BigQuery’s vector similarity search isn’t just for finding "similar" things; it’s a way to perform complex, multi-criteria filtering and retrieval using high-dimensional data.
Let’s see it in action. Imagine you have a table of product descriptions, each with a corresponding vector embedding generated by a machine learning model.
-- Sample table structure
CREATE TABLE your_dataset.products (
product_id STRING,
description STRING,
embedding ARRAY<FLOAT64> -- Vector embedding, e.g., 768 dimensions
);
-- Sample data
INSERT INTO your_dataset.products (product_id, description, embedding) VALUES
('prod1', 'A comfortable blue cotton t-shirt.', [0.1, 0.2, ..., 0.9]),
('prod2', 'Stylish red leather boots.', [0.3, 0.4, ..., 0.1]),
('prod3', 'Soft grey wool sweater.', [0.2, 0.3, ..., 0.8]),
('prod4', 'Lightweight running shoes, blue.', [0.1, 0.3, ..., 0.7]);
Now, you want to find products that are both similar to a query vector (e.g., representing "casual wear, comfortable") and are in stock (let’s say we add an in_stock boolean column).
-- Querying for similar and in-stock products
WITH query_vector AS (
SELECT [0.15, 0.25, ..., 0.85] AS query_emb -- Vector for "casual wear, comfortable"
)
SELECT
p.product_id,
p.description,
-- Calculate cosine similarity. Higher is more similar.
1 - (VECTOR_COSINE_DISTANCE(p.embedding, q.query_emb)) AS similarity_score
FROM
your_dataset.products p,
query_vector q
WHERE
p.in_stock IS TRUE -- Assuming an 'in_stock' boolean column
ORDER BY
similarity_score DESC
LIMIT 10;
This query combines a traditional boolean filter (p.in_stock IS TRUE) with a vector similarity calculation (VECTOR_COSINE_DISTANCE). The VECTOR_COSINE_DISTANCE function is key here; it returns a value between 0 and 2, where 0 means identical vectors, 1 means orthogonal vectors, and 2 means diametrically opposed vectors. By subtracting this distance from 1, we get a score where 1 is perfectly similar and 0 is completely dissimilar, making it easier to reason about and order.
The underlying mechanism in BigQuery leverages specialized indexing and query execution paths for vector data. When you perform a VECTOR_COSINE_DISTANCE or VECTOR_EUCLIDEAN_DISTANCE operation, BigQuery can utilize an Approximate Nearest Neighbor (ANN) index if one is present. This index, often based on algorithms like Hierarchical Navigable Small Worlds (HNSW) or Product Quantization (PQ), allows the system to quickly prune large portions of the dataset that are unlikely to contain the nearest neighbors, significantly speeding up searches that would otherwise require a brute-force comparison against every single vector. Without an ANN index, BigQuery falls back to a less efficient, but still correct, brute-force scan.
The real power emerges when you combine vector search with other BigQuery capabilities. You can join vector search results with transactional data, perform aggregations on retrieved items, or even use the similarity scores as features in downstream ML models. For instance, you could find products similar to a customer’s recently purchased item and then filter those results by products that have had a high purchase rate in the last week, all within a single, scalable query.
One crucial aspect of vector search performance is the choice of distance metric and the data type of your embeddings. While FLOAT64 is common, using BFLOAT16 can offer significant storage and performance benefits with minimal impact on accuracy for many ML models, especially when dealing with very large datasets. BigQuery’s VECTOR_COSINE_DISTANCE and VECTOR_EUCLIDEAN_DISTANCE functions are optimized to work efficiently with these types. Also, remember that the dimensionality of your vectors directly impacts index size and query time; higher dimensions generally mean more computational overhead and larger index structures.
The next challenge you’ll likely face is managing and updating these vector embeddings, especially as your product catalog or data changes.