GaussDB-Vector: A Large-Scale Persistent Real-Time Vector Database for LLM Applications

GaussDB-Vector: A Large-Scale Persistent Real-Time Vector Database for LLM Applications

Type: Research paper / system description
Category: Curated resource (vector databases, LLM infrastructure)

Overview

GaussDB-Vector is a high-performance, real-time, persistent vector database system designed for large-scale LLM and AI applications. It targets scenarios such as retrieval-augmented generation (RAG) and semantic memory, where billions of embeddings must be stored and queried with low latency and high concurrency.

Key Use Cases

• Long-term memory for LLMs (e.g., RAG-based systems)
• Semantic caching of intermediate LLM inference states
• Large-scale similarity search over billions of vectors
• Applications requiring frequent vector updates and deletions
• Hybrid search combining vector similarity with scalar filters

Features

• Real-time, persistent vector storage

  • Native vector storage supporting large-scale datasets (up to billions of embeddings).
  • Persistent design instead of in-memory only operation.

• Low-latency, scalable similarity search

  • Optimized for low-latency, high-throughput vector queries.
  • Scalable search across large, distributed deployments.

• Real-time inserts and deletes

  • Supports frequent updates to the vector dataset.
  • Handles real-time insertion and deletion of vectors without major downtime.

• High availability and user isolation

  • Designed for high availability in production environments.
  • Supports isolation between different users or workloads.

• Large-scale distributed search

  • Capable of distributed vector search across multiple nodes.
  • Supports high user concurrency.

• Hybrid scalar–vector filtered search

  • Combines vector similarity with scalar filters in a single query (e.g., metadata + embedding search).

• Graph-based vector indexing

  • Novel storage architecture tailored to a graph-based vector index.
  • Adaptable to various dataset sizes and vector dimensions.
  • Optimized I/O patterns to support high-performance queries.

• I/O-optimized storage and buffering

  • Storage architecture designed specifically to reduce I/O overhead.
  • New buffering strategies to further reduce I/O burden during search and updates.

• Acceleration techniques

  • Product quantization (PQ) for efficient compressed vector representation.
  • Parallel search execution to exploit multi-core and distributed environments.
  • Hardware acceleration via:
    • SIMD instructions
    • GPUs
    • NPUs

• Performance

  • Experimental results (as reported in the paper) indicate performance improvements of approximately 1×–5× compared to competitive vector database baselines.

Integrations / Application Context

• Designed to integrate with large language model workloads, including:
  • RAG pipelines using knowledge embeddings.
  • Semantic memory systems that cache and retrieve intermediate LLM embeddings.

Pricing

• Not applicable. This is a research paper describing the GaussDB-Vector system; no pricing information is provided in the source content.

Reference

• Sun, J., Li, G., Pan, J., Wang, J., Xie, Y., Liu, R., & Nie, W. (2025). GaussDB-Vector: A Large-Scale Persistent Real-Time Vector Database for LLM Applications. PVLDB, 18(12).
• PDF: https://sunji.greatji.com/resource/p2114-li%20(1).pdf