Question_12.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a0740787",
   "metadata": {},
   "source": [
    "# Question 12: What are the top 5 most frequently occurring hours in the log?”"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26ce9ee0",
   "metadata": {},
   "source": [
    "# Section 1: Data Preparation and Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc7b3bf8",
   "metadata": {},
   "source": [
    "**IMPORTING NECESSARY LIBRARIES**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "923c79cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://spark.apache.org/docs/latest/api/python/getting_started/quickstart_df.html\n",
    "# Importinng 'SparkSession' module which is the main entry point for DataFrame and SQL functionality. SparkSession can be used create DataFrame, register DataFrame as tables, execute SQL over tables and cache tables.\n",
    "from pyspark.sql import SparkSession\n",
    "\n",
    "# Importing 'Date (datetime.date)', 'Timestamp (datetime.datetime)' , 'Double' data type from 'Data Types' module  \n",
    "from pyspark.sql.types import DateType, TimestampType, DoubleType\n",
    "\n",
    "# importing built-in functions available for DataFrame.\n",
    "from pyspark.sql.functions import to_date, to_timestamp, col, month\n",
    "\n",
    "# Importing 'matplotlib.pyplot' module for creating and customizing plots and visualizations. \n",
    "# 'pyplot' is a part of the Matplotlib library and provides a user-friendly interface to create different types of plots.\n",
    "# https://www.geeksforgeeks.org/bar-plot-in-matplotlib/\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Importing'time' module for measuring execution time\n",
    "# https://docs.python.org/3/library/time.html\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaf7f0f0",
   "metadata": {},
   "source": [
    "**EXECUTION TIME COUNTER START**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9520f99f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Recording the starting time of the execution using the 'time.perf_counter()' function.It provides a high-resolution timer that measures the time elapsed in seconds to measure its execution time.\n",
    "executiontimestart= time.perf_counter()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5c8c3b29",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "23/08/11 21:28:38 WARN SparkSession: Using an existing Spark session; only runtime SQL configurations will take effect.\n"
     ]
    }
   ],
   "source": [
    "# Initializing SparkSession named 'ScpProject' using the SparkSession builder.\n",
    "session = SparkSession.builder.appName('ScpProject').getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "faa7ecd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "# Reading data from CSV file into a DataFrame 'bgl_log_df'\n",
    "bgl_log_df = session.read.csv(\"BGLnew.log\", sep=',', inferSchema=True, header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "64976506",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renaming the columns 'toDF()' method for better understanding into our dataframe.\n",
    "bgl_log_df = bgl_log_df.toDF('alert_message_flag', 'timestamp', 'date', 'node', 'date_and_time', 'node_repeated',\n",
    "                             'message_type', 'system_component', 'level', 'message_content')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b83ac62a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parsing 'date' column to standard DateType\n",
    "bgl_log_df = bgl_log_df.withColumn('date', to_date('date', 'yyyy.MM.dd'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fd40fd59",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parsing 'date_and_time' column to standard TimestampType\n",
    "bgl_log_df = bgl_log_df.withColumn('date_and_time', to_timestamp('date_and_time', 'yyyy-MM-dd-HH.mm.ss.SSSSSS'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6a55ab84",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Casting columns to appropriate data types\n",
    "bgl_log_df = bgl_log_df.withColumn(\"timestamp\", bgl_log_df[\"timestamp\"].cast(TimestampType())) \\\n",
    "    .withColumn(\"date\", bgl_log_df[\"date\"].cast(DateType())) \\\n",
    "    .withColumn(\"date_and_time\", bgl_log_df[\"date_and_time\"].cast(TimestampType()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "45d2410d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- alert_message_flag: string (nullable = true)\n",
      " |-- timestamp: timestamp (nullable = true)\n",
      " |-- date: date (nullable = true)\n",
      " |-- node: string (nullable = true)\n",
      " |-- date_and_time: timestamp (nullable = true)\n",
      " |-- node_repeated: string (nullable = true)\n",
      " |-- message_type: string (nullable = true)\n",
      " |-- system_component: string (nullable = true)\n",
      " |-- level: string (nullable = true)\n",
