Vector Database Management Framework
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A Vector Database Management Framework is a 3rd-Party DBMS that can be used to create vector DBMS instances.
- Context:
- It can (typically) offer VDBMS Features, such as: store, manage, and retrieve data in vector records.
- It can (often) use specialized indexing and search algorithms to handle high-dimensional vector data efficiently.
- It can (often) support operations such as similarity search, nearest neighbor search, and vector space transformations.
- It can range from being an Self-Hosted VDBMS to being a Fully-Managed VDBMS.
- ...
- Example(s):
- Elasticsearch DBMS supports clustering and high availability.
- Vespa DBMS is known for its fast data writes and vector search operators.
- Managed VDBMS Service, such as: Pinecone DBMS.
- Open-Source VDMS, such as: Chroma DBMS.
- ...
- Counter-Example(s):
- Relational DBMS, such as: Maria DBMS or PostgreSQL.
- NoSQL databases like Cassandra that do not primarily focus on vector data.
- See: Vector Space Model, Nearest Neighbor Search, High-Dimensional Data Management.
References
2024
- GPT-4
| Name | Open Source | Key Features |
|---|---|---|
| Elasticsearch | Yes | Clustering, High Availability, Automatic Node Recovery, Horizontal Scalability, Cross-Cluster Replication |
| Vespa | Yes | Fast Data Writes, Configurable Data Redundancy, Structured Filters, Text Search Operators, Vector Search Operators |
| Vald | Yes | Automatic Backups, Distributed Vector Indexes, Index Replication, Multi-Language Support |
| ScaNN | Yes | Search Space Trimming, Quantization for Maximum Inner Product Search, Euclidean Distance Support |
| Pgvector | Yes | Nearest Neighbor Search, L2 Distance, Inner Product, Cosine Distance, PostgreSQL Client Compatibility |
| Chroma | Yes | Queries, Filtering, Density Estimates, LangChain Support, Scalable API |
| Pinecone | No | Fully Managed Service, Scalability, Real-time Data Ingestion, Low-Latency Search, LangChain Integration |
| Weaviate | Yes | Fast Search, Flexibility, Modules Integration with OpenAI, Cohere |
| Faiss | Yes | Similarity Search, Clustering of Dense Vectors, Various Indexing and Search Algorithms, Large-Scale Dataset Optimization |
| Annoy | Yes | Memory Efficiency, Tree-Based Search, Euclidean/Cosine Distance Metrics |
| Milvus | Yes | Scalable Storage and Search, Metric Indexing, Multiple Programming Languages Support |
| Hnswlib | Yes | Memory Efficiency, Small-World Graph Search, Euclidean/Cosine Distance Metrics |
| FaunaDB | Not Specified | Cloud-Native, Serverless, k-d Tree Algorithm, ACID Transactions |
| Amazon Neptune | Not Specified | Fully Managed Graph Database, Gremlin and SPARQL Support, Scalable Infrastructure |
2023
- (Pan, Wang et al., 2023) ⇒ James Jie Pan, Jianguo Wang, and Guoliang Li. (2023). “Survey of Vector Database Management Systems.” doi:10.48550/arXiv.2310.14021
- NOTES:
- It thoroughly evaluates over 20 commercial Vector Database Management Systems (VDBMSs) that have emerged in recent years, focusing on the obstacles in managing vector data.
- It details the process of query processing in VDBMSs, discussing aspects like similarity scores, query types, and interfaces, along with the complexities of basic search query operators.
- It outlines various storage and indexing strategies used in VDBMSs, including partitioning techniques (like randomization and learned partitioning) and different types of indexes such as tree-based, table-based, and graph-based.
- It delves into the optimization and execution aspects of VDBMSs, explaining plan enumeration, selection, hybrid operators for predicated queries, and the utilization of hardware acceleration and distributed search techniques.
- It classifies current VDBMSs into categories such as native, extended, and search engines/libraries, analyzing their design and runtime characteristics to highlight each type's strengths.
- It acknowledges the importance of benchmarks in evaluating VDBMSs, but it doesn't provide an in-depth analysis of specific benchmarks, suggesting an area for future exploration.
- It analyzes EuclidesDB VDBMS (2018), Vearch VDBMS (2018), Pinecone VDBMS (2019), Vald (2020), Chroma (2022), Weaviate (2019), Milvus (2021), NucliaDB (2021), Qdrant (2021), Manu (2022), Marqo (2022), Vespa (2020), Cosmos DB (2023), MongoDB DBMS (2023), Neo4j DBMS (2023), Redis (2023), AnalyticDB-V (2020), PASE+PG (2020), pgvector+PG (2021), SingleStoreDB (2022), ClickHouse (2023), MyScale (2023).
- NOTES: