Leetcode #1348: Tweet Counts Per Frequency
In this guide, we solve Leetcode #1348 Tweet Counts Per Frequency in Python and focus on the core idea that makes the solution efficient.
You will see the intuition, the step-by-step method, and a clean Python implementation you can use in interviews.
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).
Quick Facts
- Difficulty: Medium
- Premium: No
- Tags: Design, Hash Table, String, Binary Search, Ordered Set, Sorting
Intuition
Fast membership checks and value lookups are the heart of this problem, which makes a hash map the natural choice.
By storing what we have already seen (or counts/indexes), we can answer the question in one pass without backtracking.
Approach
Scan the input once, using the map to detect when the condition is satisfied and to update state as you go.
This keeps the solution linear while remaining easy to explain in an interview setting.
Steps:
- Initialize a hash map for seen items or counts.
- Iterate through the input, querying/updating the map.
- Return the first valid result or the final computed value.
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
Python Solution
class TweetCounts:
def __init__(self):
self.d = {"minute": 60, "hour": 3600, "day": 86400}
self.data = defaultdict(SortedList)
def recordTweet(self, tweetName: str, time: int) -> None:
self.data[tweetName].add(time)
def getTweetCountsPerFrequency(
self, freq: str, tweetName: str, startTime: int, endTime: int
) -> List[int]:
f = self.d[freq]
tweets = self.data[tweetName]
t = startTime
ans = []
while t <= endTime:
l = tweets.bisect_left(t)
r = tweets.bisect_left(min(t + f, endTime + 1))
ans.append(r - l)
t += f
return ans
# Your TweetCounts object will be instantiated and called as such:
# obj = TweetCounts()
# obj.recordTweet(tweetName,time)
# param_2 = obj.getTweetCountsPerFrequency(freq,tweetName,startTime,endTime)
Complexity
The time complexity is O(n). The space complexity is O(n).
Edge Cases and Pitfalls
Watch for boundary values, empty inputs, and duplicate values where applicable. If the problem involves ordering or constraints, confirm the invariant is preserved at every step.
Summary
This Python solution focuses on the essential structure of the problem and keeps the implementation interview-friendly while meeting the constraints.