A Top K system identifies and returns the K most frequent, popular, or highest-scoring items from a large dataset or continuous stream of events.
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The core challenge is maintaining accurate counts across billions of events while providing real-time or near-real-time results. This requires careful trade-offs between memory usage, accuracy, and latency.
Popular Examples: Twitter Trending Topics, YouTube Trending Videos, Amazon Best Sellers, Google Trending Searches, Spotify Top Charts
In this chapter, we will explore the high-level design of a Top K system.
This problem tests your understanding of streaming algorithms, distributed counting, and trade-offs between precision and scalability.
Let's start by clarifying the requirements: