The hype of Artificial Intelligence (AI) and Retrieval-Augmented Generation (RAG) is revolutionizing databases and how they are architected. Traditional database management systems (DBMS) are being redefined to harness the capabilities of AI, transforming how data is stored, retrieved, and utilized. In this article I am sharing some of the shifts happening right now to catch up and create better DBs that can play nice with AI.

Traditional databases store data in structured formats, typically as rows and columns in tables. However, with the rise of AI, there is a need to store and query high-dimensional data such as vectors (embeddings), which represent complex data types like images, audio, and natural language.

• Embedding Vectors: When new data is inserted into the database, it can be vectorized using machine learning models, converting the data into embedding vectors. This allows for efficient similarity searches and comparisons. For example, inserting a new product description could automatically generate an embedding that captures its semantic meaning.
• Vector Databases: Specialized vector databases like Pinecone, Weaviate, FAISS (Facebook AI Similarity Search) and Azure AI Search are designed to handle and index vectorized data, enabling fast and accurate similarity searches and nearest neighbor queries.

A great example is PostgreSQL which can be extended to handle high-dimensional vector data efficiently using the pgvector extension. This capability is particularly useful for applications involving machine learning, natural language processing, and other AI-driven tasks that rely on vector representations of data.

What is pgvector?

pgvector is an extension for PostgreSQL that enables the storage, indexing, and querying of vector data. Vectors are often used to represent data in a high-dimensional space, such as word embeddings in NLP, feature vectors in machine learning, and image embeddings in computer vision.

One of the main changes to support AI is that now your index is required to support ANN (approximate nearest neighbor) queries against vector data. A typical query would be “find me the top N vectors that are most similar to this one”. Each vector may have 100s or 1000s of dimensions, and similarity is based on overall distance across all these dimensions. Your regular btree or hash table index is completely useless for this kind of query, so new types of indexes are provided as part of pgvector on PostgreSQL, or you could use Pinecone, Milvus and many solutions being developed as AI keeps demanding data, these solutions are more specialized for these workloads.

Databases are adopting hybrid indexing techniques that combine traditional indexing methods (B-trees, hash indexes) with AI-driven indexes such as neural hashes and inverted indexes for text and multimedia data.

• AI-Driven Indexing: Machine learning algorithms can optimize index structures by predicting access patterns and preemptively loading relevant data into memory, reducing query response times.

What is an Approximate Nearest Neighbor (ANN) Search? It’s an algorithm that finds a data point in a data set that’s very close to the given query point, but not necessarily the absolute closest one. An NN algorithm searches exhaustively through all the data to find the perfect match, whereas an ANN algorithm will settle for a match that’s close enough.

Source: https://www.elastic.co/blog/understanding-ann

AI-driven databases can automatically adjust configurations and optimize performance based on workload analysis. This includes automatic indexing, query optimization, and resource allocation.

• Adaptive Query Optimization: AI models predict the best execution plans for queries by learning from historical data, continuously improving query performance over time.

Predictive Maintenance: Machine learning models can predict hardware failures and performance degradation, allowing for proactive maintenance and minimizing downtime.

Some examples:

• Azure SQL Database offers built-in AI features such as automatic tuning, which includes automatic indexing and query performance optimization. Azure DBs also provide insights with machine learning to analyze database performance and recommend optimizations.
• Google BigQuery incorporates machine learning to optimize query execution and manage resources efficiently and allows users to create and execute machine learning models directly within the database.
• Amazon Aurora utilizes machine learning to optimize queries, predict database performance issues, and automate database management tasks such as indexing and resource allocation. They also integrate machine learning capabilities directly into the database, allowing for real-time predictions and automated adjustments.

The landscape of database technology is rapidly evolving, driven by the need to handle more complex data types, improve performance, and integrate seamlessly with machine learning workflows. Innovations like vectorization during inserts, enhanced indexing techniques, and automated data management are at the forefront of this transformation. As these technologies continue to mature, databases will become even more powerful, enabling new levels of efficiency, intelligence, and security in data management.