Atividade_Final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Importando bibliotecas \n",
    "import pandas as pd\n",
    "import random\n",
    "import fastavro\n",
    "import time\n",
    "import os\n",
    "import json\n",
    "\n",
    "\n",
    "import findspark\n",
    "from pyspark.sql.types import StructType\n",
    "\n",
    "findspark.init()\n",
    "\n",
    "from pyspark.sql import SparkSession\n",
    "\n",
    "spark = SparkSession.builder \\\n",
    "    .appName(\"ExemploAvro\") \\\n",
    "    .config('spark.jars.packages', 'org.apache.spark:spark:spark-avro_2.12:3.2.0').getOrCreate()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Gerando um dataframe aleatório pra gerar arquivos em csv, json e avro\n",
    "\n",
    "# Valores possíveis por coluna\n",
    "nomes = [\"Pedro\", \"Tiago\", \"João\", \"André\", \"Filipe\", \"Judas Iscariotes\", \"Mateus\", \"Tomé\", \"Bartolomeu\", \"Judas Tadeu\", \"Simão\"]\n",
    "idades = [20, 23, 19, 30, 28, 25, 31, 36, 34, 27, 39, 41, 52]\n",
    "cores_favoritas = [\"Vermelho\", \"Azul\", \"Verde\", \"Amarelo\", \"Roxo\", \"Laranja\", \"Anil\"] \n",
    "animais_favoritos = [\"Cachorro\", \"Gato\", \"Pássaro\", \"Peixe\", \"Coelho\", \"Bode\", \"Dragão de Komodo\"]  # Adicione mais valores, se desejar\n",
    "times_que_torce = [\"Flamengo\", \"Corinthians\", \"Palmeiras\", \"São Paulo\", \"Grêmio\", \"Vasco\", \"Belo\", \"Campinense\", \"Treze\"]  # Adicione mais valores, se desejar\n",
    "estados_que_nasceu = [\"Paraíba\", \"Acre\", \"São Paulo\", \"Rio de Janeiro\", \"Minas Gerais\", \"Bahia\", \"Pernambuco\"]  # Adicione mais valores, se desejar\n",
    "\n",
    "# Criando uma lista de dicionários com valores aleatórios usando random\n",
    "dados_aleatorios = []\n",
    "for i in range(1000):\n",
    "    dados_aleatorios.append({\n",
    "        \"nome\": random.choice(nomes),\n",
    "        \"idade\": random.choice(idades),\n",
    "        \"numero_favorito\": random.randint(0, 1000),\n",
    "        \"cor_favorita\": random.choice(cores_favoritas),\n",
    "        \"animal_favorito\": random.choice(animais_favoritos),\n",
    "        \"time_que_torce\": random.choice(times_que_torce),\n",
    "        \"estado_que_nasceu\": random.choice(estados_que_nasceu)\n",
    "    })\n",
    "\n",
    "df = pd.DataFrame(dados_aleatorios)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definindo o esquema Avro\n",
    "# obs: Lembrar de colocar null pra não ter problema com celulas vazias de uma base baixda da internet\n",
    "schema = {\n",
    "    \"type\": \"record\",\n",
    "    \"name\": \"ExemploAvro\",\n",
    "    \"fields\": [\n",
    "        {\"name\": \"nome\", \"type\": \"string\"},\n",
    "        {\"name\": \"idade\", \"type\": [\"int\", \"null\"]},\n",
    "        {\"name\": \"numero_favorito\", \"type\": [\"int\", \"null\"]},\n",
    "        {\"name\": \"cor_favorita\", \"type\": [\"string\", \"null\"]},\n",
    "        {\"name\": \"animal_favorito\", \"type\": [\"string\", \"null\"]},\n",
    "        {\"name\": \"time_que_torce\", \"type\": [\"string\", \"null\"]},\n",
    "        {\"name\": \"estado_que_nasceu\", \"type\": [\"string\", \"null\"]}\n",
    "    ]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_time_csv_write = time.time()\n",
    "df.to_csv(\"dados.csv\", index=False)\n",
    "csv_write_time = time.time() - start_time_csv_write\n",
    "\n",
    "start_time_json_write = time.time()\n",
    "# Salvar o DataFrame em um arquivo JSON\n",
    "df.to_json('dados.json', orient='records', lines=True)\n",
    "json_write_time = time.time() - start_time_json_write\n",
    "\n",
    "start_time_avro_write = time.time()\n",
    "with open(\"dados.avro\", \"wb\") as avro_file:\n",
    "    fastavro.writer(avro_file, schema, df.to_dict(orient=\"records\"))\n",
    "avro_write_time = time.time() - start_time_avro_write\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tempo para ler JSON: 0.0156 segundos\n",
      "Tempo de leitura em CSV: 0.0047 segundos\n",
      "Tempo de leitura em Avro: 0.0551 segundos\n"
     ]
    }
   ],
   "source": [
    "import findspark\n",
    "from pyspark.sql.types import StructType\n",
    "\n",
    "findspark.init()\n",
    "\n",
    "from pyspark.sql import SparkSession\n",
    "\n",
    "spark = SparkSession.builder \\\n",
    "    .appName(\"ExemploAvro\") \\\n",
    "    .config('spark.jars.packages', 'org.apache.spark:spark:spark-avro_2.12:3.2.0').getOrCreate()\n",
    "\n",
    "# Medindo o tempo de leitura em CSV\n",
    "start_time_csv_read = time.time()\n",
    "df_csv = pd.read_csv(\"dados.csv\")\n",
    "csv_read_time = time.time() - start_time_csv_read\n",
    "\n",
    "# Medindo o tempo de leitura em Avro\n",
    "start_time_avro_read = time.time()\n",
    "df_avro = spark.read.format(\"avro\").load(\"dados.avro\")\n",
    "avro_read_time = time.time() - start_time_avro_read\n",
    "\n",
    "# Ler dados do arquivo JSON e medir o tempo\n",
    "start_time_json = time.time()\n",
    "df = pd.read_json(\"dados.json\", lines=True)\n",
    "json_read_time = time.time() - start_time_json\n",
    "\n",
    "print(f\"Tempo para ler JSON: {json_read_time:.4f} segundos\")\n",
