Analyzing Group Lifetimes

Welcome to our new coding practice lesson! We have an interesting problem in this unit that centers around data from a social networking app. The challenge involves processing logs from this app and extracting useful information from them. This task will leverage your skills in string manipulation, working with timestamps, and task subdivision. Let's get started!

Imagine a social networking application that allows users to form groups. Each group has a unique ID ranging from 1 up to n, the total number of groups. The app keeps track of when a group is created and deleted, logging all these actions in a string.

Your task is to create a Scala function named analyzeLogs. This function will take as input a string of logs and output a List[(Int, String)] representing the groups with the longest lifetime. Each tuple in the list contains two items: the group ID (as an Int) and the group's lifetime (as a string in the format "HH:MM." By 'lifetime,' we mean the duration from when the group was created until its deletion. If a group has been created and deleted multiple times, the lifetime is the total sum of those durations. If multiple groups have the same longest lifetime, the function should return all such groups in ascending order of their IDs.

For example, if we have a log string as follows:
"1 create 09:00, 2 create 10:00, 1 delete 12:00, 3 create 13:00, 2 delete 15:00, 3 delete 16:00",
the function will return: List((2, "05:00")).

First, we need to handle timestamps and split the input string into individual log entries. In Scala, we can use the java.time.LocalTime class to represent times and the java.time.Duration class to represent durations. To split the input string into separate log entries, we can use the split method.

import java.time.LocalTime
import java.time.Duration

def analyzeLogs(logs: String): List[(Int, String)] = {
  val logList = logs.split(", ").toList  // Split the log string into individual log entries
}

Next, we need to process each log entry. For each entry, we split it into its components: the group ID, the action (create or delete), and the time. In Scala, we can use the split method and pattern matching to extract these values.

import java.time.LocalTime
import java.time.Duration

def analyzeLogs(logs: String): List[(Int, String)] = {
  val logList = logs.split(", ").toList

  for (log <- logList) {
    val parts = log.split(" ")
    val groupId = parts(0)
    val action = parts(1)
    val time = parts(2)
    // Further processing will be done in the next steps
  }
  List() // Placeholder return
}