#!/usr/bin/env python # module to parse fio histogram log files, not using pandas # runs in python v2 or v3 # to get help with the CLI: $ python fio-histo-log-pctiles.py -h # this can be run standalone as a script but is callable # assumes all threads run for same time duration # assumes all threads are doing the same thing for the entire run # percentiles: # 0 - min latency # 50 - median # 100 - max latency # TO-DO: # separate read and write stats for randrw mixed workload # report average latency if needed # prove that it works (partially done with unit tests) # to run unit tests, set UNITTEST environment variable to anything # if you do this, don't pass normal CLI parameters to it # otherwise it runs the CLI import sys, os, math, copy, time from copy import deepcopy import argparse unittest2_imported = True try: import unittest2 except ImportError: unittest2_imported = False msec_per_sec = 1000 nsec_per_usec = 1000 direction_read = 0 direction_write = 1 class FioHistoLogExc(Exception): pass # if there is an error, print message, and exit with error status def myabort(msg): print('ERROR: ' + msg) sys.exit(1) # convert histogram log file into a list of # (time_ms, direction, bsz, buckets) tuples where # - time_ms is the time in msec at which the log record was written # - direction is 0 (read) or 1 (write) # - bsz is block size (not used) # - buckets is a CSV list of counters that make up the histogram # caller decides if the expected number of counters are present def exception_suffix( record_num, pathname ): return 'in histogram record %d file %s' % (record_num+1, pathname) # log file parser raises FioHistoLogExc exceptions # it returns histogram buckets in whatever unit fio uses # inputs: # logfn: pathname to histogram log file # buckets_per_interval - how many histogram buckets to expect # log_hist_msec - if not None, expected time interval between histogram records def parse_hist_file(logfn, buckets_per_interval, log_hist_msec): previous_ts_ms_read = -1 previous_ts_ms_write = -1 with open(logfn, 'r') as f: records = [ l.strip() for l in f.readlines() ] intervals = [] last_time_ms = -1 last_direction = -1 for k, r in enumerate(records): if r == '': continue tokens = r.split(',') try: int_tokens = [ int(t) for t in tokens ] except ValueError as e: raise FioHistoLogExc('non-integer value %s' % exception_suffix(k+1, logfn)) neg_ints = list(filter( lambda tk : tk < 0, int_tokens )) if len(neg_ints) > 0: raise FioHistoLogExc('negative integer value %s' % exception_suffix(k+1, logfn)) if len(int_tokens) < 3: raise FioHistoLogExc('too few numbers %s' % exception_suffix(k+1, logfn)) direction = int_tokens[1] if direction != direction_read and direction != direction_write: raise FioHistoLogExc('invalid I/O direction %s' % exception_suffix(k+1, logfn)) time_ms = int_tokens[0] if direction == direction_read: if time_ms < previous_ts_ms_read: raise FioHistoLogExc('read timestamp in column 1 decreased %s' % exception_suffix(k+1, logfn)) previous_ts_ms_read = time_ms elif direction == direction_write: if time_ms < previous_ts_ms_write: raise FioHistoLogExc('write timestamp in column 1 decreased %s' % exception_suffix(k+1, logfn)) previous_ts_ms_write = time_ms bsz = int_tokens[2] if bsz > (1 << 24): raise FioHistoLogExc('block size too large %s' % exception_suffix(k+1, logfn)) buckets = int_tokens[3:] if len(buckets) != buckets_per_interval: raise FioHistoLogExc('%d buckets per interval but %d expected in %s' % (len(buckets), buckets_per_interval, exception_suffix(k+1, logfn))) # hack to filter out records with the same timestamp # we should not have to do this if fio logs histogram records correctly if time_ms == last_time_ms and direction == last_direction: continue last_time_ms = time_ms last_direction = direction intervals.append((time_ms, direction, bsz, buckets)) if len(intervals) == 0: raise FioHistoLogExc('no records in %s' % logfn) (first_timestamp, _, _, _) = intervals[0] if