      " |-- message_content: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Using 'printSchema()' method to display the updated schema of the DataFrame 'bgl_log_df'.\n",
    "bgl_log_df.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c51663d2",
   "metadata": {},
   "source": [
    "**DATA PREPRATION AND PREPROCESSING COMPLETED**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e72b9e71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-RECORD 0------------------------------------------------------\n",
      " alert_message_flag | -                                        \n",
      " timestamp          | 2005-06-03 22:42:50                      \n",
      " date               | 2005-06-03                               \n",
      " node               | R02-M1-N0-C:J12-U11                      \n",
      " date_and_time      | 2005-06-03 15:42:50.363779               \n",
      " node_repeated      | R02-M1-N0-C:J12-U11                      \n",
      " message_type       | RAS                                      \n",
      " system_component   | KERNEL                                   \n",
      " level              | INFO                                     \n",
      " message_content    | instruction cache parity error corrected \n",
      "-RECORD 1------------------------------------------------------\n",
      " alert_message_flag | -                                        \n",
      " timestamp          | 2005-06-03 22:42:50                      \n",
      " date               | 2005-06-03                               \n",
      " node               | R02-M1-N0-C:J12-U11                      \n",
      " date_and_time      | 2005-06-03 15:42:50.527847               \n",
      " node_repeated      | R02-M1-N0-C:J12-U11                      \n",
      " message_type       | RAS                                      \n",
      " system_component   | KERNEL                                   \n",
      " level              | INFO                                     \n",
      " message_content    | instruction cache parity error corrected \n",
      "-RECORD 2------------------------------------------------------\n",
      " alert_message_flag | -                                        \n",
      " timestamp          | 2005-06-03 22:42:50                      \n",
      " date               | 2005-06-03                               \n",
      " node               | R02-M1-N0-C:J12-U11                      \n",
      " date_and_time      | 2005-06-03 15:42:50.675872               \n",
      " node_repeated      | R02-M1-N0-C:J12-U11                      \n",
      " message_type       | RAS                                      \n",
      " system_component   | KERNEL                                   \n",
      " level              | INFO                                     \n",
      " message_content    | instruction cache parity error corrected \n",
      "-RECORD 3------------------------------------------------------\n",
      " alert_message_flag | -                                        \n",
      " timestamp          | 2005-06-03 22:42:50                      \n",
      " date               | 2005-06-03                               \n",
      " node               | R02-M1-N0-C:J12-U11                      \n",
      " date_and_time      | 2005-06-03 15:42:50.823719               \n",
      " node_repeated      | R02-M1-N0-C:J12-U11                      \n",
      " message_type       | RAS                                      \n",
      " system_component   | KERNEL                                   \n",
      " level              | INFO                                     \n",
      " message_content    | instruction cache parity error corrected \n",
      "-RECORD 4------------------------------------------------------\n",
      " alert_message_flag | -                                        \n",
      " timestamp          | 2005-06-03 22:42:50                      \n",
      " date               | 2005-06-03                               \n",
      " node               | R02-M1-N0-C:J12-U11                      \n",
      " date_and_time      | 2005-06-03 15:42:50.982731               \n",
      " node_repeated      | R02-M1-N0-C:J12-U11                      \n",
      " message_type       | RAS                                      \n",
      " system_component   | KERNEL                                   \n",
      " level              | INFO                                     \n",
      " message_content    | instruction cache parity error corrected \n",
      "-RECORD 5------------------------------------------------------\n",
      " alert_message_flag | -                                        \n",
      " timestamp          | 2005-06-03 22:42:51                      \n",
      " date               | 2005-06-03                               \n",
      " node               | R02-M1-N0-C:J12-U11                      \n",
      " date_and_time      | 2005-06-03 15:42:51.131467               \n",
