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d8296fdd BE |
1 | #!/usr/bin/env python |
2 | ||
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 | |
9 | ||
10 | # percentiles: | |
11 | # 0 - min latency | |
12 | # 50 - median | |
13 | # 100 - max latency | |
14 | ||
15 | # TO-DO: | |
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) | |
19 | ||
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 | |
23 | ||
24 | import sys, os, math, copy | |
25 | from copy import deepcopy | |
c670cb44 | 26 | import argparse |
088a092e BE |
27 | |
28 | unittest2_imported = True | |
29 | try: | |
30 | import unittest2 | |
31 | except ImportError: | |
32 | unittest2_imported = False | |
d8296fdd BE |
33 | |
34 | msec_per_sec = 1000 | |
35 | nsec_per_usec = 1000 | |
36 | ||
37 | class FioHistoLogExc(Exception): | |
38 | pass | |
39 | ||
c670cb44 | 40 | # if there is an error, print message, and exit with error status |
d8296fdd | 41 | |
c670cb44 | 42 | def myabort(msg): |
d8296fdd | 43 | print('ERROR: ' + msg) |
d8296fdd BE |
44 | sys.exit(1) |
45 | ||
d8296fdd BE |
46 | # convert histogram log file into a list of |
47 | # (time_ms, direction, bsz, buckets) tuples where | |
48 | # - time_ms is the time in msec at which the log record was written | |
49 | # - direction is 0 (read) or 1 (write) | |
50 | # - bsz is block size (not used) | |
51 | # - buckets is a CSV list of counters that make up the histogram | |
52 | # caller decides if the expected number of counters are present | |
53 | ||
54 | ||
55 | def exception_suffix( record_num, pathname ): | |
56 | return 'in histogram record %d file %s' % (record_num+1, pathname) | |
57 | ||
58 | # log file parser raises FioHistoLogExc exceptions | |
59 | # it returns histogram buckets in whatever unit fio uses | |
60 | ||
61 | def parse_hist_file(logfn, buckets_per_interval): | |
62 | max_timestamp_ms = 0.0 | |
63 | ||
64 | with open(logfn, 'r') as f: | |
65 | records = [ l.strip() for l in f.readlines() ] | |
66 | intervals = [] | |
fbcf1e71 BE |
67 | last_time_ms = -1 |
68 | last_direction = -1 | |
d8296fdd BE |
69 | for k, r in enumerate(records): |
70 | if r == '': | |
71 | continue | |
72 | tokens = r.split(',') | |
73 | try: | |
74 | int_tokens = [ int(t) for t in tokens ] | |
75 | except ValueError as e: | |
76 | raise FioHistoLogExc('non-integer value %s' % exception_suffix(k+1, logfn)) | |
77 | ||
78 | neg_ints = list(filter( lambda tk : tk < 0, int_tokens )) | |
79 | if len(neg_ints) > 0: | |
80 | raise FioHistoLogExc('negative integer value %s' % exception_suffix(k+1, logfn)) | |
81 | ||
82 | if len(int_tokens) < 3: | |
83 | raise FioHistoLogExc('too few numbers %s' % exception_suffix(k+1, logfn)) | |
84 | ||
85 | time_ms = int_tokens[0] | |
86 | if time_ms > max_timestamp_ms: | |
87 | max_timestamp_ms = time_ms | |
88 | ||
89 | direction = int_tokens[1] | |
90 | if direction != 0 and direction != 1: | |
91 | raise FioHistoLogExc('invalid I/O direction %s' % exception_suffix(k+1, logfn)) | |
92 | ||
93 | bsz = int_tokens[2] | |
94 | if bsz > (1 << 24): | |
95 | raise FioHistoLogExc('block size too large %s' % exception_suffix(k+1, logfn)) | |
96 | ||
97 | buckets = int_tokens[3:] | |
98 | if len(buckets) != buckets_per_interval: | |
99 | raise FioHistoLogExc('%d buckets per interval but %d expected in %s' % | |
100 | (len(buckets), buckets_per_interval, exception_suffix(k+1, logfn))) | |
fbcf1e71 BE |
101 | |
102 | # hack to filter out records with the same timestamp | |
103 | # we should not have to do this if fio logs histogram records correctly | |
104 | ||
105 | if time_ms == last_time_ms and direction == last_direction: | |
106 | continue | |
107 | last_time_ms = time_ms | |
108 | last_direction = direction | |
