Home_Sales_SDT.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SteveTuttle/Home_Sales/blob/main/Home_Sales_SDT.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a_KW73O2e3dw",
        "outputId": "3bd8fd8e-1a09-416f-9a16-bcd551992465"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r0% [Working]\r            \rGet:1 http://security.ubuntu.com/ubuntu jammy-security InRelease [110 kB]\n",
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            "Fetched 3,649 kB in 4s (938 kB/s)\n",
            "Reading package lists... Done\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "# Find the latest version of spark 3.x  from http://www.apache.org/dist/spark/ and enter as the spark version\n",
        "# For example:\n",
        "# spark_version = 'spark-3.4.0'\n",
        "spark_version = 'spark-3.4.0'\n",
        "os.environ['SPARK_VERSION']=spark_version\n",
        "\n",
        "# Install Spark and Java\n",
        "!apt-get update\n",
        "!apt-get install openjdk-11-jdk-headless -qq > /dev/null\n",
        "!wget -q http://www.apache.org/dist/spark/$SPARK_VERSION/$SPARK_VERSION-bin-hadoop3.tgz\n",
        "!tar xf $SPARK_VERSION-bin-hadoop3.tgz\n",
        "!pip install -q findspark\n",
        "\n",
        "# Set Environment Variables\n",
        "os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-11-openjdk-amd64\"\n",
        "os.environ[\"SPARK_HOME\"] = f\"/content/{spark_version}-bin-hadoop3\"\n",
        "\n",
        "# Start a SparkSession\n",
        "import findspark\n",
        "findspark.init()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "2XbWNf1Te5fM"
      },
      "outputs": [],
      "source": [
        "# Import packages\n",
        "from pyspark.sql import SparkSession\n",
        "import time\n",
        "\n",
        "# Create a SparkSession\n",
        "spark = SparkSession.builder.appName(\"SparkSQL\").getOrCreate()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "wOJqxG_RPSwp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eed8bbb2-53a6-48bf-b257-a09223644bb4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+----------+----------+------+--------+---------+-----------+--------+------+----------+----+\n",
            "|                  id|      date|date_built| price|bedrooms|bathrooms|sqft_living|sqft_lot|floors|waterfront|view|\n",
            "+--------------------+----------+----------+------+--------+---------+-----------+--------+------+----------+----+\n",
            "|f8a53099-ba1c-47d...|2022-04-08|      2016|936923|       4|        3|       3167|   11733|     2|         1|  76|\n",
            "|7530a2d8-1ae3-451...|2021-06-13|      2013|379628|       2|        2|       2235|   14384|     1|         0|  23|\n",
            "|43de979c-0bf0-4c9...|2019-04-12|      2014|417866|       2|        2|       2127|   10575|     2|         0|   0|\n",
            "|b672c137-b88c-48b...|2019-10-16|      2016|239895|       2|        2|       1631|   11149|     2|         0|   0|\n",
            "|e0726d4d-d595-407...|2022-01-08|      2017|424418|       3|        2|       2249|   13878|     2|         0|   4|\n",
            "|5aa00529-0533-46b...|2019-01-30|      2017|218712|       2|        3|       1965|   14375|     2|         0|   7|\n",
            "|131492a1-72e2-4a8...|2020-02-08|      2017|419199|       2|        3|       2062|    8876|     2|         0|   6|\n",
            "|8d54a71b-c520-44e...|2019-07-21|      2010|323956|       2|        3|       1506|   11816|     1|         0|  25|\n",
            "|e81aacfe-17fe-46b...|2020-06-16|      2016|181925|       3|        3|       2137|   11709|     2|         0|  22|\n",
            "|2ed8d509-7372-46d...|2021-08-06|      2015|258710|       3|        3|       1918|    9666|     1|         0|  25|\n",
            "|f876d86f-3c9f-42b...|2019-02-27|      2011|167864|       3|        3|       2471|   13924|     2|         0|  15|\n",
            "|0a2bd445-8508-4d8...|2021-12-30|      2014|337527|       2|        3|       1926|   12556|     1|         0|  23|\n",
            "|941bad30-eb49-4a7...|2020-05-09|      2015|229896|       3|        3|       2197|    8641|     1|         0|   3|\n",
            "|dd61eb34-6589-4c0...|2021-07-25|      2016|210247|       3|        2|       1672|   11986|     2|         0|  28|\n",
            "|f1e4cef7-d151-439...|2019-02-01|      2011|398667|       2|        3|       2331|   11356|     1|         0|   7|\n",
            "|ea620c7b-c2f7-4c6...|2021-05-31|      2011|437958|       3|        3|       2356|   11052|     1|         0|  26|\n",
