client/bin/fio_util.py - mirrors/cros/chromiumos/third_party/autotest - Git at Google

 # 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, logging, re, 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 = """
 <html>
   <head>
     <script type="text/javascript" src="https://www.google.com/jsapi"></script>
     <script type="text/javascript">
       google.load("visualization", "1", {packages:["corechart"]});
       google.setOnLoadCallback(drawChart);
       function drawChart() {
 """

     html_tail = """
         var chart_div = document.getElementById('chart_div');
         var chart = new google.visualization.ScatterChart(chart_div);
         chart.draw(data, options);
       }
     </script>
   </head>
   <body>
     <div id="chart_div" style="width: 100%; height: 100%;"></div>
   </body>
 </html>
 """

     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'] + pass_list
         else:
             col_name_list = ['time'] + pass_list

         return [{'label': name, 'type': 'number'} for name in col_name_list]

     @classmethod
     def _gen_data_row(cls, test_name, 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_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

         @return: list of data rows in google.visualization.DataTable format
         """
         all_row = []
         pass_count = len(pass_list)
         for pass_index, pass_str in enumerate(pass_list):
             log_file_name = str('%s_%s_%s.log' %
                                 (test_name, pass_str, test_type))
             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_name, test_type, pass_list, percentile):
         """
         Write google.visualization.DataTable object to output file.
         https://developers.google.com/chart/interactive/docs/reference

         @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
         """
         col = cls._gen_data_col(pass_list, percentile)
         row = cls._gen_data_row(test_name, 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_name, 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():
             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>.*)_                    # jobname
                       ((?P<runpass>p\d+)_)                # pass
                       (?P<type>bw|iops|lat|clat|slat).log # type
                    """
         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')

             # 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)

         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": "<name_prefix>_<modle>_<size>G",
      "graph": "<graph_prefix>_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<f>[^ ]*)', 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
	# 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, logging, re, 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 = """
	<html>
	<head>
	<script type="text/javascript" src="https://www.google.com/jsapi"></script>
	<script type="text/javascript">
	google.load("visualization", "1", {packages:["corechart"]});
	google.setOnLoadCallback(drawChart);
	function drawChart() {
	"""

	html_tail = """
	var chart_div = document.getElementById('chart_div');
	var chart = new google.visualization.ScatterChart(chart_div);
	chart.draw(data, options);
	}
	</script>
	</head>
	<body>
	<div id="chart_div" style="width: 100%; height: 100%;"></div>
	</body>
	</html>
	"""

	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'] + pass_list
	else:
	col_name_list = ['time'] + pass_list

	return [{'label': name, 'type': 'number'} for name in col_name_list]

	@classmethod
	def _gen_data_row(cls, test_name, 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_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

	@return: list of data rows in google.visualization.DataTable format
	"""
	all_row = []
	pass_count = len(pass_list)
	for pass_index, pass_str in enumerate(pass_list):
	log_file_name = str('%s_%s_%s.log' %
	(test_name, pass_str, test_type))
	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_name, test_type, pass_list, percentile):
	"""
	Write google.visualization.DataTable object to output file.
	https://developers.google.com/chart/interactive/docs/reference

	@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
	"""
	col = cls._gen_data_col(pass_list, percentile)
	row = cls._gen_data_row(test_name, 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_name, 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():
	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>.*)_ # jobname
	((?P<runpass>p\d+)_) # pass
	(?P<type>bw\|iops\|lat\|clat\|slat).log # type
	"""
	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')

	# 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)

	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": "<name_prefix>_<modle>_<size>G",
	"graph": "<graph_prefix>_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<f>[^ ])', 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