HNSW’s empirical performance profile typically places it at or near the Pareto frontier of recall-vs-latency trade-offs when benchmarked against other approximate-nearest-neighbor (ANN) indexes; although no single data structure dominates all workload regimes, HNSW consistently exhibits superior query throughput and recall at equivalent memory footprints.
HNSW V.S. KD-Trees
KD-Trees recursively bisect the feature space along coordinate-aligned hyperplanes, yielding a binary tree that routes queries to leaf buckets.
This partitioning strategy remains I/O- and cache-friendly in two or three dimensions, yet its query cost grows exponentially with the number of axes, a phenomenon colloquially labeled “the curse of dimensionality.”
HNSW abandons axis-aligned cuts in favor of a probabilistic, multi-layered proximity graph; edges are forged by heuristic nearest-neighbor selection rather than by coordinate splits.
Consequently, the expected number of distance evaluations scales poly-logarithmically with cardinality even when the embedding dimension reaches the hundreds or thousands, permitting sub-millisecond searches on data sets whose intrinsic complexity would cripple a KD-Tree.
HNSW vs. Locality-Sensitive Hashing (LSH)
Locality-Sensitive Hashing (LSH) randomly projects vectors into compact signatures, colliding near-duplicates into common buckets while dispersing dissimilar ones; at query time, only the bucket(s) whose signature matches the probe are scanned, yielding sub-linear cost.
This mechanism is especially attractive when dimensionality is vast or sparsity is extreme, because the signature length is independent of the raw feature count and the index occupies little RAM.
The trade-off, however, is probabilistic: the desired neighbor may be hashed away from the query, so recall saturates well below 100 % unless multiple tables or longer codes are used—both of which inflate memory and CPU.
HNSW, by contrast, maintains an explicit navigable graph whose edge set is curated by a hierarchical, greedy-expansion strategy; this design delivers recall in the high-nineties with only a few hundred distance computations per query.
The price is a larger RAM footprint (≈ 1–2 kB per vector), yet for applications whose accuracy SLA is stringent, the memory premium is routinely accepted.
HNSW vs. Inverted File Index (IVF)
Inverted-File (IVF) indexing reduces the search space by a k-means pre-partitioning stage: vectors are coalesced into Voronoi cells, each materialized as an inverted list anchored at the cell’s centroid.
At query time, only the lists whose centroids rank among the closest to the probe vector are visited, cutting the candidate set from millions to thousands in a handful of distance evaluations.
This coarse-to-fine filtering yields millisecond-scale latencies on billion-scale corpora and is eminently amenable to GPU batching.
The Achilles heel is recall: if the ground-truth neighbor straddles a Voronoi boundary, the requisite list may be missed unless the probe list is enlarged, which erodes the very speed advantage sought.
Moreover, the clustering must be recomputed whenever the collection grows substantially, entailing an offline, globally-synchronized retraining pass.
HNSW dispenses with rigid cells and instead constructs a multi-layered, scale-free proximity graph that is updated incrementally—each insertion performs only local edge rewiring, obviating full rebuilds.
Consequently, HNSW sustains both high recall (≥ 0.95) and steady throughput under dynamic workloads, at the cost of a larger resident index and slightly higher per-query CPU than a lean IVF system.