01.basic-benchmarks_quobyte_compare.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fbb77a64",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0608424d",
   "metadata": {},
   "outputs": [
    {
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       "      <th>h:m:s</th>\n",
       "      <th>max_rss</th>\n",
       "      <th>max_vms</th>\n",
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      "text/plain": [
       "           s    h:m:s   max_rss   max_vms   max_uss   max_pss  io_in  io_out  \\\n",
       "0  1347.0233  0:22:27  12910.21  15135.63  13073.72  13073.72   0.01    0.14   \n",
       "\n",
       "   mean_load  cpu_time  n_query  n_against  \n",
       "0     2762.7  37225.36      100      10000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def load_benchmark_and_annotate(filename, n_query, n_against):\n",
    "    df = pd.read_csv(filename, sep='\\t')\n",
    "    df['n_query'] = int(n_query)\n",
    "    df['n_against'] = int(n_against)\n",
    "    \n",
    "    return df\n",
    "\n",
    "load_benchmark_and_annotate('benchmarks/a_100_vs_a.txt', 100, 10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0bc09f7e",
   "metadata": {},
   "outputs": [
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       "      <td>500</td>\n",
       "      <td>10000</td>\n",
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       "      <th>0</th>\n",
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       "      <td>10000</td>\n",
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       "      <td>700</td>\n",
       "      <td>10000</td>\n",
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       "      <th>0</th>\n",
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       "           s    h:m:s   max_rss   max_vms   max_uss   max_pss     io_in  \\\n",
       "0  1347.0233  0:22:27  12910.21  15135.63  13073.72  13073.72      0.01   \n",
       "0  1291.4875  0:21:31  14541.96  18260.40  14601.04  14601.05      0.01   \n",
       "0  1454.4474  0:24:14  16348.15  18954.26  16429.44  16429.44      0.02   \n",
       "0  1551.7362  0:25:51  14438.77  16864.46  14666.07  14666.08      0.02   \n",
       "0  1603.6622  0:26:43  18893.50  21000.83  18968.33  18968.33      0.03   \n",
       "0  1696.6006  0:28:16  17783.73  19868.45  17888.35  17888.35      0.03   \n",
       "0  1610.4366  0:26:50  16855.73  19042.42  16989.04  16989.04      0.04   \n",
       "0  1770.2948  0:29:30  16316.45  19000.11  16440.92  16440.92      0.04   \n",
       "0  1806.3763  0:30:06  16779.61  19569.78  16286.82  16286.82  95967.58   \n",
       "\n",
       "   io_out  mean_load  cpu_time  n_query  n_against  \n",
       "0    0.14    2762.70  37225.36      100      10000  \n",
       "0    0.24    3044.57  39323.17      200      10000  \n",
       "0    0.31    2828.24  41136.09      300      10000  \n",
       "0    0.40    2751.87  42702.26      400      10000  \n",
       "0    0.48    2802.76  44947.46      500      10000  \n",
       "0    0.59    2755.67  46753.81      600      10000  \n",
       "0    0.70    3032.36  48835.22      700      10000  \n",
       "0    0.85    2863.60  50694.71      800      10000  \n",
       "0    0.97    2889.70  52198.87      900      10000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_line = [ load_benchmark_and_annotate(f\"benchmarks/a_{x}_vs_a.txt\", x, 10000) for x in range(100, 1000, 100) ]\n",
    "\n",
    "#query_line.append(load_benchmark_and_annotate('benchmarks/a_vs_a.txt', 1000, 10000))\n",
    "query_line_df = pd.concat(query_line)\n",
    "query_line_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "31b6e5ff",
   "metadata": {},
   "outputs": [
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       "      <td>500</td>\n",
       "      <td>10000</td>\n",
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       "      <td>20774.84</td>\n",
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       "           s    h:m:s   max_rss   max_vms   max_uss   max_pss     io_in  \\\n",
