AAAbstract Algorithmsinabstractalgorithms.hashnode.dev·May 3 · 22 min readCount-Min Sketch Explained: Frequency Estimation at Streaming ScaleTLDR: 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 c00
AAAbstract Algorithmsinabstractalgorithms.hashnode.dev·Apr 5 · 14 min readProbabilistic Data Structures: A Practical Guide to Bloom Filters, HyperLogLog, and Count-Min SketchTLDR: 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"00
DPDevesh Parmarinbackend-bytes.hashnode.dev·Nov 1, 2025 · 2 min readThe Memory Nightmare of Big Data: Why the Count-Min Sketch is Essential for Trend TrackingHave 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...00
VGVipul Goyalinvipultech.hashnode.dev·Sep 10, 2025 · 4 min read🚀 Bloom Filters: The Fast & Memory-Efficient Way to Check MembershipHave 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...00
STSagyam Thapainblog.sagyamthapa.com.np·Jun 25, 2025 · 3 min readAn Interactive Guide To Count Min SketchIntroduction 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...00