db-insert-into-ra-latencies-last_hop.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*This notebook gets the raw atlas traceroute measurement data from `ra_traceroute_parameters` table; prunes the data to fetch (probeid, timestamp, h1_latencies, h2_latencies) then inserts entire dataframe in `ra_latencies` table.*\n",
    "- - - "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "import pandas as pd\n",
    "import requests\n",
    "import ipaddress\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "DB_LOCATION = 'lastmile.db'\n",
    "RA_TRACEROUTE_PARAMETERS = 'ra_traceroute_parameters'\n",
    "RA_LATENCIES_LAST_HOP = 'ra_latencies_last_hop'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "con = sqlite3.connect(DB_LOCATION)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "query = '''SELECT    probeid\n",
    "                   , starttime as timestamp\n",
    "                   , GROUP_CONCAT(result_hop_no, ';') as hop_no\n",
    "                   , GROUP_CONCAT(result_hop_blob, ';') as result_hop_blob\n",
    "                   , '' as last_hop\n",
    "                   , '' as last_hop_blob                   \n",
    "                   , '' as last_hop_latencies\n",
    "           FROM      %s\n",
    "           GROUP BY  probeid, starttime\n",
    "        '''%(RA_TRACEROUTE_PARAMETERS)\n",
    "\n",
    "df = pd.read_sql(query, con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_last_hop(hop_no): last_hop = hop_no.split(';')[-1]; return last_hop\n",
    "def get_last_hop_blob(result_hop_blob): last_hop_blob = result_hop_blob.split(';')[-1]; return last_hop_blob\n",
    "\n",
    "df['last_hop'] = df['hop_no'].apply(get_last_hop)\n",
    "df['last_hop_blob'] = df['result_hop_blob'].apply(get_last_hop_blob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>probeid</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>hop_no</th>\n",
       "      <th>result_hop_blob</th>\n",
       "      <th>last_hop</th>\n",
       "      <th>last_hop_blob</th>\n",
       "      <th>last_hop_latencies</th>\n",
       "    </tr>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   probeid   timestamp                                        hop_no  \\\n",
       "0    10006  1406548034  1;2;3;4;5;6;7;8;9;10;11;12;13;14;15;16;17;18   \n",
       "1    10006  1406562432  1;2;3;4;5;6;7;8;9;10;11;12;13;14;15;16;17;18   \n",
       "2    10006  1406576835  1;2;3;4;5;6;7;8;9;10;11;12;13;14;15;16;17;18   \n",
       "3    10006  1406591230  1;2;3;4;5;6;7;8;9;10;11;12;13;14;15;16;17;18   \n",
       "4    10006  1406605631  1;2;3;4;5;6;7;8;9;10;11;12;13;14;15;16;17;18   \n",
       "\n",
       "                                     result_hop_blob last_hop  \\\n",
       "0  [{'size': 76, 'rtt': 0.559, 'ttl': 64, 'from':...       18   \n",
       "1  [{'size': 76, 'rtt': 0.579, 'ttl': 64, 'from':...       18   \n",
       "2  [{'size': 76, 'rtt': 0.58, 'ttl': 64, 'from': ...       18   \n",
       "3  [{'size': 76, 'rtt': 0.769, 'ttl': 64, 'from':...       18   \n",
       "4  [{'size': 76, 'rtt': 0.534, 'ttl': 64, 'from':...       18   \n",
       "\n",
       "                                       last_hop_blob last_hop_latencies  \n",
       "0  [{'size': 48, 'rtt': 64.929, 'ttl': 55, 'from'...                     \n",
       "1  [{'size': 48, 'rtt': 64.001, 'ttl': 55, 'from'...                     \n",
       "2  [{'size': 48, 'rtt': 64.473, 'ttl': 55, 'from'...                     \n",
       "3  [{'size': 48, 'rtt': 64.46, 'ttl': 55, 'from':...                     \n",
       "4  [{'size': 48, 'rtt': 64.059, 'ttl': 55, 'from'...                     "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del df['hop_no']\n",
    "del df['result_hop_blob']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>probeid</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>last_hop</th>\n",
       "      <th>last_hop_blob</th>\n",
       "      <th>last_hop_latencies</th>\n",
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      ],
      "text/plain": [
       "   probeid   timestamp last_hop  \\\n",
       "0    10006  1406548034       18   \n",
       "1    10006  1406562432       18   \n",
       "2    10006  1406576835       18   \n",
       "3    10006  1406591230       18   \n",
       "4    10006  1406605631       18   \n",
       "\n",
       "                                       last_hop_blob last_hop_latencies  \n",
       "0  [{'size': 48, 'rtt': 64.929, 'ttl': 55, 'from'...                     \n",
       "1  [{'size': 48, 'rtt': 64.001, 'ttl': 55, 'from'...                     \n",
       "2  [{'size': 48, 'rtt': 64.473, 'ttl': 55, 'from'...                     \n",
       "3  [{'size': 48, 'rtt': 64.46, 'ttl': 55, 'from':...                     \n",
       "4  [{'size': 48, 'rtt': 64.059, 'ttl': 55, 'from'...                     "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def return_all_rtt_field_values(json_string):\n",
    "    import json\n",
    "    try: json_data = json.loads(json_string)\n",
    "    except Exception as e: print(e, type(e), json_string)\n",
    "    try: \n",
    "        rtt_field_values = ', '.join([str(item['rtt']) for item in json_data])        \n",
    "    except Exception as e: print('*', end=\"\"); return None\n",
    "    else: return rtt_field_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def return_concat_from_field_values(a):\n",
    "    a = a.replace(\"'\", '\"')\n",
    "    a = return_all_rtt_field_values(a)\n",
    "    return a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***********************************************************************************************************************************************************************************************************************Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "**************************Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "*Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "***************************************************************************Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "****************************************************************Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "************************************************************************************Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "**********Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "*Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "****************************Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "*Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "******Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "******************************************************************Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "*Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "***********************************************************************************************************************************Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "*************************************************************************************Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "*Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "***********************************************************************************Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "********************Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "*Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "********************************************************Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "*Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "*********************************Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "*Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "***Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "********************************************************Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "****************Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "*************Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "*Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "*Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "*Expecting value: line 1 column 1 (char 0) <class 'ValueError'> nan\n",
      "*************************************************************************************************************************"
     ]
    }
   ],
   "source": [
    "def get_rtts(last_hop_blob):\n",
    "    last_hop_blob = last_hop_blob.replace('True', '\"True\"')\n",
    "    rtts = return_concat_from_field_values(last_hop_blob)\n",
    "    return rtts    \n",
    "df['last_hop_latencies'] = df['last_hop_blob'].apply(get_rtts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>probeid</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>last_hop</th>\n",
       "      <th>last_hop_blob</th>\n",
       "      <th>last_hop_latencies</th>\n",
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      "text/plain": [
       "   probeid   timestamp last_hop  \\\n",
       "0    10006  1406548034       18   \n",
       "1    10006  1406562432       18   \n",
       "2    10006  1406576835       18   \n",
       "3    10006  1406591230       18   \n",
       "4    10006  1406605631       18   \n",
       "\n",
       "                                       last_hop_blob      last_hop_latencies  \n",
       "0  [{'size': 48, 'rtt': 64.929, 'ttl': 55, 'from'...  64.929, 65.337, 63.992  \n",
       "1  [{'size': 48, 'rtt': 64.001, 'ttl': 55, 'from'...  64.001, 64.404, 64.194  \n",
       "2  [{'size': 48, 'rtt': 64.473, 'ttl': 55, 'from'...  64.473, 64.215, 64.613  \n",
       "3  [{'size': 48, 'rtt': 64.46, 'ttl': 55, 'from':...    64.46, 65.16, 63.902  \n",
       "4  [{'size': 48, 'rtt': 64.059, 'ttl': 55, 'from'...  64.059, 63.728, 64.401  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del df['last_hop_blob']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>probeid</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>last_hop</th>\n",
       "      <th>last_hop_latencies</th>\n",
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       "      <td>1406605631</td>\n",
       "      <td>18</td>\n",
       "      <td>64.059, 63.728, 64.401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   probeid   timestamp last_hop      last_hop_latencies\n",
       "0    10006  1406548034       18  64.929, 65.337, 63.992\n",
       "1    10006  1406562432       18  64.001, 64.404, 64.194\n",
       "2    10006  1406576835       18  64.473, 64.215, 64.613\n",
       "3    10006  1406591230       18    64.46, 65.16, 63.902\n",
       "4    10006  1406605631       18  64.059, 63.728, 64.401"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "probeid               137017\n",
       "timestamp             137017\n",
       "last_hop              137017\n",
       "last_hop_latencies    135815\n",
       "dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cur = con.execute('pragma foreign_keys=ON')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "index_label = ['probeid', 'timestamp']\n",
    "df = df.set_index(index_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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></th>\n",
       "      <th>last_hop</th>\n",
       "      <th>last_hop_latencies</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>probeid</th>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">10006</th>\n",
       "      <th>1406548034</th>\n",
       "      <td>18</td>\n",
       "      <td>64.929, 65.337, 63.992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1406562432</th>\n",
       "      <td>18</td>\n",
       "      <td>64.001, 64.404, 64.194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1406576835</th>\n",
       "      <td>18</td>\n",
       "      <td>64.473, 64.215, 64.613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1406591230</th>\n",
       "      <td>18</td>\n",
       "      <td>64.46, 65.16, 63.902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1406605631</th>\n",
       "      <td>18</td>\n",
       "      <td>64.059, 63.728, 64.401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   last_hop      last_hop_latencies\n",
       "probeid timestamp                                  \n",
       "10006   1406548034       18  64.929, 65.337, 63.992\n",
       "        1406562432       18  64.001, 64.404, 64.194\n",
       "        1406576835       18  64.473, 64.215, 64.613\n",
       "        1406591230       18    64.46, 65.16, 63.902\n",
       "        1406605631       18  64.059, 63.728, 64.401"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_sql(  '%s'%RA_LATENCIES_LAST_HOP\n",
    "          , con\n",
    "          , flavor='sqlite'\n",
    "          , if_exists = 'append'\n",
    "          , index_label = index_label\n",
    "         )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "con.commit()\n",
    "con.close()"
   ]
  }
 ],
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