# ADR-019: Vector Storage — Parquet+HNSW stays, Lance joins as second tier

**Status:** Accepted — 2026-04-16
**Implements:** Phase 18 from PRD (Lance evaluation)
**Supersedes:** nothing (augments ADR-008)
**Owner:** J

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## Context

Phase 18 of the PRD committed to settling "Parquet+sidecar vs Lance" with measurements, not vibes. This ADR records the benchmark outcome and the resulting architectural direction.

Input data: `data/vectors/resumes_100k_v2.parquet` — 100,000 × 768d embeddings, the same index we tuned HNSW against in Phase 15.

Benchmark harness: `crates/lance-bench/src/main.rs` — standalone binary, deliberately not integrated into the workspace's common deps to avoid forcing DataFusion/Arrow upgrades on the rest of the stack until we'd decided.

## The scorecard

All numbers measured on the same 128GB server, same 100K × 768d index, release build:

| Dimension | Parquet + HNSW (current) | Lance 4.0 IVF_PQ (candidate) | Winner |
|---|---|---|---|
| Cold load | 0.17s | 0.13s | Lance, 1.27× — *does not clear 2× decision threshold* |
| Disk size (data only) | 330.3 MB | 330.4 MB | Tie |
| Index on-disk footprint | 0 (HNSW is RAM-only) | 7.4 MB | Lance |
| Index build time | 230s (ec=80 es=30) | 16s | **Lance, 14× faster** |
| Search p50 | 873us (recall@10 = 1.00) | 2229us (recall unmeasured, likely 0.85-0.95) | **Parquet+HNSW, 2.55× faster** |
| Search p95 | 1413us | 4998us | **Parquet+HNSW, 3.54× faster** |
| Speedup vs brute force (p50) | 50.4× | 19.7× | Parquet+HNSW |
| Random row access (fetch by id) | ~35ms (full-file scan) | 311us | **Lance, 112× faster** |
| Append 10K rows | Full-file rewrite (~330MB + re-embed + re-index) | 0.08s, +31MB delta | **Lance, structurally different** |

## Applying the decision rules from EXECUTION_PLAN.md

Original rules:
- *Lance wins cold-load by ≥2× AND matches search latency → migrate*
- *Within 50% across board → stay Parquet, document ceiling*
- *Lance loses → close the door*

Strict reading: cold-load is **1.27×, not ≥2×**. Search latency is **2.55× worse, not matching**. By the written rule, we stay.

But the written rule missed something. It assumed Lance's value would show up as raw-speed wins across the whole table. The actual benchmark reveals Lance's value is **in capabilities the current stack doesn't have**, not in the metrics we scoped:

1. **Random row access** is 112× faster. Our Parquet design can't do O(1) random access to a row — RAG text retrieval is a full-file scan today. Lance makes this native.
2. **Append** is structurally different. Adding 10K rows is 0.08s on Lance; on our stack it's a full rewrite of the entire 330MB Parquet file plus re-embedding plus re-indexing.
3. **Index build** is 14× faster. The HNSW `ec=80 es=30` production default takes 230s; Lance IVF_PQ takes 16s. Hot-swap generation (Phase 16) is much more feasible at 16s per build.

## The decision

**Hybrid architecture — neither replace nor reject.**

### What stays

- `vectord::store` with Parquet + binary-blob vectors → **primary vector backend**
- `vectord::hnsw::HnswStore` → in-RAM HNSW for search at 100K-scale indexes
- All Phase 15 trial infrastructure → keeps working, unchanged
- Production default `ec=80 es=30` → still the right call for in-RAM use

### What gets added

- **`vectord::lance_store`** — second backend using Lance as the persistence layer
  - Scope: indexes where *any* of the following apply:
    - Corpus exceeds ~5M vectors (our in-RAM ceiling)
    - Workload is append-heavy (incremental ingest from streaming sources)
    - Text retrieval dominates (point lookups by doc_id for RAG)
    - Hot-swap generations are required (Phase 16)
  - Implemented as a standalone crate first (follow the pilot layout), promoted into vectord when the API stabilizes
- **Profile-level configuration** — `ModelProfile.vector_backend: Parquet | Lance` so each profile picks the tier that matches its workload

### What we keep watching (but don't act on yet)

- **Lance search latency at scale.** 2229us at 100K is worse than HNSW. 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.
- **IVF_PQ recall.** We measured latency but not recall — I picked `num_partitions=316, nbits=8, num_sub_vectors=48` blindly. A proper recall sweep is part of Phase C when we integrate Lance into the trial system.
- **Lance's own HNSW-on-disk variant** (`with_ivf_hnsw_pq_params`). Might close the in-RAM latency gap. Left for a future pilot.

## Why this isn't moving the goalposts

The EXECUTION_PLAN rule was "migrate or don't migrate." The evidence says neither is correct — one stack can't serve both the staffing SQL workload AND the LLM-brain append-heavy random-access workload at all scales. The honest answer is two backends, each doing what it's good at, selected per-profile.

This matches the dual-use framing in the 2026-04-16 PRD update: different workloads, shared substrate, per-profile specialization. We wrote that principle into the PRD; the benchmark data just made it concrete for the vector tier.

## Follow-up work (updates EXECUTION_PLAN.md)

- **Phase C (decoupled embedding refresh)** gets easier — Lance's native append removes the need to invent a "vectors delta" Parquet layer. When we build Phase C, use Lance as the embedding-layer backend.
- **Phase 16 (hot-swap)** becomes feasible — 16s index builds mean online re-trials are cheap. When we build Phase 16, Lance is the storage for index generations.
- **Phase 17 (model profiles)** gains a new field: `vector_backend: Parquet | Lance`. Default Parquet for backward compatibility. Agents can opt into Lance.

## Costs we accept

- **Second dependency tree.** Lance pulls in DataFusion 52 and Arrow 57, while our main stack runs DataFusion 47 and Arrow 55. Keeping lance-bench isolated works for a pilot; productionizing will need either workspace-wide upgrade or a firewall via a dedicated `vectord-lance` crate.
- **Second API surface.** Lance's vector-index API is different from our HNSW code. Per-profile abstraction cost is real.
- **Operational complexity.** Two vector storage implementations to debug and monitor.

Worth it because the alternative — forcing every workload through one backend — means either the staffing case or the LLM-brain case is served badly.

## Ceilings this updates in PRD

The PRD "Known ceilings" table had:

> Vector count per index | ~5M vectors on 128GB RAM | 10M+ (serious web crawl) | Phase 18 Lance migration OR mmap'd embeddings

Update to:

> Vector count per index | ~5M vectors on 128GB RAM (Parquet+HNSW in-RAM) | Past 5M | Switch that profile's `vector_backend` to Lance; IVF_PQ keeps working on disk-resident quantized codes
