# Copyright (c) 2013 The Chromium OS Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
"""Library to run fio scripts.
fio_runner launch fio and collect results.
The output dictionary can be add to autotest keyval:
results = {}
results.update(fio_util.fio_runner(job_file, env_vars))
self.write_perf_keyval(results)
Decoding class can be invoked independently.
"""
import json
import logging
import re
import common
from autotest_lib.client.bin import utils
class fio_graph_generator():
"""
Generate graph from fio log that created when specified these options.
- write_bw_log
- write_iops_log
- write_lat_log
The following limitations apply
- Log file name must be in format jobname_testpass
- Graph is generate using Google graph api -> Internet require to view.
"""
html_head = """
"""
h_title = { True: 'Percentile', False: 'Time (s)' }
v_title = { 'bw' : 'Bandwidth (KB/s)',
'iops': 'IOPs',
'lat' : 'Total latency (us)',
'clat': 'Completion latency (us)',
'slat': 'Submission latency (us)' }
graph_title = { 'bw' : 'bandwidth',
'iops': 'IOPs',
'lat' : 'total latency',
'clat': 'completion latency',
'slat': 'submission latency' }
test_name = ''
test_type = ''
pass_list = ''
@classmethod
def _parse_log_file(cls, file_name, pass_index, pass_count, percentile):
"""
Generate row for google.visualization.DataTable from one log file.
Log file is the one that generated using write_{bw,lat,iops}_log
option in the FIO job file.
The fio log file format is timestamp, value, direction, blocksize
The output format for each row is { c: list of { v: value} }
@param file_name: log file name to read data from
@param pass_index: index of current run pass
@param pass_count: number of all test run passes
@param percentile: flag to use percentile as key instead of timestamp
@return: list of data rows in google.visualization.DataTable format
"""
# Read data from log
with open(file_name, 'r') as f:
data = []
for line in f.readlines():
if not line:
break
t, v, _, _ = [int(x) for x in line.split(', ')]
data.append([t / 1000.0, v])
# Sort & calculate percentile
if percentile:
data.sort(key=lambda x: x[1])
l = len(data)
for i in range(l):
data[i][0] = 100 * (i + 0.5) / l
# Generate the data row
all_row = []
row = [None] * (pass_count + 1)
for d in data:
row[0] = {'v' : '%.3f' % d[0]}
row[pass_index + 1] = {'v': d[1]}
all_row.append({'c': row[:]})
return all_row
@classmethod
def _gen_data_col(cls, pass_list, percentile):
"""
Generate col for google.visualization.DataTable
The output format is list of dict of label and type. In this case,
type is always number.
@param pass_list: list of test run passes
@param percentile: flag to use percentile as key instead of timestamp
@return: list of column in google.visualization.DataTable format
"""
if percentile:
col_name_list = ['percentile'] + [p[0] for p in pass_list]
else:
col_name_list = ['time'] + [p[0] for p in pass_list]
return [{'label': name, 'type': 'number'} for name in col_name_list]
@classmethod
def _gen_data_row(cls, test_type, pass_list, percentile):
"""
Generate row for google.visualization.DataTable by generate all log
file name and call _parse_log_file for each file
@param test_type: type of value collected for current test. i.e. IOPs
@param pass_list: list of run passes for current test
@param percentile: flag to use percentile as key instead of timestamp
@return: list of data rows in google.visualization.DataTable format
"""
all_row = []
pass_count = len(pass_list)
for pass_index, log_file_name in enumerate([p[1] for p in pass_list]):
all_row.extend(cls._parse_log_file(log_file_name, pass_index,
pass_count, percentile))
return all_row
@classmethod
def _write_data(cls, f, test_type, pass_list, percentile):
"""
Write google.visualization.DataTable object to output file.
