Impala_SQL_Jupyter/Impala_SQL_Magic_Kerberos.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Apache Impala and SQL magic for IPython/Jupyter notebooks\n",
    "# with Kerberos authentication"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Load SQL magic extension (uses ipython-sql by Catherine Devlin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%load_ext sql"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Connect to the target database\n",
    "- ### requires Cloudera impyla package and thrift_sasl\n",
    "- ### edit the value of config_args as relevant for your environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'Connected: None@test2'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%config SqlMagic.connect_args=\"{'kerberos_service_name':'impala', 'auth_mechanism':'GSSAPI'}\"\n",
    "%sql impala://impalasrv-prod:21050/test2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Run SQL on the target using the %%sql cell magic or %sql line magic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>empno</th>\n",
       "        <th>ename</th>\n",
       "        <th>job</th>\n",
       "        <th>mgr</th>\n",
       "        <th>hiredate</th>\n",
       "        <th>sal</th>\n",
       "        <th>comm</th>\n",
       "        <th>deptno</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>7521</td>\n",
       "        <td>WARD</td>\n",
       "        <td>SALESMAN</td>\n",
       "        <td>7698</td>\n",
       "        <td>1981-02-22 00:00:00</td>\n",
       "        <td>1250</td>\n",
       "        <td>500</td>\n",
       "        <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>7902</td>\n",
       "        <td>FORD</td>\n",
       "        <td>ANALYST</td>\n",
       "        <td>7566</td>\n",
       "        <td>1981-12-03 00:00:00</td>\n",
       "        <td>3000</td>\n",
       "        <td>None</td>\n",
       "        <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>7900</td>\n",
       "        <td>JAMES</td>\n",
       "        <td>CLERK</td>\n",
       "        <td>7698</td>\n",
       "        <td>1981-12-03 00:00:00</td>\n",
       "        <td>950</td>\n",
       "        <td>None</td>\n",
       "        <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>7782</td>\n",
       "        <td>CLARK</td>\n",
       "        <td>MANAGER</td>\n",
       "        <td>7839</td>\n",
       "        <td>1981-06-09 00:00:00</td>\n",
       "        <td>2450</td>\n",
       "        <td>None</td>\n",
       "        <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>7566</td>\n",
       "        <td>JONES</td>\n",
       "        <td>MANAGER</td>\n",
       "        <td>7839</td>\n",
       "        <td>1981-04-02 00:00:00</td>\n",
       "        <td>2975</td>\n",
       "        <td>None</td>\n",
       "        <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>7698</td>\n",
       "        <td>BLAKE</td>\n",
       "        <td>MANAGER</td>\n",
       "        <td>7839</td>\n",
       "        <td>1981-05-01 00:00:00</td>\n",
       "        <td>2850</td>\n",
       "        <td>None</td>\n",
       "        <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>7839</td>\n",
       "        <td>KING</td>\n",
       "        <td>PRESIDENT</td>\n",
       "        <td>None</td>\n",
       "        <td>1981-11-17 00:00:00</td>\n",
       "        <td>5000</td>\n",
       "        <td>None</td>\n",
       "        <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>7788</td>\n",
       "        <td>SCOTT</td>\n",
       "        <td>ANALYST</td>\n",
       "        <td>7566</td>\n",
       "        <td>1987-04-19 00:00:00</td>\n",
       "        <td>3000</td>\n",
       "        <td>None</td>\n",
       "        <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>7369</td>\n",
       "        <td>SMITH</td>\n",
       "        <td>CLERK</td>\n",
       "        <td>7902</td>\n",
       "        <td>1980-12-17 00:00:00</td>\n",
       "        <td>800</td>\n",
       "        <td>None</td>\n",
       "        <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>7654</td>\n",
       "        <td>MARTIN</td>\n",
       "        <td>SALESMAN</td>\n",
       "        <td>7698</td>\n",
       "        <td>1981-09-28 00:00:00</td>\n",
       "        <td>1250</td>\n",
       "        <td>1400</td>\n",
       "        <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>7934</td>\n",
       "        <td>MILLER</td>\n",
       "        <td>CLERK</td>\n",
       "        <td>7782</td>\n",
       "        <td>1982-01-23 00:00:00</td>\n",
       "        <td>1300</td>\n",
       "        <td>None</td>\n",
       "        <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>7876</td>\n",
       "        <td>ADAMS</td>\n",
       "        <td>CLERK</td>\n",
       "        <td>7788</td>\n",
       "        <td>1987-05-23 00:00:00</td>\n",
       "        <td>1100</td>\n",
       "        <td>None</td>\n",
       "        <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>7844</td>\n",
       "        <td>TURNER</td>\n",
       "        <td>SALESMAN</td>\n",
       "        <td>7698</td>\n",
       "        <td>1981-09-08 00:00:00</td>\n",
       "        <td>1500</td>\n",
       "        <td>0</td>\n",
       "        <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>7499</td>\n",
       "        <td>ALLEN</td>\n",
       "        <td>SALESMAN</td>\n",
       "        <td>7698</td>\n",
       "        <td>1981-02-20 00:00:00</td>\n",
       "        <td>1600</td>\n",
       "        <td>300</td>\n",
       "        <td>30</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[(7521, 'WARD', 'SALESMAN', 7698, datetime.datetime(1981, 2, 22, 0, 0), 1250, 500, 30),\n",
       " (7902, 'FORD', 'ANALYST', 7566, datetime.datetime(1981, 12, 3, 0, 0), 3000, None, 20),\n",
       " (7900, 'JAMES', 'CLERK', 7698, datetime.datetime(1981, 12, 3, 0, 0), 950, None, 30),\n",
       " (7782, 'CLARK', 'MANAGER', 7839, datetime.datetime(1981, 6, 9, 0, 0), 2450, None, 10),\n",
       " (7566, 'JONES', 'MANAGER', 7839, datetime.datetime(1981, 4, 2, 0, 0), 2975, None, 20),\n",
       " (7698, 'BLAKE', 'MANAGER', 7839, datetime.datetime(1981, 5, 1, 0, 0), 2850, None, 30),\n",
       " (7839, 'KING', 'PRESIDENT', None, datetime.datetime(1981, 11, 17, 0, 0), 5000, None, 10),\n",
       " (7788, 'SCOTT', 'ANALYST', 7566, datetime.datetime(1987, 4, 19, 0, 0), 3000, None, 20),\n",
       " (7369, 'SMITH', 'CLERK', 7902, datetime.datetime(1980, 12, 17, 0, 0), 800, None, 20),\n",
       " (7654, 'MARTIN', 'SALESMAN', 7698, datetime.datetime(1981, 9, 28, 0, 0), 1250, 1400, 30),\n",
       " (7934, 'MILLER', 'CLERK', 7782, datetime.datetime(1982, 1, 23, 0, 0), 1300, None, 10),\n",
       " (7876, 'ADAMS', 'CLERK', 7788, datetime.datetime(1987, 5, 23, 0, 0), 1100, None, 20),\n",
       " (7844, 'TURNER', 'SALESMAN', 7698, datetime.datetime(1981, 9, 8, 0, 0), 1500, 0, 30),\n",
       " (7499, 'ALLEN', 'SALESMAN', 7698, datetime.datetime(1981, 2, 20, 0, 0), 1600, 300, 30)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql \n",
    "select * from emp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bind variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>empno</th>\n",
       "        <th>ename</th>\n",
       "        <th>job</th>\n",
       "        <th>mgr</th>\n",
       "        <th>hiredate</th>\n",
       "        <th>sal</th>\n",
       "        <th>comm</th>\n",
       "        <th>deptno</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>7788</td>\n",
       "        <td>SCOTT</td>\n",
       "        <td>ANALYST</td>\n",
       "        <td>7566</td>\n",
       "        <td>1987-04-19 00:00:00</td>\n",
       "        <td>3000</td>\n",
       "        <td>None</td>\n",
       "        <td>20</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[(7788, 'SCOTT', 'ANALYST', 7566, datetime.datetime(1987, 4, 19, 0, 0), 3000, None, 20)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Employee_name=\"SCOTT\"\n",
    "\n",
    "%sql select * from emp where ename=:Employee_name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional example of the integration with the IPython environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Container object of 14 artists>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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dXV34+/tz7ty5kWqbEEKILzGrx0D8/f0pLS0lNDSU4OBgtm3bhpOTE+7u7iPZPiGEEF9S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\nEQFEEARBsIkIIIIgCIJNRAARBEEQbPJ/iVCtTTKDUc4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff1ecb6de50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "myResultSet=%sql select ename \"Employee Name\", sal \"Salary\" from emp\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "matplotlib.style.use('ggplot')\n",
    "\n",
    "myResultSet.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The integration with Pandas opens many additional possibilities for data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "my_DataFrame=myResultSet.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>employee name</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TURNER</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FORD</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WARD</td>\n",
       "      <td>1250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JONES</td>\n",
       "      <td>2975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JAMES</td>\n",
       "      <td>950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  employee name  salary\n",
       "0        TURNER    1500\n",
       "1          FORD    3000\n",
       "2          WARD    1250\n",
       "3         JONES    2975\n",
       "4         JAMES     950"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_DataFrame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}