Ejercicio_3_Spark.ipynb

{
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GfH06k90ac2S"
      },
      "source": [
        "El porcentaje de contenidos que están publicados cuya última edición no tiene comentario para los usuarios que realizaron 1, >10 y >100 de las últimas ediciones (⭐⭐)"
      ]
    },
    {
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting pyspark\n",
            "  Downloading pyspark-3.4.1.tar.gz (310.8 MB)\n",
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            "Requirement already satisfied: py4j==0.10.9.7 in /usr/local/lib/python3.10/dist-packages (from pyspark) (0.10.9.7)\n",
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            "Successfully built pyspark\n",
            "Installing collected packages: pyspark\n",
            "Successfully installed pyspark-3.4.1\n",
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            "Reading package lists... Done\n",
            "Building dependency tree       \n",
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            "25 packages can be upgraded. Run 'apt list --upgradable' to see them.\n",
            "The following additional packages will be installed:\n",
            "  libxtst6 openjdk-8-jre-headless\n",
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            "Processing triggers for libc-bin (2.31-0ubuntu9.9) ...\n"
          ]
        }
      ],
      "source": [
        "!pip install pyspark\n",
        "!pip install -U -q PyDrive\n",
        "!apt update\n",
        "!apt install openjdk-8-jdk-headless -qq\n",
        "#!apt install default-jre\n",
        "#!apt install default-jdk+\n",
        "import os\n",
        "os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9SZG94oqauEi"
      },
      "outputs": [],
      "source": [
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "from pyspark.sql import *\n",
        "from pyspark.sql.functions import *\n",
        "from pyspark import SparkContext\n",
        "from pyspark.sql import SQLContext"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6bK2i59Sa4O6"
      },
      "outputs": [],
      "source": [
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZGkX_YYLa9az"
      },
      "outputs": [],
      "source": [
        "spark = SparkSession.builder.getOrCreate()\n",
        "sc = spark.sparkContext"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YvM4KRroa_Ra",
        "outputId": "801d8fa8-0f10-495d-cd5b-b31af141ed56"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pyspark/sql/context.py:112: FutureWarning: Deprecated in 3.0.0. Use SparkSession.builder.getOrCreate() instead.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "sqlContext = SQLContext(sc)\n",
        "df_contenido = sqlContext.read.csv('/content/drive/MyDrive/contents.csv', header=True, inferSchema=True, multiLine=True, escape='\"')\n",
        "rdd_contenido = df_contenido.rdd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lXIRczRTclmr"
      },
      "source": [
        "Primero necesito agrupar los Usuarios que tengan 1, >10 y > 100 comentarios entre todos los contenidos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9A0bGfWUbLfx"
      },
      "outputs": [],
      "source": [
        "rdd_filtrado = rdd_contenido.map(lambda x: (x.revisor_username, x.revisor_ip, x.revisor_comment))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z2qVlyxWh2nB"
      },
      "source": [
        "Veo la cantidad de Articulos sin comentarios para los usarios registrados y sin registrar"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UMWxmJXu2bwE"
      },
      "source": [
        "Para los registrados"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LLpefgb7iwyJ"
      },
      "outputs": [],
      "source": [
        "rdd_usuarios_registrados = rdd_filtrado.filter(lambda x: x[0] != None).cache()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q0QMXugxpYk8"
      },
      "outputs": [],
      "source": [
        "rdd_usuarios_registrados = rdd_usuarios_registrados.map(lambda x: (x[0], x[2]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PY5B5K7S6GrU"
      },
      "source": [
        "Con los usuarios no registrados"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c3z8n3IAsinY"
      },
      "outputs": [],
      "source": [