            "|f233cb41-6f33-4b0...|2021-07-18|      2016|437375|       4|        3|       1704|   11721|     2|         0|  34|\n",
            "|c797ca12-52cd-4b1...|2019-06-08|      2015|288650|       2|        3|       2100|   10419|     2|         0|   7|\n",
            "|0cfe57f3-28c2-472...|2019-10-04|      2015|308313|       3|        3|       1960|    9453|     2|         0|   2|\n",
            "|4566cd2a-ac6e-435...|2019-07-15|      2016|177541|       3|        3|       2130|   10517|     2|         0|  25|\n",
            "+--------------------+----------+----------+------+--------+---------+-----------+--------+------+----------+----+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# 1. Read in the AWS S3 bucket into a DataFrame.\n",
        "from pyspark import SparkFiles\n",
        "url = \"https://2u-data-curriculum-team.s3.amazonaws.com/dataviz-classroom/v1.2/22-big-data/home_sales_revised.csv\"\n",
        "\n",
        "# View file data for better understanding of each data point through 'SparkContext.addFile'\n",
        "spark.sparkContext.addFile(url)\n",
        "home_sales_data = spark.read.option('header', 'true').csv(SparkFiles.get('home_sales_revised.csv'), inferSchema=True, sep=',', timestampFormat=\"mm/dd/yy\")\n",
        "\n",
        "home_sales_data.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "RoljcJ7WPpnm"
      },
      "outputs": [],
      "source": [
        "# 2. Create a temporary view of the DataFrame.\n",
        "home_sales_data.createOrReplaceTempView('home_sales')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Answering the Next Four Questions Using SparkSQL\n",
        "### Creating a series of subQueries for each question posed"
      ],
      "metadata": {
        "id": "Drb3fCSEO5kV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "L6fkwOeOmqvq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "93d93b5e-d120-4c26-b999-1de3dbca7c96"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----+---------+\n",
            "|YEAR|AVG_Price|\n",
            "+----+---------+\n",
            "|2022|296363.88|\n",
            "|2021|301819.44|\n",
            "|2020|298353.78|\n",
            "|2019| 300263.7|\n",
            "+----+---------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from traitlets.config.loader import DeferredConfigString\n",
        "# 3. What is the average price for a four bedroom house sold in each year rounded to two decimal places?\n",
        "subQuery_1 = \"\"\"\n",
        "SELECT\n",
        "  YEAR(date) AS YEAR,\n",
        "  ROUND(AVG(price),2) AS AVG_Price\n",
        "FROM\n",
        "  home_sales\n",
        "WHERE bedrooms = 4\n",
        "GROUP BY YEAR\n",
        "ORDER BY YEAR DESC\n",
        "\"\"\"\n",
        "\n",
        "spark.sql(subQuery_1).show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "l8p_tUS8h8it",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f5a77b52-3d09-42a8-ec20-5cf385af2179"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----+---------+\n",
            "|YEAR|AVG_Price|\n",
            "+----+---------+\n",
            "|2017|292676.79|\n",
            "|2016|290555.07|\n",
            "|2015| 288770.3|\n",
            "|2014|290852.27|\n",
            "|2013|295962.27|\n",
            "|2012|293683.19|\n",
            "|2011|291117.47|\n",
            "|2010|292859.62|\n",
            "+----+---------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# 4. What is the average price of a home for each year the home was built that have 3 bedrooms and 3 bathrooms rounded to two decimal places?\n",
        "subQuery_2 = \"\"\"\n",
        "SELECT\n",
        "  date_built AS YEAR,\n",
        "  ROUND(AVG(price),2) AS AVG_Price\n",
        "FROM\n",
        "  home_sales\n",
        "WHERE bedrooms = 3\n",
        "  AND bathrooms = 3\n",
        "GROUP BY YEAR\n",
        "ORDER BY YEAR DESC\n",
        "\"\"\"\n",
        "\n",
        "spark.sql(subQuery_2).show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "Y-Eytz64liDU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4144fd0f-0a15-4314-e529-6e74c778e706"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----+---------+\n",
            "|YEAR|AVG_Price|\n",
            "+----+---------+\n",
            "|2017|280317.58|\n",
            "|2016| 293965.1|\n",
            "|2015|297609.97|\n",
            "|2014|298264.72|\n",
            "|2013|303676.79|\n",
            "|2012|307539.97|\n",
            "|2011|276553.81|\n",
