# Manu: A Cloud Native Vector Database Management System

arXiv: https://arxiv.org/abs/2206.13843 · HTML: https://mlsanity.com/paper/2206.13843/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2022.

## Built on by (most cited first, 18 of 18)

- [A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challenge](https://mlsanity.com/paper/2310.11703/) (2023)
- [Is My Data in Your Retrieval Database? Membership Inference Attacks Against Retrieval Augmented Generation](https://mlsanity.com/paper/2405.20446/) (2024)
- [High-Throughput Vector Similarity Search in Knowledge Graphs](https://mlsanity.com/paper/2304.01926/) (2023)
- [Top Ten Challenges Towards Agentic Neural Graph Databases](https://mlsanity.com/paper/2501.14224/) (2025)
- [Experimental Analysis of Large-scale Learnable Vector Storage Compression](https://mlsanity.com/paper/2311.15578/) (2023)
- [VELO: A Vector Database-Assisted Cloud-Edge Collaborative LLM QoS Optimization Framework](https://mlsanity.com/paper/2406.13399/) (2024)
- [VDTuner: Automated Performance Tuning for Vector Data Management Systems](https://mlsanity.com/paper/2404.10413/) (2024)
- [eCIL-MU: Embedding based Class Incremental Learning and Machine Unlearning](https://mlsanity.com/paper/2401.02457/) (2024)
- [LoRANN: Low-Rank Matrix Factorization for Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2410.18926/) (2024)
- [CaGR-RAG: Context-aware Query Grouping for Disk-based Vector Search in RAG Systems](https://mlsanity.com/paper/2505.01164/) (2025)
- [Differentially Private In-Context Learning with Nearest Neighbor Search](https://mlsanity.com/paper/2511.04332/) (2025)
- [MarineVRS: Marine Video Retrieval System with Explainability via Semantic Understanding](https://mlsanity.com/paper/2306.04593/) (2023)
- [Harnessing Scalable Transactional Stream Processing for Managing Large Language Models [Vision]](https://mlsanity.com/paper/2307.08225/) (2023)
- [Delta Tensor: Efficient Vector and Tensor Storage in Delta Lake](https://mlsanity.com/paper/2405.03708/) (2024)
- [IQLS: Framework for leveraging Metadata to enable Large Language Model based queries to complex, versatile Data](https://mlsanity.com/paper/2405.15792/) (2024)
- [MINT: Multi-Vector Search Index Tuning](https://mlsanity.com/paper/2504.20018/) (2025)
- [Exqutor: Extended Query Optimizer for Vector-augmented Analytical Queries](https://mlsanity.com/paper/2512.09695/) (2025)
- [NasZip: Software and Hardware Co-Design to Accelerate Approximate Nearest Neighbor Search with DIMM-Based Near-Data Processing](https://mlsanity.com/paper/2605.21952/) (2026)

## Abstract (arXiv)

With the development of learning-based embedding models, embedding vectors are widely used for analyzing and searching unstructured data. As vector collections exceed billion-scale, fully managed and horizontally scalable vector databases are necessary. In the past three years, through interaction with our 1200+ industry users, we have sketched a vision for the features that next-generation vector databases should have, which include long-term evolvability, tunable consistency, good elasticity, and high performance. We present Manu, a cloud native vector database that implements these features. It is difficult to integrate all these features if we follow traditional DBMS design rules. As most vector data applications do not require complex data models and strong data consistency, our design philosophy is to relax the data model and consistency constraints in exchange for the aforementioned features. Specifically, Manu firstly exposes the write-ahead log (WAL) and binlog as backbone services. Secondly, write components are designed as log publishers while all read-only analytic and search components are designed as independent subscribers to the log services. Finally, we utilize multi-version concurrency control (MVCC) and a delta consistency model to simplify the communication and cooperation among the system components. These designs achieve a low coupling among the system components, which is essential for elasticity and evolution. We also extensively optimize Manu for performance and usability with hardware-aware implementations and support for complex search semantics.