AlgoScale_Interview_Problems.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "AlgoScale_Interview_Problems.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyODWYqIdoC85CfYE7kLorGn",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pksX01/PySpark_Tutorials/blob/main/AlgoScale_Interview_Problems.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lwwJBOz8YS6O"
      },
      "source": [
        "# **PySpark Question: Count of occurrence of word 'spark' in each person's chat**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5719M5_PSofv",
        "outputId": "8f5bb860-cd58-4dee-a838-e69042e2bc1c"
      },
      "source": [
        "!pip install pyspark"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pyspark in /usr/local/lib/python3.7/dist-packages (3.2.0)\n",
            "Requirement already satisfied: py4j==0.10.9.2 in /usr/local/lib/python3.7/dist-packages (from pyspark) (0.10.9.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 109
        },
        "id": "YCUfe04SStmR",
        "outputId": "de3c2838-c4d5-4c35-d430-47d2ff20896c"
      },
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-18feb84e-7122-4601-b54a-97bc77379676\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-18feb84e-7122-4601-b54a-97bc77379676\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving chat_text_file.txt to chat_text_file (1).txt\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'chat_text_file.txt': b'Akshay: Spark is a data processing enginee which is very fast because of its in-memory processing.The current version of spark is 3.0.0.This version of spark support the delta lake feature which support the CDC concept.\\nAjay: Apache Spark is a multi-language engine for executing data engineering.\\nAkshay: Spark is a Big Data Engine.\\n'}"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cN8ad0faTAgI"
      },
      "source": [
        "***chat_text_file.txt***</br>\n",
        "Akshay: Spark is a data processing enginee which is very fast because of its in-memory processing.The current version of spark is 3.0.0.This version of spark support the delta lake feature which support the CDC concept.</br>\n",
        "Ajay: Apache Spark is a multi-language engine for executing data engineering.</br>\n",
        "Akshay: Spark is a Big Data Engine."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RKVkGZm-ZAWn"
      },
      "source": [
        "from pyspark.sql import SparkSession\n",
        "spark = SparkSession.builder.master(\"local[4]\").appName(\"Algoscale Spark Tasks\").enableHiveSupport().getOrCreate()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9GW0xsUVSWeb"
      },
      "source": [
        "rdd = spark.sparkContext.textFile('/content/chat_text_file.txt') # given file is uploaded in Google colab"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Shq9kcS-ZG3R",
        "outputId": "4d3d6850-2602-4d2e-943e-35134853a011"
      },
      "source": [
        "rdd.map(lambda x : x.split(\"\\n\")).map(lambda x : x[0].split(':')).map(lambda x : (x[0], x[1].lower().split(' ').count('spark')))\\\n",
        "    .reduceByKey(lambda x,y : x+y).collect()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('Akshay', 4), ('Ajay', 1)]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IO-p7xeqYilT"
      },
      "source": [
        "# **SQL question to find out the participants who participated in an event for atleast 3 consecutive years**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1J2PZJzHSzSw",
        "outputId": "e946425b-d271-473f-81c3-b8069ffe3110"
      },
      "source": [
        "spark.sql('create table participation (name string, year int)')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DataFrame[]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zdhCoaFNZI-u",
        "outputId": "1ed6648e-b878-43a5-ba3e-a4f5f1ff451b"
      },
      "source": [
        "spark.sql(\"insert into participation values('Carol', 1999)\")\n",
        "spark.sql(\"insert into participation values('Carol', 2000)\")\n",
        "spark.sql(\"insert into participation values('Carol', 2001)\")\n",
        "spark.sql(\"insert into participation values('Carol', 2002)\")\n",
        "spark.sql(\"insert into participation values('Faith', 1996)\")\n",
        "spark.sql(\"insert into participation values('John', 2001)\")\n",
        "spark.sql(\"insert into participation values('John', 2002)\")\n",
        "spark.sql(\"insert into participation values('John', 2003)\")\n",
        "spark.sql(\"insert into participation values('John', 2009)\")\n",
        "spark.sql(\"insert into participation values('Lyla', 1994)\")\n",
        "spark.sql(\"insert into participation values('Lyla', 1996)\")\n",
        "spark.sql(\"insert into participation values('Lyla', 1997)\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DataFrame[]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "emUUFmbVZI7X",
        "outputId": "1ce606b5-7515-48a3-83a5-85f04d8c6daa"
      },
      "source": [
        "spark.sql('select * from participation').show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----+----+\n",
            "| name|year|\n",
            "+-----+----+\n",
            "| John|2009|\n",
            "| John|2003|\n",
            "|Carol|2002|\n",
            "| Lyla|1994|\n",
            "| Lyla|1997|\n",
            "| John|2001|\n",
            "|Carol|1999|\n",
            "|Carol|2000|\n",
            "| Lyla|1996|\n",
            "|Faith|1996|\n",
            "| John|2002|\n",
            "|Carol|2001|\n",
            "+-----+----+\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DNClLRPKZI3u",
        "outputId": "5c4fd322-5536-4883-db0b-36e2b4b2d6c2"
      },
      "source": [
        "spark.sql(\"\\\n",
        "select name from (\\\n",
        "  select p1.name from participation p1 join participation p2 on (p1.name = p2.name) \\\n",
        "  where p2.year - p1.year = 1 order by p1.name, p1.year, p2.year\\\n",
        "  ) innerTbl \\\n",
        "  group by name having count(*) >= 2 \\\n",
        "\").show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----+\n",
            "| name|\n",
            "+-----+\n",
            "| John|\n",
            "|Carol|\n",
            "+-----+\n",
            "\n"
          ]
        }
      ]
    }
  ]
}