      " node_repeated      | R02-M1-N0-C:J12-U11                      \n",
      " message_type       | RAS                                      \n",
      " system_component   | KERNEL                                   \n",
      " level              | INFO                                     \n",
      " message_content    | instruction cache parity error corrected \n",
      "-RECORD 6------------------------------------------------------\n",
      " alert_message_flag | -                                        \n",
      " timestamp          | 2005-06-03 22:42:51                      \n",
      " date               | 2005-06-03                               \n",
      " node               | R02-M1-N0-C:J12-U11                      \n",
      " date_and_time      | 2005-06-03 15:42:51.293532               \n",
      " node_repeated      | R02-M1-N0-C:J12-U11                      \n",
      " message_type       | RAS                                      \n",
      " system_component   | KERNEL                                   \n",
      " level              | INFO                                     \n",
      " message_content    | instruction cache parity error corrected \n",
      "-RECORD 7------------------------------------------------------\n",
      " alert_message_flag | -                                        \n",
      " timestamp          | 2005-06-03 22:42:51                      \n",
      " date               | 2005-06-03                               \n",
      " node               | R02-M1-N0-C:J12-U11                      \n",
      " date_and_time      | 2005-06-03 15:42:51.428563               \n",
      " node_repeated      | R02-M1-N0-C:J12-U11                      \n",
      " message_type       | RAS                                      \n",
      " system_component   | KERNEL                                   \n",
      " level              | INFO                                     \n",
      " message_content    | instruction cache parity error corrected \n",
      "-RECORD 8------------------------------------------------------\n",
      " alert_message_flag | -                                        \n",
      " timestamp          | 2005-06-03 22:42:51                      \n",
      " date               | 2005-06-03                               \n",
      " node               | R02-M1-N0-C:J12-U11                      \n",
      " date_and_time      | 2005-06-03 15:42:51.601412               \n",
      " node_repeated      | R02-M1-N0-C:J12-U11                      \n",
      " message_type       | RAS                                      \n",
      " system_component   | KERNEL                                   \n",
      " level              | INFO                                     \n",
      " message_content    | instruction cache parity error corrected \n",
      "-RECORD 9------------------------------------------------------\n",
      " alert_message_flag | -                                        \n",
      " timestamp          | 2005-06-03 22:42:51                      \n",
      " date               | 2005-06-03                               \n",
      " node               | R02-M1-N0-C:J12-U11                      \n",
      " date_and_time      | 2005-06-03 15:42:51.749199               \n",
      " node_repeated      | R02-M1-N0-C:J12-U11                      \n",
      " message_type       | RAS                                      \n",
      " system_component   | KERNEL                                   \n",
      " level              | INFO                                     \n",
      " message_content    | instruction cache parity error corrected \n",
      "-RECORD 10-----------------------------------------------------\n",
      " alert_message_flag | -                                        \n",
      " timestamp          | 2005-06-03 22:42:51                      \n",
      " date               | 2005-06-03                               \n",
      " node               | R02-M1-N0-C:J12-U11                      \n",
      " date_and_time      | 2005-06-03 15:42:51.885834               \n",
      " node_repeated      | R02-M1-N0-C:J12-U11                      \n",
      " message_type       | RAS                                      \n",
      " system_component   | KERNEL                                   \n",
      " level              | INFO                                     \n",
      " message_content    | instruction cache parity error corrected \n",
      "-RECORD 11-----------------------------------------------------\n",
      " alert_message_flag | -                                        \n",
      " timestamp          | 2005-06-03 22:42:52                      \n",
      " date               | 2005-06-03                               \n",
      " node               | R02-M1-N0-C:J12-U11                      \n",
      " date_and_time      | 2005-06-03 15:42:52.041388               \n",
      " node_repeated      | R02-M1-N0-C:J12-U11                      \n",
