notebooks/search_in_files.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importando bibliotecas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%load_ext memory_profiler\n",
    "\n",
    "import os\n",
    "from os.path import join, getsize\n",
    "import re\n",
    "import urllib.request\n",
    "import zipfile\n",
    "import glob\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparando o dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "urllib.request.urlretrieve('https://s3-sa-east-1.amazonaws.com/luizalabs-tech-challenges/movies.zip', 'movie.zip')\n",
    "\n",
    "with open('movie.zip', 'rb') as f:\n",
    "    z = zipfile.ZipFile(f)\n",
    "    for name in z.namelist():\n",
    "        try:\n",
    "            z.extract(name, '.')\n",
    "        except:\n",
    "            pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problema\n",
    "\n",
    "Esse problema pode ser dividido em quatro outros sub problemas, são eles:\n",
    "\n",
    "    * listar arquivos de uma pasta\n",
    "    * procurar uma substring em um texto\n",
    "    * ler o conteudo de um arquivo\n",
    "    * ordenar alfabeticamente\n",
    "\n",
    "A baixo vou atacar cada um separadamente com o objetivo de obter a forma mais performática de resolver cada um deles, tendo como objetivo a menor velocidade de execução e o menor consumo de memória."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Listar arquivos de uma pasta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#usando o walk\n",
    "def get_files1(path):\n",
    "    _, _, files = next(os.walk(path))\n",
    "    return files\n",
    "    \n",
    "#usando o scandir\n",
    "def get_files2(path):\n",
    "    return os.scandir(path)\n",
    "    \n",
    "#usando o glob\n",
    "def get_files3(path):\n",
    "    return glob.glob(join(path, '*'))\n",
    "    \n",
    "#usando o listdir\n",
    "def get_files4(path):\n",
    "    return os.listdir(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tempo de execução"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30.9 ms ± 2.27 ms per loop (mean ± std. dev. of 10 runs, 10 loops each)\n",
      "51.5 µs ± 175 ns per loop (mean ± std. dev. of 10 runs, 10000 loops each)\n",
      "85.8 ms ± 367 µs per loop (mean ± std. dev. of 10 runs, 10 loops each)\n",
      "21.2 ms ± 529 µs per loop (mean ± std. dev. of 10 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "a1 = %timeit -r 10 -o get_files1('data')\n",
    "a2 = %timeit -r 10 -o get_files2('data')\n",
    "a3 = %timeit -r 10 -o get_files3('data')\n",
    "a4 = %timeit -r 10 -o get_files4('data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 4 artists>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/Znmk38+rgX+pqh9W1feAL4xsfzHwrar6Znd5JfDaoe2XD33dq49IZ8BNwJ8leS/w/Kr6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\nL0kNMfqS1BCjL0kN+X8v3UhvshqxtgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7571d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = [a1.average, a2.average, a3.average, a4.average]\n",
    "x = ['walk', 'scandir', 'glob', 'listdir']\n",
    "plt.bar(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Consumo de mémoria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 64.59 MiB, increment: 0.73 MiB\n",
      "peak memory: 64.08 MiB, increment: 0.00 MiB\n",
      "peak memory: 65.14 MiB, increment: 1.07 MiB\n",
      "peak memory: 64.49 MiB, increment: 0.41 MiB\n"
     ]
    }
   ],
   "source": [
    "ma1 = %memit -r 10 -o get_files1('data')\n",
    "ma2 = %memit -r 10 -o get_files2('data')\n",
    "ma3 = %memit -r 10 -o get_files3('data')\n",
    "ma4 = %memit -r 10 -o get_files4('data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 4 artists>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Q+X8Efp/Zz8NfAP8H/CqDQB05tO/HQ8v38bOfwPt+z/1gHlM+bFTVPcCtwCuBLzEI+nOB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x75e1fb0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = [\n",
    "        max(ma1.mem_usage) - ma1.baseline, \n",
    "        max(ma2.mem_usage) - ma2.baseline, \n",
    "        max(ma3.mem_usage) - ma3.baseline, \n",
    "        max(ma4.mem_usage) - ma4.baseline, \n",
    "]\n",
    "\n",
    "x = ['walk', 'scandir', 'glob', 'listdir']\n",
    "plt.bar(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusão\n",
    "\n",
    "Para listar arquivos o `scandir` levou vantagem tanto no tempo de execução quanto no consumo de mémoria.\n",
    "\n",
    "vencedor: `scandir` "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Procurar uma substring em um texto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_str1(content, q):\n",
    "    return q in content\n",
    "\n",
    "def find_str2(content, q):\n",
    "    return content.find(q) > -1\n",
    "\n",
    "def find_str3(content, q):\n",
    "    return re.match(content, q)\n",
    "\n",
    "#https://en.wikipedia.org/wiki/Rabin%E2%80%93Karp_algorithm\n",
    "def find_str4(source, pattern):\n",
    "    base = 26\n",
    "    p_hash = 0\n",
    "    s_hash = 0\n",
    "    letter_position = lambda letter:  ord(letter) - ord('A')\n",
    "\n",
    "    for letter in pattern:\n",
    "        p_hash = p_hash * base + letter_position(letter)\n",
    "\n",
    "    first_letter_base = base ** (len(pattern) - 1)\n",
    "    for i, letter in enumerate(source):\n",
    "        s_hash = s_hash * base + letter_position(letter)\n",
    "        if i < len(pattern) - 1:\n",
    "            continue\n",
    "\n",
    "        match_start_index = i - len(pattern) + 1\n",
    "        if s_hash == p_hash:\n",
    "            return match_start_index\n",
    "\n",
    "        s_hash = s_hash - letter_position(source[match_start_index]) *  first_letter_base\n",
    "    return None\n",
    "\n",
    "\n",
    "#https://en.wikipedia.org/wiki/Boyer%E2%80%93Moore_string_search_algorithm\n",
    "def find_str5(text, pattern):\n",
    "    m = len(pattern)\n",
    "    n = len(text)\n",
    "    if m > n: return -1\n",
    "    skip = []\n",
    "    for k in range(256): skip.append(m)\n",
    "    for k in range(m - 1): skip[ord(pattern[k])] = m - k - 1\n",
    "    skip = tuple(skip)\n",
    "    k = m - 1\n",
    "    while k < n:\n",
    "        j = m - 1; i = k\n",
    "        while j >= 0 and text[i] == pattern[j]:\n",
    "            j -= 1; i -= 1\n",
    "        if j == -1: return i + 1\n",
    "        k += skip[ord(text[k])]\n",
    "    return -1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tempo de execução"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "195 ns ± 6 ns per loop (mean ± std. dev. of 10 runs, 10000000 loops each)\n",
      "353 ns ± 5.21 ns per loop (mean ± std. dev. of 10 runs, 1000000 loops each)\n",
      "942 ns ± 13.1 ns per loop (mean ± std. dev. of 10 runs, 1000000 loops each)\n",
      "62.3 µs ± 2.21 µs per loop (mean ± std. dev. of 10 runs, 10000 loops each)\n",
      "31.6 µs ± 157 ns per loop (mean ± std. dev. of 10 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "content = 'Bem vindo ao desafio técnico do Luiza Labs e muito obrigado por se interessar em fazer parte do Magazine Luiza.'\n",
    "q = 'obrigado'\n",
    "s1 = %timeit -r 10 -o find_str1(content, q)\n",
    "s2 = %timeit -r 10 -o find_str2(content, q)\n",
    "s3 = %timeit -r 10 -o find_str3(content, q)\n",
    "s4 = %timeit -r 10 -o find_str4(content, q)\n",
    "s5 = %timeit -r 10 -o find_str5(content, q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 5 artists>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x763aeb0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = [s1.average, s2.average, s3.average, s4.average, s5.average]\n",
    "x = ['in', 'find', 'match', 'Rabin–Karp', 'Boyer–Moore']\n",
    "plt.bar(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Consumo de mémoria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 66.34 MiB, increment: 0.11 MiB\n",
