2017_IDUG_EMEA.ipynb

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    "# 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: F17\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;Thursday, 5th of October 09:40-10:40"
   ]
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   "source": [
    "## Setting up this Jupyter Notebook"
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    "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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    "### 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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    "### 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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    "### 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)"
   ]
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  {
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   "metadata": {},
   "source": [
    "### Jupyter Notebook Basics\n",
    "[Basics of Jupyter Notebook](BasicsOfJupyterNotebook.ipynb)"
   ]
  },
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   "metadata": {},
   "source": [
    "### Setting up Jupyter Notebook for use with SQL Magic"
   ]
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   "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"
   ]
  },
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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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       "'Connected: db2admin@SAMPLE'"
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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"
   ]
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   "source": [
    "Explicitly closing connections is not currently possible"
   ]
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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"
   ]
  },
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   "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": {
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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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   "source": [
    "%sql drop table temp_sales\n",
    "%sql create table temp_sales like ember.sales"
   ]
  },
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   "metadata": {
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   "source": [
    "%sql insert into temp_sales select * from ember.sales"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
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   "source": [
    "%sql select count(*) from temp_sales"
   ]
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  {
   "cell_type": "code",
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   "source": [
    "%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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     "slide_type": "fragment"
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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"
   ]
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   "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",
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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": 5,
   "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"
   ]
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      " * db2+ibm_db://db2admin:***@localhost:50000/SAMPLE\n",
      "Done.\n"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "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": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * db2+ibm_db://db2admin:***@localhost:50000/SAMPLE\n",
      "(ibm_db_dbi.ProgrammingError) ibm_db_dbi::ProgrammingError: SQLNumResultCols failed: [IBM][CLI Driver][DB2/NT64] SQL0551N  The statement failed because the authorization ID does not have the required authorization or privilege to perform the operation.  Authorization ID: \"DB2ADMIN\".  Operation: \"EXECUTE\". Object: \"SYSPROC.MON_GET_PKG_CACHE_STMT\".  SQLSTATE=42501\\r SQLCODE=-551 [SQL: \"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\"] (Background on this error at: http://sqlalche.me/e/f405)\n"
     ]
    }
   ],
   "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": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * db2+ibm_db://db2admin:***@localhost:50000/SAMPLE\n",
      "(ibm_db_dbi.ProgrammingError) ibm_db_dbi::ProgrammingError: SQLNumResultCols failed: [IBM][CLI Driver][DB2/NT64] SQL0551N  The statement failed because the authorization ID does not have the required authorization or privilege to perform the operation.  Authorization ID: \"DB2ADMIN\".  Operation: \"EXECUTE\". Object: \"SYSPROC.MON_GET_PKG_CACHE_STMT\".  SQLSTATE=42501\\r SQLCODE=-551 [SQL: \"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 substr(stmt_text,1,25) as STATEMENT,         ROWS_READ,         coalesce(DECIMAL(100*(FLOAT(ROWS_READ)/SUM_TAB.SUM_RR),5,2),0) AS PCT_TOT_RR,         TOTAL_CPU_TIME,         coalesce(DECIMAL(100*(FLOAT(TOTAL_CPU_TIME)/SUM_TAB.SUM_CPU),5,2),0) AS PCT_TOT_CPU,         STMT_EXEC_TIME,         coalesce(DECIMAL(100*(FLOAT(STMT_EXEC_TIME)/SUM_TAB.SUM_EXEC),5,2),0) AS PCT_TOT_EXEC,         TOTAL_SECTION_SORT_TIME,         coalesce(DECIMAL(100*(FLOAT(TOTAL_SECTION_SORT_TIME)/SUM_TAB.SUM_SORT),5,2),0) AS PCT_TOT_SRT,         NUM_EXECUTIONS,         coalesce(DECIMAL(100*(FLOAT(NUM_EXECUTIONS)/SUM_TAB.SUM_NUM_EXEC),5,2),0) AS PCT_TOT_EXECS,         DECIMAL(FLOAT(STMT_EXEC_TIME)/FLOAT(NUM_EXECUTIONS),10,2) AS AVG_EXEC_TIME,         RTRIM(STMT_TEXT) as FULL_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\"] (Background on this error at: http://sqlalche.me/e/f405)\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'DataFrame'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-da1f66f492cf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'sql'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"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 substr(stmt_text,1,25) as STATEMENT,         ROWS_READ,         coalesce(DECIMAL(100*(FLOAT(ROWS_READ)/SUM_TAB.SUM_RR),5,2),0) AS PCT_TOT_RR,         TOTAL_CPU_TIME,         coalesce(DECIMAL(100*(FLOAT(TOTAL_CPU_TIME)/SUM_TAB.SUM_CPU),5,2),0) AS PCT_TOT_CPU,         STMT_EXEC_TIME,         coalesce(DECIMAL(100*(FLOAT(STMT_EXEC_TIME)/SUM_TAB.SUM_EXEC),5,2),0) AS PCT_TOT_EXEC,         TOTAL_SECTION_SORT_TIME,         coalesce(DECIMAL(100*(FLOAT(TOTAL_SECTION_SORT_TIME)/SUM_TAB.SUM_SORT),5,2),0) AS PCT_TOT_SRT,         NUM_EXECUTIONS,         coalesce(DECIMAL(100*(FLOAT(NUM_EXECUTIONS)/SUM_TAB.SUM_NUM_EXEC),5,2),0) AS PCT_TOT_EXECS,         DECIMAL(FLOAT(STMT_EXEC_TIME)/FLOAT(NUM_EXECUTIONS),10,2) AS AVG_EXEC_TIME,         RTRIM(STMT_TEXT) as FULL_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\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'matplotlib'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'inline'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mresult\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[0m\u001b[0;32m      4\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'DataFrame'"
     ]
    }
   ],
   "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": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show the names of all columns, in a comma separated list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sort the output by a different column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert incorrect columns to FLOAT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the data on multiple dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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": true
   },
   "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": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%sql set current explain mode no"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "!db2exfmt -d SAMPLE -1 -o query_exfmt.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "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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