio_version, pctiles_wanted,
366 file_list, time_quantum, output_unit) = parse_cli_params()
368 print('bucket groups = %d' % bucket_groups)
369 print('bucket bits = %d' % bucket_bits)
370 print('time quantum = %d sec' % time_quantum)
371 print('percentiles = %s' % ','.join([ str(p) for p in pctiles_wanted ]))
372 buckets_per_group = 1 << bucket_bits
373 print('buckets per group = %d' % buckets_per_group)
374 buckets_per_interval = buckets_per_group * bucket_groups
375 print('buckets per interval = %d ' % buckets_per_interval)
376 bucket_index_range = range(0, buckets_per_interval)
377 if time_quantum == 0:
378 usage('time-quantum must be a positive number of seconds')
379 print('output unit = ' + output_unit)
380 if output_unit == 'msec':
381 time_divisor = 1000.0
382 elif output_unit == 'usec':
385 # calculate response time interval associated with each histogram bucket
387 bucket_times = time_ranges(bucket_groups, buckets_per_group, fio_version=fio_version)
389 # construct template for each histogram bucket array with buckets all zeroes
390 # we just copy this for each new histogram
392 zeroed_buckets = [ 0.0 for r in bucket_index_range ]
394 # print CSV header just like fiologparser_hist does
397 for p in pctiles_wanted:
398 header += '%3.1f, ' % p
399 print('time (millisec), percentiles in increasing order with values in ' + output_unit)
402 # parse the histogram logs
403 # assumption: each bucket has a monotonically increasing time
404 # assumption: time ranges do not overlap for a single thread's records
405 # (exception: if randrw workload, then there is a read and a write
406 # record for the same time interval)
408 max_timestamp_all_logs = 0
412 (hist_files[fn], max_timestamp_ms) = parse_hist_file(fn, buckets_per_interval)
413 except FioHistoLogExc as e:
415 max_timestamp_all_logs = max(max_timestamp_all_logs, max_timestamp_ms)
417 (end_time, time_interval_count) = get_time_intervals(time_quantum, max_timestamp_all_logs)
418 all_threads_histograms = [ ((j*time_quantum*msec_per_sec), deepcopy(zeroed_buckets))
419 for j in range(0, time_interval_count) ]
421 for logfn in hist_files.keys():
422 aligned_per_thread = align_histo_log(hist_files[logfn],
424 buckets_per_interval,
425 max_timestamp_all_logs)
426 for t in range(0, time_interval_count):
427 (_, all_threads_histo_t) = all_threads_histograms[t]
428 (_, log_histo_t) = aligned_per_thread[t]
429 pct = get_pctiles(log_histo_t, pctiles_wanted, bucket_times)
430 add_to_histo_from( all_threads_histo_t, log_histo_t )
432 print('percentiles for entire set of threads')
433 for (t_msec, all_threads_histo_t) in all_threads_histograms:
434 record = '%d, ' % t_msec
435 pct = get_pctiles(all_threads_histo_t, pctiles_wanted, bucket_times)
437 for w in pctiles_wanted:
440 pct_keys = [ k for k in pct.keys() ]
441 pct_values = [ str(pct[wanted]/time_divisor) for wanted in sorted(pct_keys) ]
442 record += ', '.join(pct_values)
451 ##### below are unit tests ##############
453 import tempfile, shutil
454 from os.path import join
455 should_not_get_here = False
457 class Test(unittest2.TestCase):
460 # a little less typing please
461 def A(self, boolean_val):
462 self.assertTrue(boolean_val)
464 # initialize unit test environment
468 d = tempfile.mkdtemp()
471 # remove anything left by unit test environment
472 # unless user sets UNITTEST_LEAVE_FILES environment variable
475 def tearDownClass(cls):
476 if not os.getenv("UNITTEST_LEAVE_FILES"):
477 shutil.rmtree(cls.tempdir)
480 self.fn = join(Test.tempdir, self.id())
482 def test_a_add_histos(self):
485 add_to_histo_from( a, b )
486 self.A(a == [2.5, 4.5])
487 self.A(b == [1.5, 2.5])
489 def test_b1_parse_log(self):