      "peak memory: 66.34 MiB, increment: 0.00 MiB\n",
      "peak memory: 66.34 MiB, increment: 0.00 MiB\n",
      "peak memory: 66.34 MiB, increment: 0.00 MiB\n",
      "peak memory: 66.34 MiB, increment: 0.00 MiB\n"
     ]
    }
   ],
   "source": [
    "content = 'Bem vindo ao desafio técnico do Luiza Labs e muito obrigado por se interessar em fazer parte do Magazine Luiza.'\n",
    "q = 'obrigado'\n",
    "ms1 = %memit -r 10 -o find_str1(content, q)\n",
    "ms2 = %memit -r 10 -o find_str2(content, q)\n",
    "ms3 = %memit -r 10 -o find_str3(content, q)\n",
    "ms4 = %memit -r 10 -o find_str4(content, q)\n",
    "ms5 = %memit -r 10 -o find_str5(content, q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 5 artists>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x75f4c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = [\n",
    "        max(ms1.mem_usage) - ms1.baseline, \n",
    "        max(ms2.mem_usage) - ms2.baseline, \n",
    "        max(ms3.mem_usage) - ms3.baseline, \n",
    "        max(ms4.mem_usage) - ms4.baseline, \n",
    "        max(ms5.mem_usage) - ms5.baseline, \n",
    "]\n",
    "\n",
    "x = ['in', 'find', 'match', 'Rabin–Karp', 'Boyer–Moore']\n",
    "plt.bar(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusão\n",
    "\n",
    "A performance desse problema esta relacionado com o tamanho do texto em que se realiza a busca. Usei os metodos nativos `in`, `find` e `match`, com a adição de mais dois algoritmos de busca em texto o `Rabin–Karp` e `Boyer–Moore`.\n",
    "\n",
    "O método que apresentou melhor custo beneficio foi o `find`.\n",
    "\n",
    "vencendor: `find`  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ler o conteudo de um arquivo "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lendo o arquivo inteiro\n",
    "def find1(path, q):\n",
    "    with(open(path)) as f:\n",
    "        content = f.read()\n",
    "    return q in content\n",
    "\n",
    "\n",
    "#lendo o arquivo em pedaços\n",
    "def find2(path, q):\n",
    "    with(open(path)) as f:\n",
    "        length = len(q)\n",
    "        part = f.read(length)\n",
    "        \n",
    "        while True:\n",
    "            if part == q:\n",
    "                return True\n",
    "\n",
    "            char = f.read(1)\n",
    "            \n",
    "            if not char:\n",
    "                return False\n",
    "\n",
    "            part = part[1:] + char\n",
    "\n",
    "            \n",
    "#lendo o arquivo por linhas            \n",
    "def find3(path, q):\n",
    "     with(open(path)) as file:\n",
    "        for line in file:\n",
    "            if q in line:\n",
    "                return True\n",
    "        return False\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "O maior arquivo é data\\the-most-dangerous-man-in-america-daniel-ellsberg-and-the-pentagon-papers.txt e tem 170 bytes\n"
     ]
    }
   ],
   "source": [
    "#procurando o maior arquivo de nosso dataset\n",
    "\n",
    "biggest = ('', -1)\n",
    "path = 'data'\n",
    "\n",
    "for item in os.listdir(path):\n",
    "    item = join(path, item)\n",
    "    itemsize = getsize(item)\n",
    "    if itemsize > biggest[1]:\n",
    "            biggest = (item, itemsize)\n",
    "\n",
    "print('O maior arquivo é {} e tem {} bytes'.format(*biggest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tempo de execução"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "125 µs ± 9.85 µs per loop (mean ± std. dev. of 10 runs, 10000 loops each)\n",
      "138 µs ± 2.34 µs per loop (mean ± std. dev. of 10 runs, 10000 loops each)\n",
      "122 µs ± 12.8 µs per loop (mean ± std. dev. of 10 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "hf1 = %timeit -r 10 -o find1('./data/the-most-dangerous-man-in-america-daniel-ellsberg-and-the-pentagon-papers.txt', 'papers')    \n",
    "hf2 = %timeit -r 10 -o find2('./data/the-most-dangerous-man-in-america-daniel-ellsberg-and-the-pentagon-papers.txt', 'papers')    \n",
