Revamp test result display
Instead of showing CLAT percentiles, which are very hard to interpret and understand, instead use the main latency buckets.
This commit is contained in:
parent
e9b69c4124
commit
8edce74b85
|
|
@ -1939,14 +1939,14 @@ def format_info_benchmark_json(config, benchmark_information):
|
|||
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)",
|
||||
"seq_read": "Sequential Read (4M blocks, queue depth 64)",
|
||||
"seq_write": "Sequential Write (4M blocks, queue depth 64)",
|
||||
"rand_read_4M": "Random Read (4M blocks, queue depth 64)",
|
||||
"rand_write_4M": "Random Write (4M blocks queue depth 64)",
|
||||
"rand_read_4K": "Random Read (4K blocks, queue depth 64)",
|
||||
"rand_write_4K": "Random Write (4K blocks, queue depth 64)",
|
||||
"rand_read_4K_lowdepth": "Random Read (4K blocks, queue depth 1)",
|
||||
"rand_write_4K_lowdepth": "Random Write (4K blocks, queue depth 1)",
|
||||
}
|
||||
|
||||
for test in benchmark_details:
|
||||
|
|
@ -1961,29 +1961,37 @@ def format_info_benchmark_json(config, benchmark_information):
|
|||
|
||||
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
|
||||
# Calculate the unified latency categories (in us)
|
||||
latency_tree = list()
|
||||
for field in job_details['latency_ns']:
|
||||
bucket = str(int(field) / 1000)
|
||||
latency_tree.append((bucket, job_details['latency_ns'][field]))
|
||||
for field in job_details['latency_us']:
|
||||
bucket = field
|
||||
latency_tree.append((bucket, job_details['latency_us'][field]))
|
||||
for field in job_details['latency_ms']:
|
||||
# That one annoying one
|
||||
if field == '>=2000':
|
||||
bucket = '>=2000000'
|
||||
else:
|
||||
bucket = str(int(field) * 1000)
|
||||
latency_tree.append((bucket, job_details['latency_ms'][field]))
|
||||
|
||||
# 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
|
||||
# Find the minimum entry without a zero
|
||||
useful_latency_tree = list()
|
||||
for element in latency_tree:
|
||||
if element[1] != 0:
|
||||
useful_latency_tree.append(element)
|
||||
|
||||
overall_label = [ 'Overall BW:',
|
||||
max_rows = 9
|
||||
if len(useful_latency_tree) > 9:
|
||||
max_rows = len(useful_latency_tree)
|
||||
elif len(useful_latency_tree) < 9:
|
||||
while len(useful_latency_tree) < 9:
|
||||
useful_latency_tree.append(('', ''))
|
||||
|
||||
# Format the static data
|
||||
overall_label = [ 'Overall BW/s:',
|
||||
'Overall IOPS:',
|
||||
'Total I/O:',
|
||||
'Runtime (s):',
|
||||
|
|
@ -1992,6 +2000,9 @@ def format_info_benchmark_json(config, benchmark_information):
|
|||
'Ctx Switches:',
|
||||
'Major Faults:',
|
||||
'Minor Faults:' ]
|
||||
while len(overall_label) < max_rows:
|
||||
overall_label.append('')
|
||||
|
||||
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'])),
|
||||
|
|
@ -2001,6 +2012,9 @@ def format_info_benchmark_json(config, benchmark_information):
|
|||
job_details['ctx'],
|
||||
job_details['majf'],
|
||||
job_details['minf'] ]
|
||||
while len(overall_data) < max_rows:
|
||||
overall_data.append('')
|
||||
|
||||
bandwidth_label = [ 'Min:',
|
||||
'Max:',
|
||||
'Mean:',
|
||||
|
|
@ -2010,6 +2024,9 @@ def format_info_benchmark_json(config, benchmark_information):
|
|||
'',
|
||||
'',
|
||||
'' ]
|
||||
while len(bandwidth_label) < max_rows:
|
||||
bandwidth_label.append('')
|
||||
|
||||
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),
|
||||
|
|
@ -2019,6 +2036,9 @@ def format_info_benchmark_json(config, benchmark_information):
|
|||
'',
|
||||
'',
|
||||
'' ]
|
||||
while len(bandwidth_data) < max_rows:
|
||||
bandwidth_data.append('')
|
||||
|
||||
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'])),
|
||||
|
|
@ -2028,6 +2048,9 @@ def format_info_benchmark_json(config, benchmark_information):
|
|||
'',
|
||||
'',
|
||||
'' ]
|
||||
while len(iops_data) < max_rows:
|
||||
iops_data.append('')
|
||||
|
||||
lat_data = [ job_details[io_class]['lat_ns']['min'],
|
||||
job_details[io_class]['lat_ns']['max'],
|
||||
job_details[io_class]['lat_ns']['mean'],
|
||||
|
|
@ -2037,25 +2060,39 @@ def format_info_benchmark_json(config, benchmark_information):
|
|||
'',
|
||||
'',
|
||||
'' ]
|
||||
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'] ]
|
||||
while len(lat_data) < max_rows:
|
||||
lat_data.append('')
|
||||
|
||||
# Format the dynamic buckets
|
||||
lat_bucket_label = list()
|
||||
lat_bucket_data = list()
|
||||
for element in useful_latency_tree:
|
||||
lat_bucket_label.append(element[0])
|
||||
lat_bucket_data.append(element[1])
