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Vector databases have recently gained significant attention due to the emergence of large language models that produce vector embeddings for text. Existing vector databases can be broadly categorized into two types: specialized and generalized. Specialized vector databases are explicitly designed and optimized for managing vector data, while generalized ones support vector data management within a general purpose database. While specialized vector databases are interesting, there is a substantial customer base interested in generalized vector databases for various reasons, e.g., a reluctance to move data out of relational databases to reduce data silos and costs, the desire to use SQL, and the need for more sophisticated query processing of vector and non-vector data. However, generalized vector databases face two main challenges: performance and interoperability of vector search with SQL, such as combining vector search with filters, joins, or even fulltext search. In this paper, we present SingleStore-V, a full-fledged generalized vector database integrated into SingleStore, a modern distributed relational database optimized for both OLAP and OLTP workloads. SingleStore-V achieves high performance and interoperability via a suite of optimizations. Experiments on standard vector benchmarks show that SingleStore-V performs comparably to Milvus, a highly-optimized specialized vector database, and significantly outperforms pgvector, a popular generalized vector database in PostgreSQL. We believe this paper will shed light on integrating vector search into relational databases in general, as many design concepts and optimizations apply to other databases.
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Accelerating Skewed Workloads With Performance Multipliers in the TurboDB Distributed Database
Distributed databases suffer from performance degradation under skewed workloads. Such workloads cause high contention, which is exacerbated by cross-node network latencies. In contrast, single-machine databases better handle skewed workloads because their centralized nature enables performance optimizations that execute contended requests more efficiently. Based on this insight, we propose a novel hybrid architecture that employs a single-machine database inside a distributed database and present TurboDB, the first distributed database that leverages this hybrid architecture to achieve up to an order of magnitude better performance than representative solutions under skewed workloads. TurboDB introduces two designs to tackle the core challenges unique to its hybrid architecture. First, Hybrid Concurrency Control is a specialized technique that coordinates the single-machine and distributed databases to collectively ensure process-ordered serializability. Second, Phalanx Replication provides fault tolerance for the single-machine database without significantly sacrificing its performance benefits. We implement TurboDB using CockroachDB and Cicada as the distributed and single-machine databases, respectively. Our evaluation shows that TurboDB significantly improves the performance of CockroachDB under skewed workloads.
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- PAR ID:
- 10516823
- Publisher / Repository:
- 21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24)
- Date Published:
- ISBN:
- 978-1-939133-39-7
- Format(s):
- Medium: X
- Location:
- SANTA CLARA, CA, USA
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
