benchmarks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Benchmarking\n",
    "This is a first stab at making benchmarks from Python instead of shell/gnuplot, which is the method that has been used until now.\n",
    "It may make sense to add code for generating new benchmark numbers, e.g. using one of the methods\n",
    "[listed in this stackoverflow article](https://stackoverflow.com/questions/3652554/calling-java-from-python), or anything else really\n",
    "\n",
    "The main motivation behind revamping the test setup is not really to use python, but to uncover more information about the\n",
    "actual performance.  For instance, it seems like the regex matcher at present has an 18% disadvantage wrs to java matching.\n",
    "However, that is based on the \"unfair test\", which is deliberately unfair, giving java a good chance to outperform rmatch.\n",
    "\n",
    "Also the the test runs are short, and there may be benefit in running more extensive tests, both with respect to\n",
    "length of input and with respect to number of regular expressions.  The complexity of the expressions should also be\n",
    "variable.\n",
    "\n",
    "However, the idea of benchmarking towards the java regex library is still a good one, and one that the revamped test regime should\n",
    "build on.\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:11.501641Z",
     "start_time": "2023-09-24T22:15:11.320026Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "TODO: Maybe fix the date formatting using [this trick](https://stackoverflow.com/questions/23294197/plotting-chart-with-epoch-time-x-axis-using-matplotlib)."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "outputs": [],
   "source": [
    "df0 = pd.read_csv('rmatch-tester/measurements/handle-the-wuthering-heights-corpus.csv')\n",
    "df1 = df0.tail(5)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:11.501885Z",
     "start_time": "2023-09-24T22:15:11.329075Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df1[\"secondsSinceEpoch\"], df1[\"memoryInMb\"], label=\"memoryInMb\")\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:11.539943Z",
     "start_time": "2023-09-24T22:15:11.394576Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df1[\"secondsSinceEpoch\"], df1[\"millisDuration\"], label=\"millisDuration\")\n",
    "plt.yscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:11.914929Z",
     "start_time": "2023-09-24T22:15:11.537248Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "outputs": [],
   "source": [
    "df = pd.read_csv('rmatch-tester/logs/logfile-2023-05-08-20:37:45.csv')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:11.923828Z",
     "start_time": "2023-09-24T22:15:11.918273Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df[\"NoOfRegexps\"], df[\" javaMillis\"], label=\"javaMillis\")\n",
    "plt.plot(df[\"NoOfRegexps\"], df[\" regexMillis\"], label=\"rmatchMillis\")\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:12.081485Z",
     "start_time": "2023-09-24T22:15:11.925641Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df[\"NoOfRegexps\"], df[\" quotient\"], label=\"quotient\")\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:12.225249Z",
     "start_time": "2023-09-24T22:15:12.104766Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "outputs": [
    {
     "data": {
      "text/plain": "0.12511474586834134"
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\" quotient\"].std()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:12.227245Z",
     "start_time": "2023-09-24T22:15:12.222847Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "outputs": [
    {
     "data": {
      "text/plain": "1.181047619047619"
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\" quotient\"].mean()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:12.230809Z",
     "start_time": "2023-09-24T22:15:12.228039Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "outputs": [
    {
     "data": {
      "text/plain": "16.22013739593032"
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\" javaMillis\"].std()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:12.236516Z",
     "start_time": "2023-09-24T22:15:12.232689Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "outputs": [
    {
     "data": {
      "text/plain": "121.65714285714286"
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\" javaMillis\"].mean()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:12.242624Z",
     "start_time": "2023-09-24T22:15:12.237978Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "outputs": [
    {
     "data": {
      "text/plain": "21.037869221761536"
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\" regexMillis\"].std()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:12.293492Z",
     "start_time": "2023-09-24T22:15:12.241407Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "outputs": [
    {
     "data": {
      "text/plain": "103.81904761904762"
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\" regexMillis\"].mean()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:12.293900Z",
     "start_time": "2023-09-24T22:15:12.250262Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:12.305791Z",
     "start_time": "2023-09-24T22:15:12.258775Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Study the large corpus test and evolution over time"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "outputs": [],
   "source": [
    "largeCorpusDf = pd.read_csv('./rmatch-tester/logs/large-corpus-log.csv')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:12.319680Z",
     "start_time": "2023-09-24T22:15:12.263403Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "outputs": [
    {
     "data": {