      " message_type       | RAS                                      \n",
      " system_component   | KERNEL                                   \n",
      " level              | INFO                                     \n",
      " message_content    | instruction cache parity error corrected \n",
      "-RECORD 12-----------------------------------------------------\n",
      " alert_message_flag | -                                        \n",
      " timestamp          | 2005-06-03 22:42:52                      \n",
      " date               | 2005-06-03                               \n",
      " node               | R02-M1-N0-C:J12-U11                      \n",
      " date_and_time      | 2005-06-03 15:42:52.199063               \n",
      " node_repeated      | R02-M1-N0-C:J12-U11                      \n",
      " message_type       | RAS                                      \n",
      " system_component   | KERNEL                                   \n",
      " level              | INFO                                     \n",
      " message_content    | instruction cache parity error corrected \n",
      "-RECORD 13-----------------------------------------------------\n",
      " alert_message_flag | -                                        \n",
      " timestamp          | 2005-06-03 22:42:52                      \n",
      " date               | 2005-06-03                               \n",
      " node               | R02-M1-N0-C:J12-U11                      \n",
      " date_and_time      | 2005-06-03 15:42:52.345821               \n",
      " node_repeated      | R02-M1-N0-C:J12-U11                      \n",
      " message_type       | RAS                                      \n",
      " system_component   | KERNEL                                   \n",
      " level              | INFO                                     \n",
      " message_content    | instruction cache parity error corrected \n",
      "-RECORD 14-----------------------------------------------------\n",
      " alert_message_flag | -                                        \n",
      " timestamp          | 2005-06-03 22:42:52                      \n",
      " date               | 2005-06-03                               \n",
      " node               | R02-M1-N0-C:J12-U11                      \n",
      " date_and_time      | 2005-06-03 15:42:52.493353               \n",
      " node_repeated      | R02-M1-N0-C:J12-U11                      \n",
      " message_type       | RAS                                      \n",
      " system_component   | KERNEL                                   \n",
      " level              | INFO                                     \n",
      " message_content    | instruction cache parity error corrected \n",
      "-RECORD 15-----------------------------------------------------\n",
      " alert_message_flag | -                                        \n",
      " timestamp          | 2005-06-03 22:42:52                      \n",
      " date               | 2005-06-03                               \n",
      " node               | R02-M1-N0-C:J12-U11                      \n",
      " date_and_time      | 2005-06-03 15:42:52.638135               \n",
      " node_repeated      | R02-M1-N0-C:J12-U11                      \n",
      " message_type       | RAS                                      \n",
      " system_component   | KERNEL                                   \n",
      " level              | INFO                                     \n",
      " message_content    | instruction cache parity error corrected \n",
      "-RECORD 16-----------------------------------------------------\n",
      " alert_message_flag | -                                        \n",
      " timestamp          | 2005-06-03 22:42:52                      \n",
      " date               | 2005-06-03                               \n",
      " node               | R02-M1-N0-C:J12-U11                      \n",
      " date_and_time      | 2005-06-03 15:42:52.807927               \n",
      " node_repeated      | R02-M1-N0-C:J12-U11                      \n",
      " message_type       | RAS                                      \n",
      " system_component   | KERNEL                                   \n",
      " level              | INFO                                     \n",
      " message_content    | instruction cache parity error corrected \n",
      "-RECORD 17-----------------------------------------------------\n",
      " alert_message_flag | -                                        \n",
      " timestamp          | 2005-06-03 22:42:52                      \n",
      " date               | 2005-06-03                               \n",
      " node               | R02-M1-N0-C:J12-U11                      \n",
      " date_and_time      | 2005-06-03 15:42:52.951717               \n",
      " node_repeated      | R02-M1-N0-C:J12-U11                      \n",
