2018_IDUG_NA.ipynb

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    "# Hello, _nbpresent_!"
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   "source": [
    "import nbpresent\n",
    "nbpresent.__version__"
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   "source": [
    "# Using Jupyter Notebook for Db2 Administration\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;Ember Crooks\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;XTIVIA\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;Session Code: F1\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;Wednesday, May 2 1:05 PM"
   ]
  },
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   "source": [
    "## Setting up this Jupyter Notebook"
   ]
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   "source": [
    "This notebook should be launched from a session of jupyter notebook that was launched from a DB2 command window\n",
    "To do this, run an administrator DB2 command window as an administrator and type 'jupyter notebook'"
   ]
  },
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   },
   "source": [
    "## Jupyter Notebook"
   ]
  },
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   "source": [
    "### What is Jupyter Notebook?\n",
    "- Open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and explanatory text.\n",
    "- Heavily used in Data Science\n",
    "- Supports a large number of programming languages, including SQL\n",
    "- Requires Python\n",
    "- Easiest to install by [installing Anaconda](https://www.continuum.io/downloads)"
   ]
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   "source": [
    "### Jupyter Notebook Terms\n",
    "- **Application**\n",
    "    - Server-client application that allows editing and running notebook document via web browser\n",
    "    - Can run on a local laptop/desktop or a remote server\n",
    "- **Kernel**\n",
    "    - Computational engine that execute the code contained in the notebook\n",
    "    - ipython kernel executes python – kernels for other languages exist\n",
    "    - Each running notebook has a different instnace of a Kernel\n",
    "- **Notebook Dashboard**\n",
    "    - Shown in browser when you launch Jupyter Notebook\n",
    "    - Features similar to a file manager\n",
    "    - Used to open notebooks and manage the running kernels"
   ]
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  {
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   "source": [
    "### More Jupyter Notebook Terms\n",
    "- **Notebook document or notebooks**\n",
    "    - Produced by the Jupyter Notebook App\n",
    "    - Contains both computer code and rich text elements\n",
    "- **Cell**\n",
    "    - Portion of a notebook that is either markdown-formatted text or code\n",
    "    - Each cell can be independently executed in any order, but convention dictates they be executed in order\n",
    "- **Magic**\n",
    "    - Called with a command line style syntax\n",
    "    - Magic Functions work at the cell or line level"
   ]
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   "source": [
    "### Installing Jupyter Notebook on Windows\n",
    "- [Download Anaconda](https://www.continuum.io/downloads)\n",
    "- Install Anaconda, accepting the defaults\n",
    "- Install at least a DB2 Client\n",
    "- Download and install [visual studio tools](http://landinghub.visualstudio.com/visual-cpp-build-tools)\n",
    "\n",
    "- Jupyter Notebook is likely to be installed on a DB2 client such as your laptop or a jump server"
   ]
  },
  {
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   "source": [
    "### Launching Jupyter Notebook on Windows\n",
    "- Launch Jupyter Notebook from a DB2 Command Window PowerShell window with the Db2 environment variables set\n",
    "- Cd to directory where Jupyter Notebooks are stored, first\n",
    "![screenshot](files/LaunchJupyterNotebook.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jupyter Notebook Basics\n",
    "[Basics of Jupyter Notebook](BasicsOfJupyterNotebook.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting up Jupyter Notebook for use with SQL Magic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Install Packages"
   ]
  },
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   "source": [
    "import sys,os,os.path\n",
    "os.environ['DB2INSTANCE']"
   ]
  },
  {
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    "## This cell only needs to be executed if these packages have not been previously installed\n",
    "#Uncomment the below line and set to the proper value if installing these for the first time\n",
    "#os.environ['IBM_DB_HOME']='C:\\Program Files\\IBM\\SQLLIB_01'\n",
    "!pip install ipython-sql\n",
    "!pip install ibm_db \n",
    "!pip install ibm_db_sa"
   ]
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   "source": [
    "Restart the Kernel if this is your first time installing the above. The next steps will fail unless you do this."
   ]
  },
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   "source": [
    "#### Import the modules and load SQL magic"
   ]
  },
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   "source": [
    "## This cell must be executed any time the Kernel is started or restarted\n",
    "import ibm_db\n",
    "import ibm_db_sa\n",
    "import sqlalchemy\n",
    "%load_ext sql"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "#### Connect to the database. \n",
    "- Change the values of user, host, and password to match your environment. \n",
    "- For connection to a local database, use 'localhost' for the host name. \n",
    "- Also change the port number and database name in the connection string.\n",
    "- Minimally, the password is stored in a separate file for easier sharing"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Connected: db2admin@SAMPLE'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "user='db2admin'\n",
    "host='localhost'\n",
    "# Define filename for passwords\n",
    "filename = 'ember_variables.py'\n",
    "# source the file\n",
    "%run $filename\n",
    "password = LocalDB2password\n",
    "\n",
    "%sql db2+ibm_db://$user:$password@$host:50000/SAMPLE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explicitly closing connections is not currently possible"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "### Using SQL in Jupyter Notebook \n",
    "- SQL magic makes SQL quick and easy\n",
    "- DB2 commands can be executed when the notebook was launched from a command window, when prefixed with !\n",
    "- Limited options available in sql magic - full ibm_db offers more options using Python or the core language of your choice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jupyter Notebook and SQL Magic Topics\n",
    "- How does commit processing work by default and how can you change it?\n",
    "- Using SQL magic for whole cell vs. line by line\n",
    "- Displaying data in interesting ways\n",
    "- Using host variables/parameter markers\n",
    "- Explains"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
