bytesort_investigation_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "lang": "en"
   },
   "source": [
    "# Working with MySQL slow log data like Data Scientist. #\n",
    "\n",
    "The possibilities of pandas for filtering and aggregating data are wide and convenient. Advanced graphics tools allow you to visualize data in a non-standard way.\n",
    "\n",
    "Obfuscated data taken from some real mysql server will be used.\n",
    "\n",
    "\n",
    "## Problem ##\n",
    "\n",
    "An accident has occurred due to a failed setup of mysql tmpdir. The ramdisk size has not been set large enough. It is necessary to estimate the safe size of tmpdir based only on the mysql slow log data\n",
    "\n",
    "Perhaps this example is contrived, but when investigating events that have already taken place, there is no way to reconfigure the server to store logs in a table. We have to deal with the information we have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import mysqlslowlog # this module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "a set of files has been downloaded and placed in the **slowlog** directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "files = sorted(glob.glob('slowlog/mariadb*'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We read and combine all the data into one dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "slowlog/mariadb-slow.log\n",
      "slowlog/mariadb-slow.log.1.gz\n",
      "slowlog/mariadb-slow.log.2.gz\n",
      "slowlog/mariadb-slow.log.3.gz\n",
      "slowlog/mariadb-slow.log.4.gz\n",
      "slowlog/mariadb-slow.log.5.gz\n",
      "slowlog/mariadb-slow.log.6.gz\n",
      "slowlog/mariadb-slow.log.7.gz\n"
     ]
    }
   ],
   "source": [
    "alldata = pd.DataFrame()\n",
    "for filename in files:\n",
    "    print(filename)\n",
    "    onefile  = mysqlslowlog.read(filename,save_sql=False)\n",
    "    alldata = pd.concat([alldata,onefile])\n",
    "    \n",
    "#alldata=alldata.sort_index()\n",
    "\n",
    "alldata = alldata.drop(columns='schema') # need obfuscate some columns for privacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp            datetime64[ns]\n",
       "sqltext                      object\n",
       "rows_sent                     int64\n",
       "rows_examined                 int64\n",
       "rows_affected                 int64\n",
       "bytes_sent                    int64\n",
       "tmp_table_sizes               int64\n",
       "tmp_tables                    int64\n",
       "tmp_disk_tables               int64\n",
       "merge_passes                  int64\n",
       "query_time                  float64\n",
       "lock_time                   float64\n",
       "full_scan                      bool\n",
       "full_join                      bool\n",
       "tmp_table                      bool\n",
       "tmp_table_on_disk              bool\n",
       "filesort                       bool\n",
       "filesort_on_disk               bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# are the data types as expected ?\n",
    "alldata.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4642, 18)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total records\n",
    "alldata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4642 entries, 0 to 521\n",
      "Data columns (total 18 columns):\n",
      " #   Column             Non-Null Count  Dtype         \n",
      "---  ------             --------------  -----         \n",
      " 0   timestamp          4642 non-null   datetime64[ns]\n",
      " 1   sqltext            4642 non-null   object        \n",
      " 2   rows_sent          4642 non-null   int64         \n",
      " 3   rows_examined      4642 non-null   int64         \n",
      " 4   rows_affected      4642 non-null   int64         \n",
      " 5   bytes_sent         4642 non-null   int64         \n",
      " 6   tmp_table_sizes    4642 non-null   int64         \n",
      " 7   tmp_tables         4642 non-null   int64         \n",
      " 8   tmp_disk_tables    4642 non-null   int64         \n",
      " 9   merge_passes       4642 non-null   int64         \n",
      " 10  query_time         4642 non-null   float64       \n",
      " 11  lock_time          4642 non-null   float64       \n",
      " 12  full_scan          4642 non-null   bool          \n",
      " 13  full_join          4642 non-null   bool          \n",
      " 14  tmp_table          4642 non-null   bool          \n",
      " 15  tmp_table_on_disk  4642 non-null   bool          \n",
