May 3 · 22 min read · TLDR: Count-Min Sketch (CMS) is a fixed-size d × w counter matrix that estimates how often any element has appeared in a stream. Insert: hash the element with each of the d hash functions to get one c
Join discussionApr 5 · 14 min read · TLDR: Probabilistic data structures trade a small, bounded probability of being wrong for orders-of-magnitude better memory efficiency and O(1) speed. Bloom Filters answer "definitely not in this set"
Join discussionNov 1, 2025 · 2 min read · Have you ever stopped to think about how social media platforms identify what's trending in real-time? With millions of new posts and billions of hashtags every day, they're facing a massive data problem. The simple solution of using a hash map to co...
Join discussionSep 10, 2025 · 4 min read · Have you ever wondered how large systems answer the simple question: “Is this item in my dataset?” Whether you’re checking if a username is taken, a web page is cached, or a key exists in a database, performance matters. That’s where Bloom filter...
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Jun 25, 2025 · 3 min read · Introduction Count min sketch is a probabilistic data structure that can estimate the frequency of items in a stream. It is an improvement over Hyperloglog. While hyperloglog can estimate the number of unique items in a fixed amount of data, count mi...
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