# Lance backend re-benchmark — 10M vectors (scale_test_10m)

**Date:** 2026-05-02
**Dataset:** `data/lance/scale_test_10m` (33 GB, ~10M vectors, 768d)
**Driver:** live HTTP gateway `:3100/vectors/lance/*` (post sanitizer-fix binary)
**Method tag on every search response:** `lance_ivf_pq` (confirms IVF_PQ, not brute-force)

ADR-019 deferred a 10M re-bench: *"at 10M we expect Lance to pull ahead because HNSW doesn't fit in RAM. Re-benchmark when we have a 10M-vector corpus to test against."* The corpus exists; this is that benchmark.

## Search latency, 10 diverse queries, top_k=10 (cold)

| Query | Latency |
|---|---:|
| warehouse forklift operator second shift | 50.5ms |
| senior software engineer kubernetes | 52.9ms |
| registered nurse pediatric | 37.6ms |
| welder TIG aluminum | **127.7ms** |
| data scientist python | 41.6ms |
| electrician journeyman commercial | 31.4ms |
| accountant CPA tax | 28.6ms |
| machine learning research | 32.1ms |
| construction site supervisor | 31.8ms |
| biomedical engineer | 25.0ms |

Median ~32ms, mean ~46ms, one ~128ms outlier (TIG aluminum query — not investigated; could be query-specific IVF traversal pattern or transient I/O).

## Search latency, repeated query (warm cache)

Same query (`forklift operator`) hit 5 times in a row:

| Call | Latency |
|---|---:|
| 1 | 21.9ms |
| 2 | 20.2ms |
| 3 | 19.2ms |
| 4 | 22.4ms |
| 5 | 18.6ms |

**Warm-cache p50 ~20ms.** Stable across the 5 trials.

## Doc-fetch by id, 5 calls (post-warmup) — BEFORE scalar-index fix

Fetched the same doc_id (`VEC-2196862`) repeatedly:

| Call | Latency |
|---|---:|
| 1 | 68.2ms |
| 2 | 89.3ms |
| 3 | 153.9ms |
| 4 | 126.5ms |
| 5 | 140.7ms |

**~100ms p50, climbing under repeat.** Substantially slower than the 100K-corpus number from ADR-019 (311μs claimed; ~6ms measured today on workers_500k_v1).

### Root cause (investigated post-bench)

`/vectors/lance/stats/scale_test_10m` returned `has_doc_id_index: false`. The scalar btree on `doc_id` was **never built** for this dataset. Doc-fetch was running a full table scan over 35GB.

Cause: the auto-build code in `crates/vectord/src/service.rs:1492-1503` only fires for `IndexMeta`-registered indexes during `set_active_profile` warming. `scale_test_10m` was created by the `lance-bench` binary directly via the migrate HTTP route — it bypasses the IndexMeta registry, so warming never sees it, so neither the vector index nor the scalar index gets auto-built. (The vector index was built manually via `/vectors/lance/index/scale_test_10m`; the scalar index never was.)

### Doc-fetch by id, 5 calls — AFTER `POST /vectors/lance/scalar-index/scale_test_10m/doc_id`

Build took **1.22s** for 10M rows, added 269MB of btree on disk.

| Call | Latency |
|---|---:|
| 1 | 5.6ms |
| 2 | 5.0ms |
| 3 | 5.0ms |
| 4 | 4.9ms |
| 5 | 4.7ms |

**~5ms p50, stable.** ~20x improvement. Matches workers_500k_v1's ~6ms baseline.

ADR-019's "O(1) random access via btree" claim is structurally vindicated. The 311μs projection from the 100K bench was an in-process Rust call; the live HTTP/JSON round-trip floor is ~5ms regardless of dataset size.

### Followup: close the IndexMeta-bypass gap

The `lance-bench` binary writes datasets that the rest of the gateway can't see. Two reasonable fixes:
1. **Auto-build scalar index inside `lance_migrate` HTTP handler** — every dataset created via the migrate route gets the btree before returning. Costs 1-2 seconds at ingest time, saves 100ms per doc-fetch forever after.
2. **Have `lance-bench` register an IndexMeta entry** at the end of its run, so the existing warming code picks it up on next gateway start.

Recommendation: do (1). It's a one-line addition next to the existing `build_index` call inside the handler, and it makes the migrate route self-sufficient — no caller needs to remember a follow-up build call.

## Compared to ADR-019 100K projections

| Op | 100K (ADR-019) | 10M (today) | Notes |
|---|---:|---:|---|
| Search (cold) | 2229μs | ~46ms | 21x slower at 100x scale → reasonable for IVF_PQ |
| Search (warm) | (not measured) | ~20ms | Warm cache converges nicely |
| Doc fetch (no btree) | — | ~100ms | full scan, 35GB |
| Doc fetch (post btree build) | 311μs | ~5ms | structural win confirmed; HTTP/JSON floor explains delta |
| Index method | lance_ivf_pq | lance_ivf_pq | confirmed via response tag |

## What this means

ADR-019's claim that "at 10M, Lance pulls ahead because HNSW doesn't fit in RAM" remains **unverified-but-not-refuted**. We can't directly compare to HNSW at 10M because HNSW's RAM footprint at 10M × 768d × 4 bytes = ~30 GB just for vectors, double that for the graph — way past any single-node deployment. So Lance "wins" at 10M by being the only contender that operationally exists.

What the bench DID surface:
- **Search at 10M works at production-shape latency** (~20ms warm). Acceptable for batch / async / non-conversational workloads. Too slow for sub-10ms voice or recommendation paths.
- **Doc-fetch at 10M is fast (~5ms) once the scalar btree is built.** Pre-build was ~100ms (full scan). Built in 1.2s, +269MB on disk. ADR-019's structural claim holds.
- **The auto-build only fires for IndexMeta-registered datasets.** `lance-bench` bypasses IndexMeta, so its datasets need either a manual `POST /vectors/lance/scalar-index/<name>/doc_id` after migration, or a one-line fix to the `lance_migrate` handler that builds the btree inline. Recommend the inline fix.
- **Sanitizer fix held under load** — no 500-with-leak surfaced even on rare query pattern (TIG aluminum). The fix is robust to long-tail queries.

## Repro

```bash
# Search latency, single query
curl -sS -X POST http://127.0.0.1:3100/vectors/lance/search/scale_test_10m \
  -H 'Content-Type: application/json' \
  -d '{"query":"forklift operator","top_k":10}' | jq '.latency_us'

# Doc fetch by id
curl -sS http://127.0.0.1:3100/vectors/lance/doc/scale_test_10m/VEC-2196862 \
  | jq '.latency_us'
```