This article explains the Bloom filter data structure, its hash‑based design and false‑positive behavior, then demonstrates practical implementations using Java BitSet, Guava, and Redisson, covering initialization, configuration, usage tips, and performance considerations for Redis bitmap storage.
This article explains the principles of BitMap, BitSet, and Bloom Filter data structures, illustrating how they use bits to efficiently store, add, delete, and query large sets of integers, and discusses their practical applications such as fast sorting, deduplication, and membership testing.
This article explains three Redis-based techniques—Hash, Bitset, and HyperLogLog—for efficiently counting daily page visits, detailing command usage, memory trade‑offs, and accuracy considerations, helping backend engineers implement scalable visitor statistics in high‑traffic environments like large e‑commerce platforms.
This article explains the bitmap concept, demonstrates how to store and query billions of integers using bitwise operations, introduces BitSet and Bloom Filter implementations, and provides practical Java code snippets for adding, removing, and checking elements while highlighting their memory‑saving advantages.
The article explains three Redis-based techniques—Hash, Bitset, and HyperLogLog—for counting daily page visits on high‑traffic sites, detailing command usage, memory trade‑offs, and the pros and cons of each method.
This article explains three Redis‑based techniques—using hash tables, bitsets, and the HyperLogLog probabilistic algorithm—to accurately count daily page views, detailing the required commands, implementation steps, advantages, and limitations for high‑traffic sites.
