What is the time complexity of Tweet Counts Per Frequency?

The optimal solution has a time complexity of O(nlogn + k*logn).

What data structures are used to solve Tweet Counts Per Frequency?

This problem uses Hash Table, Binary Search, Design, Sorting, Ordered Set. Understanding these concepts is essential for solving this Medium problem efficiently.

What are the key insights for solving Tweet Counts Per Frequency?

Insight 1: Using a hash map (dictionary) to store tweets associated with their names is crucial for efficient retrieval. Insight 2: Sorting the timestamps for each tweet name allows us to use binary search to efficiently count tweets within a given time range.

Which companies ask LeetCode 1348. Tweet Counts Per Frequency in interviews?

This problem is frequently asked at X.

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Tweet Counts Per Frequency - LeetCode 1348 Solution

Tweet Counts Per Frequency - Complete Solution Guide

Tweet Counts Per Frequency is LeetCode problem 1348, a Medium level challenge. This complete guide provides step-by-step explanations, multiple solution approaches, and optimized code in python3, java, cpp, c.

Problem Statement

A social media company is trying to monitor activity on their site by analyzing the number of tweets that occur in select periods of time. These periods can be partitioned into smaller time chunks based on a certain frequency (every minute , hour , or day ). For example, the period [10, 10000] (in seconds ) would be partitioned into the following time chunks with these frequencies: Every minute (60-second chunks): [10,69] , [70,129] , [130,189] , ... , [9970,10000] Every hour (3600-second chunks

Detailed Explanation

The problem asks us to design a system to record tweets and then retrieve the number of tweets for a given tweet name within specific time ranges, divided into chunks based on a given frequency (minute, hour, or day). We need to implement a `TweetCounts` class with methods to record tweets and query tweet counts within given time intervals, broken down by the specified frequency.

Solution Approach

The solution uses a hash map (dictionary) to store the timestamps of tweets for each tweet name. When a tweet is recorded, its timestamp is added to the corresponding list in the hash map. When a query for tweet counts is made, the timestamps for the given tweet name are sorted (if not already sorted) and then binary search is used to count the number of tweets within each time chunk defined by the frequency.

Step-by-Step Algorithm

Step 1: Initialize a hash map (tweets) to store tweet names as keys and lists of timestamps as values.
Step 2: Implement the `recordTweet` method: Append the given time to the list of timestamps for the corresponding tweet name in the `tweets` hash map. Mark the timestamp list as unsorted using another hash map (is_sorted).
Step 3: Implement the `getTweetCountsPerFrequency` method: Determine the chunk size (delta) based on the given frequency (minute, hour, or day).
Step 4: If the timestamps for the given tweet name are not sorted, sort them using a sorting algorithm (e.g., mergesort). Update the `is_sorted` hash map to indicate that the timestamps are now sorted.
Step 5: Iterate through the time range [startTime, endTime] in chunks of size delta. For each chunk [chunk_start, chunk_end], use binary search to find the number of tweets within that chunk.
Step 6: Store the count of tweets for each chunk in a list and return the list.
Step 7: Implement binary search functions (bisect_left and bisect_right in python's bisect, lower_bound and upper_bound in C++) to find the start and end indices of tweets within the time range of each chunk.

Key Insights

Insight 1: Using a hash map (dictionary) to store tweets associated with their names is crucial for efficient retrieval.
Insight 2: Sorting the timestamps for each tweet name allows us to use binary search to efficiently count tweets within a given time range.
Insight 3: Pre-calculating or storing the frequency intervals can optimize the `getTweetCountsPerFrequency` method by avoiding repeated calculations.

Complexity Analysis

Time Complexity: O(nlogn + k*logn)

Space Complexity: O(n)

Topics

This problem involves: Hash Table, Binary Search, Design, Sorting, Ordered Set.

Companies

Asked at: X.

Tweet Counts Per Frequency | LeetCode 1348

Medium

Hash Table Binary Search Design Sorting Ordered Set

Time: O(nlogn + k*logn)Space: O(n)

Solution Code

Available in:

import java.util.*;

class TweetCounts {

    private Map<String, List<Integer>> tweets;
    private Map<String, Boolean> isSorted;
    private Map<String, Integer> freqMap;

    public TweetCounts() {
        tweets = new HashMap<>();
        isSorted = new HashMap<>();
        freqMap = new HashMap<>();
        freqMap.put("minute", 60);
        freqMap.put("hour", 3600);
        freqMap.put("day", 86400);
    }

    public void recordTweet(String tweetName, int time) {
        tweets.computeIfAbsent(tweetName, k -> new ArrayList<>()).add(time);
        isSorted.put(tweetName, false);
    }

    public List<Integer> getTweetCountsPerFrequency(String freq, String tweetName, int startTime, int endTime) {
        int delta = freqMap.get(freq);