            "|2010|285010.22|\n",
            "+----+---------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# 5. What is the average price of a home for each year built that have 3 bedrooms, 3 bathrooms, with two floors,\n",
        "# and are greater than or equal to 2,000 square feet rounded to two decimal places?\n",
        "subQuery_3 = \"\"\"\n",
        "SELECT\n",
        "  date_built AS YEAR,\n",
        "  ROUND(AVG(price),2) AS AVG_Price\n",
        "FROM\n",
        "  home_sales\n",
        "WHERE bedrooms = 3\n",
        "  AND bathrooms = 3\n",
        "  AND floors = 2\n",
        "  AND sqft_living >= 2000\n",
        "GROUP BY YEAR\n",
        "ORDER BY YEAR DESC\n",
        "\"\"\"\n",
        "\n",
        "spark.sql(subQuery_3).show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GUrfgOX1pCRd",
        "outputId": "cc253e6c-14c9-4e4a-acae-cf1bc6fe3d19"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----+----------+\n",
            "|view| AVG_Price|\n",
            "+----+----------+\n",
            "| 100| 1026669.5|\n",
            "|  99|1061201.42|\n",
            "|  98|1053739.33|\n",
            "|  97|1129040.15|\n",
            "|  96|1017815.92|\n",
            "|  95| 1054325.6|\n",
            "|  94| 1033536.2|\n",
            "|  93|1026006.06|\n",
            "|  92| 970402.55|\n",
            "|  91|1137372.73|\n",
            "|  90|1062654.16|\n",
            "|  89|1107839.15|\n",
            "|  88|1031719.35|\n",
            "|  87| 1072285.2|\n",
            "|  86|1070444.25|\n",
            "|  85|1056336.74|\n",
            "|  84|1117233.13|\n",
            "|  83|1033965.93|\n",
            "|  82| 1063498.0|\n",
            "|  81|1053472.79|\n",
            "+----+----------+\n",
            "only showing top 20 rows\n",
            "\n",
            "--- 1.0991384983062744 seconds ---\n"
          ]
        }
      ],
      "source": [
        "# 6. What is the \"view\" rating for the average price of a home, rounded to two decimal places, where the homes are greater than\n",
        "# or equal to $350,000? Although this is a small dataset, determine the run time for this query.\n",
        "\n",
        "start_time = time.time()\n",
        "\n",
        "subQuery_4 = \"\"\"\n",
        "SELECT\n",
        "  view,\n",
        "  ROUND(AVG(price),2) AS AVG_Price\n",
        "FROM\n",
        "  home_sales\n",
        "GROUP BY view\n",
        "HAVING AVG(price) >= 350000\n",
        "ORDER BY view DESC\n",
        "\"\"\"\n",
        "\n",
        "spark.sql(subQuery_4).show()\n",
        "print(\"--- %s seconds ---\" % (time.time() - start_time))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "KAhk3ZD2tFy8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c76ae583-2578-4584-ad91-2c594e864783"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DataFrame[]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# 7. Cache the the temporary table home_sales.\n",
        "spark.sql('cache table home_sales')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "4opVhbvxtL-i",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b71f4d3-5ad8-48d0-ae9d-212d14d1685a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# 8. Check if the table is cached.\n",
        "spark.catalog.isCached('home_sales')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5GnL46lwTSEk",
        "outputId": "3063c235-d3d3-42f3-81e3-791229488b44"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----+----------+\n",
            "|view| AVG_Price|\n",
            "+----+----------+\n",
            "| 100| 1026669.5|\n",
            "|  99|1061201.42|\n",
            "|  98|1053739.33|\n",
            "|  97|1129040.15|\n",
            "|  96|1017815.92|\n",
            "|  95| 1054325.6|\n",
            "|  94| 1033536.2|\n",
            "|  93|1026006.06|\n",
            "|  92| 970402.55|\n",
            "|  91|1137372.73|\n",
            "|  90|1062654.16|\n",
            "|  89|1107839.15|\n",
            "|  88|1031719.35|\n",
            "|  87| 1072285.2|\n",
            "|  86|1070444.25|\n",
            "|  85|1056336.74|\n",
            "|  84|1117233.13|\n",
            "|  83|1033965.93|\n",
            "|  82| 1063498.0|\n",
            "|  81|1053472.79|\n",
            "+----+----------+\n",
            "only showing top 20 rows\n",
            "\n",
            "--- 0.5665831565856934 seconds ---\n"
          ]
        }
      ],
      "source": [
        "# 9. Using the cached data, run the query that filters out the view ratings with average price\n",