|
||||
|
||||
# Column default widths
|
||||
overall_label_length = 0
|
||||
overall_column_length = 0
|
||||
bandwidth_label_length = 0
|
||||
bandwidth_column_length = 11
|
||||
iops_column_length = 4
|
||||
latency_column_length = 12
|
||||
latency_bucket_label_length = 0
|
||||
|
||||
# Column layout:
|
||||
# General Bandwidth IOPS Latency Percentiles
|
||||
# --------- ---------- -------- -------- ---------------
|
||||
# Size Min Min Min A
|
||||
# BW Max Max Max B
|
||||
# IOPS Mean Mean Mean ...
|
||||
# Runtime StdDev StdDev StdDev Z
|
||||
# UsrCPU Samples Samples
|
||||
# SysCPU
|
||||
# CtxSw
|
||||
# MajFault
|
||||
# MinFault
|
||||
|
||||
# Set column widths
|
||||
for item in overall_label:
|
||||
_item_length = len(str(item))
|
||||
if _item_length > overall_label_length:
|
||||
|
|
@ -2091,15 +2128,10 @@ def format_info_benchmark_json(config, benchmark_information):
|
|||
if _item_length > latency_column_length:
|
||||
latency_column_length = _item_length
|
||||
|
||||
for item in lat_percentile_label:
|
||||
for item in lat_bucket_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
|
||||
if _item_length > latency_bucket_label_length:
|
||||
latency_bucket_label_length = _item_length
|
||||
|
||||
# Top row (Headers)
|
||||
ainformation.append('{bold}\
|
||||
|
|
@ -2108,8 +2140,8 @@ def format_info_benchmark_json(config, benchmark_information):
|
|||
{bandwidth: <{bandwidth_length}} \
|
||||
{iops: <{iops_length}} \
|
||||
{latency: <{latency_length}} \
|
||||
{latency_percentile_label: <{latency_percentile_label_length}} \
|
||||
{latency_percentile: <{latency_percentile_length}} \
|
||||
{latency_bucket_label: <{latency_bucket_label_length}} \
|
||||
{latency_bucket} \
|
||||
{end_bold}'.format(
|
||||
bold=ansiprint.bold(),
|
||||
end_bold=ansiprint.end(),
|
||||
|
|
@ -2117,19 +2149,18 @@ def format_info_benchmark_json(config, benchmark_information):
|
|||
overall_label_length=overall_label_length,
|
||||
bandwidth_label='',
|
||||
bandwidth_label_length=bandwidth_label_length,
|
||||
bandwidth='Bandwidth',
|
||||
bandwidth='Bandwidth/s',
|
||||
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,
|
||||
latency_bucket_label='Latency Buckets (μs/%)',
|
||||
latency_bucket_label_length=latency_bucket_label_length,
|
||||
latency_bucket='',
|
||||
))
|
||||
|
||||
for idx, _ in enumerate(overall_data):
|
||||
for idx in range(0, max_rows):
|
||||
# Top row (Headers)
|
||||
ainformation.append('{bold}\
|
||||
{overall_label: >{overall_label_length}} \
|
||||
|
|
@ -2138,8 +2169,8 @@ def format_info_benchmark_json(config, benchmark_information):
|
|||
{bandwidth: <{bandwidth_length}} \
|
||||
{iops: <{iops_length}} \
|
||||
{latency: <{latency_length}} \
|
||||
{latency_percentile_label: >{latency_percentile_label_length}} \
|
||||
{latency_percentile: <{latency_percentile_length}} \
|
||||
{latency_bucket_label: >{latency_bucket_label_length}} \
|
||||
{latency_bucket} \
|
||||
{end_bold}'.format(
|
||||
bold='',
|
||||
end_bold='',
|
||||
|
|
@ -2155,10 +2186,9 @@ def format_info_benchmark_json(config, benchmark_information):
|
|||
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,
|
||||
latency_bucket_label=lat_bucket_label[idx],
|
||||
latency_bucket_label_length=latency_bucket_label_length,
|
||||
latency_bucket=lat_bucket_data[idx],
|
||||
))
|
||||
|
||||
return '\n'.join(ainformation)
|
||||
|
|
|
|||
Loading…
Reference in New Issue