109 | ||
d8296fdd BE |
110 | intervals.append((time_ms, direction, bsz, buckets)) |
111 | if len(intervals) == 0: | |
112 | raise FioHistoLogExc('no records in %s' % logfn) | |
113 | return (intervals, max_timestamp_ms) | |
114 | ||
115 | ||
116 | # compute time range for each bucket index in histogram record | |
117 | # see comments in https://github.com/axboe/fio/blob/master/stat.h | |
118 | # for description of bucket groups and buckets | |
119 | # fio v3 bucket ranges are in nanosec (since response times are measured in nanosec) | |
120 | # but we convert fio v3 nanosecs to floating-point microseconds | |
121 | ||
122 | def time_ranges(groups, counters_per_group, fio_version=3): | |
123 | bucket_width = 1 | |
124 | bucket_base = 0 | |
125 | bucket_intervals = [] | |
126 | for g in range(0, groups): | |
127 | for b in range(0, counters_per_group): | |
128 | rmin = float(bucket_base) | |
129 | rmax = rmin + bucket_width | |
130 | if fio_version == 3: | |
131 | rmin /= nsec_per_usec | |
132 | rmax /= nsec_per_usec | |
133 | bucket_intervals.append( [rmin, rmax] ) | |
134 | bucket_base += bucket_width | |
135 | if g != 0: | |
136 | bucket_width *= 2 | |
137 | return bucket_intervals | |
138 | ||
139 | ||
140 | # compute number of time quantum intervals in the test | |
141 | ||
142 | def get_time_intervals(time_quantum, max_timestamp_ms): | |
143 | # round down to nearest second | |
144 | max_timestamp = max_timestamp_ms // msec_per_sec | |
145 | # round up to nearest whole multiple of time_quantum | |
146 | time_interval_count = (max_timestamp + time_quantum) // time_quantum | |
147 | end_time = time_interval_count * time_quantum | |
148 | return (end_time, time_interval_count) | |
149 | ||
150 | # align raw histogram log data to time quantum so | |
151 | # we can then combine histograms from different threads with addition | |
152 | # for randrw workload we count both reads and writes in same output bucket | |
153 | # but we separate reads and writes for purposes of calculating | |
154 | # end time for histogram record. | |
155 | # this requires us to weight a raw histogram bucket by the | |
156 | # fraction of time quantum that the bucket overlaps the current | |
157 | # time quantum interval | |
158 | # for example, if we have a bucket with 515 samples for time interval | |
159 | # [ 1010, 2014 ] msec since start of test, and time quantum is 1 sec, then | |
160 | # for time quantum interval [ 1000, 2000 ] msec, the overlap is | |
161 | # (2000 - 1010) / (2000 - 1000) = 0.99 | |
162 | # so the contribution of this bucket to this time quantum is | |
163 | # 515 x 0.99 = 509.85 | |
164 | ||
165 | def align_histo_log(raw_histogram_log, time_quantum, bucket_count, max_timestamp_ms): | |
166 | ||
167 | # slice up test time int intervals of time_quantum seconds | |
168 | ||
169 | (end_time, time_interval_count) = get_time_intervals(time_quantum, max_timestamp_ms) | |
170 | time_qtm_ms = time_quantum * msec_per_sec | |
171 | end_time_ms = end_time * msec_per_sec | |
172 | aligned_intervals = [] | |
173 | for j in range(0, time_interval_count): | |
174 | aligned_intervals.append(( | |
175 | j * time_qtm_ms, | |
176 | [ 0.0 for j in range(0, bucket_count) ] )) | |
177 | ||
178 | log_record_count = len(raw_histogram_log) | |
179 | for k, record in enumerate(raw_histogram_log): | |
180 | ||
181 | # find next record with same direction to get end-time | |
182 | # have to avoid going past end of array | |
183 | # for fio randrw workload, | |
184 | # we have read and write records on same time interval | |
185 | # sometimes read and write records are in opposite order | |
186 | # assertion checks that next read/write record | |