       "0  1162.8157  0:19:22  18997.52  23878.00  19488.38  19488.66      0.01   \n",
       "0  1412.3448  0:23:32  14810.07  17239.73  14174.27  14174.55      0.01   \n",
       "0  1313.0555  0:21:53  17390.10  21158.68  17320.14  17320.41      0.02   \n",
       "0  1528.5183  0:25:28  13448.81  15992.88  13542.36  13542.63      0.02   \n",
       "0  1505.2579  0:25:05  13926.41  17138.85  13939.65  13939.92      0.03   \n",
       "0  1473.2980  0:24:33  19966.12  23967.04  20016.52  20016.78      0.03   \n",
       "0  1723.1636  0:28:43  16750.52  21506.70  16766.94  16767.20      0.04   \n",
       "0  1740.7366  0:29:00  13400.32  16234.74  13229.82  13230.06      0.04   \n",
       "0  1742.6810  0:29:02  16575.49  20774.84  16739.16  16739.44  12070.06   \n",
       "\n",
       "   io_out  mean_load  cpu_time  n_query  n_against  \n",
       "0     0.0    2888.42  33587.05      100      10000  \n",
       "0     0.0    2487.27  35129.06      200      10000  \n",
       "0     0.0    2773.60  36418.75      300      10000  \n",
       "0     0.0    2473.59  37809.40      400      10000  \n",
       "0     0.0    2614.97  39362.13      500      10000  \n",
       "0     0.0    2794.10  41165.41      600      10000  \n",
       "0     0.0    2463.08  42442.27      700      10000  \n",
       "0     0.0    2509.93  43691.62      800      10000  \n",
       "0     0.0    2612.59  45529.02      900      10000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quo_query_line = [ load_benchmark_and_annotate(f\"benchmarks.quobyte/a_{x}_vs_a.txt\", x, 10000) for x in range(100, 1000, 100) ]\n",
    "quo_query_line_df = pd.concat(quo_query_line)\n",
    "quo_query_line_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f69c666a",
   "metadata": {},
   "outputs": [
    {
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       "           s    h:m:s   max_rss   max_vms   max_uss   max_pss  io_in  io_out  \\\n",
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       "\n",
       "   mean_load  cpu_time  n_query  n_against  \n",
       "0    2112.52   3256.75     1000       1000  \n",
       "0    2450.90   6594.63     1000       2000  \n",
       "0    2943.38  10428.58     1000       3000  \n",
       "0    3003.24  14358.91     1000       4000  \n",
       "0    2722.32  19182.34     1000       5000  \n",
       "0    2635.62  28288.41     1000       6000  \n",
       "0    2923.52  37104.07     1000       7000  \n",
       "0    3018.64  42844.40     1000       8000  \n",
       "0    3052.70  48122.82     1000       9000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "against_line = [ load_benchmark_and_annotate(f\"benchmarks/a_vs_a_{x}.txt\", 1000, x) for x in range(1000, 10000, 1000) ]\n",
    "#against_line.append(load_benchmark_and_annotate('benchmarks/a_vs_a.txt', 1000, 10000))\n",
    "against_line_df = pd.concat(against_line)\n",
    "against_line_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5a3fe289",
   "metadata": {},
   "outputs": [
    {
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       "           s    h:m:s   max_rss   max_vms   max_uss   max_pss  io_in  io_out  \\\n",
       "0   237.4116  0:03:57   7684.28   8800.33   7802.29   7802.59   0.00     0.0   \n",
       "0   366.2607  0:06:06   9817.54  10801.36   9873.40   9873.68   0.12     0.0   \n",
       "0   470.3574  0:07:50  10213.26  11778.20  10169.59  10169.86   0.19     0.0   \n",
       "0   614.2592  0:10:14  13133.09  16322.82  13194.47  13194.76   0.25     0.0   \n",
       "0  1006.9718  0:16:46  15449.10  17748.61  15016.04  15016.34   0.31     0.0   \n",
       "0  1066.2326  0:17:46  13807.07  16115.25  13897.41  13897.72   0.38     0.0   \n",
       "0  1105.0427  0:18:25  15063.78  17894.73  15197.97  15198.23   0.44     0.0   \n",
       "0  1238.7524  0:20:38  17603.96  20879.96  17490.12  17490.42   0.50     0.0   \n",
       "0  1429.6253  0:23:49  15088.23  17541.75  14948.63  14948.93   0.61     0.0   \n",
       "\n",