      "text/plain": "         timestamp                          testSeriesId metadata   \n0    1684151426228  99d2b216-dc52-44c0-b379-3a74a11681a7   master  \\\n1    1684151734259  e3b088d2-65a6-4766-994f-a6bcb020ae63   master   \n2    1684152005265  42bb90f0-1649-490b-8826-f918c9092e6a   master   \n3    1684152107237  700981ac-a4cd-49a1-9ee3-f17d288559a9   master   \n4    1684152163314  0f75451c-5881-456c-9eea-d3e5207e35c1   master   \n..             ...                                   ...      ...   \n171  1695575996695  7bb391a5-2f39-4e45-bbb3-c812e251f1c2   master   \n172  1695578259490  bed0c449-1742-48f5-b5e4-3fe421ba43f3   master   \n173  1695591108293  79cae25a-affe-41af-8d98-654cb5149ee3   master   \n174  1695592582360  0c2e04cf-451a-41ed-ab2c-0c9e9328663c   master   \n175  1695592836207  ce299a2c-f979-4b64-953a-fc79e563c775   master   \n\n    matcherTypeName1  usedMemoryInMb1  durationInMillis1 matcherTypeName2   \n0             rmatch              917               4475             java  \\\n1             rmatch              599               6621             java   \n2             rmatch              247               4169             java   \n3             rmatch              769              19949             java   \n4             rmatch              171               9560             java   \n..               ...              ...                ...              ...   \n171           rmatch             3244             222493             java   \n172           rmatch             1222             161874             java   \n173           rmatch             2115             238636             java   \n174           rmatch             2747             253478             java   \n175           rmatch             1991             172404             java   \n\n     usedMemoryInMb2  durationInMillis2  noOfMatches  noOfMismatches   \n0                 54                391            0           96068  \\\n1                 55                375            0           96068   \n2                 54                351        96068               0   \n3                148               1709       207832            6111   \n4                100                950       142322             262   \n..               ...                ...          ...             ...   \n171              409              20023       657323              12   \n172              245              11678       318135          339188   \n173              413              47162       657323              12   \n174              406               6414       657323              12   \n175              407              10512       657323              12   \n\n     noOfRegexps  corpusLength  2to1Ratio  \n0        1154369       1179077   0.000000  \n1        1154369       1179077   0.000000  \n2        1154369       1179077   0.000000  \n3        1154369       1179077   0.000000  \n4        1154369       1179077   0.000000  \n..           ...           ...        ...  \n171        19566        662077  11.111872  \n172        19566        662077  13.861449  \n173        19566        662077   5.059921  \n174        19566        662077  39.519490  \n175        19566        662077  16.400684  \n\n[176 rows x 14 columns]",
      "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>timestamp</th>\n      <th>testSeriesId</th>\n      <th>metadata</th>\n      <th>matcherTypeName1</th>\n      <th>usedMemoryInMb1</th>\n      <th>durationInMillis1</th>\n      <th>matcherTypeName2</th>\n      <th>usedMemoryInMb2</th>\n      <th>durationInMillis2</th>\n      <th>noOfMatches</th>\n      <th>noOfMismatches</th>\n      <th>noOfRegexps</th>\n      <th>corpusLength</th>\n      <th>2to1Ratio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1684151426228</td>\n      <td>99d2b216-dc52-44c0-b379-3a74a11681a7</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>917</td>\n      <td>4475</td>\n      <td>java</td>\n      <td>54</td>\n      <td>391</td>\n      <td>0</td>\n      <td>96068</td>\n      <td>1154369</td>\n      <td>1179077</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1684151734259</td>\n      <td>e3b088d2-65a6-4766-994f-a6bcb020ae63</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>599</td>\n      <td>6621</td>\n      <td>java</td>\n      <td>55</td>\n      <td>375</td>\n      <td>0</td>\n      <td>96068</td>\n      <td>1154369</td>\n      <td>1179077</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1684152005265</td>\n      <td>42bb90f0-1649-490b-8826-f918c9092e6a</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>247</td>\n      <td>4169</td>\n      <td>java</td>\n      <td>54</td>\n      <td>351</td>\n      <td>96068</td>\n      <td>0</td>\n      <td>1154369</td>\n      <td>1179077</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1684152107237</td>\n      <td>700981ac-a4cd-49a1-9ee3-f17d288559a9</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>769</td>\n      <td>19949</td>\n      <td>java</td>\n      <td>148</td>\n      <td>1709</td>\n      <td>207832</td>\n      <td>6111</td>\n      <td>1154369</td>\n      <td>1179077</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1684152163314</td>\n      <td>0f75451c-5881-456c-9eea-d3e5207e35c1</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>171</td>\n      <td>9560</td>\n      <td>java</td>\n      <td>100</td>\n      <td>950</td>\n      <td>142322</td>\n      <td>262</td>\n      <td>1154369</td>\n      <td>1179077</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>171</th>\n      <td>1695575996695</td>\n      <td>7bb391a5-2f39-4e45-bbb3-c812e251f1c2</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>3244</td>\n      <td>222493</td>\n      <td>java</td>\n      <td>409</td>\n      <td>20023</td>\n      <td>657323</td>\n      <td>12</td>\n      <td>19566</td>\n      <td>662077</td>\n      <td>11.111872</td>\n    </tr>\n    <tr>\n      <th>172</th>\n      <td>1695578259490</td>\n      <td>bed0c449-1742-48f5-b5e4-3fe421ba43f3</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>1222</td>\n      <td>161874</td>\n      <td>java</td>\n      <td>245</td>\n      <td>11678</td>\n      <td>318135</td>\n      <td>339188</td>\n      <td>19566</td>\n      <td>662077</td>\n      <td>13.861449</td>\n    </tr>\n    <tr>\n      <th>173</th>\n      <td>1695591108293</td>\n      <td>79cae25a-affe-41af-8d98-654cb5149ee3</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>2115</td>\n      <td>238636</td>\n      <td>java</td>\n      <td>413</td>\n      <td>47162</td>\n      <td>657323</td>\n      <td>12</td>\n      <td>19566</td>\n      <td>662077</td>\n      <td>5.059921</td>\n    </tr>\n    <tr>\n      <th>174</th>\n      <td>1695592582360</td>\n      <td>0c2e04cf-451a-41ed-ab2c-0c9e9328663c</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>2747</td>\n      <td>253478</td>\n      <td>java</td>\n      <td>406</td>\n      <td>6414</td>\n      <td>657323</td>\n      <td>12</td>\n      <td>19566</td>\n      <td>662077</td>\n      <td>39.519490</td>\n    </tr>\n    <tr>\n      <th>175</th>\n      <td>1695592836207</td>\n      <td>ce299a2c-f979-4b64-953a-fc79e563c775</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>1991</td>\n      <td>172404</td>\n      <td>java</td>\n      <td>407</td>\n      <td>10512</td>\n      <td>657323</td>\n      <td>12</td>\n      <td>19566</td>\n      <td>662077</td>\n      <td>16.400684</td>\n    </tr>\n  </tbody>\n</table>\n<p>176 rows × 14 columns</p>\n</div>"