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    }
   },
   "source": [
    "### Jupyter Notebook and Commit Processing\n",
    "- Autocommit is on\n",
    "- More advanced controls available through traditonal python syntax for the ibm_db driver"
   ]
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   "cell_type": "code",
   "execution_count": 3,
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n",
      "Done.\n"
     ]
    },
    {
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       "[]"
      ]
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "%sql drop table temp_sales\n",
    "%sql create table temp_sales like ember.sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql insert into temp_sales select * from ember.sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "slideshow": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n"
     ]
    },
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     "execution_count": 5,
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   "source": [
    "%sql select count(*) from temp_sales"
   ]
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   "metadata": {
    "collapsed": false,
    "slideshow": {
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      "41 rows affected.\n",
      "Done.\n",
      "Done.\n"
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    "%sql insert into temp_sales select * from ember.sales\n",
    "%sql rollback\n",
    "%sql select count(*) from temp_sales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "### Using SQL Magic at the Cell Level vs. the Line Level\n",
    "- Line Level\n",
    "    - Each line is prefixed with %\n",
    "    - If a command fails, subsequent lines are still executed\n",
    "- Cell Level\n",
    "    - Using SQL Magic at the cell level involves starting a Cell with %%sql\n",
    "    - All lines in the cell are then interpreted as SQL\n",
    "    - If a command fails, subsequent lines are not executed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "slideshow": {
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   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n",
      "(ibm_db_dbi.ProgrammingError) ibm_db_dbi::ProgrammingError: SQLNumResultCols failed: [IBM][CLI Driver][DB2/NT64] SQL0204N  \"DB2ADMIN.DUAL\" is an undefined name.  SQLSTATE=42704\\r SQLCODE=-204 [SQL: 'select * from dual;']\n"
     ]
    }
   ],
   "source": [
    "%%sql\n",
    "--sql magic at the cell level\n",
    "select * from syscat.tables;\n",
    "select * from dual;\n",
    "select * from syscat.bufferpools;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "slideshow": {
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   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "Done.\n",
      "(ibm_db_dbi.ProgrammingError) ibm_db_dbi::ProgrammingError: SQLNumResultCols failed: [IBM][CLI Driver][DB2/NT64] SQL0204N  \"DB2ADMIN.DUAL\" is an undefined name.  SQLSTATE=42704\\r SQLCODE=-204 [SQL: 'select * from dual;']\n",
      "Done.\n"
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       "<table>\n",
       "    <tr>\n",
       "        <th>bpname</th>\n",
       "        <th>bufferpoolid</th>\n",
       "        <th>dbpgname</th>\n",
       "        <th>npages</th>\n",
       "        <th>PAGESIZE</th>\n",
       "        <th>estore</th>\n",
       "        <th>numblockpages</th>\n",
       "        <th>blocksize</th>\n",
       "        <th>ngname</th>\n",
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       "    <tr>\n",
       "        <td>IBMDEFAULTBP</td>\n",
       "        <td>1</td>\n",
       "        <td>None</td>\n",
       "        <td>250</td>\n",
       "        <td>8192</td>\n",
       "        <td>N</td>\n",
       "        <td>0</td>\n",
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   "source": [
    "#sql magic at the line level\n",
    "%sql select * from syscat.tables;\n",
    "%sql select * from dual;\n",
    "%sql select * from syscat.bufferpools;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Displaying Data in Interesting Ways"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Import stuff we need\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import datetime as dt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# All plots should be inline, inside the notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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   "outputs": [
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     "text": [
      "Done.\n"
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HAt8hLAIyBhgP/KYJpyjVDD8WuBfoYWb1LTh3GrA5Yej/s8BfzWyXEsd9GXgT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\n/wHd8RyyH8XGRgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20a8dba2128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result= %sql select typename \\\n",
    "    , count(*) as count \\\n",
    "from syscat.columns \\\n",
    "group by typename \\\n",
    "order by count desc\n",
    "\n",
    "result.pie()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>rows_read</th>\n",
       "        <th>pct_tot_rr</th>\n",
       "        <th>total_cpu_time</th>\n",
       "        <th>pct_tot_cpu</th>\n",
       "        <th>stmt_exec_time</th>\n",
       "        <th>pct_tot_exec</th>\n",
       "        <th>total_section_sort_time</th>\n",
       "        <th>pct_tot_srt</th>\n",
       "        <th>num_executions</th>\n",
       "        <th>pct_tot_exec_1</th>\n",
       "        <th>avg_exec_time</th>\n",
       "        <th>STATEMENT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>711</td>\n",
       "        <td>99.16</td>\n",
       "        <td>0</td>\n",
       "        <td>0.00</td>\n",
       "        <td>24</td>\n",
       "        <td>53.33</td>\n",
       "        <td>0</td>\n",
       "        <td>0.00</td>\n",
       "        <td>1</td>\n",
       "        <td>5.55</td>\n",
       "        <td>24.00</td>\n",
       "        <td>select * from syscat.tables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>3</td>\n",
       "        <td>0.41</td>\n",
       "        <td>0</td>\n",
       "        <td>0.00</td>\n",
       "        <td>2</td>\n",
       "        <td>4.44</td>\n",
       "        <td>0</td>\n",
       "        <td>0.00</td>\n",
       "        <td>3</td>\n",
       "        <td>16.66</td>\n",
       "        <td>0.66</td>\n",
       "        <td>SELECT POLICY FROM SYSTOOLS.POLICY WHERE MED=&#x27;DB2CommonMED&#x27; AND DECISION=&#x27;NOP&#x27; AND NAME=&#x27;CommonPolicy&#x27;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>0</td>\n",
       "        <td>0.00</td>\n",
       "        <td>31250</td>\n",
       "        <td>100.00</td>\n",
       "        <td>18</td>\n",
       "        <td>40.00</td>\n",
       "        <td>2</td>\n",
       "        <td>100.00</td>\n",
       "        <td>1</td>\n",
       "        <td>5.55</td>\n",
       "        <td>18.00</td>\n",
       "        <td>select typename     , count(*) as count from syscat.columns group by typename order by count desc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>0</td>\n",
