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:
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bba73980de
commit
7c8ba56561
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@ -1939,14 +1939,14 @@ def format_info_benchmark_json(config, benchmark_information):
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ainformation.append('{}Storage Benchmark details:{}'.format(ansiprint.bold(), ansiprint.end()))
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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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nice_test_name_map = {
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"seq_read": "Sequential Read (4M blocks)",
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"seq_read": "Sequential Read (4M blocks, queue depth 64)",
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"seq_write": "Sequential Write (4M blocks)",
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"seq_write": "Sequential Write (4M blocks, queue depth 64)",
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"rand_read_4M": "Random Read (4M blocks)",
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"rand_read_4M": "Random Read (4M blocks, queue depth 64)",
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"rand_write_4M": "Random Write (4M blocks)",
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"rand_write_4M": "Random Write (4M blocks queue depth 64)",
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"rand_read_4K": "Random Read (4K blocks)",
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"rand_read_4K": "Random Read (4K blocks, queue depth 64)",
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"rand_write_4K": "Random Write (4K blocks)",
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"rand_write_4K": "Random Write (4K blocks, queue depth 64)",
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"rand_read_4K_lowdepth": "Random Read (4K blocks, single-queue)",
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"rand_read_4K_lowdepth": "Random Read (4K blocks, queue depth 1)",
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"rand_write_4K_lowdepth": "Random Write (4K blocks, single-queue)",
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"rand_write_4K_lowdepth": "Random Write (4K blocks, queue depth 1)",
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}
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}
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for test in benchmark_details:
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for test in benchmark_details:
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@ -1961,29 +1961,37 @@ def format_info_benchmark_json(config, benchmark_information):
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job_details = benchmark_details[test]['jobs'][0]
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job_details = benchmark_details[test]['jobs'][0]
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overall_label_length = 0
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# Calculate the unified latency categories (in us)
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overall_column_length = 0
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latency_tree = list()
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bandwidth_label_length = 0
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for field in job_details['latency_ns']:
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bandwidth_column_length = 9
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bucket = str(int(field) / 1000)
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iops_column_length = 4
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latency_tree.append((bucket, job_details['latency_ns'][field]))
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latency_column_length = 12
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for field in job_details['latency_us']:
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latency_percentile_label_length = 6
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bucket = field
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latency_percentile_column_length = 12
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latency_tree.append((bucket, job_details['latency_us'][field]))
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for field in job_details['latency_ms']:
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# That one annoying one
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if field == '>=2000':
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bucket = '>=2000000'
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else:
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bucket = str(int(field) * 1000)
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latency_tree.append((bucket, job_details['latency_ms'][field]))
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# Column layout:
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# Find the minimum entry without a zero
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# General Bandwidth IOPS Latency CLAT Percentile
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useful_latency_tree = list()
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# --------- ---------- -------- -------- ---------------
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for element in latency_tree:
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# Size Min Min Min 1.00
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if element[1] != 0:
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# BW Max Max Max 5.00
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useful_latency_tree.append(element)
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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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max_rows = 9
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if len(useful_latency_tree) > 9:
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max_rows = len(useful_latency_tree)
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elif len(useful_latency_tree) < 9:
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while len(useful_latency_tree) < 9:
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useful_latency_tree.append(('', ''))
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# Format the static data
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overall_label = [ 'Overall BW/s:',
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'Overall IOPS:',
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'Overall IOPS:',
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'Total I/O:',
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'Total I/O:',
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'Runtime (s):',
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'Runtime (s):',
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@ -1992,6 +2000,9 @@ def format_info_benchmark_json(config, benchmark_information):
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'Ctx Switches:',
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'Ctx Switches:',
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'Major Faults:',
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'Major Faults:',
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'Minor Faults:' ]
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'Minor Faults:' ]
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while len(overall_label) < max_rows:
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overall_label.append('')
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overall_data = [ format_bytes_tohuman(int(job_details[io_class]['bw_bytes'])),
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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_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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format_bytes_tohuman(int(job_details[io_class]['io_bytes'])),
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@ -2001,6 +2012,9 @@ def format_info_benchmark_json(config, benchmark_information):
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job_details['ctx'],
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job_details['ctx'],
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job_details['majf'],
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job_details['majf'],
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job_details['minf'] ]
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job_details['minf'] ]
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while len(overall_data) < max_rows:
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overall_data.append('')
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bandwidth_label = [ 'Min:',
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bandwidth_label = [ 'Min:',
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'Max:',
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'Max:',
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'Mean:',
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'Mean:',
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@ -2010,6 +2024,9 @@ def format_info_benchmark_json(config, benchmark_information):
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'',
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'',
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'',
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'',
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'' ]
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'' ]
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while len(bandwidth_label) < max_rows:
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bandwidth_label.append('')
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bandwidth_data = [ format_bytes_tohuman(int(job_details[io_class]['bw_min']) * 1024),
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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_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_mean']) * 1024),
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@ -2019,6 +2036,9 @@ def format_info_benchmark_json(config, benchmark_information):
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'',
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'',
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'',
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'',
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'' ]
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'' ]
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while len(bandwidth_data) < max_rows:
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bandwidth_data.append('')
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iops_data = [ format_ops_tohuman(int(job_details[io_class]['iops_min'])),
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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_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_mean'])),
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@ -2028,6 +2048,9 @@ def format_info_benchmark_json(config, benchmark_information):
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'',
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'',
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'',
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'',
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'' ]
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'' ]
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while len(iops_data) < max_rows:
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iops_data.append('')
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lat_data = [ job_details[io_class]['lat_ns']['min'],
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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']['max'],
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job_details[io_class]['lat_ns']['mean'],
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job_details[io_class]['lat_ns']['mean'],
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@ -2037,25 +2060,39 @@ def format_info_benchmark_json(config, benchmark_information):
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'',
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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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while len(lat_data) < max_rows:
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'99.95%:',
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lat_data.append('')
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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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# Format the dynamic buckets
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lat_bucket_label = list()
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lat_bucket_data = list()
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for element in useful_latency_tree:
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lat_bucket_label.append(element[0])
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lat_bucket_data.append(element[1])
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# Column default widths
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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 = 11
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iops_column_length = 4
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latency_column_length = 12
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latency_bucket_label_length = 0
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# Column layout:
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# General Bandwidth IOPS Latency Percentiles
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# --------- ---------- -------- -------- ---------------
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# Size Min Min Min A
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# BW Max Max Max B
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# IOPS Mean Mean Mean ...
