original 성능 테스트

original.ipynb

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    "colab": {
      "name": "original 성능 테스트",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNISOn6rL2lkmv3qljCh3ta",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/swcho/607c16011a4f5b9e6713564d060c74ef/original.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tQf84llzBUbj",
        "outputId": "de7bd3bb-6237-49ea-b90d-8c3b1adb2dcf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "from io import StringIO\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "csv_str = \"\"\"\n",
        "from,to,len,fetchTime,name,reDrawTime\n",
        "1604644330605,1604644335957,14653,284,original,59\n",
        "1604644325247,1604644330604,14518,627,original,20\n",
        "1604644319836,1604644325246,14608,445,original,21\n",
        "1604644314441,1604644319835,14511,342,original,18\n",
        "1604644309089,1604644314440,14406,265,original,18\n",
        "1604644303702,1604644309088,14418,238,original,11\n",
        "1604644298357,1604644303701,14330,269,original,11\n",
        "1604644292956,1604644298356,14273,252,original,11\n",
        "1604644287561,1604644292955,14201,336,original,16\n",
        "1604644282171,1604644287559,14509,287,original,16\n",
        "1604644276787,1604644282170,14431,524,original,11\n",
        "1604644271411,1604644276786,14448,241,original,10\n",
        "1604644266032,1604644271410,14323,250,original,14\n",
        "1604644260626,1604644266031,14411,232,original,10\n",
        "1604644255219,1604644260624,14491,251,original,9\n",
        "1604644249840,1604644255218,14377,279,original,15\n",
        "1604644244445,1604644249839,14542,302,original,17\n",
        "1604644239067,1604644244443,14584,574,original,12\n",
        "1604644233682,1604644239066,14449,205,original,13\n",
        "1604644228299,1604644233681,14395,234,original,10\n",
        "1604644222922,1604644228298,14284,224,original,10\n",
        "1604644217510,1604644222921,14346,239,original,10\n",
        "1604644212101,1604644217509,14494,232,original,16\n",
        "1604644206696,1604644212100,14361,228,original,10\n",
        "1604644201286,1604644206694,14388,240,original,17\n",
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        "1604644185150,1604644190525,14306,257,original,17\n",
        "1604644179749,1604644185148,14229,296,original,23\n",
        "1604644174349,1604644179748,14646,237,original,11\n",
        "1604644169014,1604644174348,14349,277,original,15\n",
        "1604644163638,1604644169013,14317,243,original,9\n",
        "1604644158300,1604644163637,14170,197,original,12\n",
        "1604644152932,1604644158299,14588,233,original,11\n",
        "1604644147590,1604644152931,14497,231,original,14\n",
        "1604644142230,1604644147589,14385,225,original,11\n",
        "1604644136842,1604644142229,14196,220,original,14\n",
        "1604644131428,1604644136841,14413,253,original,11\n",
        "1604644125998,1604644131427,14286,240,original,14\n",
        "1604644120582,1604644125997,14379,214,original,10\n",
        "1604644115192,1604644120580,14316,248,original,15\n",
        "1604644109785,1604644115191,14318,301,original,17\n",
        "1604644104417,1604644109784,14544,256,original,19\n",
        "1604644099030,1604644104416,14278,239,original,10\n",
        "1604644093631,1604644099029,14365,200,original,14\n",
        "1604644088255,1604644093630,14401,256,original,10\n",
        "1604644082862,1604644088254,14476,236,original,13\n",
        "1604644077475,1604644082861,14536,251,original,12\n",
        "1604644072105,1604644077474,14391,242,original,22\n",
        "1604644066707,1604644072104,14374,284,original,11\n",
        "1604644061282,1604644066706,14458,224,original,14\n",
        "1604644055883,1604644061281,14511,219,original,15\n",
        "1604644050486,1604644055882,14528,295,original,15\n",
        "1604644045119,1604644050485,14454,226,original,10\n",
        "1604644039712,1604644045118,14386,305,original,12\n",
        "1604644035958,1604644039711,10001,257,original,7\n",
        "1604644335958,1604644336957,2742,86,original,24\n",
        "1604644336959,1604644337957,2771,79,original,11\n",
        "1604644337958,1604644338957,2701,88,original,6\n",
        "1604644338958,1604644339957,2694,73,original,10\n",
        "1604644339958,1604644340957,2627,112,original,6\n",
        "1604644340958,1604644341957,2682,96,original,7\n",
        "1604644341958,1604644342957,2686,77,original,17\n",
        "1604644342959,1604644343955,2640,84,original,7\n",
        "1604644343958,1604644344957,2648,94,original,5\n",