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "largeCorpusDf\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:12.320914Z",
     "start_time": "2023-09-24T22:15:12.271122Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "outputs": [],
   "source": [
    "largeCorpusDf[\"regexPerMilli1\"] = (largeCorpusDf[\"noOfRegexps\"]  *  largeCorpusDf[\"corpusLength\"]) /   largeCorpusDf[\"durationInMillis1\"]\n",
    "largeCorpusDf[\"regexPerMilli2\"] = (largeCorpusDf[\"noOfRegexps\"]  *  largeCorpusDf[\"corpusLength\"]) /   largeCorpusDf[\"durationInMillis2\"]\n",
    "largeCorpusDf[\"regex2to1ratio\"] = largeCorpusDf[\"regexPerMilli2\"] /   largeCorpusDf[\"regexPerMilli1\"]\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:12.321196Z",
     "start_time": "2023-09-24T22:15:12.288531Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "outputs": [
    {
     "data": {
      "text/plain": "         timestamp                          testSeriesId metadata   \n0    1684151426228  99d2b216-dc52-44c0-b379-3a74a11681a7   master  \\\n1    1684151734259  e3b088d2-65a6-4766-994f-a6bcb020ae63   master   \n2    1684152005265  42bb90f0-1649-490b-8826-f918c9092e6a   master   \n3    1684152107237  700981ac-a4cd-49a1-9ee3-f17d288559a9   master   \n4    1684152163314  0f75451c-5881-456c-9eea-d3e5207e35c1   master   \n..             ...                                   ...      ...   \n171  1695575996695  7bb391a5-2f39-4e45-bbb3-c812e251f1c2   master   \n172  1695578259490  bed0c449-1742-48f5-b5e4-3fe421ba43f3   master   \n173  1695591108293  79cae25a-affe-41af-8d98-654cb5149ee3   master   \n174  1695592582360  0c2e04cf-451a-41ed-ab2c-0c9e9328663c   master   \n175  1695592836207  ce299a2c-f979-4b64-953a-fc79e563c775   master   \n\n    matcherTypeName1  usedMemoryInMb1  durationInMillis1 matcherTypeName2   \n0             rmatch              917               4475             java  \\\n1             rmatch              599               6621             java   \n2             rmatch              247               4169             java   \n3             rmatch              769              19949             java   \n4             rmatch              171               9560             java   \n..               ...              ...                ...              ...   \n171           rmatch             3244             222493             java   \n172           rmatch             1222             161874             java   \n173           rmatch             2115             238636             java   \n174           rmatch             2747             253478             java   \n175           rmatch             1991             172404             java   \n\n     usedMemoryInMb2  durationInMillis2  noOfMatches  noOfMismatches   \n0                 54                391            0           96068  \\\n1                 55                375            0           96068   \n2                 54                351        96068               0   \n3                148               1709       207832            6111   \n4                100                950       142322             262   \n..               ...                ...          ...             ...   \n171              409              20023       657323              12   \n172              245              11678       318135          339188   \n173              413              47162       657323              12   \n174              406               6414       657323              12   \n175              407              10512       657323              12   \n\n     noOfRegexps  corpusLength  2to1Ratio  regexPerMilli1  regexPerMilli2   \n0        1154369       1179077   0.000000    3.041542e+08    3.481048e+09  \\\n1        1154369       1179077   0.000000    2.055717e+08    3.629573e+09   \n2        1154369       1179077   0.000000    3.264788e+08    3.877749e+09   \n3        1154369       1179077   0.000000    6.822848e+07    7.964248e+08   \n4        1154369       1179077   0.000000    1.423734e+08    1.432726e+09   \n..           ...           ...        ...             ...             ...   \n171        19566        662077  11.111872    5.822295e+04    6.469659e+05   \n172        19566        662077  13.861449    8.002643e+04    1.109282e+06   \n173        19566        662077   5.059921    5.428434e+04    2.746745e+05   \n174        19566        662077  39.519490    5.110581e+04    2.019675e+06   \n175        19566        662077  16.400684    7.513862e+04    1.232325e+06   \n\n     regex2to1ratio  \n0         11.445013  \n1         17.656000  \n2         11.877493  \n3         11.672908  \n4         10.063158  \n..              ...  \n171       11.111871  \n172       13.861449  \n173        5.059921  \n174       39.519489  \n175       16.400685  \n\n[176 rows x 17 columns]",
