Question_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a0740787",
   "metadata": {},
   "source": [
    "# Question 2: How many fatal log entries in the month of September resulted from a ”major internal error”"
   ]
  },
  {
   "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",
    "\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",
    "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 21:12:43 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"
   ]
  },
  {
   "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": "f92f6c63",
   "metadata": {},
   "source": [
    "**TOTAL NUMBER OF LOG ENTRIES WITH LEVEL AS 'FATAL'** -- STEP 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dd8e13d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "855195"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering fatal log entries and counting them\n",
    "fatal_logs = bgl_log_df.filter(col(\"level\") == \"FATAL\")\n",
    "fatal_logs_count = fatal_logs.count()\n",
    "fatal_logs_count"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6ffab0a",
   "metadata": {},
   "source": [
    "**TOTAL NUMBER OF LOG ENTRIES WITH LEVEL AS 'FATAL' IN THE MONTH OF 'SEPTEMBER'**--STEP 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3fdeacc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "81818"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering fatal log entries for September and counting them\n",
    "fatal_logs_september = bgl_log_df.filter((col(\"level\") == \"FATAL\") & (month(col(\"date\")) == 9))\n",
    "fatal_logs_september_count = fatal_logs_september.count()\n",
    "fatal_logs_september_count"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c1acc19",
   "metadata": {},
   "source": [
    "<span style=\"color:blue\">***TOTAL NUMBER OF 'FATAL' LOG ENTRIES IN THE MONTH OF SEPTEMBER RESULTED FROM 'major intenral error'***--FINAL ANSWER</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "de528ad1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering fatal log entries with \"major internal error\" in September and counting them\n",
    "major_internal_error_september = fatal_logs_september.filter(col(\"message_content\").contains(\"major internal error\"))\n",
    "major_internal_error_september_count = major_internal_error_september.count()\n",
    "major_internal_error_september_count"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a09fd07",
   "metadata": {},
   "source": [
    "<span style=\"color:blue\">***LIST OF ALL 'FATAL' LOG ENTRIES IN THE MONTH OF SEPTEMBER RESULTED FROM 'major intenral error'***--FINAL ANSWER</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c3fb536a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 17:>                                                         (0 + 1) / 1]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-RECORD 0-------------------------------------------------------------------------\n",
      " alert_message_flag | KERNMC                                                      \n",
      " timestamp          | 2005-09-19 13:13:01                                         \n",
      " date               | 2005-09-19                                                  \n",
      " node               | R12-M0-N0-C:J12-U01                                         \n",
      " date_and_time      | 2005-09-19 06:13:01.71549                                   \n",
      " node_repeated      | R12-M0-N0-C:J12-U01                                         \n",
      " message_type       | RAS                                                         \n",
      " system_component   | KERNEL                                                      \n",
      " level              | FATAL                                                       \n",
      " message_content    | machine check interrupt (bit=0x06): L3 major internal error \n",
      "-RECORD 1-------------------------------------------------------------------------\n",
      " alert_message_flag | KERNMC                                                      \n",
      " timestamp          | 2005-09-20 20:57:10                                         \n",
      " date               | 2005-09-20                                                  \n",
      " node               | R21-M1-ND-C:J15-U11                                         \n",
      " date_and_time      | 2005-09-20 13:57:10.519732                                  \n",
      " node_repeated      | R21-M1-ND-C:J15-U11                                         \n",
      " message_type       | RAS                                                         \n",
      " system_component   | KERNEL                                                      \n",
      " level              | FATAL                                                       \n",
      " message_content    | machine check interrupt (bit=0x06): L3 major internal error \n",
      "-RECORD 2-------------------------------------------------------------------------\n",
      " alert_message_flag | KERNMC                                                      \n",
      " timestamp          | 2005-09-20 20:57:12                                         \n",
      " date               | 2005-09-20                                                  \n",
      " node               | R21-M1-N4-C:J14-U11                                         \n",
      " date_and_time      | 2005-09-20 13:57:12.610002                                  \n",
      " node_repeated      | R21-M1-N4-C:J14-U11                                         \n",
      " message_type       | RAS                                                         \n",
      " system_component   | KERNEL                                                      \n",
      " level              | FATAL                                                       \n",
      " message_content    | machine check interrupt (bit=0x06): L3 major internal error \n",
      "-RECORD 3-------------------------------------------------------------------------\n",
      " alert_message_flag | KERNMC                                                      \n",
      " timestamp          | 2005-09-20 20:57:14                                         \n",
      " date               | 2005-09-20                                                  \n",
      " node               | R34-M0-NC-C:J16-U01                                         \n",
      " date_and_time      | 2005-09-20 13:57:14.946287                                  \n",
      " node_repeated      | R34-M0-NC-C:J16-U01                                         \n",
      " message_type       | RAS                                                         \n",
