May 3 · 18 min read · TLDR: HyperLogLog estimates the number of distinct elements in a dataset using ~12 KB of memory regardless of cardinality — with ±0.81% error. The insight: if you hash every element to a random bit st
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 discussionJan 12 · 14 min read · From predicting driver locations in real-time to syncing files across devices, from counting ad impressions to mapping social connections, modern applications demand solutions to computational problems that seem deceptively simple at first glance. Ho...
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Dec 16, 2024 · 5 min read · The problem Imagine you’re running a large scale online store. Thousands of users visit your website every second, and you want to know how many unique users visit each day. This sounds straightforward just track each user by their IP address or logi...
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Aug 20, 2024 · 8 min read · The HyperLogLog algorithm is a clever way to count how many unique items are in a big dataset without using a lot of memory. Imagine trying to count every different item in a huge list, like all the different words in a book. Doing it the regular way...
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Feb 2, 2024 · 8 min read · Calculating the exact number of distinct items in a collection can be a difficult task if the collection is very large or if you have a very limited amount of memory. Typical approaches can be: using a hash map (uses more memory) using a bit map (u...
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