      " message_type       | RAS                                      \n",
      " system_component   | KERNEL                                   \n",
      " level              | INFO                                     \n",
      " message_content    | instruction cache parity error corrected \n",
      "-RECORD 18-----------------------------------------------------\n",
      " alert_message_flag | -                                        \n",
      " timestamp          | 2005-06-03 22:42:53                      \n",
      " date               | 2005-06-03                               \n",
      " node               | R02-M1-N0-C:J12-U11                      \n",
      " date_and_time      | 2005-06-03 15:42:53.12578                \n",
      " node_repeated      | R02-M1-N0-C:J12-U11                      \n",
      " message_type       | RAS                                      \n",
      " system_component   | KERNEL                                   \n",
      " level              | INFO                                     \n",
      " message_content    | instruction cache parity error corrected \n",
      "-RECORD 19-----------------------------------------------------\n",
      " alert_message_flag | -                                        \n",
      " timestamp          | 2005-06-03 22:42:53                      \n",
      " date               | 2005-06-03                               \n",
      " node               | R02-M1-N0-C:J12-U11                      \n",
      " date_and_time      | 2005-06-03 15:42:53.276129               \n",
      " node_repeated      | R02-M1-N0-C:J12-U11                      \n",
      " message_type       | RAS                                      \n",
      " system_component   | KERNEL                                   \n",
      " level              | INFO                                     \n",
      " message_content    | instruction cache parity error corrected \n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# We are using the 'show()' method to display the DataFrame 'bgl_log_df' in a vertical format.\n",
    "# The 'vertical=True' argument is used to display the DataFrame vertically, showing each row in a single line.\n",
    "# The 'truncate=False' argument is used to prevent truncation of column values, ensuring complete visibility of the data.\n",
    "bgl_log_df.show(truncate=False, vertical=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4fa8c6f",
   "metadata": {},
   "source": [
    "# Section 2: Log Data Analysis : Spark SQL"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d77834c0",
   "metadata": {},
   "source": [
    "**TOTAL NUMBER OF LOG ENTRIES IN THE LOG DATASET COUNT**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8a06cc1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4747963"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count total logs in the DataFrame\n",
    "total_logs_count = bgl_log_df.count()\n",
    "\n",
    "# Display the total number of logs\n",
    "total_logs_count"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff7a3f8a",
   "metadata": {},
   "source": [
    "**CREATING TEMPORARY VIEW OF THE DATAFRAME**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c2dcd0c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using the 'createOrReplaceTempView()' method to create a temporary view of the DataFrame 'bgl_log_df'.\n",
    "# This temporary view, named \"bgllogtable,\" allows us to perform SQL queries and leverage the power of SQL to interact with the DataFrame and perform complex data analysis and transformations.\n",
    "# NOTE: The temporary view exists for the duration of the SparkSession and can be queried using Spark SQL syntax.\n",
    "bgl_log_df.createOrReplaceTempView(\"bgllogtable\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d20ecc04",
   "metadata": {},
   "source": [
    "<span style=\"color:blue\">**SQL Query for Question 12: Top 5 Most Frequent Occurring Hours**</SPAN>\n",
    "\n",
    "***1. SELECT HOUR(date_and_time) AS hour_HH, COUNT(*) AS Occurance_Count***\n",
    "   - We are selecting the 'hour' part from the 'date_and_time' column using the 'HOUR()' function and aliasing it as 'hour_HH'.\n",
    "   - The 'COUNT()' function is used to calculate the number of occurrences for each hour and is aliased as 'Occurance_Count'.\n",
    "\n",
    "***2. FROM bgllogtable***\n",
    "   - We are selecting data from the 'bgllogtable' where the log entries are stored.\n",
    "\n",
    "***3. GROUP BY HOUR(date_and_time)***\n",
    "   - We are grouping the log entries by the 'hour' part of the 'date_and_time' column.\n",
    "   - This helps in counting the occurrences of log entries for each hour.\n",
    "\n",
    "***4. ORDER BY Occurance_Count DESC***\n",
    "   - We are arranging the result in descending order based on the 'Occurance_Count'.\n",
    "   - This will ensure that the most frequent hours appear at the top.\n",
    "\n",
