# hashing

> Browse 4 items tagged with hashing.

## Items

- [Locality Sensitive Hashing (LSH)](https://vectordb.ever.works/items/locality-sensitive-hashing-lsh) — Algorithmic technique for approximate nearest neighbor search in high-dimensional spaces using hash functions to map similar items to the same buckets with high probability.
- [DET-LSH](https://vectordb.ever.works/items/det-lsh) — DET-LSH is a locality-sensitive hashing scheme that introduces a dynamic encoding tree structure to accelerate approximate nearest neighbor (ANN) search in high-dimensional spaces. While it is a research algorithm rather than a production database, it directly targets the core operation behind vector databases—efficient ANN search over vector embeddings—and is relevant for designing or optimizing vector indexing components within vector database systems.
- [Efficient Locality Sensitive Hashing](https://vectordb.ever.works/items/efficient-locality-sensitive-hashing) — This work by Jingfan Meng is a comprehensive research thesis on efficient locality-sensitive hashing (LSH), covering algorithmic solutions, core primitives, and applications for approximate nearest neighbor search. It is relevant to vector databases because LSH-based indexing is a foundational technique for scalable similarity search over high-dimensional vectors, informing the design of vector indexes, retrieval engines, and similarity search modules in modern vector database systems.
- [Hashing](https://vectordb.ever.works/items/hashing) — A set of libraries and methods focused on hashing for similarity search in vector databases, directly impacting the performance of large-scale vector search systems.

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