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