--- /dev/null
+#!/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
+from copy import deepcopy
+
+import unittest2
+
+msec_per_sec = 1000
+nsec_per_usec = 1000
+
+class FioHistoLogExc(Exception):
+ pass
+
+# if there is an error, print message, print syntax, and exit with error status
+
+def usage(msg):
+ print('ERROR: ' + msg)
+ print('usage: fio-histo-log-pctiles.py ')
+ print(' [ --fio-version 2|3 (default 3) ]')
+ print(' [ --bucket-groups positive-int (default 29) ]')
+ print(' [ --bucket-bits small-positive-int (default 6) ]')
+ print(' [ --percentiles p1,p2,...,pN ] (default 0,50,95,99,100)')
+ print(' [ --time-quantum positive-int (default 1 sec) ]')
+ print(' [ --output-unit msec|usec|nsec (default msec) ]')
+ print(' log-file1 log-file2 ...')
+ 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
+
+def parse_hist_file(logfn, buckets_per_interval):
+ max_timestamp_ms = 0.0
+
+ with open(logfn, 'r') as f:
+ records = [ l.strip() for l in f.readlines() ]
+ intervals = []
+ 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))
+
+ time_ms = int_tokens[0]
+ if time_ms > max_timestamp_ms:
+ max_timestamp_ms = time_ms
+
+ direction = int_tokens[1]
+ if direction != 0 and direction != 1:
+ raise FioHistoLogExc('invalid I/O direction %s' % exception_suffix(k+1, logfn))
+
+ 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)))
+ intervals.append((time_ms, direction, bsz, buckets))
+ if len(intervals) == 0:
+ raise FioHistoLogExc('no records in %s' % logfn)
+ return (intervals, max_timestamp_ms)
+
+
+# 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, max_timestamp_ms):
+ # round down to nearest second
+ max_timestamp = max_timestamp_ms // msec_per_sec
+ # round up to nearest whole multiple of time_quantum
+ time_interval_count = (max_timestamp + time_quantum) // time_quantum
+ end_time = 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, max_timestamp_ms):
+
+ # slice up test time int intervals of time_quantum seconds
+
+ (end_time, time_interval_count) = get_time_intervals(time_quantum, 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((
+ 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
+
+ qtm_start_ms = (time_msec // time_qtm_ms) * time_qtm_ms
+ qtm_end_ms = ((time_msec + time_qtm_ms) // time_qtm_ms) * time_qtm_ms
+ qtm_index = qtm_start_ms // 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
+
+ # 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]
+
+# 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
+
+ total_so_far = 0
+ for b, io_count in enumerate(buckets):
+ total_so_far += io_count
+ pct_lt = 100.0 * float(total_so_far) / total_ios
+ # a single bucket could satisfy multiple pctiles
+ # so this must be a while loop
+ # consider both the 0-percentile (min latency)
+ # and 100-percentile (max latency) case here
+ while ((next_pctile == 100.0 and pct_lt >= almost_100) or
+ (next_pctile < 100.0 and pct_lt > next_pctile)):
+ # FIXME: interpolate between these fractions
+ range_max_time = time_ranges[b][1]
+ pctile_result[next_pctile] = range_max_time
+ 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
+
+
+# parse parameters
+# returns a tuple of command line parameters
+# parameters have default values unless otherwise shown
+
+def parse_cli_params():
+
+ # default values for input parameters
+
+ fio_version = 3 # we are using fio 3.x now
+ bucket_groups = None # defaulting comes later
+ bucket_bits = 6 # default in fio 3.x
+ pctiles_wanted = [ 0, 50, 90, 95, 99, 100 ]
+ time_quantum = 1
+ output_unit = 'usec'
+
+ # parse command line parameters and display them
+
+ argindex = 1
+ argct = len(sys.argv)
+ if argct < 2:
+ usage('must supply at least one histogram log file')
+ while argindex < argct:
+ if argct < argindex + 2:
+ break
+ pname = sys.argv[argindex]
+ pval = sys.argv[argindex+1]
+ if not pname.startswith('--'):
+ break
+ argindex += 2
+ pname = pname[2:]
+
+ if pname == 'bucket-groups':
+ bucket_groups = int(pval)
+ elif pname == 'bucket-bits':
+ bucket_bits = int(pval)
+ elif pname == 'time-quantum':
+ time_quantum = int(pval)
+ elif pname == 'percentiles':
+ pctiles_wanted = [ float(p) for p in pval.split(',') ]
+ elif pname == 'output-unit':
+ if pval == 'msec' or pval == 'usec':
+ output_unit = pval
+ else:
+ usage('output-unit must be usec (microseconds) or msec (milliseconds)')
+ elif pname == 'fio-version':
+ if pval != '2' and pval != '3':
+ usage('invalid fio version, must be 2 or 3')
+ fio_version = int(pval)
+ else:
+ usage('invalid parameter name --%s' % pname)
+
+ if not bucket_groups:
+ # default changes based on fio version
+ if fio_version == 2:
+ bucket_groups = 19
+ else:
+ # default in fio 3.x
+ bucket_groups = 29