      " system_component   | KERNEL                                                      \n",
      " level              | FATAL                                                       \n",
      " message_content    | machine check interrupt (bit=0x06): L3 major internal error \n",
      "-RECORD 4-------------------------------------------------------------------------\n",
      " alert_message_flag | KERNMC                                                      \n",
      " timestamp          | 2005-09-20 20:57:15                                         \n",
      " date               | 2005-09-20                                                  \n",
      " node               | R30-M1-N4-C:J14-U11                                         \n",
      " date_and_time      | 2005-09-20 13:57:15.132838                                  \n",
      " node_repeated      | R30-M1-N4-C:J14-U11                                         \n",
      " message_type       | RAS                                                         \n",
      " system_component   | KERNEL                                                      \n",
      " level              | FATAL                                                       \n",
      " message_content    | machine check interrupt (bit=0x06): L3 major internal error \n",
      "-RECORD 5-------------------------------------------------------------------------\n",
      " alert_message_flag | KERNMC                                                      \n",
      " timestamp          | 2005-09-20 20:57:15                                         \n",
      " date               | 2005-09-20                                                  \n",
      " node               | R37-M0-N4-C:J14-U01                                         \n",
      " date_and_time      | 2005-09-20 13:57:15.363924                                  \n",
      " node_repeated      | R37-M0-N4-C:J14-U01                                         \n",
      " message_type       | RAS                                                         \n",
      " system_component   | KERNEL                                                      \n",
      " level              | FATAL                                                       \n",
      " message_content    | machine check interrupt (bit=0x06): L3 major internal error \n",
      "-RECORD 6-------------------------------------------------------------------------\n",
      " alert_message_flag | KERNMC                                                      \n",
      " timestamp          | 2005-09-20 20:57:19                                         \n",
      " date               | 2005-09-20                                                  \n",
      " node               | R12-M1-NC-C:J16-U11                                         \n",
      " date_and_time      | 2005-09-20 13:57:19.547756                                  \n",
      " node_repeated      | R12-M1-NC-C:J16-U11                                         \n",
      " message_type       | RAS                                                         \n",
      " system_component   | KERNEL                                                      \n",
      " level              | FATAL                                                       \n",
      " message_content    | machine check interrupt (bit=0x06): L3 major internal error \n",
      "-RECORD 7-------------------------------------------------------------------------\n",
      " alert_message_flag | KERNMC                                                      \n",
      " timestamp          | 2005-09-25 09:29:39                                         \n",
      " date               | 2005-09-25                                                  \n",
      " node               | R07-M0-N2-C:J10-U01                                         \n",
      " date_and_time      | 2005-09-25 02:29:39.31386                                   \n",
      " node_repeated      | R07-M0-N2-C:J10-U01                                         \n",
      " message_type       | RAS                                                         \n",
      " system_component   | KERNEL                                                      \n",
      " level              | FATAL                                                       \n",
      " message_content    | machine check interrupt (bit=0x06): L3 major internal error \n",
      "-RECORD 8-------------------------------------------------------------------------\n",
      " alert_message_flag | KERNMC                                                      \n",
      " timestamp          | 2005-09-28 05:25:51                                         \n",
      " date               | 2005-09-27                                                  \n",
      " node               | R45-M0-N8-C:J10-U01                                         \n",
      " date_and_time      | 2005-09-27 22:25:51.318668                                  \n",
      " node_repeated      | R45-M0-N8-C:J10-U01                                         \n",
      " message_type       | RAS                                                         \n",
      " system_component   | KERNEL                                                      \n",
      " level              | FATAL                                                       \n",
      " message_content    | machine check interrupt (bit=0x06): L3 major internal error \n",
      "-RECORD 9-------------------------------------------------------------------------\n",
      " alert_message_flag | -                                                           \n",
      " timestamp          | 2005-09-28 05:27:28                                         \n",
      " date               | 2005-09-27                                                  \n",
      " node               | R45-M0-N8-C:J10-U01                                         \n",
      " date_and_time      | 2005-09-27 22:27:28.325396                                  \n",
      " node_repeated      | R45-M0-N8-C:J10-U01                                         \n",
      " message_type       | RAS                                                         \n",
      " system_component   | KERNEL                                                      \n",
      " level              | FATAL                                                       \n",
      " message_content    | disable flagging of DDR UE's as major internal error.0      \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "major_internal_error_september.show(truncate=False, vertical=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f84cc462",