       "   mean_load  cpu_time  n_query  n_against  \n",
       "0    1627.00   3862.69     1000       1000  \n",
       "0    2141.66   7844.31     1000       2000  \n",
       "0    2601.59  12237.12     1000       3000  \n",
       "0    2798.82  17192.36     1000       4000  \n",
       "0    2256.98  22727.47     1000       5000  \n",
       "0    2525.65  26929.75     1000       6000  \n",
       "0    2813.19  31087.26     1000       7000  \n",
       "0    2941.14  36433.50     1000       8000  \n",
       "0    2825.72  40397.22     1000       9000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quo_against_line = [ load_benchmark_and_annotate(f\"benchmarks.quobyte/a_vs_a_{x}.txt\", 1000, x) for x in range(1000, 10000, 1000) ]\n",
    "#against_line.append(load_benchmark_and_annotate('benchmarks/a_vs_a.txt', 1000, 10000))\n",
    "quo_against_line_df = pd.concat(quo_against_line)\n",
    "quo_against_line_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "850d18f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fig = plt.figure()\n",
    "fig, axs = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "ax = axs[0]\n",
    "ax.plot(query_line_df.n_query, query_line_df['s'] / 60, '-p', label='orig')\n",
    "ax.plot(quo_query_line_df.n_query, quo_query_line_df['s'] / 60, '-p', label='quobyte')\n",
    "\n",
    "ax.axis(xmin=0, ymin=0, ymax=35)\n",
    "ax.set_xlabel('num queries')\n",
    "ax.set_ylabel('walltime (m)')\n",
    "ax.set_title('varying number of queries (10k subjects)')\n",
    "ax.legend(loc='lower right')\n",
    "\n",
    "\n",
    "ax = axs[1]\n",
    "ax.plot(against_line_df.n_against, against_line_df['s'] / 60, '-p', label='orig')\n",
    "ax.plot(quo_against_line_df.n_against, quo_against_line_df['s'] / 60, '-p', label='quobyte')\n",
    "\n",
    "ax.axis(xmin=0, ymin=0, ymax=35)\n",
    "ax.set_xlabel('num subject datasets')\n",
    "ax.set_ylabel('walltime (m)')\n",
    "ax.set_title('varying number of subjects (1000 queries)')\n",
    "ax.legend(loc='lower right')\n",
    "\n",
    "plt.savefig('subsample_benchmarks.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f11cf360",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>s</th>\n",
       "      <th>h:m:s</th>\n",
       "      <th>max_rss</th>\n",
       "      <th>max_vms</th>\n",
       "      <th>max_uss</th>\n",
       "      <th>max_pss</th>\n",
       "      <th>io_in</th>\n",
       "      <th>io_out</th>\n",
       "      <th>mean_load</th>\n",
       "      <th>cpu_time</th>\n",
       "      <th>ident</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1394.1251</td>\n",
       "      <td>0:23:14</td>\n",
       "      <td>16106.77</td>\n",
       "      <td>18410.19</td>\n",
       "      <td>16167.91</td>\n",
       "      <td>16167.94</td>\n",
       "      <td>95974.41</td>\n",
       "      <td>1.14</td>\n",
       "      <td>2914.11</td>\n",
       "      <td>40626.79</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1394.1750</td>\n",
       "      <td>0:23:14</td>\n",
       "      <td>17553.49</td>\n",
       "      <td>20057.43</td>\n",
       "      <td>17685.95</td>\n",
       "      <td>17685.99</td>\n",
       "      <td>94003.57</td>\n",
       "      <td>1.16</td>\n",
       "      <td>2771.80</td>\n",
       "      <td>38643.81</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1357.7237</td>\n",
       "      <td>0:22:37</td>\n",
       "      <td>18257.87</td>\n",
       "      <td>20041.38</td>\n",
       "      <td>18277.77</td>\n",
       "      <td>18277.80</td>\n",
       "      <td>91280.15</td>\n",
       "      <td>1.01</td>\n",
       "      <td>2794.07</td>\n",
       "      <td>37936.37</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1505.0091</td>\n",
       "      <td>0:25:05</td>\n",
       "      <td>16107.48</td>\n",
       "      <td>19187.17</td>\n",
       "      <td>16137.28</td>\n",
       "      <td>16137.32</td>\n",
       "      <td>93738.74</td>\n",
       "      <td>1.02</td>\n",
