Vector Databases and Similarity Search: A Practical Guide for Data Teams
Picture the scene: a data team at a Manchester e-commerce firm wants to find every product image that looks like a customer’s photo of a broken lamp. They write a SQL query, hit run, and watch it crawl through millions of rows doing exact string and integer comparisons that tell them nothing about visual similarity. We have seen this pattern repeatedly — relational engines built for transactions simply were not designed to answer “what is most similar to this?” questions. That gap is exactly why vector databases have moved from research curiosity to production infrastructure across the UK tech scene, from London fintechs to Edinburgh research labs.
Why Vector Databases Matter for Modern Data Analytics
The shift we are watching is fundamental: from exact-match retrieval to approximate nearest neighbour (ANN) search over high-dimensional embeddings. This is not a niche optimisation — it changes what questions a data platform can answer.
The Limits of Traditional SQL for Unstructured Data
SQL databases index data with B-trees and hash indexes, which excel at equality and range predicates. Ask PostgreSQL to find the row whose 768-dimensional embedding is closest to a query vector and the B-tree is useless — there is no meaningful ordering across dimensions. You can brute-force a distance calculation with a sequential scan, but latency grows linearly with table size. At ten million rows, that is already too slow for interactive search, and at a billion rows it is hopeless.
How Embeddings Turn Everything into Vectors
Embedding models such as BERT for text and ResNet for images convert unstructured content into dense numeric vectors where semantic similarity becomes geometric proximity. A sentence about mortgage rates and one about home loans land close together in vector space even with no shared keywords. Once everything — text, images, audio, user behaviour — becomes a vector, similarity search becomes the core retrieval primitive, and that is what vector databases are built to serve.
Core Techniques Behind Similarity Search
Before choosing a tool, we think every data engineer should understand the maths and the index structures underneath it. The good news is that the concepts are intuitive once you strip away the jargon.
Distance Metrics: Cosine, Euclidean, and Dot Product
Three metrics dominate practice. Cosine similarity measures the angle between vectors and ignores magnitude, which suits text embeddings. Euclidean distance measures straight-line separation and works well when magnitude carries meaning. Dot product combines both and is common when vectors are already normalised. The practical rule we follow: match the metric to how the embedding model was trained, because mixing them silently degrades recall.
HNSW, IVF-PQ, and Product Quantisation Explained
Exact nearest neighbour search is O(n) per query, so production systems use approximate indexes:
- HNSW (Hierarchical Navigable Small World) — a multi-layer graph that greedily navigates towards close neighbours. Excellent recall and latency, but memory-hungry.
- IVF-PQ (Inverted File with Product Quantisation) — clusters vectors into cells, then compresses each vector into short codes. Dramatically lower memory at some recall cost.
- FAISS — Meta AI’s open-source library implementing both, and widely used in UK research labs for billion-scale similarity search.
Leading Vector Databases and Tools in 2025
The market has consolidated around a handful of credible options, and UK adoption is broad: fintechs in London and research groups at UCL and Edinburgh are running these tools in both production and experimental settings.
Managed Services vs Self-Hosted Options
Pinecone offers a fully managed service with minimal operational burden. Weaviate, Milvus, and Qdrant are open-source and self-hostable, giving teams control over data residency — a real consideration under UK GDPR. The trade-off is familiar: managed services cost more per vector but save engineering time, while self-hosted deployments demand tuning expertise.
pgvector and the Postgres Ecosystem
pgvector has become a popular choice for UK startups wanting vector search inside PostgreSQL. It adds vector columns and ANN indexes to an engine teams already run, which means no new operational surface, transactional consistency with existing tables, and SQL joins between embeddings and business data. For datasets under roughly ten million vectors, it is often the pragmatic first step.
Real-World Use Cases Across UK Industries
Vector search is no longer theoretical in the UK — it is embedded in services people use daily, and each case ties back to broader big data management challenges around scale, freshness, and governance.
Recommendations and Personalisation
BBC iPlayer uses recommendation systems that rely on vector similarity for personalised content, matching viewing embeddings against programme embeddings to surface what a user is likely to watch next. The same pattern powers retail product recommendations and music discovery.
Anomaly Detection and Semantic Search
UK banks apply vector techniques to fraud detection by embedding transaction patterns and flagging those far from normal clusters. Meanwhile, London legal firms use semantic search over contracts and case law, where keyword matching fails but embedding retrieval finds conceptually relevant clauses in milliseconds.
Benchmarking and Performance Tuning for Vector Workloads
Getting vectors into an index is easy; getting them fast, accurate, and affordable at scale is the hard part. This is where the DASFAA community’s benchmarking efforts and tools like ANN-Benchmarks, widely cited by UK data scientists, become essential reference points.
Recall, Latency, and Memory Trade-offs
Every ANN index sits on a three-way trade-off. Higher recall usually means more memory and slower queries. Aggressive quantisation cuts memory but loses accuracy. We recommend measuring recall@k against a brute-force ground truth on a representative sample before tuning anything else.
Indexing Strategies for Billion-Scale Datasets
At billion scale, sharding and quantisation are unavoidable. FAISS supports GPU-accelerated IVF-PQ indexes that fit billions of vectors into a single node’s memory, while distributed systems like Milvus partition across nodes. Index build times can run into hours, so plan rebuilds around data refresh cycles rather than on demand.
The Road Ahead: DASFAA, Research, and Open Challenges
The DASFAA conference series has been a key forum for database systems research since 2014, with UK universities like Edinburgh and UCL regularly contributing. Vector search has become a recurring theme precisely because it sits at the intersection of database systems and big data management.
What DASFAA 2014 Taught Us About Scalability
Looking back at DASFAA 2014, the dominant concerns were distributed query processing and scalable indexing — problems that anticipated today’s vector workloads. The lesson we took from that era is that scalability is a systems problem, not just an algorithmic one.
Open Problems in Vector Search and Data Analytics
Standardised evaluation remains the biggest gap. Filtered vector search, hybrid SQL-plus-vector query planning, and cost-based index selection are all under-researched. We expect these to dominate future DASFAA tracks.
FAQ
Do we need a dedicated vector database if we already use PostgreSQL?
Not always. pgvector handles millions of vectors well and keeps everything in one transactional system. Move to a dedicated engine when you exceed roughly ten million vectors, need sub-50ms latency at high query volume, or require GPU-accelerated indexing.
Which distance metric should we choose?
Use the metric your embedding model was trained with. Cosine similarity is the safe default for text embeddings; Euclidean and dot product suit models where magnitude is meaningful. Always validate with recall@k on real data.
Is approximate nearest neighbour search accurate enough for production?
Yes, when tuned properly. Well-configured HNSW indexes routinely achieve over 95% recall@10 with millisecond latency. Measure against exact search on a sample before trusting any configuration.
How do UK data protection rules affect vector database choice?
Embeddings can encode personal data, so data residency matters. Self-hosted options like Qdrant, Weaviate, and Milvus, or pgvector inside your existing UK-hosted Postgres, give you the clearest control under UK GDPR.
What is the fastest way to start experimenting?
Install pgvector in a local PostgreSQL instance and index a few hundred thousand embeddings, then benchmark FAISS with the same data. Comparing both takes an afternoon and tells you more than any vendor benchmark.
Vector databases are not a silver bullet — they complement SQL rather than replace it. Our advice to UK data teams is simple: experiment with pgvector and FAISS on your own data before committing to a managed service, because the right answer depends on your scale, latency budget, and compliance constraints far more than on any feature checklist.