AnalysingResponseTime.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysing response time of the Nginx server\n",
    "\n",
    "Nginx was configured with the following logging format:\n",
    "\n",
    "```\n",
    "log_format time_request '[$time_local] \"$request\" $status $body_bytes_sent $htt$\n",
    "    '$request_time';\n",
    "```\n",
    "\n",
    "Which logs the time it takes for the response to be generated. The code below parses the log and builds histograms of the response times for the posting itself and for generating the dashboard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "from pandas import DataFrame as df\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "mpl.rc('figure',  figsize=(10, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('privalytics_times.log', 'r') as f:\n",
    "    log_data = f.read()\n",
    "log_data = log_data.split('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[26/Feb/2019:05:58:52 +0000] \"GET /static/privalytics/vendor/daterangepicker/daterangepicker.css HTTP/1.1\" 304 0 - \"Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:65.0) Gecko/20100101 Firefox/65.0\" 0.000'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_data[22]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "lineformat = re.compile(\n",
    "            r\"\"\"\\[(?P<dateandtime>\\d{2}\\/[a-z]{3}\\/\\d{4}:\\d{2}:\\d{2}:\\d{2} (\\+|\\-)\\d{4})\\] [\"](?P<method>.+) (?P<url>.+)(http\\/1\\.1\\\") (?P<statuscode>\\d{3}) (?P<bytessent>\\d+) - ([\"](?P<useragent>.+)[\"]) (?P<response_time>\\d.\\d+)\"\"\",\n",
    "            re.IGNORECASE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "datadict = []\n",
    "for log_line in log_data:\n",
    "    info = re.search(lineformat, log_line)\n",
    "    if info:\n",
    "        datadict.append(info.groupdict()) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The loop above ignores any case in which the regular expression fails to find the values. It could be improved, to warn about the number of lines missed, etc. but it seems to work OK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas\n",
    "\n",
    "Even if I am not a fan of Pandas, it is very useful to organize table-like data such as the logs from Nginx.\n",
    "\n",
    "First we need to convert the columns to the appropriate data tipes, including the dateandtime column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df.from_dict(datadict)\n",
    "data['response_time'] = data['response_time'].astype(np.float16)\n",
    "data['statuscode'] = data['statuscode'].astype(np.int16)\n",
    "data['bytessent'] = data['bytessent'].astype(np.int16)\n",
    "data['dateandtime'] = pd.to_datetime(data['dateandtime'], format='%d/%b/%Y:%H:%M:%S %z')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bytessent</th>\n",
       "      <th>dateandtime</th>\n",
       "      <th>method</th>\n",
       "      <th>response_time</th>\n",
       "      <th>statuscode</th>\n",
       "      <th>url</th>\n",
       "      <th>useragent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3388</td>\n",
       "      <td>2019-02-26 05:57:10+00:00</td>\n",
       "      <td>GET</td>\n",
       "      <td>0.018997</td>\n",
       "      <td>200</td>\n",
       "      <td>/</td>\n",
       "      <td>Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:65....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>989</td>\n",
       "      <td>2019-02-26 05:57:10+00:00</td>\n",
       "      <td>GET</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>200</td>\n",
       "      <td>/static/privalytics/css/pricing.css</td>\n",
       "      <td>Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:65....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>204</td>\n",
       "      <td>2019-02-26 05:57:10+00:00</td>\n",
       "      <td>GET</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>200</td>\n",
       "      <td>/static/privalytics/css/showcase.css</td>\n",
       "      <td>Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:65....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5712</td>\n",
       "      <td>2019-02-26 05:57:10+00:00</td>\n",
       "      <td>GET</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>200</td>\n",
       "      <td>/static/privalytics/img/analytics.png</td>\n",
       "      <td>Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:65....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6691</td>\n",
       "      <td>2019-02-26 05:57:10+00:00</td>\n",
       "      <td>GET</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>200</td>\n",
       "      <td>/static/privalytics/img/user.png</td>\n",
       "      <td>Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:65....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bytessent               dateandtime method  response_time  statuscode  \\\n",
       "0       3388 2019-02-26 05:57:10+00:00    GET       0.018997         200   \n",
       "1        989 2019-02-26 05:57:10+00:00    GET       0.000000         200   \n",
       "2        204 2019-02-26 05:57:10+00:00    GET       0.000000         200   \n",
       "3       5712 2019-02-26 05:57:10+00:00    GET       0.000000         200   \n",
       "4       6691 2019-02-26 05:57:10+00:00    GET       0.000000         200   \n",
       "\n",
       "                                      url  \\\n",
       "0                                      /    \n",
       "1    /static/privalytics/css/pricing.css    \n",
       "2   /static/privalytics/css/showcase.css    \n",
       "3  /static/privalytics/img/analytics.png    \n",
       "4       /static/privalytics/img/user.png    \n",
       "\n",
       "                                           useragent  \n",
       "0  Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:65....  \n",
       "1  Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:65....  \n",
       "2  Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:65....  \n",
       "3  Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:65....  \n",
       "4  Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:65....  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# API Tracker\n",
    "\n",
    "First, let's see what response times we get for the API endpoint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "api_tracker = data[data['url'].str.startswith('/api/tracker')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4243.000000\n",
       "mean       36.861778\n",
       "std        69.238701\n",
       "min        16.998291\n",
       "25%        20.996094\n",
       "50%        22.003174\n",
       "75%        26.000977\n",
       "max      2626.953125\n",
       "Name: response_time, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = api_tracker[(api_tracker['method'] == 'POST') & (api_tracker['statuscode']==200)]['response_time']*1000\n",
    "t.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Response time for tracker posts to endpoint /api/tracker')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = t.hist(bins=1200, log=True)\n",
    "# plt.xlim(0, 100)\n",
    "plt.xscale('log')\n",
    "plt.tick_params(labelsize=14)\n",
    "plt.xlabel('Response time (ms)', size=18)\n",
    "plt.ylabel('Requests', size=18)\n",
    "plt.title('Response time for tracker posts to endpoint /api/tracker', size=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pay attention to the log-log scale. Response times are Below 30ms in 75% of the cases. Also, note that we are looking only at valid requests (status 200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also look at the non 200 response codes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    179.000000\n",
       "mean      22.615993\n",
       "std       57.311069\n",
       "min        0.000000\n",
       "25%        4.001617\n",
       "50%        6.000519\n",
       "75%        9.502411\n",
       "max      367.919922\n",
       "Name: response_time, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = api_tracker[(api_tracker['method'] == 'POST') & (api_tracker['statuscode']!=200)]['response_time']*1000\n",
    "t.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Response time for tracker posts to endpoint /api/tracker')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = t.hist(bins=10, log=True)\n",
    "# plt.xlim(0, 100)\n",
    "plt.xscale('log')\n",
    "plt.tick_params(labelsize=14)\n",
    "plt.xlabel('Response time (ms)', size=18)\n",
    "plt.ylabel('Requests', size=18)\n",
    "plt.title('Response time for tracker posts to endpoint /api/tracker', size=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dashboard\n",
    "\n",
    "Generating the dashboard is much more expensive, since the database queries are much more complex."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws_dashboard = data[data['url'].str.startswith('/ws')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     160.000000\n",
       "mean      785.873657\n",
       "std       607.873657\n",
       "min         0.000000\n",
       "25%       360.839844\n",
       "50%       798.828125\n",
       "75%      1206.054688\n",
       "max      4042.968750\n",
       "Name: response_time, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = ws_dashboard[ws_dashboard['method'] == 'GET']['response_time']*1000\n",
    "t.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Response time for dashboard at endpoint /ws')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t.hist(bins=20)\n",
    "# plt.xlim(0, 100)\n",
    "plt.tick_params(labelsize=14)\n",
    "plt.xlabel('Response time (ms)', size=18)\n",
    "plt.ylabel('Requests', size=18)\n",
    "plt.title('Response time for dashboard at endpoint /ws', size=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generating the dashboard takes around 1 second in most cases. This can be improved with caching and some database optimizations, such as pre-compiling statistics instead of generating them every time the page is requested. It is also important to see what happens when user select ranges of dates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       4.000000\n",
       "mean      730.590820\n",
       "std       517.140991\n",
       "min        62.011719\n",
       "25%       453.491211\n",
       "50%       847.167969\n",
       "75%      1124.267578\n",
       "max      1166.015625\n",
       "Name: response_time, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = ws_dashboard[ws_dashboard['method'] == 'POST']['response_time']*1000\n",
    "t.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Response time for dashboard at endpoint /ws')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t.hist()\n",
    "# plt.xlim(0, 100)\n",
    "plt.tick_params(labelsize=14)\n",
    "plt.xlabel('Response time (ms)', size=18)\n",
    "plt.ylabel('Requests', size=18)\n",
    "plt.title('Response time for dashboard at endpoint /ws', size=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Response time for tracker at endpoint /api/tracker')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "api_tracker[(api_tracker['method'] == 'POST') & (api_tracker['statuscode']==200)].plot('dateandtime', 'response_time')\n",
    "plt.yscale('log')\n",
    "plt.tick_params(labelsize=14)\n",
    "plt.xlabel('Date', size=18)\n",
    "plt.ylabel('Response time (s)', size=18)\n",
    "plt.title('Response time for tracker at endpoint /api/tracker', size=14)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}