       "      <td>2605.24</td>\n",
       "      <td>39209.39</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1596.4828</td>\n",
       "      <td>0:26:36</td>\n",
       "      <td>14046.44</td>\n",
       "      <td>15868.38</td>\n",
       "      <td>14061.43</td>\n",
       "      <td>14061.46</td>\n",
       "      <td>91948.11</td>\n",
       "      <td>0.96</td>\n",
       "      <td>2367.13</td>\n",
       "      <td>37791.57</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           s    h:m:s   max_rss   max_vms   max_uss   max_pss     io_in  \\\n",
       "0  1394.1251  0:23:14  16106.77  18410.19  16167.91  16167.94  95974.41   \n",
       "0  1394.1750  0:23:14  17553.49  20057.43  17685.95  17685.99  94003.57   \n",
       "0  1357.7237  0:22:37  18257.87  20041.38  18277.77  18277.80  91280.15   \n",
       "0  1505.0091  0:25:05  16107.48  19187.17  16137.28  16137.32  93738.74   \n",
       "0  1596.4828  0:26:36  14046.44  15868.38  14061.43  14061.46  91948.11   \n",
       "\n",
       "   io_out  mean_load  cpu_time ident  \n",
       "0    1.14    2914.11  40626.79     a  \n",
       "0    1.16    2771.80  38643.81     b  \n",
       "0    1.01    2794.07  37936.37     c  \n",
       "0    1.02    2605.24  39209.39     d  \n",
       "0    0.96    2367.13  37791.57     e  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def load_benchmark_replicate(filename, ident):\n",
    "    df = pd.read_csv(filename, sep='\\t')\n",
    "    df['ident'] = ident\n",
    "    \n",
    "    return df\n",
    "\n",
    "replicates_df = [ load_benchmark_replicate(f\"benchmarks/a_vs_{x}.txt\", x) for x in ('a','b','c','d','e')]\n",
    "#query_line.append(load_benchmark_and_annotate('benchmarks/a_vs_a.txt', 1000, 10000))\n",
    "replicates_df = pd.concat(replicates_df)\n",
    "replicates_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1474a721",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>s</th>\n",
       "      <th>h:m:s</th>\n",
       "      <th>max_rss</th>\n",
       "      <th>max_vms</th>\n",
       "      <th>max_uss</th>\n",
       "      <th>max_pss</th>\n",
       "      <th>io_in</th>\n",
       "      <th>io_out</th>\n",
       "      <th>mean_load</th>\n",
       "      <th>cpu_time</th>\n",
       "      <th>ident</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1828.5562</td>\n",
       "      <td>0:30:28</td>\n",
       "      <td>17199.63</td>\n",
       "      <td>19372.52</td>\n",
       "      <td>17137.26</td>\n",
       "      <td>17137.49</td>\n",
       "      <td>95859.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2552.00</td>\n",
       "      <td>46664.91</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1736.2214</td>\n",
       "      <td>0:28:56</td>\n",
       "      <td>20809.34</td>\n",
       "      <td>24031.92</td>\n",
       "      <td>20838.29</td>\n",
       "      <td>20838.55</td>\n",
       "      <td>92243.98</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2637.59</td>\n",
       "      <td>45794.45</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1664.0652</td>\n",
       "      <td>0:27:44</td>\n",
       "      <td>17074.38</td>\n",
       "      <td>19697.34</td>\n",
       "      <td>17159.77</td>\n",
       "      <td>17160.03</td>\n",
       "      <td>91210.98</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2665.93</td>\n",
       "      <td>44363.13</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1649.0302</td>\n",
       "      <td>0:27:29</td>\n",
       "      <td>16548.58</td>\n",
       "      <td>20109.86</td>\n",
       "      <td>16397.11</td>\n",
       "      <td>16397.37</td>\n",
       "      <td>93627.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2758.07</td>\n",
       "      <td>45481.58</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1613.8850</td>\n",
       "      <td>0:26:53</td>\n",
       "      <td>19621.16</td>\n",
       "      <td>25938.22</td>\n",
       "      <td>19731.92</td>\n",
       "      <td>19732.19</td>\n",