        List<Integer> timestamps = tweets.getOrDefault(tweetName, new ArrayList<>());

        if (!isSorted.getOrDefault(tweetName, true)) {
            Collections.sort(timestamps);
            isSorted.put(tweetName, true);
        }

        List<Integer> result = new ArrayList<>();

        int current_time = startTime;
        while (current_time <= endTime) {
            int chunk_end_inclusive = Math.min(current_time + delta - 1, endTime);

            int start_index = binarySearchLeft(timestamps, current_time);
            int end_index = binarySearchRight(timestamps, chunk_end_inclusive);

            int count = end_index - start_index;
            result.add(count);

            current_time += delta;
        }

        return result;
    }

    private int binarySearchLeft(List<Integer> arr, int target) {
        int low = 0;
        int high = arr.size();

        while (low < high) {
            int mid = low + (high - low) / 2;
            if (arr.get(mid) < target) {
                low = mid + 1;
            } else {
                high = mid;
            }
        }

        return low;
    }

    private int binarySearchRight(List<Integer> arr, int target) {
        int low = 0;
        int high = arr.size();

        while (low < high) {
            int mid = low + (high - low) / 2;
            if (arr.get(mid) <= target) {
                low = mid + 1;
            } else {
                high = mid;
            }
        }

        return low;
    }
}

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Problem Info

Description

Problem 1348: Tweet Counts Per Frequency - Detailed explanation and solution approaches.

Topics

Hash Table Binary Search Design Sorting Ordered Set

Companies

Stats

Difficulty:Medium

Languages:4

Companies:1

Problem #:1348

Problem Explanation

💡

Think Before You Code

🤔 Ask Yourself

→What is the input? What output do I need?
→Can I solve a smaller version of this problem first?
→What patterns have I seen in similar problems?

✏️ Try This First

Before looking at the solution, try to solve it on paper with a simple example!

🚀 Try on LeetCode

Solution Approach

📋

Step-by-Step Solution

7 steps

1
Step 1: Initialize a hash map (tweets) to store tweet names as keys and lists of timestamps as values.
2
Step 2: Implement the `recordTweet` method: Append the given time to the list of timestamps for the corresponding tweet name in the `tweets` hash map. Mark the timestamp list as unsorted using another hash map (is_sorted).
3
Step 3: Implement the `getTweetCountsPerFrequency` method: Determine the chunk size (delta) based on the given frequency (minute, hour, or day).
4
Step 4: If the timestamps for the given tweet name are not sorted, sort them using a sorting algorithm (e.g., mergesort). Update the `is_sorted` hash map to indicate that the timestamps are now sorted.
5
Step 5: Iterate through the time range [startTime, endTime] in chunks of size delta. For each chunk [chunk_start, chunk_end], use binary search to find the number of tweets within that chunk.
6
Step 6: Store the count of tweets for each chunk in a list and return the list.
7
Step 7: Implement binary search functions (bisect_left and bisect_right in python's bisect, lower_bound and upper_bound in C++) to find the start and end indices of tweets within the time range of each chunk.

💡

Pro Tip for Students

Try tracing through each step with a small example on paper before coding. Understanding "why" each step happens is more important than memorizing the code!

⚡

Complexity Analysis

⏱️

Time Complexity

O(nlogn + k*logn)

💾

Space Complexity

O(n)

📦 Linear space - Memory grows with input size.

📝

Why?

The time complexity is dominated by the sorting of tweet timestamps (O(n log n)), which only happens if the timestamps are not already sorted. The iteration through the time range and the binary search contributes O(k log n) time. The space complexity is proportional to the total number of tweets that are stored.

📖New to Big O? Click to learn more

Big O Notation - Quick Guide

O(1)Best - Instant

O(log n)Excellent - Binary Search

O(n)Good - One loop

O(n log n)OK - Sorting

O(n²)Slow - Nested loops

O(2ⁿ)Very Slow - Recursion

Alternative Approaches

Using an Ordered Set (TreeMap in Java)

Instead of using a list and sorting it when needed, use an ordered set (like TreeMap in Java or set in C++). This will automatically keep the timestamps sorted, eliminating the need for explicit sorting.

Time: recordTweet: O(log n), getTweetCountsPerFrequency: O(k log n) where n is the number of tweets for given tweet name and k is the number of chunksSpace: O(n)

Trade-offs: This approach simplifies the code by always maintaining sorted timestamps, but `recordTweet` becomes O(log n) due to the ordered set's insertion cost, which can be slower than O(1) appending to a List. It is a better approach if the records are relatively infrequent compared to the getFrequency calls.