187 | # can be separated by at most 2 other records | |
188 | ||
189 | (time_msec, direction, sz, interval_buckets) = record | |
190 | if k+1 < log_record_count: | |
191 | (time_msec_end, direction2, _, _) = raw_histogram_log[k+1] | |
192 | if direction2 != direction: | |
193 | if k+2 < log_record_count: | |
194 | (time_msec_end, direction2, _, _) = raw_histogram_log[k+2] | |
195 | if direction2 != direction: | |
196 | if k+3 < log_record_count: | |
197 | (time_msec_end, direction2, _, _) = raw_histogram_log[k+3] | |
198 | assert direction2 == direction | |
199 | else: | |
200 | time_msec_end = end_time_ms | |
201 | else: | |
202 | time_msec_end = end_time_ms | |
203 | else: | |
204 | time_msec_end = end_time_ms | |
205 | ||
206 | # calculate first quantum that overlaps this histogram record | |
207 | ||
208 | qtm_start_ms = (time_msec // time_qtm_ms) * time_qtm_ms | |
209 | qtm_end_ms = ((time_msec + time_qtm_ms) // time_qtm_ms) * time_qtm_ms | |
210 | qtm_index = qtm_start_ms // time_qtm_ms | |
211 | ||
212 | # for each quantum that overlaps this histogram record's time interval | |
213 | ||
214 | while qtm_start_ms < time_msec_end: # while quantum overlaps record | |
215 | ||
216 | # calculate fraction of time that this quantum | |
217 | # overlaps histogram record's time interval | |
218 | ||
219 | overlap_start = max(qtm_start_ms, time_msec) | |
220 | overlap_end = min(qtm_end_ms, time_msec_end) | |
221 | weight = float(overlap_end - overlap_start) | |
222 | weight /= (time_msec_end - time_msec) | |
223 | (_,aligned_histogram) = aligned_intervals[qtm_index] | |
224 | for bx, b in enumerate(interval_buckets): | |
225 | weighted_bucket = weight * b | |
226 | aligned_histogram[bx] += weighted_bucket | |
227 | ||
228 | # advance to the next time quantum | |
229 | ||
230 | qtm_start_ms += time_qtm_ms | |
231 | qtm_end_ms += time_qtm_ms | |
232 | qtm_index += 1 | |
233 | ||
234 | return aligned_intervals | |
235 | ||
236 | # add histogram in "source" to histogram in "target" | |
237 | # it is assumed that the 2 histograms are precisely time-aligned | |
238 | ||
239 | def add_to_histo_from( target, source ): | |
240 | for b in range(0, len(source)): | |
241 | target[b] += source[b] | |
242 | ||
243 | # compute percentiles | |
244 | # inputs: | |
245 | # buckets: histogram bucket array | |
246 | # wanted: list of floating-pt percentiles to calculate | |
247 | # time_ranges: [tmin,tmax) time interval for each bucket | |
248 | # returns None if no I/O reported. | |
249 | # otherwise we would be dividing by zero | |
250 | # think of buckets as probability distribution function | |
251 | # and this loop is integrating to get cumulative distribution function | |
252 | ||
253 | def get_pctiles(buckets, wanted, time_ranges): | |
254 | ||
255 | # get total of IO requests done | |
256 | total_ios = 0 | |
257 | for io_count in buckets: | |
258 | total_ios += io_count | |
259 | ||
260 | # don't return percentiles if no I/O was done during interval | |
261 | if total_ios == 0.0: | |
262 | return None | |
263 | ||
264 | pctile_count = len(wanted) | |
265 | ||
266 | # results returned as dictionary keyed by percentile | |
267 | pctile_result = {} | |
268 | ||
269 | # index of next percentile in list | |
270 | pctile_index = 0 | |
271 | ||
272 | # next percentile | |
273 | next_pctile = wanted[pctile_index] | |
274 | ||
275 | # no one is interested in percentiles bigger than this but not 100.0 | |
276 | # this prevents floating-point error from preventing loop exit | |
277 | almost_100 = 99.9999 | |
278 | ||
0456267b BE |
279 | # pct is the percentile corresponding to |
280 | # all I/O requests up through bucket b | |
281 | pct = 0.0 | |
d8296fdd BE |
282 | total_so_far = 0 |