    "hf3 = %timeit -r 10 -o find3('./data/the-most-dangerous-man-in-america-daniel-ellsberg-and-the-pentagon-papers.txt', 'papers')    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa5f770>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = [hf1.average, hf2.average, hf3.average]\n",
    "x = ['read', 'chunk', 'line']\n",
    "plt.bar(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Consumo de mémoria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 67.88 MiB, increment: 0.00 MiB\n",
      "peak memory: 67.88 MiB, increment: 0.00 MiB\n",
      "peak memory: 67.88 MiB, increment: 0.00 MiB\n"
     ]
    }
   ],
   "source": [
    "mhf1 = %memit -r 10 -o find1('./data/the-most-dangerous-man-in-america-daniel-ellsberg-and-the-pentagon-papers.txt', 'papers')  \n",
    "mhf2 = %memit -r 10 -o find2('./data/the-most-dangerous-man-in-america-daniel-ellsberg-and-the-pentagon-papers.txt', 'papers')\n",
    "mhf3 = %memit -r 10 -o find3('./data/the-most-dangerous-man-in-america-daniel-ellsberg-and-the-pentagon-papers.txt', 'papers')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XgW8B+xc87FeB25J8F9i7lvM+FfiXz5KkxreSJEmNYZAkNYZBktQYBklSYxgkSY1hkCQ1\nhkGS1BgGSVLzv36hR4bBjtAUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb505f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = [\n",
    "    max(mhf1.mem_usage) - mhf1.baseline, \n",
    "    max(mhf2.mem_usage) - mhf2.baseline, \n",
    "    max(mhf3.mem_usage) - mhf3.baseline,\n",
    "]\n",
    "x = ['read', 'chunk', 'line']\n",
    "plt.bar(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusão\n",
    "\n",
    "Arquivos pequenos com pouco conteúdo podem ser carregados com o `read` de uma só vez para a mémoria. Já arquivos grandes precisam ser carregodos em pedaços (`chunks`) para que o consumo de mémoria se mantenha baixo, mas sendo penalizado com perda de velocidade. Já ler os arquivos por linhas apresenta o melhor custo beneficio, uma vez que apenas uma linha do arquivo é carregada para a mémoria por vez.\n",
    "\n",
    "vencendor: `line`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ordenar alfabeticamente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sort1(results):\n",
    "    return sorted(results)\n",
    "\n",
    "def sort2(results):\n",
    "    results.sort()\n",
    "    return results\n",
    "\n",
    "def quicksort(arr):\n",
    "    if len(arr) < 2:\n",
    "        return arr\n",
    "\n",
    "    pivot = arr[0]\n",
    "    lesser = quicksort([x for x in arr if x < pivot])\n",
    "    greater = quicksort([x for x in arr if x > pivot])\n",
    "\n",
    "    return lesser + [pivot] + greater\n",
    "\n",
    "def merge(left, right):\n",
    "    if not len(left) or not len(right):\n",
    "        return left or right\n",
    "\n",
    "    result = []\n",
    "    i, j = 0, 0\n",
    "    while (len(result) < len(left) + len(right)):\n",
    "        if left[i] < right[j]:\n",
    "            result.append(left[i])\n",
    "            i+= 1\n",
    "        else:\n",
    "            result.append(right[j])\n",
    "            j+= 1\n",
    "        if i == len(left) or j == len(right):\n",
    "            result.extend(left[i:] or right[j:])\n",
    "            break \n",
    "\n",
    "    return result\n",
    "\n",
    "def mergesort(list):\n",
    "    if len(list) < 2:\n",
    "        return list\n",
    "\n",
    "    middle = int(len(list) / 2)\n",
    "    left = mergesort(list[:middle])\n",
    "    right = mergesort(list[middle:])\n",
    "\n",
    "    return merge(left, right)\n",
    "\n",
    "def ins_sort(k):\n",
    "    for i in range(1,len(k)):\n",
    "        j = i                    \n",
    "        temp = k[j]              \n",
    "        while j > 0 and temp < k[j-1]: \n",
    "            k[j] = k[j-1] \n",
    "            j=j-1 \n",
    "        k[j] = temp\n",