       "      <td>91417.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2771.49</td>\n",
       "      <td>44728.82</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           s    h:m:s   max_rss   max_vms   max_uss   max_pss     io_in  \\\n",
       "0  1828.5562  0:30:28  17199.63  19372.52  17137.26  17137.49  95859.33   \n",
       "0  1736.2214  0:28:56  20809.34  24031.92  20838.29  20838.55  92243.98   \n",
       "0  1664.0652  0:27:44  17074.38  19697.34  17159.77  17160.03  91210.98   \n",
       "0  1649.0302  0:27:29  16548.58  20109.86  16397.11  16397.37  93627.83   \n",
       "0  1613.8850  0:26:53  19621.16  25938.22  19731.92  19732.19  91417.85   \n",
       "\n",
       "   io_out  mean_load  cpu_time ident  \n",
       "0     0.0    2552.00  46664.91     a  \n",
       "0     0.0    2637.59  45794.45     b  \n",
       "0     0.0    2665.93  44363.13     c  \n",
       "0     0.0    2758.07  45481.58     d  \n",
       "0     0.0    2771.49  44728.82     e  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quo_replicates_df = [ load_benchmark_replicate(f\"benchmarks.quobyte/a_vs_{x}.txt\", x) for x in ('a','b','c','d','e')]\n",
    "#query_line.append(load_benchmark_and_annotate('benchmarks/a_vs_a.txt', 1000, 10000))\n",
    "quo_replicates_df = pd.concat(quo_replicates_df)\n",
    "quo_replicates_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b8dbec83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean (minutes) 24.158385666666664, std 1.650659128576706\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(18, 4))\n",
    "\n",
    "ax = axs[0]\n",
    "ax.boxplot(replicates_df.s)\n",
    "ax.set_title('time (s)')\n",
    "\n",
    "ax = axs[1]\n",
    "ax.boxplot(quo_replicates_df.s)\n",
    "\n",
    "ax.set_title('time (s) - quobyte')\n",
    "\n",
    "replicates_df['m'] = replicates_df['s'] / 60.0\n",
    "\n",
    "print(f\"mean (minutes) {replicates_df.m.mean()}, std {replicates_df.m.std()}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8cb4d0cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean 16414.41, std 1619.5661713403376\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(18, 4))\n",
    "\n",
    "ax = axs[0]\n",
    "ax.boxplot(replicates_df.max_rss)\n",
    "ax.set_title('max RSS (mb)')\n",
    "\n",
    "ax = axs[1]\n",
    "ax.boxplot(quo_replicates_df.max_rss)\n",
    "ax.set_title('max RSS (mb) - quobyte')\n",
    "\n",
    "print(f\"mean {replicates_df.max_rss.mean()}, std {replicates_df.max_rss.std()}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0f4a6b97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean 93388.996, std 1851.2003899578276\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(18, 4))\n",
    "\n",
    "ax = axs[0]\n",
    "ax.boxplot(replicates_df.io_in)\n",
    "ax.set_title('I/O in (mb)')\n",
    "\n",
    "ax = axs[1]\n",
    "ax.boxplot(quo_replicates_df.io_in)\n",
    "ax.set_title('I/O in (mb) - quobyte')\n",
    "\n",
    "print(f\"mean {replicates_df.io_in.mean()}, std {replicates_df.io_in.std()}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d6d0b779",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_threads_benchmark_and_annotate(filename, n_threads):\n",
    "    df = pd.read_csv(filename.format(threads=n_threads), sep='\\t')\n",
    "    df['threads'] = n_threads\n",
    "    \n",
    "    return df\n",
    "\n",
    "\n",
    "x = [ load_threads_benchmark_and_annotate(f'benchmarks/a_vs_a_1000_t{t}.txt', t) for t in (4,8,16,36,40,44,48,52,56,60,64) ]\n",
    "x.append(load_threads_benchmark_and_annotate(f'benchmarks/a_vs_a_1000.txt', 32))\n",
    "\n",
    "threads_df = pd.concat(x).sort_values(by='threads')\n",
    "\n",
    "x2 = [ load_threads_benchmark_and_annotate(f'benchmarks.quobyte/a_vs_a_1000_t{t}.txt', t) for t in (4,8,16,36,40,44,48,52,56,60,64) ]\n",
    "x2.append(load_threads_benchmark_and_annotate(f'benchmarks.quobyte/a_vs_a_1000.txt', 32))\n",
    "\n",
    "quo_threads_df = pd.concat(x2).sort_values(by='threads')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1722f685",
