3_spark_sample_code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cb48c421",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48d5c841",
   "metadata": {},
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://github.com/philhoonoh/spark/blob/main/3_spark_sample_code.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View Source</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1d998d4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pyspark             3.3.0\r\n"
     ]
    }
   ],
   "source": [
    "!pip list | grep pyspark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aaa4a1a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql import SparkSession"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3811010a",
   "metadata": {},
   "source": [
    "### 1. hdfs 에 파일올리기\n",
    "```bash\n",
    "$ hadoop fs -ls\n",
    "$ hadoop fs -mkdir /user/philhoonoh\n",
    "$ hadoop fs hadoop fs -put /Users/philhoonoh/Desktop/Hadoop/data.csv /user/philhoonoh/\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7a87a65",
   "metadata": {},
   "source": [
    "### 2. Spark Session 으로 data.csv를 dataframe 으로 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f65b1a63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/08/23 13:53:30 WARN Utils: Your hostname, PHILui-MacBook-Pro.local resolves to a loopback address: 127.0.0.1; using 172.16.100.49 instead (on interface en0)\n",
      "22/08/23 13:53:30 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting default log level to \"WARN\".\n",
      "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/08/23 13:53:30 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n"
     ]
    }
   ],
   "source": [
    "spark = SparkSession.builder.getOrCreate()\n",
    "# spark.stop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cbc742e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DataFrame[InvoiceNo: string, StockCode: string, Description: string, Quantity: int, InvoiceDate: string, UnitPrice: double, CustomerID: int, Country: string]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = spark.read.csv(\"hdfs://localhost:9000/user/philhoonoh/data.csv\", header=\"true\", inferSchema=\"true\")\n",
    "data.cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4e21df47",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "[Stage 2:>                                                          (0 + 1) / 1]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+---------+--------------------+--------+--------------+---------+----------+--------------+\n",
      "|InvoiceNo|StockCode|         Description|Quantity|   InvoiceDate|UnitPrice|CustomerID|       Country|\n",
      "+---------+---------+--------------------+--------+--------------+---------+----------+--------------+\n",
      "|   536365|   85123A|WHITE HANGING HEA...|       6|12/1/2010 8:26|     2.55|     17850|United Kingdom|\n",
      "|   536365|    71053| WHITE METAL LANTERN|       6|12/1/2010 8:26|     3.39|     17850|United Kingdom|\n",
      "|   536365|   84406B|CREAM CUPID HEART...|       8|12/1/2010 8:26|     2.75|     17850|United Kingdom|\n",
      "|   536365|   84029G|KNITTED UNION FLA...|       6|12/1/2010 8:26|     3.39|     17850|United Kingdom|\n",
      "|   536365|   84029E|RED WOOLLY HOTTIE...|       6|12/1/2010 8:26|     3.39|     17850|United Kingdom|\n",
      "+---------+---------+--------------------+--------+--------------+---------+----------+--------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "data.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b73f0d81",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 3:=============================>                             (2 + 2) / 4]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------------+-------------+\n",
      "|        Country|sum(Quantity)|\n",
      "+---------------+-------------+\n",
      "|         Sweden|        35637|\n",
      "|      Singapore|         5234|\n",
      "|        Germany|       117448|\n",
      "|         France|       110480|\n",
      "|         Greece|         1556|\n",
      "|        Belgium|        23152|\n",
      "|        Finland|        10666|\n",
      "|          Italy|         7999|\n",
      "|           EIRE|       142637|\n",
      "|      Lithuania|          652|\n",
      "|         Norway|        19247|\n",
      "|          Spain|        26824|\n",
      "|        Denmark|         8188|\n",
      "|      Hong Kong|         4769|\n",
      "|        Iceland|         2458|\n",
      "|         Israel|         4353|\n",
      "|Channel Islands|         9479|\n",
      "|         Cyprus|         6317|\n",
      "|   Saudi Arabia|           75|\n",
      "|    Switzerland|        30325|\n",
      "+---------------+-------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "data.groupby('Country').sum('Quantity').show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a0cb099",
   "metadata": {},
   "source": [
    "### 3. Spark dataframe -> Pandas Dataframe\n",
    "    - Spark dataframe runs on multiple nodes\n",
    "    - Pandas dataframe runs on a single machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9d2e691",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pandas = data.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8a6f1531",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>536365</td>\n",
       "      <td>22752</td>\n",
       "      <td>SET 7 BABUSHKA NESTING BOXES</td>\n",
       "      <td>2</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>7.65</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>536365</td>\n",
       "      <td>21730</td>\n",
       "      <td>GLASS STAR FROSTED T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>4.25</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>536366</td>\n",
       "      <td>22633</td>\n",
       "      <td>HAND WARMER UNION JACK</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:28</td>\n",
       "      <td>1.85</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>536366</td>\n",
       "      <td>22632</td>\n",
       "      <td>HAND WARMER RED POLKA DOT</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:28</td>\n",
