Implement alert queries and spike detection

You are building an in-memory alert analytics component. Each alert has:

• timestamp (integer seconds since epoch)
• severity (one of a small fixed set, e.g., INFO/WARN/ERROR/CRITICAL )

Implement data structures / APIs to support the following:

1. Recent alerts (rolling 15 minutes)

• countRecent(now) returns the number of alerts with timestamps in $(now-15*60, now]$ .

2. Severity distribution for the current hour

• severityDist(now) returns a map severity -> count for alerts whose timestamps fall within the current hour window $[hourStart(now), now]$ (or $[hourStart, hourStart+3600)$ if you prefer full-hour reporting—state your assumption).
• The system may receive many alerts per second; updates and queries should be efficient.

3. Spike detection on per-minute counts Given an array cnt[0..n-1] where cnt[i] is the number of alerts in minute i , compute an array nextHigher[0..n-1] where:

• nextHigher[i] is the smallest index j > i such that cnt[j] > cnt[i]
• if no such j exists, nextHigher[i] = -1

Notes

• You can treat (1) and (2) as streaming/online; alerts arrive over time.
• State any assumptions about out-of-order timestamps, retention limits, and memory.