Topic
probabilistic
3 articles
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...
Count-Min Sketch Explained: Frequency Estimation at Streaming Scale
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 column per row, increment those d counters. Query: ...
Bloom Filters Explained: Membership Testing with Zero False Negatives
TLDR: A Bloom filter is a bit array of m bits + k independent hash functions that sets k bits on insert and checks those same k bits on lookup. If any checked bit is 0, the element is definitely not in the set — false negatives are mathematically imp...