283 | for b, io_count in enumerate(buckets): | |
0456267b BE |
284 | if io_count == 0: |
285 | continue | |
d8296fdd | 286 | total_so_far += io_count |
0456267b BE |
287 | # last_pct_lt is the percentile corresponding to |
288 | # all I/O requests up to, but not including, bucket b | |
289 | last_pct = pct | |
290 | pct = 100.0 * float(total_so_far) / total_ios | |
d8296fdd BE |
291 | # a single bucket could satisfy multiple pctiles |
292 | # so this must be a while loop | |
0456267b BE |
293 | # for 100-percentile (max latency) case, no bucket exceeds it |
294 | # so we must stop there. | |
295 | while ((next_pctile == 100.0 and pct >= almost_100) or | |
296 | (next_pctile < 100.0 and pct > next_pctile)): | |
297 | # interpolate between min and max time for bucket time interval | |
298 | # we keep the time_ranges access inside this loop, | |
299 | # even though it could be above the loop, | |
300 | # because in many cases we will not be even entering | |
301 | # the loop so we optimize out these accesses | |
d8296fdd | 302 | range_max_time = time_ranges[b][1] |
0456267b BE |
303 | range_min_time = time_ranges[b][0] |
304 | offset_frac = (next_pctile - last_pct)/(pct - last_pct) | |
305 | interpolation = range_min_time + (offset_frac*(range_max_time - range_min_time)) | |
306 | pctile_result[next_pctile] = interpolation | |
d8296fdd BE |
307 | pctile_index += 1 |
308 | if pctile_index == pctile_count: | |
309 | break | |
310 | next_pctile = wanted[pctile_index] | |
311 | if pctile_index == pctile_count: | |
312 | break | |
313 | assert pctile_index == pctile_count | |
314 | return pctile_result | |
315 | ||
316 | ||
c670cb44 | 317 | # this is really the main program |
d8296fdd | 318 | |
c670cb44 BE |
319 | def compute_percentiles_from_logs(): |
320 | parser = argparse.ArgumentParser() | |
321 | parser.add_argument("--fio-version", dest="fio_version", | |
322 | default="3", choices=[2,3], type=int, | |
323 | help="fio version (default=3)") | |
324 | parser.add_argument("--bucket-groups", dest="bucket_groups", default="29", type=int, | |
325 | help="fio histogram bucket groups (default=29)") | |
326 | parser.add_argument("--bucket-bits", dest="bucket_bits", | |
327 | default="6", type=int, | |
328 | help="fio histogram buckets-per-group bits (default=6 means 64 buckets/group)") | |
329 | parser.add_argument("--percentiles", dest="pctiles_wanted", | |
4b34a0eb | 330 | default=[ 0., 50., 95., 99., 100.], type=float, nargs='+', |
c670cb44 BE |
331 | help="fio histogram buckets-per-group bits (default=6 means 64 buckets/group)") |
332 | parser.add_argument("--time-quantum", dest="time_quantum", | |
333 | default="1", type=int, | |
334 | help="time quantum in seconds (default=1)") | |
335 | parser.add_argument("--output-unit", dest="output_unit", | |
336 | default="usec", type=str, | |
337 | help="Latency percentile output unit: msec|usec|nsec (default usec)") | |
4b34a0eb BE |
338 | parser.add_argument("file_list", nargs='+', |
339 | help='list of files, preceded by " -- " if necessary') | |
c670cb44 | 340 | args = parser.parse_args() |
c670cb44 | 341 | |
4b34a0eb BE |
342 | # default changes based on fio version |
343 | if args.fio_version == 2: | |
344 | args.bucket_groups = 19 | |
d8296fdd | 345 | |
c670cb44 | 346 | # print parameters |
d8296fdd | 347 | |
4b34a0eb | 348 | print('fio version = %d' % args.fio_version) |
c670cb44 BE |
349 | print('bucket groups = %d' % args.bucket_groups) |
350 | print('bucket bits = %d' % args.bucket_bits) | |
351 | print('time quantum = %d sec' % args.time_quantum) | |
352 | print('percentiles = %s' % ','.join([ str(p) for p in args.pctiles_wanted ])) | |
353 | buckets_per_group = 1 << args.bucket_bits | |
d8296fdd | 354 | print('buckets per group = %d' % buckets_per_group) |
