fully automate seq_latency js creation
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askiiart
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EA85979611654C30
4 changed files with 192 additions and 135 deletions
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@ -1,52 +1,46 @@
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let ctx = document.getElementById("seq_read_latency_chart");
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const labels = [
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"Null 25 GiB file",
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"Random 25 GiB file",
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"100 million-sided polygon data",
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"Linux LTS kernel",
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];
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const labels = ['Null 25 GiB file', 'Random 25 GiB file', '100 million-sided polygon data', 'Linux LTS kernel', '1024 null files', '1024 random files']
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let data = [
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{
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label: "DwarFS",
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data: [0.37114600000000003, 14.15143, 2.95083, 0.001523],
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backgroundColor: "rgb(255, 99, 132)",
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label: 'DwarFS',
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data: [0.37114600000000003, 14.15143, 2.95083, 0.001523, 0.014287000000000001, 0.013595000000000001],
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backgroundColor: 'rgb(255, 99, 132)',
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},
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{
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label: "fuse-archive (tar)",
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data: [0.393568, 0.397626, 0.07750499999999999, 0.0012230000000000001],
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backgroundColor: "rgb(75, 192, 192)",
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label: 'fuse-archive (tar)',
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data: [0.393568, 0.397626, 0.07750499999999999, 0.0012230000000000001, 0.013405, 0.013465],
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backgroundColor: 'rgb(75, 192, 192)',
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},
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{
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label: "Btrfs",
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data: [
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0.027922000000000002, 0.290906, 0.14088399999999998,
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0.0013930000000000001,
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],
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backgroundColor: "rgb(54, 162, 235)",
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label: 'Btrfs',
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data: [0.027922000000000002, 0.290906, 0.14088399999999998, 0.0013930000000000001, 0.0032860000000000003, 0.003326],
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backgroundColor: 'rgb(54, 162, 235)',
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},
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];
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]
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let config = {
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type: "bar",
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type: 'bar',
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data: {
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datasets: data,
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labels,
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labels
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},
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options: {
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plugins: {
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title: {
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display: true,
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text: "Sequential Read Latency - in ms",
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text: 'Sequential Read Latency - in ms'
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},
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},
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responsive: true,
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interaction: {
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intersect: false,
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},
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},
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}
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};
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Chart.defaults.borderColor = "#eee";
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Chart.defaults.borderColor = "#eee"
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Chart.defaults.color = "#eee";
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let ctx = document.getElementById("seq_read_latency_chart");
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new Chart(ctx, config);
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@ -2,6 +2,10 @@
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import csv
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import re
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# a bunch of horrible code to make the chart.js code
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class HelperFunctions:
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def get_fs(dir):
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if dir.endswith('dwarfs'):
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@ -20,6 +24,10 @@ class HelperFunctions:
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return '100 million-sided polygon data'
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elif filename.startswith('kernel'):
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return 'Linux LTS kernel'
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elif filename == 'small-files/random':
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return '1024 random files'
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elif filename == 'small-files/null':
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return '1024 null files'
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def convert_time(time: str, unit: str) -> int:
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unit_exponents = ['ns', 'µs', 'ms', 's']
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@ -33,15 +41,18 @@ class HelperFunctions:
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else:
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current_unit = 's'
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unit_multiplier = unit_exponents.index(current_unit) - unit_exponents.index(unit)
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return HelperFunctions.time_int(time) * (1000 ** unit_multiplier)
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unit_multiplier = unit_exponents.index(current_unit) - unit_exponents.index(
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unit
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)
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return HelperFunctions.time_num(time) * (1000**unit_multiplier)
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def time_int(time: str):
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time = re.sub("[^0-9\\.]", "", time)
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def time_num(time: str):
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time = re.sub('[^0-9\\.]', '', time)
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return float(time)
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def sequential_latency():
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def get_seq_latency_data() -> tuple:
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# format: { 'labels': ['btrfs'], 'btrfs': [9, 8, 4, 6]}
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datasets = {'labels': []}
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with open('assets/benchmarking-dwarfs/data/benchmark-data.csv', 'rt') as f:
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for line in csv.reader(f):
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@ -56,18 +67,68 @@ def sequential_latency():
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datasets[fs] = []
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datasets[fs].append(line[3])
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return datasets
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# NOTE: this will break if the bulk data contains a larger unit than the single file data, but that's unlikely to happen so I'm not gonna deal with it
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largest_time_unit = 'ns'
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for key in datasets.keys():
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if key == 'labels':
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continue
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for item in datasets[key]:
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if item.endswith('ms'):
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largest_time_unit = 'ms'
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elif item.endswith('µs') and largest_time_unit != 'ms':
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largest_time_unit = 'µs'
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elif (
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item.endswith('ns')
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and largest_time_unit != 'ms'
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and largest_time_unit != 'µs'
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):
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largest_time_unit = 'ns'
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elif re.sub('[0-9]', '', item) == 's':
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largest_time_unit = 's'
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break