       "        <td>0.00</td>\n",
       "        <td>0</td>\n",
       "        <td>0.00</td>\n",
       "        <td>0</td>\n",
       "        <td>0.00</td>\n",
       "        <td>0</td>\n",
       "        <td>0.00</td>\n",
       "        <td>9</td>\n",
       "        <td>50.00</td>\n",
       "        <td>0.00</td>\n",
       "        <td>SET CURRENT LOCK TIMEOUT 5</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[(711, Decimal('99.16'), 0, Decimal('0.00'), 24, Decimal('53.33'), 0, Decimal('0.00'), 1, Decimal('5.55'), Decimal('24.00'), 'select * from syscat.tables'),\n",
       " (3, Decimal('0.41'), 0, Decimal('0.00'), 2, Decimal('4.44'), 0, Decimal('0.00'), 3, Decimal('16.66'), Decimal('0.66'), \"SELECT POLICY FROM SYSTOOLS.POLICY WHERE MED='DB2CommonMED' AND DECISION='NOP' AND NAME='CommonPolicy'\"),\n",
       " (0, Decimal('0.00'), 31250, Decimal('100.00'), 18, Decimal('40.00'), 2, Decimal('100.00'), 1, Decimal('5.55'), Decimal('18.00'), 'select typename     , count(*) as count from syscat.columns group by typename order by count desc'),\n",
       " (0, Decimal('0.00'), 0, Decimal('0.00'), 0, Decimal('0.00'), 0, Decimal('0.00'), 9, Decimal('50.00'), Decimal('0.00'), 'SET CURRENT LOCK TIMEOUT 5')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql WITH SUM_TAB (SUM_RR, SUM_CPU, SUM_EXEC, SUM_SORT, SUM_NUM_EXEC) AS ( \\\n",
    "        SELECT  nullif(FLOAT(SUM(ROWS_READ)),0), \\\n",
    "                nullif(FLOAT(SUM(TOTAL_CPU_TIME)),0), \\\n",
    "                nullif(FLOAT(SUM(STMT_EXEC_TIME)),0), \\\n",
    "                nullif(FLOAT(SUM(TOTAL_SECTION_SORT_TIME)),0), \\\n",
    "                nullif(FLOAT(SUM(NUM_EXECUTIONS)),0) \\\n",
    "            FROM TABLE(MON_GET_PKG_CACHE_STMT ( 'D', NULL, NULL, -2)) AS T \\\n",
    "        ) \\\n",
    "SELECT \\\n",
    "        ROWS_READ, \\\n",
    "        DECIMAL(100*(FLOAT(ROWS_READ)/SUM_TAB.SUM_RR),5,2) AS PCT_TOT_RR, \\\n",
    "        TOTAL_CPU_TIME, \\\n",
    "        DECIMAL(100*(FLOAT(TOTAL_CPU_TIME)/SUM_TAB.SUM_CPU),5,2) AS PCT_TOT_CPU, \\\n",
    "        STMT_EXEC_TIME, \\\n",
    "        DECIMAL(100*(FLOAT(STMT_EXEC_TIME)/SUM_TAB.SUM_EXEC),5,2) AS PCT_TOT_EXEC, \\\n",
    "        TOTAL_SECTION_SORT_TIME, \\\n",
    "        DECIMAL(100*(FLOAT(TOTAL_SECTION_SORT_TIME)/SUM_TAB.SUM_SORT),5,2) AS PCT_TOT_SRT, \\\n",
    "        NUM_EXECUTIONS, \\\n",
    "        DECIMAL(100*(FLOAT(NUM_EXECUTIONS)/SUM_TAB.SUM_NUM_EXEC),5,2) AS PCT_TOT_EXEC, \\\n",
    "        DECIMAL(FLOAT(STMT_EXEC_TIME)/FLOAT(NUM_EXECUTIONS),10,2) AS AVG_EXEC_TIME, \\\n",
    "        RTRIM(STMT_TEXT) as STATEMENT \\\n",
    "    FROM TABLE(MON_GET_PKG_CACHE_STMT ( 'D', NULL, NULL, -2)) AS T, SUM_TAB \\\n",
    "    WHERE DECIMAL(100*(FLOAT(ROWS_READ)/SUM_TAB.SUM_RR),5,2) > 10 \\\n",
    "        OR DECIMAL(100*(FLOAT(TOTAL_CPU_TIME)/SUM_TAB.SUM_CPU),5,2) >10 \\\n",
    "        OR DECIMAL(100*(FLOAT(STMT_EXEC_TIME)/SUM_TAB.SUM_EXEC),5,2) >10 \\\n",
    "        OR DECIMAL(100*(FLOAT(TOTAL_SECTION_SORT_TIME)/SUM_TAB.SUM_SORT),5,2) >10 \\\n",
    "        OR DECIMAL(100*(FLOAT(NUM_EXECUTIONS)/SUM_TAB.SUM_NUM_EXEC),5,2) >10 \\\n",
    "    ORDER BY ROWS_READ DESC FETCH FIRST 20 ROWS ONLY WITH UR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rows_read</th>\n",
       "      <th>total_cpu_time</th>\n",
       "      <th>stmt_exec_time</th>\n",
       "      <th>total_section_sort_time</th>\n",
       "      <th>num_executions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>178.000000</td>\n",
       "      <td>11718.750000</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>355.333646</td>\n",
       "      <td>14959.798559</td>\n",
       "      <td>10.246951</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>7812.500000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>178.500000</td>\n",
       "      <td>19531.250000</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>711.000000</td>\n",
       "      <td>31250.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        rows_read  total_cpu_time  stmt_exec_time  total_section_sort_time  \\\n",
       "count    4.000000        4.000000        4.000000                      4.0   \n",
       "mean   178.000000    11718.750000       13.500000                      0.5   \n",
       "std    355.333646    14959.798559       10.246951                      1.0   \n",
       "min      0.000000        0.000000        0.000000                      0.0   \n",
       "25%      0.000000        0.000000        9.000000                      0.0   \n",
       "50%      0.500000     7812.500000       15.000000                      0.0   \n",
       "75%    178.500000    19531.250000       19.500000                      0.5   \n",
       "max    711.000000    31250.000000       24.000000                      2.0   \n",
       "\n",
       "       num_executions  \n",
       "count             4.0  \n",
       "mean              1.0  \n",
       "std               0.0  \n",
       "min               1.0  \n",
       "25%               1.0  \n",
       "50%               1.0  \n",
       "75%               1.0  \n",
       "max               1.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result= %sql WITH SUM_TAB (SUM_RR, SUM_CPU, SUM_EXEC, SUM_SORT, SUM_NUM_EXEC) AS ( \\\n",
    "        SELECT  nullif(FLOAT(SUM(ROWS_READ)),0), \\\n",
    "                nullif(FLOAT(SUM(TOTAL_CPU_TIME)),0), \\\n",
    "                nullif(FLOAT(SUM(STMT_EXEC_TIME)),0), \\\n",
    "                nullif(FLOAT(SUM(TOTAL_SECTION_SORT_TIME)),0), \\\n",
    "                nullif(FLOAT(SUM(NUM_EXECUTIONS)),0) \\\n",
    "            FROM TABLE(MON_GET_PKG_CACHE_STMT ( 'D', NULL, NULL, -2)) AS T \\\n",
    "        ) \\\n",
    "SELECT substr(stmt_text,1,25) as STATEMENT, \\\n",
    "        ROWS_READ, \\\n",
    "        coalesce(DECIMAL(100*(FLOAT(ROWS_READ)/SUM_TAB.SUM_RR),5,2),0) AS PCT_TOT_RR, \\\n",
    "        TOTAL_CPU_TIME, \\\n",
    "        coalesce(DECIMAL(100*(FLOAT(TOTAL_CPU_TIME)/SUM_TAB.SUM_CPU),5,2),0) AS PCT_TOT_CPU, \\\n",
    "        STMT_EXEC_TIME, \\\n",
    "        coalesce(DECIMAL(100*(FLOAT(STMT_EXEC_TIME)/SUM_TAB.SUM_EXEC),5,2),0) AS PCT_TOT_EXEC, \\\n",
    "        TOTAL_SECTION_SORT_TIME, \\\n",
    "        coalesce(DECIMAL(100*(FLOAT(TOTAL_SECTION_SORT_TIME)/SUM_TAB.SUM_SORT),5,2),0) AS PCT_TOT_SRT, \\\n",
    "        NUM_EXECUTIONS, \\\n",
    "        coalesce(DECIMAL(100*(FLOAT(NUM_EXECUTIONS)/SUM_TAB.SUM_NUM_EXEC),5,2),0) AS PCT_TOT_EXECS, \\\n",
    "        DECIMAL(FLOAT(STMT_EXEC_TIME)/FLOAT(NUM_EXECUTIONS),10,2) AS AVG_EXEC_TIME, \\\n",
    "        RTRIM(STMT_TEXT) as FULL_STATEMENT \\\n",
    "    FROM TABLE(MON_GET_PKG_CACHE_STMT ( 'D', NULL, NULL, -2)) AS T, SUM_TAB \\\n",
    "    WHERE DECIMAL(100*(FLOAT(ROWS_READ)/SUM_TAB.SUM_RR),5,2) > 10 \\\n",
    "        OR DECIMAL(100*(FLOAT(TOTAL_CPU_TIME)/SUM_TAB.SUM_CPU),5,2) >10 \\\n",
    "        OR DECIMAL(100*(FLOAT(STMT_EXEC_TIME)/SUM_TAB.SUM_EXEC),5,2) >10 \\\n",
    "        OR DECIMAL(100*(FLOAT(TOTAL_SECTION_SORT_TIME)/SUM_TAB.SUM_SORT),5,2) >10 \\\n",