c670cb44 | 355 | buckets_per_interval = buckets_per_group * args.bucket_groups |
d8296fdd BE |
356 | print('buckets per interval = %d ' % buckets_per_interval) |
357 | bucket_index_range = range(0, buckets_per_interval) | |
c670cb44 BE |
358 | if args.time_quantum == 0: |
359 | print('ERROR: time-quantum must be a positive number of seconds') | |
360 | print('output unit = ' + args.output_unit) | |
361 | if args.output_unit == 'msec': | |
d8296fdd | 362 | time_divisor = 1000.0 |
c670cb44 | 363 | elif args.output_unit == 'usec': |
d8296fdd BE |
364 | time_divisor = 1.0 |
365 | ||
366 | # calculate response time interval associated with each histogram bucket | |
367 | ||
c670cb44 | 368 | bucket_times = time_ranges(args.bucket_groups, buckets_per_group, fio_version=args.fio_version) |
d8296fdd BE |
369 | |
370 | # construct template for each histogram bucket array with buckets all zeroes | |
371 | # we just copy this for each new histogram | |
372 | ||
373 | zeroed_buckets = [ 0.0 for r in bucket_index_range ] | |
374 | ||
375 | # print CSV header just like fiologparser_hist does | |
376 | ||
377 | header = 'msec, ' | |
c670cb44 | 378 | for p in args.pctiles_wanted: |
d8296fdd | 379 | header += '%3.1f, ' % p |
c670cb44 | 380 | print('time (millisec), percentiles in increasing order with values in ' + args.output_unit) |
d8296fdd BE |
381 | print(header) |
382 | ||
383 | # parse the histogram logs | |
384 | # assumption: each bucket has a monotonically increasing time | |
385 | # assumption: time ranges do not overlap for a single thread's records | |
386 | # (exception: if randrw workload, then there is a read and a write | |
387 | # record for the same time interval) | |
388 | ||
389 | max_timestamp_all_logs = 0 | |
390 | hist_files = {} | |
c670cb44 | 391 | for fn in args.file_list: |
d8296fdd BE |
392 | try: |
393 | (hist_files[fn], max_timestamp_ms) = parse_hist_file(fn, buckets_per_interval) | |
394 | except FioHistoLogExc as e: | |
c670cb44 | 395 | myabort(str(e)) |
d8296fdd BE |
396 | max_timestamp_all_logs = max(max_timestamp_all_logs, max_timestamp_ms) |
397 | ||
c670cb44 BE |
398 | (end_time, time_interval_count) = get_time_intervals(args.time_quantum, max_timestamp_all_logs) |
399 | all_threads_histograms = [ ((j*args.time_quantum*msec_per_sec), deepcopy(zeroed_buckets)) | |
d8296fdd BE |
400 | for j in range(0, time_interval_count) ] |
401 | ||
402 | for logfn in hist_files.keys(): | |
403 | aligned_per_thread = align_histo_log(hist_files[logfn], | |
c670cb44 | 404 | args.time_quantum, |
d8296fdd BE |
405 | buckets_per_interval, |
406 | max_timestamp_all_logs) | |
407 | for t in range(0, time_interval_count): | |
408 | (_, all_threads_histo_t) = all_threads_histograms[t] | |
409 | (_, log_histo_t) = aligned_per_thread[t] | |
d8296fdd BE |
410 | add_to_histo_from( all_threads_histo_t, log_histo_t ) |
411 | ||
c670cb44 BE |
412 | # calculate percentiles across aggregate histogram for all threads |
413 | ||
d8296fdd BE |
414 | for (t_msec, all_threads_histo_t) in all_threads_histograms: |
415 | record = '%d, ' % t_msec | |
c670cb44 | 416 | pct = get_pctiles(all_threads_histo_t, args.pctiles_wanted, bucket_times) |
d8296fdd | 417 | if not pct: |
c670cb44 | 418 | for w in args.pctiles_wanted: |
d8296fdd BE |
419 | record += ', ' |
420 | else: | |
421 | pct_keys = [ k for k in pct.keys() ] | |
422 | pct_values = [ str(pct[wanted]/time_divisor) for wanted in sorted(pct_keys) ] | |
423 | record += ', '.join(pct_values) | |
424 | print(record) | |
425 | ||
426 | ||
427 | ||
428 | #end of MAIN PROGRAM | |
429 | ||
430 | ||
d8296fdd BE |
431 | ##### below are unit tests ############## |
432 | ||
088a092e BE |
433 | if unittest2_imported: |