+
+ filename_list = sys.argv[argindex:]
+ for f in filename_list:
+ if not os.path.exists(f):
+ usage('file %s does not exist' % f)
+ return (bucket_groups, bucket_bits, fio_version, pctiles_wanted,
+ filename_list, time_quantum, output_unit)
+
+
+# this is really the main program
+
+def compute_percentiles_from_logs():
+ (bucket_groups, bucket_bits, fio_version, pctiles_wanted,
+ file_list, time_quantum, output_unit) = parse_cli_params()
+
+ print('bucket groups = %d' % bucket_groups)
+ print('bucket bits = %d' % bucket_bits)
+ print('time quantum = %d sec' % time_quantum)
+ print('percentiles = %s' % ','.join([ str(p) for p in pctiles_wanted ]))
+ buckets_per_group = 1 << bucket_bits
+ print('buckets per group = %d' % buckets_per_group)
+ buckets_per_interval = buckets_per_group * bucket_groups
+ print('buckets per interval = %d ' % buckets_per_interval)
+ bucket_index_range = range(0, buckets_per_interval)
+ if time_quantum == 0:
+ usage('time-quantum must be a positive number of seconds')
+ print('output unit = ' + output_unit)
+ if output_unit == 'msec':
+ time_divisor = 1000.0
+ elif output_unit == 'usec':
+ time_divisor = 1.0
+
+ # calculate response time interval associated with each histogram bucket
+
+ bucket_times = time_ranges(bucket_groups, buckets_per_group, fio_version=fio_version)
+
+ # 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 ]
+
+ # print CSV header just like fiologparser_hist does
+
+ header = 'msec, '
+ for p in pctiles_wanted:
+ header += '%3.1f, ' % p
+ print('time (millisec), percentiles in increasing order with values in ' + output_unit)
+ print(header)
+
+ # 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)
+
+ max_timestamp_all_logs = 0
+ hist_files = {}
+ for fn in file_list:
+ try:
+ (hist_files[fn], max_timestamp_ms) = parse_hist_file(fn, buckets_per_interval)
+ except FioHistoLogExc as e:
+ usage(str(e))
+ max_timestamp_all_logs = max(max_timestamp_all_logs, max_timestamp_ms)
+
+ (end_time, time_interval_count) = get_time_intervals(time_quantum, max_timestamp_all_logs)
+ all_threads_histograms = [ ((j*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],
+ time_quantum,
+ buckets_per_interval,
+ max_timestamp_all_logs)
+ for t in range(0, time_interval_count):
+ (_, all_threads_histo_t) = all_threads_histograms[t]
+ (_, log_histo_t) = aligned_per_thread[t]
+ pct = get_pctiles(log_histo_t, pctiles_wanted, bucket_times)
+ add_to_histo_from( all_threads_histo_t, log_histo_t )
+
+ print('percentiles for entire set of threads')
+ for (t_msec, all_threads_histo_t) in all_threads_histograms:
+ record = '%d, ' % t_msec
+ pct = get_pctiles(all_threads_histo_t, pctiles_wanted, bucket_times)
+ if not pct:
+ for w in 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 ##############
+
+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, max_timestamp) = parse_hist_file(self.fn, 4) # 4 buckets per interval
+ self.A(len(raw_histo_log) == 2 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, max_timestamp_ms) = parse_hist_file(self.fn, 4)
+ 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)
+ 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)
+ 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)
+ 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)
+ 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)
+ 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, max_timestamp_ms) = parse_hist_file(self.fn+'_good', 4)
+ 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)
+ 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)
+ 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, max_timestamp_ms) = parse_hist_file(self.fn, 4)
+ self.A(max_timestamp_ms == 100)
+ aligned_log = align_histo_log(raw_histo_log, 5, 4, max_timestamp_ms)
+ self.A(len(aligned_log) == 1)
+ (time_ms0, h) = aligned_log[0]
+ self.A(time_ms0 == 0 and h == [1.0, 2.0, 3.0, 4.0])
+
+ # 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, max_timestamp_ms) = parse_hist_file(self.fn, 4)
+ self.A(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, 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))
+
+ def test_e1_get_pctiles(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, max_timestamp_ms) = parse_hist_file(self.fn, 128)
+ self.A(max_timestamp_ms == 9000)
+ aligned_log = align_histo_log(raw_histo_log, 5, 128, 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 = { 0:0.001, 50:0.066, 100:0.256 }
+ self.A(pct_vs_time[1] == expected_pctiles)
+
+# we are using this module as a standalone program
+
+if __name__ == '__main__':
+ if os.getenv('UNITTEST'):
+ sys.exit(unittest2.main())
+ else:
+ compute_percentiles_from_logs()
+
projects / fio.git / blobdiff