2 # Note: this script is python2 and python3 compatible.
7 This script converts fio's json+ latency data to CSV format.
11 Run the following fio jobs:
12 $ fio --output=fio-jsonplus.output --output-format=json+ --ioengine=null \
13 --time_based --runtime=3s --size=1G --slat_percentiles=1 \
14 --clat_percentiles=1 --lat_percentiles=1 \
15 --name=test1 --rw=randrw \
16 --name=test2 --rw=read \
17 --name=test3 --rw=write
20 $ fio_jsonplus_clat2csv fio-jsonplus.output fio-jsonplus.csv
22 You will end up with the following 3 files:
24 -rw-r--r-- 1 root root 77547 Mar 24 15:17 fio-jsonplus_job0.csv
25 -rw-r--r-- 1 root root 65413 Mar 24 15:17 fio-jsonplus_job1.csv
26 -rw-r--r-- 1 root root 63291 Mar 24 15:17 fio-jsonplus_job2.csv
28 fio-jsonplus_job0.csv will look something like:
30 nsec, read_slat_ns_count, read_slat_ns_cumulative, read_slat_ns_percentile, read_clat_ns_count, read_clat_ns_cumulative, read_clat_ns_percentile, read_lat_ns_count, read_lat_ns_cumulative, read_lat_ns_percentile, write_slat_ns_count, write_slat_ns_cumulative, write_slat_ns_percentile, write_clat_ns_count, write_clat_ns_cumulative, write_clat_ns_percentile, write_lat_ns_count, write_lat_ns_cumulative, write_lat_ns_percentile, trim_slat_ns_count, trim_slat_ns_cumulative, trim_slat_ns_percentile, trim_clat_ns_count, trim_clat_ns_cumulative, trim_clat_ns_percentile, trim_lat_ns_count, trim_lat_ns_cumulative, trim_lat_ns_percentile,
31 12, , , , 3, 3, 6.11006798673e-07, , , , , , , 2, 2, 4.07580840603e-07, , , , , , , , , , , , ,
32 13, , , , 1364, 1367, 0.000278415431262, , , , , , , 1776, 1778, 0.000362339367296, , , , , , , , , , , , ,
33 14, , , , 181872, 183239, 0.037320091594, , , , , , , 207436, 209214, 0.0426358089929, , , , , , , , , , , , ,
34 15, , , , 1574811, 1758050, 0.358060167469, , , , , , , 1661435, 1870649, 0.381220345946, , , , , , , , , , , , ,
35 16, , , , 2198478, 3956528, 0.805821835713, , , , , , , 2154571, 4025220, 0.820301275606, , , , , , , , , , , , ,
36 17, , , , 724335, 4680863, 0.953346372218, , , , , , , 645351, 4670571, 0.951817627138, , , , , , , , , , , , ,
37 18, , , , 71837, 4752700, 0.96797733735, , , , , , , 61084, 4731655, 0.964265961171, , , , , , , , , , , , ,
38 19, , , , 15915, 4768615, 0.971218728417, , , , , , , 18419, 4750074, 0.968019576923, , , , , , , , , , , , ,
39 20, , , , 12651, 4781266, 0.973795344087, , , , , , , 14176, 4764250, 0.970908509921, , , , , , , , , , , , ,
41 168960, , , , , , , , , , , , , 1, 4906999, 0.999999388629, 1, 4906997, 0.999998981048, , , , , , , , , ,
42 177152, , , , , , , , , , , , , 1, 4907000, 0.999999592419, 1, 4906998, 0.999999184838, , , , , , , , , ,
43 183296, , , , , , , , , , , , , 1, 4907001, 0.99999979621, 1, 4906999, 0.999999388629, , , , , , , , , ,
44 189440, , , , , , , 1, 4909925, 0.999999185324, , , , , , , , , , , , , , , , , , ,
45 214016, , , , 1, 4909928, 0.999999796331, 2, 4909927, 0.999999592662, , , , , , , , , , , , , , , , , , ,
46 246784, , , , , , , , , , , , , , , , 1, 4907000, 0.999999592419, , , , , , , , , ,
47 272384, , , , 1, 4909929, 1.0, 1, 4909928, 0.999999796331, , , , , , , , , , , , , , , , , , ,
48 329728, , , , , , , , , , , , , 1, 4907002, 1.0, 1, 4907001, 0.99999979621, , , , , , , , , ,
49 1003520, , , , , , , , , , , , , , , , 1, 4907002, 1.0, , , , , , , , , ,
50 1089536, , , , , , , 1, 4909929, 1.0, , , , , , , , , , , , , , , , , , ,
52 The first line says that there were three read IOs with 12ns clat,
53 the cumulative number of read IOs at or below 12ns was two, and
54 12ns was the 0.0000611th percentile for read latency. There were
55 two write IOs with 12ns clat, the cumulative number of write IOs
56 at or below 12ns was two, and 12ns was the 0.0000408th percentile
59 The job had one write IO complete at 168960ns and 4906999 write IOs
60 completed at or below this duration. Also this duration was the
61 99.99994th percentile for write latency. There was one write IO
62 with a total latency of 168960ns, this duration had a cumulative
63 frequency of 4906997 write IOs and was the 99.9998981048th percentile
64 for write total latency.
66 The last line says that one read IO had 1089536ns total latency, this
67 duration had a cumulative frequency of 4909929 and represented the 100th
68 percentile for read total latency.
70 Running the following:
72 $ fio_jsonplus_clat2csv fio-jsonplus.output fio-jsonplus.csv --validate
73 fio-jsonplus_job0.csv validated
74 fio-jsonplus_job1.csv validated
75 fio-jsonplus_job2.csv validated
77 will check the CSV data against the json+ output to confirm that the CSV
81 from __future__ import absolute_import
82 from __future__ import print_function
89 DDIR_LIST = ['read', 'write', 'trim']
90 LAT_LIST = ['slat_ns', 'clat_ns', 'lat_ns']
93 """Parse command-line arguments."""
95 parser = argparse.ArgumentParser()
96 parser.add_argument('source',
97 help='fio json+ output file containing completion '
99 parser.add_argument('dest',
100 help='destination file stub for latency data in CSV '
101 'format. job number will be appended to filename')
102 parser.add_argument('--debug', '-d', action='store_true',
103 help='enable debug prints')
104 parser.add_argument('--validate', action='store_true',
105 help='validate CSV against JSON output')
106 args = parser.parse_args()
111 def percentile(idx, run_total):
112 """Return a percentile for a specified index based on a running total.
115 idx index for which to generate percentile.
116 run_total list of cumulative sums.
119 Percentile represented by the specified index.
122 total = run_total[len(run_total)-1]
126 return float(run_total[idx]) / total
129 def more_bins(indices, bins):
130 """Determine whether we have more bins to process.
133 indices a dict containing the last index processed in each bin.
134 bins a dict contaiing a set of bins to process.
137 True if the indices do not yet point to the end of each bin in bins.
138 False if the indices point beyond their respective bins.
141 for key, value in six.iteritems(indices):
142 if value < len(bins[key]):
148 def debug_print(debug, *args):
149 """Print debug messages.
152 debug emit messages if True.
153 *args arguments for print().
160 def get_csvfile(dest, jobnum):
161 """Generate CSV filename from command-line arguments and job numbers.
164 dest file specification for CSV filename.
168 A string that is a new filename that incorporates the job number.
171 stub, ext = os.path.splitext(dest)
172 return stub + '_job' + str(jobnum) + ext
175 def validate(args, jsondata, col_labels):
176 """Validate CSV data against json+ output.
178 This function checks the CSV data to make sure that it was correctly
179 generated from the original json+ output. json+ 'bins' objects are
180 constructed from the CSV data and then compared to the corresponding
181 objects in the json+ data. An AssertionError will appear if a mismatch
184 Percentiles and cumulative counts are not checked.
187 args command-line arguments for this script.
188 jsondata json+ output to compare against.
189 col_labels column labels for CSV data.
192 0 if no mismatches found.
195 colnames = [c.strip() for c in col_labels.split(',')]
197 for jobnum in range(len(jsondata['jobs'])):
198 job_data = jsondata['jobs'][jobnum]
199 csvfile = get_csvfile(args.dest, jobnum)
201 with open(csvfile, 'r') as csvsource:
202 csvlines = csvsource.read().split('\n')
204 assert csvlines[0] == col_labels
205 debug_print(args.debug, 'col_labels match for', csvfile)
207 # create 'bins' objects from the CSV data
209 for ddir in DDIR_LIST:
212 counts[ddir][lat] = {}
215 for line in csvlines:
216 if line.strip() == "":
218 values = line.split(',')
222 val = values[colnames.index(col)]
223 if val.strip() != "":
225 ddir, lat, _, _ = col.split('_')
227 counts[ddir][lat][nsec] = count
229 assert count == job_data[ddir][lat]['bins'][nsec]
231 print("mismatch:", csvfile, ddir, lat, nsec, "ns")
234 # compare 'bins' objects created from the CSV data
235 # with corresponding 'bins' objects in the json+ output
236 for ddir in DDIR_LIST:
238 if lat in job_data[ddir] and 'bins' in job_data[ddir][lat]:
239 assert job_data[ddir][lat]['bins'] == counts[ddir][lat]
240 debug_print(args.debug, csvfile, ddir, lat, "bins match")
242 assert counts[ddir][lat] == {}
243 debug_print(args.debug, csvfile, ddir, lat, "bins empty")
245 print(csvfile, "validated")
251 """Starting point for this script.
253 In standard mode, this script will generate CSV data from fio json+ output.
254 In validation mode it will check to make sure that counts in CSV files
255 match the counts in the json+ data.
260 with open(args.source, 'r') as source:
261 jsondata = json.loads(source.read())
263 ddir_lat_list = list(ddir + '_' + lat for ddir, lat in itertools.product(DDIR_LIST, LAT_LIST))
264 debug_print(args.debug, 'ddir_lat_list: ', ddir_lat_list)
265 col_labels = 'nsec, '
266 for ddir_lat in ddir_lat_list:
267 col_labels += "{0}_count, {0}_cumulative, {0}_percentile, ".format(ddir_lat)
268 debug_print(args.debug, 'col_labels: ', col_labels)
271 return validate(args, jsondata, col_labels)
273 for jobnum in range(0, len(jsondata['jobs'])):
277 for ddir in DDIR_LIST:
278 ddir_data = jsondata['jobs'][jobnum][ddir]
280 ddir_lat = ddir + '_' + lat
281 if lat not in ddir_data or 'bins' not in ddir_data[lat]:
283 debug_print(args.debug, 'job', jobnum, ddir_lat, 'not found')
286 debug_print(args.debug, 'job', jobnum, ddir_lat, 'processing')
287 bins[ddir_lat] = [[int(key), value] for key, value in
288 six.iteritems(ddir_data[lat]['bins'])]
289 bins[ddir_lat] = sorted(bins[ddir_lat], key=lambda bin: bin[0])
291 run_total[ddir_lat] = [0 for x in range(0, len(bins[ddir_lat]))]
292 run_total[ddir_lat][0] = bins[ddir_lat][0][1]
293 for index in range(1, len(bins[ddir_lat])):
294 run_total[ddir_lat][index] = run_total[ddir_lat][index-1] + \
295 bins[ddir_lat][index][1]
297 csvfile = get_csvfile(args.dest, jobnum)
298 with open(csvfile, 'w') as output:
299 output.write(col_labels + "\n")
302 # Have a counter for each ddir_lat pairing
303 # In each round, pick the shortest remaining duration
304 # and output a line with any values for that duration
306 indices = {x: 0 for x in ddir_lat_list}
307 while more_bins(indices, bins):
308 debug_print(args.debug, 'indices: ', indices)
309 min_lat = 17112760320
310 for ddir_lat in ddir_lat_list:
311 if indices[ddir_lat] < len(bins[ddir_lat]):
312 min_lat = min(bins[ddir_lat][indices[ddir_lat]][0], min_lat)
314 output.write("{0}, ".format(min_lat))
316 for ddir_lat in ddir_lat_list:
317 if indices[ddir_lat] < len(bins[ddir_lat]) and \
318 min_lat == bins[ddir_lat][indices[ddir_lat]][0]:
319 count = bins[ddir_lat][indices[ddir_lat]][1]
320 cumulative = run_total[ddir_lat][indices[ddir_lat]]
321 ptile = percentile(indices[ddir_lat], run_total[ddir_lat])
322 output.write("{0}, {1}, {2}, ".format(count, cumulative, ptile))
323 indices[ddir_lat] += 1
325 output.write(", , , ")
328 print("{0} generated".format(csvfile))
331 if __name__ == '__main__':