    "print(f\"Tempo de leitura em CSV: {csv_read_time:.4f} segundos\")\n",
    "print(f\"Tempo de leitura em Avro: {avro_read_time:.4f} segundos\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# Criar um gráfico de barras\n",
    "tempos = [json_read_time, csv_read_time, avro_read_time]\n",
    "formatos = [\"JSON\", \"CSV\", \"Avro\"]\n",
    "\n",
    "plt.bar(formatos, tempos)\n",
    "plt.xlabel(\"Formato\")\n",
    "plt.ylabel(\"Tempo de Leitura (s)\")\n",
    "plt.title(\"Tempo de Leitura em Diferentes Formatos\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tempo de leitura em CSV (1000 linhas): 0.0095 segundos\n",
      "Tempo de leitura em Avro (1000 linhas): 0.0263 segundos\n",
      "Tempo de leitura em JSON (1000 linhas): 0.0170 segundos\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Tempo de leitura em CSV (10000 linhas): 0.0170 segundos\n",
      "Tempo de leitura em Avro (10000 linhas): 0.0261 segundos\n",
      "Tempo de leitura em JSON (10000 linhas): 0.0537 segundos\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Tempo de leitura em CSV (100000 linhas): 0.0968 segundos\n",
      "Tempo de leitura em Avro (100000 linhas): 0.0207 segundos\n",
      "Tempo de leitura em JSON (100000 linhas): 0.5141 segundos\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Tempo de leitura em CSV (1000000 linhas): 0.8493 segundos\n",
      "Tempo de leitura em Avro (1000000 linhas): 0.0223 segundos\n",
      "Tempo de leitura em JSON (1000000 linhas): 7.0906 segundos\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import random\n",
    "import fastavro\n",
    "import time\n",
    "import os\n",
    "import json\n",
    "import findspark\n",
    "from pyspark.sql import SparkSession\n",
    "\n",
    "findspark.init()\n",
    "\n",
    "spark = SparkSession.builder \\\n",
    "    .appName(\"ExemploAvro\") \\\n",
    "    .config('spark.jars.packages', 'org.apache.spark:spark-avro_2.12:3.2.0').getOrCreate()\n",
    "\n",
    "# Função para gerar dados aleatórios com um número especificado de linhas\n",
    "def gerar_dados_aleatorios(num_linhas):\n",
    "    nomes = [\"Pedro\", \"Tiago\", \"João\", \"André\", \"Filipe\", \"Judas Iscariotes\", \"Mateus\", \"Tomé\", \"Bartolomeu\", \"Judas Tadeu\", \"Simão\"]\n",
    "    idades = [20, 23, 19, 30, 28, 25, 31, 36, 34, 27, 39, 41, 52]\n",
    "    cores_favoritas = [\"Vermelho\", \"Azul\", \"Verde\", \"Amarelo\", \"Roxo\", \"Laranja\", \"Anil\"] \n",
    "    animais_favoritos = [\"Cachorro\", \"Gato\", \"Pássaro\", \"Peixe\", \"Coelho\", \"Bode\", \"Dragão de Komodo\"]\n",
    "    times_que_torce = [\"Flamengo\", \"Corinthians\", \"Palmeiras\", \"São Paulo\", \"Grêmio\", \"Vasco\", \"Belo\", \"Campinense\", \"Treze\"]\n",
    "    estados_que_nasceu = [\"Paraíba\", \"Acre\", \"São Paulo\", \"Rio de Janeiro\", \"Minas Gerais\", \"Bahia\", \"Pernambuco\"]\n",
    "\n",
    "    dados_aleatorios = []\n",
    "    for i in range(num_linhas):\n",
    "        dados_aleatorios.append({\n",
    "            \"nome\": random.choice(nomes),\n",
    "            \"idade\": random.choice(idades),\n",
    "            \"numero_favorito\": random.randint(0, 1000),\n",
    "            \"cor_favorita\": random.choice(cores_favoritas),\n",
    "            \"animal_favorito\": random.choice(animais_favoritos),\n",
    "            \"time_que_torce\": random.choice(times_que_torce),\n",
    "            \"estado_que_nasceu\": random.choice(estados_que_nasceu)\n",
    "        })\n",
    "\n",
    "    return pd.DataFrame(dados_aleatorios)\n",
    "\n",
    "# Lista de números de linhas para os cenários\n",
    "num_linhas_cenarios = [1000, 10000, 100000, 1000000]\n",
    "\n",
    "for num_linhas in num_linhas_cenarios:\n",
    "    df = gerar_dados_aleatorios(num_linhas)\n",
    "\n",
    "    start_time_csv_write = time.time()\n",
    "    df.to_csv(f\"dados_{num_linhas}.csv\", index=False)\n",
    "    csv_write_time = time.time() - start_time_csv_write\n",
    "\n",
    "    start_time_json_write = time.time()\n",
    "    df.to_json(f'dados_{num_linhas}.json', orient='records', lines=True)\n",
    "    json_write_time = time.time() - start_time_json_write\n",
    "\n",
    "    start_time_avro_write = time.time()\n",
    "    with open(f\"dados_{num_linhas}.avro\", \"wb\") as avro_file:\n",
    "        fastavro.writer(avro_file, schema, df.to_dict(orient=\"records\"))\n",
    "    avro_write_time = time.time() - start_time_avro_write\n",
    "\n",
    "    # Medindo o tempo de leitura em CSV\n",
    "    start_time_csv_read = time.time()\n",
    "    df_csv = pd.read_csv(f\"dados_{num_linhas}.csv\")\n",
    "    csv_read_time = time.time() - start_time_csv_read\n",
    "\n",
    "    # Medindo o tempo de leitura em Avro\n",
    "    start_time_avro_read = time.time()\n",
    "    df_avro = spark.read.format(\"avro\").load(f\"dados_{num_linhas}.avro\")\n",
    "    avro_read_time = time.time() - start_time_avro_read\n",
    "\n",
    "    # Ler dados do arquivo JSON e medir o tempo\n",
    "    start_time_json_read = time.time()\n",
    "    df = pd.read_json(f\"dados_{num_linhas}.json\", lines=True)\n",