    "<span style=\"color:blue\">**The output of this SQL query will provide the top 5 hours and their corresponding occurrence counts, which can be used for further analysis and visualization to gain insights into the log entry patterns.**<span style=\"color:blue\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "93c12d18",
   "metadata": {},
   "outputs": [],
   "source": [
    "SQL_question12=\"\"\"\n",
    "SELECT HOUR(date_and_time) AS hour_HH, COUNT(*) AS Occurance_Count\n",
    "FROM bgllogtable\n",
    "GROUP BY HOUR(date_and_time)\n",
    "ORDER BY Occurance_Count DESC;\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "691ab842",
   "metadata": {},
   "source": [
    "<span style=\"color:blue\">***THE TOP 5 MOST FREQUENTLY OCCURING HOURS IN THE LOG***<span style=\"color:blue\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aaf2bfe4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 6:=============================>                             (3 + 3) / 6]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+---------------+\n",
      "|hour_HH|Occurance_Count|\n",
      "+-------+---------------+\n",
      "|     11|         388648|\n",
      "|     10|         379141|\n",
      "|     12|         354389|\n",
      "|      8|         315017|\n",
      "|      9|         308801|\n",
      "+-------+---------------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "# Using the 'session' object to execute an SQL query and display the results.\n",
    "sql_result_df=session.sql(SQL_question12)\n",
    "sql_result_df.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "87d3d36a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "#converting the Spark DataFrame to a Pandas DataFrame for plotting\n",
    "pandas_df = sql_result_df.toPandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e97f844a",
   "metadata": {},
   "source": [
    "# Section 3: Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe1a45cb",
   "metadata": {},
   "source": [
    "<span style=\"color:blue\">***VISUALISING THE TOP 5 MOST FREQUENTLY OCCURING HOURS IN THE LOG***</span> MARKED IN <span style=\"color:red\">RED</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a18cd835",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# https://www.geeksforgeeks.org/bar-plot-in-matplotlib/\n",
    "# Plotting the bar chart\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.bar(pandas_df[\"hour_HH\"], pandas_df[\"Occurance_Count\"], color=\"blue\")\n",
    "\n",
    "# Highlight the top five occurrences in red\n",
    "top_five = pandas_df.iloc[:5]\n",
    "plt.bar(top_five[\"hour_HH\"], top_five[\"Occurance_Count\"], color=\"red\")\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel(\"Hour of the Day\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Top Five Most Frequent Hours in the Log\")\n",
    "plt.xticks(pandas_df['hour_HH'])\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f69c48b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Measuring end of execution time. \n",
    "#https://docs.python.org/3/library/time.html\n",
    "#returns the value (in fractional seconds) of a performance counter.It does include time elapsed during sleep and is system-wide.\n",
    "executiontimeend= time.perf_counter()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9d897ef2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.194009243990877"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the total execution time by subtracting the start time from the end time.\n",
    "# Calculating the total execution time as  time.perf_counter() returns the absolute value of the counter\n",
    "# https://stackoverflow.com/questions/25785243/understanding-time-perf-counter-and-time-process-time\n",
    "totalexecution_time= executiontimeend - executiontimestart\n",
    "totalexecution_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3efd5715",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <div>\n",
       "                <p><b>SparkSession - hive</b></p>\n",
       "                \n",
       "        <div>\n",
       "            <p><b>SparkContext</b></p>\n",
       "\n",
       "            <p><a href=\"http://192.168.64.4:4046\">Spark UI</a></p>\n",
       "\n",
       "            <dl>\n",
       "              <dt>Version</dt>\n",
       "                <dd><code>v3.4.0</code></dd>\n",
       "              <dt>Master</dt>\n",
       "                <dd><code>local[*]</code></dd>\n",
       "              <dt>AppName</dt>\n",
       "                <dd><code>PySparkShell</code></dd>\n",
       "            </dl>\n",
       "        </div>\n",
       "        \n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "<pyspark.sql.session.SparkSession at 0xffff49f777c0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all the information on the 'spark runtime environment', executors sumamry, jobs and its stages can be found on 'Spark UI'\n",
    "spark"
   ]
  }
 ],
 "metadata": {
  "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}