    "        OR DECIMAL(100*(FLOAT(NUM_EXECUTIONS)/SUM_TAB.SUM_NUM_EXEC),5,2) >10 \\\n",
    "    ORDER BY ROWS_READ DESC FETCH FIRST 20 ROWS ONLY WITH UR\n",
    "%matplotlib inline\n",
    "df=result.DataFrame()\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show number of rows and number of columns of the output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 13)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show the names of all columns, in a comma separated list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['STATEMENT', 'rows_read', 'pct_tot_rr', 'total_cpu_time', 'pct_tot_cpu',\n",
       "       'stmt_exec_time', 'pct_tot_exec', 'total_section_sort_time',\n",
       "       'pct_tot_srt', 'num_executions', 'pct_tot_execs', 'avg_exec_time',\n",
       "       'full_statement'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sort the output by a different column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>STATEMENT</th>\n",
       "      <th>rows_read</th>\n",
       "      <th>pct_tot_rr</th>\n",
       "      <th>total_cpu_time</th>\n",
       "      <th>pct_tot_cpu</th>\n",
       "      <th>stmt_exec_time</th>\n",
       "      <th>pct_tot_exec</th>\n",
       "      <th>total_section_sort_time</th>\n",
       "      <th>pct_tot_srt</th>\n",
       "      <th>num_executions</th>\n",
       "      <th>pct_tot_execs</th>\n",
       "      <th>avg_exec_time</th>\n",
       "      <th>full_statement</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>select * from syscat.tabl</td>\n",
       "      <td>711</td>\n",
       "      <td>99.85</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>24</td>\n",
       "      <td>44.44</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>25.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>select * from syscat.tables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>select typename     , cou</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>31250</td>\n",
       "      <td>66.66</td>\n",
       "      <td>18</td>\n",
       "      <td>33.33</td>\n",
       "      <td>2</td>\n",
       "      <td>100.00</td>\n",
       "      <td>1</td>\n",
       "      <td>25.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>select typename     , count(*) as count from s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WITH SUM_TAB (SUM_RR, SUM</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15625</td>\n",
       "      <td>33.33</td>\n",
       "      <td>12</td>\n",
       "      <td>22.22</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>25.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>WITH SUM_TAB (SUM_RR, SUM_CPU, SUM_EXEC, SUM_S...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>select * from syscat.buff</td>\n",
       "      <td>1</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>25.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>select * from syscat.bufferpools</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   STATEMENT  rows_read pct_tot_rr  total_cpu_time  \\\n",
       "0  select * from syscat.tabl        711      99.85               0   \n",
       "3  select typename     , cou          0       0.00           31250   \n",
       "2  WITH SUM_TAB (SUM_RR, SUM          0       0.00           15625   \n",
       "1  select * from syscat.buff          1       0.14               0   \n",
       "\n",
       "  pct_tot_cpu  stmt_exec_time pct_tot_exec  total_section_sort_time  \\\n",
       "0        0.00              24        44.44                        0   \n",
       "3       66.66              18        33.33                        2   \n",
       "2       33.33              12        22.22                        0   \n",
       "1        0.00               0         0.00                        0   \n",
       "\n",
       "  pct_tot_srt  num_executions pct_tot_execs avg_exec_time  \\\n",
       "0        0.00               1         25.00         24.00   \n",
       "3      100.00               1         25.00         18.00   \n",
       "2        0.00               1         25.00         12.00   \n",
       "1        0.00               1         25.00          0.00   \n",
       "\n",
       "                                      full_statement  \n",
       "0                        select * from syscat.tables  \n",
       "3  select typename     , count(*) as count from s...  \n",
       "2  WITH SUM_TAB (SUM_RR, SUM_CPU, SUM_EXEC, SUM_S...  \n",
       "1                   select * from syscat.bufferpools  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort output by a different column\n",
    "df.sort_values(by=['stmt_exec_time'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some data types may not be what is expected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STATEMENT                  object\n",
       "rows_read                   int64\n",
       "pct_tot_rr                 object\n",
       "total_cpu_time              int64\n",
       "pct_tot_cpu                object\n",
       "stmt_exec_time              int64\n",
       "pct_tot_exec               object\n",
       "total_section_sort_time     int64\n",
       "pct_tot_srt                object\n",
       "num_executions              int64\n",
       "pct_tot_execs              object\n",
       "avg_exec_time              object\n",
       "full_statement             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert incorrect columns to FLOAT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df[['pct_tot_rr']]=df[['pct_tot_rr']].astype(float)\n",
    "df[['pct_tot_cpu']]=df[['pct_tot_cpu']].astype(float)\n",
    "df[['pct_tot_exec']]=df[['pct_tot_exec']].astype(float)\n",
    "df[['pct_tot_srt']]=df[['pct_tot_srt']].astype(float)\n",
    "df[['pct_tot_execs']]=df[['pct_tot_execs']].astype(float)\n",
    "df[['avg_exec_time']]=df[['avg_exec_time']].astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verify the data type conversion worked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STATEMENT                   object\n",
       "rows_read                    int64\n",
       "pct_tot_rr                 float64\n",
       "total_cpu_time               int64\n",
       "pct_tot_cpu                float64\n",
       "stmt_exec_time               int64\n",
       "pct_tot_exec               float64\n",
       "total_section_sort_time      int64\n",
       "pct_tot_srt                float64\n",
       "num_executions               int64\n",
       "pct_tot_execs              float64\n",
       "avg_exec_time              float64\n",