490 with open(self.fn, 'w') as f:
491 f.write('1234, 0, 4096, 1, 2, 3, 4\n')
492 f.write('5678,1,16384,5,6,7,8 \n')
493 (raw_histo_log, max_timestamp) = parse_hist_file(self.fn, 4) # 4 buckets per interval
494 self.A(len(raw_histo_log) == 2 and max_timestamp == 5678)
495 (time_ms, direction, bsz, histo) = raw_histo_log[0]
496 self.A(time_ms == 1234 and direction == 0 and bsz == 4096 and histo == [ 1, 2, 3, 4 ])
497 (time_ms, direction, bsz, histo) = raw_histo_log[1]
498 self.A(time_ms == 5678 and direction == 1 and bsz == 16384 and histo == [ 5, 6, 7, 8 ])
500 def test_b2_parse_empty_log(self):
501 with open(self.fn, 'w') as f:
504 (raw_histo_log, max_timestamp_ms) = parse_hist_file(self.fn, 4)
505 self.A(should_not_get_here)
506 except FioHistoLogExc as e:
507 self.A(str(e).startswith('no records'))
509 def test_b3_parse_empty_records(self):
510 with open(self.fn, 'w') as f:
512 f.write('1234, 0, 4096, 1, 2, 3, 4\n')
513 f.write('5678,1,16384,5,6,7,8 \n')
515 (raw_histo_log, max_timestamp_ms) = parse_hist_file(self.fn, 4)
516 self.A(len(raw_histo_log) == 2 and max_timestamp_ms == 5678)
517 (time_ms, direction, bsz, histo) = raw_histo_log[0]
518 self.A(time_ms == 1234 and direction == 0 and bsz == 4096 and histo == [ 1, 2, 3, 4 ])
519 (time_ms, direction, bsz, histo) = raw_histo_log[1]
520 self.A(time_ms == 5678 and direction == 1 and bsz == 16384 and histo == [ 5, 6, 7, 8 ])
522 def test_b4_parse_non_int(self):
523 with open(self.fn, 'w') as f:
524 f.write('12, 0, 4096, 1a, 2, 3, 4\n')
526 (raw_histo_log, _) = parse_hist_file(self.fn, 4)
528 except FioHistoLogExc as e:
529 self.A(str(e).startswith('non-integer'))
531 def test_b5_parse_neg_int(self):
532 with open(self.fn, 'w') as f:
533 f.write('-12, 0, 4096, 1, 2, 3, 4\n')
535 (raw_histo_log, _) = parse_hist_file(self.fn, 4)
537 except FioHistoLogExc as e:
538 self.A(str(e).startswith('negative integer'))
540 def test_b6_parse_too_few_int(self):
541 with open(self.fn, 'w') as f:
544 (raw_histo_log, _) = parse_hist_file(self.fn, 4)
546 except FioHistoLogExc as e:
547 self.A(str(e).startswith('too few numbers'))
549 def test_b7_parse_invalid_direction(self):
550 with open(self.fn, 'w') as f:
551 f.write('100, 2, 4096, 1, 2, 3, 4\n')
553 (raw_histo_log, _) = parse_hist_file(self.fn, 4)
555 except FioHistoLogExc as e:
556 self.A(str(e).startswith('invalid I/O direction'))
558 def test_b8_parse_bsz_too_big(self):
559 with open(self.fn+'_good', 'w') as f:
560 f.write('100, 1, %d, 1, 2, 3, 4\n' % (1<<24))
561 (raw_histo_log, max_timestamp_ms) = parse_hist_file(self.fn+'_good', 4)
562 with open(self.fn+'_bad', 'w') as f:
563 f.write('100, 1, 20000000, 1, 2, 3, 4\n')
565 (raw_histo_log, _) = parse_hist_file(self.fn+'_bad', 4)
567 except FioHistoLogExc as e:
568 self.A(str(e).startswith('block size too large'))
570 def test_b9_parse_wrong_bucket_count(self):
571 with open(self.fn, 'w') as f:
572 f.write('100, 1, %d, 1, 2, 3, 4, 5\n' % (1<<24))
574 (raw_histo_log, _) = parse_hist_file(self.fn, 4)
576 except FioHistoLogExc as e:
577 self.A(str(e).__contains__('buckets per interval'))
579 def test_c1_time_ranges(self):
580 ranges = time_ranges(3, 2) # fio_version defaults to 3
581 expected_ranges = [ # fio_version 3 is in nanoseconds
582 [0.000, 0.001], [0.001, 0.002], # first group
583 [0.002, 0.003], [0.003, 0.004], # second group same width
584 [0.004, 0.006], [0.006, 0.008]] # subsequent groups double width
585 self.A(ranges == expected_ranges)
586 ranges = time_ranges(3, 2, fio_version=3)
587 self.A(ranges == expected_ranges)
588 ranges = time_ranges(3, 2, fio_version=2)
589 expected_ranges_v2 = [ [ 1000.0 * min_or_max for min_or_max in time_range ]
590 for time_range in expected_ranges ]
591 self.A(ranges == expected_ranges_v2)
592 # see fio V3 stat.h for why 29 groups and 2^6 buckets/group
593 normal_ranges_v3 = time_ranges(29, 64)
594 # for v3, bucket time intervals are measured in nanoseconds
595 self.A(len(normal_ranges_v3) == 29 * 64 and normal_ranges_v3[-1][1] == 64*(1<<(29-1))/1000.0)
596 normal_ranges_v2 = time_ranges(19, 64, fio_version=2)
597 # for v2, bucket time intervals are measured in microseconds so we have fewer buckets
598 self.A(len(normal_ranges_v2) == 19 * 64 and normal_ranges_v2[-1][1] == 64*(1<<(19-1)))
600 def test_d1_align_histo_log_1_quantum(self):
601 with open(self.fn, 'w') as f:
602 f.write('100, 1, 4096, 1, 2, 3, 4')
603 (raw_histo_log, max_timestamp_ms) = parse_hist_file(self.fn, 4)
604 self.A(max_timestamp_ms == 100)
605 aligned_log = align_histo_log(raw_histo_log, 5, 4, max_timestamp_ms)
606 self.A(len(aligned_log) == 1)
607 (time_ms0, h) = aligned_log[0]
608 self.A(time_ms0 == 0 and h == [1.0, 2.0, 3.0, 4.0])
610 # we need this to compare 2 lists of floating point numbers for equality
611 # because of floating-point imprecision
613 def compare_2_floats(self, x, y):
614 if x == 0.0 or y == 0.0:
615 return (x+y) < 0.0000001
617 return (math.fabs(x-y)/x) < 0.00001
619 def is_close(self, buckets, buckets_expected):
620 if len(buckets) != len(buckets_expected):
622 compare_buckets = lambda k: self.compare_2_floats(buckets[k], buckets_expected[k])
623 indices_close = list(filter(compare_buckets, range(0, len(buckets))))
624 return len(indices_close) == len(buckets)
626 def test_d2_align_histo_log_2_quantum(self):
627 with open(self.fn, 'w') as f:
628 f.write('2000, 1, 4096, 1, 2, 3, 4\n')
629 f.write('7000, 1, 4096, 1, 2, 3, 4\n')
630 (raw_histo_log, max_timestamp_ms) = parse_hist_file(self.fn, 4)
631 self.A(max_timestamp_ms == 7000)
632 (_, _, _, raw_buckets1) = raw_histo_log[0]
633 (_, _, _, raw_buckets2) = raw_histo_log[1]
634 aligned_log = align_histo_log(raw_histo_log, 5, 4, max_timestamp_ms)
635 self.A(len(aligned_log) == 2)
636 (time_ms1, h1) = aligned_log[0]
637 (time_ms2, h2) = aligned_log[1]
638 # because first record is from time interval [2000, 7000]
639 # we weight it according
640 expect1 = [float(b) * 0.6 for b in raw_buckets1]
641 expect2 = [float(b) * 0.4 for b in raw_buckets1]
642 for e in range(0, len(expect2)):
643 expect2[e] += raw_buckets2[e]
644 self.A(time_ms1 == 0 and self.is_close(h1, expect1))
645 self.A(time_ms2 == 5000 and self.is_close(h2, expect2))
647 def test_e1_get_pctiles(self):
648 with open(self.fn, 'w') as f:
649 buckets = [ 100 for j in range(0, 128) ]
650 f.write('9000, 1, 4096, %s\n' % ', '.join([str(b) for b in buckets]))
651 (raw_histo_log, max_timestamp_ms) = parse_hist_file(self.fn, 128)
652 self.A(max_timestamp_ms == 9000)
653 aligned_log = align_histo_log(raw_histo_log, 5, 128, max_timestamp_ms)
654 time_intervals = time_ranges(4, 32)
655 # since buckets are all equal, then median is halfway through time_intervals
656 # and max latency interval is at end of time_intervals
657 self.A(time_intervals[64][1] == 0.066 and time_intervals[127][1] == 0.256)
658 pctiles_wanted = [ 0, 50, 100 ]
660 for (time_ms, histo) in aligned_log:
661 pct_vs_time.append(get_pctiles(histo, pctiles_wanted, time_intervals))
662 self.A(pct_vs_time[0] == None) # no I/O in this time interval
663 expected_pctiles = { 0:0.001, 50:0.066, 100:0.256 }
664 self.A(pct_vs_time[1] == expected_pctiles)
666 # we are using this module as a standalone program
668 if __name__ == '__main__':
669 if os.getenv('UNITTEST'):
670 sys.exit(unittest2.main())
672 compute_percentiles_from_logs()