      "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>timestamp</th>\n      <th>testSeriesId</th>\n      <th>metadata</th>\n      <th>matcherTypeName1</th>\n      <th>usedMemoryInMb1</th>\n      <th>durationInMillis1</th>\n      <th>matcherTypeName2</th>\n      <th>usedMemoryInMb2</th>\n      <th>durationInMillis2</th>\n      <th>noOfMatches</th>\n      <th>noOfMismatches</th>\n      <th>noOfRegexps</th>\n      <th>corpusLength</th>\n      <th>2to1Ratio</th>\n      <th>regexPerMilli1</th>\n      <th>regexPerMilli2</th>\n      <th>regex2to1ratio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1684151426228</td>\n      <td>99d2b216-dc52-44c0-b379-3a74a11681a7</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>917</td>\n      <td>4475</td>\n      <td>java</td>\n      <td>54</td>\n      <td>391</td>\n      <td>0</td>\n      <td>96068</td>\n      <td>1154369</td>\n      <td>1179077</td>\n      <td>0.000000</td>\n      <td>3.041542e+08</td>\n      <td>3.481048e+09</td>\n      <td>11.445013</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1684151734259</td>\n      <td>e3b088d2-65a6-4766-994f-a6bcb020ae63</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>599</td>\n      <td>6621</td>\n      <td>java</td>\n      <td>55</td>\n      <td>375</td>\n      <td>0</td>\n      <td>96068</td>\n      <td>1154369</td>\n      <td>1179077</td>\n      <td>0.000000</td>\n      <td>2.055717e+08</td>\n      <td>3.629573e+09</td>\n      <td>17.656000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1684152005265</td>\n      <td>42bb90f0-1649-490b-8826-f918c9092e6a</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>247</td>\n      <td>4169</td>\n      <td>java</td>\n      <td>54</td>\n      <td>351</td>\n      <td>96068</td>\n      <td>0</td>\n      <td>1154369</td>\n      <td>1179077</td>\n      <td>0.000000</td>\n      <td>3.264788e+08</td>\n      <td>3.877749e+09</td>\n      <td>11.877493</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1684152107237</td>\n      <td>700981ac-a4cd-49a1-9ee3-f17d288559a9</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>769</td>\n      <td>19949</td>\n      <td>java</td>\n      <td>148</td>\n      <td>1709</td>\n      <td>207832</td>\n      <td>6111</td>\n      <td>1154369</td>\n      <td>1179077</td>\n      <td>0.000000</td>\n      <td>6.822848e+07</td>\n      <td>7.964248e+08</td>\n      <td>11.672908</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1684152163314</td>\n      <td>0f75451c-5881-456c-9eea-d3e5207e35c1</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>171</td>\n      <td>9560</td>\n      <td>java</td>\n      <td>100</td>\n      <td>950</td>\n      <td>142322</td>\n      <td>262</td>\n      <td>1154369</td>\n      <td>1179077</td>\n      <td>0.000000</td>\n      <td>1.423734e+08</td>\n      <td>1.432726e+09</td>\n      <td>10.063158</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>171</th>\n      <td>1695575996695</td>\n      <td>7bb391a5-2f39-4e45-bbb3-c812e251f1c2</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>3244</td>\n      <td>222493</td>\n      <td>java</td>\n      <td>409</td>\n      <td>20023</td>\n      <td>657323</td>\n      <td>12</td>\n      <td>19566</td>\n      <td>662077</td>\n      <td>11.111872</td>\n      <td>5.822295e+04</td>\n      <td>6.469659e+05</td>\n      <td>11.111871</td>\n    </tr>\n    <tr>\n      <th>172</th>\n      <td>1695578259490</td>\n      <td>bed0c449-1742-48f5-b5e4-3fe421ba43f3</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>1222</td>\n      <td>161874</td>\n      <td>java</td>\n      <td>245</td>\n      <td>11678</td>\n      <td>318135</td>\n      <td>339188</td>\n      <td>19566</td>\n      <td>662077</td>\n      <td>13.861449</td>\n      <td>8.002643e+04</td>\n      <td>1.109282e+06</td>\n      <td>13.861449</td>\n    </tr>\n    <tr>\n      <th>173</th>\n      <td>1695591108293</td>\n      <td>79cae25a-affe-41af-8d98-654cb5149ee3</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>2115</td>\n      <td>238636</td>\n      <td>java</td>\n      <td>413</td>\n      <td>47162</td>\n      <td>657323</td>\n      <td>12</td>\n      <td>19566</td>\n      <td>662077</td>\n      <td>5.059921</td>\n      <td>5.428434e+04</td>\n      <td>2.746745e+05</td>\n      <td>5.059921</td>\n    </tr>\n    <tr>\n      <th>174</th>\n      <td>1695592582360</td>\n      <td>0c2e04cf-451a-41ed-ab2c-0c9e9328663c</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>2747</td>\n      <td>253478</td>\n      <td>java</td>\n      <td>406</td>\n      <td>6414</td>\n      <td>657323</td>\n      <td>12</td>\n      <td>19566</td>\n      <td>662077</td>\n      <td>39.519490</td>\n      <td>5.110581e+04</td>\n      <td>2.019675e+06</td>\n      <td>39.519489</td>\n    </tr>\n    <tr>\n      <th>175</th>\n      <td>1695592836207</td>\n      <td>ce299a2c-f979-4b64-953a-fc79e563c775</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>1991</td>\n      <td>172404</td>\n      <td>java</td>\n      <td>407</td>\n      <td>10512</td>\n      <td>657323</td>\n      <td>12</td>\n      <td>19566</td>\n      <td>662077</td>\n      <td>16.400684</td>\n      <td>7.513862e+04</td>\n      <td>1.232325e+06</td>\n      <td>16.400685</td>\n    </tr>\n  </tbody>\n</table>\n<p>176 rows × 17 columns</p>\n</div>"
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "largeCorpusDf"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:12.334849Z",
     "start_time": "2023-09-24T22:15:12.304990Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(largeCorpusDf[\"timestamp\"], largeCorpusDf[\"regex2to1ratio\"], label=\"quotient\")\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:12.515796Z",
     "start_time": "2023-09-24T22:15:12.308019Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lastTen = largeCorpusDf.iloc[-10:]\n",
    "plt.plot(lastTen[\"timestamp\"], lastTen[\"regex2to1ratio\"], label=\"quotient\")\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:12.634944Z",
     "start_time": "2023-09-24T22:15:12.482950Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "outputs": [
    {
     "data": {
