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HyperLogLog Explained: Counting Billions of Unique Items with 12 KB
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 string, the maximum length of leading zeros you obse...
•17 min read
Probabilistic Data Structures: A Practical Guide to Bloom Filters, HyperLogLog, and Count-Min Sketch
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" in constant time with zero false negatives. Hyper...
•13 min read