    "    json_read_time = time.time() - start_time_json_read\n",
    "\n",
    "    print(f\"Tempo de leitura em CSV ({num_linhas} linhas): {csv_read_time:.4f} segundos\")\n",
    "    print(f\"Tempo de leitura em Avro ({num_linhas} linhas): {avro_read_time:.4f} segundos\")\n",
    "    print(f\"Tempo de leitura em JSON ({num_linhas} linhas): {json_read_time:.4f} segundos\")\n",
    "    \n",
    "    tempos = [json_read_time, csv_read_time, avro_read_time]\n",
    "    formatos = [\"JSON\", \"CSV\", \"Avro\"]\n",
    "\n",
    "    plt.bar(formatos, tempos)\n",
    "    plt.xlabel(\"Formato\")\n",
    "    plt.ylabel(\"Tempo de Leitura (s)\")\n",
    "    plt.title(f\"Tempo de Leitura para {num_linhas} linhas\")\n",
    "    plt.show()\n",
    "\n",
    "    print()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tempo de leitura em CSV (1000 linhas): 0.0228 segundos\n",
      "Tempo de leitura em Avro (1000 linhas): 0.0260 segundos\n",
      "Tempo de leitura em JSON (1000 linhas): 0.0158 segundos\n"
     ]
    },
    {
     "data": {
      "image/png": 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DDz8sdezw8HAtWLBAU6ZM0bXXXquGDRuqa9eu6tatm5o0aaKhQ4fqiSeeULVq1fTqq6+qQYMGys7OvmC953Tr1k0eHh7q1auXhg0bppMnT+rll19Ww4YNnd5O54wrP8uePXtqxowZ6t69u+666y4dPnxY8+bN07XXXquvv/7apeP81THy8vL00ksvSZJ9CfS5c+fK19dXvr6+DouGTJ06Vb1791a3bt3Uv39/ffvtt5o7d67uu+8++zOH0h/hq0OHDhoyZIi+++471a9fX/Pnz1dxcbEmTZrkcHxXx6xMXH3PJ0+erA0bNqhnz55q2rSpDh8+rPnz56tx48bq3LlzBc4AQIWoyKUWAaAyOn+p+XMWLlxohIeHGzVr1jTq1KljhIaGGk8++aRx6NAhe5+mTZsaPXv2LLGvJOPhhx92aDu3/PXUqVPtbeeWZv/xxx+Nbt26GbVq1TL8/f2NlJQUo7i42GH/3377zRg1apQRGBho1KhRw2jRooUxdepUhyXbL2T+/PlGs2bNDE9PTyMiIsLYsGGDERMTU2Lp9qKiIuOFF14w2rRpY3h6ehp169Y1wsPDjUmTJhl5eXkXPMa5ZbudvZo3b27vt337duPOO+80/Pz8DE9PT6Np06ZGv379jIyMDHsfZ8vE5+TkGD179jTq1KlTYpn8bdu2GVFRUYaHh4fRpEkTY8aMGaUuNe/sZ2YYhrFq1Srj+uuvN7y8vIzg4GDjhRdeMF599dVSl2I/f+6u/iwXLVpktGjRwvD09DRCQkKM1157zely+c7+HLk7hjPn/iw6e/15yfZz3n//fSMsLMzw9PQ0GjdubIwfP94oKioq0e/YsWPG0KFDDT8/P6NWrVpGTEyMsWXLFqc1uDrm+dxZav7P59o5koyUlBSHfZ19PIKz99KV9zwjI8O4/fbbjcDAQMPDw8MIDAw0kpKSjO+///6icwNQ9VgMoxI/6Q0AV5jBgwfr3XffdXoLHi4v/CwBAOfjmS8AAAAAMAHhCwAAAABMQPgCAAAAABPwzBcAAAAAmIArXwAAAABgAsIXAAAAAJiAD1m+RDabTYcOHVKdOnVksVgquhwAAAAAFcQwDP32228KDAyU1Vr69S3C1yU6dOiQgoKCKroMAAAAAJXEgQMH1Lhx41K3E74uUZ06dST98QZ7e3tXcDUAAAAAKkp+fr6CgoLsGaE0hK9LdO5WQ29vb8IXAAAAgIs+jsSCGwAAAABgAsIXAAAAAJiA8AUAAAAAJiB8AQAAAIAJCF8AAAAAYALCFwAAAACYgPAFAACAK868efMUHBwsLy8vRUVFafPmzRfsv3z5coWEhMjLy0uhoaFas2aNw/ann35aISEhql27turWrau4uDht2rSpPKeAyxDhCwAAAFeUZcuWKTk5WSkpKcrKylK7du0UHx+vw4cPO+2/ceNGJSUlaejQodq+fbsSEhKUkJCgb7/91t7nuuuu09y5c/XNN9/o3//+t4KDg9WtWzcdOXLErGnhMmAxDMOo6CIuR/n5+fLx8VFeXh4fsgwAAHAZiYqKUvv27TV37lxJks1mU1BQkEaMGKExY8aU6J+YmKiCggKtXr3a3tahQweFhYUpLS3N6THO/a64bt06xcbGls9EUGm4mg248gUAAIArRlFRkbZt26a4uDh7m9VqVVxcnDIzM53uk5mZ6dBfkuLj40vtX1RUpIULF8rHx0ft2rUru+Jx2SN8AQAA4Ipx9OhRFRcXy9/f36Hd399fOTk5TvfJyclxqf/q1at11VVXycvLSzNnztTatWtVv379sp0ALmuELwAAAKAM3HzzzdqxY4c2btyo7t27q1+/fqU+R4YrE+ELAAAAV4z69eurWrVqys3NdWjPzc1VQECA030CAgJc6l+7dm1de+216tChgxYtWqTq1atr0aJFZTsBXNYIXwAAALhieHh4KDw8XBkZGfY2m82mjIwMRUdHO90nOjraob8krV27ttT+fx63sLDwrxeNKqN6RRcAAAAAmCk5OVmDBg1SRESEIiMjNWvWLBUUFGjIkCGSpIEDB6pRo0ZKTU2VJI0cOVIxMTGaPn26evbsqaVLl2rr1q1auHChJKmgoEDPPvusevfurauvvlpHjx7VvHnzdPDgQf3973+vsHmi8iF8AQAA4IqSmJioI0eOaOLEicrJyVFYWJjS09Pti2pkZ2fLav3fDWIdO3bUkiVLNH78eI0bN04tWrTQypUr1bZtW0lStWrVtGvXLr3xxhs6evSo/Pz81L59e33xxRdq06ZNhcwRlROf83WJ+JwvAAAAABKf8wUAAAAAlQrhCwAAAABMwDNfAAAAlVDwmI8qugSg0tv3fM+KLsEtXPkCAAAAABMQvgAAAADABIQvAAAAADAB4QsAAAAATED4AgAAAAATEL4AAAAAwASELwAAAAAwAeELAAAAAExA+AIAAAAAExC+AAAAAMAEhC8AAAAAMAHhCwAAAABMUCnC17x58xQcHCwvLy9FRUVp8+bNF+y/fPlyhYSEyMvLS6GhoVqzZo1925kzZzR69GiFhoaqdu3aCgwM1MCBA3Xo0CGHMYKDg2WxWBxezz//fLnMDwAAAAAqPHwtW7ZMycnJSklJUVZWltq1a6f4+HgdPnzYaf+NGzcqKSlJQ4cO1fbt25WQkKCEhAR9++23kqRTp04pKytLEyZMUFZWllasWKHdu3erd+/eJcaaPHmyfvnlF/trxIgR5TpXAAAAAFcui2EYRkUWEBUVpfbt22vu3LmSJJvNpqCgII0YMUJjxowp0T8xMVEFBQVavXq1va1Dhw4KCwtTWlqa02Ns2bJFkZGR2r9/v5o0aSLpjytfjz76qB599NFLqjs/P18+Pj7Ky8uTt7f3JY0BAABQmuAxH1V0CUClt+/5nhVdgiTXs0GFXvkqKirStm3bFBcXZ2+zWq2Ki4tTZmam030yMzMd+ktSfHx8qf0lKS8vTxaLRb6+vg7tzz//vPz8/HTDDTdo6tSpOnv2bKljFBYWKj8/3+EFAAAAAK6qXpEHP3r0qIqLi+Xv7+/Q7u/vr127djndJycnx2n/nJwcp/1Pnz6t0aNHKykpySGFPvLII7rxxhtVr149bdy4UWPHjtUvv/yiGTNmOB0nNTVVkyZNcmd6AAAAAGBXoeGrvJ05c0b9+vWTYRhasGCBw7bk5GT719dff708PDw0bNgwpaamytPTs8RYY8eOddgnPz9fQUFB5Vc8AAAAgCqlQsNX/fr1Va1aNeXm5jq05+bmKiAgwOk+AQEBLvU/F7z279+vTz/99KLPZUVFRens2bPat2+fWrZsWWK7p6en01AGAAAAAK6o0Ge+PDw8FB4eroyMDHubzWZTRkaGoqOjne4THR3t0F+S1q5d69D/XPDas2eP1q1bJz8/v4vWsmPHDlmtVjVs2PASZwMAAAAApavw2w6Tk5M1aNAgRUREKDIyUrNmzVJBQYGGDBkiSRo4cKAaNWqk1NRUSdLIkSMVExOj6dOnq2fPnlq6dKm2bt2qhQsXSvojePXt21dZWVlavXq1iouL7c+D1atXTx4eHsrMzNSmTZt08803q06dOsrMzNSoUaN09913q27duhXzRgAAAACo0io8fCUmJurIkSOaOHGicnJyFBYWpvT0dPuiGtnZ2bJa/3eBrmPHjlqyZInGjx+vcePGqUWLFlq5cqXatm0rSTp48KBWrVolSQoLC3M41vr169WlSxd5enpq6dKlevrpp1VYWKhmzZpp1KhRDs90AQAAAEBZqvDP+bpc8TlfAACgPPE5X8DF8TlfAAAAAIASCF8AAAAAYALCFwAAAACYgPAFAAAAACYgfAEAAACACQhfAAAAAGACwhcAAAAAmIDwBQAAAAAmIHwBAAAAgAkIXwAAAABgAsIXAAAAAJiA8AUAAAAAJiB8AQAAAIAJCF8AAAAAYALCFwAAAACYgPAFAAAAACYgfAEAAACACQhfAAAAAGACwhcAAH8yb948BQcHy8vLS1FRUdq8efMF+y9fvlwhISHy8vJSaGio1qxZY9925swZjR49WqGhoapdu7YCAwM1cOBAHTp0qLynAQCohAhfAAD8f8uWLVNycrJSUlKUlZWldu3