    "    return k"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tempo de execução"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.6 µs ± 56.5 ns per loop (mean ± std. dev. of 10 runs, 1000000 loops each)\n",
      "557 ns ± 3.55 ns per loop (mean ± std. dev. of 10 runs, 1000000 loops each)\n",
      "41.5 µs ± 295 ns per loop (mean ± std. dev. of 10 runs, 10000 loops each)\n",
      "51.3 µs ± 1.44 µs per loop (mean ± std. dev. of 10 runs, 10000 loops each)\n",
      "4.41 µs ± 236 ns per loop (mean ± std. dev. of 10 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "content = 'Bem vindo ao desafio técnico do Luiza Labs e muito obrigado por se interessar em fazer parte do Magazine Luiza.'.split()\n",
    "z1 = %timeit -r 10 -o sort1(content)\n",
    "z2 = %timeit -r 10 -o sort2(content)\n",
    "z3 = %timeit -r 10 -o quicksort(content)\n",
    "z4 = %timeit -r 10 -o mergesort(content)\n",
    "z5 = %timeit -r 10 -o ins_sort(content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 5 artists>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xb86890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = [z1.average, z2.average, z3.average, z4.average, z5.average]\n",
    "x = ['sorted', 'sort', 'quicksort', 'mergesort', 'ins_sort']\n",
    "plt.bar(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Consumo de mémoria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 71.64 MiB, increment: 0.00 MiB\n",
      "peak memory: 71.64 MiB, increment: 0.00 MiB\n",
      "peak memory: 71.64 MiB, increment: 0.00 MiB\n",
      "peak memory: 71.64 MiB, increment: 0.00 MiB\n",
      "peak memory: 71.64 MiB, increment: 0.00 MiB\n"
     ]
    }
   ],
   "source": [
    "content = 'Bem vindo ao desafio técnico do Luiza Labs e muito obrigado por se interessar em fazer parte do Magazine Luiza.'.split()\n",
    "mz1 = %memit -r 10 -o sort1(content)\n",
    "mz2 = %memit -r 10 -o sort2(content)\n",
    "mz3 = %memit -r 10 -o quicksort(content)\n",
    "mz4 = %memit -r 10 -o mergesort(content)\n",
    "mz5 = %memit -r 10 -o ins_sort(content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 5 artists>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAD9CAYAAAC4EtBTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAEm1JREFUeJzt3H+w3XV95/HnS4JYpcuvRKWENM5C\n18Glo+UMjlW3VOTXdm1oYVdot4bWXWZ3ZDvWZbs47BZFdystHRwrbSdVNKVOQWyr8UcXkR+ytYrc\naArECmQpLhFacUJxU6o08N4/zid6PnfOzU3uOeEk5vmYOXO/P97f7/f9uTnnvM73+z03qSokSdrp\nWbNuQJK0bzEYJEkdg0GS1DEYJEkdg0GS1DEYJEkdg0GS1DEYJEkdg0GS1Fk26waWYvny5bV69epZ\ntyFJ+5WNGzd+s6pWLFa3XwbD6tWrmZubm3UbkrRfSfK13anzUpIkqWMwSJI6BoMkqWMwSJI6BoMk\nqWMwSJI6BoMkqWMwSJI6BoMkqWMwSJI6BoMkqWMwSJI6BoMkqWMwSJI6BoMkqWMwSJI6BoMkqWMw\nSJI6BoMkqWMwSJI6BoMkqWMwSJI6BoMkqWMwSJI6UwmGJGcmuTfJliSXjFl/SJLr2/o7kqyet35V\nku1JLp5GP5KkpZs4GJIcBFwNnAWcAJyf5IR5ZW8EHquq44CrgCvmrb8K+LNJe5EkTW4aZwwnA1uq\n6oGqehK4Dlgzr2YNsL5NfwQ4NUkAkpwNPABsnkIvkqQJTSMYjgEeGpnf2paNramqHcDjwFFJngf8\nV+DtU+hDkjQF0wiGjFlWu1nzduCqqtq+6EGSC5PMJZl79NFHl9CmJGl3LJvCPrYCx47MrwQeXqBm\na5JlwGHANuDlwLlJfgM4HHg6yber6r3zD1JV64B1AIPBYH7wSJKmZBrBcCdwfJIXAV8HzgN+bl7N\nBmAt8HngXOCWqirg1TsLkrwN2D4uFCRJz5yJg6GqdiS5CLgROAi4pqo2J7kcmKuqDcD7gWuTbGF4\npnDepMeVJO0dGX5w378MBoOam5ubdRuStF9JsrGqBovV+ZfPkqSOwSBJ6hgMkqSOwSBJ6hgMkqSO\nwSBJ6hgMkqSOwSBJ6hgMkqSOwSBJ6hgMkqSOwSBJ6hgMkqSOwSBJ6hgMkqSOwSBJ6hgMkqSOwSBJ\n6hgMkqSOwSBJ6hgMkqSOwSBJ6hgMkqSOwSBJ6hgMkqSOwSBJ6hgMkqSOwSBJ6hgMkqSOwSBJ6hgM\nkqTOVIIhyZlJ7k2yJcklY9YfkuT6tv6OJKvb8tOSbExyd/v5mmn0I0lauomDIclBwNXAWcAJwPlJ\nTphX9kbgsao6DrgKuKIt/ybwuqo6EVgLXDtpP5KkyUzjjOFkYEtVPVBVTwLXAWvm1awB1rfpjwCn\nJklVfbmqHm7LNwPPSXLIFHqSJC3RNILhGOChkfmtbdnYmqraATwOHDWv5hzgy1X1nSn0JElaomVT\n2EfGLKs9qUnyEoaXl05f8CDJhcCFAKtWrdrzLiVJu2UaZwxbgWNH5lcCDy9Uk2QZcBiwrc2vBP4U\neENV/Z+FDlJV66pqUFWDFStWTKFtSdI40wiGO4Hjk7woybOB84AN82o2MLy5DHAucEtVVZLDgU8C\nb62qz02hF0nShCYOhnbP4CLgRuCvgA9X1eYklyf56Vb2fuCoJFuAtwA7v9J6EXAc8N+TbGqP50/a\nkyRp6VI1/3bAvm8wGNTc3Nys25Ck/UqSjVU1WKzOv3yWJHUMBklSx2CQJHUMBklSx2CQJHUMBklS\nx2CQJHUMBklSx2CQJHUMBklSx2CQJHUMBklSx2CQJHUMBklSx2CQJHUMBklSx2CQJHUMBklSx2CQ\nJHUMBklSx2CQJHUMBklSx2CQJHUMBklSx2CQJHUMBklSx2CQJHUMBklSx2CQJHUMBklSZyrBkOTM\nJPcm2ZLkkjHrD0lyfVt/R5LVI+ve2pbfm+SMafQjSVq6iYMhyUHA1cBZwAnA+UlOmFf2RuCxqjoO\nuAq4om17AnAe8BLgTOB32v4kSTMyjTOGk4EtVfVAVT0JXAesmVezBljfpj8CnJokbfl1VfWdqvpr\nYEvbnyRpRqYRDMcAD43Mb23LxtZU1Q7gceCo3dxWkvQMmkYwZMyy2s2a3dl2uIPkwiRzSeYeffTR\nPWxRkrS7phEMW4FjR+ZXAg8vVJNkGXAYsG03twWgqtZV1aCqBitWrJhC25KkcaYRDHcCxyd5UZJn\nM7yZvGFezQZgbZs+F7ilqqotP699a+lFwPHAF6fQkyRpiZZNuoOq2pHkIuBG4CDgmqranORyYK6q\nNgDvB65NsoXhmcJ5bdvNST4MfAXYAbypqp6atCdJ0tJl+MF9/zIYDGpubm7WbUjSfiXJxqoaLFbn\nXz5LkjoGgySpYzBIkjoGgySpYzBIkjoGgySpYzBIkjoGgySpYzBIkjoGgySpYzBIkjoGgySpYzBI\nkjoGgySpYzBIkjoGgySpYzBIkjoGgySpYzBIkjoGgySpYzBIkjoGgySpYzBIkjoGgySpYzBIkjoG\ngySpYzBIkjoGgySpYzBIkjoGgySpYzBIkjoTBUOSI5PclOT+9vOIBerWtpr7k6xty56b5JNJvppk\nc5J3TdKLJGk6Jj1juAS4uaqOB25u850kRwKXAS8HTgYuGwmQK6vqxcDLgFcmOWvCfiRJE5o0GNYA\n69v0euDsMTVnADdV1baqegy4CTizqp6oqlsBqupJ4EvAygn7kSRNaNJgeEFVPQLQfj5/TM0xwEMj\n81vbsu9KcjjwOoZnHZKkGVq2WEGSzwAvHLPq0t08RsYsq5H9LwP+CHhPVT2wiz4uBC4EWLVq1W4e\nWpK0pxYNhqp67ULrkvxtkqOr6pEkRwPfGFO2FThlZH4lcNvI/Drg/qp69yJ9rGu1DAaD2lWtJGnp\nJr2UtAFY26bXAh8bU3MjcHqSI9pN59PbMpK8EzgMePOEfUiSpmTSYHgXcFqS+4HT2jxJBkneB1BV\n24B3AHe2x+VVtS3JSoaXo04AvpRkU5J/N2E/kqQJpWr/uyozGAxqbm5u1m1I0n4lycaqGixW518+\nS5I6BoMkqWMwSJI6BoMkqWMwSJI6BoMkqWMwSJI6BoMkqWMwSJI6BoMkqWMwSJI6BoMkqWMwSJI6\nBoMkqWMwSJI6BoMkqWMwSJI6BoMkqWMwSJI6BoMkqWMwSJI6BoMkqWMwSJI6BoMkqWMwSJI6BoMk\nqWMwSJI6BoMkqWMwSJI6BoMkqTNRMCQ5MslNSe5vP49YoG5tq7k/ydox6zckuWeSXiRJ0zHpGcMl\nwM1VdTxwc5vvJDkSuAx4OXAycNlogCT5WWD7hH1IkqZk0mBYA6xv0+uBs8fUnAHcVFXbquox4Cbg\nTIAkhwJvAd45YR+SpCmZNBheUFWPALSfzx9Tcwzw0Mj81rYM4B3AbwFPTNiHJGlKli1WkOQzwAvH\nrLp0N4+RMcsqyUuB46rqV5Ks3o0+LgQuBFi1atVuHlqStKcWDYaqeu1C65L8bZKjq+qRJEcD3xhT\nthU4ZWR+JXAb8ArgpCQPtj6en+S2qjqFMapqHbAOYDAY1GJ9S5KWZtJLSRuAnd8yWgt8bEzNjcDp\nSY5oN51PB26sqt+tqh+qqtXAq4D7FgoFSdIzZ9JgeBdwWpL7gdPaPEkGSd4HUFXbGN5LuLM9Lm/L\nJEn7oFTtf1dlBoNBzc3NzboNSdqvJNlYVYPF6vzLZ0lSx2CQJHUMBklSx2CQJHUMBklSx2CQJHUM\nBklSx2CQJHUMBklSx2CQJHUMBklSx2CQJHUMBklSx2CQJHUMBklSx2CQJHUMBklSx2CQJHUMBklS\nx2CQJHUMBklSx2CQJHUMBklSx2CQJHUMBklSJ1U16x72WJJHga/Nuo9dWA58c9ZNzNCBPP4Deexw\nYI9/fxj7D1fVisWK9stg2Nclmauqwaz7mJUDefwH8tjhwB7/99PYvZQkSeoYDJKkjsGwd6ybdQMz\ndiCP/0AeOxzY4/++Gbv3GCRJHc8YJEkdg0H7jSQvTfIvZ3TsQZL3LFKzfS8c981Jnjvt/T6TklyQ\n5Idm3cfetpRxJlmd5J691dNSGQwjkvzFrHuA7z5Zfm7WfexKkmUzON5LgZkEQ1XNVdUvP5PHTHIQ\n8GZgvw2GNoYLgO/rYPh+G6fBMKKqfnzWPbQ3wNXAXgmGFjpfTfK+JPck+VCS1yb5XJL7k5yc5HlJ\nrklyZ5IvJ1nTtr0gyQ1JPg58OsmzkvxOks1JPpHkU0nObbUnJflsko1JbkxydFv+y0m+kuSuJNe1\nZUcm+Whb9oUkP9qWvy3JuiSfBv4AuBx4fZJNSV4/hd/FpUnuTfKZJH+U5OIktyUZtPXLkzzYpk9J\n8ok2fWiSDyS5u/V8zrz9Lk/y+SQ/leToJLe3nu9J8upWc37b/p4kV4xsuz3J5UnuAC5l+EZza5Jb\nJx3vJNpz4pNJ/rL1/Pokp7bnx93t+XJIq30wya8l+XPgfGAAfKj9Dn5gluNYzDTHuYvXwElt/58H\n3jS70e5CVfloD2B7+3kKcBvwEeCrwIf43o36dwFfAe4CrtzFvv41cA/wl8DtbdlzgA8AdwNfBn6y\nLb8AuAH4OHAL8AXgcWAT8CtTHuNqYAdwIsMPBhuBa4AAa4CPAv8T+Let/nDgPuB5rc+twJFt3bnA\np9p+Xgg81pYdDPwFsKLVvR64pk0/DByyc9/t528Dl7Xp1wCb2vTbWn8/MPJ7eu+Ufg8ntX+H5wL/\nBNgCXNz+3QetZjnw4Mhz4hNt+grg3SP7OmLn8wd4AXAHcFpb9p+BS9v0QcAPMnyz/7/ACmBZ+zc/\nu9UU8G9G9v0gsHwfeG2cA/z+yPxhwEPAj7T5PwDePNLzr47Ufvd3uq8/pjXORV4DdwE/0aZ/E7hn\n1uOe/3hGLwfsZ14GvIThG9nngFcm+QrwM8CLq6qSHL6L7X8NOKOqvj5S9yaAqjoxyYsZfur+kbbu\nFcCPVtW2JKcAF1fVv5r+sAD466q6GyDJZuDmNp67GQbHSuCnk1zc6p8DrGrTN1XVtjb9KuCGqnoa\n+JuRT7X/DPjnwE1JYPiG+EhbdxfDT1UfZRhCO/dzDkBV3ZLkqCSHtXUbquofpjj2nV4N/GlVPQGQ\nZMMebPta4LydM1X1WJs8GLgZeFNVfbYtuxO4JsnBwEeralOS1wC3VdWj7dgfAv4Fw9/HU8AfL31Y\ne83dwJXt7OYTwLcYPo/ua+vXM3x+v7vNX//MtzgV0xrn2NdAe14fPvL8uBY4a+qjmJCXkhb2xara\n2t70NjF8w/wW8G3gfUl+FnhiF9t/Dvhgkn/P8EkBwzfAawGq6qsM/7+nncEw+oa7t31nZPrpkfmn\nGX6CDXBOVb20PVZV1V+1mr8f2TYL7D/A5pHtT6yq09u6nwKuZviJfWOGl87G7Wfn96j/fsy6aRn3\nXe0dfO918ZwFtssutt0InPHdA1TdzvBN/+vAtUnewMK/N4BvV9VTi/T9jGtvjDvPsn6d4dnlruzN\nf7e9ZorjXOg1sNBzZ59iMCxs9M3zKWBZVe0ATmb4ie5s4H8ttHFV/QfgvwHHApuSHMWu3xD2pRfS\njcB/Svuok+RlC9T9OXBOu9fwAoaXWwDuBVYkeUXb/uAkL0nyLODYqroV+FWGl6kOBW4Hfr7VngJ8\ns6q+NeZ4/4/hpZhpuB34mXYt+AeB17XlDzJ8Y4DhZbFxPg1ctHMmyRFtsoBfAl6c5JK27oeBb1TV\n7wPvB36M4aWmn2j3Ig5ieH36s4