Vector Database Interview Guide and Technical Analysis

This article is an interview experience sharing and technical analysis of vector databases. It systematically explains the core concepts, technical principles, selection recommendations, and application scenarios of vector databases.

1. Core Definition

Definition: A vector database is a database specifically designed for storing and retrieving high-dimensional vectors. Its core capability is approximate nearest neighbor search, which can quickly find the most similar results to a query vector from a large-scale vector set.
Essential differences from ordinary databases:
Ordinary databases (e.g., MySQL): Good at handling exact match queries.
Vector databases: Good at handling semantic similarity search. It measures content similarity by calculating the distance in high-dimensional space between vectors, thereby understanding semantics.

2. Why Do We Need Specialized Vector Databases?

B-tree indexes in ordinary relational databases (e.g., MySQL, PostgreSQL) are designed for exact matching and are not suitable for similarity search of high-dimensional vectors. Brute-force computation on massive vectors is extremely inefficient. Vector databases solve this core performance issue through specialized indexing algorithms.

3. Core Indexing Algorithms

The article focuses on two mainstream indexing algorithms, which are also technical highlights in interviews:

HNSW: Based on multi-layer graph structure navigation, with fast query speed and high accuracy, but high memory usage during index construction. Suitable for scenarios requiring high recall and low latency.
IVF: Based on clustering, dividing vectors into different "buckets" for search, with low memory usage, suitable for ultra-large-scale data, but slightly lower accuracy than HNSW.

4. Core Capabilities of Vector Databases

A production-grade vector database, in addition to ANN search, needs to have the following key features:

Metadata filtering: Supports adding filter conditions during retrieval to achieve hybrid retrieval based on attributes (e.g., department, time).
Real-time updates: Supports incremental writes, modifications, and deletions of data without rebuilding the entire index.
Keyword search integration: Supports combining vector search with keyword search like BM25 to achieve hybrid recall, improving the retrieval effect for both exact terms and semantics.

5. Selection Recommendations and Product Comparison

The article provides specific recommendations from three dimensions: data scale, deployment method, and functional requirements, and compares mainstream options:

Database	Deployment	Suitable Scale	Main Advantages	Main Disadvantages
Chroma	Local/Embedded	Small scale (dev/test)	Zero configuration, very fast to start, good integration with LangChain/LlamaIndex	Not suitable for production, lacks distributed and advanced features
Qdrant	Self-hosted/Cloud	Small to medium scale (millions)	Good performance, concise API, comprehensive documentation, supports hybrid retrieval	Needs tuning for ultra-large scale
Milvus	Self-hosted (distributed)	Large scale (hundreds of millions)	Horizontally scalable, comprehensive features, mature community ecosystem	Complex deployment and operation
Pinecone	Fully managed cloud service	Medium to large scale	No operation required, ready to use out of the box	High cost, potential data compliance risks
pgvector	PostgreSQL extension	Small to medium scale	No need to introduce new components, can JOIN with business data, simple operation	Weaker performance than dedicated vector databases

6. Interview Summary and Pitfalls

Accurately understand that the core of vector databases is ANN search, not just "storing vectors".
Selection should not only consider GitHub Stars, but also comprehensively consider data scale, deployment, and functional requirements.
Technically, it is necessary to understand the differences and applicable scenarios of HNSW and IVF algorithms.

AI Interview Questions: Vector Database Interview Guide and Technical Analysis