      "text/plain": "         timestamp                          testSeriesId metadata   \n166  1695498945369  11cf7403-c574-4523-ab83-eff784e71e06   master  \\\n167  1695499619109  0191bf8b-48a0-4a5d-95a0-9e7f41452a19   master   \n168  1695499982839  94540fdc-e65c-4a02-ac8e-3f3297c0a6c9   master   \n169  1695500237614  93b4c6c8-d003-4220-a0da-f28cee4f47ce   master   \n170  1695575363673  b198d2e2-4802-4acd-af92-bc38218d1e95   master   \n171  1695575996695  7bb391a5-2f39-4e45-bbb3-c812e251f1c2   master   \n172  1695578259490  bed0c449-1742-48f5-b5e4-3fe421ba43f3   master   \n173  1695591108293  79cae25a-affe-41af-8d98-654cb5149ee3   master   \n174  1695592582360  0c2e04cf-451a-41ed-ab2c-0c9e9328663c   master   \n175  1695592836207  ce299a2c-f979-4b64-953a-fc79e563c775   master   \n\n    matcherTypeName1  usedMemoryInMb1  durationInMillis1 matcherTypeName2   \n166           rmatch             1316             201812             java  \\\n167           rmatch             1715             250615             java   \n168           rmatch              635             246290             java   \n169           rmatch             1652             162909             java   \n170           rmatch             2771             206664             java   \n171           rmatch             3244             222493             java   \n172           rmatch             1222             161874             java   \n173           rmatch             2115             238636             java   \n174           rmatch             2747             253478             java   \n175           rmatch             1991             172404             java   \n\n     usedMemoryInMb2  durationInMillis2  noOfMatches  noOfMismatches   \n166              408              14858       657323              12  \\\n167              409              31242       657323              12   \n168              407              10769       657323              12   \n169              406               8117       657323              12   \n170              408              32328       657323              12   \n171              409              20023       657323              12   \n172              245              11678       318135          339188   \n173              413              47162       657323              12   \n174              406               6414       657323              12   \n175              407              10512       657323              12   \n\n     noOfRegexps  corpusLength  2to1Ratio  regexPerMilli1  regexPerMilli2   \n166        19566        662077  13.582716    64189.436614    8.718669e+05  \\\n167        19566        662077   8.021733    51689.637819    4.146405e+05   \n168        19566        662077  22.870275    52597.338836    1.202916e+06   \n169        19566        662077  20.070099    79518.004420    1.595934e+06   \n170        19566        662077   6.392724    62682.414847    4.007114e+05   \n171        19566        662077  11.111872    58222.948956    6.469659e+05   \n172        19566        662077  13.861449    80026.431558    1.109282e+06   \n173        19566        662077   5.059921    54284.343444    2.746745e+05   \n174        19566        662077  39.519490    51105.810295    2.019675e+06   \n175        19566        662077  16.400684    75138.619649    1.232325e+06   \n\n     regex2to1ratio  \n166       13.582716  \n167        8.021734  \n168       22.870276  \n169       20.070100  \n170        6.392725  \n171       11.111871  \n172       13.861449  \n173        5.059921  \n174       39.519489  \n175       16.400685  ",
      "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>timestamp</th>\n      <th>testSeriesId</th>\n      <th>metadata</th>\n      <th>matcherTypeName1</th>\n      <th>usedMemoryInMb1</th>\n      <th>durationInMillis1</th>\n      <th>matcherTypeName2</th>\n      <th>usedMemoryInMb2</th>\n      <th>durationInMillis2</th>\n      <th>noOfMatches</th>\n      <th>noOfMismatches</th>\n      <th>noOfRegexps</th>\n      <th>corpusLength</th>\n      <th>2to1Ratio</th>\n      <th>regexPerMilli1</th>\n      <th>regexPerMilli2</th>\n      <th>regex2to1ratio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>166</th>\n      <td>1695498945369</td>\n      <td>11cf7403-c574-4523-ab83-eff784e71e06</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>1316</td>\n      <td>201812</td>\n      <td>java</td>\n      <td>408</td>\n      <td>14858</td>\n      <td>657323</td>\n      <td>12</td>\n      <td>19566</td>\n      <td>662077</td>\n      <td>13.582716</td>\n      <td>64189.436614</td>\n      <td>8.718669e+05</td>\n      <td>13.582716</td>\n    </tr>\n    <tr>\n      <th>167</th>\n      <td>1695499619109</td>\n      <td>0191bf8b-48a0-4a5d-95a0-9e7f41452a19</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>1715</td>\n      <td>250615</td>\n      <td>java</td>\n      <td>409</td>\n      <td>31242</td>\n      <td>657323</td>\n      <td>12</td>\n      <td>19566</td>\n      <td>662077</td>\n      <td>8.021733</td>\n      <td>51689.637819</td>\n      <td>4.146405e+05</td>\n      <td>8.021734</td>\n    </tr>\n    <tr>\n      <th>168</th>\n      <td>1695499982839</td>\n      <td>94540fdc-e65c-4a02-ac8e-3f3297c0a6c9</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>635</td>\n      <td>246290</td>\n      <td>java</td>\n      <td>407</td>\n      <td>10769</td>\n      <td>657323</td>\n      <td>12</td>\n      <td>19566</td>\n      <td>662077</td>\n      <td>22.870275</td>\n      <td>52597.338836</td>\n      <td>1.202916e+06</td>\n      <td>22.870276</td>\n    </tr>\n    <tr>\n      <th>169</th>\n      <td>1695500237614</td>\n      <td>93b4c6c8-d003-4220-a0da-f28cee4f47ce</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>1652</td>\n      <td>162909</td>\n      <td>java</td>\n      <td>406</td>\n      <td>8117</td>\n      <td>657323</td>\n      <td>12</td>\n      <td>19566</td>\n      <td>662077</td>\n      <td>20.070099</td>\n      <td>79518.004420</td>\n      <td>1.595934e+06</td>\n      <td>20.070100</td>\n    </tr>\n    <tr>\n      <th>170</th>\n      <td>1695575363673</td>\n      <td>b198d2e2-4802-4acd-af92-bc38218d1e95</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>2771</td>\n      <td>206664</td>\n      <td>java</td>\n      <td>408</td>\n      <td>32328</td>\n      <td>657323</td>\n      <td>12</td>\n      <td>19566</td>\n      <td>662077</td>\n      <td>6.392724</td>\n      <td>62682.414847</td>\n      <td>4.007114e+05</td>\n      <td>6.392725</td>\n    </tr>\n    <tr>\n      <th>171</th>\n      <td>1695575996695</td>\n      <td>7bb391a5-2f39-4e45-bbb3-c812e251f1c2</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>3244</td>\n      <td>222493</td>\n      <td>java</td>\n      <td>409</td>\n      <td>20023</td>\n      <td>657323</td>\n      <td>12</td>\n      <td>19566</td>\n      <td>662077</td>\n      <td>11.111872</td>\n      <td>58222.948956</td>\n      <td>6.469659e+05</td>\n      <td>11.111871</td>\n    </tr>\n    <tr>\n      <th>172</th>\n      <td>1695578259490</td>\n      <td>bed0c449-1742-48f5-b5e4-3fe421ba43f3</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>1222</td>\n      <td>161874</td>\n      <td>java</td>\n      <td>245</td>\n      <td>11678</td>\n      <td>318135</td>\n      <td>339188</td>\n      <td>19566</td>\n      <td>662077</td>\n      <td>13.861449</td>\n      <td>80026.431558</td>\n      <td>1.109282e+06</td>\n      <td>13.861449</td>\n    </tr>\n    <tr>\n      <th>173</th>\n      <td>1695591108293</td>\n      <td>79cae25a-affe-41af-8d98-654cb5149ee3</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>2115</td>\n      <td>238636</td>\n      <td>java</td>\n      <td>413</td>\n      <td>47162</td>\n      <td>657323</td>\n      <td>12</td>\n      <td>19566</td>\n      <td>662077</td>\n      <td>5.059921</td>\n      <td>54284.343444</td>\n      <td>2.746745e+05</td>\n      <td>5.059921</td>\n    </tr>\n    <tr>\n      <th>174</th>\n      <td>1695592582360</td>\n      <td>0c2e04cf-451a-41ed-ab2c-0c9e9328663c</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>2747</td>\n      <td>253478</td>\n      <td>java</td>\n      <td>406</td>\n      <td>6414</td>\n      <td>657323</td>\n      <td>12</td>\n      <td>19566</td>\n      <td>662077</td>\n      <td>39.519490</td>\n      <td>51105.810295</td>\n      <td>2.019675e+06</td>\n      <td>39.519489</td>\n    </tr>\n    <tr>\n      <th>175</th>\n      <td>1695592836207</td>\n      <td>ce299a2c-f979-4b64-953a-fc79e563c775</td>\n      <td>master</td>\n      <td>rmatch</td>\n      <td>1991</td>\n      <td>172404</td>\n      <td>java</td>\n      <td>407</td>\n      <td>10512</td>\n      <td>657323</td>\n      <td>12</td>\n      <td>19566</td>\n      <td>662077</td>\n      <td>16.400684</td>\n      <td>75138.619649</td>\n      <td>1.232325e+06</td>\n      <td>16.400685</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "lastTen"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:12.652285Z",
     "start_time": "2023-09-24T22:15:12.645267Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "outputs": [],
   "source": [
    "import sqlite3"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:12.652638Z",
     "start_time": "2023-09-24T22:15:12.650495Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         timestamp                          testSeriesId metadata   \n",
      "0    1684151426228  99d2b216-dc52-44c0-b379-3a74a11681a7   master  \\\n",
      "1    1684151734259  e3b088d2-65a6-4766-994f-a6bcb020ae63   master   \n",
      "2    1684152005265  42bb90f0-1649-490b-8826-f918c9092e6a   master   \n",
      "3    1684152107237  700981ac-a4cd-49a1-9ee3-f17d288559a9   master   \n",
      "4    1684152163314  0f75451c-5881-456c-9eea-d3e5207e35c1   master   \n",
      "..             ...                                   ...      ...   \n",
      "171  1695575996695  7bb391a5-2f39-4e45-bbb3-c812e251f1c2   master   \n",
      "172  1695578259490  bed0c449-1742-48f5-b5e4-3fe421ba43f3   master   \n",
      "173  1695591108293  79cae25a-affe-41af-8d98-654cb5149ee3   master   \n",
      "174  1695592582360  0c2e04cf-451a-41ed-ab2c-0c9e9328663c   master   \n",
      "175  1695592836207  ce299a2c-f979-4b64-953a-fc79e563c775   master   \n",
      "\n",
      "    matcherTypeName1  usedMemoryInMb1  durationInMillis1 matcherTypeName2   \n",
      "0             rmatch              917               4475             java  \\\n",
      "1             rmatch              599               6621             java   \n",
      "2             rmatch              247               4169             java   \n",
      "3             rmatch              769              19949             java   \n",
      "4             rmatch              171               9560             java   \n",
      "..               ...              ...                ...              ...   \n",
      "171           rmatch             3244             222493             java   \n",
      "172           rmatch             1222             161874             java   \n",
      "173           rmatch             2115             238636             java   \n",
      "174           rmatch             2747             253478             java   \n",
      "175           rmatch             1991             172404             java   \n",
      "\n",
      "     usedMemoryInMb2  durationInMillis2  noOfMatches  noOfMismatches   \n",
      "0                 54                391            0           96068  \\\n",
      "1                 55                375            0           96068   \n",
      "2                 54                351        96068               0   \n",
      "3                148               1709       207832            6111   \n",
      "4                100                950       142322             262   \n",
      "..               ...                ...          ...             ...   \n",
      "171              409              20023       657323              12   \n",
      "172              245              11678       318135          339188   \n",
      "173              413              47162       657323              12   \n",
      "174              406               6414       657323              12   \n",
      "175              407              10512       657323              12   \n",
      "\n",
      "     noOfRegexps  corpusLength  2to1Ratio  \n",
      "0        1154369       1179077   0.000000  \n",
      "1        1154369       1179077   0.000000  \n",
      "2        1154369       1179077   0.000000  \n",
      "3        1154369       1179077   0.000000  \n",
      "4        1154369       1179077   0.000000  \n",
      "..           ...           ...        ...  \n",
      "171        19566        662077  11.111872  \n",
      "172        19566        662077  13.861449  \n",
      "173        19566        662077   5.059921  \n",
      "174        19566        662077  39.519490  \n",
      "175        19566        662077  16.400684  \n",
      "\n",
      "[176 rows x 14 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "df = pd.read_csv('./rmatch-tester/logs/large-corpus-log.csv')\n",
    "print(df)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:12.679467Z",
     "start_time": "2023-09-24T22:15:12.654787Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "outputs": [],
   "source": [
    "import os\n",
    "dbfile = 'measurements.sqlite'\n",