      " 16  filesort           4642 non-null   bool          \n",
      " 17  filesort_on_disk   4642 non-null   bool          \n",
      "dtypes: bool(6), datetime64[ns](1), float64(2), int64(8), object(1)\n",
      "memory usage: 720.8 KB\n"
     ]
    }
   ],
   "source": [
    "# summary of the memory occupied by the data:\n",
    "alldata.info(memory_usage=\"deep\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert bytes to megabytes.\n",
    "# In this form, the data is more familiar to evaluate.\n",
    "alldata['tmp_table_sizes_mb'] = alldata['tmp_table_sizes']/(1024*1024)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accident ##\n",
    "We are interested in the behavior of the server in the vicinity of 2022-07-14.\n",
    "Let's delete the extra data on the time interval.\n",
    "\n",
    "We are also interested in queries that involve sorting on disk\n",
    "\n",
    "The logical expression that combines all these conditions looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = alldata[(pd.Timestamp('2022-07-14 00:00') <alldata.timestamp )\n",
    "             & (alldata.timestamp < pd.Timestamp('2022-07-14 19:00'))\n",
    "             & alldata.tmp_table_on_disk]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "evaluate whether the data is loaded and preprocessed correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>timestamp</th>\n",
       "      <th>sqltext</th>\n",
       "      <th>rows_sent</th>\n",
       "      <th>rows_examined</th>\n",
       "      <th>rows_affected</th>\n",
       "      <th>bytes_sent</th>\n",
       "      <th>tmp_table_sizes</th>\n",
       "      <th>tmp_tables</th>\n",
       "      <th>tmp_disk_tables</th>\n",
       "      <th>merge_passes</th>\n",
       "      <th>query_time</th>\n",
       "      <th>lock_time</th>\n",
       "      <th>full_scan</th>\n",
       "      <th>full_join</th>\n",
       "      <th>tmp_table</th>\n",
       "      <th>tmp_table_on_disk</th>\n",
       "      <th>filesort</th>\n",
       "      <th>filesort_on_disk</th>\n",
       "      <th>tmp_table_sizes_mb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-07-14 00:00:10</td>\n",
       "      <td></td>\n",
       "      <td>100</td>\n",
       "      <td>1766939</td>\n",
       "      <td>0</td>\n",
       "      <td>344376</td>\n",
       "      <td>543612928</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8.830811</td>\n",
       "      <td>0.000167</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>518.429688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-07-14 00:05:10</td>\n",
       "      <td></td>\n",
       "      <td>100</td>\n",
       "      <td>1766919</td>\n",
       "      <td>0</td>\n",
       "      <td>348708</td>\n",
       "      <td>543465472</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8.492314</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>518.289062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-07-14 00:10:10</td>\n",
       "      <td></td>\n",
       "      <td>100</td>\n",
       "      <td>1766922</td>\n",
       "      <td>0</td>\n",
       "      <td>348607</td>\n",
       "      <td>543457280</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8.634478</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>518.281250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2022-07-14 00:15:10</td>\n",
       "      <td></td>\n",
       "      <td>100</td>\n",
       "      <td>1766922</td>\n",
       "      <td>0</td>\n",
       "      <td>348607</td>\n",
       "      <td>543457280</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8.627150</td>\n",
       "      <td>0.000126</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>518.281250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2022-07-14 00:20:09</td>\n",
       "      <td></td>\n",
       "      <td>100</td>\n",
       "      <td>1766922</td>\n",
       "      <td>0</td>\n",
       "      <td>348607</td>\n",
       "      <td>543457280</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8.635135</td>\n",
       "      <td>0.000192</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>518.281250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            timestamp sqltext  rows_sent  rows_examined  rows_affected  \\\n",
       "0 2022-07-14 00:00:10                100        1766939              0   \n",
       "3 2022-07-14 00:05:10                100        1766919              0   \n",
       "4 2022-07-14 00:10:10                100        1766922              0   \n",
