Practice/Ramp/Leetcode 1348. Tweet Counts Per Frequency

Leetcode 1348. Tweet Counts Per Frequency

CodingMust

Problem

Design and implement an event analytics system that tracks timestamped events and provides aggregated counts over time intervals. Your system must support two primary operations:

1. Recording events: Store a timestamp associated with a named event type
2. Querying aggregated counts: Given an event name, a time granularity (minute, hour, or day), and a time range, return an array where each element represents the count of events that occurred in each consecutive time bucket within that range

The time granularities are defined as:

• minute: 60 seconds
• hour: 3600 seconds
• day: 86400 seconds

For a query with start time startTime and end time endTime, divide the inclusive range [startTime, endTime] into contiguous buckets of the specified size. Return a list containing the event count for each bucket in chronological order.

Requirements

1. Implement a class EventAnalytics with a constructor and two methods
2. recordEvent(eventName, timestamp): Records that an event of type eventName occurred at the given timestamp (in seconds)
3. getEventCountsPerFrequency(eventName, frequency, startTime, endTime): Returns a list of integers representing event counts per time bucket
4. Handle multiple distinct event names independently
5. Support arbitrary query ranges and handle cases where buckets contain zero events
6. The time buckets should cover the entire range [startTime, endTime] inclusive

Constraints

• 1 <= eventName.length <= 50
• 0 <= timestamp, startTime, endTime <= 10^9
• startTime <= endTime
• frequency is one of: "minute", "hour", "day"
• At most 10^4 total calls to recordEvent and getEventCountsPerFrequency
• Event names consist of lowercase English letters and digits

Examples

Example 1:

` Operations: analytics = EventAnalytics() analytics.recordEvent("click", 10) analytics.recordEvent("click", 70) analytics.recordEvent("click", 130) result = analytics.getEventCountsPerFrequency("click", "minute", 0, 180)

Output: [1, 1, 1] Explanation:

• Bucket 0-59: contains event at timestamp 10 (count = 1)
• Bucket 60-119: contains event at timestamp 70 (count = 1)
• Bucket 120-179: contains event at timestamp 130 (count = 1)
• Note: 180 would start a new bucket, but since endTime is 180 and it's inclusive, we need to include bucket 180-239 However, no events exist at exactly 180, and we stop at the bucket containing endTime. `

Example 2:

` Operations: analytics = EventAnalytics() analytics.recordEvent("payment", 5) analytics.recordEvent("payment", 10) analytics.recordEvent("payment", 65) result = analytics.getEventCountsPerFrequency("payment", "minute", 0, 120)

Output: [2, 1, 0] Explanation:

• Bucket 0-59: contains events at 5 and 10 (count = 2)
• Bucket 60-119: contains event at 65 (count = 1)
• Bucket 120-179: contains no events (count = 0), but is included since endTime=120 falls in this bucket `

Example 3:

` Operations: analytics = EventAnalytics() analytics.recordEvent("error", 1000) analytics.recordEvent("error", 5000) analytics.recordEvent("success", 2000) result1 = analytics.getEventCountsPerFrequency("error", "hour", 0, 7199) result2 = analytics.getEventCountsPerFrequency("success", "hour", 0, 7199)

Output: result1 = [2, 0] result2 = [1, 0] Explanation: Different event names are tracked independently `