How does high dimensionality affect similarity search systems at scale?

It reduces recall accuracy and makes brute-force search inefficient.

In high-dimensional vector databases, linear scan becomes expensive and tree-based indexing structures degrade. Approximate nearest neighbor (ANN) methods like HNSW or IVF-PQ are used to trade accuracy for speed. The curse of dimensionality makes exact search both computationally and statistically unreliable due to distance concentration.