1348. Tweet Counts Per Frequency - Detailed Explanation

Problem Statement

You are asked to design a system to record tweets and later retrieve the tweet counts per specified time frequency over a given time range. The system should support two main operations:

1. recordTweet(tweetName, time):
   Record a tweet with a given name at a given timestamp (in seconds).

2. getTweetCountsPerFrequency(freq, tweetName, startTime, endTime):
   Given a frequency (which can be "minute", "hour", or "day"), a tweet name, and a time range ([startTime, endTime]), return an array representing the count of tweets in each time bucket.

   • For "minute", the bucket size is 60 seconds.
   • For "hour", the bucket size is 3600 seconds.
   • For "day", the bucket size is 86400 seconds.

Example 1

• Input:

  TweetCounts tweetCounts = new TweetCounts();
  tweetCounts.recordTweet("tweet1", 0);
  tweetCounts.recordTweet("tweet1", 60);
  tweetCounts.recordTweet("tweet1", 10);
  tweetCounts.getTweetCountsPerFrequency("minute", "tweet1", 0, 59);

• Output: [2]

• Explanation:
  With a range of 0 to 59 seconds and "minute" frequency (bucket size = 60), only the tweets at times 0 and 10 are in the first bucket.

Example 2

• Input:

  tweetCounts.getTweetCountsPerFrequency("minute", "tweet1", 0, 60);

• Output: [2, 1]

• Explanation:
  The range [0, 60] is divided into:

  • Bucket 1: [0, 59] → contains tweets at 0 and 10 (count = 2)
  • Bucket 2: [60, 60] → contains tweet at 60 (count = 1)

Example 3

• Input:

  tweetCounts.getTweetCountsPerFrequency("hour", "tweet1", 0, 210);

• Output: [3]

• Explanation:
  With "hour" frequency (bucket size = 3600 seconds), the entire range [0, 210] falls into a single bucket containing all 3 tweets.

Constraints

• Tweet Timestamps: Provided as integers (seconds) and can be large.
• Operation Count: Total operations (recording and querying) can be up to 10⁴.
• Order of Inputs: Tweets may not be recorded in chronological order.

Hints

1. Use a HashMap:
   Store each tweet's timestamps in a list mapped by tweet name. This makes it easy to retrieve and process the tweets for a given tweet name.

2. Bucket Computation:
   Calculate the number of buckets using:

   numBuckets = ((endTime - startTime) // bucketSize) + 1 
   

   Then, determine the bucket index for a tweet timestamp using:

   bucketIndex = (timestamp - startTime) // bucketSize
   

3. Optimize with Sorting and Binary Search:
   If the tweet timestamps are not stored in order, sort them before processing a query. Binary search can be used to quickly locate the first timestamp within the range, reducing unnecessary iterations.

Approaches

Approach 1: Brute Force

• Method:

  • For each recordTweet, simply append the timestamp to a list.
  • For getTweetCountsPerFrequency, iterate through every timestamp in the list and check if it falls within ([startTime, endTime]). Then, determine its bucket and increment the count.

• Pros:
  Simple and straightforward to implement.

• Cons:
  Inefficient if many tweets are recorded, as every query may iterate through all recorded tweets.

• Time Complexity:
  O(n) per query, where n is the total number of tweets for that tweet name.

Approach 2: Optimized with Sorting and Binary Search

• Method:

  • For each tweet name, store all timestamps in a list.
  • Before processing a query, sort the list (or maintain a sorted list).
  • Use binary search to find the starting point for timestamps within the query range.
  • Iterate only over the tweets in the range and compute the corresponding bucket.

• Pros:
  More efficient queries since you only process tweets within the query range.

• Cons:
  Additional complexity for sorting and handling potential re-sorting if tweets are recorded out-of-order.

• Time Complexity:
  O(log n + k) per query, where k is the number of tweets in the range.

Step-by-Step Walkthrough and Visual Example

Assume we have recorded the following tweets for "tweet1":
[0, 10, 60]
Now, consider the query:

getTweetCountsPerFrequency("minute", "tweet1", 0, 60)

1. Bucket Calculation:

   • Frequency "minute" → bucket size = 60 seconds.
   • Total buckets = ((60 - 0) // 60) + 1 = 2.

2. Assigning Tweets to Buckets:

   • Tweet at time 0:
     Bucket index = (0 - 0) // 60 = 0.
   • Tweet at time 10:
     Bucket index = (10 - 0) // 60 = 0.
   • Tweet at time 60:
     Bucket index = (60 - 0) // 60 = 1.

3. Final Result:

   • Bucket 0 contains 2 tweets.
   • Bucket 1 contains 1 tweet.
     Output: [2, 1]

Code Implementation

Python Code