first_timestamp < 1000000: start_time = 0 # assume log_unix_epoch = 0 elif log_hist_msec != None: start_time = first_timestamp - log_hist_msec elif len(intervals) > 1: (second_timestamp, _, _, _) = intervals[1] start_time = first_timestamp - (second_timestamp - first_timestamp) else: raise FioHistoLogExc('no way to estimate test start time') (end_timestamp, _, _, _) = intervals[-1] return (intervals, start_time, end_timestamp) # compute time range for each bucket index in histogram record # see comments in https://github.com/axboe/fio/blob/master/stat.h # for description of bucket groups and buckets # fio v3 bucket ranges are in nanosec (since response times are measured in nanosec) # but we convert fio v3 nanosecs to floating-point microseconds def time_ranges(groups, counters_per_group, fio_version=3): bucket_width = 1 bucket_base = 0 bucket_intervals = [] for g in range(0, groups): for b in range(0, counters_per_group): rmin = float(bucket_base) rmax = rmin + bucket_width if fio_version == 3: rmin /= nsec_per_usec rmax /= nsec_per_usec bucket_intervals.append( [rmin, rmax] ) bucket_base += bucket_width if g != 0: bucket_width *= 2 return bucket_intervals # compute number of time quantum intervals in the test def get_time_intervals(time_quantum, min_timestamp_ms, max_timestamp_ms): # round down to nearest second max_timestamp = max_timestamp_ms // msec_per_sec min_timestamp = min_timestamp_ms // msec_per_sec # round up to nearest whole multiple of time_quantum time_interval_count = ((max_timestamp - min_timestamp) + time_quantum) // time_quantum end_time = min_timestamp + (time_interval_count * time_quantum) return (end_time, time_interval_count) # align raw histogram log data to time quantum so # we can then combine histograms from different threads with addition # for randrw workload we count both reads and writes in same output bucket # but we separate reads and writes for purposes of calculating # end time for histogram record. # this requires us to weight a raw histogram bucket by the # fraction of time quantum that the bucket overlaps the current # time quantum interval # for example, if we have a bucket with 515 samples for time interval # [ 1010, 2014 ] msec since start of test, and time quantum is 1 sec, then # for time quantum interval [ 1000, 2000 ] msec, the overlap is # (2000 - 1010) / (2000 - 1000) = 0.99 # so the contribution of this bucket to this time quantum is # 515 x 0.99 = 509.85 def align_histo_log(raw_histogram_log, time_quantum, bucket_count, min_timestamp_ms, max_timestamp_ms): # slice up test time int intervals of time_quantum seconds (end_time, time_interval_count) = get_time_intervals(time_quantum, min_timestamp_ms, max_timestamp_ms) time_qtm_ms = time_quantum * msec_per_sec end_time_ms = end_time * msec_per_sec aligned_intervals = [] for j in range(0, time_interval_count): aligned_intervals.append(( min_timestamp_ms + (j * time_qtm_ms), [ 0.0 for j in range(0, bucket_count) ] )) log_record_count = len(raw_histogram_log) for k, record in enumerate(raw_histogram_log): # find next record with same direction to get end-time # have to avoid going past end of array # for fio randrw workload, # we have read and write records on same time interval # sometimes read and write records are in opposite order # assertion checks that next read/write record # can be separated by at most 2 other records (time_msec, direction, sz, interval_buckets) = record if k+1 < log_record_count: (time_msec_end, direction2, _, _) = raw_histogram_log[k+1] if direction2 != direction: if k+2 < log_record_count: (time_msec_end, direction2, _, _) = raw_histogram_log[k+2] if direction2 != direction: if k+3 < log_record_count: (time_msec_end, direction2, _, _) = raw_histogram_log[k+3] assert direction2 == direction else: time_msec_end = end_time_ms else: time_msec_end = end_time_ms else: time_msec_end = end_time_ms # calculate