About Vector Database

A vector database is best understood not as “just another NoSQL,” but as a full database management system built around high‑dimensional vectors and similarity search, rather than scalar values and Boolean predicates. Recent academic surveys treat vector databases (often abbreviated VDBs or VDBMSs) as a distinct class of systems that sit at the intersection of information retrieval, high‑dimensional geometry, and traditional database design. This article synthesizes those research results to explain what a vector database is, how it works, and how it differs from both classical databases and standalone approximate nearest neighbor (ANN) libraries—using only academically grounded or neutral sources.

From Traditional Databases to Vector Databases

Scalar data vs. vector data

Classical relational and NoSQL databases are optimized around scalar attributes: integers, floats, strings, timestamps, Booleans, and so on. Queries operate primarily via exact or range predicates, for example:

• WHERE age > 30 AND country = ‘IN’
• WHERE title LIKE ‘%database%’

Indexes such as B‑trees, hash indexes, and bitmap indexes exploit the order and discrete structure of these domains to accelerate evaluation of such predicates.

However, modern AI systems—especially deep learning, recommendation models, and large language models (LLMs)—increasingly represent data as dense real‑valued vectors in high‑dimensional spaces. Some canonical examples include:

• Word embeddings: Mapping each word to a real vector so that semantic similarity corresponds to geometric closeness.
• Sentence/document embeddings: Representing full texts as vectors in several hundred to thousands of dimensions.
• Image and audio embeddings: CNNs or other encoders map media into vectors whose distances encode similarity.

In all these cases, meaningful queries are not “title = ‘foo’” but “find items whose embedding is closest to this new embedding.”

Nearest neighbor and similarity search

Formally, nearest neighbor search (NNS) is: given a set of points in a metric space and a query point, find the point in the set closest to the query under some distance function. Its k‑nearest neighbor variant returns the k closest points.

Similarity search generalizes this to any similarity or distance function. Common ones in vector spaces include:

• Inner product: Measures the alignment of vectors.
• Cosine similarity: Measures the cosine of the angle between two vectors.
• Euclidean (Minkowski L2) distance: Measures the straight-line distance between points.
• Manhattan (Minkowski L1) distance: Measures distance along axes at right angles.
• Hamming distance: Used for discrete or binary vectors.

Similarity search has a long history in multimedia databases and information retrieval and is known to be challenging at scale in high dimensions due to the “curse of dimensionality.” In high-dimensional spaces, most points become nearly equidistant, and many classical indexing schemes degrade toward a linear scan.

What Is a Vector Database?

Recent surveys give a precise systems‑level definition. A vector database management system (VDBMS) is defined as a system that stores entities as high‑dimensional feature vectors plus attributes, and supports high‑performance similarity search with traditional DBMS capabilities such as transactions, indexing, query optimization, and scalability. More broadly, a vector database is a database built to manage high‑dimensional vector data, with core functions of vector storage and vector retrieval, particularly via approximate nearest neighbor techniques.

Combining these views: a vector database is a database management system whose primary data model consists of high‑dimensional vectors (often with associated metadata), and whose core query primitive is vector similarity search—typically via approximate nearest neighbor algorithms—integrated with traditional DB capabilities such as storage management, indexing, query processing, and distribution.

This definition distinguishes a full vector database from:

• A simple embedding table in a relational DB with no specialized indexing.
• A standalone ANN library (such as FAISS) that provides search but lacks transactional storage, query languages, or distributed execution.

Core Concepts Underlying Vector Databases

Vectors and embeddings

In NLP and many other domains, an embedding is a learned mapping from discrete objects like words, sentences, or images to continuous vectors in a space where semantic similarity corresponds to geometric closeness.

Key properties include:

• Semantically similar items have embeddings that are close in vector space.
• Embeddings are often dense (most dimensions are non‑zero) and high‑dimensional (hundreds to thousands of dimensions).
• Embeddings can be generated by models like Word2Vec, GloVe, BERT, CLIP, or custom encoders.

A vector database typically stores the original object (text, image ID, user ID), its embedding vector, and additional scalar attributes (metadata) such as timestamps, categories, or numeric features.