       "full_statement              object\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the data on multiple dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZbmYLzOxdM9thZpvM7K9m9u09tWFm95nZsqjnz4TbXlXDtn8J1123h347oh7nfGGVfd4y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\nJEmSVDGTcEmSJKliJuGSJElSxf4fS2R2eqiUACEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20a8f711048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x='STATEMENT', y=['pct_tot_rr','pct_tot_cpu','pct_tot_exec','pct_tot_srt'], kind='barh')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>full_statement</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>select * from syscat.tables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>select * from syscat.bufferpools</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WITH SUM_TAB (SUM_RR, SUM_CPU, SUM_EXEC, SUM_SORT, SUM_NUM_EXEC) AS (         SELECT  nullif(FLOAT(SUM(ROWS_READ)),0),                 nullif(FLOAT(SUM(TOTAL_CPU_TIME)),0),                 nullif(FLOAT(SUM(STMT_EXEC_TIME)),0),                 nullif(FLOAT(SUM(TOTAL_SECTION_SORT_TIME)),0),                 nullif(FLOAT(SUM(NUM_EXECUTIONS)),0)             FROM TABLE(MON_GET_PKG_CACHE_STMT ( 'D', NULL, NULL, -2)) AS T         ) SELECT         ROWS_READ,         DECIMAL(100*(FLOAT(ROWS_READ)/SUM_TAB.SUM_RR),5,2) AS PCT_TOT_RR,         TOTAL_CPU_TIME,         DECIMAL(100*(FLOAT(TOTAL_CPU_TIME)/SUM_TAB.SUM_CPU),5,2) AS PCT_TOT_CPU,         STMT_EXEC_TIME,         DECIMAL(100*(FLOAT(STMT_EXEC_TIME)/SUM_TAB.SUM_EXEC),5,2) AS PCT_TOT_EXEC,         TOTAL_SECTION_SORT_TIME,         DECIMAL(100*(FLOAT(TOTAL_SECTION_SORT_TIME)/SUM_TAB.SUM_SORT),5,2) AS PCT_TOT_SRT,         NUM_EXECUTIONS,         DECIMAL(100*(FLOAT(NUM_EXECUTIONS)/SUM_TAB.SUM_NUM_EXEC),5,2) AS PCT_TOT_EXEC,         DECIMAL(FLOAT(STMT_EXEC_TIME)/FLOAT(NUM_EXECUTIONS),10,2) AS AVG_EXEC_TIME,         RTRIM(STMT_TEXT) as STATEMENT     FROM TABLE(MON_GET_PKG_CACHE_STMT ( 'D', NULL, NULL, -2)) AS T, SUM_TAB     WHERE DECIMAL(100*(FLOAT(ROWS_READ)/SUM_TAB.SUM_RR),5,2) &gt; 10         OR DECIMAL(100*(FLOAT(TOTAL_CPU_TIME)/SUM_TAB.SUM_CPU),5,2) &gt;10         OR DECIMAL(100*(FLOAT(STMT_EXEC_TIME)/SUM_TAB.SUM_EXEC),5,2) &gt;10         OR DECIMAL(100*(FLOAT(TOTAL_SECTION_SORT_TIME)/SUM_TAB.SUM_SORT),5,2) &gt;10         OR DECIMAL(100*(FLOAT(NUM_EXECUTIONS)/SUM_TAB.SUM_NUM_EXEC),5,2) &gt;10     ORDER BY ROWS_READ DESC FETCH FIRST 20 ROWS ONLY WITH UR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>select typename     , count(*) as count from syscat.columns group by typename order by count desc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        full_statement\n",
       "0  select * from syscat.tables                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        \n",
       "1  select * from syscat.bufferpools                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   \n",
       "2  WITH SUM_TAB (SUM_RR, SUM_CPU, SUM_EXEC, SUM_SORT, SUM_NUM_EXEC) AS (         SELECT  nullif(FLOAT(SUM(ROWS_READ)),0),                 nullif(FLOAT(SUM(TOTAL_CPU_TIME)),0),                 nullif(FLOAT(SUM(STMT_EXEC_TIME)),0),                 nullif(FLOAT(SUM(TOTAL_SECTION_SORT_TIME)),0),                 nullif(FLOAT(SUM(NUM_EXECUTIONS)),0)             FROM TABLE(MON_GET_PKG_CACHE_STMT ( 'D', NULL, NULL, -2)) AS T         ) SELECT         ROWS_READ,         DECIMAL(100*(FLOAT(ROWS_READ)/SUM_TAB.SUM_RR),5,2) AS PCT_TOT_RR,         TOTAL_CPU_TIME,         DECIMAL(100*(FLOAT(TOTAL_CPU_TIME)/SUM_TAB.SUM_CPU),5,2) AS PCT_TOT_CPU,         STMT_EXEC_TIME,         DECIMAL(100*(FLOAT(STMT_EXEC_TIME)/SUM_TAB.SUM_EXEC),5,2) AS PCT_TOT_EXEC,         TOTAL_SECTION_SORT_TIME,         DECIMAL(100*(FLOAT(TOTAL_SECTION_SORT_TIME)/SUM_TAB.SUM_SORT),5,2) AS PCT_TOT_SRT,         NUM_EXECUTIONS,         DECIMAL(100*(FLOAT(NUM_EXECUTIONS)/SUM_TAB.SUM_NUM_EXEC),5,2) AS PCT_TOT_EXEC,         DECIMAL(FLOAT(STMT_EXEC_TIME)/FLOAT(NUM_EXECUTIONS),10,2) AS AVG_EXEC_TIME,         RTRIM(STMT_TEXT) as STATEMENT     FROM TABLE(MON_GET_PKG_CACHE_STMT ( 'D', NULL, NULL, -2)) AS T, SUM_TAB     WHERE DECIMAL(100*(FLOAT(ROWS_READ)/SUM_TAB.SUM_RR),5,2) > 10         OR DECIMAL(100*(FLOAT(TOTAL_CPU_TIME)/SUM_TAB.SUM_CPU),5,2) >10         OR DECIMAL(100*(FLOAT(STMT_EXEC_TIME)/SUM_TAB.SUM_EXEC),5,2) >10         OR DECIMAL(100*(FLOAT(TOTAL_SECTION_SORT_TIME)/SUM_TAB.SUM_SORT),5,2) >10         OR DECIMAL(100*(FLOAT(NUM_EXECUTIONS)/SUM_TAB.SUM_NUM_EXEC),5,2) >10     ORDER BY ROWS_READ DESC FETCH FIRST 20 ROWS ONLY WITH UR\n",
       "3  select typename     , count(*) as count from syscat.columns group by typename order by count desc                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_colwidth', -1)\n",
    "df[['full_statement']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Host Variables / Parameter Markers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This statement is prepared using the literal value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>tabschema</th>\n",
       "        <th>tabname</th>\n",
       "        <th>controlauth</th>\n",
       "        <th>deleteauth</th>\n",
       "        <th>insertauth</th>\n",
       "        <th>selectauth</th>\n",
       "        <th>updateauth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>DB2ADMIN</td>\n",
       "        <td>TEMP_SALES</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>ADVISE_INDEX</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>ADVISE_INSTANCE</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>ADVISE_MQT</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>ADVISE_PARTITION</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>ADVISE_TABLE</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>ADVISE_WORKLOAD</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>EXPLAIN_ACTUALS</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>EXPLAIN_ARGUMENT</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>EXPLAIN_DIAGNOSTIC</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>EXPLAIN_DIAGNOSTIC_DATA</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>EXPLAIN_INSTANCE</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>EXPLAIN_OBJECT</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>EXPLAIN_OPERATOR</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>EXPLAIN_PREDICATE</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>EXPLAIN_STATEMENT</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>EXPLAIN_STREAM</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>HMON_ATM_INFO</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>HMON_COLLECTION</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>OBJECT_METRICS</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>POLICY</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[('DB2ADMIN', 'TEMP_SALES', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'ADVISE_INDEX', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'ADVISE_INSTANCE', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'ADVISE_MQT', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'ADVISE_PARTITION', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'ADVISE_TABLE', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'ADVISE_WORKLOAD', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'EXPLAIN_ACTUALS', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'EXPLAIN_ARGUMENT', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'EXPLAIN_DIAGNOSTIC', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'EXPLAIN_DIAGNOSTIC_DATA', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'EXPLAIN_INSTANCE', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'EXPLAIN_OBJECT', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'EXPLAIN_OPERATOR', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'EXPLAIN_PREDICATE', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'EXPLAIN_STATEMENT', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'EXPLAIN_STREAM', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'HMON_ATM_INFO', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'HMON_COLLECTION', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'OBJECT_METRICS', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'POLICY', 'Y', 'G', 'G', 'G', 'G')]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_id = 'DB2ADMIN'\n",