first quantum that overlaps this histogram record offset_from_min_ts = time_msec - min_timestamp_ms qtm_start_ms = min_timestamp_ms + (offset_from_min_ts // time_qtm_ms) * time_qtm_ms qtm_end_ms = min_timestamp_ms + ((offset_from_min_ts + time_qtm_ms) // time_qtm_ms) * time_qtm_ms qtm_index = offset_from_min_ts // time_qtm_ms # for each quantum that overlaps this histogram record's time interval while qtm_start_ms < time_msec_end: # while quantum overlaps record # some histogram logs may be longer than others if len(aligned_intervals) <= qtm_index: break # calculate fraction of time that this quantum # overlaps histogram record's time interval overlap_start = max(qtm_start_ms, time_msec) overlap_end = min(qtm_end_ms, time_msec_end) weight = float(overlap_end - overlap_start) weight /= (time_msec_end - time_msec) (_,aligned_histogram) = aligned_intervals[qtm_index] for bx, b in enumerate(interval_buckets): weighted_bucket = weight * b aligned_histogram[bx] += weighted_bucket # advance to the next time quantum qtm_start_ms += time_qtm_ms qtm_end_ms += time_qtm_ms qtm_index += 1 return aligned_intervals # add histogram in "source" to histogram in "target" # it is assumed that the 2 histograms are precisely time-aligned def add_to_histo_from( target, source ): for b in range(0, len(source)): target[b] += source[b] # calculate total samples in the histogram buckets def get_samples(buckets): return reduce( lambda x,y: x + y, buckets) # compute percentiles # inputs: # buckets: histogram bucket array # wanted: list of floating-pt percentiles to calculate # time_ranges: [tmin,tmax) time interval for each bucket # returns None if no I/O reported. # otherwise we would be dividing by zero # think of buckets as probability distribution function # and this loop is integrating to get cumulative distribution function def get_pctiles(buckets, wanted, time_ranges): # get total of IO requests done total_ios = 0 for io_count in buckets: total_ios += io_count # don't return percentiles if no I/O was done during interval if total_ios == 0.0: return None pctile_count = len(wanted) # results returned as dictionary keyed by percentile pctile_result = {} # index of next percentile in list pctile_index = 0 # next percentile next_pctile = wanted[pctile_index] # no one is interested in percentiles bigger than this but not 100.0 # this prevents floating-point error from preventing loop exit almost_100 = 99.9999 # pct is the percentile corresponding to # all I/O requests up through bucket b pct = 0.0 total_so_far = 0 for b, io_count in enumerate(buckets): if io_count == 0: continue total_so_far += io_count # last_pct_lt is the percentile corresponding to # all I/O requests up to, but not including, bucket b last_pct = pct pct = 100.0 * float(total_so_far) / total_ios # a single bucket could satisfy multiple pctiles # so this must be a while loop # for 100-percentile (max latency) case, no bucket exceeds it # so we must stop there. while ((next_pctile == 100.0 and pct >= almost_100) or (next_pctile < 100.0 and pct > next_pctile)): # interpolate between min and max time for bucket time interval # we keep the time_ranges access inside this loop, # even though it could be above the loop, # because in many cases we will not be even entering # the loop so we optimize out these accesses range_max_time = time_ranges[b][1] range_min_time = time_ranges[b][0] offset_frac = (next_pctile - last_pct)/(pct - last_pct) interpolation = range_min_time + (offset_frac*(range_max_time - range_min_time)) pctile_result[next_pctile] = interpolation pctile_index += 1 if pctile_index == pctile_count: break next_pctile = wanted[pctile_index] if pctile_index == pctile_count: break assert pctile_index == pctile_count return pctile_result # this is really the main program def compute_percentiles_from_logs(): parser = argparse.ArgumentParser() parser.add_argument("--fio-version", dest="fio_version", default="3", choices=[2,3], type=int, help="fio