Distances, similarity scores, and query semantics

Vector databases must choose or support multiple similarity scores or distance metrics. The choice of score affects the ranking of results and must be aligned with how embeddings are trained and how users interpret “similarity.” Selecting and learning appropriate metrics is an active research area involving metric learning and Mahalanobis distances.

Exact vs. approximate nearest neighbors

Exact k‑nearest neighbor search on large, high‑dimensional datasets is often prohibitively expensive; computing distances to all vectors costs operations proportional to the size of the dataset per query.

The approximate nearest neighbor (ANN) problem relaxes this, asking for points whose distance is within a specific factor of the true nearest neighbor distance. ANN algorithms can achieve sublinear query time with controlled error guarantees at the cost of storing auxiliary data structures. This trade‑off—slightly approximate results for large gains in speed and scalability—is central to vector databases. Graph‑based ANN methods, particularly HNSW and its variants, currently dominate practical large‑scale similarity search due to their empirical performance.

Architecture of a Vector Database System

A generic architecture for VDBMSs consists of two major modules: a query processor and a storage manager, plus supporting components such as an optimizer and execution engine.

High-level architecture

• Data ingestion & embedding: Raw entities (texts, images) arrive. An embedding model transforms them into vectors. The model may be external or integrated directly into the database.
• Storage manager: Persists vectors and associated attributes, maintaining vector indexes and separate indexes for scalar attributes.
• Query processor: Parses queries via APIs or extended SQL, chooses similarity scores, and selects appropriate operators.
• Indexing subsystem: Builds and maintains ANN indexes, such as graph, tree, or table‑based structures.
• Execution engine: Executes search and update operators, often in a distributed manner, managing concurrency and transactions.
• Optimizer: Vital for systems that integrate vector search with relational operations like joins and filters.

Native vs. integrated architectures

• Native vector databases: Purpose‑built for vector data and similarity queries. They implement their own storage layer and indexing, focusing on maximum performance for vector workloads.
• Extended relational/NoSQL systems: Existing DBMSs augmented with vector types and distance operators, such as SingleStore-V or PostgreSQL with pgvector. These benefit from mature relational features but must carefully integrate ANN indexes with existing paths.
• Search engines and ANN libraries: Tools like FAISS or Lucene provide similarity search but lack full DBMS features like transactions or multi‑table schemas.

Storage and Indexing Techniques

The heart of a vector database is its ability to answer similarity queries fast. This requires specialized index structures targeted at high‑dimensional vectors. Key obstacles include the high cost of vector comparisons, large vector sizes, and the lack of traditional order for indexing.

Table-based indexes: hashing and quantization

These partition the data space into buckets and store vectors that map to the same bucket.

• Locality-Sensitive Hashing (LSH): Uses hash functions where nearby points collide with higher probability.
• Learning-to-hash (L2H): Hash functions are learned from data to minimize collisions among dissimilar vectors.
• Product quantization (PQ): Vectors are compressed by partitioning dimensions into subspaces and quantizing them into a small codebook, enabling memory‑efficient indexing.

Tree-based indexes

Structures like kd‑trees and random projection trees recursively partition the vector space. While they work well in low‑dimensional spaces, their performance tends to degrade as dimensionality grows.

Graph-based indexes

Graph‑based ANN methods have become the standard for large‑scale similarity search. Vectors are nodes in a graph, and edges connect “neighbor” nodes. Search proceeds by graph traversal, moving from entry points to nodes closer to the query. Hierarchical Navigable Small World (HNSW) graphs offer fast queries with high recall but can be more complex to build and update.

Compression and disk-resident designs

Because vectors are large, VDBMSs use quantization to reduce memory footprint and disk-resident index designs (like SPANN) to store bulk vectors on disk while keeping index metadata in memory.

Query Processing in Vector Databases

Query types

• Approximate k-Nearest Neighbor (ANN) queries: The primary query type, returning vectors whose distance to a query is within an acceptable factor of the true nearest neighbor.
• Range queries: Returning all vectors within a specific radius.
• Hybrid (predicated) queries: Combining vector similarity with attribute filters (e.g., searching for items under a certain price that are similar to a specific embedding).
• Batched and multi-vector queries: Processing multiple queries together or combining similarity across multiple embeddings for a single entity.