        "#  greater than or equal to $350,000. Determine the runtime and compare it to uncached runtime.\n",
        "\n",
        "start_time = time.time()\n",
        "# rerun subQuery_4 after cache\n",
        "subQuery_4 = \"\"\"\n",
        "SELECT\n",
        "  view,\n",
        "  ROUND(AVG(price),2) AS AVG_Price\n",
        "FROM\n",
        "  home_sales\n",
        "GROUP BY view\n",
        "HAVING AVG(price) >= 350000\n",
        "ORDER BY view DESC\n",
        "\"\"\"\n",
        "\n",
        "spark.sql(subQuery_4).show()\n",
        "print(\"--- %s seconds ---\" % (time.time() - start_time))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "Qm12WN9isHBR"
      },
      "outputs": [],
      "source": [
        "# 10. Partition by the \"date_built\" field on the formatted parquet home sales data\n",
        "home_sales_data.write.partitionBy(\"date_built\").mode(\"overwrite\").parquet(\"home_sales_part\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "AZ7BgY61sRqY"
      },
      "outputs": [],
      "source": [
        "# 11. Read the parquet formatted data.\n",
        "p_hsd_p = spark.read.parquet('home_sales_part')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "J6MJkHfvVcvh"
      },
      "outputs": [],
      "source": [
        "# 12. Create a temporary table for the parquet data.\n",
        "p_hsd_p.createOrReplaceTempView('home_sales_part')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G_Vhb52rU1Sn",
        "outputId": "2dbd3281-d373-4abb-f09e-c6a446fe9b4a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----+----------+\n",
            "|view| AVG_Price|\n",
            "+----+----------+\n",
            "| 100| 1026669.5|\n",
            "|  99|1061201.42|\n",
            "|  98|1053739.33|\n",
            "|  97|1129040.15|\n",
            "|  96|1017815.92|\n",
            "|  95| 1054325.6|\n",
            "|  94| 1033536.2|\n",
            "|  93|1026006.06|\n",
            "|  92| 970402.55|\n",
            "|  91|1137372.73|\n",
            "|  90|1062654.16|\n",
            "|  89|1107839.15|\n",
            "|  88|1031719.35|\n",
            "|  87| 1072285.2|\n",
            "|  86|1070444.25|\n",
            "|  85|1056336.74|\n",
            "|  84|1117233.13|\n",
            "|  83|1033965.93|\n",
            "|  82| 1063498.0|\n",
            "|  81|1053472.79|\n",
            "+----+----------+\n",
            "only showing top 20 rows\n",
            "\n",
            "--- 0.5538718700408936 seconds ---\n"
          ]
        }
      ],
      "source": [
        "# 13. Run the query that filters out the view ratings with average price of greater than or equal to $350,000\n",
        "# with the parquet DataFrame. Round your average to two decimal places.\n",
        "# Determine the runtime and compare it to the cached version.\n",
        "\n",
        "start_time = time.time()\n",
        "# rerun subQuery_4 after partition\n",
        "subQuery_4 = \"\"\"\n",
        "SELECT\n",
        "  view,\n",
        "  ROUND(AVG(price),2) AS AVG_Price\n",
        "FROM\n",
        "  home_sales\n",
        "GROUP BY view\n",
        "HAVING AVG(price) >= 350000\n",
        "ORDER BY view DESC\n",
        "\"\"\"\n",
        "\n",
        "spark.sql(subQuery_4).show()\n",
        "print(\"--- %s seconds ---\" % (time.time() - start_time))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "hjjYzQGjtbq8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9c3692f7-aea2-4e2f-a224-4cb3191c2ade"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DataFrame[]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# 14. Uncache the home_sales temporary table.\n",
        "spark.sql('uncache table home_sales')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "Sy9NBvO7tlmm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a951eb4d-abb9-491c-a945-997943e15dbd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "All is clear\n"
          ]
        }
      ],
      "source": [
        "# 15. Check if the home_sales is no longer cached\n",
        "if spark.catalog.isCached('home_sales'):\n",
        "  print('The home_sales table is still cached!')\n",
        "else:\n",
        "  print('All is clear')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "Si-BNruRUGK3"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "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": "3.10.4"
    },
    "nteract": {
      "version": "0.28.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}