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Rhea Kapoor
Rhea Kapoor

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The Engineering Tradeoffs Behind HNSW-Based Vector Search

Building scalable vector search always presents an infrastructure dilemma: how do we balance accuracy against latency when datasets outgrow brute-force computation? Having tested multiple graph-based approaches for real-time production use, I've found Hierarchical Navigable Small Worlds (HNSW) strikes a practical engineering balance for medium-sized datasets (1M-100M vectors). Today, I'll break down what makes it work and where friction surfaces during implementation.

Why NSW Falls Short First

A Navigable Small World graph connects vectors so most nodes are reachable within a few hops. During insertion (Figure 1), we:

  1. Start from a random entry node
  2. Greedily traverse to nearest neighbors
  3. Insert new vectors by connecting them to their top-K closest nodes found

Search works similarly: from an entry point, hop to the neighbor minimizing distance to the query. But during my tests on datasets like GloVe-100D (1.2M vectors), NSW consistently hit three failure modes:

  • Low-dimensional clustering caused prolonged searches in crowded regions
  • No escape from local minima despite restarts
  • Inconsistent latency during scale tests (>50ms variance at 95th percentile)

The core issue? A single graph layer forces coarse and fine searches to compete.

How Hierarchy Solves This

HNSW's elegance lies in separating search scales across multiple layers (Figure 2):

  • Layer 0 (top): Few vectors, long-range connections (coarse navigation)
  • Layer L (bottom): All vectors, short-range connections (fine-grained search)

This structure introduces valuable properties:

  1. Top layers prune irrelevant regions early
  2. Controlled descent minimizes point revisits
  3. Natural protection against directional bias

Construction: Layer by Layer

When adding a new vector, I sample its maximum insertion layer l_max using a geometric distribution (higher layers = exponentially less likely). Then we:

  1. Start search at top layer (coarse)
  2. Greedily traverse to local minimum
  3. Drop to next layer via existing neighbors
  4. Repeat until reaching layer l_max
  5. Insert the vector with connections to top-M neighbors

Here's Python-esque insertion logic:

def insert_vector(vector, m=12, mL=0.62):
    current_layer = random_geometric_layer(maxL)  # High layers rare
    entry_node = random_top_node()
    current_layer = max_layer
    path = [entry_node]

    # Descend until insertion layer
    while current_layer > vector_layer:
        nearest = greedy_search(vector, path[-1], current_layer)
        path.append(nearest)
        current_layer -= 1

    # Insert and connect neighbors
    for layer in range(min(current_layer, vector_layer), -1, -1):
        neighbors = select_neighbors(vector, layer, m)
        for node in neighbors:
            bidirectional_connect(vector, node, layer)
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The select_neighbors heuristic is critical—simplified implementations favor closest distance, but HNSW optimizes for graph connectivity using heuristic criteria.

Search: Controlled Descent is Key

Query execution mirrors insertion’s hierarchical traversal:

  1. Enter at top layer (coarse hop zones)
  2. Greedy search to local minimum
  3. Drop down layer via closest neighbor
  4. Repeat refinement until bottom layer
  5. Return top-K neighbors from final layer

(Animation shows query path shrinking between layers)

Practical Implementation Notes

After integrating HNSW in three pipeline variants, I documented these engineering considerations:

Parameter Impact Misconfiguration Risk
Construction M Graph connectivity Poor recall/ fragmented graph
Search EF Candidate set size High latency or OOM crashes
Layer Decay (mL) Vector distribution per layer Over-indexing slow layers

Benchmark on 10M SIFT vectors (AWS c6i.8xlarge):

M=16, efConstruction=200 → Build time: 45 min
efSearch=80 → Latency: 2.7ms@P95, Recall: 98.3%
efSearch=40 → Latency: 1.1ms@P95, Recall: 94.1%
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Key deployment tradeoffs observed:

Pros

  • Sub-millisecond search viable on commodity hardware
  • On-disk persistence straightforward (layers = separate files)
  • Tunable recall/latency via EF parameter

Cons

  • Build-time memory bloat: Needed 64GB RAM for 10M 768D vectors
  • High dimensions (>1024D) destabilize layer navigation
  • No native support for incremental updates without rebuild

When HNSW Isn't the Answer

DiskANN dominates at billion-scale, trading memory for SSD throughput. FLAT indexes remain preferable for sub-1M vectors where brute-force outperforms graph traversal. For consistency-critical systems, consider supplementing with streaming indices.

Moving Forward

HNSW delivers remarkable "good enough" performance out of the box. But I'm increasingly curious about hybrid approaches that combine it with quantization—could we shrink memory overhead while preserving layer navigation? Future testing will involve product image retrieval at 100M+ scale. For those exploring implementations, refer to resources like the original paper and pedagogical examples. Remember: effective vector search is less about theoretical superiority than mapping algorithms to hardware constraints.

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