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What data structure can we use to keep the players' data?
Keep a map (dictionary) of player scores.
For each top(K) function call, find the maximum K scores and add them.
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A heap allows you to maintain the K largest scores without sorting the entire dataset. By keeping a min-heap of size K, smaller elements are removed as larger scores appear, making the operation more efficient for large leaderboards.
Yes, design-style problems like Design A Leaderboard are common in FAANG and other top tech interviews. They test understanding of data structures, efficient updates, and how to balance time complexity across multiple operations.
A hash table is ideal for storing player IDs and their scores because it allows fast updates and lookups. For retrieving the top K scores, either sorting or a min-heap can be used depending on performance needs.
A common optimal approach uses a hash map to store each player's score and a min-heap to compute the top K scores. The hash map enables constant-time updates, while the heap efficiently tracks the highest K values. This reduces the cost of repeatedly sorting all scores.