434 | import tempfile, shutil | |
435 | from os.path import join | |
436 | should_not_get_here = False | |
d8296fdd | 437 | |
088a092e | 438 | class Test(unittest2.TestCase): |
d8296fdd BE |
439 | tempdir = None |
440 | ||
441 | # a little less typing please | |
442 | def A(self, boolean_val): | |
443 | self.assertTrue(boolean_val) | |
444 | ||
445 | # initialize unit test environment | |
446 | ||
447 | @classmethod | |
448 | def setUpClass(cls): | |
449 | d = tempfile.mkdtemp() | |
450 | Test.tempdir = d | |
451 | ||
452 | # remove anything left by unit test environment | |
453 | # unless user sets UNITTEST_LEAVE_FILES environment variable | |
454 | ||
455 | @classmethod | |
456 | def tearDownClass(cls): | |
457 | if not os.getenv("UNITTEST_LEAVE_FILES"): | |
458 | shutil.rmtree(cls.tempdir) | |
459 | ||
460 | def setUp(self): | |
461 | self.fn = join(Test.tempdir, self.id()) | |
462 | ||
463 | def test_a_add_histos(self): | |
464 | a = [ 1.0, 2.0 ] | |
465 | b = [ 1.5, 2.5 ] | |
466 | add_to_histo_from( a, b ) | |
467 | self.A(a == [2.5, 4.5]) | |
468 | self.A(b == [1.5, 2.5]) | |
469 | ||
470 | def test_b1_parse_log(self): | |
471 | with open(self.fn, 'w') as f: | |
472 | f.write('1234, 0, 4096, 1, 2, 3, 4\n') | |
473 | f.write('5678,1,16384,5,6,7,8 \n') | |
474 | (raw_histo_log, max_timestamp) = parse_hist_file(self.fn, 4) # 4 buckets per interval | |
475 | self.A(len(raw_histo_log) == 2 and max_timestamp == 5678) | |
476 | (time_ms, direction, bsz, histo) = raw_histo_log[0] | |
477 | self.A(time_ms == 1234 and direction == 0 and bsz == 4096 and histo == [ 1, 2, 3, 4 ]) | |
478 | (time_ms, direction, bsz, histo) = raw_histo_log[1] | |
479 | self.A(time_ms == 5678 and direction == 1 and bsz == 16384 and histo == [ 5, 6, 7, 8 ]) | |
480 | ||
481 | def test_b2_parse_empty_log(self): | |
482 | with open(self.fn, 'w') as f: | |
483 | pass | |
484 | try: | |
485 | (raw_histo_log, max_timestamp_ms) = parse_hist_file(self.fn, 4) | |
486 | self.A(should_not_get_here) | |
487 | except FioHistoLogExc as e: | |
488 | self.A(str(e).startswith('no records')) | |
489 | ||
490 | def test_b3_parse_empty_records(self): | |
491 | with open(self.fn, 'w') as f: | |
492 | f.write('\n') | |
493 | f.write('1234, 0, 4096, 1, 2, 3, 4\n') | |
494 | f.write('5678,1,16384,5,6,7,8 \n') | |
495 | f.write('\n') | |
496 | (raw_histo_log, max_timestamp_ms) = parse_hist_file(self.fn, 4) | |
497 | self.A(len(raw_histo_log) == 2 and max_timestamp_ms == 5678) | |
498 | (time_ms, direction, bsz, histo) = raw_histo_log[0] | |
499 | self.A(time_ms == 1234 and direction == 0 and bsz == 4096 and histo == [ 1, 2, 3, 4 ]) | |
500 | (time_ms, direction, bsz, histo) = raw_histo_log[1] | |
501 | self.A(time_ms == 5678 and direction == 1 and bsz == 16384 and histo == [ 5, 6, 7, 8 ]) | |
502 | ||
503 | def test_b4_parse_non_int(self): | |
504 | with open(self.fn, 'w') as f: | |
505 | f.write('12, 0, 4096, 1a, 2, 3, 4\n') | |
506 | try: | |
507 | (raw_histo_log, _) = parse_hist_file(self.fn, 4) | |
508 | self.A(False) | |
509 | except FioHistoLogExc as e: | |
510 | self.A(str(e).startswith('non-integer')) | |
511 | ||
512 | def test_b5_parse_neg_int(self): | |
513 | with open(self.fn, 'w') as f: | |
514 | f.write('-12, 0, 4096, 1, 2, 3, 4\n') | |
515 | try: | |
516 | (raw_histo_log, _) = parse_hist_file(self.fn, 4) | |
517 | self.A(False) | |
518 | except FioHistoLogExc as e: | |
519 | self.A(str(e).startswith('negative integer')) | |
520 | ||
521 | def test_b6_parse_too_few_int(self): | |
522 | with open(self.fn, 'w') as f: | |
523 | f.write('0, 0\n') | |
524 | try: | |
525 | (raw_histo_log, _) = parse_hist_file(self.fn, 4) | |
526 | self.A(False) | |
527 | except FioHistoLogExc as e: | |