aKT4+XocPH3baf+PGjUpKStLQoUO1fft2JSQkKCEhQd9++60k6dSpU8rKytKECROUlZWlFStWaPfu3erdu7eZ0wIAVBIWwzCMii7icpSfny8fHx/l5eXJ29u7ossBAJSBqKgotW/fXnPnzpUk2Ww2BQUFacSIERozZkyJ/omJiSooKNDq1avtbR06dFBYWJjS0tKcHmPLli2KjIzU/v371aRJk/KZCKqE4DEfVXQJQKW37/meFV2CJNezAVe+AACQVFRUpG3btikuLs7eZrVaFRcXp8zMTKf7ZGZmOvSXpPj4+FL7S1JeXp4sFot8fX3LpG4AwOWD8AUAgKSjR4+quLhY/v7+Du3+/v7Kyclxuk9OTo5b/U+fPq3Ro0crKSmJuyYA4ApE+AIAwARnzpxRv379ZBiGFixYUNHlAAAqQPWKLgAAgMqgfv36qlatmnJzcx3ac3NzFRAQ4HSfgIAAl/qfC1779+/Xp59+ylUvALhCceULAABJHh4eCg8PV0ZGhr3NZrMpIyND0dHRTveJjo526C9Ja9eudeh/Lnjt2bNH69atk5+fX/lMAABQ6XHlCwCA/y85OVmDBg1SRESEIiMjNWvWLBUUFGjIkCGSpIEDB6pRo0ZKTU2VJI0cOVIxMTGaPn26evbsqaVLl2rr1q1auHChpD+CV9++fZWVlaXVq1eruLjY/jxYvXr15OHhUTETBQBUCMIXAAD/X2Jioo4cOaKJEycqJydHYWFhSk9Pty+qkZ2dLav1fzeNdOzYUUuWLNH48eM1btw4tWjRQitXrlTbtm0lSQcPHtSqVaskSWFhYQ7HWr9+vbp06WLKvAAAlQOf83WJ+JwvAABQnvicL+Di+JwvAAAAAEAJhC8AAAAAMAHPfAHAZYZbkYALqyy3IQHA+bjyBQAAAAAmIHwBAAAAgAkIXwAAAABgAsIXAAAAAJiA8AUAAAAAJiB8AQAAAIAJCF8AAAAAYALCFwAAAACYgPAFAAAAACYgfAEAAACACQhfAAAAAGACwhcAAAAAmIDwBQAAAAAmIHwBAAAAgAkIXwAAAABgAsIXAAAAAJiA8AUAAAAAJiB8AQAAAIAJCF8AAAAAYALCFwAAAACYgPAFAAAAACYgfAEAAACACQhfAAAAAGACwhcAAAAAmIDwBQAAAAAmIHwBAAAAgAkIXwAAAABgAsIXAAAAAJiA8AUAAAAAJiB8AQAAAIAJCF8AAAAAYALCFwAAAACYgPAFAAAAACYgfAEAAACACQhfAAAAAGACwhcAAAAAmIDwBQAAAAAmIHwBAAAAgAkIXwAAAABgAsIXAAAAAJiA8AUAAAAAJiB8AQAAAIAJKkX4mjdvnoKDg+Xl5aWoqCht3rz5gv2XL1+ukJAQeXl5KTQ0VGvWrLFvO3PmjEaPHq3Q0FDVrl1bgYGBGjhwoA4dOuQwxrFjxzRgwAB5e3vL19dXQ4cO1cmTJ8tlfgAAAABQ4eFr2bJlSk5OVkpKirKystSuXTvFx8fr8OHDTvtv3LhRSUlJGjp0qLZv366EhAQlJCTo22+/lSSdOnVKWVlZmjBhgrKysrRixQrt3r1bvXv3dhhnwIAB+u9//6u1a9dq9erV2rBhgx544IFyny8AAACAK5PFMAyjIguIiopS+/btNXfuXEmSzWZTUFCQRowYoTFjxpTon5iYqIKCAq1evdre1qFDB4WFhSktLc3pMbZs2aLIyEjt379fTZo00c6dO9W6dWtt2bJFERERkqT09HT16NFDP//8swIDAy9ad35+vnx8fJSXlydvb+9LmToAXJLgMR9VdAlApbbv+Z4VXUKZ4FwHLq6ynO+uZoMKvfJVVFSkbdu2KS4uzt5mtVoVFxenzMxMp/tkZmY69Jek+Pj4UvtLUl5eniwWi3x9fe1j+Pr62oOXJMXFxclqtWrTpk1OxygsLFR+fr7DCwAAAABcVaHh6+jRoyouLpa/v79Du7+/v3Jycpzuk5OT41b/06dPa/To0UpKSrKn0JycHDVs2NChX/Xq1VWvXr1Sx0lNTZWPj4/9FRQU5NIcAQAAAECqBM98laczZ86oX79+MgxDCxYs+EtjjR07Vnl5efbXgQMHyqhKAAAAAFeC6hV58Pr166tatWrKzc11aM/NzVVAQIDTfQICAlzqfy547d+/X59++qnDvZcBAQElFvQ4e/asjh07VupxPT095enp6fLcAAAAAODPKvTKl4eHh8LDw5WRkWFvs9lsysjIUHR0tNN9oqOjHfpL0tq1ax36nwtee/bs0bp16+Tn51dijBMnTmjbtm32tk8//VQ2m01RUVFlMTUAAAAAcFChV74kKTk5WYMGDVJERIQiIyM1a9YsFRQUaMiQIZKkgQMHqlGjRkpNTZUkjRw5UjExMZo+fbp69uyppUuXauvWrVq4cKGkP4JX3759lZWVpdWrV6u4uNj+HFe9evXk4eGhVq1aqXv37rr//vuVlpamM2fOaPjw4erfv79LKx0CAAAAgLsqPHwlJibqyJEjmjhxonJychQWFqb09HT7ohrZ2dmyWv93ga5jx45asmSJxo8fr3HjxqlFixZauXKl2rZtK0k6ePCgVq1aJUkKCwtzONb69evVpUsXSdLixYs1fPhwxcbGymq1qk+fPpozZ075TxgAAADAFanCP+frcsXnfAGoKHz2D3BhleVzf/4qznXg4irL+X5ZfM4XAAAAAFwpCF8AAAAAYALCFwAAAACYgPAFAAAAACYgfAEAAACACQhfAAAAAGACwhcAAAAAmIDwBQAAAAAmIHwBAAAAgAkIXwAAAABgAsIXAAAAAJiA8AUAAAAAJiB8AQAAAIAJCF8AAAAAYALCFwAAAACYgPAFAAAAACYgfAEAAACACQhfAAAAAGACwhcAAAAAmIDwBQAAAAAmIHwBAAAAgAkIXwAAAABgAsIXAAAAAJiA8AUAAAAAJiB8AQAAAIAJCF8AAAAAYALCFwAAAACYgPAFAAAAACYgfAEAAACACQhfAAAAAGACwhcAAAAAmKD6peyUnZ2t/fv369SpU2rQoIHatGkjT0/Psq4NAAAAAKoMl8PXvn37tGDBAi1dulQ///yzDMOwb/Pw8NBNN92kBx54QH369JHVygU1AAAAAPgzl1LSI488onbt2mnv3r2aMmWKvvvuO+Xl5amoqEg5OTlas2aNOnfurIkTJ+r666/Xli1byrtuAAAAALisuHTlq3bt2vrpp5/k5+dXYlvDhg3VtWtXde3aVSkpKUpPT9eBAwfUvn37Mi8WAAAAAC5XLoWv1NRUlwfs3r37JRcDAAAAAFWV2w9n/f777zp16pT9+/3792vWrFn6+OOPy7QwAAAAAKhK3A5ft99+u958801J0okTJxQVFaXp06crISFBCxYsKPMCAQAAAKAqcDt8ZWVl6aabbpIkvfvuu/L399f+/fv15ptvas6cOWVeIAAAAABUBW6Hr1OnTqlOnTqSpE8++UR33nmnrFarOnTooP3795d5gQAAAABQFbgdvq699lqtXLlSBw4c0Mcff6xu3bpJkg4fPixvb+8yLxAAAAAAqgK3w9fEiRP1+OOPKzg4WFFRUYqOjpb0x1WwG264ocwLBAAAAICqwKWl5v+sb9++6ty5s3755Re1a9fO3h4bG6s77rijTIsDAAAAgKrC7fAlSQEBAQoICHBoi4yMLJOCAAAAAKAqcum2wwcffFA///yzSwMuW7ZMixcv/ktFAQAAAEBV49KVrwYNGqhNmzbq1KmTevXqpYiICAUGBsrLy0vHjx/Xd999p3//+99aunSpAgMDtXDhwvKuGwAAAAAuKy6Fr2eeeUbDhw/XK6+8ovnz5+u7775z2F6nTh3FxcVp4cKF6t69e7kUCgAAAACXM5ef+fL399dTTz2lp556SsePH1d2drZ+//131a9fX82bN5fFYinPOgEAAADgsnZJC27UrVtXdevWLetaAAAAAKDKcvtzvgAAAAAA7iN8AQAAAIAJCF+AC+bNm6fg4GB5eXkpKipKmzdvvmD/5cuXKyQkRF5eXgoNDdWaNWvs286cOaPRo0crNDRUtWvXVmBgoAYOHKhDhw6V9zQAAABQgQhfwEUsW7ZMycnJSklJUVZWltq1a6f4+HgdPnzYaf+NGzcqKSlJQ4cO1fbt25WQkKCEhAR9++23kqRTp04pKytLEyZMUFZWllasWKHdu3erd+/eZk4LAAAAJrMYhmFUdBGXo/z8fPn4+CgvL0/e3t4VXQ7KUVRUlNq3b6+5c+dKkmw2m4KCgjRixAiNGTOmRP/ExEQVFBRo9erV9rYOHTooLCxMaWlpTo+xZcsWRUZGav/+/WrSpEn5TARVRvCYjyq6BKBS2/d8z4ouoUxwrgMXV1nOd1ezwSWtdvjuu+/qnXfeUXZ2toqKihy2ZWVlXcqQQKVUVFSkbdu2aezYsfY2q9WquLg4ZWZmOt0nMzNTycnJDm3x8fFauXJlqcfJy8uTxWKRr69vWZQNAACASsjt2w7nzJmjIUOGyN/fX9u3b1dkZKT8/Pz0008/6dZbby2PGoEKc/ToURUXF8vf39+h3d/fXzk5OU73ycnJcav/6dOnNXr0aCUlJXEVFQAAoApzO3zNnz9fCxcu1EsvvSQPDw89+eSTWrt2rR555BHl5eWVR41AlXXmzBn169dPhmFowYIFFV0OAAAAypHb4Ss7O1sdO3aUJNWsWVO//fabJOmee+7R22+/XbbVARWsfv36qlatmnJzcx3ac3NzFRAQ4HSfgIAAl/qfC1779+/X2rVrueoFAABQxbkdvgICAnTs2DFJUpMmTfSf//xHkrR3716xdgeqGg8PD4WHhysjI8PeZrPZlJGRoejoaKf7REdHO/SXpLVr1zr0Pxe89uzZo3Xr1snPz698JgAAAIBKw+0FN7p27apVq1bphhtu0JAhQzRq1Ci9++672rp1q+68887yqBGoUMnJyRo0aJAiIiIUGRmpWbNmqaCgQEOGDJEkDRw4UI0aNVJqaqokaeTIkYqJidH06dPVs2dPLV26VFu3btXChQsl/RG8+vbtq6ysLK1evVrFxcX258Hq1asnDw+PipkoAAAAypXb4WvhwoWy2WySpIcfflh+fn7auHGjevfurWHDhpV5gUBFS0xM1JEjRzRx4kTl5OQoLCxM6enp9kU1srOzZbX+7yJyx44dtWTJEo0fP17jxo1TixYttHLlSrVt21aSdPDgQa1atUqSFBYW5nCs9evXq0uXLqbMCwAAAOZy63O+zp49q+eee0733nuvGjduXJ51VXp8zheAisJn/wAXVlk+9+ev4lwHLq6ynO+uZgO3nvmqXr26XnzxRZ09e/YvFwgAAAAAVxK3bzuMjY3V559/ruDg4HIoB5eKfx0DLqyy/MsYAAC4crkdvm699VaNGTNG33zzjcLDw1W7dm2H7b179y6z4gAAAACgqnA7fD300EOSpBkzZpTYZrFYVFxc/NerAgAAAIAqxu3wdW6lQwAAAACA69z+kGUAAAAAgPvcvvI1efLkC26fOHHiJRcDAAAAAFWV2+Hr/fffd/j+zJkz2rt3r6pXr67mzZsTvgAAAADACbfD1/bt20u05efna/DgwbrjjjvKpCgAAAAAqGrK5Jkvb29vTZo0SRMmTCiL4QAAAACgyimzBTfy8vKUl5dXVsMBAAAAQJXidviaM2eOw2v27NkaM2aMEhMTdeutt7pdwLx58xQcHCwvLy9FRUVp8+bNF+y/fPlyhYSEyMvLS6GhoVqzZo3D9hUrVqhbt27y8/OTxWLRjh07SozRpUsXWSwWh9eDDz7odu0AAAAA4Cq3n/maOXOmw/dWq1UNGjTQoEGDNHbsWLfGWrZsmZKTk5WWlqaoqCjNmjVL8fHx2r17txo2bFii/8aNG5WUlKTU1FTddtttWrJkiRISEpSVlaW2bdtKkgoKCtS5c2f169dP999/f6nHvv/++x1WbqxVq5ZbtQMAAACAO9wOX3v37i2zg8+YMUP333+/hgwZIklKS0vTRx99pFdffVVjxowp0X/27Nnq3r27nnjiCUnSM888o7Vr12ru3LlKS0uTJN1zzz2SpH379l3w2LVq1VJAQECZzQUAAAAALsTt2w7vvfde/fbbbyXaCwoKdO+997o8TlFRkbZt26a4uLj/FWO1Ki4uTpmZmU73yczMdOgvSfHx8aX2v5DFixerfv36atu2rcaOHatTp05dsH9hYaHy8/MdXgAAAADgKrfD1xtvvKHff/+9RPvvv/+uN9980+Vxjh49quLiYvn7+zu0+/v7Kycnx+k+OTk5bvUvzV133aV//vOfWr9+vcaOHau33npLd9999wX3SU1NlY+Pj/0VFBTk1jEBAAAAXNlcvu0wPz9fhmHIMAz99ttv8vLysm8rLi7WmjVrnD6nVRk98MAD9q9DQ0N19dVXKzY2Vj/++KOaN2/udJ+xY8cqOTnZ/n1+fj4BDAAAAIDLXA5fvr6+9pUBr7vuuhLbLRaLJk2a5PKB69evr2rVqik3N9ehPTc3t9RnsQICAtzq76qoqChJ0g8//FBq+PL09JSnp+dfOg4AAACAK5fL4Wv9+vUyDENdu3bVe++9p3r16tm3eXh4qGnTpgoMDHT5wB4eHgoPD1dGRoYSEhIkSTabTRkZGRo+fLjTfaKjo5WRkaFHH33U3rZ27VpFR0e7fFxnzi1Hf/XVV/+lcQAAAACgNC6Hr5iYGEl/rHbYpEkTWSyWv3zw5ORkDRo0SBEREYqMjNSsWbNUUFBgX/1w4MCBatSokVJTUyVJI0eOVExMjKZPn66ePXtq6dKl2rp1qxYuXGgf89ixY8rOztahQ4ckSbt375b0x1WzgIAA/fjjj1qyZIl69OghPz8/ff311xo1apT+9re/6frrr//LcwIAAAAAZ1wKX19//bXatm0rq9WqvLw8ffPNN6X2dSfAJCYm6siRI5o4caJycnIUFham9PR0+6Ia2dnZslr/tyZIx44dtWTJEo0fP17jxo1TixYttHLlSvtnfEnSqlWr7OFNkvr37y9JSklJ0dNPPy0PDw+tW7fOHvSCgoLUp08fjR8/3uW6AQAAAMBdFsMwjIt1slqtysnJUcOGDWW1WmWxWORsN4vFouLi4nIptLLJz8+Xj4+P8vLy5O3tXdHlKHjMRxVdAlCp7Xu+Z0WXUGY434ELqyrnO+c6cHGV5Xx3NRu4dOVr7969atCggf1rAAAAAIB7XApfTZs2dfo1AAAAAMA1bn/IsiS99dZb6tSpkwIDA7V//35J0qxZs/TBBx+UaXEAAAAAUFW4Hb4WLFig5ORk9ejRQydOnLA/4+Xr66tZs2aVdX0AAAAAUCW4Hb5eeuklvfzyy3rqqadUrVo1e3tERMQFV0EEAAAAgCuZ2+Fr7969uuGGG0q0e3p6qqCgoEyKAgAAAICqxu3w1axZM+3YsaNEe3p6ulq1alUWNQEAAABAlePSaod/lpycrIcfflinT5+WYRjavHmz3n77baWmpuqVV14pjxoBAAAA4LLndvi67777VLNmTY0fP16nTp3SXXfdpcDAQM2ePVv9+/cvjxoBAAAA4LJ3SUvNDxgwQHv27NHJkyeVk5Ojn3/+WUlJSdq4cWNZ1wcAAAAAVYLbV77+rFatWqpVq5Ykac+ePbrpppvsS88DAAAAAP7nkq58AQAAAADcQ/gCAAAAABMQvgAAAADABC4/87Vq1aoLbt+7d+9fLgYAAAAAqiqXw1dCQsJF+1gslr9SCwAAAABUWS6HL5vNVp51AAAAAECVxjNfAAAAAGACwhcAAAAAmIDwBQAAAAAmIHwBAAAAgAkIXwAAAABggksKXydOnNArr7yisWPH6tixY5KkrKwsHTx4sEyLAwAAAICqwuWl5s/5+uuvFRcXJx8fH+3bt0/333+/6tWrpxUrVig7O1tvvvlmedQJAAAAAJc1t698JScna/DgwdqzZ4+8vLzs7T169NCGDRvKtDgAAAAAqCrcDl9btmzRsGHDSrQ3atRIOTk5ZVIUAAAAAFQ1bocvT09P5efnl2j//vvv1aBBgzIpCgAAAACqGrfDV+/evTV58mSdOXNGkmSxWJSdna3Ro0erT58+ZV4gAAAAAFQFboev6dOn6+TJk2rYsKF+//13xcTE6Nprr1WdOnX07LPPlkeNAAAAAHDZc3u1Qx8fH61du1b//ve/9fXXX+vkyZO68cYbFRcXVx71AQAAAECV4Hb4Oqdz587q3LlzWdYCAAAAAFWWS+Frzpw5Lg/4yCOPXHIxAAAAAFBVuRS+Zs6c6fD9kSNHdOrUKfn6+kqSTpw4oVq1aqlhw4aELwAAAABwwqUFN/bu3Wt/PfvsswoLC9POnTt17NgxHTt2TDt37tSNN96oZ555przrBQAAAIDLkturHU6YMEEvvfSSWrZsaW9r2bKlZs6cqfHjx5dpcQAAAABQVbgdvn755RedPXu2RHtxcbFyc3PLpCgAAAAAqGrcDl+xsbEaNmyYsrKy7G3btm3T//3f/7HcPAAAAACUwu3w9eqrryogIEARERHy9PSUp6enIiMj5e/vr1deeaU8agQAAACAy57bn/PVoEEDrVmzRnv27NHOnTslSSEhIbruuuvKvDgAAAAAqCou+UOWW7RooRYtWpRlLQAAAABQZbl92yEAAAAAwH2ELwAAAAAwAeELAAAAAExA+AIAAAAAE1zSghsnTpzQokWL7KsdtmnTRvfee698fHzKtDgAAAAAqCrcvvK1detWNW/eXDNnztSxY8d07NgxzZgxQ82bN3f44GUAAAAAwP+4feVr1KhR6t27t15++WVVr/7H7mfPntV9992nRx99VBs2bCjzIgEAAADgcud2+Nq6datD8JKk6tWr68knn1RERESZFgcAAAAAVYXbtx16e3srOzu7RPuBAwdUp06dMikKAAAAAKoat8NXYmKihg4dqmXLlunAgQM6cOCAli5dqvvuu09JSUnlUSMAAAAAXPbcvu1w2rRpslgsGjhwoM6ePStJqlGjhv7v//5Pzz//fJkXCAAAAABVgdvhy8PDQ7Nnz1Zqaqp+/PFHSVLz5s1Vq1atMi8OAAAAAKqKS/qcL0mqVauWfH197V8DAAAAAErn9jNfZ8+e1YQJE+Tj46Pg4GAFBwfLx8dH48eP15kzZ8qjRgAAAAC47Ll95WvEiBFasWKFXnzxRUVHR0uSMjMz9fTTT+vXX3/VggULyrxIAAAAALjcuR2+lixZoqVLl+rWW2+1t11//fUKCgpSUlIS4QsAAAAAnHD7tkNPT08FBweXaG/WrJk8PDzKoiYAAAAAqHLcDl/Dhw/XM888o8LCQntbYWGhnn32WQ0fPrxMiwMAAACAqsLt2w63b9+ujIwMNW7cWO3atZMkffXVVyoqKlJsbKzuvPNOe98VK1aUXaUAAAAAcBlzO3z5+vqqT58+Dm1BQUFlVhAAAAAAVEVuh6/XXnutPOoAAAAAgCrN7We+AAAAAADuc/vK16+//qqJEydq/fr1Onz4sGw2m8P2Y8eOlVlxAAAAAFBVuB2+7rnnHv3www8aOnSo/P39ZbFYyqMuAAAAAKhS3A5fX3zxhf7973/bVzoEAAAAAFyc2898hYSE6Pfffy+PWgAAAACgynI7fM2fP19PPfWUPv/8c/3666/Kz893eAEAAAAASrqkz/nKz89X165dHdoNw5DFYlFxcXGZFQcAAAAAVYXb4WvAgAGqUaOGlixZwoIbAAAAAOAit8PXt99+q+3bt6tly5blUQ8AAAAAVEluP/MVERGhAwcOlEctAAAAAFBluX3la8SIERo5cqSeeOIJhYaGqkaNGg7br7/++jIrDgAAAACqCrfDV2JioiTp3nvvtbdZLBYW3AAAAACAC3A7fO3du7c86gAAAACAKs3tZ76aNm16wZe75s2bp+DgYHl5eSkqKkqbN2++YP/ly5crJCREXl5eCg0N1Zo1axy2r1ixQt26dZOfn58sFot27NhRYozTp0/r4Ycflp+fn6666ir16dNHubm5btcOAAAAAK5yO3xJ0ltvvaVOnTopMDBQ+/fvlyTNmjVLH3zwgVvjLFu2TMnJyUpJSVFWVpbatWun+Ph4HT582Gn/jRs3KikpSUOHDtX27duVkJCghIQEffvtt/Y+BQUF6ty5s1544YVSjztq1Ch9+OGHWr58uT7//HMdOnRId955p1u1AwAAAIA73A5fCxYsUHJysnr06KETJ07Yn/Hy9fXVrFmz3BprxowZuv/++zVkyBC1bt1aaWlpqlWrll599VWn/WfPnq3u3bvriSeeUKtWrfTMM8/oxhtv1Ny5c+197rnnHk2cOFFxcXFOx8jLy9OiRYs0Y8YMde3aVeHh4Xrttde0ceNG/ec//3GrfgAAAABwldvh66WXXtLLL7+sp556StWqVbO3R0RE6JtvvnF5