bench/Benchmark.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "import pandas as pd\r\n",
      "import sarparse\r\n",
      "\r\n",
      "def runtime(df):\r\n",
      "    return df.iloc[-1]['time'] - df.iloc[0]['time']\r\n",
      "\r\n",
      "df_nosh = pd.DataFrame(sarparse.parse_pidstat(\"output_nosh.pidstat.gz\"))\r\n",
      "df_nosh['time'] = pd.to_datetime(df_nosh['time'])\r\n",
      "df_nosh.set_index(\"time\")\r\n",
      "\r\n",
      "df_sh = pd.DataFrame(sarparse.parse_pidstat(\"output_sh.pidstat.gz\"))\r\n",
      "df_sh['time'] = pd.to_datetime(df_sh['time'])\r\n",
      "df_sh.set_index(\"time\")\r\n"
     ]
    }
   ],
   "source": [
    "%run stats.py\n",
    "!cat stats.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>UID</th>\n",
       "      <th>PID</th>\n",
       "      <th>minflt_s</th>\n",
       "      <th>majflt_s</th>\n",
       "      <th>VSZ</th>\n",
       "      <th>RSS</th>\n",
       "      <th>Percent_MEM</th>\n",
       "      <th>Command</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-05-15 00:08:08</td>\n",
       "      <td>1001</td>\n",
       "      <td>24757</td>\n",
       "      <td>4695.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2971812.0</td>\n",
       "      <td>2719988.0</td>\n",
       "      <td>4.40</td>\n",
       "      <td>python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-05-15 00:08:09</td>\n",
       "      <td>1001</td>\n",
       "      <td>24757</td>\n",
       "      <td>1461.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3948436.0</td>\n",
       "      <td>3915212.0</td>\n",
       "      <td>6.33</td>\n",
       "      <td>python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-05-15 00:08:10</td>\n",
       "      <td>1001</td>\n",
       "      <td>24757</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3948436.0</td>\n",
       "      <td>3915212.0</td>\n",
       "      <td>6.33</td>\n",
       "      <td>python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-05-15 00:08:11</td>\n",
       "      <td>1001</td>\n",
       "      <td>24757</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3948436.0</td>\n",
       "      <td>3915212.0</td>\n",
       "      <td>6.33</td>\n",
       "      <td>python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-05-15 00:08:12</td>\n",
       "      <td>1001</td>\n",
       "      <td>24757</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3948436.0</td>\n",
       "      <td>3915212.0</td>\n",
       "      <td>6.33</td>\n",
       "      <td>python</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 time   UID    PID  minflt_s  majflt_s        VSZ        RSS  \\\n",
       "0 2016-05-15 00:08:08  1001  24757    4695.0       0.0  2971812.0  2719988.0   \n",
       "1 2016-05-15 00:08:09  1001  24757    1461.0       0.0  3948436.0  3915212.0   \n",
       "2 2016-05-15 00:08:10  1001  24757      25.0       0.0  3948436.0  3915212.0   \n",
       "3 2016-05-15 00:08:11  1001  24757      10.0       0.0  3948436.0  3915212.0   \n",
       "4 2016-05-15 00:08:12  1001  24757       5.0       0.0  3948436.0  3915212.0   \n",
       "\n",
       "   Percent_MEM Command  \n",
       "0         4.40  python  \n",
       "1         6.33  python  \n",
       "2         6.33  python  \n",
       "3         6.33  python  \n",
       "4         6.33  python  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nosh.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>UID</th>\n",
       "      <th>PID</th>\n",
       "      <th>minflt_s</th>\n",
       "      <th>majflt_s</th>\n",
       "      <th>VSZ</th>\n",
       "      <th>RSS</th>\n",
       "      <th>Percent_MEM</th>\n",
       "      <th>Command</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-05-15 21:13:37</td>\n",
       "      <td>1001</td>\n",
       "      <td>12511</td>\n",
       "      <td>2339.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>42136.0</td>\n",
       "      <td>8488.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-05-15 21:13:38</td>\n",
       "      <td>1001</td>\n",
       "      <td>12511</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42136.0</td>\n",
       "      <td>8488.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-05-15 21:13:39</td>\n",
       "      <td>1001</td>\n",
       "      <td>12511</td>\n",
       "      <td>65375.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>367660.0</td>\n",
       "      <td>267848.0</td>\n",
       "      <td>0.43</td>\n",
       "      <td>python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-05-15 21:13:40</td>\n",
       "      <td>1001</td>\n",
       "      <td>12511</td>\n",
       "      <td>32903.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>693184.0</td>\n",
       "      <td>450136.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>python</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-05-15 21:13:41</td>\n",
       "      <td>1001</td>\n",
       "      <td>12511</td>\n",
       "      <td>24165.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1018708.0</td>\n",
       "      <td>916756.0</td>\n",
       "      <td>1.48</td>\n",
       "      <td>python</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 time   UID    PID  minflt_s  majflt_s        VSZ       RSS  \\\n",
       "0 2016-05-15 21:13:37  1001  12511    2339.0      50.0    42136.0    8488.0   \n",
       "1 2016-05-15 21:13:38  1001  12511       0.0       0.0    42136.0    8488.0   \n",
       "2 2016-05-15 21:13:39  1001  12511   65375.0       2.0   367660.0  267848.0   \n",
       "3 2016-05-15 21:13:40  1001  12511   32903.0       0.0   693184.0  450136.0   \n",
       "4 2016-05-15 21:13:41  1001  12511   24165.0       0.0  1018708.0  916756.0   \n",
       "\n",
       "   Percent_MEM Command  \n",
       "0         0.01  python  \n",
       "1         0.01  python  \n",
       "2         0.43  python  \n",
       "3         0.73  python  \n",
       "4         1.48  python  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sh.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('0 days 00:23:00')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runtime(df_nosh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('0 days 01:21:02')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runtime(df_sh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f69615458d0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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JjBo1ivXr13PrrbcCkJGRAcCiRYssGSsiIl1TrVHLV8UH+SJ3B9tPCxGhfiFcHnsJSVEX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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f696149d2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.plt.plot(df_nosh['RSS'], label=\"Current\")\n",
    "sns.plt.plot(df_sh['RSS'], label=\"Sparsetable\")\n",
    "sns.plt.xlabel(\"Time (s)\")\n",
    "sns.plt.ylabel(\"RSS (bytes)\")\n",
    "sns.plt.legend(loc=\"best\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}