       "      <td>1.85</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>536367</td>\n",
       "      <td>84879</td>\n",
       "      <td>ASSORTED COLOUR BIRD ORNAMENT</td>\n",
       "      <td>32</td>\n",
       "      <td>12/1/2010 8:34</td>\n",
       "      <td>1.69</td>\n",
       "      <td>13047.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "5    536365     22752         SET 7 BABUSHKA NESTING BOXES         2   \n",
       "6    536365     21730    GLASS STAR FROSTED T-LIGHT HOLDER         6   \n",
       "7    536366     22633               HAND WARMER UNION JACK         6   \n",
       "8    536366     22632            HAND WARMER RED POLKA DOT         6   \n",
       "9    536367     84879        ASSORTED COLOUR BIRD ORNAMENT        32   \n",
       "\n",
       "      InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0  12/1/2010 8:26       2.55     17850.0  United Kingdom  \n",
       "1  12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
       "2  12/1/2010 8:26       2.75     17850.0  United Kingdom  \n",
       "3  12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
       "4  12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
       "5  12/1/2010 8:26       7.65     17850.0  United Kingdom  \n",
       "6  12/1/2010 8:26       4.25     17850.0  United Kingdom  \n",
       "7  12/1/2010 8:28       1.85     17850.0  United Kingdom  \n",
       "8  12/1/2010 8:28       1.85     17850.0  United Kingdom  \n",
       "9  12/1/2010 8:34       1.69     13047.0  United Kingdom  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pandas.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc3ffa17",
   "metadata": {},
   "source": [
    "### 4. Using SQL on pandas dataframe \n",
    "    - dataframe 으로 temp view 생성 \n",
    "    - spark session 에서 sql 문 요청"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "398becca",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.createOrReplaceTempView(\"data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "02d7a13b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(InvoiceNo='536365', StockCode='85123A', Description='WHITE HANGING HEART T-LIGHT HOLDER', Quantity=6, InvoiceDate='12/1/2010 8:26', UnitPrice=2.55, CustomerID=17850, Country='United Kingdom'),\n",
       " Row(InvoiceNo='536365', StockCode='71053', Description='WHITE METAL LANTERN', Quantity=6, InvoiceDate='12/1/2010 8:26', UnitPrice=3.39, CustomerID=17850, Country='United Kingdom'),\n",
       " Row(InvoiceNo='536365', StockCode='84406B', Description='CREAM CUPID HEARTS COAT HANGER', Quantity=8, InvoiceDate='12/1/2010 8:26', UnitPrice=2.75, CustomerID=17850, Country='United Kingdom'),\n",
       " Row(InvoiceNo='536365', StockCode='84029G', Description='KNITTED UNION FLAG HOT WATER BOTTLE', Quantity=6, InvoiceDate='12/1/2010 8:26', UnitPrice=3.39, CustomerID=17850, Country='United Kingdom'),\n",
       " Row(InvoiceNo='536365', StockCode='84029E', Description='RED WOOLLY HOTTIE WHITE HEART.', Quantity=6, InvoiceDate='12/1/2010 8:26', UnitPrice=3.39, CustomerID=17850, Country='United Kingdom'),\n",
       " Row(InvoiceNo='536365', StockCode='22752', Description='SET 7 BABUSHKA NESTING BOXES', Quantity=2, InvoiceDate='12/1/2010 8:26', UnitPrice=7.65, CustomerID=17850, Country='United Kingdom'),\n",
       " Row(InvoiceNo='536365', StockCode='21730', Description='GLASS STAR FROSTED T-LIGHT HOLDER', Quantity=6, InvoiceDate='12/1/2010 8:26', UnitPrice=4.25, CustomerID=17850, Country='United Kingdom')]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spark.sql(\"SELECT * FROM data WHERE InvoiceNo=536365\").collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e625c734",
   "metadata": {},
   "source": [
    "### 5. Pyspark Dataframe with Plot\n",
    "    - Plotting PySpark Dataframes using standard python plot libraries like Seaborn require conversion to Pandas...(As far as I know)\n",
    "    - That being said, python will crash due to massive dataset when using pandas dataframe\n",
    "    - Thus in order to take advantage of distributing processing, do all the necessary transformations on PySpark Dataframe before transforming it into Pandas Dataframe \n",
    "    - As an alternative, Sample PySpark DataFrame before converting to Pandas\n",
    "    \n",
    "```python\n",
    "# data.sample(withReplacement, fraction, seed = None)\n",
    "sample_data = data.sample(True, 0.5, 42)\n",
    "pandas_df = sample_data.toPandas()\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4993f8de",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_data = data.groupby('Country').sum('Quantity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "92922f4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[Country: string, sum(Quantity): bigint]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "499af49f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "pandas_data = result_data.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "97441c2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>sum(Quantity)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>35637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>5234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Germany</td>\n",
       "      <td>117448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>France</td>\n",
       "      <td>110480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Greece</td>\n",
       "      <td>1556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country  sum(Quantity)\n",
       "0     Sweden          35637\n",
       "1  Singapore           5234\n",
       "2    Germany         117448\n",
       "3     France         110480\n",
       "4     Greece           1556"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "743f6add",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Country', ylabel='sum(Quantity)'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = pandas_data['Country'], y = pandas_data['sum(Quantity)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d0f14ed2",
   "metadata": {},
   "outputs": [],
   "source": [
    "spark.stop()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pyspark",
   "language": "python",
   "name": "pyspark"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}