528 | self.A(str(e).startswith('too few numbers')) | |
529 | ||
530 | def test_b7_parse_invalid_direction(self): | |
531 | with open(self.fn, 'w') as f: | |
532 | f.write('100, 2, 4096, 1, 2, 3, 4\n') | |
533 | try: | |
534 | (raw_histo_log, _) = parse_hist_file(self.fn, 4) | |
535 | self.A(False) | |
536 | except FioHistoLogExc as e: | |
537 | self.A(str(e).startswith('invalid I/O direction')) | |
538 | ||
539 | def test_b8_parse_bsz_too_big(self): | |
540 | with open(self.fn+'_good', 'w') as f: | |
541 | f.write('100, 1, %d, 1, 2, 3, 4\n' % (1<<24)) | |
542 | (raw_histo_log, max_timestamp_ms) = parse_hist_file(self.fn+'_good', 4) | |
543 | with open(self.fn+'_bad', 'w') as f: | |
544 | f.write('100, 1, 20000000, 1, 2, 3, 4\n') | |
545 | try: | |
546 | (raw_histo_log, _) = parse_hist_file(self.fn+'_bad', 4) | |
547 | self.A(False) | |
548 | except FioHistoLogExc as e: | |
549 | self.A(str(e).startswith('block size too large')) | |
550 | ||
551 | def test_b9_parse_wrong_bucket_count(self): | |
552 | with open(self.fn, 'w') as f: | |
553 | f.write('100, 1, %d, 1, 2, 3, 4, 5\n' % (1<<24)) | |
554 | try: | |
555 | (raw_histo_log, _) = parse_hist_file(self.fn, 4) | |
556 | self.A(False) | |
557 | except FioHistoLogExc as e: | |
558 | self.A(str(e).__contains__('buckets per interval')) | |
559 | ||
560 | def test_c1_time_ranges(self): | |
561 | ranges = time_ranges(3, 2) # fio_version defaults to 3 | |
562 | expected_ranges = [ # fio_version 3 is in nanoseconds | |
563 | [0.000, 0.001], [0.001, 0.002], # first group | |
564 | [0.002, 0.003], [0.003, 0.004], # second group same width | |
565 | [0.004, 0.006], [0.006, 0.008]] # subsequent groups double width | |
566 | self.A(ranges == expected_ranges) | |
567 | ranges = time_ranges(3, 2, fio_version=3) | |
568 | self.A(ranges == expected_ranges) | |
569 | ranges = time_ranges(3, 2, fio_version=2) | |
570 | expected_ranges_v2 = [ [ 1000.0 * min_or_max for min_or_max in time_range ] | |
571 | for time_range in expected_ranges ] | |
572 | self.A(ranges == expected_ranges_v2) | |
573 | # see fio V3 stat.h for why 29 groups and 2^6 buckets/group | |
574 | normal_ranges_v3 = time_ranges(29, 64) | |
575 | # for v3, bucket time intervals are measured in nanoseconds | |
576 | self.A(len(normal_ranges_v3) == 29 * 64 and normal_ranges_v3[-1][1] == 64*(1<<(29-1))/1000.0) | |
577 | normal_ranges_v2 = time_ranges(19, 64, fio_version=2) | |
578 | # for v2, bucket time intervals are measured in microseconds so we have fewer buckets | |
579 | self.A(len(normal_ranges_v2) == 19 * 64 and normal_ranges_v2[-1][1] == 64*(1<<(19-1))) | |
580 | ||
581 | def test_d1_align_histo_log_1_quantum(self): | |
582 | with open(self.fn, 'w') as f: | |
583 | f.write('100, 1, 4096, 1, 2, 3, 4') | |
584 | (raw_histo_log, max_timestamp_ms) = parse_hist_file(self.fn, 4) | |
585 | self.A(max_timestamp_ms == 100) | |
586 | aligned_log = align_histo_log(raw_histo_log, 5, 4, max_timestamp_ms) | |
587 | self.A(len(aligned_log) == 1) | |
588 | (time_ms0, h) = aligned_log[0] | |
589 | self.A(time_ms0 == 0 and h == [1.0, 2.0, 3.0, 4.0]) | |
590 | ||
591 | # we need this to compare 2 lists of floating point numbers for equality | |
592 | # because of floating-point imprecision | |
593 | ||
594 | def compare_2_floats(self, x, y): | |
595 | if x == 0.0 or y == 0.0: | |
596 | return (x+y) < 0.0000001 | |
597 | else: | |
598 | return (math.fabs(x-y)/x) < 0.00001 | |
599 | ||
600 | def is_close(self, buckets, buckets_expected): | |
601 | if len(buckets) != len(buckets_expected): | |
602 | return False | |
603 | compare_buckets = lambda k: self.compare_2_floats(buckets[k], buckets_expected[k]) | |
604 | indices_close = list(filter(compare_buckets, range(0, len(buckets)))) | |
