Application logs are used in analysis of interacti...

Creado el: 8 de febrero de 2025

Respondido usando O3 Mini por Chat01

Pregunta

Application logs are used in analysis of interactions with an application and may be used to detect specific actions.
A log file is provided as a string array where each entry is in the form "user_id timestamp action". Each of the values is separated by a space.
• Both user_id and timestamp consist only of digits, are at most 9 digits long and start with a non-zero digit.
• timestamp represents the time in seconds since the application was last launched
• action will be either "sign-in" or "sign-out"
Given a log with entries in no particular order, return an array of strings that denote user_ids of users who signed out in maxSpan seconds or less after signing in.
Example
n = 7
logs = ["30 99 sign-in", "30 105 sign-out", "12 100
sign-in", "20 80 sign-in", "12 120 sign-out", "20 101 sign-out", "21 110 sign-in"]
maxSpan = 20
Confiden

ID Sign in Sign out Time delta

30
99
105
6
12
100
120
20
20
80
101
21

21 110
The users with id's 30 and 12 were not signed in for
more than maxSpan = 20 seconds. In sorted
numerical order, the return array is ["12", "30"].
Function Description
Complete the function processLogs in the editor below.
The function has the following parameters):
string logs[n]: each logs[i] denotes the ith entry in the logs
int maxSpan: the maximum difference in seconds between when a user logs in and logs out for the user to be included in the result
Returns:
stringl]: a string array of user id's, sorted ascending by numeric value
HackerRank Confidential
Constraints
• 1 ≤ n, maxSpan ≤ 105
• 1 ≤ maxpansn
• Each user_ id contains only characters in the range ascii['0'-'9'], is at most 9 digits long and starts with a non-zero digit.
• Each timestamp contains only characters in the range ascii('0-9'] and begins with a non-zero digit
• 0 ‹ timestamp ≤ 109
• Each action is either "sign-in" or "sign-out".
• Each user_id's sign-in timestamp < sign-out timestamp
• Each user signs in for only 1 session.
• The result will contain at least one element.

in java

Pensamiento

Analyzing log data

The task asks to identify users who signed out within a maxSpan of signing in based on unsorted application logs. Each log entry includes the user_id, timestamp, and action (sign-in or sign-out). Since each user only signs in once, I just need to find matching sign-in and sign-out actions and calculate the time difference for each user. If the difference is less than or equal to maxSpan, that user's ID should be included. I'll now look for an efficient approach to process and match those logs.

Designing the solution

Okay, each user has a "sign-in" and "sign-out," and I need to calculate the time difference between them. If that difference is ≤ maxSpan, I’ll include the user. In the example, user 30, whose sign-in at 99 and sign-out at 105 gives a difference of 6 seconds, should be included. But for user 20, the difference is too large (21 seconds). What's tricky is sorting the results by numeric order, which we'll handle as strings. Now, I need to implement this variety of checks in Java, possibly using a HashMap or TreeMap. Let's go!

Implementing the solution

I’m considering a couple of possibilities for the Java solution. The two-map approach seems solid since each log has a sign-in and sign-out event, but logs can come out of order. I can track the sign-in and sign-out times separately in two maps. Then, I can compute the difference when both are available. One tricky part is sorting numerically by user_id. I’ll need to convert user_id to an integer for sorting, then convert it back to a string for the final output. I’ll finalize the function based on this plan.

Reasoned for 10 seconds

Respuesta

Below is one acceptable Java solution. In this solution, we process the logs by storing each user’s sign‐in and sign‐out timestamps in separate maps. Then, we iterate through the sign‐in entries, and if the corresponding sign‐out exists and the time difference is less than or equal to maxSpan, we add that user id (converted to an integer so that sorting is done numerically) to the result list. Finally, we sort the list and convert the integers back to strings.

Below is the complete code:

java
import java.util.*; public class LogProcessor { public static List<String> processLogs(String[] logs, int maxSpan) { // Map to store the sign-in time for each user. Map<String, Integer> signInMap = new HashMap<>(); // Map to store the sign-out time for each user. Map<String, Integer> signOutMap = new HashMap<>(); // Process each log entry. for (String log : logs) { // Split the log entry by space. String[] parts = log.split(" "); String userId = parts[0]; int timestamp = Integer.parseInt(parts[1]); String action = parts[2]; if (action.equals("sign-in")) { signInMap.put(userId, timestamp); } else if (action.equals("sign-out")) { signOutMap.put(userId, timestamp); } } // List to store valid user IDs (as integers for numeric sorting). List<Integer> validUsers = new ArrayList<>(); // Check each user who has signed in. for (String userId : signInMap.keySet()) { if (signOutMap.containsKey(userId)) { int signInTime = signInMap.get(userId); int signOutTime = signOutMap.get(userId); if (signOutTime - signInTime <= maxSpan) { validUsers.add(Integer.parseInt(userId)); } } } // Sort numerically. Collections.sort(validUsers); // Convert the user IDs back to strings. List<String> result = new ArrayList<>(); for (int id : validUsers) { result.add(String.valueOf(id)); } return result; } // For testing purposes. public static void main(String[] args) { String[] logs = { "30 99 sign-in", "30 105 sign-out", "12 100 sign-in", "20 80 sign-in", "12 120 sign-out", "20 101 sign-out", "21 110 sign-in" }; int maxSpan = 20; List<String> validUsers = processLogs(logs, maxSpan); System.out.println(validUsers); // Expected output: ["12", "30"] } }

Explanation

  1. Parsing the Logs:
    Each log entry is split using split(" ") into userId, timestamp, and action. We then parse the timestamp from a string to an integer.

  2. Storing the Timestamps:
    We use two maps: one for sign-in times and one for sign-out times. This ensures that even if the logs are unsorted, we correctly associate the sign-in and sign-out events for each user.

  3. Determining Valid Users:
    We iterate through all users that have a sign-in event. For each, we check if there is a corresponding sign-out and if the difference (signOutTime - signInTime) is within the allowed maxSpan.

  4. Sorting Numerically:
    Since user IDs are strings but need to be sorted numerically, we convert them to integers, sort them, and then convert back to strings.

  5. Testing:
    The main method includes a test with the example provided. When run, it prints the expected output ["12", "30"].

This solution meets the problem requirements and handles the logs even when they are not in chronological order.

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