w3zTEvWYbftnmiqv4QuBL4cWB1kuNayS+wj49hd0xxnGNfA1X1\nd8DjSV7V6n5+b4xjUl5K2gNJDgWeW1WfSvIFhtelF6r9p1V1B3BHktcxDIidb4C3tEtIqxg+gX5s\n3uazfiG9g+Gp8l0tHB4Exl3W+mPgVIb3Uu5j+Ib3eFU9meFN6Pe0U+dlbX/3AX/YlgW4qqr+Lsnb\ngA8kuYvhWdjaBfq6FbgkySbg16tqyZcrqupLSa5neDb4NeB/t1VXAh9O8gsMr/2P807g6gy/ZvgU\n8HbgT9p+n0pyHvDxJN9iGPj/Jck/MrwH8YaqeiTJW9t4Anyqqj62wLHWAX+W5JGq+smljncKTgR+\nM8nTwD8C/5Hh9fcb2lnfncDvLbDtB4HfS/IPwCv20qXBaZnaOBl+sJj/GtgM/CLDy4tPMPwQts/x\nL59HJNleVYfOv8af5L3AHMN/xI8xvMQQhjef1y+wrz8Bjm91NzP82uEhDJ9UJzG87PCWqro1yQUM\nb1pd1LY9mOHZyHLgg1V11d4Z8eSSHFpV29sZ0ReBV1bV38y6rz3Vwml7VV05616kWTMYNJEktzG8\nJPRs4Deq6oMzbWiJDAbpewwGSVLHewwTSnIpw79ZGHVDVf2PWfQjSZPyjEGS1PHrqpKkjsEgSeoY\nDJKkjsEgSeoYDJKkzv8HQLHywSuRFtYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xba08f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = [\n",
    "    max(mz1.mem_usage) - mz1.baseline, \n",
    "    max(mz2.mem_usage) - mz2.baseline, \n",
    "    max(mz3.mem_usage) - mz3.baseline,\n",
    "    max(mz4.mem_usage) - mz4.baseline, \n",
    "    max(mz5.mem_usage) - mz5.baseline,\n",
    "]\n",
    "x = ['sorted', 'sort', 'quicksort', 'mergesort', 'ins_sort']\n",
    "plt.bar(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusão\n",
    "\n",
    "Foram testados os método de ordenação nativos `sort`, `sorted` ambos usam o `Timsort` que é o algoritmos de ordenação padrão do python, foram adicionados mais três algoritmos o `quicksort`, `mergesort` e o `insertion sort`. As implementações nativas se mostraram superior, visto que são implementadas em C. Já entre o `sort` e o `sorted`, o primeiro levou vantagem por fazer a ordenação na propria instância da lista sem necessidade da criação de uma copia.\n",
    "\n",
    "vencendor: `sort` "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Soluções\n",
    "\n",
    "Comparação entre diferentes abordagens da solução."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.67 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n",
      "peak memory: 62.03 MiB, increment: 0.16 MiB\n"
     ]
    }
   ],
   "source": [
    "#solução simples\n",
    "\n",
    "from search1 import search1\n",
    "tsolucao1 = %timeit -r 1 -o search1('walt disney', 'data')\n",
    "msolucao1 = %memit -r 1 -o search1('walt disney', 'data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.18 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n",
      "peak memory: 63.48 MiB, increment: 0.08 MiB\n"
     ]
    }
   ],
   "source": [
    "#solução com threads\n",
    "\n",
    "from search2 import search2\n",
    "tsolucao2 = %timeit -r 1 -o search2('walt disney', 'data')\n",
    "msolucao2 = %memit -r 1 -o search2('walt disney', 'data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.7 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n",
      "peak memory: 65.99 MiB, increment: 0.77 MiB\n"
     ]
    }
   ],
   "source": [
    "#solução rodando em paralelo (com pool)\n",
    "\n",
    "from search3 import search3\n",
    "tsolucao3 = %timeit -r 1 -o search3('walt disney', 'data')\n",
    "msolucao3 = %memit -r 1 -o search3('walt disney', 'data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.22 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n",
      "peak memory: 66.11 MiB, increment: 0.70 MiB\n"
     ]
    }
   ],
   "source": [
    "#solução rodando em paralelo (com fila)\n",
    "\n",
    "from search4 import search4\n",
    "tsolucao4 = %timeit -r 1 -o search4('walt disney', 'data')\n",
    "msolucao4 = %memit -r 1 -o search4('walt disney', 'data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.08 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n",
      "peak memory: 66.32 MiB, increment: 0.51 MiB\n"
     ]
    }
   ],
   "source": [
    "#solução rodando em paralelo (com temp files)\n",
    "\n",
    "from search5 import search5\n",
    "tsolucao5 = %timeit -r 1 -o search5('walt disney', 'data')\n",
