The choice of index type directly impacts query latency distribution by balancing between computational cost and approximation accuracy. Flat brute-force indexes provide deterministic, predictable latency but scale poorly. Approximate methods like HNSW and IVF reduce latency at the cost of variability, influenced by their algorithmic trade-offs and parameterization.
Flat (Brute-Force) Indexes yield consistent latency because every query computes distances to all vectors in the dataset. For example, searching a 1M-vector dataset with a flat index will always take roughly the same time, as there’s no approximation or early termination. Latency grows linearly with dataset size, making it impractical for large-scale systems, but the lack of variability makes it useful for benchmarks or small datasets where exact results are critical. However, outliers (e.g., queries against unusually high-dimensional data) may slightly affect latency due to hardware-level optimizations like SIMD instructions, but the impact is minimal compared to algorithmic factors.
HNSW (Hierarchical Navigable Small World) graphs reduce average latency significantly by traversing a hierarchy of proximity graphs. However, latency varies based on query complexity and graph topology. For instance, a query requiring traversal through multiple "long-distance" edges in the graph may take longer than one that finds neighbors quickly. Parameters like efSearch (the size of the dynamic candidate list during search) directly influence this: higher efSearch improves recall at the cost of latency. In practice, HNSW often exhibits a long-tail distribution—most queries are fast, but a small fraction require more hops, leading to higher variability. This makes HNSW suitable for applications prioritizing low average latency over strict worst-case guarantees.
IVF (Inverted File Index) partitions data into clusters and restricts searches to a subset of them (controlled by nprobe). Latency distribution here depends on cluster quality and how well the query aligns with cluster centroids. For example, if a query vector lies near a centroid, the search finishes quickly by scanning only a few clusters. Conversely, ambiguous queries (falling between clusters) may require scanning more clusters to achieve adequate recall, increasing latency. Cluster imbalance—where some clusters contain far more vectors than others—can also cause variability. Tuning nprobe allows developers to trade off between latency (scanning fewer clusters) and accuracy (scanning more), but uneven data distributions can still lead to unpredictable spikes in latency for certain queries.
In summary, flat indexes offer predictability at the expense of scalability, HNSW sacrifices consistency for lower average latency, and IVF introduces variability tied to clustering effectiveness and parameter choices. The optimal choice depends on the application’s tolerance for latency variance versus its need for speed or accuracy.