    "%sql select tabschema \\\n",
    "    , tabname \\\n",
    "    , controlauth \\\n",
    "    , deleteauth \\\n",
    "    , insertauth \\\n",
    "    , selectauth \\\n",
    "    , updateauth \\\n",
    "from syscat.tabauth \\\n",
    "where grantee = '{check_id}'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This statement is prepared using a parameter marker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>tabschema</th>\n",
       "        <th>tabname</th>\n",
       "        <th>controlauth</th>\n",
       "        <th>deleteauth</th>\n",
       "        <th>insertauth</th>\n",
       "        <th>selectauth</th>\n",
       "        <th>updateauth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>POLICY</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>HMON_ATM_INFO</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>HMON_COLLECTION</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>DB2ADMIN</td>\n",
       "        <td>TEMP_SALES</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>EXPLAIN_INSTANCE</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>EXPLAIN_STATEMENT</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>EXPLAIN_ARGUMENT</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>EXPLAIN_OBJECT</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>EXPLAIN_OPERATOR</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>EXPLAIN_PREDICATE</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>EXPLAIN_STREAM</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>EXPLAIN_DIAGNOSTIC</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>EXPLAIN_DIAGNOSTIC_DATA</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>OBJECT_METRICS</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>ADVISE_INSTANCE</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>ADVISE_INDEX</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>ADVISE_WORKLOAD</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>ADVISE_MQT</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>ADVISE_PARTITION</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>ADVISE_TABLE</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>SYSTOOLS</td>\n",
       "        <td>EXPLAIN_ACTUALS</td>\n",
       "        <td>Y</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "        <td>G</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[('SYSTOOLS', 'POLICY', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'HMON_ATM_INFO', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'HMON_COLLECTION', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('DB2ADMIN', 'TEMP_SALES', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'EXPLAIN_INSTANCE', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'EXPLAIN_STATEMENT', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'EXPLAIN_ARGUMENT', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'EXPLAIN_OBJECT', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'EXPLAIN_OPERATOR', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'EXPLAIN_PREDICATE', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'EXPLAIN_STREAM', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'EXPLAIN_DIAGNOSTIC', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'EXPLAIN_DIAGNOSTIC_DATA', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'OBJECT_METRICS', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'ADVISE_INSTANCE', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'ADVISE_INDEX', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'ADVISE_WORKLOAD', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'ADVISE_MQT', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'ADVISE_PARTITION', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'ADVISE_TABLE', 'Y', 'G', 'G', 'G', 'G'),\n",
       " ('SYSTOOLS', 'EXPLAIN_ACTUALS', 'Y', 'G', 'G', 'G', 'G')]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_id = 'DB2ADMIN'\n",
    "%sql select tabschema \\\n",
    "    , tabname \\\n",
    "    , controlauth \\\n",
    "    , deleteauth \\\n",
    "    , insertauth \\\n",
    "    , selectauth \\\n",
    "    , updateauth \\\n",
    "from syscat.tabauth \\\n",
    "where grantee = :check_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explain Information in Jupyter Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If Explain Tables do not Exist, Create Them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# This cell only needs to be executed if the explain tables do not exist\n",
    "%sql call sysproc.sysinstallobjects('EXPLAIN','C',NULL,NULL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n",
      "Done.\n"
     ]
    },
    {
     "ename": "InternalError",
     "evalue": "(ibm_db_dbi.InternalError) ibm_db_dbi::InternalError: Fetch Failure: [IBM][CLI Driver] CLI0115E  Invalid cursor state. SQLSTATE=24000 SQLCODE=-99999",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\ibm_db-2.0.7-py3.5.egg\\ibm_db_dbi.py\u001b[0m in \u001b[0;36m_fetch_helper\u001b[0;34m(self, fetch_size)\u001b[0m\n\u001b[1;32m   1413\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1414\u001b[0;31m                 \u001b[0mrow\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mibm_db\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfetch_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstmt_handler\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1415\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0minst\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mException\u001b[0m: Fetch Failure: [IBM][CLI Driver] CLI0115E  Invalid cursor state. SQLSTATE=24000 SQLCODE=-99999",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mInternalError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\sqlalchemy\\engine\\result.py\u001b[0m in \u001b[0;36mfetchall\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    963\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 964\u001b[0;31m             \u001b[0ml\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprocess_rows\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fetchall_impl\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    965\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_soft_close\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\sqlalchemy\\engine\\result.py\u001b[0m in \u001b[0;36m_fetchall_impl\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    