    "if os.path.exists(dbfile):\n",
    "    os.remove(dbfile)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:12.698373Z",
     "start_time": "2023-09-24T22:15:12.664544Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "outputs": [],
   "source": [
    "\n",
    "table_name = 'Measurements'\n",
    "\n",
    "query = f'Create table if not Exists {table_name} (timestamp,testSeriesId,metadata,matcherTypeName1,usedMemoryInMb1,durationInMillis1,matcherTypeName2,usedMemoryInMb2,durationInMillis2,noOfMatches,noOfMismatches,noOfRegexps,corpusLength,\"2to1Ratio\")'\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:12.698648Z",
     "start_time": "2023-09-24T22:15:12.671630Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "outputs": [],
   "source": [
    "conn = sqlite3.connect(dbfile)\n",
    "conn.execute(query)\n",
    "df.to_sql(table_name,conn,if_exists='replace',index=False)\n",
    "conn.commit()\n",
    "conn.close()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:12.742106Z",
     "start_time": "2023-09-24T22:15:12.674405Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         timestamp                          testSeriesId metadata   \n",
      "0    1684151426228  99d2b216-dc52-44c0-b379-3a74a11681a7   master  \\\n",
      "1    1684151734259  e3b088d2-65a6-4766-994f-a6bcb020ae63   master   \n",
      "2    1684152005265  42bb90f0-1649-490b-8826-f918c9092e6a   master   \n",
      "3    1684152107237  700981ac-a4cd-49a1-9ee3-f17d288559a9   master   \n",
      "4    1684152163314  0f75451c-5881-456c-9eea-d3e5207e35c1   master   \n",
      "..             ...                                   ...      ...   \n",
      "171  1695575996695  7bb391a5-2f39-4e45-bbb3-c812e251f1c2   master   \n",
      "172  1695578259490  bed0c449-1742-48f5-b5e4-3fe421ba43f3   master   \n",
      "173  1695591108293  79cae25a-affe-41af-8d98-654cb5149ee3   master   \n",
      "174  1695592582360  0c2e04cf-451a-41ed-ab2c-0c9e9328663c   master   \n",
      "175  1695592836207  ce299a2c-f979-4b64-953a-fc79e563c775   master   \n",
      "\n",
      "    matcherTypeName1  usedMemoryInMb1  durationInMillis1 matcherTypeName2   \n",
      "0             rmatch              917               4475             java  \\\n",
      "1             rmatch              599               6621             java   \n",
      "2             rmatch              247               4169             java   \n",
      "3             rmatch              769              19949             java   \n",
      "4             rmatch              171               9560             java   \n",
      "..               ...              ...                ...              ...   \n",
      "171           rmatch             3244             222493             java   \n",
      "172           rmatch             1222             161874             java   \n",
      "173           rmatch             2115             238636             java   \n",
      "174           rmatch             2747             253478             java   \n",
      "175           rmatch             1991             172404             java   \n",
      "\n",
      "     usedMemoryInMb2  durationInMillis2  noOfMatches  noOfMismatches   \n",
      "0                 54                391            0           96068  \\\n",
      "1                 55                375            0           96068   \n",
      "2                 54                351        96068               0   \n",
      "3                148               1709       207832            6111   \n",
      "4                100                950       142322             262   \n",
      "..               ...                ...          ...             ...   \n",
      "171              409              20023       657323              12   \n",
      "172              245              11678       318135          339188   \n",
      "173              413              47162       657323              12   \n",
      "174              406               6414       657323              12   \n",
      "175              407              10512       657323              12   \n",
      "\n",
      "     noOfRegexps  corpusLength  2to1Ratio  \n",
      "0        1154369       1179077   0.000000  \n",
      "1        1154369       1179077   0.000000  \n",
      "2        1154369       1179077   0.000000  \n",
      "3        1154369       1179077   0.000000  \n",
      "4        1154369       1179077   0.000000  \n",
      "..           ...           ...        ...  \n",
      "171        19566        662077  11.111872  \n",
      "172        19566        662077  13.861449  \n",
      "173        19566        662077   5.059921  \n",
      "174        19566        662077  39.519490  \n",
      "175        19566        662077  16.400684  \n",
      "\n",
      "[176 rows x 14 columns]\n"
     ]
    }
   ],
   "source": [
    "conn = sqlite3.connect(dbfile)\n",
    "r_df = pd.read_sql(\"select * from Measurements\",conn)\n",
    "print(r_df)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:12.821817Z",
     "start_time": "2023-09-24T22:15:12.688923Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "outputs": [],
   "source": [
    "with open('summarize-large-corpus-trials.sql', 'r') as file:\n",
    "    summarizerSql = file.read().rstrip()\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:12.822018Z",
     "start_time": "2023-09-24T22:15:12.701201Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "outputs": [
    {
     "data": {
      "text/plain": "\"select min(timestamp) as timestamp,\\n       testSeriesId,\\n       metadata,\\n       noOfMatches,\\n       noOfMismatches,\\n       corpusLength,\\n       noOfRegexps,\\n       min(durationInMillis2) as 'minJavaDuration',\\n       max(durationInMillis2) as 'maxJavaDuration',\\n       avg(durationInMillis2) as 'avgJavaDuration',\\n       min(durationInMillis1) as 'minRegexDuration',\\n       max(durationInMillis1) as 'maxRegexDuration',\\n       avg(durationInMillis1) as 'avgRegexDuration',\\n--       stdev(durationInMillis2) as 'stdevJavaDuration',\\n--       stdev(durationInMillis1) as 'stdevRegexDuration',\\n       ((1.0*min(durationInMillis1))/6154) as 'againstBestJavaRatio',\\n       ((1.0*min(durationInMillis1))/(1.0*min(durationInMillis2))) as 'minMinRatio',\\n       ((1.0*max(durationInMillis1))/(1.0*max(durationInMillis2))) as 'maxMaxRatio',\\n       ((1.0*avg(durationInMillis1))/(1.0*avg(durationInMillis2))) as 'avgAvgRatio'\\n--       ((1.0*stdev(durationInMillis1))/(1.0*stdev(durationInMillis2))) as 'stdevStdevRatio'\\nfrom Measurements  where timestamp >= 1685300128023 group by testSeriesId  order by timestamp asc;\""
