1#!bin/python3 2 3import os 4import plotly.graph_objects as go 5from functools import reduce 6 7def spread(amount, arr): 8 """ 9 duplicate elements of the array by the amount. 10 Ex.: spread(3, ["a", "b"]) -> ["a", "a", "a", "b", "b", "b"] 11 """ 12 return reduce(lambda x, y: x+y, map(lambda e: ([e] * amount), arr)) 13 14# 8x6 matrices where each row is all the attempt for the same benchmark 15data_single_file = { 16 'copy': { 17 'v1': [ 18 # 1B 19 0.036, 0.04, 0.044, 0.036, 0.037, 0.036, 0.043, 0.037, 20 # 8MB-1 21 0.042, 0.039, 0.043, 0.05, 0.043, 0.049, 0.055, 0.045, 22 # 8MB+1 23 0.06, 0.06, 0.094, 0.076, 0.045, 0.077, 0.062, 0.071, 24 # 128MB 25 0.832, 0.869, 0.842, 0.739, 0.892, 0.784, 0.772, 0.904, 26 # 4GB 27 34.113, 37.754, 36.9, 36.369, 35.358, 34.21, 36.525, 44.225, 28 # 30GB 29 6.463, 5.726, 5.994, 9.275, 10.091, 5.152, 5.85, 6.524 30 ], 31 'v2': [ 32 1.053, 0.056, 0.043, 0.066, 0.056, 0.056, 0.045, 0.045, 33 0.058, 0.062, 0.071, 0.069, 0.066, 0.066, 0.074, 0.166, 34 0.095, 0.072, 0.088, 0.135, 0.088, 0.076, 0.068, 0.105, 35 0.371, 1.886, 5.315, 0.4, 0.367, 2.364, 0.387, 0.42, 36 3.565, 3.869, 3.789, 2.447, 5.423, 2.046, 5.478, 5.175, 37 7.366, 7.946, 8.59, 8.243, 8.332, 7.607, 7.606, 8.041 38 ], 39 }, 40 'download': { 41 'disk': { 42 'v1': [ 43 0.025, 0.025, 0.024, 0.042, 0.026, 0.023, 0.024, 0.025, 44 0.187, 0.117, 0.136, 0.113, 0.117, 0.116, 0.113, 0.114, 45 0.306, 0.144, 0.248, 0.122, 0.116, 0.115, 0.113, 0.148, 46 0.499, 0.344, 0.273, 0.286, 0.332, 0.447, 0.348, 0.29, 47 3.139, 2.811, 2.571, 2.509, 2.724, 2.484, 2.522, 2.563, 48 52.904, 52.058, 45.316, 44.583, 50.343, 50.369, 53.261, 47.356 49 ], 50 'v2': [ 51 0.031, 0.04, 0.036, 0.027, 0.037, 0.049, 0.042, 0.026, 52 0.166, 0.13, 0.105, 0.122, 0.108, 0.118, 0.12, 0.111, 53 0.201, 0.133, 0.233, 0.13, 0.122, 0.122, 0.117, 0.126, 54 0.334, 0.241, 0.186, 0.184, 0.194, 0.239, 0.236, 0.191, 55 1.628, 1.565, 1.367, 1.641, 1.488, 1.353, 1.423, 1.451, 56 23.328, 25.317, 26.666, 25.147, 25.899, 25.879, 23.408, 26.406 57 ] 58 }, 59 'tmpfs': { 60 'v1': [ 61 0.038, 0.034, 0.032, 0.034, 0.032, 0.031, 0.03, 0.044, 62 0.178, 0.113, 0.113, 0.114, 0.114, 0.113, 0.121, 0.113, 63 0.211, 0.135, 0.116, 0.114, 0.114, 0.114, 0.115, 0.113, 64 0.455, 0.372, 0.278, 0.258, 0.258, 0.255, 0.275, 0.256, 65 2.474, 2.392, 2.377, 2.472, 2.511, 2.554, 2.547, 2.579, 66 45.586, 44.304, 43.423, 46.81, 51.413, 50.372, 49.704, 42.938 67 ], 68 'v2': [ 69 0.046, 0.021, 0.041, 0.024, 0.026, 0.149, 0.026, 0.026, 70 0.178, 0.12, 0.123, 0.117, 0.125, 0.124, 0.122, 0.152, 71 0.202, 0.198, 0.122, 0.127, 0.108, 0.109, 0.14, 0.12, 72 0.352, 0.274, 0.183, 0.193, 0.202, 0.199, 0.191, 0.191, 73 1.493, 1.373, 1.345, 1.459, 1.474, 1.453, 1.393, 1.353, 74 17.945, 17.071, 16.921, 17.742, 17.338, 21.262, 20.168, 19.728 75 ] 76 } 77 }, 78 'upload': { 79 'disk': { 80 'v1': [ 81 0.032, 0.02, 0.018, 0.018, 0.017, 0.018, 0.019, 0.018, 82 0.227, 0.143, 0.146, 0.171, 0.165, 0.142, 0.139, 0.129, 83 0.145, 0.15, 0.129, 0.149, 0.153, 0.15, 0.171, 0.126, 84 0.398, 0.362, 0.318, 0.334, 0.351, 3.419, 0.339, 0.287, 85 6.058, 6.073, 1.941, 1.943, 3.692, 5.452, 5.313, 5.935, 86 10.684, 10.801, 10.221, 12.64, 11.778, 12.351, 10.516, 12.691, 87 88 ], 89 'v2': [ 90 0.02, 0.019, 0.029, 0.029, 0.035, 0.023, 0.03, 0.034, 91 0.267, 0.417, 0.2, 0.287, 0.252, 0.253, 0.27, 0.29, 92 0.218, 0.283, 0.294, 0.266, 0.359, 0.272, 0.318, 0.236, 93 0.468, 0.487, 0.509, 0.559, 0.55, 0.497, 0.44, 0.476, 94 4.037, 5.459, 5.771, 2.844, 2.987, 5.337, 2.614, 5.106, 95 37.123, 37.304, 38.615, 32.718, 37.608, 40.833, 48.343, 39.442 96 ] 97 }, 98 'tmpfs': { 99 'v1': [ 100 0.037, 0.018, 0.015, 0.02, 0.015, 0.021, 0.016, 0.02, 101 0.25, 0.161, 0.172, 0.153, 0.602, 0.145, 0.221, 0.173, 102 0.178, 0.161, 0.206, 0.156, 0.196, 0.157, 0.165, 0.171, 103 0.476, 0.353, 0.355, 1.103, 0.325, 0.351, 0.324, 0.68, 104 1.202, 3.178, 0.903, 4.687, 0.744, 2.987, 5.67, 0.91, 105 11.233, 10.044, 8.174, 7.526, 9.358, 10.046, 9.606, 10.037 106 ], 107 'v2': [ 108 0.033, 0.031, 0.025, 0.034, 0.034, 0.03, 0.033, 0.041, 109 0.267, 0.31, 0.316, 0.264, 0.302, 0.286, 0.269, 0.268, 110 0.236, 0.293, 0.315, 0.293, 0.252, 0.266, 0.249, 0.317, 111 0.582, 0.487, 0.547, 0.465, 0.46, 0.445, 0.621, 0.468, 112 2.394, 2.434, 2.476, 4.944, 3.26, 2.454, 2.468, 2.397, 113 33.261, 41.114, 33.014, 32.97, 34.138, 33.972, 33.001, 34.12 114 ] 115 } 116 } 117} 118 119# 8x3 matrices where each row is all the attempt for the same benchmark 120data_directory = { 121 'download': { 122 "disk": { 123 "v1": [ 124 # 1Bx1000 125 24.68, 20.315, 19.796, 17.808, 16.791, 18.575, 19.795, 19.865, 126 # 4Kx1000 127 20.448, 19.639, 17.199, 19.143, 20.359, 19.607, 20.298, 19.523, 128 # 16Mx1000 129 15.236, 15.127, 15.62, 16.003, 16.594, 17.597, 17.261, 17.779 130 ], 131 "v2": [ 132 0.624, 0.427, 0.435, 0.449, 0.416, 0.414, 0.517, 0.429, 133 0.617, 0.48, 0.519, 0.432, 0.403, 0.411, 0.463, 0.402, 134 3.291, 2.619, 2.698, 2.488, 2.618, 2.473, 2.428, 2.487 135 ] 136 }, 137 "tmpfs": { 138 "v1": [ 139 19.468, 19.334, 19.428, 18.189, 17.86, 17.717, 17.384, 18.513, 140 22.286, 18.909, 19.058, 19.645, 19.646, 20.196, 19.321, 19.678, 141 17.807, 16.571, 16.324, 15.872, 16.626, 16.139, 16.437, 16.003 142 ], 143 "v2": [ 144 0.607, 0.474, 0.432, 0.392, 0.427, 0.359, 0.357, 0.323, 145 0.633, 0.447, 0.476, 0.429, 0.398, 0.413, 0.476, 0.381, 146 3.028, 2.692, 2.667, 2.543, 2.632, 2.578, 2.585, 2.492 147 ] 148 } 149 }, 150 'upload': { 151 "disk": { 152 "v1": [ 153 0.341, 0.448, 0.487, 0.947, 0.304, 0.261, 0.297, 0.301, 154 3.949, 0.392, 3.71, 1.005, 0.371, 0.354, 0.389, 0.371, 