version (default=3)") parser.add_argument("--bucket-groups", dest="bucket_groups", default="29", type=int, help="fio histogram bucket groups (default=29)") parser.add_argument("--bucket-bits", dest="bucket_bits", default="6", type=int, help="fio histogram buckets-per-group bits (default=6 means 64 buckets/group)") parser.add_argument("--percentiles", dest="pctiles_wanted", default=[ 0., 50., 95., 99., 100.], type=float, nargs='+', help="fio histogram buckets-per-group bits (default=6 means 64 buckets/group)") parser.add_argument("--time-quantum", dest="time_quantum", default="1", type=int, help="time quantum in seconds (default=1)") parser.add_argument("--log-hist-msec", dest="log_hist_msec", type=int, default=None, help="log_hist_msec value in fio job file") parser.add_argument("--output-unit", dest="output_unit", default="usec", type=str, help="Latency percentile output unit: msec|usec|nsec (default usec)") parser.add_argument("file_list", nargs='+', help='list of files, preceded by " -- " if necessary') args = parser.parse_args() # default changes based on fio version if args.fio_version == 2: args.bucket_groups = 19 # print parameters print('fio version = %d' % args.fio_version) print('bucket groups = %d' % args.bucket_groups) print('bucket bits = %d' % args.bucket_bits) print('time quantum = %d sec' % args.time_quantum) print('percentiles = %s' % ','.join([ str(p) for p in args.pctiles_wanted ])) buckets_per_group = 1 << args.bucket_bits print('buckets per group = %d' % buckets_per_group) buckets_per_interval = buckets_per_group * args.bucket_groups print('buckets per interval = %d ' % buckets_per_interval) bucket_index_range = range(0, buckets_per_interval) if args.log_hist_msec != None: print('log_hist_msec = %d' % args.log_hist_msec) if args.time_quantum == 0: print('ERROR: time-quantum must be a positive number of seconds') print('output unit = ' + args.output_unit) if args.output_unit == 'msec': time_divisor = float(msec_per_sec) elif args.output_unit == 'usec': time_divisor = 1.0 # construct template for each histogram bucket array with buckets all zeroes # we just copy this for each new histogram zeroed_buckets = [ 0.0 for r in bucket_index_range ] # calculate response time interval associated with each histogram bucket bucket_times = time_ranges(args.bucket_groups, buckets_per_group, fio_version=args.fio_version) # parse the histogram logs # assumption: each bucket has a monotonically increasing time # assumption: time ranges do not overlap for a single thread's records # (exception: if randrw workload, then there is a read and a write # record for the same time interval) test_start_time = 0 test_end_time = 1.0e18 hist_files = {} for fn in args.file_list: try: (hist_files[fn], log_start_time, log_end_time) = parse_hist_file(fn, buckets_per_interval, args.log_hist_msec) except FioHistoLogExc as e: myabort(str(e)) # we consider the test started when all threads have started logging test_start_time = max(test_start_time, log_start_time) # we consider the test over when one of the logs has ended test_end_time = min(test_end_time, log_end_time) if test_start_time >= test_end_time: raise FioHistoLogExc('no time interval when all threads logs overlapped') if test_start_time > 0: print('all threads running as of unix epoch time %d = %s' % ( test_start_time/float(msec_per_sec), time.ctime(test_start_time/1000.0))) (end_time, time_interval_count) = get_time_intervals(args.time_quantum, test_start_time, test_end_time) all_threads_histograms = [ ((j*args.time_quantum*msec_per_sec), deepcopy(zeroed_buckets)) for j in range(0, time_interval_count) ] for logfn in hist_files.keys(): aligned_per_thread = align_histo_log(hist_files[logfn], args.time_quantum, buckets_per_interval, test_start_time, test_end_time) for t in range(0, time_interval_count): (_, all_threads_histo_t) = all_threads_histograms[t] (_, log_histo_t) = aligned_per_thread[t] add_to_histo_from( all_threads_histo_t, log_histo_t ) # calculate percentiles across aggregate histogram for all threads # print CSV header just like fiologparser_hist does header = 'msec-since-start, samples, ' for p in args.pctiles_wanted: if p == 0.: next_pctile_header = 'min' elif p == 100.: next_pctile_header = 'max' elif p == 50.: next_pctile_header = 'median' else: next_pctile_header = '%3.1f' % p header += '%s, ' % next_pctile_header print('time (millisec), percentiles in increasing order with values in ' + args.output_unit) print(header) for (t_msec, all_threads_histo_t) in all_threads_histograms: samples = get_samples(all_threads_histo_t) record = '%8d, %8d, ' % (t_msec, samples) pct = get_pctiles(all_threads_histo_t, args.pctiles_wanted, bucket_times) if not pct: for w in args.pctiles_wanted: record += ', ' else: pct_keys = [ k for k in pct.keys() ] pct_values = [ str(pct[wanted]/time_divisor) for wanted in sorted(pct_keys) ] record += ', '.join(pct_values) print(record) #end of MAIN PROGRAM ##### below are unit tests ############## if unittest2_imported: import tempfile, shutil from os.path import join should_not_get_here = False class Test(unittest2.TestCase): tempdir = None # a little less typing please def A(self, boolean_val): self.assertTrue(boolean_val) # initialize unit test environment @classmethod def setUpClass(cls): d = tempfile.mkdtemp() Test.tempdir = d # remove anything left by unit test environment # unless user sets UNITTEST_LEAVE_FILES environment variable @classmethod def tearDownClass(cls): if not os.getenv("UNITTEST_LEAVE_FILES"): shutil.rmtree(cls.tempdir) def setUp(self): self.fn = join(Test.tempdir, self.id()) def test_a_add_histos(self): a = [ 1.0, 2.0 ] b = [ 1.5, 2.5 ] add_to_histo_from( a, b ) self.A(a == [2.5, 4.5]) self.A(b == [1.5, 2.5]) def test_b1_parse_log(self): with open(self.fn, 'w') as f: f.write('1234, 0, 4096, 1, 2, 3, 4\n') f.write('5678,1,16384,5,6,7,8 \n') (raw_histo_log, min_timestamp, max_timestamp) = parse_hist_file(self.fn, 4, None) # 4 buckets per interval # if not log_unix_epoch=1, then min_timestamp will always be set to zero self.A(len(raw_histo_log) == 2 and min_timestamp == 0 and max_timestamp == 5678) (time_ms, direction, bsz, histo) = raw_histo_log[0] self.A(time_ms == 1234 and direction == 0 and bsz == 4096 and histo == [ 1, 2, 3, 4 ]) (time_ms, direction, bsz, histo) = raw_histo_log[1] self.A(time_ms == 5678 and direction == 1 and bsz == 16384 and histo == [ 5, 6, 7, 8 ]) def test_b2_parse_empty_log(self): with open(self.fn, 'w') as f: pass try: (raw_histo_log, _, _) = parse_hist_file(self.fn, 4, None) self.A(should_not_get_here) except FioHistoLogExc as e: self.A(str(e).startswith('no records')) def test_b3_parse_empty_records(self): with open(self.fn, 'w') as f: f.write('\n') f.write('1234, 0, 4096, 1, 2, 3, 4\n') f.write('5678,1,16384,5,6,7,8 \n') f.write('\n') (raw_histo_log, _, max_timestamp_ms) = parse_hist_file(self.fn, 4, None) self.A(len(raw_histo_log) == 2 and max_timestamp_ms == 5678) (time_ms, direction, bsz, histo) = raw_histo_log[0] self.A(time_ms == 1234 and direction == 0 and bsz == 4096 and histo == [ 1, 2, 3, 4 ]) (time_ms, direction, bsz, histo) = raw_histo_log[1] self.A(time_ms == 5678 and direction == 1 and bsz == 16384 and histo == [ 5, 6, 7, 8 ]) def test_b4_parse_non_int(self): with open(self.fn, 'w') as f: f.write('12, 0, 4096, 1a, 2, 3, 4\n') try: (raw_histo_log, _, _) = parse_hist_file(self.fn, 4, None) self.A(False) except FioHistoLogExc as e: self.A(str(e).startswith('non-integer')) def test_b5_parse_neg_int(self): with open(self.fn, 'w') as f: f.write('-12, 0, 4096, 1, 2, 3, 4\n') try: (raw_histo_log, _, _) = parse_hist_file(self.fn, 4, None) self.A(False) except FioHistoLogExc as e: self.A(str(e).startswith('negative integer')) def test_b6_parse_too_few_int(self): with open(self.fn, 'w') as f: f.write('0, 0\n') try: (raw_histo_log, _, _) = parse_hist_file(self.fn, 4, None) self.A(False) except FioHistoLogExc as e: self.A(str(e).startswith('too few numbers')) def test_b7_parse_invalid_direction(self): with open(self.fn, 'w') as f: f.write('100, 2, 4096, 1, 2, 3, 4\n') try: (raw_histo_log, _, _) = parse_hist_file(self.fn, 4, None) self.A(False) except FioHistoLogExc as e: self.A(str(e).startswith('invalid I/O direction')) def test_b8_parse_bsz_too_big(self): with open(self.fn+'_good', 'w') as f: f.write('100, 1, %d, 1, 2, 3, 4\n' % (1<<24)) (raw_histo_log, _, _) = parse_hist_file(self.fn+'_good', 4, None) with open(self.fn+'_bad', 'w') as f: f.write('100, 1, 20000000, 1, 2, 3, 4\n') try: (raw_histo_log, _, _) = parse_hist_file(self.fn+'_bad', 4, None) self.A(False) except FioHistoLogExc as e: self.A(str(e).startswith('block size too large')) def test_b9_parse_wrong_bucket_count(self): with open(self.fn, 'w') as f: f.write('100, 1, %d, 1, 2, 3, 4, 5\n' % (1<<24)) try: (raw_histo_log, _, _) = parse_hist_file(self.fn, 4, None) self.A(False) except FioHistoLogExc as e: self.A(str(e).__contains__('buckets per interval')) def test_c1_time_ranges(self): ranges = time_ranges(3, 2) # fio_version defaults to 3 expected_ranges = [ # fio_version 3 is in nanoseconds [0.000, 0.001], [0.001, 0.002], # first group [0.002, 0.003], [0.003, 0.004], # second group same width [0.004, 0.006], [0.006, 0.008]] # subsequent groups double width self.A(ranges == expected_ranges) ranges = time_ranges(3, 2, fio_version=3) self.A(ranges == expected_ranges) ranges = time_ranges(3, 2, fio_version=2) expected_ranges_v2 = [ [ 1000.0 * min_or_max for min_or_max in time_range ] for time_range in expected_ranges ] self.A(ranges == expected_ranges_v2) # see fio V3 stat.h for why 29 groups and 2^6 buckets/group normal_ranges_v3 = time_ranges(29, 64) # for v3, bucket time intervals are measured in nanoseconds self.A(len(normal_ranges_v3) == 29 * 64 and normal_ranges_v3[-1][1] == 64*(1<<(29-1))/1000.0) normal_ranges_v2 = time_ranges(19, 64, fio_version=2) # for v2, bucket time intervals are measured in microseconds so we have fewer buckets self.A(len(normal_ranges_v2) == 19 * 64 and normal_ranges_v2[-1][1] == 64*(1<<(19-1))) def test_d1_align_histo_log_1_quantum(self): with open(self.fn, 'w') as f: f.write('100, 1, 4096, 1, 2, 3, 4') (raw_histo_log, min_timestamp_ms, max_timestamp_ms) = parse_hist_file(self.fn, 4, None) self.A(min_timestamp_ms == 0 and max_timestamp_ms == 100) aligned_log = align_histo_log(raw_histo_log, 5, 4, min_timestamp_ms, max_timestamp_ms) self.A(len(aligned_log) == 1) (time_ms0, h) = aligned_log[0] self.A(time_ms0 == 0 and h == [1., 2., 3., 4.]) # handle case with log_unix_epoch=1 timestamps, 1-second time quantum # here both records will be separated into 2 aligned intervals def test_d1a_align_2rec_histo_log_epoch_1_quantum_1sec(self): with open(self.fn, 'w') as f: f.write('1536504002123, 1, 4096, 1, 2, 3, 4\n') f.write('1536504003123, 1, 4096, 4, 3, 2, 1\n') (raw_histo_log, min_timestamp_ms, max_timestamp_ms) = parse_hist_file(self.fn, 4, None) self.A(min_timestamp_ms == 1536504001123 and max_timestamp_ms == 1536504003123) aligned_log = align_histo_log(raw_histo_log, 1, 4, min_timestamp_ms, max_timestamp_ms) self.A(len(aligned_log) == 3) (time_ms0, h) = aligned_log[0] self.A(time_ms0 == 1536504001123 and h == [0., 0., 0., 0.]) (time_ms1, h) = aligned_log[1] self.A(time_ms1 == 1536504002123 and h == [1., 2., 3., 4.]) (time_ms2, h) = aligned_log[2] self.A(time_ms2 == 1536504003123 and h == [4., 3., 2., 1.]) # handle case with log_unix_epoch=1 timestamps, 5-second time quantum # here both records will be merged into a single aligned time interval def test_d1b_align_2rec_histo_log_epoch_1_quantum_5sec(self): with open(self.fn, 'w') as f: f.write('1536504002123, 1, 4096, 1, 2, 3, 4\n') f.write('1536504003123, 1, 4096, 4, 3, 2, 1\n') (raw_histo_log, min_timestamp_ms, max_timestamp_ms) = parse_hist_file(self.fn, 