blob: f9df2a3dada8d48e0bf60bf7f2d4572f168213c7 [file] [log] [blame]
#!/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()