https://developers.google.com/chart/interactive/docs/reference
@param f: html file to update
@param test_type: type of value collected for current test. i.e. IOPs
@param pass_list: list of run passes for current test
@param percentile: flag to use percentile as key instead of timestamp
"""
col = cls._gen_data_col(pass_list, percentile)
row = cls._gen_data_row(test_type, pass_list, percentile)
data_dict = {'cols' : col, 'rows' : row}
f.write('var data = new google.visualization.DataTable(')
json.dump(data_dict, f)
f.write(');\n')
@classmethod
def _write_option(cls, f, test_name, test_type, percentile):
"""
Write option to render scatter graph to output file.
https://google-developers.appspot.com/chart/interactive/docs/gallery/scatterchart
@param test_name: name of current workload. i.e. randwrite
@param test_type: type of value collected for current test. i.e. IOPs
@param percentile: flag to use percentile as key instead of timestamp
"""
option = {'pointSize': 1}
if percentile:
option['title'] = ('Percentile graph of %s for %s workload' %
(cls.graph_title[test_type], test_name))
else:
option['title'] = ('Graph of %s for %s workload over time' %
(cls.graph_title[test_type], test_name))
option['hAxis'] = {'title': cls.h_title[percentile]}
option['vAxis'] = {'title': cls.v_title[test_type]}
f.write('var options = ')
json.dump(option, f)
f.write(';\n')
@classmethod
def _write_graph(cls, test_name, test_type, pass_list, percentile=False):
"""
Generate graph for test name / test type
@param test_name: name of current workload. i.e. randwrite
@param test_type: type of value collected for current test. i.e. IOPs
@param pass_list: list of run passes for current test
@param percentile: flag to use percentile as key instead of timestamp
"""
logging.info('fio_graph_generator._write_graph %s %s %s',
test_name, test_type, str(pass_list))
if percentile:
out_file_name = '%s_%s_percentile.html' % (test_name, test_type)
else:
out_file_name = '%s_%s.html' % (test_name, test_type)
with open(out_file_name, 'w') as f:
f.write(cls.html_head)
cls._write_data(f, test_type, pass_list, percentile)
cls._write_option(f, test_name, test_type, percentile)
f.write(cls.html_tail)
def __init__(self, test_name, test_type, pass_list):
"""
@param test_name: name of current workload. i.e. randwrite
@param test_type: type of value collected for current test. i.e. IOPs
@param pass_list: list of run passes for current test
"""
self.test_name = test_name
self.test_type = test_type
self.pass_list = pass_list
def run(self):
"""
Run the graph generator.
"""
self._write_graph(self.test_name, self.test_type, self.pass_list, False)
self._write_graph(self.test_name, self.test_type, self.pass_list, True)
def fio_parse_dict(d, prefix):
"""
Parse fio json dict
Recursively flaten json dict to generate autotest perf dict
@param d: input dict
@param prefix: name prefix of the key
"""
# No need to parse something that didn't run such as read stat in write job.
if 'io_bytes' in d and d['io_bytes'] == 0:
return {}
results = {}
for k, v in d.items():
# remove >, >=, <, <=
for c in '>=<':
k = k.replace(c, '')
key = prefix + '_' + k
if type(v) is dict:
results.update(fio_parse_dict(v, key))
else:
results[key] = v
return results
def fio_parser(lines, prefix=None):
"""
Parse the json fio output
This collects all metrics given by fio and labels them according to unit
of measurement and test case name.
@param lines: text output of json fio output.
@param prefix: prefix for result keys.
"""
results = {}
fio_dict = json.loads(lines)
if prefix:
prefix = prefix + '_'
else:
prefix = ''
results[prefix + 'fio_version'] = fio_dict['fio version']
if 'disk_util' in fio_dict:
results.update(fio_parse_dict(fio_dict['disk_util'][0],
prefix + 'disk'))
for job in fio_dict['jobs']:
job_prefix = '_' + prefix + job['jobname']
job.pop('jobname')
for k, v in job.iteritems():
# Igonre "job options", its alphanumerc keys confuses tko.
# Besides, these keys are redundant.
if k == 'job options':
continue
results.update(fio_parse_dict({k:v}, job_prefix))
return results
def fio_generate_graph():
"""
Scan for fio log file in output directory and send data to generate each
graph to fio_graph_generator class.
