## Problem You are building an alert pipeline for a monitoring system. Raw alerts arrive in a stream. Design a filter that: deduplicates alerts with the same (source, type) within a time window W; suppresses alerts below a minimum severity threshold; and rate-limits any single source to at most K alerts per minute. ```python from dataclasses import dataclass from typing import Optional @dataclass class Alert: id: str source: str alert_type: str severity: int # 1 (low) to 5 (critical) timestamp: float class AlertFilter: def __init__(self, min_severity: int, dedup_window_s: float, rate_limit_k: int): ... def process(self, alert: Alert) -> Optional[Alert]: # Returns alert if it passes all filters, else None ... ``` **Example:** Two identical alerts within 60 s -> second is suppressed. An alert with severity 1 when min_severity=3 -> suppressed. ## Follow-ups 1. What data structure efficiently tracks the dedup window without growing unboundedly? 2. How would you implement the rate limiter using a sliding window vs. token bucket? 3. How do you handle clock skew if alerts arrive out of order? 4. How would you persist filter state across a service restart?

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Filter Alerts: Design an Alert Filtering System to Suppress Duplicate and Low-Priority Notifications

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Problem You are building an alert pipeline for a monitoring system. Raw alerts arrive in a stream. Design a filter that: deduplicates alerts with the same (source, type) within a time window W; suppresses alerts below a minimum severity threshold; and rate-limits any single source to at most K alerts per minute. Example: Two identical alerts within 60 s -> second is suppressed. An alert with severity 1 when min_severity=3 -> suppressed. Follow-ups What data structure efficiently tracks the dedup…

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Filter Alerts: Design an Alert Filtering System to Suppress Duplicate and Low-Priority Notifications

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