nKKiIm3bts0hJFmtVsXFxSkzM9PpPpmZmSVCVXx8fKn9ndm2bZvOnDnjME5ISIiaNGlywXEKCwuVn5/v8AIAAAAAV7kdvvbu3asbbrihRLunp6cKCgpcHufo0aMqLi6Wv7+/Q7u/v79ycnKc7pOTk+NW/9LG8PDwkK+vr1vjpKamysfHx/4KCgpy+ZgAAAAA4Hb4atasmdNFLNLT09WqVauyqKlSGjt2rPLy8uwvPmgaAAAAgDtcXmp+8uTJevzxx5WcnKyHH35Yp0+flmEY2rx5s95++22lpqbqlVdecfnA9evXV7Vq1UqsMpibm6uAgACn+wQEBLjVv7QxioqKdOLECYerXxcbx9PTU56eni4fBwAAAAD+zOUrX5MmTdLJkyd133336YUXXtD48eN16tQp3XXXXVqwYIFmz56t/v37u3xgDw8PhYeHKyMjw95ms9mUkZGh6Ohop/tER0c79JektWvXltrfmfDwcNWoUcNhnN27dys7O9utcQAAAADAHS5f+TIMw/71gAEDNGDAAJ06dUonT55Uw4YNL+ngycnJGjRokCIiIhQZGalZs2apoKBAQ4YMkSQNHDhQjRo1UmpqqiRp5MiRiomJ0fTp09WzZ08tXbpUW7du1cKFC+1jHjt2TNnZ2Tp06JCkP4KV9McVr4CAAPn4+Gjo0KFKTk5WvXr15O3trREjRig6OlodOnS4pHkAAAAAwMW4HL4kyWKxOHxfq1Yt1apV65IPnpiYqCNHjmjixInKyclRWFiY0tPT7YtqZGdny2r938W5jh07asmSJRo/frzGjRunFi1aaOXKlWrbtq29z6pVq+zhTZL9alxKSoqefvppSdLMmTNltVrVp08fFRYWKj4+XvPnz7/keQAAAADAxViMP1/SugCr1SofH58SAex8x44dK5PCKrv8/Hz5+PgoLy9P3t7eFV2Ogsd8VNElAJXavud7VnQJZYbzHbiwqnK+c64DF1dZzndXs4FbV74mTZokHx+fv1wcAAAAAFxp3Apf/fv3v+TnuwAAAADgSubyaocXu90QAAAAAFA6l8OXi4+GAQAAAACccPm2Q5vNVp51AAAAAECV5vKVLwAAAADApSN8AQAAAIAJCF8AAAAAYALCFwAAAACYgPAFAAAAACYgfAEAAACACQhfAAAAAGACwhcAAAAAmIDwBQAAAAAmIHwBAAAAgAkIXwAAAABgAsIXAAAAAJiA8AUAAAAAJiB8AQAAAIAJCF8AAAAAYALCFwAAAACYgPAFAAAAACYgfAEAAACACQhfAAAAAGACwhcAAAAAmIDwBQAAAAAmIHwBAAAAgAkIXwAAAABgAsIXAAAAAJiA8AUAAAAAJiB8AQAAAIAJCF8AAAAAYALCFwAAAACYgPAFAAAAACYgfAEAAACACQhfAAAAAGACwhcAAAAAmIDwBQAAAAAmIHwBAAAAgAkIXwAAAABgAsIXAAAAAJiA8AUAAAAAJiB8AQAAAIAJCF8AAAAAYALCFwAAAACYgPAFAAAAACYgfAEAAACACQhfAAAAAGACwhcAAAAAmIDwBQAAAAAmIHwBAAAAgAkIXwAAAABgAsIXAAAAAJiA8AUAAAAAJiB8AQAAAIAJCF8AAAAAYALCFwAAAACYgPAFAAAAACYgfAEAAACACQhfAAAAAGACwhcAAAAAmIDwBQAAAAAmIHwBAAAAgAkIXwAAAABgAsIXAAAAAJiA8AUAAAAAJiB8AQAAAIAJCF8AAAAAYALCFwAAAACYgPAFAAAAACYgfAEAAACACQhfAAAAAGACwhcAAAAAmKBShK958+YpODhYXl5eioqK0ubNmy/Yf/ny5QoJCZGXl5dCQ0O1Zs0ah+2GYWjixIm6+uqrVbNmTcXFxWnPnj0OfYKDg2WxWBxezz//fJnPDQAAAACkShC+li1bpuTkZKWkpCgrK0vt2rVTfHy8Dh8+7LT/xo0blZSUpKFDh2r79u1KSEhQQkKCvv32W3ufF198UXPmzFFaWpo2bdqk2rVrKz4+XqdPn3YYa/Lkyfrll1/srxEjRpTrXAEAAABcuSo8fM2YMUP333+/hgwZotatWystLU21atXSq6++6rT/7Nmz1b17dz3xxBNq1aqVnnnmGd14442aO3eupD+ues2aNUvjx4/X7bffruuvv15vvvmmDh06pJUrVzqMVadOHQUEBNhftWvXLu/pAgAAALhCVWj4Kioq0rZt2xQXF2dvs1qtiouLU2ZmptN9MjMzHfpLUnx8vL3/3r17lZOT49DHx8dHUVFRJcZ8/vnn5efnpxtuuEFTp07V2bNnS621sLBQ+fn5Di8AAAAAcFX1ijz40aNHVVxcLH9/f4d2f39/7dq1y+k+OTk5Tvvn5OTYt59rK62PJD3yyCO68cYbVa9ePW3cuFFjx47VL7/8ohkzZjg9bmpqqiZNmuTeBAEAAADg/6vQ8FWRkpOT7V9ff/318vDw0LBhw5SamipPT88S/ceOHeuwT35+voKCgkypFQAAAMDlr0JvO6xfv76qVaum3Nxch/bc3FwFBAQ43ScgIOCC/c/9150xJSkqKkpnz57Vvn37nG739PSUt7e3wwsAAAAAXFWh4cvDw0Ph4eHKyMiwt9lsNmVkZCg6OtrpPtHR0Q79JWnt2rX2/s2aNVNAQIBDn/z8fG3atKnUMSVpx44dslqtatiw4V+ZEgAAAAA4VeG3HSYnJ2vQoEGKiIhQZGSkZs2apYKCAg0ZMkSSNHDgQDVq1EipqamSpJEjRyomJkbTp09Xz549tXTpUm3dulULFy6UJFksFj366KOaMmWKWrRooWbNmmnChAkKDAxUQkKCpD8W7di0aZNuvvlm1alTR5mZmRo1apTuvvtu1a1bt0LeBwAAAABVW4WHr8TERB05ckQTJ05UTk6OwsLClJ6ebl8wIzs7W1br/y7QdezYUUuWLNH48eM1btw4tWjRQitXrlTbtm3tfZ588kkVFBTogQce0IkTJ9S5c2elp6fLy8tL0h+3EC5dulRPP/20CgsL1axZM40aNcrhmS4AAAAAKEsWwzCMii7icpSfny8fHx/l5eVViue/gsd8VNElAJXavud7VnQJZYbzHbiwqnK+c64DF1dZzndXs0GFf8gyAAAAAFwJCF8AAAAAYALCFwAAAACYgPAFAAAAACYgfAEAAACACQhfAAAAAGACwhcAAAAAmIDwBQAAAAAmIHwBAAAAgAkIXwAAAABgAsIXAAAAAJiA8AUAAAAAJiB8AQAAAIAJCF8AAAAAYALCFwAAAACYgPAFAAAAACYgfAEAAACACQhfAAAAAGACwhcAAAAAmIDwBQAAAAAmIHwBAAAAgAkIXwAAAABgAsIXAAAAAJiA8AUAAAAAJiB8AQAAAIAJCF8AAAAAYALCFwAAAACYgPAFAAAAACYgfAEAAACACQhfAAAAAGACwhcAAAAAmIDwBQAAAAAmIHwBAAAAgAkIXwAAAABgAsIXAAAAAJiA8AUAAAAAJiB8AQAAAIAJCF8AAAAAYALCFwAAAACYgPAFAAAAACYgfAEAAACACQhfAAAAAGACwhcAAAAAmIDwBQAAAAAmIHwBAAAAgAkIXwAAAABgAsIXAAAAAJiA8AUAAAAAJiB8AQAAAIAJCF8AAAAAYALCFwAAAACYgPAFAAAAACYgfAEAAACACQhfAAAAAGACwhcAAAAAmIDwBQAAAAAmIHwBAAAAgAkIXwAAAABgAsIXAAAAAJiA8AUAAAAAJiB8AQAAAIAJCF8AAAAAYALCFwAAAACYgPAFAAAAACYgfAEAAACACQhfAAAAAGACwhcAAAAAmIDwBQAAAAAmIHwBAAAAgAkIXwAAAABgAsIXAAAAAJiA8AUAAAAAJiB8AQAAAIAJCF8AAAAAYIJKEb7mzZun4OBgeXl5KSoqSps3b75g/+XLlyskJEReXl4KDQ3VmjVrHLYbhqGJEyfq6quvVs2aNRUXF6c9e/Y49Dl27JgGDBggb29v+fr6aujQoTp58mSZzw0AAAAApEoQvpYtW6bk5GSlpKQoKytL7dq1U3x8vA4fPuy0/8aNG5WUlKShQ4dq+/btSkhIUEJCgr799lt7nxdffFFz5sxRWlqaNm3apNq1ays+Pl6nT5+29xkwYID++9//au3atVq9erU2bNigBx54oNznCwAAAODKZDEMw6jIAqKiotS+fXvNnTtXkmSz2RQUFKQRI0ZozJgxJfonJiaqoKBAq1evtrd16NBBYWFhSktLk2EYCgwM1GOPPabHH39ckpSXlyd/f3+9/vrr6t+/v3bu3KnWrVtry5YtioiIkCSlp6erR48e+vnnnxUYGHjRuvPz8+Xj46O8vDx5e3uXxVvxlwSP+aiiSwAqtX3P96zoEsoM5ztwYVXlfOdcBy6uspzvrmaD6ibWVEJRUZG2bdumsWPH2tusVqvi4uKUmZnpdJ/MzEwlJyc7tMXHx2vlypWSpL179yonJ0dxcXH27T4+PoqKilJmZqb69++vzMxM+fr62oOXJMXFxclqtWrTpk264447Shy3sLBQhYWF9u/z8vIk/fFGVwa2wlMVXQJQqVWWc7UscL4DF1ZVznfOdeDiKsv5fq6Oi13XqtDwdfToURUXF8vf39+h3d/fX7t27XK6T05OjtP+OTk59u3n2i7Up2HDhg7bq1evrnr16tn7nC81NVWTJk0q0R4UFFTa9ABUIj6zKroCAGbhfAeuHJXtfP/tt9/k4+NT6vYKDV+Xk7FjxzpccbPZbDp27Jj8/PxksVgqsDJURvn5+QoKCtKBAwcqxW2pAMoH5zpw5eB8x4UYhqHffvvtoo8vVWj4ql+/vqpVq6bc3FyH9tzcXAUEBDjdJyAg4IL9z/03NzdXV199tUOfsLAwe5/zF/Q4e/asjh07VupxPT095enp6dDm6+t74Qniiuft7c1f0MAVgHMduHJwvqM0F7ridU6Frnbo4eGh8PBwZWRk2NtsNpsyMjIUHR3tdJ/o6GiH/pK0du1ae/9mzZopICDAoU9+fr42bdpk7xMdHa0TJ05o27Zt9j6ffvqpbDaboqKiymx+AAAAAHBOhd92mJycrEGDBikiIkKRkZGaNWuWCgoKNGTIEEnSwIED1ahRI6WmpkqSRo4cqZiYGE2fPl09e/bU0qVLtXXrVi1cuFCSZLFY9Oijj2rKlClq0aKFmjVrpgkTJigwMFAJCQmSpFatWql79+66//