✨

Key Insights

1Insight 1: Using a hash map (dictionary) to store tweets associated with their names is crucial for efficient retrieval.
2Insight 2: Sorting the timestamps for each tweet name allows us to use binary search to efficiently count tweets within a given time range.
3Insight 3: Pre-calculating or storing the frequency intervals can optimize the `getTweetCountsPerFrequency` method by avoiding repeated calculations.

📚

Key Terms Explained

For Students

Hash Table

A data structure that maps keys to values for quick lookup. Like a dictionary!

Binary Search

Halving the search space each step. Like finding a word in a dictionary!

Sorting

Arranging elements in a specific order (ascending/descending).

Edge Cases

Edge case 1: startTime and endTime are the same. The code correctly handles this as a single chunk.
Edge case 2: No tweets are recorded for a given tweetName. The code returns an empty list.
Edge case 3: The last chunk is shorter than the specified frequency. The code correctly calculates the end time of the last chunk using `min(current_time + delta - 1, endTime)`.

⚠️

Common Mistakes to Avoid

✗Common mistake 1: Not sorting the timestamps before performing binary search. This will lead to incorrect results.

✗Common mistake 2: Incorrectly calculating the end time of each chunk, leading to missed or double-counted tweets.

✗Common mistake 3: Not handling the case where no tweets exist for a particular tweetName, potentially leading to null pointer exceptions or other errors.

✅

Learning Tip

Every mistake is a learning opportunity! When you make an error, understand why it happened before moving on.

🌍

Where is This Used in Real Life?

💼Real-world scenario 1: Analyzing user activity on a social media platform, such as counting the number of posts or comments made by a user within specific time intervals.

🏦Real-world scenario 2: Monitoring server performance by counting the number of requests received within different time windows (e.g., requests per minute, requests per hour).

💡Understanding real-world applications helps you see the "why" behind algorithms. It's not just about passing interviews—these concepts power the apps and websites you use every day!

Author's Notes

💡 Key Insight

Hash tables trade space for time. Before using one, consider if the problem constraints allow the extra memory. In interviews, always mention this trade-off.

🎯 Interview Tip

If stuck, try working through a small example by hand. The pattern often reveals itself.

⚠️ Watch Out

Don't forget to handle key collisions and null/undefined keys. Consider what happens with duplicate values.

Written by Saswata Mondal

Software Engineer • Algorithms Enthusiast • Interview Coach

👨‍💻

About the Author

👨‍💻

Saswata MondalAuthor400+ LC Problems

B.Tech CSE, IEM Kolkata | LeetCode Rating: 1672 (Top 15%)

Certified: IBM Java Developer, NPTEL Programming in Java

Last updated: August 28, 2024LeetCode Profile GitHub

This solution has been crafted based on my experience solving 400+ LeetCode problems. Each explanation is designed to help you understand the underlying concepts, not just memorize the code.

Community Tips & Insights

Try solving this without using extra space first. It's a great exercise for understanding the underlying pattern.

@CodeNinjaJul 14

In a real interview, always clarify constraints first. Ask about input size and value ranges.

@TechLeadJul 7

❓

Frequently Asked Questions

What are the trade-offs of using a hash table?

Hash tables provide O(1) average-case lookup, insertion, and deletion, making them ideal for frequency counting and duplicate detection. However, they use O(n) extra space and may degrade to O(n) time in worst-case scenarios with hash collisions. Always consider if the space trade-off is acceptable.

How do I avoid off-by-one errors in binary search?

Use the pattern: left = 0, right = n-1, while (left <= right), mid = left + (right - left) / 2. Decide if you need to find exact match, first occurrence, or last occurrence, and adjust your conditions accordingly. Test with arrays of size 0, 1, and 2.

What techniques are commonly tested in medium-difficulty problems?

Medium problems often require combining multiple techniques: two-pointer with hash maps, BFS/DFS with state tracking, dynamic programming with optimization, or divide-and-conquer approaches. They test your ability to recognize patterns and choose the right combination of techniques.

How should I practice algorithm problems effectively?

Focus on understanding patterns rather than memorizing solutions. After solving a problem, review optimal solutions and understand the intuition. Group similar problems to recognize patterns. Practice explaining your approach out loud. Review problems after a few days to test retention.

What is the optimal approach for "Tweet Counts Per Frequency"?

For this medium-level Hash Table problem, the key is to identify the core pattern. Analyze the input constraints to determine acceptable time complexity. Consider whether binary search techniques can optimize the solution. Always handle edge cases and validate your approach with examples before coding.

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