914\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 915\u001b[0;31m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcursor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfetchall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    916\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\ibm_db-2.0.7-py3.5.egg\\ibm_db_dbi.py\u001b[0m in \u001b[0;36mfetchall\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1460\u001b[0m         \"\"\"\n\u001b[0;32m-> 1461\u001b[0;31m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fetch_helper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1462\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\ibm_db-2.0.7-py3.5.egg\\ibm_db_dbi.py\u001b[0m in \u001b[0;36m_fetch_helper\u001b[0;34m(self, fetch_size)\u001b[0m\n\u001b[1;32m   1417\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrow_list\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1418\u001b[0;31m                     \u001b[1;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessages\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessages\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1419\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInternalError\u001b[0m: ibm_db_dbi::InternalError: Fetch Failure: [IBM][CLI Driver] CLI0115E  Invalid cursor state. SQLSTATE=24000 SQLCODE=-99999",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mInternalError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-23-f1e8105aed5c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'sql set current explain mode explain'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[1;31m# Below will return CLI0115E, but that is expected and it works fine.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'sql select bpname from syscat.bufferpools'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\u001b[0m in \u001b[0;36mmagic\u001b[0;34m(self, arg_s)\u001b[0m\n\u001b[1;32m   2156\u001b[0m         \u001b[0mmagic_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marg_s\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpartition\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m' '\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2157\u001b[0m         \u001b[0mmagic_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlstrip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprefilter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mESC_MAGIC\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2158\u001b[0;31m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmagic_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2159\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2160\u001b[0m     \u001b[1;31m#-------------------------------------------------------------------------\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line)\u001b[0m\n\u001b[1;32m   2077\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'local_ns'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2078\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2079\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2080\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   2081\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<decorator-gen-124>\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\IPython\\core\\magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[1;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    189\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<decorator-gen-123>\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\IPython\\core\\magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[1;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    189\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\sql\\magic.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m     85\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 87\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msql\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparsed\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'sql'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muser_ns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     88\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m~\u001b[0m\u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumn_local_vars\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\sql\\run.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(conn, sql, config, user_namespace)\u001b[0m\n\u001b[1;32m    283\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfeedback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    284\u001b[0m                 \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minterpret_rowcount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrowcount\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 285\u001b[0;31m         \u001b[0mresultset\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mResultSet\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    286\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mautopandas\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    287\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mresultset\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\sql\\run.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, sqlaproxy, sql, config)\u001b[0m\n\u001b[1;32m    103\u001b[0m                 \u001b[0mlist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msqlaproxy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfetchmany\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlimit\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m                 \u001b[0mlist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msqlaproxy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfetchall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    106\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfield_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0munduplicate_field_names\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpretty\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprettytable\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPrettyTable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfield_names\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\sqlalchemy\\engine\\result.py\u001b[0m in \u001b[0;36mfetchall\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    968\u001b[0m             self.connection._handle_dbapi_exception(\n\u001b[1;32m    969\u001b[0m                 \u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 970\u001b[0;31m                 self.cursor, self.context)\n\u001b[0m\u001b[1;32m    971\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    972\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mfetchmany\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36m_handle_dbapi_exception\u001b[0;34m(self, e, statement, parameters, cursor, context)\u001b[0m\n\u001b[1;32m   1339\u001b[0m                 util.raise_from_cause(\n\u001b[1;32m   1340\u001b[0m                     \u001b[0msqlalchemy_exception\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1341\u001b[0;31m                     \u001b[0mexc_info\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1342\u001b[0m                 )\n\u001b[1;32m   1343\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\sqlalchemy\\util\\compat.py\u001b[0m in \u001b[0;36mraise_from_cause\u001b[0;34m(exception, exc_info)\u001b[0m\n\u001b[1;32m    200\u001b[0m     \u001b[0mexc_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexc_value\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexc_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexc_info\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m     \u001b[0mcause\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexc_value\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mexc_value\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mexception\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 202\u001b[0;31m     \u001b[0mreraise\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexception\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexception\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtb\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mexc_tb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcause\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcause\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    203\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    204\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mpy3k\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\sqlalchemy\\util\\compat.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(tp, value, tb, cause)\u001b[0m\n\u001b[1;32m    183\u001b[0m             \u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__cause__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcause\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mtb\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m             \u001b[1;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    186\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\sqlalchemy\\engine\\result.py\u001b[0m in \u001b[0;36mfetchall\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    962\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    963\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 964\u001b[0;31m             \u001b[0ml\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprocess_rows\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fetchall_impl\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    965\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_soft_close\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    966\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\sqlalchemy\\engine\\result.py\u001b[0m in \u001b[0;36m_fetchall_impl\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    913\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_fetchall_impl\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 915\u001b[0;31m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcursor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfetchall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    916\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    917\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_non_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\ibm_db-2.0.7-py3.5.egg\\ibm_db_dbi.py\u001b[0m in \u001b[0;36mfetchall\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1459\u001b[0m         \u001b[0mafter\u001b[0m \u001b[0mexecuting\u001b[0m \u001b[0man\u001b[0m \u001b[0mSQL\u001b[0m \u001b[0mstatement\u001b[0m \u001b[0mwhich\u001b[0m \u001b[0mproduces\u001b[0m \u001b[0ma\u001b[0m \u001b[0mresult\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1460\u001b[0m         \"\"\"\n\u001b[0;32m-> 1461\u001b[0;31m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fetch_helper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1462\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1463\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mnextset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Program Files\\Anaconda3\\lib\\site-packages\\ibm_db-2.0.7-py3.5.egg\\ibm_db_dbi.py\u001b[0m in \u001b[0;36m_fetch_helper\u001b[0;34m(self, fetch_size)\u001b[0m\n\u001b[1;32m   1416\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessages\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_get_exception\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minst\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1417\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrow_list\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1418\u001b[0;31m                     \u001b[1;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessages\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessages\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1419\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1420\u001b[0m                     \u001b[1;32mreturn\u001b[0m \u001b[0mrow_list\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInternalError\u001b[0m: (ibm_db_dbi.InternalError) ibm_db_dbi::InternalError: Fetch Failure: [IBM][CLI Driver] CLI0115E  Invalid cursor state. SQLSTATE=24000 SQLCODE=-99999"
     ]
    }
   ],
   "source": [
    "%sql set current explain mode explain\n",
    "# Below will return CLI0115E, but that is expected and it works fine.\n",
    "%sql select bpname from syscat.bufferpools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql set current explain mode no"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connecting to the Database.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DB2 Universal Database Version 11.1, 5622-044 (c) Copyright IBM Corp. 1991, 2015\n",
      "Licensed Material - Program Property of IBM\n",
      "IBM DATABASE 2 Explain Table Format Tool\n",
      "\n",
      "Connect to Database Successful.\n",
      "Using SYSTOOLS schema for Explain tables.\n",
      "Output is in query_exfmt.txt.\n",
      "Executing Connect Reset -- Connect Reset was Successful.\n"
     ]
    }
   ],
   "source": [
    "!db2exfmt -d SAMPLE -1 -o query_exfmt.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "-----------\n",
      "\tTotal Cost: \t\t0.0034452\n",
      "\tQuery Degree:\t\t1\n",
      "\n",
      "\n",
      "       Rows\n",
      "      RETURN\n",
      "      (   1)\n",
      "       Cost\n",
      "        I/O\n",
      "        |\n",
      "         1\n",
      "      IXSCAN\n",
      "      (   2)\n",
      "     0.0034452\n",
      "         0\n",
      "        |\n",
      "         1\n",
      "  INDEX: SYSIBM\n",
      " INDBUFFERPOOLS01\n",
      "        Q1\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open(\"query_exfmt.txt\") as f:\n",
    "    for line in f:\n",
    "        if line.rstrip() == \"Access Plan:\":\n",
    "            print(\"\")\n",
    "            for line in f:\n",
    "                if line.rstrip() == \"Extended Diagnostic Information:\":\n",
    "                    break\n",
    "                print(line.rstrip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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