        "rdd_usuarios_no_registrados = rdd_filtrado.filter(lambda x: x[1] != None).cache()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BxMMSUTVrWd7"
      },
      "outputs": [],
      "source": [
        "rdd_usuarios_no_registrados = rdd_usuarios_no_registrados.map(lambda x: (x[1], x[2]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rn3MYTOnGWw2"
      },
      "source": [
        "Agrupo los usuarios que revisaron un articulo y no comentaron nada\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M6-1pWMd-cXd"
      },
      "source": [
        "Primero con los usuarios registrados"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HUg37Fvh6oQn"
      },
      "outputs": [],
      "source": [
        "rdd_ur_sin_comentarios = rdd_usuarios_registrados.filter(lambda x: x[1] == None).cache()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DooKCZqA8HQQ"
      },
      "outputs": [],
      "source": [
        "rdd_ur_sin_comentarios = rdd_ur_sin_comentarios.map(lambda x: (x[0], 1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dmuzznes8-Qn"
      },
      "outputs": [],
      "source": [
        "rdd_ur_sin_comentarios = rdd_ur_sin_comentarios.reduceByKey(lambda x, y: x + y).cache()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZnARBcYGGbEJ"
      },
      "source": [
        "Con los no registrados"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "txZfk7un7vvJ"
      },
      "outputs": [],
      "source": [
        "rdd_unr_sin_comentarios = rdd_usuarios_no_registrados.filter(lambda x: x[1] == None).cache()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B3lhcHX18s_H"
      },
      "outputs": [],
      "source": [
        "rdd_unr_sin_comentarios = rdd_unr_sin_comentarios.map(lambda x: (x[0], 1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "11Mki6NL9JEA"
      },
      "outputs": [],
      "source": [
        "rdd_unr_sin_comentarios = rdd_unr_sin_comentarios.reduceByKey(lambda x, y: x + y).cache()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZkD9n_Oa-VN3"
      },
      "source": [
        "Caculo las cantidades por los grupos, en 1 es con los usarios con una revision, 10 con los usuarios con mas de 10 revision y 100 con los usarios con mas de 100 revisiones."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CHvkwwKMGe3B"
      },
      "source": [
        "Con los usuarios registrados (ur)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "24Ml9M8ibFpm"
      },
      "outputs": [],
      "source": [
        "cantidad_ur_sin_comentarios_1 = rdd_ur_sin_comentarios.filter(lambda x: x[1] == 1).map(lambda x: x[1]).sum()\n",
        "cantidad_ur_sin_comentarios_10 = rdd_ur_sin_comentarios.filter(lambda x: x[1] > 10).map(lambda x: x[1]).sum()\n",
        "cantidad_ur_sin_comentarios_100 = rdd_ur_sin_comentarios.filter(lambda x: x[1] > 100).map(lambda x: x[1]).sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8-S35mGsHD5h"
      },
      "source": [
        "Con los usuarios no registrados (unr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4LywAHzRHCo6"
      },
      "outputs": [],
      "source": [
        "cantidad_unr_sin_comentarios_1 = rdd_unr_sin_comentarios.filter(lambda x: x[1] == 1).map(lambda x: x[1]).sum()\n",
        "cantidad_unr_sin_comentarios_10 = rdd_unr_sin_comentarios.filter(lambda x: x[1] > 10).map(lambda x: x[1]).sum()\n",
        "cantidad_unr_sin_comentarios_100 = rdd_unr_sin_comentarios.filter(lambda x: x[1] > 100).map(lambda x: x[1]).sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1CaWQAHyI6FJ"
      },
      "source": [
        "El total de articulos sin comentarios por todos los usuarios en los grupos es la suma de los no registrados con los registrados\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gWKispBaJA8A"
      },
      "outputs": [],
      "source": [
        " total_articulos_sin_comentarios_1 = cantidad_ur_sin_comentarios_1 + cantidad_unr_sin_comentarios_1\n",
        " total_articulos_sin_comentarios_10 = cantidad_ur_sin_comentarios_10 + cantidad_unr_sin_comentarios_10\n",
        " total_articulos_sin_comentarios_100 = cantidad_ur_sin_comentarios_100 + cantidad_unr_sin_comentarios_100"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M9NIKeTV_94g"
      },
      "source": [
        "Tengo las cantidades de los articulos sin comentarios en cada subgrupo, veo que pasa con los usarios registrados y no resgistrados y veo cuantos comentaron."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eNxnQHjZAHR8"