   "metadata": {},
   "outputs": [
    {
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       "          s    h:m:s   max_rss   max_vms   max_uss   max_pss    io_in  io_out  \\\n",
       "0  814.4291  0:13:34   2246.58   2380.33   2243.58   2244.08    89.14    0.11   \n",
       "0  484.0369  0:08:04   2661.74   3352.14   2661.75   2662.25  1189.09    0.11   \n",
       "0  329.2121  0:05:29   6144.40   7038.87   6169.71   6170.21  7404.52    0.10   \n",
       "0  154.1568  0:02:34   8026.29   8707.47   8042.18   8042.25     0.07    0.11   \n",
       "0  251.5666  0:04:11   8824.62  10318.54   8824.39   8824.65     0.00    0.09   \n",
       "0  238.9409  0:03:58   8506.40  10415.53   8506.89   8507.14     0.00    0.09   \n",
       "0  245.7476  0:04:05   8379.21   9790.57   8344.01   8344.26     0.00    0.09   \n",
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       "\n",
       "   mean_load  cpu_time  threads  \n",
       "0     341.55   2782.05        4  \n",
       "0     649.08   3142.19        8  \n",
       "0    1137.80   3746.52       16  \n",
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       "0    1590.29   3729.21       56  \n",
       "0    1699.32   3821.86       60  \n",
       "0    1430.49   3833.95       64  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threads_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "311692ea",
   "metadata": {},
   "outputs": [
    {
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       "      <td>193.3999</td>\n",
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       "      <td>10145.53</td>\n",
       "      <td>8556.44</td>\n",
       "      <td>8556.70</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>10843.75</td>\n",
       "      <td>9245.59</td>\n",
       "      <td>9245.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1752.85</td>\n",
       "      <td>3391.84</td>\n",
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       "      <th>0</th>\n",
       "      <td>177.9364</td>\n",
       "      <td>0:02:57</td>\n",
       "      <td>10051.96</td>\n",
       "      <td>12198.20</td>\n",
       "      <td>10050.84</td>\n",
       "      <td>10051.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1953.76</td>\n",
       "      <td>3476.61</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0.0</td>\n",
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       "      <th>0</th>\n",
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       "      <td>11957.71</td>\n",
       "      <td>15372.31</td>\n",
       "      <td>11958.42</td>\n",
       "      <td>11958.68</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2216.51</td>\n",
       "      <td>3841.88</td>\n",
       "      <td>60</td>\n",
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       "      <th>0</th>\n",
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       "      <td>15743.64</td>\n",
       "      <td>12812.48</td>\n",
       "      <td>12812.76</td>\n",
       "      <td>1346.62</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2190.40</td>\n",
       "      <td>3900.65</td>\n",
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       "          s    h:m:s   max_rss   max_vms   max_uss   max_pss    io_in  io_out  \\\n",
       "0  730.2899  0:12:10   2834.26   3305.06   2859.93   2860.22    20.21     0.0   \n",
       "0  555.4759  0:09:15   3092.03   3682.67   3123.50   3124.25    23.80     0.0   \n",
       "0  322.4711  0:05:22   5016.30   5616.88   4961.63   4962.34    15.32     0.0   \n",
       "0  237.4116  0:03:57   7684.28   8800.33   7802.29   7802.59     0.00     0.0   \n",
       "0  188.2942  0:03:08   9008.73  11004.65   9008.57   9008.85     0.00     0.0   \n",
       "0  193.3999  0:03:13   8596.90  10145.53   8556.44   8556.70     0.00     0.0   \n",
       "0  180.6055  0:03:00   9618.16  11638.85   9612.49   9612.77     0.00     0.0   \n",
