With the rise of big data and machine learning applications, the demand for storing and querying high-dimensional vector data has gradually increased. In this context, vector databases and traditional relational databases began to emerge in different data worlds. They each solved the storage and query needs of different types of data in their own unique ways. This article will explore the differences between vector databases and relational databases, as well as their applicable scenarios and underlying implementation principles.
Before we dive into these two database types, let's review their basic concepts. A vector database is a database system specifically designed to store and query vector data, which can be numeric vectors, embedded vectors, or other types of vectors. Their main task is to perform efficient similarity search in large-scale vector data sets, which plays a key role in recommender systems, natural language processing, image recognition and other fields. Different from this, a relational database is a database used to store structured data. The data is organized in tabular form and uses SQL query language to perform complex relational operations. These databases are widely used in enterprise applications, transaction processing, and data warehousing.
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- Features: Milvus is an open source vector database that supports vector indexing and similarity search. It provides a variety of distance measurement methods and indexing algorithms to support large-scale vector data storage and efficient retrieval.
- Advantages: Open source, scalable, supports multiple vector data types and query operations.
- Disadvantages: Relatively new and may not be as stable as traditional databases in some aspects.