      },
      "outputs": [],
      "source": [
        "rdd_ur_comentaron = rdd_usuarios_registrados.map(lambda x: (x[0], 1)).reduceByKey(lambda x, y: x + y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nYgCJZL1AoPq"
      },
      "outputs": [],
      "source": [
        "rdd_unr_comentaron = rdd_usuarios_no_registrados.map(lambda x: (x[0], 1)).reduceByKey(lambda x, y: x + y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rJ-T6-5GB-sZ"
      },
      "source": [
        "Creo todos los sub grupos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BGrR7_4eYMu7"
      },
      "outputs": [],
      "source": [
        "cantidad_comentarios_ur_comentaron_1 = rdd_ur_comentaron.filter(lambda x: x[1] == 1).map(lambda x: x[1]).sum()\n",
        "cantidad_comentarios_ur_comentaron_10 = rdd_ur_comentaron.filter(lambda x: x[1] > 10).map(lambda x: x[1]).sum()\n",
        "cantidad_comentarios_ur_comentaron_100 = rdd_ur_comentaron.filter(lambda x: x[1] > 100).map(lambda x: x[1]).sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QSRPgw_eYUT-"
      },
      "outputs": [],
      "source": [
        "cantidad_comentarios_unr_comentaron_1 = rdd_unr_comentaron.filter(lambda x: x[1] == 1).map(lambda x: x[1]).sum()\n",
        "cantidad_comentarios_unr_comentaron_10 = rdd_unr_comentaron.filter(lambda x: x[1] > 10).map(lambda x: x[1]).sum()\n",
        "cantidad_comentarios_unr_comentaron_100 = rdd_unr_comentaron.filter(lambda x: x[1] > 100).map(lambda x: x[1]).sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "48CvDVhIMiEY"
      },
      "source": [
        "Veo las cantidad total de comentarios para cada grupo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mQycRg0ANWvY"
      },
      "outputs": [],
      "source": [
        "cantidad_comentarios_1 = cantidad_comentarios_ur_comentaron_1 + cantidad_comentarios_unr_comentaron_1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GIJGhThiNeRw"
      },
      "outputs": [],
      "source": [
        "cantidad_comentarios_10 = cantidad_comentarios_ur_comentaron_10 + cantidad_comentarios_unr_comentaron_10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qm981T3vNivY"
      },
      "outputs": [],
      "source": [
        "cantidad_comentarios_100 = cantidad_comentarios_ur_comentaron_100 + cantidad_comentarios_unr_comentaron_100"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iRKRZK3PDtvA"
      },
      "source": [
        "Calculo los porcentajes pedidos para cada grupo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xrhDUuJdDpeY"
      },
      "outputs": [],
      "source": [
        "porcentaje_1 = 100*(total_articulos_sin_comentarios_1/cantidad_comentarios_1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gDZtYMpWDxH3"
      },
      "outputs": [],
      "source": [
        "porcentaje_10 = 100*(total_articulos_sin_comentarios_10)/(cantidad_comentarios_10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Vht71_xvEFlf"
      },
      "outputs": [],
      "source": [
        "porcentaje_100 = 100*(total_articulos_sin_comentarios_100)/(cantidad_comentarios_100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lDs9NTWkEkrY",
        "outputId": "f26457a8-6bd0-4c83-daf2-4a118de96aa0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "40.43636300241455"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "porcentaje_1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vt2IqU-1Emom",
        "outputId": "1bcfb15f-fdca-4bac-aff2-61ed64df7eb1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "9.830704298834851"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "porcentaje_10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_znU_qQ1Oa_g",
        "outputId": "3dc07382-b136-4d81-fca0-94c59779c270"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "8.417951820682088"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "porcentaje_100"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5vvFuhzFmx4v"
      },
      "source": [
        "En mi caso, yo considere tanto a los usarios registrados como los no registrados ya que no se hace mencion en el enunciado de trabajar con algunos de los dos tipos de usuarios."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X5s_2f9vnYfX"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}