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Tweet Counts Per Frequency

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): [10,3609], [3610,7209], [7210,10000]
  • Every day (86400-second chunks): [10,10000]

Notice that the last chunk may be shorter than the specified frequency's chunk size and will always end with the end time of the period (10000 in the above example).

Design and implement an API to help the company with their analysis.

Implement the TweetCounts class:

  • TweetCounts() Initializes the TweetCounts object.
  • void recordTweet(String tweetName, int time) Stores the tweetName at the recorded time (in seconds).
  • List<Integer> getTweetCountsPerFrequency(String freq, String tweetName, int startTime, int endTime) Returns a list of integers representing the number of tweets with tweetName in each time chunk for the given period of time [startTime, endTime] (in seconds) and frequency freq.
    • freq is one of "minute", "hour", or "day" representing a frequency of every minute, hour, or day respectively.

 

Example:

Input
["TweetCounts","recordTweet","recordTweet","recordTweet","getTweetCountsPerFrequency","getTweetCountsPerFrequency","recordTweet","getTweetCountsPerFrequency"]
[[],["tweet3",0],["tweet3",60],["tweet3",10],["minute","tweet3",0,59],["minute","tweet3",0,60],["tweet3",120],["hour","tweet3",0,210]]

Output
[null,null,null,null,[2],[2,1],null,[4]]

Explanation
TweetCounts tweetCounts = new TweetCounts();
tweetCounts.recordTweet("tweet3", 0);                              // New tweet "tweet3" at time 0
tweetCounts.recordTweet("tweet3", 60);                             // New tweet "tweet3" at time 60
tweetCounts.recordTweet("tweet3", 10);                             // New tweet "tweet3" at time 10
tweetCounts.getTweetCountsPerFrequency("minute", "tweet3", 0, 59); // return [2]; chunk [0,59] had 2 tweets
tweetCounts.getTweetCountsPerFrequency("minute", "tweet3", 0, 60); // return [2,1]; chunk [0,59] had 2 tweets, chunk [60,60] had 1 tweet
tweetCounts.recordTweet("tweet3", 120);                            // New tweet "tweet3" at time 120
tweetCounts.getTweetCountsPerFrequency("hour", "tweet3", 0, 210);  // return [4]; chunk [0,210] had 4 tweets

 

Constraints:

  • 0 <= time, startTime, endTime <= 109
  • 0 <= endTime - startTime <= 104
  • There will be at most 104 calls in total to recordTweet and getTweetCountsPerFrequency.

Solution Explanation

This problem requires designing a system to track tweet counts within specified time intervals and frequencies. The solution utilizes a data structure to efficiently store and retrieve tweet information.

Data Structures

The optimal data structure for this problem is a combination of a hash map and a sorted set (or a similar structure offering efficient range queries).

  • Hash Map: A hash map (dictionary in Python) is used to store tweets, with the tweet name as the key and a sorted set (or similar) as the value. This allows for O(1) average-case lookup of tweets by name.

  • Sorted Set: A sorted set (e.g., SortedList in Python, TreeMap in Java, multiset in C++) stores the timestamps of tweets for a given tweet name. The sorted nature enables efficient range queries using binary search to count tweets within specific time intervals.

Algorithm

The TweetCounts class has three methods:

  1. __init__ (Constructor): Initializes the hash map data and a dictionary d to map frequency strings ("minute", "hour", "day") to their corresponding durations in seconds.

  2. recordTweet(tweetName, time): Adds the tweet with tweetName and timestamp time to the sorted set associated with tweetName in the data hash map.

  3. getTweetCountsPerFrequency(freq, tweetName, startTime, endTime): This is the core logic.

    • It determines the chunk size f based on the input freq.
    • It retrieves the sorted set of timestamps for the given tweetName.
    • It iterates through the time range [startTime, endTime] in chunks of size f.
    • For each chunk, it uses binary search (or equivalent operations provided by the sorted set) to count the number of tweets within that chunk.
    • It appends the count to the result list ans.
    • Finally, it returns the ans list.

Time and Space Complexity Analysis

  • recordTweet:

    • Time Complexity: O(log n), where n is the number of tweets for a given tweet name (due to insertion in a sorted set).
    • Space Complexity: O(1) (assuming hash map insertion is O(1) on average).

  • getTweetCountsPerFrequency:

    • Time Complexity: O(m log n), where m is the number of chunks and n is the number of tweets for a given tweet name. The log n factor comes from the binary search (or equivalent) operation within each chunk.
    • Space Complexity: O(m), where m is the number of chunks, to store the result list.

Code Examples (Python, Java, C++)

The code examples in the previous response demonstrate the implementation of the TweetCounts class in Python, Java, and C++. They utilize the appropriate data structures mentioned above to achieve the required efficiency. Note that the specific implementations for sorted sets may vary slightly depending on the chosen language and libraries.