605 | return len(indices_close) == len(buckets) | |
606 | ||
607 | def test_d2_align_histo_log_2_quantum(self): | |
608 | with open(self.fn, 'w') as f: | |
609 | f.write('2000, 1, 4096, 1, 2, 3, 4\n') | |
610 | f.write('7000, 1, 4096, 1, 2, 3, 4\n') | |
611 | (raw_histo_log, max_timestamp_ms) = parse_hist_file(self.fn, 4) | |
612 | self.A(max_timestamp_ms == 7000) | |
613 | (_, _, _, raw_buckets1) = raw_histo_log[0] | |
614 | (_, _, _, raw_buckets2) = raw_histo_log[1] | |
615 | aligned_log = align_histo_log(raw_histo_log, 5, 4, max_timestamp_ms) | |
616 | self.A(len(aligned_log) == 2) | |
617 | (time_ms1, h1) = aligned_log[0] | |
618 | (time_ms2, h2) = aligned_log[1] | |
619 | # because first record is from time interval [2000, 7000] | |
620 | # we weight it according | |
621 | expect1 = [float(b) * 0.6 for b in raw_buckets1] | |
622 | expect2 = [float(b) * 0.4 for b in raw_buckets1] | |
623 | for e in range(0, len(expect2)): | |
624 | expect2[e] += raw_buckets2[e] | |
625 | self.A(time_ms1 == 0 and self.is_close(h1, expect1)) | |
626 | self.A(time_ms2 == 5000 and self.is_close(h2, expect2)) | |
627 | ||
0456267b BE |
628 | # what to expect if histogram buckets are all equal |
629 | def test_e1_get_pctiles_flat_histo(self): | |
d8296fdd BE |
630 | with open(self.fn, 'w') as f: |
631 | buckets = [ 100 for j in range(0, 128) ] | |
632 | f.write('9000, 1, 4096, %s\n' % ', '.join([str(b) for b in buckets])) | |
633 | (raw_histo_log, max_timestamp_ms) = parse_hist_file(self.fn, 128) | |
634 | self.A(max_timestamp_ms == 9000) | |
635 | aligned_log = align_histo_log(raw_histo_log, 5, 128, max_timestamp_ms) | |
636 | time_intervals = time_ranges(4, 32) | |
637 | # since buckets are all equal, then median is halfway through time_intervals | |
638 | # and max latency interval is at end of time_intervals | |
639 | self.A(time_intervals[64][1] == 0.066 and time_intervals[127][1] == 0.256) | |
640 | pctiles_wanted = [ 0, 50, 100 ] | |
641 | pct_vs_time = [] | |
642 | for (time_ms, histo) in aligned_log: | |
643 | pct_vs_time.append(get_pctiles(histo, pctiles_wanted, time_intervals)) | |
644 | self.A(pct_vs_time[0] == None) # no I/O in this time interval | |
0456267b | 645 | expected_pctiles = { 0:0.000, 50:0.064, 100:0.256 } |
d8296fdd BE |
646 | self.A(pct_vs_time[1] == expected_pctiles) |
647 | ||
0456267b BE |
648 | # what to expect if just the highest histogram bucket is used |
649 | def test_e2_get_pctiles_highest_pct(self): | |
650 | fio_v3_bucket_count = 29 * 64 | |
651 | with open(self.fn, 'w') as f: | |
652 | # make a empty fio v3 histogram | |
653 | buckets = [ 0 for j in range(0, fio_v3_bucket_count) ] | |
654 | # add one I/O request to last bucket | |
655 | buckets[-1] = 1 | |
656 | f.write('9000, 1, 4096, %s\n' % ', '.join([str(b) for b in buckets])) | |
657 | (raw_histo_log, max_timestamp_ms) = parse_hist_file(self.fn, fio_v3_bucket_count) | |
658 | self.A(max_timestamp_ms == 9000) | |
659 | aligned_log = align_histo_log(raw_histo_log, 5, fio_v3_bucket_count, max_timestamp_ms) | |
660 | (time_ms, histo) = aligned_log[1] | |
661 | time_intervals = time_ranges(29, 64) | |
662 | expected_pctiles = { 100.0:(64*(1<<28))/1000.0 } | |
663 | pct = get_pctiles( histo, [ 100.0 ], time_intervals ) | |
664 | self.A(pct == expected_pctiles) | |
665 | ||
d8296fdd BE |
666 | # we are using this module as a standalone program |
667 | ||
668 | if __name__ == '__main__': | |
669 | if os.getenv('UNITTEST'): | |
088a092e BE |
670 | if unittest2_imported: |
671 | sys.exit(unittest2.main()) | |
672 | else: | |
673 | raise Exception('you must install unittest2 module to run unit test') | |
d8296fdd BE |
674 | else: |
675 | compute_percentiles_from_logs() | |
676 |