Impala_SQL_Jupyter/Impala_Basic_Kerberos.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IPython/Jupyter notebooks for Apache Impala\n",
    "# with Kerberos authentication"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Connect to the target database\n",
    "- ### requires Cloudera impyla package and thrift_sasl\n",
    "- ### edit the value kerberos_service_name as relevant for your environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from impala.dbapi import connect\n",
    "conn = connect(host='impalasrv-prod', port=21050, kerberos_service_name='impala', auth_mechanism='GSSAPI')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Run a query and fetch the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(7876,\n",
       "  'ADAMS',\n",
       "  'CLERK',\n",
       "  7788,\n",
       "  datetime.datetime(1987, 5, 23, 0, 0),\n",
       "  1100,\n",
       "  None,\n",
       "  20),\n",
       " (7499,\n",
       "  'ALLEN',\n",
       "  'SALESMAN',\n",
       "  7698,\n",
       "  datetime.datetime(1981, 2, 20, 0, 0),\n",
       "  1600,\n",
       "  300,\n",
       "  30)]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur = conn.cursor()\n",
    "\n",
    "cur.execute('select * from test2.emp limit 2')\n",
    "\n",
    "cur.fetchall()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Integration with pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7698</td>\n",
       "      <td>BLAKE</td>\n",
       "      <td>MANAGER</td>\n",
       "      <td>7839.0</td>\n",
       "      <td>1981-05-01</td>\n",
       "      <td>2850</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7900</td>\n",
       "      <td>JAMES</td>\n",
       "      <td>CLERK</td>\n",
       "      <td>7698.0</td>\n",
       "      <td>1981-12-03</td>\n",
       "      <td>950</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7566</td>\n",
       "      <td>JONES</td>\n",
       "      <td>MANAGER</td>\n",
       "      <td>7839.0</td>\n",
       "      <td>1981-04-02</td>\n",
       "      <td>2975</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7782</td>\n",
       "      <td>CLARK</td>\n",
       "      <td>MANAGER</td>\n",
       "      <td>7839.0</td>\n",
       "      <td>1981-06-09</td>\n",
       "      <td>2450</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7902</td>\n",
       "      <td>FORD</td>\n",
       "      <td>ANALYST</td>\n",
       "      <td>7566.0</td>\n",
       "      <td>1981-12-03</td>\n",
       "      <td>3000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   empno  ename      job     mgr   hiredate   sal  comm  deptno\n",
       "0   7698  BLAKE  MANAGER  7839.0 1981-05-01  2850   NaN      30\n",
       "1   7900  JAMES    CLERK  7698.0 1981-12-03   950   NaN      30\n",
       "2   7566  JONES  MANAGER  7839.0 1981-04-02  2975   NaN      20\n",
       "3   7782  CLARK  MANAGER  7839.0 1981-06-09  2450   NaN      10\n",
       "4   7902   FORD  ANALYST  7566.0 1981-12-03  3000   NaN      20"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur = conn.cursor()\n",
    "\n",
    "cur.execute('select * from test2.emp')\n",
    "\n",
    "from impala.util import as_pandas\n",
    "df = as_pandas(cur)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More examples of integration with IPython ecosystem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc66cfd8ed0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEECAYAAAAvY19bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/wGn1bTvnvrSwQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc66cfd8590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cur = conn.cursor()\n",
    "cur.execute('select ename, sal from test2.emp')\n",
    "df = as_pandas(cur)\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "matplotlib.style.use('ggplot')\n",
    "\n",
    "df.plot()"
   ]
  },
  {
   "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
}