# MicroNN: An On-device Disk-resident Updatable Vector Database

arXiv: https://arxiv.org/abs/2504.05573 · HTML: https://mlsanity.com/paper/2504.05573/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Vector search and ANN indexes, one of 15 directions in Retrieval-augmented generation.
- Builds on 19 of its 45 references that are in the map.
- First posted on arXiv 8 Apr 2025.

## Placed in

- Retrieval-augmented generation / [Vector search and ANN indexes](https://mlsanity.com/rag/vector-search-and-ann-indexes/)

## Builds on (most cited first, 19 of 19)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [A Comprehensive Survey of Deep Learning for Image Captioning](https://mlsanity.com/paper/1810.04020/) (2018)
- [Deep Learning Based Text Classification: A Comprehensive Review](https://mlsanity.com/paper/2004.03705/) (2020)
- [Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba](https://mlsanity.com/paper/1803.02349/) (2018)
- [Fast Approximate Nearest Neighbor Search With The Navigating Spreading-out Graph](https://mlsanity.com/paper/1707.00143/) (2017)
- [PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest](https://mlsanity.com/paper/2007.03634/) (2020)
- [Survey of Vector Database Management Systems](https://mlsanity.com/paper/2310.14021/) (2023)
- [FreshDiskANN: A Fast and Accurate Graph-Based ANN Index for Streaming Similarity Search](https://mlsanity.com/paper/2105.09613/) (2021)
- [Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors](https://mlsanity.com/paper/1802.02422/) (2018)
- [ACORN: Performant and Predicate-Agnostic Search Over Vector Embeddings and Structured Data](https://mlsanity.com/paper/2403.04871/) (2024)
- [SPFresh: Incremental In-Place Update for Billion-Scale Vector Search](https://mlsanity.com/paper/2410.14452/) (2024)
- [Related Pins at Pinterest: The Evolution of a Real-World Recommender System](https://mlsanity.com/paper/1702.07969/) (2017)
- [Applying Deep Learning To Airbnb Search](https://mlsanity.com/paper/1810.09591/) (2018)
- [Results of the Big ANN: NeurIPS'23 competition](https://mlsanity.com/paper/2409.17424/) (2024)
- [High-Throughput Vector Similarity Search in Knowledge Graphs](https://mlsanity.com/paper/2304.01926/) (2023)
- [HD-Index: Pushing the Scalability-Accuracy Boundary for Approximate kNN Search in High-Dimensional Spaces](https://mlsanity.com/paper/1804.06829/) (2018)
- [Binary Embedding-based Retrieval at Tencent](https://mlsanity.com/paper/2302.08714/) (2023)

## Built on by (most cited first, 6 of 6)

- [Quake: Adaptive Indexing for Vector Search](https://mlsanity.com/paper/2506.03437/) (2025)
- [Scalable Disk-Based Approximate Nearest Neighbor Search with Page-Aligned Graph](https://mlsanity.com/paper/2509.25487/) (2025)
- [A Super Fast K-means for Indexing Vector Embeddings](https://mlsanity.com/paper/2603.20009/) (2026)
- [Semantic Recall for Vector Search](https://mlsanity.com/paper/2604.20417/) (2026)
- [Stop Indexing at Full Precision: Revisiting Clustering for Vector Embeddings](https://mlsanity.com/paper/2608.14648/) (2026)
- [Dynamic Flow, Static Graph: KV Cache Reuse for Efficient LLM Serving on Mobile NPUs](https://mlsanity.com/paper/2609.34727/) (2026)

## Abstract (arXiv)

Nearest neighbour search over dense vector collections has important applications in information retrieval, retrieval augmented generation (RAG), and content ranking. Performing efficient search over large vector collections is a well studied problem with many existing approaches and open source implementations. However, most state-of-the-art systems are generally targeted towards scenarios using large servers with an abundance of memory, static vector collections that are not updatable, and nearest neighbour search in isolation of other search criteria. We present Micro Nearest Neighbour (MicroNN), an embedded nearest-neighbour vector search engine designed for scalable similarity search in low-resource environments. MicroNN addresses the problem of on-device vector search for real-world workloads containing updates and hybrid search queries that combine nearest neighbour search with structured attribute filters. In this scenario, memory is highly constrained and disk-efficient index structures and algorithms are required, as well as support for continuous inserts and deletes. MicroNN is an embeddable library that can scale to large vector collections with minimal resources. MicroNN is used in production and powers a wide range of vector search use-cases on-device. MicroNN takes less than 7 ms to retrieve the top-100 nearest neighbours with 90% recall on publicly available million-scale vector benchmark while using ~10 MB of memory.