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for key in datasets.keys():
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if key == 'labels':
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continue
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for i in range(len(datasets[key])):
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datasets[key][i] = HelperFunctions.convert_time(
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datasets[key][i], largest_time_unit
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)
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def singles():
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pass
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with open('assets/benchmarking-dwarfs/data/bulk.csv', 'rt') as f:
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for line in csv.reader(f):
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if line[2] != 'bulk_sequential_read_latency':
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continue
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fs = HelperFunctions.get_fs(line[0])
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label = HelperFunctions.get_label(line[1])
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datasets['labels'].append(label) if label not in datasets[
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'labels'
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] else False
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for item in line[3:]:
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if item.endswith('ms'):
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largest_time_unit = 'ms'
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elif item.endswith('µs') and largest_time_unit != 'ms':
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largest_time_unit = 'µs'
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elif (
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item.endswith('ns')
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and largest_time_unit != 'ms'
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and largest_time_unit != 'µs'
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):
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largest_time_unit = 'ns'
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elif re.sub('[0-9]', '', item) == 's':
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largest_time_unit = 's'
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break
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def bulk():
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pass
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for i in range(len(line[3:])):
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line[i + 3] = HelperFunctions.convert_time(item, largest_time_unit)
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datasets[fs].append(sum(line[3:]) / len(line[3:]))
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if __name__ == '__main__':
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return (datasets, largest_time_unit)
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def seq_latency():
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with open('assets/benchmarking-dwarfs/js/seq_latency.js', 'wt') as f:
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# from https://github.com/chartjs/Chart.js/blob/master/docs/scripts/utils.js (CHART_COLORS)
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# modified so similar color aren't adjacent
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chart_colors = [
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@ -80,14 +141,15 @@ if __name__ == '__main__':
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"'rgb(201, 203, 207)'", # grey
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]
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print('Sequential latency:')
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#print('Sequential latency:')
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labels_code = 'const labels = $labels$'
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dataset_code = '''
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{
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label: '$label$',
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data: $data$,
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backgroundColor: $color$,
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},'''
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},
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'''
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config_code = '''
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let config = {
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@ -111,36 +173,37 @@ let config = {
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};
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'''
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data = sequential_latency()
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data, largest_time_unit = get_seq_latency_data()
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labels_code = labels_code.replace('$labels$', format(data['labels']))
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print(labels_code)
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f.write(labels_code)
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data.pop('labels')
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print('let data = [', end='')
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largest_time_unit = 'ns'
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f.write('\nlet data = [')
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for fs in data.keys():
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for item in data[fs]:
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if item.endswith('ms'):
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largest_time_unit = 'ms'
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elif item.endswith('µs') and largest_time_unit != 'ms':
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largest_time_unit = 'µs'
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elif item.endswith('ns') and largest_time_unit != 'ms' and largest_time_unit != 'µs':
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largest_time_unit = 'ns'
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elif re.sub('[0-9]', '', item) == 's':
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largest_time_unit = 's'
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break
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for i in range(len(data[fs])):
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data[fs][i] = HelperFunctions.convert_time(data[fs][i], largest_time_unit)
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print(
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f.write(
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dataset_code.replace('$label$', fs)
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.replace('$data$', format(data[fs]))
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.replace('$color$', format(chart_colors[list(data.keys()).index(fs)])),
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end=''
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.replace('$color$', format(chart_colors[list(data.keys()).index(fs)]))
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)
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print('\n]\n')
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f.write('\n]\n')
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title = 'Sequential Read Latency'
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print(config_code.replace('$title$', title).replace('$timeunit$', largest_time_unit))
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print('\nChart.defaults.borderColor = "#eee"')
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print('Chart.defaults.color = "#eee";')
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f.write(
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config_code.replace('$title$', title).replace('$timeunit$', largest_time_unit)
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)
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f.write('\nChart.defaults.borderColor = "#eee"\n')
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f.write('Chart.defaults.color = "#eee";\n')
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f.write('let ctx = document.getElementById("seq_read_latency_chart");\n')
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f.write('new Chart(ctx, config);\n')
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def singles():
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pass
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def bulk():
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pass
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if __name__ == '__main__':
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seq_latency()
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@ -126,7 +126,6 @@
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</ol>
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<h2 id="footnotes">Footnotes</h2>
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<!-- JavaScript for graphs goes hereeeeeee -->
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<!-- EXAMPLE HERE -->
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<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
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<script src="/assets/benchmarking-dwarfs/js/seq_latency.js"></script>
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<section id="footnotes"
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generates regular polygons and writes their data to a file. I
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chose this because it was an artificial and reproducible yet
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fairly compressible dataset (without being extremely
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compressible like null data).
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compressible like null data).<br />
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<details open>
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<summary>
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3-sided regular polygon data
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