Tweet Counts Per Frequency

Tweet Counts Per Frequency

What is this problem about?

The "Tweet Counts Per Frequency coding problem" is a system design and data structure challenge. You need to implement a class that tracks tweets and their timestamps. You must be able to record a tweet and then query the total number of tweets for a specific user within a time interval [startTime, endTime], broken down into chunks (minute, hour, or day). This is a simplified version of a real-world analytics dashboard for social media.

Why is this asked in interviews?

This "Tweet Counts Per Frequency interview question" is often used by companies like Twitter (X) and Google to test your ability to design a data structure that supports both efficient insertions and range queries. It evaluates your knowledge of "Ordered Sets" or "Sorted Maps" and your ability to perform binary search on timestamps. It also tests your logic in handling time-based chunking.

Algorithmic pattern used

The "Hash Table, Design, Sorting, Binary Search, Ordered Set interview pattern" is essential here. We use a Hash Map where the key is the tweetName and the value is a sorted list (or an OrderedSet) of timestamps. For a query, we first find the list of timestamps for the user. We then use binary search (bisect_left or lower_bound) to find the range of timestamps within [startTime, endTime]. Finally, we iterate through this range and increment counters in an array corresponding to the correct time chunk based on the frequency (60 for minute, 3600 for hour, 86400 for day).

Example explanation

1. recordTweet("user1", 10)
2. recordTweet("user1", 65)
3. recordTweet("user1", 120)
4. getTweetCountsPerFrequency("minute", "user1", 0, 150)
   • Chunk size: 60 seconds.
   • Intervals: [0, 59], [60, 119], [120, 150].
   • Count for [0, 59]: tweet at 10 -> count 1.
   • Count for [60, 119]: tweet at 65 -> count 1.
   • Count for [120, 150]: tweet at 120 -> count 1. Result: [1, 1, 1].

Common mistakes candidates make

A common error in the "Tweet Counts Per Frequency coding problem" is not sorting the timestamps, which makes the range query $O(n)$ instead of $O(\log n)$. Another mistake is incorrect calculation of the number of chunks, particularly at the boundary of endTime. Some candidates also fail to use the correct user-specific data structure, attempting to store all tweets in a single global list.

Interview preparation tip

When designing a system for "Time-Series Data," always consider using sorted data structures. Being able to find all events within a time range is a fundamental requirement. Practice using std::map in C++, TreeMap in Java, or the bisect module in Python to handle these scenarios efficiently.