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Add format parsing for format 1 storage benchmarks

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This commit is contained in:
Joshua Boniface
2021-10-02 04:46:44 -04:00
parent f70f052df1
commit 4a9805578e
2 changed files with 239 additions and 2 deletions
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239
client-cli/pvc/cli_lib/ceph.py
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@@ -1619,6 +1619,7 @@ def format_info_benchmark(config, oformat, benchmark_information):
# It is extensable in the future should newer formats be required.
benchmark_matrix = {
0: format_info_benchmark_legacy,
1: format_info_benchmark_json,
}
benchmark_version = benchmark_information[0]['test_format']
@@ -1628,7 +1629,7 @@ def format_info_benchmark(config, oformat, benchmark_information):
elif oformat == 'json':
return dumps(benchmark_information)
else:
return benchmark_matrix.get(benchmark_version, lambda: 'Invalid format function')(config, benchmark_information[0])
return benchmark_matrix[benchmark_version](config, benchmark_information[0])
def format_info_benchmark_legacy(config, benchmark_information):
@@ -1918,3 +1919,239 @@ def format_info_benchmark_legacy(config, benchmark_information):
ainformation.append('')
return '\n'.join(ainformation)
def format_info_benchmark_json(config, benchmark_information):
if benchmark_information['benchmark_result'] == "Running":
return "Benchmark test is still running."
benchmark_details = benchmark_information['benchmark_result']
# Format a nice output; do this line-by-line then concat the elements at the end
ainformation = []
ainformation.append('{}Storage Benchmark details:{}'.format(ansiprint.bold(), ansiprint.end()))
nice_test_name_map = {
"seq_read": "Sequential Read (4M blocks)",
"seq_write": "Sequential Write (4M blocks)",
"rand_read_4M": "Random Read (4M blocks)",
"rand_write_4M": "Random Write (4M blocks)",
"rand_read_4K": "Random Read (4K blocks)",
"rand_write_4K": "Random Write (4K blocks)",
"rand_read_4K_lowdepth": "Random Read (4K blocks, single-queue)",
"rand_write_4K_lowdepth": "Random Write (4K blocks, single-queue)",
}
for test in benchmark_details:
ainformation.append('')
io_class = None
for _io_class in ['read', 'write']:
if benchmark_details[test]['jobs'][0][_io_class]['io_bytes'] > 0:
io_class = _io_class
if io_class is None:
continue
job_details = benchmark_details[test]['jobs'][0]
overall_label_length = 0
overall_column_length = 0
bandwidth_label_length = 0
bandwidth_column_length = 9
iops_column_length = 4
latency_column_length = 12
latency_percentile_label_length = 6
latency_percentile_column_length = 12
# Column layout:
# General Bandwidth IOPS Latency CLAT Percentile
# --------- ---------- -------- -------- ---------------
# Size Min Min Min 1.00
# BW Max Max Max 5.00
# IOPS Mean Mean Mean 10.00
# Runtime StdDev StdDev StdDev 50.00
# UsrCPU Samples Samples 90.00
# SysCPU 99.50
# CtxSw 99.90
# MajFault 99.95
# MinFault 99.99
overall_label = [ 'Overall BW:',
'Overall IOPS:',
'Total I/O:',
'Runtime (s):',
'User CPU %:',
'System CPU %:',
'Ctx Switches:',
'Major Faults:',
'Minor Faults:' ]
overall_data = [ format_bytes_tohuman(int(job_details[io_class]['bw_bytes'])),
format_ops_tohuman(int(job_details[io_class]['iops'])),
format_bytes_tohuman(int(job_details[io_class]['io_bytes'])),
job_details['job_runtime'] / 1000,
job_details['usr_cpu'],
job_details['sys_cpu'],
job_details['ctx'],
job_details['majf'],
job_details['minf'] ]
bandwidth_label = [ 'Min:',
'Max:',
'Mean:',
'StdDev:',
'Samples:',
'',
'',
'',
'' ]
bandwidth_data = [ format_bytes_tohuman(int(job_details[io_class]['bw_min']) * 1024),
format_bytes_tohuman(int(job_details[io_class]['bw_max']) * 1024),
format_bytes_tohuman(int(job_details[io_class]['bw_mean']) * 1024),
format_bytes_tohuman(int(job_details[io_class]['bw_dev']) * 1024),
job_details[io_class]['bw_samples'],
'',
'',
'',
'' ]
iops_data = [ format_ops_tohuman(int(job_details[io_class]['iops_min'])),
format_ops_tohuman(int(job_details[io_class]['iops_max'])),
format_ops_tohuman(int(job_details[io_class]['iops_mean'])),
format_ops_tohuman(int(job_details[io_class]['iops_stddev'])),
job_details[io_class]['iops_samples'],
'',
'',
'',
'' ]
lat_data = [ job_details[io_class]['lat_ns']['min'],