    "msolucao5 = %memit -r 1 -o search5('walt disney', 'data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.36 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n",
      "peak memory: 66.50 MiB, increment: 0.25 MiB\n"
     ]
    }
   ],
   "source": [
    "#solução rodando em paralelo (com manager)\n",
    "\n",
    "from search6 import search6\n",
    "tsolucao6 = %timeit -r 1 -o search6('walt disney', 'data')\n",
    "msolucao6 = %memit -r 1 -o search6('walt disney', 'data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.04 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n",
      "peak memory: 66.30 MiB, increment: 0.00 MiB\n"
     ]
    }
   ],
   "source": [
    "#solução rodando em paralelo (shared memory)\n",
    "\n",
    "from search7 import search7\n",
    "tsolucao7 = %timeit -r 1 -o search7('walt disney', 'data')\n",
    "msolucao7 = %memit -r 1 -o search7('walt disney', 'data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.31 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n",
      "peak memory: 66.34 MiB, increment: 0.00 MiB\n"
     ]
    }
   ],
   "source": [
    "#solução rodando em paralelo (consumer)\n",
    "\n",
    "from search8 import search8\n",
    "tsolucao8 = %timeit -r 1 -o search8('walt disney', 'data')\n",
    "msolucao8 = %memit -r 1 -o search8('walt disney', 'data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "873 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n",
      "peak memory: 66.33 MiB, increment: 0.03 MiB\n"
     ]
    }
   ],
   "source": [
    "#solução rodando em paralelo (consumer/shared memory)\n",
    "\n",
    "from search9 import search9\n",
    "tsolucao9 = %timeit -r 1 -o search9('walt disney', 'data')\n",
    "msolucao9 = %memit -r 1 -o search9('walt disney', 'data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tempo de execução"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 9 artists>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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h+teDw4Cabrf+fbbRGHDBgOWMUfb77mtrMZOvu4lcOIRyILlbfX4vB+47zX7S7XsR5dYp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\nhrskNchwl6QG/T838ag3BwzzagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x812cc70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = [\n",
    "    tsolucao1.average,\n",
    "    tsolucao2.average,\n",
    "    tsolucao3.average,\n",
    "    tsolucao4.average,\n",
    "    tsolucao5.average,\n",
    "    tsolucao6.average,    \n",
    "    tsolucao7.average,\n",
    "    tsolucao8.average,\n",
    "    tsolucao9.average,\n",
    "]\n",
    "x = ['simples', 'thread', 'pool', 'queue', 'temp', 'manager', 'shared', 'consumer', 'consumer/shared']\n",
    "plt.bar(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Consumo de mémoria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 9 artists>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xcb4ff0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = [\n",
    "    max(msolucao1.mem_usage) - msolucao1.baseline,\n",
    "    max(msolucao2.mem_usage) - msolucao2.baseline,\n",
    "    max(msolucao3.mem_usage) - msolucao3.baseline,\n",
    "    max(msolucao4.mem_usage) - msolucao4.baseline,\n",
    "    max(msolucao5.mem_usage) - msolucao5.baseline,\n",
    "    max(msolucao6.mem_usage) - msolucao6.baseline,    \n",
    "    max(msolucao7.mem_usage) - msolucao7.baseline,\n",
    "    max(msolucao8.mem_usage) - msolucao8.baseline,\n",
    "    max(msolucao9.mem_usage) - msolucao9.baseline,\n",
    "]\n",
    "x = ['simples', 'thread', 'pool', 'queue', 'temp', 'manager', 'shared', 'consumer', 'consumer/shared']\n",
    "plt.bar(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusão\n",
    "\n",
    "A abordagem que apresentou melhores resultados foi a de rodar em paralelo usando mémoria compartilhada para armazenar os resultados dos processos. Uma abordagem com threads pode ser aplicada se o ambiente só apresentar apenas um processador."
   ]
  }
 ],
 "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
}