Recent changes (master)

Jens Axboe
2016-05-18T12:00:06Z

The following changes since commit 15a0c8ee4e1a5434075ebc2c9f48e96e5e892196:

Windows crash in ctime_r() (2016-05-16 19:25:48 -0600)

are available in the git repository at:

git://git.kernel.dk/fio.git master

for you to fetch changes up to 8c4693e2e578613f517dc42b38e204bf77fdab1d:

add -A option for better stats (2016-05-17 18:48:30 -0400)

----------------------------------------------------------------
Ben England (1):
add -A option for better stats

Jens Axboe (1):
init: cleanup random inits

init.c | 35 +++++++++++++++-------------
options.c | 4 ++--
tools/fiologparser.py | 64 +++++++++++++++++++++++++++++++++++++++++++++++++++
3 files changed, 85 insertions(+), 18 deletions(-)

---

Diff of recent changes:

diff --git a/init.c b/init.c
index e8c8afb..7166ea7 100644
--- a/init.c
+++ b/init.c
@@ -919,6 +919,23 @@ static int exists_and_not_file(const char *filename)
return 1;
}

+static void init_rand_file_service(struct thread_data *td)
+{
+ unsigned long nranges = td->o.nr_files << FIO_FSERVICE_SHIFT;
+ const unsigned int seed = td->rand_seeds[FIO_RAND_FILE_OFF];
+
+ if (td->o.file_service_type == FIO_FSERVICE_ZIPF) {
+ zipf_init(&td->next_file_zipf, nranges, td->zipf_theta, seed);
+ zipf_disable_hash(&td->next_file_zipf);
+ } else if (td->o.file_service_type == FIO_FSERVICE_PARETO) {
+ pareto_init(&td->next_file_zipf, nranges, td->pareto_h, seed);
+ zipf_disable_hash(&td->next_file_zipf);
+ } else if (td->o.file_service_type == FIO_FSERVICE_GAUSS) {
+ gauss_init(&td->next_file_gauss, nranges, td->gauss_dev, seed);
+ gauss_disable_hash(&td->next_file_gauss);
+ }
+}
+
static void td_fill_rand_seeds_internal(struct thread_data *td, bool use64)
{
int i;
@@ -929,22 +946,8 @@ static void td_fill_rand_seeds_internal(struct thread_data *td, bool use64)

if (td->o.file_service_type == FIO_FSERVICE_RANDOM)
init_rand_seed(&td->next_file_state, td->rand_seeds[FIO_RAND_FILE_OFF], use64);
- else if (td->o.file_service_type & __FIO_FSERVICE_NONUNIFORM) {
- unsigned long nranges;
-
- nranges = td->o.nr_files << FIO_FSERVICE_SHIFT;
-
- if (td->o.file_service_type == FIO_FSERVICE_ZIPF) {
- zipf_init(&td->next_file_zipf, nranges, td->zipf_theta, td->rand_seeds[FIO_RAND_FILE_OFF]);
- zipf_disable_hash(&td->next_file_zipf);
- } else if (td->o.file_service_type == FIO_FSERVICE_PARETO) {
- pareto_init(&td->next_file_zipf, nranges, td->pareto_h, td->rand_seeds[FIO_RAND_FILE_OFF]);
- zipf_disable_hash(&td->next_file_zipf);
- } else if (td->o.file_service_type == FIO_FSERVICE_GAUSS) {
- gauss_init(&td->next_file_gauss, nranges, td->gauss_dev, td->rand_seeds[FIO_RAND_FILE_OFF]);
- gauss_disable_hash(&td->next_file_gauss);
- }
- }
+ else if (td->o.file_service_type & __FIO_FSERVICE_NONUNIFORM)
+ init_rand_file_service(td);

init_rand_seed(&td->file_size_state, td->rand_seeds[FIO_RAND_FILE_SIZE_OFF], use64);
init_rand_seed(&td->trim_state, td->rand_seeds[FIO_RAND_TRIM_OFF], use64);
diff --git a/options.c b/options.c
index a925663..07589c4 100644
--- a/options.c
+++ b/options.c
@@ -788,7 +788,7 @@ static int str_fst_cb(void *data, const char *str)
break;
case FIO_FSERVICE_GAUSS:
if (val < 0.00 || val >= 100.00) {
- log_err("fio: normal deviation out of range (0 < input < 100.0 )\n");
+ log_err("fio: normal deviation out of range (0 <= input < 100.0)\n");
return 1;
}
if (parse_dryrun())
@@ -1048,7 +1048,7 @@ static int str_random_distribution_cb(void *data, const char *str)
td->o.pareto_h.u.f = val;
} else {
if (val < 0.00 || val >= 100.0) {
- log_err("fio: normal deviation out of range (0 < input < 100.0)\n");
+ log_err("fio: normal deviation out of range (0 <= input < 100.0)\n");
return 1;
}
if (parse_dryrun())
diff --git a/tools/fiologparser.py b/tools/fiologparser.py
index 0574099..00e4d30 100755
--- a/tools/fiologparser.py
+++ b/tools/fiologparser.py
@@ -14,12 +14,16 @@
# to see per-interval average completion latency.