155 8.009, 6.948, 7.37, 5.141, 3.546, 4.866, 4.325, 5.609 156 ], 157 "v2": [ 158 4.057, 3.504, 3.361, 3.363, 3.337, 3.319, 3.334, 3.305, 159 4.124, 3.671, 3.582, 3.655, 3.481, 3.659, 3.434, 3.463, 160 5.349, 9.489, 7.626, 7.471, 9.632, 7.452, 7.21, 7.082 161 ] 162 }, 163 "tmpfs": { 164 "v1": [ 165 0.274, 0.212, 0.224, 0.24, 0.244, 0.222, 0.216, 0.239, 166 0.384, 0.364, 0.363, 0.329, 0.349, 0.411, 0.392, 0.347, 167 6.916, 6.307, 6.492, 8.0, 7.968, 7.322, 4.969, 6.121 168 ], 169 "v2": [ 170 3.547, 3.212, 3.191, 3.068, 3.036, 3.015, 3.116, 3.069, 171 4.091, 3.563, 3.495, 3.435, 3.468, 3.434, 3.479, 3.408, 172 5.531, 6.018, 5.639, 8.545, 5.927, 8.506, 6.945, 8.973 173 ] 174 } 175 } 176} 177 178directory_sizes = spread(8, [ "1Bx1000", "4Kx1000", "16Mx1000" ]) 179single_file_sizes = spread(8, [ "1B", "8MB-1", "8MB+1", "128MB", "4GB", "30GB" ]) 180 181v1_color = 'rgb(116, 116, 116)' 182v2_color = 'rgb(21, 21, 21)' 183 184def bar_graph(name, sizes, data, log_y_axis=True): 185 figure = go.Figure() 186 figure.add_trace(go.Box( 187 y=data['v1'], 188 x=sizes, 189 name='v1', 190 marker_color=v1_color, 191 boxpoints=False, 192 boxmean=True, 193 line_width=0.8 194 )) 195 figure.add_trace(go.Box( 196 y=data['v2'], 197 x=sizes, 198 name='v2', 199 marker_color=v2_color, 200 boxmean=True, 201 boxpoints=False, 202 line_width=0.8 203 )) 204 if log_y_axis: 205 figure.update_yaxes(type="log") 206 figure.update_layout( 207 title=name, 208 yaxis_title='seconds', 209 boxmode='group' 210 ) 211 return figure 212 213if not os.path.exists("../images"): 214 os.mkdir("../images") 215 216bar_graph("upload-tmpfs", single_file_sizes, data_single_file['upload']['tmpfs']).write_image("../images/upload-tmpfs.png") 217bar_graph("upload-disk", single_file_sizes, data_single_file['upload']['disk']).write_image("../images/upload-disk.png") 218bar_graph("download-tmpfs", single_file_sizes, data_single_file['download']['tmpfs']).write_image("../images/download-tmpfs.png") 219bar_graph("download-disk", single_file_sizes, data_single_file['download']['disk']).write_image("../images/download-disk.png") 220bar_graph("copy", single_file_sizes, data_single_file['copy']).write_image("../images/copy.png") 221 222bar_graph("download-directory-tmpfs", directory_sizes, data_directory['download']['tmpfs']).write_image("../images/download-directory-tmpfs.png") 223bar_graph("download-directory-disk", directory_sizes, data_directory['download']['disk']).write_image("../images/download-directory-disk.png") 224bar_graph("upload-directory-tmpfs", directory_sizes, data_directory['upload']['tmpfs']).write_image("../images/upload-directory-tmpfs.png") 225bar_graph("upload-directory-disk", directory_sizes, data_directory['upload']['disk']).write_image("../images/upload-directory-disk.png") 226
