Key Operations in Count-Min Sketch

Initialization: Set the number of rows and columns that you want in the Count-Min Sketch.

Update: To increase an element’s count, hash it through each hash function and update the array’s associated buckets.

Query: Find the lowest count across the related buckets after hashing an element with each hash algorithm to determine its estimated frequency.

Count-Min Sketch is a probabilistic data structure which approximates the frequency of items in a stream of data. It uses little memory while handling massive amounts of data and producing approximations of the answers. In this post, we’ll explore the idea behind the Count-Min Sketch, how it’s implemented in Python, and discuss its uses and drawbacks.