#!/usr/bin/python2.7

# Note: this script is python2 and python3 compatible.

#

# fio_jsonplus_clat2csv

#

# This script converts fio's json+ completion latency data to CSV format.

#

# For example:

#

# Run the following fio jobs:

# ../fio --output=fio-jsonplus.output --output-format=json+ --name=test1

#  	--ioengine=null --time_based --runtime=5s --size=1G --rw=randrw

# 	--name=test2 --ioengine=null --time_based --runtime=3s --size=1G

# 	--rw=read --name=test3 --ioengine=null --time_based --runtime=4s

# 	--size=8G --rw=write

#

# Then run:

# fio_jsonplus_clat2csv fio-jsonplus.output fio-latency.csv

#

# You will end up with the following 3 files

#

# -rw-r--r-- 1 root root  6467 Jun 27 14:57 fio-latency_job0.csv

# -rw-r--r-- 1 root root  3985 Jun 27 14:57 fio-latency_job1.csv

# -rw-r--r-- 1 root root  4490 Jun 27 14:57 fio-latency_job2.csv

#

# fio-latency_job0.csv will look something like:

#

# clat_nsec, read_count, read_cumulative, read_percentile, write_count,

# 	write_cumulative, write_percentile, trim_count, trim_cumulative,

# 	trim_percentile,

# 25, 1, 1, 1.50870705013e-07, , , , , , ,

# 26, 12, 13, 1.96131916517e-06, 947, 947, 0.000142955890032, , , ,

# 27, 843677, 843690, 0.127288105112, 838347, 839294, 0.126696959629, , , ,

# 28, 1877982, 2721672, 0.410620573454, 1870189, 2709483, 0.409014312345, , , ,

# 29, 4471, 2726143, 0.411295116376, 7718, 2717201, 0.410179395301, , , ,

# 30, 2142885, 4869028, 0.734593687087, 2138164, 4855365, 0.732949340025, , , ,

# ...

# 2544, , , , 2, 6624404, 0.999997433738, , , ,

# 2576, 3, 6628178, 0.99999788781, 4, 6624408, 0.999998037564, , , ,

# 2608, 4, 6628182, 0.999998491293, 4, 6624412, 0.999998641391, , , ,

# 2640, 3, 6628185, 0.999998943905, 2, 6624414, 0.999998943304, , , ,

# 2672, 1, 6628186, 0.999999094776, 3, 6624417, 0.999999396174, , , ,

# 2736, 1, 6628187, 0.999999245646, 1, 6624418, 0.99999954713, , , ,

# 2768, 2, 6628189, 0.999999547388, 1, 6624419, 0.999999698087, , , ,

# 2800, , , , 1, 6624420, 0.999999849043, , , ,

# 2832, 1, 6628190, 0.999999698259, , , , , , ,

# 4192, 1, 6628191, 0.999999849129, , , , , , ,

# 5792, , , , 1, 6624421, 1.0, , , ,

# 10304, 1, 6628192, 1.0, , , , , , ,

#

# The first line says that you had one read IO with 25ns clat,

# the cumulative number of read IOs at or below 25ns is 1, and

# 25ns is the 0.00001509th percentile for read latency

#

# The job had 2 write IOs complete in 2544ns,

# 6624404 write IOs completed in 2544ns or less,

# and this represents the 99.99974th percentile for write latency

#

# The last line says that one read IO had 10304ns clat,

# 6628192 read IOs had 10304ns or shorter clat, and

# 10304ns is the 100th percentile for read latency

#

from __future__ import absolute_import

from __future__ import print_function

import os

import json

import argparse

import six

from six.moves import range

def parse_args():

    parser = argparse.ArgumentParser()

    parser.add_argument('source',

                        help='fio json+ output file containing completion '

                             'latency data')

    parser.add_argument('dest',

                        help='destination file stub for latency data in CSV '

                             'format. job number will be appended to filename')

    args = parser.parse_args()

    return args

def percentile(idx, run_total):

    total = run_total[len(run_total)-1]

    if total == 0:

        return 0

    return float(run_total[idx]) / total

def more_lines(indices, bins):

    for key, value in six.iteritems(indices):

        if value < len(bins[key]):

            return True

    return False

def main():

    args = parse_args()

    with open(args.source, 'r') as source:

        jsondata = json.loads(source.read())

    for jobnum in range(0, len(jsondata['jobs'])):

        bins = {}

        run_total = {}

        ddir_set = set(['read', 'write', 'trim'])

        prev_ddir = None

        for ddir in ddir_set:

            if 'bins' in jsondata['jobs'][jobnum][ddir]['clat_ns']:

                bins_loc = 'clat_ns'

            elif 'bins' in jsondata['jobs'][jobnum][ddir]['lat_ns']:

                bins_loc = 'lat_ns'

            else:

                raise RuntimeError("Latency bins not found. "

                                   "Are you sure you are using json+ output?")

            bins[ddir] = [[int(key), value] for key, value in

                          six.iteritems(jsondata['jobs'][jobnum][ddir][bins_loc]

                          ['bins'])]

            bins[ddir] = sorted(bins[ddir], key=lambda bin: bin[0])

            run_total[ddir] = [0 for x in range(0, len(bins[ddir]))]

            if len(bins[ddir]) > 0:

                run_total[ddir][0] = bins[ddir][0][1]

                for x in range(1, len(bins[ddir])):

                    run_total[ddir][x] = run_total[ddir][x-1] + \

                        bins[ddir][x][1]

        stub, ext = os.path.splitext(args.dest)

        outfile = stub + '_job' + str(jobnum) + ext

        with open(outfile, 'w') as output:

            output.write("{0}ec, ".format(bins_loc))

            ddir_list = list(ddir_set)

            for ddir in ddir_list:

                output.write("{0}_count, {0}_cumulative, {0}_percentile, ".

                             format(ddir))

            output.write("\n")

#

# Have a counter for each ddir

# In each round, pick the shortest remaining duration

# and output a line with any values for that duration

#

            indices = {x: 0 for x in ddir_list}

            while more_lines(indices, bins):

                min_lat = 17112760320

                for ddir in ddir_list:

                    if indices[ddir] < len(bins[ddir]):

                        min_lat = min(bins[ddir][indices[ddir]][0], min_lat)

                output.write("{0}, ".format(min_lat))

                for ddir in ddir_list:

                    if indices[ddir] < len(bins[ddir]) and \

                       min_lat == bins[ddir][indices[ddir]][0]:

                        count = bins[ddir][indices[ddir]][1]

                        cumulative = run_total[ddir][indices[ddir]]

                        ptile = percentile(indices[ddir], run_total[ddir])

                        output.write("{0}, {1}, {2}, ".format(count,

                                     cumulative, ptile))

                        indices[ddir] += 1

                    else:

                        output.write(", , , ")

                output.write("\n")

            print("{0} generated".format(outfile))

if __name__ == '__main__':

    main()