job_details[io_class]['lat_ns']['max'],
job_details[io_class]['lat_ns']['mean'],
job_details[io_class]['lat_ns']['stddev'],
'',
'',
'',
'',
'' ]
lat_percentile_label = [ '99.99%:',
'99.95%:',
'99.9%:',
'99.5%:',
'99%:',
'90%:',
'50%:',
'10%:',
'1%:' ]
lat_percentile_data = [ job_details[io_class]['clat_ns']['percentile']['99.990000'],
job_details[io_class]['clat_ns']['percentile']['99.950000'],
job_details[io_class]['clat_ns']['percentile']['99.900000'],
job_details[io_class]['clat_ns']['percentile']['99.500000'],
job_details[io_class]['clat_ns']['percentile']['99.000000'],
job_details[io_class]['clat_ns']['percentile']['90.000000'],
job_details[io_class]['clat_ns']['percentile']['50.000000'],
job_details[io_class]['clat_ns']['percentile']['10.000000'],
job_details[io_class]['clat_ns']['percentile']['1.000000'] ]
for item in overall_label:
_item_length = len(str(item))
if _item_length > overall_label_length:
overall_label_length = _item_length
for item in overall_data:
_item_length = len(str(item))
if _item_length > overall_column_length:
overall_column_length = _item_length
test_name_length = len(nice_test_name_map[test])
if test_name_length > overall_label_length + overall_column_length:
_diff = test_name_length - (overall_label_length + overall_column_length)
overall_column_length += _diff
for item in bandwidth_label:
_item_length = len(str(item))
if _item_length > bandwidth_label_length:
bandwidth_label_length = _item_length
for item in bandwidth_data:
_item_length = len(str(item))
if _item_length > bandwidth_column_length:
bandwidth_column_length = _item_length
for item in iops_data:
_item_length = len(str(item))
if _item_length > iops_column_length:
iops_column_length = _item_length
for item in lat_data:
_item_length = len(str(item))
if _item_length > latency_column_length:
latency_column_length = _item_length
for item in lat_percentile_label:
_item_length = len(str(item))
if _item_length > latency_percentile_label_length:
latency_percentile_label_length = _item_length
for item in lat_percentile_data:
_item_length = len(str(item))
if _item_length > latency_percentile_column_length:
latency_percentile_column_length = _item_length
# Top row (Headers)
ainformation.append('{bold}\
{overall_label: <{overall_label_length}} \
{bandwidth_label: <{bandwidth_label_length}} \
{bandwidth: <{bandwidth_length}} \
{iops: <{iops_length}} \
{latency: <{latency_length}} \
{latency_percentile_label: <{latency_percentile_label_length}} \
{latency_percentile: <{latency_percentile_length}} \
{end_bold}'.format(
bold=ansiprint.bold(),
end_bold=ansiprint.end(),
overall_label=nice_test_name_map[test],
overall_label_length=overall_label_length,
bandwidth_label='',
bandwidth_label_length=bandwidth_label_length,
bandwidth='Bandwidth',
bandwidth_length=bandwidth_column_length,
iops='IOPS',
iops_length=iops_column_length,
latency='Latency (μs)',
latency_length=latency_column_length,
latency_percentile_label='CLAT Percentiles (μs)',
latency_percentile_label_length=latency_percentile_label_length,
latency_percentile='',
latency_percentile_length=latency_percentile_column_length,
))
for idx, _ in enumerate(overall_data):
# Top row (Headers)
ainformation.append('{bold}\
{overall_label: >{overall_label_length}} \
{overall: <{overall_length}} \
{bandwidth_label: >{bandwidth_label_length}} \
{bandwidth: <{bandwidth_length}} \
{iops: <{iops_length}} \
{latency: <{latency_length}} \
{latency_percentile_label: >{latency_percentile_label_length}} \
{latency_percentile: <{latency_percentile_length}} \
{end_bold}'.format(
bold='',
end_bold='',
overall_label=overall_label[idx],
overall_label_length=overall_label_length,
overall=overall_data[idx],
overall_length=overall_column_length,
bandwidth_label=bandwidth_label[idx],
bandwidth_label_length=bandwidth_label_length,
bandwidth=bandwidth_data[idx],
bandwidth_length=bandwidth_column_length,
iops=iops_data[idx],
iops_length=iops_column_length,
latency=lat_data[idx],
latency_length=latency_column_length,
latency_percentile_label=lat_percentile_label[idx],
latency_percentile_label_length=latency_percentile_label_length,
latency_percentile=lat_percentile_data[idx],
latency_percentile_length=latency_percentile_column_length,
))
return '\n'.join(ainformation)