       "5 2022-07-14 00:15:10                100        1766922              0   \n",
       "6 2022-07-14 00:20:09                100        1766922              0   \n",
       "\n",
       "   bytes_sent  tmp_table_sizes  tmp_tables  tmp_disk_tables  merge_passes  \\\n",
       "0      344376        543612928           3                2             0   \n",
       "3      348708        543465472           3                2             0   \n",
       "4      348607        543457280           3                2             0   \n",
       "5      348607        543457280           3                2             0   \n",
       "6      348607        543457280           3                2             0   \n",
       "\n",
       "   query_time  lock_time  full_scan  full_join  tmp_table  tmp_table_on_disk  \\\n",
       "0    8.830811   0.000167       True      False       True               True   \n",
       "3    8.492314   0.000195       True      False       True               True   \n",
       "4    8.634478   0.000161       True      False       True               True   \n",
       "5    8.627150   0.000126       True      False       True               True   \n",
       "6    8.635135   0.000192       True      False       True               True   \n",
       "\n",
       "   filesort  filesort_on_disk  tmp_table_sizes_mb  \n",
       "0      True             False          518.429688  \n",
       "3      True             False          518.289062  \n",
       "4      True             False          518.281250  \n",
       "5      True             False          518.281250  \n",
       "6      True             False          518.281250  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try a naive way to display the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter',x='timestamp',y='tmp_table_sizes_mb',figsize=(15,3),alpha=0.3,s=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obviously, these events are difficult to perceive as a process. \n",
    "Therefore, we regroup the data and for this we need pandas!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp\n",
       "2022-07-14 00:00:00    518.429688\n",
       "2022-07-14 00:05:00    518.289062\n",
       "2022-07-14 00:10:00    518.281250\n",
       "2022-07-14 00:15:00    518.281250\n",
       "2022-07-14 00:20:00    518.281250\n",
       "Freq: 5T, Name: tmp_table_sizes_mb, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sortbytes=df.resample('5min',on='timestamp')['tmp_table_sizes_mb'].sum()\n",
    "sortbytes.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sortbytes.plot(figsize=(15,3),grid=True,rot=45,\n",
    "               ylabel='Mbytes per 5min',\n",
    "               title='Disk sorts');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Due to the accident, not all requests completed and ended up in the log.\n",
    "It can be seen that before the accident, the amount of bytes increased. And after the crash, 12GB was successfully sorted.\n",
    "\n",
    "Smaller interval will show whether the excess at the time of the accident was critical:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alldata[(pd.Timestamp('2022-07-14 10:00') <alldata.timestamp )  \\\n",
    "         & (alldata.timestamp < pd.Timestamp('2022-07-14 15:10')\n",
    "         )\n",
    "         & alldata.tmp_table_on_disk] \\\n",
    "    .resample('1min',on='timestamp')['tmp_table_sizes_mb'].sum() \\\n",
    "    .plot(figsize=(15,3),grid=True,rot=45,ylabel='Mb per 1min',title='Disk sorts');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Code changes   ## \n",
    "A after changes in the code, the amount of sorted data has decreased significantly.\n",
    "We can draw this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alldata[(pd.Timestamp('2022-07-14 00:00') <alldata.timestamp )  \\\n",
    "         & (alldata.timestamp < pd.Timestamp('2022-07-18 0:00')\n",
    "         )\n",
    "         & alldata.tmp_table_on_disk] \\\n",
    "    .resample('5min',on='timestamp')['tmp_table_sizes_mb'] \\\n",
    "    .sum() \\\n",
    "    .plot(figsize=(15,3),grid=True,rot=0,\n",
    "          ylabel='Mb per 5min',\n",
    "          title='Disk sorts');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.2"
  },
  "nbTranslate": {
   "displayLangs": [
    "*"
   ],
   "hotkey": "alt-t",
   "langInMainMenu": true,
   "sourceLang": "en",
   "targetLang": "fr",
   "useGoogleTranslate": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}