       "0  193.5016  0:03:13   9250.39  10843.75   9245.59   9245.84     0.00     0.0   \n",
       "0  177.9364  0:02:57  10051.96  12198.20  10050.84  10051.13     0.00     0.0   \n",
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       "0  178.0744  0:02:58  12812.36  15743.64  12812.48  12812.76  1346.62     0.0   \n",
       "\n",
       "   mean_load  cpu_time  threads  \n",
       "0     371.60   2713.87        4  \n",
       "0     643.01   3571.32        8  \n",
       "0    1140.63   3677.28       16  \n",
       "0    1627.00   3862.69       32  \n",
       "0    1506.60   2836.83       36  \n",
       "0    1573.39   3043.12       40  \n",
       "0    1747.33   3155.81       44  \n",
       "0    1752.85   3391.84       48  \n",
       "0    1953.76   3476.61       52  \n",
       "0    2050.62   3676.81       56  \n",
       "0    2216.51   3841.88       60  \n",
       "0    2190.40   3900.65       64  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quo_threads_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8a522d68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fig = plt.figure()\n",
    "fig, axs = plt.subplots(1, 3, figsize=(18, 4))\n",
    "\n",
    "ax = axs[0]\n",
    "ax.plot(threads_df.threads, threads_df.s, '-p', label='observed times')\n",
    "ax.plot(quo_threads_df.threads, quo_threads_df.s, '-p', label='observed times (quobyte)')\n",
    "\n",
    "ax.plot([4, 8, 16, 32, 64], [484 * 2, 484, 484 / 2, 484 / 4, 484 / 8], '--', label='perfect scaling')\n",
    "ax.axis(xmin=0, ymin=0)\n",
    "ax.set_xlabel('num threads')\n",
    "ax.set_ylabel('walltime (s)')\n",
    "ax.set_title('walltime')\n",
    "ax.legend(loc='upper right')\n",
    "\n",
    "ax = axs[1]\n",
    "ax.plot(threads_df.threads, threads_df.cpu_time, '-p')\n",
    "ax.plot(quo_threads_df.threads, quo_threads_df.cpu_time, '-p')\n",
    "\n",
    "ax.axis(xmin=0, ymin=0, ymax=4000)\n",
    "ax.set_xlabel('num threads')\n",
    "ax.set_ylabel('total CPU time (s)')\n",
    "ax.set_title('total compute')\n",
    "\n",
    "ax = axs[2]\n",
    "ax.plot(threads_df.threads, threads_df.max_rss, '-p')\n",
    "ax.plot(quo_threads_df.threads, quo_threads_df.max_rss, '-p')\n",
    "\n",
    "ax.axis(xmin=0, ymin=0)\n",
    "ax.set_xlabel('num threads')\n",
    "ax.set_ylabel('max RSS (GB)')\n",
    "ax.set_title('memory usage')\n",
    "\n",
    "plt.savefig('basic_benchmarks.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4a413afd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fig = plt.figure()\n",
    "fig, axs = plt.subplots(1, 3, figsize=(18, 4))\n",
    "\n",
    "ax = axs[0]\n",
    "ax.plot(threads_df.threads, threads_df.s, '-p', label='observed times')\n",
    "ax.plot(quo_threads_df.threads, quo_threads_df.s, '-p', label='observed times (quobyte)')\n",
    "\n",
    "ax.plot([4, 8, 16, 32, 64], [484 * 2, 484, 484 / 2, 484 / 4, 484 / 8], '--', label='perfect scaling')\n",
    "ax.axis(xmin=0)\n",
    "ax.set_xlabel('num threads')\n",
    "ax.set_ylabel('walltime (s)')\n",
    "ax.set_title('walltime')\n",
    "ax.set_yscale('log')\n",
    "ax.legend(loc='upper right')\n",
    "\n",
    "ax = axs[1]\n",
    "ax.plot(threads_df.threads, threads_df.cpu_time, '-p')\n",
    "ax.plot(quo_threads_df.threads, quo_threads_df.cpu_time, '-p')\n",
    "\n",
    "ax.axis(xmin=0, ymin=0, ymax=4000)\n",
    "ax.set_xlabel('num threads')\n",
    "ax.set_ylabel('total CPU time (s)')\n",
    "ax.set_title('total compute')\n",
    "\n",
    "ax = axs[2]\n",
    "ax.plot(threads_df.threads, threads_df.max_rss, '-p')\n",
    "ax.plot(quo_threads_df.threads, quo_threads_df.max_rss, '-p')\n",
    "\n",
    "ax.axis(xmin=0, ymin=0)\n",
    "ax.set_xlabel('num threads')\n",
    "ax.set_ylabel('max RSS (GB)')\n",
    "ax.set_title('memory usage')\n",
    "\n",
    "plt.savefig('basic_benchmarks.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c4e0830",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py38arm",
   "language": "python",
   "name": "py38arm"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}