import argparse
+import numpy
+import scipy

def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--interval', required=False, type=int, default=1000, help='interval of time in seconds.')
parser.add_argument('-d', '--divisor', required=False, type=int, default=1, help='divide the results by this value.')
parser.add_argument('-f', '--full', dest='full', action='store_true', default=False, help='print full output.')
+ parser.add_argument('-A', '--all', dest='allstats', action='store_true', default=False,
+ help='print all stats for each interval.')
parser.add_argument('-a', '--average', dest='average', action='store_true', default=False, help='print the average for each interval.')
parser.add_argument('-s', '--sum', dest='sum', action='store_true', default=False, help='print the sum for each interval.')
parser.add_argument("FILE", help="collectl log output files to parse", nargs="+")
@@ -70,6 +74,57 @@ def print_averages(ctx, series):
start += ctx.interval
end += ctx.interval

+# FIXME: this routine is computationally inefficient
+# and has O(N^2) behavior
+# it would be better to make one pass through samples
+# to segment them into a series of time intervals, and
+# then compute stats on each time interval instead.
+# to debug this routine, use
+# # sort -n -t ',' -k 2 small.log
+# on your input.
+# Sometimes scipy interpolates between two values to get a percentile
+
+def my_extend( vlist, val ):
+ vlist.extend(val)
+ return vlist
+
+array_collapser = lambda vlist, val: my_extend(vlist, val)
+
+def print_all_stats(ctx, series):
+ ftime = get_ftime(series)
+ start = 0
+ end = ctx.interval
+ print('start-time, samples, min, avg, median, 90%, 95%, 99%, max')
+ while (start < ftime): # for each time interval
+ end = ftime if ftime < end else end
+ sample_arrays = [ s.get_samples(start, end) for s in series ]
+ samplevalue_arrays = []
+ for sample_array in sample_arrays:
+ samplevalue_arrays.append(
+ [ sample.value for sample in sample_array ] )
+ #print('samplevalue_arrays len: %d' % len(samplevalue_arrays))
+ #print('samplevalue_arrays elements len: ' + \
+ #str(map( lambda l: len(l), samplevalue_arrays)))
+ # collapse list of lists of sample values into list of sample values
+ samplevalues = reduce( array_collapser, samplevalue_arrays, [] )
+ #print('samplevalues: ' + str(sorted(samplevalues)))
+ # compute all stats and print them
+ myarray = scipy.fromiter(samplevalues, float)
+ mymin = scipy.amin(myarray)
+ myavg = scipy.average(myarray)
+ mymedian = scipy.median(myarray)
+ my90th = scipy.percentile(myarray, 90)
+ my95th = scipy.percentile(myarray, 95)
+ my99th = scipy.percentile(myarray, 99)
+ mymax = scipy.amax(myarray)
+ print( '%f, %d, %f, %f, %f, %f, %f, %f, %f' % (
+ start, len(samplevalues),
+ mymin, myavg, mymedian, my90th, my95th, my99th, mymax))
+
+ # advance to next interval
+ start += ctx.interval
+ end += ctx.interval
+

def print_default(ctx, series):
ftime = get_ftime(series)
@@ -112,6 +167,13 @@ class TimeSeries():
self.last = sample
self.samples.append(sample)

+ def get_samples(self, start, end):
+ sample_list = []
+ for s in self.samples:
+ if s.start >= start and s.end <= end:
+ sample_list.append(s)
+ return sample_list
+
def get_value(self, start, end):
value = 0
for sample in self.samples:
@@ -147,6 +209,8 @@ if __name__ == '__main__':
print_averages(ctx, series)
elif ctx.full:
print_full(ctx, series)
+ elif ctx.allstats:
+ print_all_stats(ctx, series)
else:
print_default(ctx, series)