4, None) self.A(min_timestamp_ms == 1536504001123 and max_timestamp_ms == 1536504003123) aligned_log = align_histo_log(raw_histo_log, 5, 4, min_timestamp_ms, max_timestamp_ms) self.A(len(aligned_log) == 1) (time_ms0, h) = aligned_log[0] self.A(time_ms0 == 1536504001123 and h == [5., 5., 5., 5.]) # we need this to compare 2 lists of floating point numbers for equality # because of floating-point imprecision def compare_2_floats(self, x, y): if x == 0.0 or y == 0.0: return (x+y) < 0.0000001 else: return (math.fabs(x-y)/x) < 0.00001 def is_close(self, buckets, buckets_expected): if len(buckets) != len(buckets_expected): return False compare_buckets = lambda k: self.compare_2_floats(buckets[k], buckets_expected[k]) indices_close = list(filter(compare_buckets, range(0, len(buckets)))) return len(indices_close) == len(buckets) def test_d2_align_histo_log_2_quantum(self): with open(self.fn, 'w') as f: f.write('2000, 1, 4096, 1, 2, 3, 4\n') f.write('7000, 1, 4096, 1, 2, 3, 4\n') (raw_histo_log, min_timestamp_ms, max_timestamp_ms) = parse_hist_file(self.fn, 4, None) self.A(min_timestamp_ms == 0 and max_timestamp_ms == 7000) (_, _, _, raw_buckets1) = raw_histo_log[0] (_, _, _, raw_buckets2) = raw_histo_log[1] aligned_log = align_histo_log(raw_histo_log, 5, 4, min_timestamp_ms, max_timestamp_ms) self.A(len(aligned_log) == 2) (time_ms1, h1) = aligned_log[0] (time_ms2, h2) = aligned_log[1] # because first record is from time interval [2000, 7000] # we weight it according expect1 = [float(b) * 0.6 for b in raw_buckets1] expect2 = [float(b) * 0.4 for b in raw_buckets1] for e in range(0, len(expect2)): expect2[e] += raw_buckets2[e] self.A(time_ms1 == 0 and self.is_close(h1, expect1)) self.A(time_ms2 == 5000 and self.is_close(h2, expect2)) # what to expect if histogram buckets are all equal def test_e1_get_pctiles_flat_histo(self): with open(self.fn, 'w') as f: buckets = [ 100 for j in range(0, 128) ] f.write('9000, 1, 4096, %s\n' % ', '.join([str(b) for b in buckets])) (raw_histo_log, min_timestamp_ms, max_timestamp_ms) = parse_hist_file(self.fn, 128, None) self.A(min_timestamp_ms == 0 and max_timestamp_ms == 9000) aligned_log = align_histo_log(raw_histo_log, 5, 128, min_timestamp_ms, max_timestamp_ms) time_intervals = time_ranges(4, 32) # since buckets are all equal, then median is halfway through time_intervals # and max latency interval is at end of time_intervals self.A(time_intervals[64][1] == 0.066 and time_intervals[127][1] == 0.256) pctiles_wanted = [ 0, 50, 100 ] pct_vs_time = [] for (time_ms, histo) in aligned_log: pct_vs_time.append(get_pctiles(histo, pctiles_wanted, time_intervals)) self.A(pct_vs_time[0] == None) # no I/O in this time interval expected_pctiles = { 0:0.000, 50:0.064, 100:0.256 } self.A(pct_vs_time[1] == expected_pctiles) # what to expect if just the highest histogram bucket is used def test_e2_get_pctiles_highest_pct(self): fio_v3_bucket_count = 29 * 64 with open(self.fn, 'w') as f: # make a empty fio v3 histogram buckets = [ 0 for j in range(0, fio_v3_bucket_count) ] # add one I/O request to last bucket buckets[-1] = 1 f.write('9000, 1, 4096, %s\n' % ', '.join([str(b) for b in buckets])) (raw_histo_log, min_timestamp_ms, max_timestamp_ms) = parse_hist_file(self.fn, fio_v3_bucket_count, None) self.A(min_timestamp_ms == 0 and max_timestamp_ms == 9000) aligned_log = align_histo_log(raw_histo_log, 5, fio_v3_bucket_count, min_timestamp_ms, max_timestamp_ms) (time_ms, histo) = aligned_log[1] time_intervals = time_ranges(29, 64) expected_pctiles = { 100.0:(64*(1<<28))/1000.0 } pct = get_pctiles( histo, [ 100.0 ], time_intervals ) self.A(pct == expected_pctiles) # we are using this module as a standalone program if __name__ == '__main__': if os.getenv('UNITTEST'): if unittest2_imported: sys.exit(unittest2.main()) else: raise Exception('you must install unittest2 module to run unit test') else: compute_percentiles_from_logs()