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summarizerSql"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:12.822288Z",
     "start_time": "2023-09-24T22:15:12.707387Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        timestamp                          testSeriesId   \n",
      "0   1685300128023  A9B69B90-AAB2-4AD3-A421-5E232DF8C8BD  \\\n",
      "1   1685374976474  8383FDD9-A509-4046-8F04-FD430BB07BA8   \n",
      "2   1685380210149  28CC3751-BA44-4B0D-BA81-27F159A3079C   \n",
      "3   1685390284561  58C63D2D-F066-415C-AE24-E045B873D387   \n",
      "4   1685395218314  73EE6D74-51C3-46AD-9650-47FAD020E03E   \n",
      "..            ...                                   ...   \n",
      "75  1695575996695  7bb391a5-2f39-4e45-bbb3-c812e251f1c2   \n",
      "76  1695578259490  bed0c449-1742-48f5-b5e4-3fe421ba43f3   \n",
      "77  1695591108293  79cae25a-affe-41af-8d98-654cb5149ee3   \n",
      "78  1695592582360  0c2e04cf-451a-41ed-ab2c-0c9e9328663c   \n",
      "79  1695592836207  ce299a2c-f979-4b64-953a-fc79e563c775   \n",
      "\n",
      "                              metadata  noOfMatches  noOfMismatches   \n",
      "0   u/la3lma/refactoring-a-la-chatgpt2      1144409               8  \\\n",
      "1                               master      1144409               8   \n",
      "2      u/la3lma/optimize-using-chatgtp      1144409               8   \n",
      "3   u/la3lma/refactoring-a-la-chatgpt2      1144409               8   \n",
      "4                               master      1144409               8   \n",
      "..                                 ...          ...             ...   \n",
      "75                              master       657323              12   \n",
      "76                              master       318135          339188   \n",
      "77                              master       657323              12   \n",
      "78                              master       657323              12   \n",
      "79                              master       657323              12   \n",
      "\n",
      "    corpusLength  noOfRegexps  minJavaDuration  maxJavaDuration   \n",
      "0        1154369        19566            10009            21266  \\\n",
      "1        1154369        19566             6147            17757   \n",
      "2        1154369        19566             9492            63137   \n",
      "3        1154369        19566             6154            20161   \n",
      "4        1154369        19566             8824            14363   \n",
      "..           ...          ...              ...              ...   \n",
      "75        662077        19566            20023            20023   \n",
      "76        662077        19566            11678            11678   \n",
      "77        662077        19566            47162            47162   \n",
      "78        662077        19566             6414             6414   \n",
      "79        662077        19566            10512            10512   \n",
      "\n",
      "    avgJavaDuration  minRegexDuration  maxRegexDuration  avgRegexDuration   \n",
      "0           14724.6            134888            265588          192971.0  \\\n",
      "1           11564.7             69792            164391          141148.1   \n",
      "2           16955.2            102796            209465          147334.0   \n",
      "3           11824.1             72052            202999          142595.8   \n",
      "4           10677.6            151514            343727          204564.9   \n",
      "..              ...               ...               ...               ...   \n",
      "75          20023.0            222493            222493          222493.0   \n",
      "76          11678.0            161874            161874          161874.0   \n",
      "77          47162.0            238636            238636          238636.0   \n",
      "78           6414.0            253478            253478          253478.0   \n",
      "79          10512.0            172404            172404          172404.0   \n",
      "\n",
      "    againstBestJavaRatio  minMinRatio  maxMaxRatio  avgAvgRatio  \n",
      "0              21.918752    13.476671    12.488855    13.105348  \n",
      "1              11.340916    11.353831     9.257814    12.205081  \n",
      "2              16.703932    10.829751     3.317627     8.689606  \n",
      "3              11.708157    11.708157    10.068895    12.059759  \n",
      "4              24.620409    17.170671    23.931421    19.158322  \n",
      "..                   ...          ...          ...          ...  \n",
      "75             36.154209    11.111871    11.111871    11.111871  \n",
      "76             26.303867    13.861449    13.861449    13.861449  \n",
      "77             38.777381     5.059921     5.059921     5.059921  \n",
      "78             41.189145    39.519489    39.519489    39.519489  \n",
      "79             28.014950    16.400685    16.400685    16.400685  \n",
      "\n",
      "[80 rows x 17 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "conn = sqlite3.connect(dbfile)\n",
    "r_df = pd.read_sql(summarizerSql, conn)\n",
    "\n",
    "print(r_df)\n",
    "conn.close()\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:12.822668Z",
     "start_time": "2023-09-24T22:15:12.716538Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lastTen = r_df.iloc[-100:]\n",
    "plt.plot(r_df[\"timestamp\"], lastTen[\"minMinRatio\"], label=\"quotient\")\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:12.912737Z",
     "start_time": "2023-09-24T22:15:12.732863Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-09-24T22:15:12.912949Z",
     "start_time": "2023-09-24T22:15:12.905202Z"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}