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# Runtime StdDev StdDev StdDev Z
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# UsrCPU Samples Samples
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# SysCPU
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# CtxSw
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# MajFault
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# MinFault
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# Set column widths
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for item in overall_label:
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for item in overall_label:
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_item_length = len(str(item))
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_item_length = len(str(item))
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if _item_length > overall_label_length:
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if _item_length > overall_label_length:
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@ -2091,25 +2128,20 @@ def format_info_benchmark_json(config, benchmark_information):
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if _item_length > latency_column_length:
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if _item_length > latency_column_length:
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latency_column_length = _item_length
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latency_column_length = _item_length
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for item in lat_percentile_label:
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for item in lat_bucket_label:
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_item_length = len(str(item))
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_item_length = len(str(item))
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if _item_length > latency_percentile_label_length:
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if _item_length > latency_bucket_label_length:
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latency_percentile_label_length = _item_length
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latency_bucket_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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# Top row (Headers)
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ainformation.append('{bold}\
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ainformation.append('{bold}\
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{overall_label: <{overall_label_length}} \
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{overall_label: <{overall_label_length}} \
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{bandwidth_label: <{bandwidth_label_length}} \
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{bandwidth_label: <{bandwidth_label_length}} \
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{bandwidth: <{bandwidth_length}} \
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{bandwidth: <{bandwidth_length}} \
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{iops: <{iops_length}} \
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{iops: <{iops_length}} \
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{latency: <{latency_length}} \
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{latency: <{latency_length}} \
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{latency_percentile_label: <{latency_percentile_label_length}} \
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{latency_bucket_label: <{latency_bucket_label_length}} \
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{latency_percentile: <{latency_percentile_length}} \
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{latency_bucket} \
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{end_bold}'.format(
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{end_bold}'.format(
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bold=ansiprint.bold(),
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bold=ansiprint.bold(),
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end_bold=ansiprint.end(),
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end_bold=ansiprint.end(),
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@ -2117,29 +2149,28 @@ def format_info_benchmark_json(config, benchmark_information):
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overall_label_length=overall_label_length,
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overall_label_length=overall_label_length,
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bandwidth_label='',
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bandwidth_label='',
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bandwidth_label_length=bandwidth_label_length,
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bandwidth_label_length=bandwidth_label_length,
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bandwidth='Bandwidth',
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bandwidth='Bandwidth/s',
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bandwidth_length=bandwidth_column_length,
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bandwidth_length=bandwidth_column_length,
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iops='IOPS',
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iops='IOPS',
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iops_length=iops_column_length,
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iops_length=iops_column_length,
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latency='Latency (μs)',
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latency='Latency (μs)',
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latency_length=latency_column_length,
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latency_length=latency_column_length,
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latency_percentile_label='CLAT Percentiles (μs)',
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latency_bucket_label='Latency Buckets (μs/%)',
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latency_percentile_label_length=latency_percentile_label_length,
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latency_bucket_label_length=latency_bucket_label_length,
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latency_percentile='',
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latency_bucket='',
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latency_percentile_length=latency_percentile_column_length,
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))
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))
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for idx, _ in enumerate(overall_data):
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for idx in range(0, max_rows):
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# Top row (Headers)
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# Top row (Headers)
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ainformation.append('{bold}\
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ainformation.append('{bold}\
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{overall_label: >{overall_label_length}} \
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{overall_label: >{overall_label_length}} \
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{overall: <{overall_length}} \
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{overall: <{overall_length}} \
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{bandwidth_label: >{bandwidth_label_length}} \
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{bandwidth_label: >{bandwidth_label_length}} \
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{bandwidth: <{bandwidth_length}} \
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{bandwidth: <{bandwidth_length}} \
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{iops: <{iops_length}} \
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{iops: <{iops_length}} \
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{latency: <{latency_length}} \
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{latency: <{latency_length}} \
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{latency_percentile_label: >{latency_percentile_label_length}} \
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{latency_bucket_label: >{latency_bucket_label_length}} \
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{latency_percentile: <{latency_percentile_length}} \
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{latency_bucket} \
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{end_bold}'.format(
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{end_bold}'.format(
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bold='',
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bold='',
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end_bold='',
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end_bold='',
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@ -2155,10 +2186,9 @@ def format_info_benchmark_json(config, benchmark_information):
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iops_length=iops_column_length,
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iops_length=iops_column_length,
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latency=lat_data[idx],
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latency=lat_data[idx],
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latency_length=latency_column_length,
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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_bucket_label=lat_bucket_label[idx],
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latency_percentile_label_length=latency_percentile_label_length,
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latency_bucket_label_length=latency_bucket_label_length,
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latency_percentile=lat_percentile_data[idx],
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latency_bucket=lat_bucket_data[idx],
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latency_percentile_length=latency_percentile_column_length,
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))
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))
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return '\n'.join(ainformation)
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return '\n'.join(ainformation)
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