Popular Content Counter

Design a content-popularity tracker with increment, decrement, and most-popular operations. Follow-ups push toward O(1) operations and deterministic tie-breaking when multiple content IDs share the top score. Requirements Implement: increasePopularity(contentId) decreasePopularity(contentId) getMostPopular() / mostPopular() increasePopularity increments the content score. decreasePopularity decrements the content score. getMostPopular returns a content ID with the highest current popularity. Follow-ups include optimizing the operations to O(1) and deciding what to return when multiple content IDs tie. One tie-break variant asks for the earliest content ID that reached the top score through increasePopularity. Notes Candidates explicitly connect the optimized follow-up to the LC 432-style all-O(1) data structure. Clarify whether scores can become negative, whether zero-score content should remain tracked, and what to return when the structure is empty. Preparation Implement the simple hash-map plus scan version first. Then implement the bucketed doubly linked list version with content IDs grouped by count.