Vector databases and traditional databases differ primarily in their data models, indexing mechanisms, and use cases.
1. Data Model
- Traditional Databases (Relational & NoSQL): Store structured or semi-structured data (e.g., tables, key-value pairs, documents). They handle exact matches, range queries, and SQL-based filtering.
- Vector Databases: Store high-dimensional vector embeddings (numerical representations of unstructured data like text, images, or audio). They are optimized for similarity search rather than exact matches.
2. Querying Mechanism
- Traditional Databases: Use exact or indexed lookups (e.g.,
WHERE id = 123 or WHERE age > 30). Performance is optimized for structured queries.
- Vector Databases: Use approximate nearest neighbor (ANN) search to find vectors most similar to a query vector (e.g., finding the most relevant image based on embedding).
3. Indexing
- Traditional Databases: Rely on B-trees, hash indexes, or inverted indexes for fast lookups.
- Vector Databases: Use specialized indexing techniques like HNSW (Hierarchical Navigable Small World), IVF (Inverted File Index), or PQ (Product Quantization) to efficiently search in high-dimensional spaces.
4. Use Cases
- Traditional Databases: Best for transactional data (e.g., user accounts, orders), structured reporting, and CRUD operations.
- Vector Databases: Ideal for AI/ML applications like semantic search, recommendation systems, image/video retrieval, and natural language processing (NLP).
Example
- Traditional Database (e.g., PostgreSQL): Storing customer records and querying by
customer_id or email.
- Vector Database (e.g., Tencent Cloud VectorDB): Storing embeddings of product descriptions and retrieving the most similar products based on a user’s query using semantic matching.
For AI-driven applications requiring similarity search, Tencent Cloud VectorDB provides optimized performance, scalability, and integration with machine learning pipelines. It supports hybrid search (combining vector and keyword queries) and ensures low-latency retrieval for large-scale datasets.