77lZaWpjNnzmj48OHq37+/SysdAgAAAIC7Kjx8JSYm6siRI5o4caJycnIUFham9PR0+4IZ2dnZslr/d4GuY8eOWrJkicaPH69x48apRYsWWrlypdq2bWvv8+STT6qgoEAPPPCATpw4oc6dOys9PV1eXl72PosXL9bw4cMVGxsrq9WqPn36aM6cOeZNHFWap6enUlJSStyqCqBq4VwHrhyc7ygLFf45XwAAAABwJajQZ74AAAAA4EpB+AIAAAAAExC+AAAAAMAEhC8AAAAAMAHhCyjF4MGD7R9PcOTIEf3f//2fmjRpIk9PTwUEBCg+Pl5ffvmlwz4bN25Ujx49VLduXXl5eSk0NFQzZsxQcXGxQz+LxSIvLy/t37/foT0hIUGDBw8uz2kBcEFOTo5GjBiha665Rp6engoKClKvXr3snyH51VdfqXfv3mrYsKG8vLwUHBysxMREHT58WNu2bZPFYtF//vMfp2PHxsbqzjvvNHM6AEqRmZmpatWqqWfPnhVdCq4QhC/ABX369NH27dv1xhtv6Pvvv9eqVavUpUsX/frrr/Y+77//vmJiYtS4cWOtX79eu3bt0siRIzVlyhT1799f5y8sarFYNHHiRLOnAuAi9u3bp/DwcH366aeaOnWqvvnmG6Wnp+vmm2/Www8/rCNHjig2Nlb16tXTxx9/rJ07d+q1115TYGCgCgoKFB4ernbt2unVV191Ovb69es1dOjQCpgZgPMtWrRII0aM0IYNG3To0KFLHqeoqKgMq0KVZgBwatCgQcbtt99uHD9+3JBkfPbZZ6X2PXnypOHn52fceeedJbatWrXKkGQsXbrU3ibJePzxxw2r1Wp888039vbbb7/dGDRoUJnOA4B7br31VqNRo0bGyZMnS2w7fvy48f777xvVq1c3zpw5U+oYc+bMMby9vY2CggKH9pSUFCMwMNA4e/ZsmdcNwD2//fabcdVVVxm7du0yEhMTjWeffdYwDMNISkoy+vXr59C3qKjI8PPzM9544w3DMAwjJibGePjhh42RI0cafn5+RpcuXQzDMIzPPvvMaN++veHh4WEEBAQYo0ePvuDfFbjycOULuIirrrpKV111lVauXKnCwkKnfT755BP9+uuvevzxx0ts69Wrl6677jq9/fbbDu2dOnXSbbfdpjFjxpRL3QDcd+zYMaWnp+vhhx9W7dq1S2z39fVVQECAzp49q/fff7/EFe1zBgwYoMLCQr377rv2NsMw9MYbb2jw4MGqVq1auc0BgGveeecdhYSEqGXLlrr77rv16quvyjAMDRgwQB9++KFOnjxp7/vxxx/r1KlTuuOOO+xtb7zxhjw8PPTll18qLS1NBw8eVI8ePdS+fXt99dVXWrBggRYtWqQpU6ZUxPRQSRG+gIuoXr26Xn/9db3xxhvy9fVVp06dNG7cOH399df2Pt9//70kqVWrVk7HCAkJsff5s9TUVKWnp+uLL74on+IBuOWHH36QYRgKCQkptU+HDh00btw43XXXXapfv75uvfVWTZ06Vbm5ufY+9erV0x133OFw6+H69eu1b98+DRkypFznAMA1ixYt0t133y1J6t69u/Ly8vT5558rPj5etWvX1vvvv2/vu2TJEvXu3Vt16tSxt7Vo0UIvvviiWrZsqZYtW2r+/PkKCgrS3LlzFRISooSEBE2aNEnTp0+XzWYzfX6onAhfgAv69OmjQ4cOadWqVerevbs+++wz3XjjjXr99dcd+pX2r+Clad26tQYOHMjVL6CScPUcfvbZZ5WTk6O0tDS1adNGaWlpCgkJ0TfffGPvc++992rDhg368ccfJUmvvvqqYmJidO2115ZL7QBct3v3bm3evFlJSUmS/viH1sTERC1atEjVq1dXv379tHjxYklSQUGBPvjgAw0YMMBhjPDwcIfvd+7cqejoaFksFntbp06ddPLkSf3888/lPCNcLghfgIu8vLx0yy23aMKECdq4caMGDx6slJQUSdJ1110n6Y+/eJ3ZuXOnvc/5Jk2apKysLK1cubJc6gbguhYtWshisWjXrl0X7evn56e///3vmjZtmnbu3KnAwEBNmzbNvj02NlZNmjTR66+/rvz8fK1YsYKFNoBKYtGiRTp79qwCAwNVvXp1Va9eXQsWLNB7772nvLw8DRgwQBkZGTp8+LBWrlypmjVrqnv37g5jOLs1GbgYwhdwiVq3bq2CggJJUrdu3VSvXj1Nnz69RL9Vq1Zpz5499n9dO19QUJCGDx+ucePGlViSHoC56tWrp/j4eM2bN89+fv/ZiRMnnO7n4eGh5s2bO+xjtVo1ZMgQvfHGG1qyZIk8PDzUt2/f8iodgIvOnj2rN998U9OnT9eOHTvsr6+++kqBgYF6++231bFjRwUFBWnZsmVavHix/v73v6tGjRoXHLdVq1bKzMx0uIL+5Zdfqk6dOmrcuHF5TwuXCcIXcBG//vqrunbtqn/+85/6+uuvtXfvXi1fvlwvvviibr/9dkl//OvXP/7xD33wwQd64IEH9PXXX2vfvn1atGiRBg8erL59+6pfv36lHmPs2LE6dOiQ1q1bZ9a0AJRi3rx5Ki4uVmRkpN577z3t2bNHO3fu1Jw5cxQdHa3Vq1fr7rvv1urVq/X9999r9+7dmjZtmtasWWP/O+GcIUOG6ODBgxo3bpySkpJUs2bNCpoVgHNWr16t48ePa+jQoWrbtq3Dq0+fPlq0aJEk6a677lJaWprWrl1b4pZDZx566CEdOHBAI0aM0K5du/TBBx8oJSVFycnJslr5lRt/4E8CUAqbzabq1avrqquuUlRUlGbOnKm//e1vatu2rSZMmKD7779fc+fOtffv27ev1q9fr+zsbN10001q2bKlZs6cqaeeekpLly51uAf8fPXq1dPo0aN1+vRpM6YG4AKuueYaZWVl6eabb9Zjjz2mtm3b6pZbblFGRoYWLFig1q1bq1atWnrssccUFhamDh066J133tErr7yie+65x2GsJk2aKC4uTsePH9e9995bQTMC8GeLFi1SXFycfHx8Smzr06ePtm7dqq+//loDBgzQd999p0aNGqlTp04XHbdRo0Zas2aNNm/erHbt2unBBx/U0KFDNX78+PKYBi5TFsPdFQKAK0T37t117bXXOgQsAAAA4FJx5Qs4z/Hjx7V69Wp99tlniouLq+hyAAAAUEVUr+gCgMrm3nvv1ZYtW/TYY4+VeH4DAAAAuFTcdggAAAAAJuC2QwAAAAAwAeELAAAAAExA+AIAAAAAExC+AAAAAMAEhC8AAAAAMAHhCwAAAABMQPgCAFQ5gwcPlsViKfH64YcfKro0B/v27ZPFYtGOHTsquhQAgAn4kGUAQJXUvXt3vfbaaw5tDRo0cHucoqIieXh4lFVZAIArGFe+AABVkqenpwICAhxe1apV0+eff67IyEh5enrq6quv1pgxY3T27Fn7fl26dNHw4cP16KOPqn79+oqPj9dnn30mi8Wijz/+WDfccINq1qyprl276vDhw/rXv/6lVq1aydvbW3fddZdOnTplHys9PV2dO3eWr6+v/Pz8dNttt+nHH3+0b2/WrJkk6YYbbpDFYlGXLl0kSTabTZMnT1bjxo3l6empsLAwpaenm/PGAQDKDeELAHDFOHjwoHr06KH27dvrq6++0oIFC7Ro0SJNmTLFod8bb7whDw8Pffnll0pLS7O3P/3005o7d642btyoAwcOqF+/fpo1a5aWLFmijz76SJ988oleeukle/+CggIlJydr69atysjIkNVq1R133CGbzSZJ2rx5syRp3bp1+uWXX7RixQpJ0uzZszV9+nRNmzZNX3/9teLj49W7d2/t2bOnvN8iAEA5shiGYVR0EQAAlKXBgwfrn//8p7y8vOxtt956q6677jq999572rlzpywWiyRp/vz5Gj16tPLy8mS1WtWlSxfl5+crKyvLvu9nn32mm2++WevWrVNsbKwk6fnnn9fYsWP1448/6pprrpEkPfjgg9q3b1+pV6mOHj2qBg0a6JtvvlHbtm21b98+NWvWTNu3b1dYWJi9X6NGjfTwww9r3Lhx9rbIyEi1b99e8+bNK7P3CQBgLq58AQCqpJtvvlk7duywv+bMmaOdO3cqOjraHrwkqVOnTjp58qR+/vlne1t4eLjTMa+//nr71/7+/qpVq5Y9eJ1rO3z4sP37PXv2KCkpSddcc428vb0VHBwsScrOzi617vz8fB06dEidOnVyaO/UqZN27tzp2uQBAJUSC24AAKqk2rVr69prr73kfZ2pUaOG/WuLxeLw/bm2c7cUSlKvXr3UtGlTvfzyywoMDJTNZlPbtm1VVFR0SXUBAC5vXPkCAFwxWrVqpczMTP35jvsvv/xSderUUePGjcv0WL/++qt2796t8ePHKzY2Vq1atdLx48cd+pxbRbG4uNje5u3trcDAQH355ZcOfb/88ku1bt26TGsEAJiL8AUAuGI89NBDOnDggEaMGKFdu3bpgw8+UEpKipKTk2W1lu3/EuvWrSs/Pz8tXLhQP/zwgz799FMlJyc79GnYsKFq1qyp9PR05ebmKi8vT5L0xBNP6IUXXtCyZcu0e/dujRkzRjt27NDIkSPLtEYAgLkIXwCAK0ajRo20Zs0abd68We3atdODDz6ooUOHavz48WV+LKvVqqVLl2rbtm1q27atRo0apalTpzr0qV69uubMmaN//OMfCgwM1O233y5JeuSRR5ScnKzHHntMoaGhSk9P16pVq9SiRYsyrxMAYB5WOwQAAAAAE3DlCwAAAABMQPgCAAAAABMQvgAAAADABIQvAAAAADAB4QsAAAAATED4AgAAAAATEL4AAAAAwASELwAAAAAwAeELAAAAAExA+AIAAAAAExC+AAAAAMAE/w83PCn4vhgeWwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Tempo de leitura em CSV (10000 linhas): 0.0178 segundos\n",
      "Tempo de leitura em Avro (10000 linhas): 0.0161 segundos\n",
      "Tempo de leitura em JSON (10000 linhas): 0.0620 segundos\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Tempo de leitura em CSV (100000 linhas): 0.0969 segundos\n",
      "Tempo de leitura em Avro (100000 linhas): 0.0173 segundos\n",
      "Tempo de leitura em JSON (100000 linhas): 0.5025 segundos\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Tempo de leitura em CSV (1000000 linhas): 0.7982 segundos\n",
      "Tempo de leitura em Avro (1000000 linhas): 0.0297 segundos\n",
      "Tempo de leitura em JSON (1000000 linhas): 5.5098 segundos\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import random\n",
    "import fastavro\n",
    "import time\n",
    "import os\n",
    "import json\n",
    "import findspark\n",
    "from pyspark.sql import SparkSession\n",
    "\n",
    "findspark.init()\n",
    "\n",
    "spark = SparkSession.builder \\\n",
    "    .appName(\"ExemploAvro\") \\\n",
    "    .config('spark.jars.packages', 'org.apache.spark:spark-avro_2.12:3.2.0').getOrCreate()\n",
    "\n",
    "# Função para gerar dados aleatórios com um número especificado de linhas\n",
    "def gerar_dados_aleatorios(num_linhas):\n",
    "    nomes = [\"Pedro\", \"Tiago\", \"João\", \"André\", \"Filipe\", \"Judas Iscariotes\", \"Mateus\", \"Tomé\", \"Bartolomeu\", \"Judas Tadeu\", \"Simão\"]\n",
    "    idades = [20, 23, 19, 30, 28, 25, 31, 36, 34, 27, 39, 41, 52]\n",
    "    cores_favoritas = [\"Vermelho\", \"Azul\", \"Verde\", \"Amarelo\", \"Roxo\", \"Laranja\", \"Anil\"] \n",
    "    animais_favoritos = [\"Cachorro\", \"Gato\", \"Pássaro\", \"Peixe\", \"Coelho\", \"Bode\", \"Dragão de Komodo\"]\n",
    "    times_que_torce = [\"Flamengo\", \"Corinthians\", \"Palmeiras\", \"São Paulo\", \"Grêmio\", \"Vasco\", \"Belo\", \"Campinense\", \"Treze\"]\n",
    "    estados_que_nasceu = [\"Paraíba\", \"Acre\", \"São Paulo\", \"Rio de Janeiro\", \"Minas Gerais\", \"Bahia\", \"Pernambuco\"]\n",
    "\n",
    "    dados_aleatorios = []\n",
    "    for i in range(num_linhas):\n",
    "        dados_aleatorios.append({\n",
    "            \"nome\": random.choice(nomes),\n",
    "            \"idade\": random.choice(idades),\n",
    "            \"numero_favorito\": random.randint(0, 1000),\n",
    "            \"cor_favorita\": random.choice(cores_favoritas),\n",
    "            \"animal_favorito\": random.choice(animais_favoritos),\n",
    "            \"time_que_torce\": random.choice(times_que_torce),\n",
    "            \"estado_que_nasceu\": random.choice(estados_que_nasceu)\n",
    "        })\n",
    "\n",
    "    return pd.DataFrame(dados_aleatorios)\n",
    "\n",
    "# Lista de números de linhas para os cenários\n",
    "num_linhas_cenarios = [1000, 10000, 100000, 1000000]\n",
    "\n",
    "for num_linhas in num_linhas_cenarios:\n",
    "    df = gerar_dados_aleatorios(num_linhas)\n",
    "\n",
    "    start_time_csv_write = time.time()\n",
    "    df.to_csv(f\"dados_{num_linhas}.csv\", index=False)\n",
    "    csv_write_time = time.time() - start_time_csv_write\n",
    "\n",
    "    start_time_json_write = time.time()\n",
    "    df.to_json(f'dados_{num_linhas}.json', orient='records', lines=True)\n",
    "    json_write_time = time.time() - start_time_json_write\n",
    "\n",
    "    start_time_avro_write = time.time()\n",
    "    with open(f\"dados_{num_linhas}.avro\", \"wb\") as avro_file:\n",
    "        fastavro.writer(avro_file, schema, df.to_dict(orient=\"records\"))\n",
    "    avro_write_time = time.time() - start_time_avro_write\n",
    "\n",
    "    # Medindo o tempo de leitura em CSV\n",
    "    start_time_csv_read = time.time()\n",
    "    df_csv = pd.read_csv(f\"dados_{num_linhas}.csv\")\n",
    "    csv_read_time = time.time() - start_time_csv_read\n",
    "\n",
    "    # Medindo o tempo de leitura em Avro\n",
    "    start_time_avro_read = time.time()\n",
    "    df_avro = spark.read.format(\"avro\").load(f\"dados_{num_linhas}.avro\")\n",
    "    avro_read_time = time.time() - start_time_avro_read\n",
    "\n",
    "    # Ler dados do arquivo JSON e medir o tempo\n",
    "    start_time_json_read = time.time()\n",
    "    df = pd.read_json(f\"dados_{num_linhas}.json\", lines=True)\n",
    "    json_read_time = time.time() - start_time_json_read\n",
    "\n",
    "    print(f\"Tempo de leitura em CSV ({num_linhas} linhas): {csv_read_time:.4f} segundos\")\n",
    "    print(f\"Tempo de leitura em Avro ({num_linhas} linhas): {avro_read_time:.4f} segundos\")\n",
    "    print(f\"Tempo de leitura em JSON ({num_linhas} linhas): {json_read_time:.4f} segundos\")\n",
    "    \n",
    "    tempos = [json_read_time, csv_read_time, avro_read_time]\n",
    "    formatos = [\"JSON\", \"CSV\", \"Avro\"]\n",
    "\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    bars = plt.bar(formatos, tempos)\n",
    "    plt.xlabel(\"Formato\")\n",
    "    plt.ylabel(\"Tempo de Leitura (s)\")\n",
    "    plt.title(f\"Tempo de Leitura para {num_linhas} linhas\")\n",
    "\n",
    "    # Adicionar rótulos de dados nas barras do gráfico\n",
    "    for bar, tempo in zip(bars, tempos):\n",
    "        plt.text(bar.get_x() + bar.get_width() / 2, tempo, f\"{tempo:.2f}\", ha='center', va='bottom')\n",
    "\n",
    "    plt.show()\n",
    "    print()"
   ]
  }
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