Query interfaces

Vector databases expose these queries through APIs/SDKs for native systems or SQL extensions for integrated systems (e.g., using a distance operator like <-> in a SELECT statement).

Operators and search algorithms

Internal operators include vector operators (insert, update, delete) and search operators (projection and top‑k selection). Systems choose between brute-force search for small datasets and index-based ANN search for production environments.

Query Optimization and Execution

Hybrid query processing

Combining vector similarity with scalar predicates is challenging. Research identifies several strategies:

• Block-first (predicate-first): Use attribute indexes to identify candidates first, then run vector search only within those results.
• Visit-first (vector-first): Perform vector search but check attributes during the traversal, pruning candidates early.
• Over-retrieval: Retrieve more neighbors than requested and then filter them by attributes.

Distributed and federated execution

Production systems typically use sharding, where vectors are partitioned across nodes. A coordinator broadcasts queries to shards (scatter) and then merges the local top‑k results into a global result (gather). Federated engines like FedVSE operate across autonomous local databases to enforce privacy.

Hardware acceleration

To reduce latency, VDBMSs exploit CPU cache locality, SIMD instructions for vectorizing distance computations, and GPUs for massive parallelism during index traversals.

Types of Vector Databases and Design Space

The design space spans axes such as:

• Native vs. Integrated: Purpose-built engines versus vector capabilities added to relational systems.
• Index Type: Graph-based versus table-based or hybrid.
• Data Manipulation: Direct vector manipulation versus indirect manipulation where the system handles the embedding.
• Deployment: Single-node versus distributed or federated clusters. Systems like VBase aim to unify online vector search and relational processing as first-class operators.

Use Cases and Applications

Vector databases respond to the needs of several data‑intensive AI applications:

• Retrieval-Augmented Generation (RAG): Retrieving relevant document passages to ground LLM responses.
• Recommendation systems: Using user and item embeddings for personalized discovery.
• Content-based retrieval: Finding visually similar images or video frames.
• Biometrics: Using face and voice embeddings for identification.
• Drug discovery: Searching molecular or protein embeddings for exploration.
• Semantic search: Enabling text matching based on meaning rather than keywords.

How Vector Databases Differ from Traditional Systems

Versus traditional relational/NoSQL databases

Relational systems rely on Boolean predicates and discrete structures like B‑trees. Vector databases rely on similarity search in continuous spaces and use ANN structures based on graph connectivity or partitioning. Distance evaluations dominate query time in vector databases, requiring different cost models than traditional systems.

Versus full-text search engines

Search engines like Lucene use inverted indexes over tokens and rank using lexical metrics like BM25. Vector databases operate on learned embeddings to capture semantic similarity beyond exact term overlap. While some modern systems combine both (hybrid retrieval), a full vector database integrates deeper data management features like transactions and query optimization.

Evaluation Metrics and Benchmarks

Performance is evaluated based on:

• Recall: The fraction of true nearest neighbors retrieved.
• Precision: The fraction of retrieved neighbors that are truly among the nearest.
• Latency and Throughput: Speed and volume of queries.
• Index Build Time: Time to construct the data structures.
• Footprint: The memory and disk space consumed.

Current Research Challenges and Trends

Emerging directions in the field include:

• Metric selection: Designing similarity scores that align with human intent.
• Dynamic updates: Supporting high‑throughput insertions and deletions without hurting index quality.
• Privacy: Developing federated search engines that do not reveal raw data.
• LLM integration: Tight coupling between databases and models in RAG pipelines.
• Learned indexes: Developing index structures that adapt to specific data distributions.

Summary

A vector database is a full database system whose data model, indexing, and execution are all built around high-dimensional similarity search. They manage data derived from unstructured sources by using embeddings that encode semantics into geometry. Architecturally, they consist of specialized query processors and storage managers capable of overcoming the curse of dimensionality to deliver sub‑second latency over billions of vectors. As the field matures, research is shifting from simply making ANN algorithms fast to creating integrated systems that treat vectors as first‑class citizens alongside traditional data. For modern AI infrastructure, vector databases represent the bridge between raw semantic embeddings and scalable, manageable data systems.