"""
log_types = ['bw', 'iops', 'lat', 'clat', 'slat']
# move fio log to result dir
for log_type in log_types:
logging.info('log_type %s', log_type)
logs = utils.system_output('ls *_%s.*log' % log_type, ignore_status=True)
if not logs:
continue
pattern = r"""(?P.*)_ # jobname
((?Pp\d+)_|) # pass
(?Pbw|iops|lat|clat|slat) # type
(.(?P\d+)|) # thread id for newer fio.
.log
"""
matcher = re.compile(pattern, re.X)
pass_list = []
current_job = ''
for log in logs.split():
match = matcher.match(log)
if not match:
logging.warn('Unknown log file %s', log)
continue
jobname = match.group('jobname')
runpass = match.group('runpass') or '1'
if match.group('thread'):
runpass += '_' + match.group('thread')
# All files for particular job name are group together for create
# graph that can compare performance between result from each pass.
if jobname != current_job:
if pass_list:
fio_graph_generator(current_job, log_type, pass_list).run()
current_job = jobname
pass_list = []
pass_list.append((runpass, log))
if pass_list:
fio_graph_generator(current_job, log_type, pass_list).run()
cmd = 'mv *_%s.*log results' % log_type
utils.run(cmd, ignore_status=True)
utils.run('mv *.html results', ignore_status=True)
def fio_runner(test, job, env_vars,
name_prefix=None,
graph_prefix=None):
"""
Runs fio.
Build a result keyval and performence json.
The JSON would look like:
{"description": "__G",
"graph": "_1m_write_wr_lat_99.00_percent_usec",
"higher_is_better": false, "units": "us", "value": "xxxx"}
{...
@param test: test to upload perf value
@param job: fio config file to use
@param env_vars: environment variable fio will substituete in the fio
config file.
@param name_prefix: prefix of the descriptions to use in chrome perfi
dashboard.
@param graph_prefix: prefix of the graph name in chrome perf dashboard
and result keyvals.
@return fio results.
"""
# running fio with ionice -c 3 so it doesn't lock out other
# processes from the disk while it is running.
# If you want to run the fio test for performance purposes,
# take out the ionice and disable hung process detection:
# "echo 0 > /proc/sys/kernel/hung_task_timeout_secs"
# -c 3 = Idle
# Tried lowest priority for "best effort" but still failed
ionice = 'ionice -c 3'
options = ['--output-format=json']
fio_cmd_line = ' '.join([env_vars, ionice, 'fio',
' '.join(options),
'"' + job + '"'])
fio = utils.run(fio_cmd_line)
logging.debug(fio.stdout)
fio_generate_graph()
filename = re.match('.*FILENAME=(?P[^ ]*)', env_vars).group('f')
diskname = utils.get_disk_from_filename(filename)
if diskname:
model = utils.get_disk_model(diskname)
size = utils.get_disk_size_gb(diskname)
perfdb_name = '%s_%dG' % (model, size)
else:
perfdb_name = filename.replace('/', '_')
if name_prefix:
perfdb_name = name_prefix + '_' + perfdb_name
result = fio_parser(fio.stdout, prefix=name_prefix)
if not graph_prefix:
graph_prefix = ''
for k, v in result.iteritems():
# Remove the prefix for value, and replace it the graph prefix.
if name_prefix:
k = k.replace('_' + name_prefix, graph_prefix)
# Make graph name to be same as the old code.
if k.endswith('bw'):
test.output_perf_value(description=perfdb_name, graph=k, value=v,
units='KB_per_sec', higher_is_better=True)
elif k.rstrip('0').endswith('clat_percentile_99.'):
test.output_perf_value(description=perfdb_name, graph=k, value=v,
units='us', higher_is_better=False)
return result