NEXUS is a production-grade, full-text and semantic search engine built from scratch, implementing advanced data structures and distributed systems concepts. It focuses on probabilistic optimization, sub-millisecond latency, and hybrid AI-powered search. The project demonstrates core technologies like LSM Trees, Bloom Filters, HNSW Graphs, and W-TinyLFU caches, integrated into a high-performance pipeline. It also includes a LeetCode algorithm library with implementations of classic interview patterns and provides insights into distributed crawling and persistent storage.
This article introduces QuadTrees, a data structure for efficiently organizing and searching spatial data. It explains the concept, use cases (collision detection, map services, AI image upscaling), and provides a TypeScript implementation with basic point and rectangle classes.
This article combines theory with hands-on implementation in Flutter to make learning data structures engaging and practical.
In this paper, we revisit one of the simplest problems in data structures: the task of inserting elements into an open-addressed hash table so that elements can later be retrieved with as few probes as possible. We show that, even without reordering elements over time, it is possible to construct a hash table that achieves far better expected search complexities (both amortized and worst-case) than were previously thought possible. Along the way, we disprove the central conjecture left by Yao in his seminal paper 'Uniform Hashing is Optimal'. All of our results come with matching lower bounds.
Huacheng Yu's team later proved that the Bender et al.'s hash table was as efficient as any data structure could possibly be, marking the end of decades-long efforts to solve Peterson's problem regarding the optimal balance between time and space efficiency in hash tables.