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        "1604644351959,1604644352957,2670,71,original,11\n",
        "1604644352959,1604644353957,2702,99,original,8\n",
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        "1604644354958,1604644355957,2656,84,original,7\n",
        "1604644355958,1604644356957,2694,78,original,8\n",
        "1604644356958,1604644357957,2738,111,original,6\n",
        "1604644357958,1604644358957,2730,74,original,6\n",
        "1604644358958,1604644359957,2747,106,original,11\n",
        "1604644359958,1604644360957,2709,84,original,6\n",
        "1604644360958,1604644361957,2665,77,original,8\n",
        "1604644361958,1604644362957,2710,77,original,6\n",
        "1604644362958,1604644363957,2660,88,original,6\n",
        "1604644363958,1604644364957,2654,77,original,6\n",
        "1604644364959,1604644365957,2686,88,original,5\n",
        "\"\"\"\n",
        "\n",
        "csv = StringIO(csv_str)\n",
        "df = pd.read_csv(csv)\n",
        "df.head()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>from</th>\n",
              "      <th>to</th>\n",
              "      <th>len</th>\n",
              "      <th>fetchTime</th>\n",
              "      <th>name</th>\n",
              "      <th>reDrawTime</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1604644330605</td>\n",
              "      <td>1604644335957</td>\n",
              "      <td>14653</td>\n",
              "      <td>284</td>\n",
              "      <td>original</td>\n",
              "      <td>59</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1604644325247</td>\n",
              "      <td>1604644330604</td>\n",
              "      <td>14518</td>\n",
              "      <td>627</td>\n",
              "      <td>original</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1604644319836</td>\n",
              "      <td>1604644325246</td>\n",
              "      <td>14608</td>\n",
              "      <td>445</td>\n",
              "      <td>original</td>\n",
              "      <td>21</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1604644314441</td>\n",
              "      <td>1604644319835</td>\n",
              "      <td>14511</td>\n",
              "      <td>342</td>\n",
              "      <td>original</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1604644309089</td>\n",
              "      <td>1604644314440</td>\n",
              "      <td>14406</td>\n",
              "      <td>265</td>\n",
              "      <td>original</td>\n",
              "      <td>18</td>\n",
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            ],
            "text/plain": [
              "            from             to    len  fetchTime      name  reDrawTime\n",
              "0  1604644330605  1604644335957  14653        284  original          59\n",
              "1  1604644325247  1604644330604  14518        627  original          20\n",
              "2  1604644319836  1604644325246  14608        445  original          21\n",
              "3  1604644314441  1604644319835  14511        342  original          18\n",
              "4  1604644309089  1604644314440  14406        265  original          18"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k6IvldisL9j-",
        "outputId": "1c42a114-e2a0-4e8d-d27b-5624c3377f6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        }
      },
      "source": [
        "# 초기 드로잉 구간\n",
        "df_initial = df.where(14000 < df['len'])\n",
        "print('fetchTime:', df_initial['fetchTime'].mean())\n",
        "print('reDrawTimee:', df_initial['reDrawTime'].mean())\n",
        "pd.concat([df_initial['fetchTime'], df_initial['reDrawTime']]).plot()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fetchTime: 272.5090909090909\n",
            "reDrawTimee: 14.290909090909091\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd130dad4e0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tng-oxIUN2Ta",
        "outputId": "594ea9ca-9339-4737-ad96-81edd18e5cb7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        }
      },
      "source": [
        "# 2초당 업데이트 구간\n",
        "df_update = df.where(df['len'] < 3000)\n",
        "print('fetchTime:', df_update['fetchTime'].mean())\n",
        "print('reDrawTimee:', df_update['reDrawTime'].mean())\n",
        "pd.concat([df_update['fetchTime'], df_update['reDrawTime']]).plot()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fetchTime: 86.03333333333333\n",
            "reDrawTimee: 8.7\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd130c0c400>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}