   "metadata": {},
   "source": [
    "# Section 3: Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79e05dff",
   "metadata": {},
   "source": [
    "** GROUPINNG CALCULATING COUNT OF EACH DISTINCT 'LEVEL' IN THE DATASET**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d5639fa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the percentage of each 'level' log out of the total number of logs\n",
    "log_level_types_counts = bgl_log_df.groupBy(\"level\").count().orderBy(\"level\")\n",
    "log_level_types_counts_percentage = log_level_types_counts.withColumn(\"percentage\", (log_level_types_counts[\"count\"] / total_logs_count) * 100)\n",
    "log_level_types_counts_percentage = log_level_types_counts_percentage.withColumn(\"percentage\", col(\"percentage\").cast(DoubleType()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "287034e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "# Convert the PySpark DataFrame to a Pandas DataFrame for plotting\n",
    "log_level_types_counts_percentage_df_pd = log_level_types_counts_percentage.toPandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9ad351c",
   "metadata": {},
   "source": [
    "**VISUALISING LOG 'LEVEL' TYPES AND ITS PERCENTAGE** 'LEVEL' 'FATAL' is highlighted in <span style=\"color:red\"> red</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "99a4d6fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the bar chart for log levels percentage\n",
    "# https://www.geeksforgeeks.org/bar-plot-in-matplotlib/\n",
    "plt.figure(figsize=(12, 6))\n",
    "bars = plt.bar(log_level_types_counts_percentage_df_pd['level'], log_level_types_counts_percentage_df_pd['percentage'], color='b', edgecolor='black')\n",
    "for bar in bars:\n",
    "    height = bar.get_height()\n",
    "    plt.gca().text(bar.get_x() + bar.get_width() / 2, height, f\"{height:.2f}%\", ha='center', va='bottom')\n",
    "plt.xlabel('Level')\n",
    "plt.ylabel('Percentage')\n",
    "plt.title('Percentage of Log Levels')\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.yticks(log_level_types_counts_percentage_df_pd['percentage'])\n",
    "fatal_index = log_level_types_counts_percentage_df_pd[log_level_types_counts_percentage_df_pd['level'] == 'FATAL'].index[0]\n",
    "bars[fatal_index].set_color('red')\n",
    "plt.text(0.95, 0.95, f\"Total Logs: {total_logs_count}\", transform=plt.gca().transAxes, ha='right', va='top')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32377c8e",
   "metadata": {},
   "source": [
    "**GROUPING FATAL LOG ENTRIES BY MONTH AND COUNTING NUMBER OF FATAL LOG ENTRIES IN EVERY MONTH** 'SEPTEMBER' MONTH 'FATAL' is highlighted in <span style=\"color:red\"> red</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a5a0705a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grouping fatal log entries by month and counting the number of entries for each month\n",
    "fatal_logs_by_month = fatal_logs.groupBy(month(\"date\").alias(\"month\")).count().orderBy(\"month\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ff78b36a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "# Convert the DataFrame to a Pandas DataFrame for plotting\n",
    "fatal_logs_by_month_pd = fatal_logs_by_month.toPandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4833650",
   "metadata": {},
   "source": [
    "**VISUALISING NUMBER OF FATAL LOG ENTRIES IN EVERY MONTH** 'SEPTEMBER' MONTH 'FATAL' is highlighted in <span style=\"color:red\"> red</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "21737c14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the bar graph for fatal entries by month\n",
    "# https://www.geeksforgeeks.org/bar-plot-in-matplotlib/\n",
    "plt.figure(figsize=(10, 6))\n",
    "bars = plt.bar(fatal_logs_by_month_pd['month'], fatal_logs_by_month_pd['count'], color='b', edgecolor='black')\n",
    "september_index = fatal_logs_by_month_pd[fatal_logs_by_month_pd['month'] == 9].index[0]\n",
    "bars[september_index].set_color('red')\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Number of Fatal Entries')\n",
    "plt.title('Number of Fatal Entries for Each Month')\n",
    "plt.xticks(fatal_logs_by_month_pd['month'])\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "for bar in bars:\n",
    "    height = bar.get_height()\n",
    "    plt.gca().text(bar.get_x() + bar.get_width() / 2, height, f\"{int(height)}\", ha='center', va='bottom')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "11a98520",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group by date and count the occurrences for each day\n",
    "error_counts_per_day = major_internal_error_september.groupBy(\"date\").count().orderBy(\"date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "04c02ba8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "# Convert the PySpark DataFrame to a Pandas DataFrame for plotting\n",
    "error_counts_per_day_pd = error_counts_per_day.toPandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cf1b55b",
   "metadata": {},
   "source": [
    "<span style=\"color:blue\">**VISUALISING NUMBER OF FATAL LOG ENTRIES IN THE MONTH OF SEPTEMBER OVER TIME RESULTING FROM 'major intenral error**</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5a9c2745",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the bar chart for major internal errors over time in September\n",
    "# https://www.geeksforgeeks.org/bar-plot-in-matplotlib/\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.bar(error_counts_per_day_pd['date'], error_counts_per_day_pd['count'], color='b')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Number of Major Internal Errors')\n",
    "plt.title('Major Internal Errors Over Time (September)')\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1309b95e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#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": "markdown",
   "id": "297dc04a",
   "metadata": {},
   "source": [
    "**EXECUTION TIME COUNTER STOP- TOTAL EXECUTION TIME GENERATED BELOW**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9d897ef2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.299458110006526"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 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": 27,
   "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://192.168.64.4:4040\">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 0xffff583977f0>"
      ]
     },
     "execution_count": 27,
     "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"
   ]
  }
 ],
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    "name": "ipython",
    "version": 3
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